7,419 Matching Annotations
  1. Last 7 days
    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

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

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      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.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      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

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The 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.

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

      Learn more at Review Commons


      Reply to the reviewers

      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.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      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.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      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.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors 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.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      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.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      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.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      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

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      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

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by 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.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      Reply to the Reviewers

      I thank the Referees for their...

      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In this 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.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      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.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      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.
  2. Jan 2026
    1. 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

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

    1. 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

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary

      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

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Reply to the reviewers

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-02932

      Corresponding author(s): Amit Tzur

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

      • *

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

      1. General Statements

      We thank all Referees for their insightful comments and thoughtful review of our manuscript.

      • *

      2. Point-by-point description of the revisions

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

      • *

      __! Original comments by Reviewers #1-3 are in gray. __


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

      The study highlights a dephosphorylation switch mediated by PP2A as a critical mechanism for coupling E2F7/8 degradation to mitotic exit and G1 phase. The study is clear and experiments are well conducted with appropriate controls

      I have some concerns highlighted below:

      Point 1. In this sentence: This intricate network of feedback mechanisms ensures the orderly progression of the cell cycle. What feedback mechanism are the authors referring to?

      Thank you for pointing this out. We aimed for a general comment. The original line was replaced with: “The intricate network of (de)phosphorylation and (de)ubiquitination events in cycling cells establishes feedback mechanisms that ensure orderly cell cycle progression.

      Point 2. Characterization of disorder in the N-terminal segments of E2F7 and E2F8

      What does it mean disorder in this title?

      “Disorder” is a structural biology term for describing an unstructured (floppy) region in a protein. We suggest the following title in hope to improve clarity: “The N-terminal segments of E2F7 and E2F8 are intrinsically unstructured”

      Point 3. In the paragraph on the untimely degradation of E2F8 the authors keep referring to APC/C Cdc20, however the degradation is triggered by the Ken box which is specifically recognised by APC/C Cdh1. Can it be due to another ligase not APC/C?

      In our anaphase-like system, Cdh1 cannot associate with the APC/C due to persistently high Cdk1 activity, maintained by the presence of non-degradable Cyclin B1. While the KEN-box is classically recognized as a Cdh1-specific motif, previous studies have also clearly demonstrated that APC/C-Cdc20 can mediate the degradation of KEN-box substrates. For example, BubR1 interacts with Cdc20 via two KEN-box motifs (PMIDs: 25383541, 27939943 and 17406666). Nek2A is targeted for degradation by the APC/C in mitotic egg extracts lacking Cdh1, in a manner that depends on both D-box and KEN-box motifs (PMID: 11742988). CENP-F degradation in Cdh1-null cells has been shown to be dependent on both Cdc20 and a KEN-motif (PMID: 20053638). Thus, the most simple explanation for our results is that degradation is KEN box dependent and controlled by Cdc20.

      Regarding alternative E3 ligases, KEN-box mutant variants of non-phosphorylatable E2F8 remained stable in APC/CCdc20-active extracts, suggesting that this degradation is indeed APC/C-specific.

      Please also see our response to Reviewer #3, Point 3.

      Point 4. The assays to detect dephosphorylation are rather indirect so it is difficult to establish whether phosphorylation of CDK1 and dephosphorylation by PP2A on the fragments is direct.

      First, the phosphorylation sites analyzed in this study conform to the full and most canonical Cdk1 consensus motif: S/TPxK/R. While recognizing that other kinases are proline directed as well, the cell cycle dependent manner of this control, and presence of a similar CDK-dependent mechanism for Cdc6, points us towards considering the role of CDKs.

      Second, consistent with the direct role of CDK1 in this regulation, NMR experiments demonstrate conformational shifts of recombinant E2F8 following incubation with Cdk1–Cyclin B1 (not included in manuscript, but shown here for reviewer consideration); see Figure below. We have not yet established equivalent biochemical systems for PP2A.

      Figure legend: NMR-based monitoring of E2F7 (a-c) and E2F8 (d-f) phosphorylation by Cdk1.

      a(d). 15N,1H-HSQC spectrum of E2F7(E2F8) prior to addition of Cdk1. Threonine residues of interest, T45 (T20) conforming to the consensus sequence (followed by a proline), and T84 (T60) lacking the signature sequence are annotated. b(e). Strips from the 3D-HNCACB spectrum used for assigning E2F7(E2F8) residues. Black (green) peaks indicate a correlation with the 13Cα (13Cβ) of the same and previous residues. The chemical shifts assigned to T45 (T20) and T84 (T60) match the expected values for K44(K19) and P83(P59), thereby confirming the assignment. c(f). Top, overlay of subspectra before adding Cdk1 (black) and after 16 h of activity (red) at 298 K. Bottom, change in intensities of the T45/T84 in E2F7 and T20/T60 in E2F8 showing how NMR monitors phosphorylation and distinguishes between various threonine residues.


      Third, PP2A is likely the principal phosphatase counteracting Cdk1-mediated phosphorylation during mitotic exit, targeting numerous APC/C substrates (PMID: 31494926). In light of our findings and the extensive literature, it is therefore reasonable to propose that E2F7 and E2F8 may also be direct PP2A targets.

      Fourth, we cannot fully exclude the possibility that dephosphorylation of E2F7 and E2F8 by PP2A occurs indirectly. Nevertheless, indirect studies of PP2A substrate identification in the literature often rely on similar genetic perturbations, chemical inhibition, cell-free systems (coupled with immunodepletion, inhibitory peptides/proteins, and small-molecule inhibitors), and phosphoproteomics. Moreover, more direct assays are not without caveats, as they lack the cellular stoichiometric context, an important limitation for relatively promiscuous enzymes such as phosphatases.

      Importantly, repeated attempts (conventional [Co-IP] and less conventional [affinity microfluidics]) to detect interactions between PP2A and E2F7 and E2F8 were unsuccessful. This result was unfortunate but not surprising, given that transient substrate–phosphatase interactions are often challenging to capture experimentally.

      Given our evidence showing the regulation of E2F7 and E2F8 degradation in a manner that depends on Cdk1 and PP2A, the title of the manuscript remains appropriate: "Cdk1 and PP2A constitute a molecular switch controlling orderly degradation of atypical E2Fs.”

      Please also see our response to Reviewer #3 Point 1.

      Point 5. Although there seems to be a control by phosphorylation and dephosphorylation (which could be indirect), it is difficult to establish the functional consequences of this observation. The authors propose a feedback mechanism which regulates the temporal activation inactivation of E2F7/8 however, there are no evidence in support of this.

      The components being studied here have been extensively characterized, as have the direct and indirect interactions that connect them and ensure orderly cell cycle progression. For example: i) The E2F1–E2F7/8 transcriptional circuitry functions as a negative feedback loop; ii) Cdk1 and PP2A counteract one another’s activity; iii) E2F1 promotes the disassembly of APC/CCdh1; iv) E2F7 and E2F8 are APC/C substrates with cell cycle-relevant degradation patterns; and v) Loss of Cdh1 leads to premature S-phase entry.

      Our study brings these components together into a coherent regulatory module operating in cycling cells, revealed through cell-free biochemistry and newly developed methodologies with broad applicability to signaling research. We believe that advancing mechanistic understanding at this level of central regulators is impactful. And notably, this is a model, which we expect others in the field to test. We stand behind the result of each individual experiment and based on those findings are proposing a feedback circuit.

      To address your suggestion, we incorporated phenotypic analyses (see Figure on the next page). Although modest and variable due to transient overexpression, these data align with the mechanistic model proposed in our study.

      In Panel a, overexpression of E2F7 or E2F8 reduces E2F1 and its target Plk1, consistent with the established negative feedback within the E2F1–E2F7/8 transcriptional circuitry. A broader impact on cell cycle progression was also evident: G1-phase cells increased and S-phase cells decreased (Panel b), hinting at a delayed G1–S transition when E2F1, an essential driver of S-phase and mitotic entry, is downregulated by excess E2F7 or E2F8.

      We next examined the effects of hyper- vs. hypo-phosphorylation–mimicking mutants of E2F7 and E2F8 on E2F1 and Plk1 (Panels c and d). Both raw data (top) and quantification (bottom) are shown. Despite ectopic overexpression, our experimental conditions highlighted the diffenrential outcome of the two phospho-mutant variants. Speificially, E2F1 and Plk1 levels were consistently higher upon expression of non-phosphorylatable variants of E2F7 (T45A/T68A) and E2F8 (T45D/T68D) relative to their phophomimetic counterparts (T45D/T68D; T20D/T44D). These findings suggest that E2F1 downregulation is more pronounced when E2F7/E2F8 are hyper-phosphorylated at Cdk1-regulated sites that control their half-lives. Furthermore, the proportion of S-phase cells was consistently lower for the phospho-mimicking mutants compared with the non-phosphorylatable variants, with complementary, though less pronounced, shifts in G1-phase cells (Panel e).

      Figure legend: Evidence for cell cycle control linked to Cdk1–PP2A regulation of the E2F1–E2F7/E2F8 axis.

      a) Immunoblot analysis showing reduced E2F1 and its target protein Plk1 upon E2F7/E2F8 overexpression. Antibodies used for immunoblotting (IB) are indicated. b) Cell cycle phase distribution after E2F7/E2F8 overexpression, based on DNA content. Left: representative histograms. Right: quantification of G1- and S-phase cells. Means (x) with individual biological replicates (color-coded; N = 4) are shown. c,d) Top: E2F1 and Plk1 protein levels in cells expressing phosphomimetic (TT-DD) or non-phosphorylatable (TT-AA) E2F7 (c) or E2F8 (d) variants. Antibodies used are indicated (*distorted signal excluded). Bottom: quantification relative to loading controls. Means (x) with individual values (N = 3/4) are shown. e) Cell cycle phase distribution following expression of E2F7/E2F8 phospho-mutant variants. Means (x) with individual values (N = 4) are shown. All experiments were performed in HEK293T cells. Cells were fixed 40–44 h post-transfection. DNA content was assessed using propidium iodide (PI). Mutation sites: T45/T68 (E2F7); T20/T44 (E2F8. Statistical significance was determined by two-tailed Student’s t-test; P-values are indicated.


      Taken together, these results support a model in which Cdk1-site (de)phosphorylation modulates the stability of E2F7 and E2F8, thereby shaping E2F1 output and influencing cell cycle preogresion.

      Point 6. Reviewer #1 (Significance (Required)):

      The study is a good and well conducted work to understand the mechanisms regulating degradation of E2F7/8 by APC/C. This is crucial to establish coordinated cell cycle progression. While the hypothesis that disruption of this mechanism is likely responsible for altered cell cycle progression, there are no evidence this is just a back up pathway, whose functional significance could be limited to lack of APC/C Cdh1 activity. These experiments are rather difficult but the authors could comment on the limitation of the study and emphasise the hypothetical alterations which could result from the alterations of the described feedback loop

      We thank Reviewer #1 for this comment. Accordingly, we have expanded the discussion to further elaborate on the potential molecular outcomes and limitations of our study.

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

      Summary: The authors provide strong biochemical evidence that the regulation of E2F7 and E2F8 by APC is affected by CDK1 phosphorylation and potentially by PP2A dependent dephosphorylation. The authors use both full length and N-terminal fragments of E2F8 in cell-free systems to monitor protein stability during mitotic exit. The detailed investigation of the critical residues in the N-terminal domain of E2F8 (T20/T44) is well supported by the combination of biochemical and cell biology approaches.

      We thank Reviewer #2 for their encouraging feedback.

      Point 1. Major: It is unclear how critical the APC-dependent destruction of E2F7 and E2F8 is for cell cycle progression or other cellular processes. Prior studies have reported that Cyclin F regulation of E2F7 is critical for DNA repair and G2-phase progression. This study would be improved if the authors could provide a cellular phenotype caused by the lack of APC dependent regulation of E2F8 and/or E2F7.

      We thank Reviewers #2 and #1 for this comment, which prompted substantial revisions. Below, we reiterate our response to Reviewer #1.

      The molecular components examined in this study are well established in the literature. Key principles include: (i) the reciprocal regulation between E2F1 and its repressors, E2F7 and E2F8, which forms a transcriptional feedback loop; (ii) the opposing activities of Cdk1 and PP2A; (iii) the capacity of E2F1 to attenuate APC/CCdh1 activity; (iv) the fact that E2F7 and E2F8 are APC/C substrates with defined cell cycle–dependent degradation patterns; and (v) the requirement for Cdh1 to prevent premature S-phase entry.

      Our study integrates these elements into a unified framework operating in proliferating cells. This framework is supported by biochemical reconstitution experiments and newly developed methodological tools, which we anticipate will be broadly applicable for dissecting signaling pathways. We view this type of mechanistic synthesis as valuable for the field. Importantly, we do not present this as a definitive model, but rather as a testable regulatory circuit constructed from robust individual findings.

      In response to your request, we incorporated additional phenotypic analyses (see Figure, next page). Although modest and variable due to transient overexpression, the results are consistent with the regulatory architecture we propose.

      In panel a, elevating E2F7 or E2F8 levels reduces E2F1 and its downstream target Plk1, consistent with the established inhibitory feedback exerted by E2F7 and E2F8 on E2F1. Additionally, we observed an increase in G1-phase cells and a decrease in S-phase cells (Panel b), hinting at a delayed G1–S transition when E2F1, a key transcriptional engine of S- and M-phase entry, is downregulated by excess E2F7 or E2F8.

      Figure legend: Evidence for cell cycle control linked to Cdk1–PP2A regulation of the E2F1–E2F7/E2F8 axis.

      a) Immunoblot analysis showing reduced E2F1 and its target protein Plk1 upon E2F7/E2F8 overexpression. Antibodies used for immunoblotting (IB) are indicated. b) Cell cycle phase distribution after E2F7/E2F8 overexpression, based on DNA content. Left: representative histograms. Right: quantification of G1- and S-phase cells. Means (x) with individual biological replicates (color-coded; N = 4) are shown. c,d) Top: E2F1 and Plk1 protein levels in cells expressing phosphomimetic (TT-DD) or non-phosphorylatable (TT-AA) E2F7 (c) or E2F8 (d) variants. Antibodies used are indicated (*distorted signal excluded). Bottom: quantification relative to loading controls. Means (x) with individual values (N = 3/4) are shown. e) Cell cycle phase distribution following expression of E2F7/E2F8 phospho-mutant variants. Means (x) with individual values (N = 4) are shown. All experiments were performed in HEK293T cells. Cells were fixed 40–44 h post-transfection. DNA content was assessed using propidium iodide (PI). Mutation sites: T45/T68 (E2F7); T20/T44 (E2F8. Statistical significance was determined by two-tailed Student’s t-test; P-values are indicated.


      We next examined how phospho-regulation of E2F7 and E2F8 influences cell cycle control by comparing the effects of phospho-mimetic and non-phosphorylatable variants on E2F1 levels and cell cycle distribution (panels c and d). Both the raw data and the corresponding quantitative analyses are presented. Despite exogenous overexpression, we identified conditions that distinguish the behaviors of the two mutant classes. Cells expressing the phospho-mimetic variants consistently exhibited lower E2F1 and Plk1 levels than those expressing the non-phosphorylatable forms. This pattern supports a model in which phosphorylation of key Cdk1 sites in E2F7 and E2F8 elevates their stability, thereby enhancing their ability to suppress E2F1. Panel e extends these observations to cell cycle behavior: compared with the non-phosphorylatable variants, The phospho-mimetic forms of E2F7 and E2F8 consistently lower the proportion of S-phase cells, accompanied by corresponding shifts in the G1 population.

      The central aim of this manuscript is to define how the Cdk1–PP2A axis is integrated into the APC/C–E2F1 regulatory network controlling cell cycle progression. Collectively, our findings support a model in which Cdk1/PP2A-dependent (de)phosphorylation modulates the stability of E2F7 and E2F8, thereby fine-tuning E2F1 activity and cell cycle progression.

      Point 2. Minor: All optional: It would have been interesting to see the T20A/T44A/KM in the live cell experiment (Figure 3F).

      This is an excellent point. Following Reviewer #2’s request, we generated a stable cell line expressing a KEN-box mutant variant of E2F8-T20A/T44A (N80 fragment). The figure below demonstrates the impact of the KEN-box mutation on the dynamics of N80-E2F8-T20A/T44A in HeLa cells. Together, our data from both cellular and cell-free systems show that the temporal dynamics of both wild-type and non-phosphorylatable variants of E2F8 depends on the KEN degron. Please note that due to differences in the flow cytometer settings used for acquiring the original measurements and those newly generated at the Reviewer’s request, the numeric data for N80-E2F8-T20A/T44A-KEN mutant will not be integrated into the original plots shown in the original Figure 3c–e in the manuscript.

      Figure legend: Dynamics of mutant variants of N80-E2F8-EGFP in HeLa cells.

      Top: Bivariate plots showing DNA content (DAPI) vs. EGFP fluorescence, with G1/G1-S phases and G2/M phases highlighted (black and gray frames, respectively). Bottom: Histograms showing EGFP signal distributions within these cell cycle phases. Blue arrows highlight subpopulations of G2/M cells with relatively low EGFP levels. The data was generated by flow cytometry.


      Point 3. Figure 4C-D - include the corresponding blots for the WT E2F7.

      This is a good point, which we previously overlooked. The requested data will be integrated in the revised manuscript.

      Point 4. It is unclear how selective or potent the PP2A inhibitors are that are used in Figure 5. Is it possible to include known targets of PP2A (positive controls for PP2A inhibition) in the analysis performed in Figure 5?

      Thank you for this helpful suggestion. Following Reviewer #2’s comment, we performed gel-shift assays of Cdc20 and C-terminal fragment of KIF4 (Residues: 732-1232), both known targets of PP2A (PMIDs: 26811472; 27453045). See data below.

      __Figure legend: PP2A inhibitor LB-100 block protein dephosphorylation in G1-like extracts. __

      Time-dependent gel shifts of mitotically phosphorylated Cdc20 and the C-terminal fragment of KIF4 (residues 732–1232) following incubation in G1 extracts supplemented with LB-100 or okadaic acid (OA; positive control). Substrates (IVT, 35S-labeled) were resolved by PhosTag SDS–PAGE and autoradiography.


      Point 5. Is the APC still active in LB-100 or OA treated conditions? Is it possible to demonstrate the APC is active using known substrates in this assay (e.g., Securin (Cdc20) and Geminin (Cdh1) or similar).

      This is an excellent point and we should have clarified this previously. Importantly, treatment with 250 µM LB-100 does not abolish APC/C-mediated degradation (otherwise, the assay would not be viable), but it does attenuate degradation kinetics. This is reflected by the prolonged half-lives of Securin and Geminin relative to mock-treated extracts (see below). Consistently, we noted in the manuscript: “Although APC/C-mediated degradation is also affected, it remains efficient, allowing us to measure relative half-lives of APC/C targets that cannot undergo PP2A-mediated dephosphorylation.” Following this comment, and one by Reviewer #3, these data will be included in the revised manuscript.


      __Figure legend: APC/C-specific activity in cell extracts treated with LB-100. __

      Time-dependent degradation of EGFP–Geminin (N-terminal fragment of 110 amino acids) and Securin in extracts supplemented with LB-100 and/or UbcH10 (recombinant). A control reaction contained dominant-negative (DN) UbcH10. Proteins (IVT, 35S-labeled) were resolved by SDS-PAGE and autoradiography.


      Reviewer #2 (Significance (Required)): Advance: A detailed analysis is provided for the critical N-terminal residues in E2F7 and E2F8 that when phosphorylated are capable of restricting APC destruction. The work builds on prior work that had identified the APC regulation of E2F7 and E2F8.

      Point 6. Audience: The manuscript would certainly appeal to a broad basic research audience that is interested in the regulation of APC substrates and/or E2F axis control via E2F7 & E2F8. The study could have a broader interest if the destruction of E2F7 or E2F8 could be shown to be biologically relevant (e.g., critical for cell fate decision G1 vs G0, G1 length, timely S-phase onset, or expression of E2F1 target genes in the subsequent cell cycle).

      To clarify, we subdivided Reviewers’ comments into separate points. Reviewer #2’s Points 1 and 6 address essentially the same issue; our detailed response is therefore provided under Point 1. We again thank Reviewer #2 for raising this concern, which led to substantial revisions to both the manuscript text and the supporting data.

      We thank Reviewer #2 for their constructive comments and criticism.

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

      This manuscript presents a well-structured study on the regulatory interplay between Cdk and Phosphatase in controlling the degradation of atypical E2Fs, E2F7 and E2F8. The work is relevant in the field of cell cycle regulation and provides new mechanistic insights into how phosphorylation and dephosphorylation govern APC/C-mediated degradation. The use of complementary cell-based and in vitro approaches strengthens the study, and the findings have significant implications for understanding the timing of transcriptional regulation in cell cycle progression.

      Point 1. However, several points in this paper require further clarification for it to have a meaningful impact on the research community. The characterization of the phosphatase is unclear to me. The use of OA is necessary to guide the research, but it is not precise enough to rule out PP1 and then identify which PP2A is involved - PP2A-B55 or PP2A-B56. To clarify this, the regulatory subunits should either be eliminated or inhibited using the inhibitors developed by Jakob Nilsson's team.

      We are grateful for this comment, which prompted an extensive series of experiments that have undoubtedly strengthened our manuscript.

      First, we wish to clarify that LB-100, unlike okadaic acid (OA), is not considered a PP1 inhibitor.

      Second, we have conducted a large set of experiments to address this important question of the strict identity of the phosphatase involved in the dephosphorylation of atypical E2Fs.

      I. We initially attempted to immunodeplete the catalytic subunit of PP2A (α) from G1 extracts as a means to validate PP2A-dependent dephosphorylation. In retrospect, this was a naïve approach given the protein’s high abundance; although immunoprecipitation was successful, immunodepletion was inefficient, preventing us from using this strategy (see Panel a in the figure below). As an alternative, we incubated immunopurified PP2A-Cα with mitotic phosphorylated E2F7 and E2F8 fragments (illustrated in Panel b). A time-dependent gel-shift assay demonstrated enhanced dephosphorylation in the presence of immunopurified PP2A-Cα (Panel c) compared to immunopurified Plk1 (control reaction), suggesting that mitotically phosphorylated E2F7 and E2F8 are targeted by PP2A.

      Figure legend: Immunopurified PP2A-Cα facilitates dephosphorylation of E2F7 and E2F8 in cell extracts. a) Inefficient immunodepletion (ID) of the catalytic subunit α of PP2A (PP2A-Cα) from cell extracts despite three rounds of immunopurification, as detected by immunoblotting (IB) with anti-PP2A-Cα and anti-BIP (loading control; LC) antibodies (BD bioscience, Cat#: 610555; Cell Signaling Technology, Cat#: 3177). Briefly, G1 cell extracts were diluted to ~10 mg/mL in a final volume of 65 μL. Anti-PP2A-Cα antibodies (3 μg) were coupled to protein G magnetic DynabeadsTM (15 μL; Novex, Cat#: 10004D) for 20 min at 20 °C. For each depletion round, antibody-coupled beads were incubated with cell extracts for 15 min at 20 °C. Cell extracts and beads were sampled after each step to assess immunodepletion and immunopurification (IP) efficiency. Equivalent immunopurification steps are shown for Plk1 (bottom). b) Schematic of the dephosphorylation assay using mitotically phosphorylated in vitro translated (IVT) targets and immuno-purified PP2A-Cα/Plk1. c) Dephosphorylation of mitotically phosphorylated E2F7 and E2F8 fragments, detected by electrophoretic mobility shifts in Phos-Tag SDS-PAGE. Immunopurified Plk1 was used for control reactions (antibodies: Santa Cruz Biotechnology: Cat#: SC-17783). *Image was altered to improve visualization of mobility shifts.


      II. Next, we used pan-B55-specific antibodies for immunodepletion of all B55-type subunits. This approach was unsuccessful despite five rounds of immunopurification (see Panel a in the figure below). Both suboptimal binding and the high abundance of endogenous B55 subunits likely contributed to this outcome. Thus, dephosphorylation in B55-depleted extracts could not be tested.

      Figure legend: PP2A-B55 facilitates dephosphorylation of E2F7 and E2F8 fragments.

      a) __Immunodepletion (ID) of B55 subunits in G1 extracts is inefficient despite five rounds of immunopurification; assessed by immunoblotting (IB) using anti-pan-B55 and anti-Cdk1 (loading control; LC) antibodies (see previous figure for more details). Cell extracts and beads were sampled after each round to monitor immunodepletion and immunopurification efficiency. b) Schematic of a dephospho-rylation assay using immuno-purified B55 subunits. __c) __Dephosphorylation of mitotically phosphorylated E2F7 and E2F8 fragments by immuno-purified B55. Control reactions performed with immuno-purified Plk1. d) __Schematic of a dephosphorylation assay performed in G1 cell extracts supplemented with B55-interacting (B55i) or control peptides (see peptide sequence on next page). RO-3306 was added to limit Cdk1 activity. __e) __Dephosphorylation of E2F7 and E2F8 fragments (mitotically phosphorylated) in G1 extracts supplemented with B55-interacting/control peptides. __f) __Schematic of the dephosphorylation assay using in vitro–translated B55/B56 subunits (unlabeled). __g) __Dephosphorylation of mitotically phosphorylated E2F7 (top) and E2F8 (bottom) fragments in reticulocyte lysate containing B55/B56 subunits. Dephosphorylation was assessed by electrophoretic mobility shifts in Phos-Tag SDS-PAGE. Panels marked with an asterisk were adjusted to improve visualization of gel-shifts. Arrowheads denote distinct, time-dependent mobility-shifted forms of E2F7 and E2F8 fragments. Antibodies used: anti-pan-B55 (ProteinTech, Cat#: 13123-1-AP); anti-Plk1 (Santa Cruz Biotechnology, Cat#: SC-17783); anti-Cdk1 (Santa Cruz Biotechnology, Cat#: SC-53217). Dynabeads™ (Novex, Cat#: 10004D) were used for immunopurification.


      As with PP2A-Cα, we incubated immunoprecipitated B55 subunits with mitotically phosphorylated E2F7 and E2F8 fragments (illustrated in Panel b). The results were less definitive compared to PP2A-Cα; nevertheless, they demonstrated accelerated dephosphorylation in the presence of immunopurified B55 subunits (Panel c) relative to Plk1 (control). These results hint at B55-mediated dephosphorylation of E2F7 and E2F8.

      III. Given that PP2A-B55 could be immunodepleted satisfactorily, despite successful immunoprecipitation, we ordered the B55-specific peptide and corresponding control peptide reported recently by Jakob Nilsson’s team as PP2A-B55 inhibitors (see below).

      Figure legend: Adapted from Kruse, T., et al., 2024; ____Science Advances. Figure 3, Panel B. ____PMID: 39356758.


      Despite our long-anticipated wait for these peptides to arrive, this line of experimentation proved disappointing. We wish to elaborate:

      The study by Kruse et al. (PMID: 39356758) is an elegant integration of classical enzymology, performed at the highest level, with structural insight into the conserved PP2A-B55 binding pocket that governs substrate specificity. Their work identified a consensus peptide that binds PP2A-B55 specifically with nanomolar affinity.

      Kruse et al. provide compelling evidence for a direct and specific interaction between their reported B55 inhibitor (B55i) and PP2A-B55. Their data show that the engineered inhibitor disrupts the binding of helical elements that underlie substrate recognition by PP2A-B55.

      However, we could not find direct evidence of PP2A-B55 enzymatic inhibition by the B55i peptide; for example, a B55-specific in vitro dephosphorylation assay demonstrating sensitivity to B55i in a dose-dependent manner. To the best of our understanding, the sole functional consequence described by Kruse et al. was the delay in mitotic exit observed upon expression of YFP-tagged B55i peptides in cells. However, this approach is indirect, given the long interval between cell manipulation and analysis and the complexity of mitotic exit. Furthermore, we assumed that the requested reagents had been validated in cell-free extracts; however, Kruse et al. do not report any experiments performed in these systems. We, in fact, became uncertain whether we had correctly understood Reviewer #3’s request to use these reagents and therefore sought clarification from the Editor.

      In vitro, Kruse et al. reported nanomolar binding affinities for B55i (Figure S14). In our cell extracts, however, we required concentrations of approximately 250 μM to detect an effect on dephosphorylation, evident as altered electrophoretic mobility of both E2F7 and E2F8 (Panel e). At this concentration, the peptide also caused nonspecific effects, rendering the extracts highly viscous (‘gooey’), at times preventing part of the reaction mixture from passing through a 10 μL pipette tip.

      The gel-shift assays shown in Panel e (Page 16) do demonstrate delayed dephosphorylation in extracts treated with the B55i peptide relative to the control peptide. Nevertheless, we prefer to exclude these data because the peptide concentrations required for the assay compromised extract integrity. Moreover, we believe that the PP2A-B55–specific peptide described by Nilsson et al. requires additional validation before it can be considered a reliable functional inhibitor in cell-free systems or in vivo. Accordingly, we are unable to directly address the experiments as suggested.

      IV. In the final set of experiments (Page 16, Panels f and g), we supplemented dephosphorylation reactions with in vitro–translated B55/B56 subunits (illustrated in Panel f). Although the expected concentration of in vitro–translated proteins in reticulocyte lysate is relatively low (100–400 nM), we reasoned that supplementing the reactions with excess of regulatory B subunits (non-radioactive) could still promote dephosphorylation in a differential manner that reflects the B55/B56 preference of E2F7 and E2F8.

      We cloned and in vitro expressed all nine B55/B56 regulatory subunits. While the exact amount of each subunit introduced into the reaction cannot be precisely determined, their expression levels were reasonably uniform (see figure below).

      __Figure legend: Expression of B55/B56 subunits in reticulocyte lysate. __B55/B56 subunits were cloned into the pCS2 vector and expressed in reticulocyte lysate supplemented with ³⁵S-Methionin. Proteins were resolved by SDS–PAGE and autoradiography.


      Returning to Panel g (Page 16), B55 subunits facilitated the accumulation of lower–electrophoretic mobility forms of both E2F7 and E2F8 fragments to the greatest extent. This is evident from the distinct lower–mobility species that emerge over time (marked by arrowheads) and the smear intensity corresponding to the buildup of dephosphorylated forms. Among the tested subunits, B55β exerted the strongest effect on both substrates, suggesting that mitotically phosphorylated E2F7 and E2F8 display a heightened preference for the PP2A-B55β holoenzyme. Control reactions with reticulocyte lysate are also shown.

      Taken together, our original and newly added data indicate that PP2A, specifically PP2A-B55, counteracts Cdk1-dependent phosphorylation during mitotic exit. Importantly, cell cycle regulators such as Cdc20 can be targeted by both PP2A-B55 and PP2A-B56 holoenzymes. Thus, while we are confident in concluding that mitotically phosphorylated E2F7 and E2F8 are targeted by PP2A-B55, we cannot rule out the possibility of functional interactions between E2F7/E2F8 and PP2A-B56.

      V. Last, but certainly not least, we used AlphaFold 3 to model interactions between the N-terminal fragments of E2F7 and E2F8 and the PP2A regulatory subunits. To clarify: for us, AlphaFold 3 remains very much a computational “black box,” and although this may sound like an overstatement, we did not anticipate obtaining meaningful or interpretable output.

      According to the AlphaFold 3 developer guidelines, the Interface Predicted Template Modeling (IPTM) score is the primary confidence metric for protein–protein interaction predictions. IPTM values above 0.8 indicate high-confidence predictions, whereas values below 0.6 likely reflect failed interaction predictions. In our models, none of the predicted interactions exceeded 0.6 (see figure below). Nevertheless, for both E2F7 and E2F8 fragments, IPTM scores were consistently higher for B55 subunits than for B56 subunits, with B55β yielding the highest scores (each interaction was modeled five times).

      __Figure legend: AlphaFold 3 predicts preferential interactions between E2F7 and E2F8 and PP2A-B55β. __Protein–protein interaction predictions between N-terminal fragments of E2F7 and E2F8 and B55/B56 regulatory subunits of PP2A were generated using AlphaFold 3 (AF3). The plot shows IPTM scores from five models per protein pair.


      Even if one assumes a scenario in which AlphaFold 3 scores are inaccurate or effectively random, such non-specific behavior would not be expected to produce: (i) a reproducible preference of two distinct substrates for B55β and B55γ, in that order (the modeled fragments of E2F7 and E2F8 share The ability of AlphaFold 3, and specifically the IPTM metric, to predict bona fide PP2A B55/B56–substrate interactions remains unvalidated. Accordingly, we do not rely on these predictions as experimental evidence. Nonetheless, in retrospect, the IPTM scores for the E2F7 and E2F8 fragments proved, unexpectedly, to be highly informative. While we are not the first to explore AlphaFold in the context of PP2A phosphatases (e.g., Kruse et al.), at this early stage of AlphaFold 3 these observations are compelling and may ultimately have implications for PP2A-mediated signaling that extend well beyond the cell-cycle field.

      Point 2. It would also be valuable for this study to investigate the mechanisms underlying this regulation. In particular, is it exclusive to E2F7-8 or could other substrates contribute to the generalisation of this regulatory process?

      Assuming Reviewer #3 is referring to the cell cycle mechanism regulating E2F7 and E2F8 half-life via conditional degrons, we wish to clarify that the temporal dynamics of APC/C targets regulated by dephosphorylation has been demonstrated previously. Examples include KIFC1, CDC6, and Aurora A (PMIDs: 24510915; 16153703; 12208850, respectively).

      Point 3. The observation that Cdc20 may target E2F8 is interesting but needs to be further clarified to ensure that weak Cdh1 activity does not contribute to this degradation. Elimination of Cdc20 would be necessary to support the authors' conclusion.

      We gratefully acknowledge this input. The newly implemented experiment and corresponding findings are presented on the next page. The immunodepletion (ID) procedure (Panel a) achieved >60% reduction of Cdc20 and Plk1 in mitotic extracts (Panel b), as confirmed by immunoblotting (IB). Plk1-depleted extracts were used to validate extract-specific activity after successive rounds of immunodepletion at 20°C. Bead-bound Cdc20 and Plk1 were also analyzed by IB for validation (Panel b, right).

      As expected, the phospho-mimetic E2F8 fragment (T20D/T44D) remained stable in Plk1- and Cdc20-depleted mitotic extracts, serving as negative control (Panel c). In contrast, degradation of the non-phosphorylatable variant (T20A/T44A), as well as the APC/CCdc20 substrate Securin (positive control), was strongly hampered in Cdc20-depleted extracts compared to Plk1-depleted extracts. These results confirm that the untimely degradation of the non-phosphorylatable E2F8 in mitotic extracts is Cdc20-dependent.

      Figure legend: Untimely degradation of the non-phosphorylatable E2F8 in mitotic extracts is Cdc20-dependent.____a) Schematic of the immunodepletion (ID) protocol; additional technical details are provided below. b) Plk1 (top) and Cdc20 (bottom) levels in NDB mitotic extracts before and after three rounds of immunodepletion, as detected by immunoblotting (IB). Plk1 and Cdc20 levels were normalized to Tubulin and Cdk1, respectively. Both normalized and raw values are presented as percentages. Immunoprecipitation (IP) efficiency is shown on the right. c) Degradation profiles of phospho-mutant E2F8 variants and Securin (positive control) in NDB mitotic extracts depleted of Plk1 (control) or Cdc20.

      __ ---__

      Point 4. This study focuses on two proteins of the E2F family. These two proteins share similar domains, phosphorylation sites and a KEN box. However, their sensitivity to APC is different. What might explain this difference? Are there any inhibitory sequences for E2F7? Or why is the KEN box functional in E2F8 but not in E2F7?

      This is an excellent question. Here are our thoughts: The processivity of polyubiquitination by the APC/C varies between substrates in ways that influence degradation rate and timing (PMID: 16413484). Although E2F7 and E2F8 are related, their sequence identity is below

      50%, and their C-terminal domains differ substantially (see below) [FIGURE]. These structural differences likely contribute to differences in APC/C-mediated processivity and, consequently, to variations in protein half-lives. Additionally, E2F8 contains two functional KEN-boxes involved in its degradation, whereas E2F7 has only one. This may increase the kon rate of E2F8 for the APC/C, further enhancing its recognition and ubiquitination. Furthermore, re-examining the study by de Bruin and Westendorp (PMID: 26882548, Figure 2f; copied below), we note that the dynamic of inducibly expressed EGFP-tagged E2F7 in cells exiting mitosis is milder compared to E2F8 (see the black lines in both charts). This, as well as the oversensitivity of E2F7 degradation to Cdh1 downregulation accord with E2F7 being less potent substrate of APC/CCdh1.

      Figure legend: Adapted from Boekhout et al., 2016; ____EMBO Reports. Figure 2, Panel F. ____PMID: 26882548.


      The stability of the E2F7 fragment in cells and extracts was unexpected. We initially hypothesized that the unique N-terminal tail of E2F7 masks the KEN-box, functioning as an inhibitory sequence. However, removal of this region did not restore degradation (original manuscript; Figure 1e). Furthermore, extending the fragment by 20 additional residues failed to confer degradation (original manuscript; Figure S2). These observations suggest that E2F7 may require a distal or modular docking site for APC/C recognition. We did not pursue this question further.

      Point 5. An additional element that could strengthen this work would be referencing the study by Catherine Lindon: J Cell Biol, 2004 Jan 19;164(2):233-241. doi: 10.1083/jcb.200309035. In Figure 1 of this article, there is a degradation kinetics analysis of APC/C complex substrates such as Aurora-A/B, Plk1, cyclin B1, and Cdc20. This could help position the degradation of E2F7/8 relative to known APC/C targets. This can be achieved by synchronizing cells with nocodazole and then removing the drug to allow cells to progress and complete mitosis.

      This is an interesting point and one we should have clarified better previously. The temporal dynamics of E2F8 in synchronized HeLa S3 cells, relative to three known APC/C substrates, were reported in our previous study (PMID: 31995441; Figure 1a, copied on the right). Specifically, protein levels were measured for Cyclin B1, Securin, and Kifc1. Unlike Cyclin B1 and Securin, which are targeted by both APC/CCdc20 and APC/CCdh1, Kifc1 is degraded exclusively by APC/CCdh1. Cells were released from a thymidine–nocodazole block.

      Following Reviewer #3’s comment, we re-blotted the original HeLa S3 synchronous extracts. The new data [FIGURE] can be incorporated into the revised manuscript if requested.

      Point 6. Minor points: Does phosphorylation of E2F7-8 proteins alter their NMR profile? This could help understand how phosphorylation/dephosphorylation affects their sensitivity to the APC/C complex.

      Excellent suggestion. Indeed, we had originally aimed to include a more extensive set of NMR data in this manuscript. Our goal was to monitor E2F7 and E2F8 fragments in cell extracts and assess structural changes induced by phosphorylation and dephosphorylation during mitosis and mitotic exit. However, purifying the E2F7 fragment proved more challenging than anticipated. In addition, the extract-to-substrate ratio requires further optimization: Substrate concentrations must be high enough for reliable NMR detection, but below levels that would saturate the enzymatic activity in the extracts.

      That said, the short answer to the reviewer’s question is Yes: NMR profiles of E2F7 and E2F8 fragment do change following incubation with recombinant Cdk1–Cyclin B1 (see next page). If possible, we wish to exclude these NMR data from the manuscript.

      Point 7. Do these substrates bind to the APC/C complex before degradation? Does E2F7 bind better than E2F8?

      We were unable to detect interactions between endogenous E2F7 and E2F8 and the APC/C complex. In general, detecting endogenous E2F8, and especially E2F7, by immunoblotting proved challenging, making co-immunoprecipitation (Co-IP) even more difficult.

      Figure legend: NMR-based monitoring of E2F7 (a-c) and E2F8 (d-f) phosphorylation by Cdk1.

      a(d). 15N,1H-HSQC spectrum of E2F7(E2F8) prior to addition of Cdk1. Threonine residues of interest, T45 (T20) conforming to the consensus sequence (followed by a proline), and T84 (T60) lacking the signature sequence are annotated. b(e). Strips from the 3D-HNCACB spectrum used for assigning E2F7(E2F8) residues. Black (green) peaks indicate a correlation with the 13Cα (13Cβ) of the same and previous residues. The chemical shifts assigned to T45 (T20) and T84 (T60) match the expected values for K44(K19) and P83(P59), thereby confirming the assignment. c(f). Top, overlay of subspectra before adding Cdk1 (black) and after 16 h of activity (red) at 298 K. Bottom, change in intensities of the T45/T84 in E2F7 and T20/T60 in E2F8 showing how NMR monitors phosphorylation and distinguishes between various threonine residues.


      However, interactions between EGFP-tagged E2F7 snd E2F8 and Cdh1 have been demonstrated previously (PMID: 26882548, Figure 2e). In contrast, only the N-terminal fragment of E2F8, but not the corresponding fragment of E2F7, was found to bind Cdh1 (see figure on the right). This observation is consistent with the stability of the E2F7 fragment in APC/C-active extracts.

      __Figure legend: N-terminal fragment of E2F8 but not E2F7 binds Cdh1. __

      Co-Immunoprecipitation (IP) was performed in HEK293 cells transfected with EGFP-tagged E2F7/E2F8 fragments, using GFP-Trap® (Chromotek, Cat#: GTMA-20). Antibodies used for immunoblotting: ant-GFP (Santa Cruz Biotechnology: Cat#: SC-9996); anti-Cdh1 (Sigma-Aldrich, Cat#: MABT1323).


      Point 8. Why do the authors state that 250 µM of LB-100 has little effect on APC/C activity?

      We thank Reviewers #2 and 3 for raising this point. As shown in the manuscript, treatment with 250 µM LB-100 does not abolish APC/C-mediated degradation (otherwise, the assay would not be viable). However, it does attenuate degradation kinetics, as reflected by the prolonged half-lives of Securin and Geminin (see figure below).

      __Figure legend: APC/C-specific activity in cell extracts treated with LB-100. __

      Time-dependent degradation of EGFP–Geminin (N-terminal fragment of 110 amino acids) and Securin in extracts supplemented with LB-100 and/or UbcH10 (recombinant). A control reaction contained dominant-negative (DN) UbcH10. Proteins (IVT, 35S-labeled) were resolved by SDS-PAGE and autoradiography.


      Point 9. How can E2F8 be a substrate for both the SCF and APC/C complexes? (If I understood correctly.)

      This can happen because they are degraded by different E3 at different times during the cell cycle. To clarify further, certain proteins can be targeted by both the APC/C and SCF complexes, reflecting distinct regulatory needs. A classic example is CDC25A, as shown by M. Pagano and A. Hershko in 2002 (PMID: 12234927). Additional examples include the APC/C inhibitor EMI1 (PMIDs: 12791267 [SCF] and 29875408 [APC/C]).

      Reviewer #3 (Significance (Required)): This manuscript presents a well-structured study on the regulatory interplay between Cdk and Phosphatase in controlling the degradation of atypical E2Fs, E2F7 and E2F8. The work is relevant in the field of cell cycle regulation and provides new mechanistic insights into how phosphorylation and dephosphorylation govern APC/C-mediated degradation. The use of complementary cell-based and in vitro approaches strengthens the study, and the findings have significant implications for understanding the timing of transcriptional regulation in cell cycle progression.

      We wish to thank Reviewer #3 for their positive and encouraging view of our work.

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

      Learn more at Review Commons


      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.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The 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).

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The 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

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

      Learn more at Review Commons


      Reply to the reviewers

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

  3. Dec 2025
    1. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      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. *

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In this 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.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      This study identifies the E3 ubiquitin ligase Makorin 2 (MKRN2) as a novel regulator of stress granule (SG) dynamics and proteostasis. Using APEX proximity proteomics, the authors demonstrate that inhibition of the ubiquitin-activating enzyme UBA1 with TAK243 alters the SG proteome, leading to depletion of several E3 ligases, chaperones, and VCP cofactors. Detailed characterization of MKRN2 reveals that it localizes to SGs in a ubiquitination-dependent manner and is required for proper SG assembly, coalescence, and disassembly. Functionally, MKRN2 prevents the accumulation of defective ribosomal products (DRiPs) within SGs, thereby maintaining granulostasis. The study provides compelling evidence that ubiquitination, mediated specifically by MKRN2, plays a critical role in surveilling stress-damaged proteins within SGs and maintaining their dynamic liquid-like properties.

      Major issues:

      1. Figures 1-2: Temporal dynamics of ubiquitination in SGs. The APEX proteomics was performed at a single timepoint (90 min heat stress), yet the live imaging data show that SG dynamics and TAK243 effects vary considerably over time:
        • The peak or SG nucleation was actually at 10-30 min (Figure 1B).
        • TAK243 treatment causes earlier SG nucleation (Figure 1B) but delayed disassembly (Figure 1A-B, D). A temporal proteomic analysis at multiple timepoints (e.g., 30 min, 60 min, 90 min of heat stress, and during recovery) would reveal whether MKRN2 and other ubiquitination-dependent proteins are recruited to SGs dynamically during the stress response. It would also delineate whether different E3 ligases predominate at different stages of the SG lifecycle. While such experiments may be beyond the scope of the current study, the authors should at minimum discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition.
      2. Figures 2D-E, 3G: MKRN2 localization mechanism requires clarification. The authors demonstrate that MKRN2 localization to SGs is dependent on active ubiquitination, as TAK243 treatment significantly reduces MKRN2 partitioning into SGs (Figure 2D-E). However, several mechanistic questions remain:
        • Does MKRN2 localize to SGs through binding to ubiquitinated substrates within SGs, or does MKRN2 require its own ubiquitination activity to enter SGs?
        • The observation that MKRN2 overexpression increases SG ubiquitin content (Figure 3G-H) could indicate either: (a) MKRN2 actively ubiquitinates substrates within SGs, or (b) MKRN2 recruitment brings along pre-ubiquitinated substrates from the cytoplasm.
        • Is MKRN2 localization to SGs dependent on its E3 ligase activity? A catalytically inactive mutant of MKRN2 would help distinguish whether MKRN2 must actively ubiquitinate proteins to remain in SGs or whether it binds to ubiquitinated proteins independently of its catalytic activity. The authors should clarify whether MKRN2's SG localization depends on its catalytic activity or on binding to ubiquitinated proteins, as this would fundamentally affect the interpretation of its role in SG dynamics.
      3. Figures 3-4: Discrepancy between assembly and disassembly phenotypes. MKRN2 knockdown produces distinct phenotypes during SG assembly versus disassembly. During assembly: smaller, more numerous SGs that fail to coalesce (Figure 3A-E), while during disassembly: delayed SG clearance (Figure 4A-D). These phenotypes may reflect different roles for MKRN2 at different stages, but the mechanism underlying this stage-specificity is unclear:
        • Does MKRN2 have different substrates or utilize different ubiquitin chain types during assembly versus disassembly?
        • The increased SG liquidity upon MKRN2 depletion (Figure 3F) seems paradoxical with delayed disassembly- typically more liquid condensates disassemble faster. The authors interpret this as decreased coalescence into "dense and mature SGs," but this requires clarification.
        • How does prevention of DRiP accumulation relate to the assembly defect? One would predict that DRiP accumulation would primarily affect disassembly (by reducing liquidity), yet MKRN2 depletion impacts both assembly dynamics and DRiP accumulation. The authors should discuss how MKRN2's role in preventing DRiP accumulation mechanistically connects to both the assembly and disassembly phenotypes.
      4. Figure 5: Incomplete characterization of MKRN2 substrates. While the authors convincingly demonstrate that MKRN2 prevents DRiP accumulation in SGs (Figure 5C-D), the direct substrates of MKRN2 remain unknown. The authors acknowledge in the limitations that "the direct MKRN2 substrates and ubiquitin-chain types (K63/K48) are currently unknown." However, several approaches could strengthen the mechanistic understanding:
        • Do DRiPs represent direct MKRN2 substrates? Co-immunoprecipitation of MKRN2 followed by ubiquitin-chain specific antibodies (K48 vs K63) could reveal whether MKRN2 mediates degradative (K48) or non-degradative (K63) ubiquitination.
        • Given that VCP cofactors (such as UFD1L, PLAA) are depleted from SGs upon UBA1 inhibition (Figure 2C) and these cofactors recognize ubiquitinated substrates, does MKRN2 function upstream of VCP recruitment? Testing whether MKRN2 depletion affects VCP cofactor localization to SGs would clarify this pathway.
        • The authors note that MKRN2 knockdown produces a phenotype reminiscent of VCP inhibition-smaller, more numerous SGs with increased DRiP partitioning. This similarity suggests MKRN2 may function in the same pathway as VCP. Direct epistasis experiments would strengthen this connection.
      5. Alternative explanations for the phenotype of delayed disassembly with TAK243 or MKRN2 depletion- the authors attribute this to DRiP accumulation, but TAK243 affects global ubiquitination. Could impaired degradation of other SG proteins (not just DRiPs) contribute to delayed disassembly? Does proteasome inhibition (MG-132 treatment) phenocopy the MKRN2 depletion phenotype? This would support that MKRN2-mediated proteasomal degradation (via K48 ubiquitin chains) is key to the phenotype.
      6. Comparison with other E3 ligases (Supplementary Figure 5): The authors show that CNOT4 and ZNF598 depletion also affect SG dynamics, though to lesser extents than MKRN2. However:
        • Do these E3 ligases also prevent DRiP accumulation in SGs? Testing OP-puro partitioning in CNOT4- or ZNF598-depleted cells would reveal whether DRiP clearance is a general feature of SG-localized E3 ligases or specific to MKRN2.
        • Are there redundant or compensatory relationships between these E3 ligases? Do double knockdowns have additive effects?
        • The authors note that MKRN2 is "the most highly SG-depleted E3 upon TAK243 treatment"-does this mean MKRN2 has the strongest dependence on active ubiquitination for its SG localization, or simply that it has the highest basal level of SG partitioning?

      Significance

      This is a well-executed study that identifies MKRN2 as an important regulator of stress granule dynamics and proteostasis. The combination of proximity proteomics, live imaging, and functional assays provides strong evidence for MKRN2's role in preventing DRiP accumulation and maintaining granulostasis. However, key mechanistic questions remain, particularly regarding MKRN2's direct substrates, the ubiquitin chain types it generates, and how its enzymatic activity specifically prevents DRiP accumulation while promoting both SG coalescence and disassembly. Addressing the suggested revisions, particularly those related to MKRN2's mechanism of SG localization and substrate specificity, would significantly strengthen the manuscript and provide clearer insights into how ubiquitination maintains the dynamic properties of stress granules under proteotoxic stress.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

      Major comments:

      • Are the key conclusions convincing?

      The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary.

      1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment.

      2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity.

      3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation.

      1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms.

      2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance.

      3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. - Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. - Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. - Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

      Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning. - Are the data and the methods presented in such a way that they can be reproduced?

      Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions. - Are the experiments adequately replicated and statistical analysis adequate?

      Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential.

      Minor comments:

      • Specific experimental issues that are easily addressable.
        • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used
        • In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers.
        • In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates.
        • DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily.
        • The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results.
        • For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.
      • Are prior studies referenced appropriately?

        • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature
        • At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover.
        • When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly.
        • Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers.
        • The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference.
        • Are the text and figures clear and accurate?
        • In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript.
        • At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics.
        • Line 86: adjust a missing bracket
        • There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.
        • At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation.
        • At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus.
        • At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data.
        • Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal.

      Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      • In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.
      • At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays."
      • At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation.

      Significance

      General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

      • This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

      Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

      • The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).
      • Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.
      • Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.
      • There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.
      • A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

      Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

      The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

      Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      *The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

      Thank you for your positive feedback.

      *There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms? *

      In a separate technical manuscript describing the application of T-ChIC in mouse cells (Zeller, Blotenburg et al 2024, bioRxiv, 2024.05. 09.593364), we have provided a direct comparison of data quality between T-ChIC and other single-cell methods for chromatin-RNA co-profiling (Please refer to Fig. 1C,D and Fig. S1D, E, of the preprint). We show that compared to other methods, T-ChIC is able to better preserve the expected biological relationship between the histone modifications and gene expression in single cells.

      *In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors? *

      While we haven't profiled these other modifications using T-ChIC in Zebrafish, we have previously published high quality data on these histone modifications using the sortChIC method, on which T-ChIC is based (Zeller, Yeung et al 2023). In our comparison, we find that histone modification profiles between T-ChIC and sortChIC are very similar (Fig. S1C in Zeller, Blotenburg et al 2024). Therefore the method is expected to work as well for the other histone marks.

      *T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary? *

      We used the published scRNA-seq dataset with a larger number of cells to homogenize our cell type labels with these datasets, but we also cross-referenced our cluster-specific marker genes with ZFIN and homogenized the cell type labels with ZFIN ontology. This way our annotation is in line with previous datasets but not biased by it. Due the relatively smaller size of our data, we didn't expect to identify unique, rare cell types, but our full-length total RNA assay helps us identify non-coding RNAs such as miRNA previously undetected in scRNA assays, which we have now highlighted in new figure S1c .

      *Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH? *

      We appreciate that the ISH staining could be useful to validate the expression pattern of genes identified in this study. But to validate the relationships between the histone marks and gene expression, we need to combine these stainings with functional genomics experiments, such as PRC2-related knockouts. Due to their complexity, such experiments are beyond the scope of this manuscript (see also reply to reviewer #3, comment #4 for details).

      *In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern? *

      Thanks for the suggestion. In this revision, we have reanalysed a dataset of mouse ChIP-seq of H3K27me3 during mouse embryonic development by Xiang et al (Nature Genetics 2019) and find similar evidence of spreading of H3K27me3 signal from their pre-marked promoter regions at E5.5 epiblast upon differentiation (new Figure S4i). This observation, combined with the fact that the mechanism of pre-marking of promoters by PRC1-PRC2 interaction seems to be conserved between the two species (see (Hickey et al., 2022), (Mei et al., 2021) & (Chen et al., 2021)), suggests that the dynamics of H3K27me3 pattern establishment is conserved across vertebrates. But we think a high-resolution profiling via a method like T-ChIC would be more useful to demonstrate the dynamics of signal spreading during mouse embryonic development in the future. We have discussed this further in our revised manuscript.

      Reviewer #1 (Significance (Required)):

      *The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

      Thank you very much for your supportive remarks.

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

      *Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication. *

      We thank the reviewer for appreciating the quality of our study.

      *Major concerns: *

        • A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay. *

      We focused on the lineage-specific transcription factor activity during gastrulation in Figure 4 and S8 of the manuscript and discovered several interesting regulators active at this stage. During our analysis of the temporal dimension for the rest of the manuscript, we also classified the cells by their germ layer and "latent" developmental time by taking the full advantage of the single-cell nature of our data. Additionally, we have now added the cell-type-specific H3K27-demethylation results for 24hpf in response to your comment below. We hope that these results, together with our openly available dataset would demonstrate the advantage of the single-cell aspect of our dataset.

      1. *The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue. *

      Indeed we see that ~30% of the bins enriched in the pluripotent stage spread, while 70% do not seem to spread. In line with earlier observations(Hickey et al., 2022; Vastenhouw et al., 2010), we find that H3K27me3 is almost absent in the zygote and is still being accumulated until 24hpf and beyond. Therefore the majority of the sites in the genome still seem to be in the process of gaining H3K27me3 until 24hpf, explaining why we see mostly "spreading" and "stable" states. Considering most of these sites are at promoters and show signs of bivalency, we think that these sites are marked for activation or silencing at later stages. We have discussed this in the manuscript ("discussion"). However, in response to this and earlier comment, we went back and searched for genes that show H3K27-demethylation in the most mature cell types (at 24 hpf) in our data, and found a subset of genes that show K27 demethylation after acquiring them earlier. Interestingly, most of the top genes in this list are well-known as developmentally important for their corresponding cell types. We have added this new result and discussed it further in the manuscript (Fig. 2d,e, , Supplementary table 3).

      *Minors: *

        • The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them. *

      We have cited more single-cell chromatin and multiome studies focussed on early embryogenesis in the introduction now.

      *2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show. *

      Figure 1a. shows a summary of our Zebrafish TChIC workflow, which contains the unique sample multiplexing and sorting strategy to reduce batch effects, which was not applied in the original TChIC workflow. We have now clarified this in "Results".

      1. *It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C. *

      We have added the numbers to the corresponding legends.

      1. *Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages. *

      The citation seems to be correct (Fig. S4C supplements Fig. 2C, but shows mesodermal lineage cells) but the description of the legend was a bit misleading. We have clarified this now.

      *5. Figure 4C has not been cited or mentioned in the main text. Please check. *

      Thanks for pointing it out. We have cited it in Results now.

      Reviewer #2 (Significance (Required)):

      *Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. *

      *Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited. *

      *Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting. *

      *The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc. *

      *I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc. *

      Thank you for your remarks.

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

      *This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors. *

      *Major concerns *

      1. *Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset. *

      Thanks for pointing this out. We had, in fact, performed T-ChIC experiments in four rounds of biological replicates (independent clutch of embryos) and merged the data to create our resource. Although not all timepoints were profiled in each replicate, two timepoints (10 and 24hpf) are present in all four, and the celltype composition of these replicates from these 2 timepoints are very similar. We have added new plots in figure S2f and added (new) supplementary table (#1) to highlight the presence of biological replicates.

      2. *The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off. *

      Thank you for this suggestion. We did use 10-fold cross validation during training and obtained the R2 values of TF motifs from the independent test set as an unbiased estimate. However, the cutoff of R2 > 0.6 to select the TFs for classification was indeed arbitrary. In the revised version, we now report the FDR-adjusted p-values for these R2 estimates based on permutation tests, and select TFs with a cutoff of padj supplementary table #4 to include the p-values for all tested TFs. However, we see that our arbitrary cutoff of 0.6 was in fact, too stringent, and we can classify many more TFs based on the FDR cutoffs. We also updated our reported numbers in Fig. 4c to reflect this. Moreover, supplementary table #4 contains the complete list of TFs used in the analysis to allow others to choose their own cutoff.

      3. *Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes. *

      We agree that independent validation of the functions of our predicted TFs on target gene activity would be important. During this revision, we analysed recently published scRNA-seq data of Saunders et al. (2023) (Saunders et al., 2023), which includes CRISPR-mediated F0 knockouts of a couple of our predicted TFs, but the scRNAseq was performed at later stages (24hpf onward) compared to our H3K4me1 analysis (which was 4-12 hpf). Therefore, we saw off-target genes being affected in lineages where these TFs are clearly not expressed (attached Fig 1). We therefore didn't include these results in the manuscript. In future, we aim to systematically test the TFs predicted in our study with CRISPRi or similar experiments.

      4. *The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression. *

      We appreciate the suggestion that indeed PRC2-disruption followed by T-ChIC or other forms of validation would be needed to confirm whether the H3K27me3 spreading is indeed causally linked to the silencing of the identified target genes. But performing this validation is complicated because of multiple reasons: 1) due to the EZH2 contribution from maternal RNA and the contradicting effects of various EZH2 zygotic mutations (depending on where the mutation occurs), the only properly validated PRC2-related mutant seems to be the maternal-zygotic mutant MZezh2, which requires germ cell transplantation (see Rougeot et al. 2019 (Rougeot et al., 2019)) , and San et al. 2019 (San et al., 2019) for details). The use of inhibitors have been described in other studies (den Broeder et al., 2020; Huang et al., 2021), but they do not show a validation of the H3K27me3 loss or a similar phenotype as the MZezh2 mutants, and can present unwanted side effects and toxicity at a high dose, affecting gene expression results. Moreover, in an attempt to validate, we performed our own trials with the EZH2 inhibitor (GSK123) and saw that this time window might be too short to see the effect within 24hpf (attached Fig. 2). Therefore, this validation is a more complex endeavor beyond the scope of this study. Nevertheless, our further analysis of H3K27me3 de-methylation on developmentally important genes (new Fig. 2e-f, Sup. table 3) adds more confidence that the polycomb repression plays an important role, and provides enough ground for future follow up studies.

      *Minor concerns *

      1. *Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development. *

      We agree that H3K27me3 alone would not be sufficient to fully understand the repressive chromatin state. Extension to other chromatin marks and DNA methylation would be the focus of our follow up works.

      *2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided. *

      As mentioned in the manuscript, we provide an open-source pre-processing pipeline "scChICflow" to perform all these steps (github.com/bhardwaj-lab/scChICflow). We have now also provided the configuration files on our zenodo repository (see below), which can simply be plugged into this pipeline together with the fastq files from GEO to obtain the processed dataset that we describe in the manuscript. Additionally, we have also clarified the peak calling and post-processing steps in the manuscript now.

      *3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added. *

      We have now publicly released the .h5ad files with raw counts, normalized counts, and complete gene and cell-level metadata, along with signal tracks (bigwigs) and peaks on GEO. Additionally, we now also released the source datasets and notebooks (.Rmarkdown format) on Zenodo that can be used to replicate the figures in the manuscript, and updated our statements on "Data and code availability".

      *4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference. *

      Thank you for spotting them. We have fixed these issues.

      Reviewer #3 (Significance (Required)):

      The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

      Thank you for your comments. We hope to have addressed your concerns in this revised version of our manuscript.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors.

      Major concerns

      1. Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset.
      2. The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off.
      3. Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes.
      4. The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression.

      Minor concerns

      1. Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development.
      2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided.
      3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added.
      4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference.

      Significance

      The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication.

      Major concerns:

      1. A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay.
      2. The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue.

      Minors:

      1. The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them.
      2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show.
      3. It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C.
      4. Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages.
      5. Figure 4C has not been cited or mentioned in the main text. Please check.

      Significance

      Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited.

      Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting.

      The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc.

      I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

      There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms?

      In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors?

      T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary?

      Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH?

      In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern?

      Significance

      The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

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

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements

      We thank the reviewers for their overall support, thorough review, and thoughtful comments. The points raised were all warranted and we feel that addressing them has improved the quality of our manuscript. Below we respond to each of the points raised.

      2. Point-by-point description of the revisions

      Reviewer #1

      Minor comments:

      Are the lgl-1; pac-1 M-Z- double mutants dead? Only the phenotype of pac-1(M-Z-); lgl-1 (M+Z-) is shown. In figures and text throughout, it should be clear whether mutants are referring to zygotic loss or both maternal and zygotic loss, as this distinction could have major implications on the interpretation of experiments.

      Almost all experiments we performed used a combination of RNAi of lgl-1 in a homozygous pac-1 null mutant background, or the other way around. RNAi should eliminate maternal product, but we hesitate to use the terminology M/Z since it has previously been used for protein degradation strategies.

      We have updated the text and figure 1 to address the potential of maternal product masking earlier phenotypes, and performed additional RNAi experiments to demonstrate that the phenotypes obtained by RNAi for either pac-1 or lgl-1 in a homozygous mutant background for the other are the same as for the genetic double mutant. The results are shown as additional images and quantifications in figure 1B,C. We also updated the legend to figure 1 to make it clear that double genetic mutants are obtained from heterozygous lgl-1/+ parents.

      Regarding the phenotype of lgl-1; pac-1 M-Z- double mutants: assuming the reviewer refers to M-Z- double genetic mutants, we cannot make such embryos as the pac-1(M-Z-); lgl-1(M+Z-) animals are already lethal.

      In Figure 1C, it would be more appropriate to show a fully elongated WT embryo to contrast with arrested elongation in mutant embryos.

      We agree with the reviewer and have replaced the 2-fold WT embryo with a 3-fold embryo.

      Is the lateral spread of DLG-1 in double mutant embryos a result of failure to polarize DLG-1, or failure to maintain polarity? This should be straightforward to address in higher time resolution movies.

      We have analyzed additional embryos at early stages of development. In lgl-1; pac-1 embryos we never see the appearance of complete junctions: defects are apparent already at dorsal intercalation. We interpret these results as a failure to properly polarize DLG-1. We have added additional images to Figure S2 and added this sentence to the text: Imaging of embryos from early stages of development on showed that normal continuous junctional DLG-1 bands are never established in pac-1(RNAi); lgl-1(mib201) embryos (Fig. S2B).

      The lack of enhancement of hmp-1(fe4) by lgl-1(RNAi) is quite interesting, given that pac-1 does enhance hmp-1(fe4). To rule out the possibility that this result stems from incomplete lgl-1 RNAi, this experiment should be repeated using the lgl-1 null mutant.

      We have done this experiment by recreating the fe4 S823F mutation in the lgl-1(null) mutant background as well as in the wild-type CGC1 background using CRISPR/Cas9. The phenotype of both was similar, but differs from that of the original PE97 strain. In the original strain, there is ~50% embryonic lethality but worms that complete embryogenesis grow up to be fertile adults. In our new "fe4" strains, nearly all animals are severely malformed with little to no elongation taking place. We are able to maintain both strains (with and without lgl-1) homozygous but with difficulty as only ~5% of animals grow up and give progeny. Apparently, there are genetic differences between PE97 and our CGC1 background that cause phenotypic differences despite having the same amino acid change in HMP-1.

      Nevertheless, using our original embryonic viability criterium of 'hatching', loss of lgl-1 does not enhance the S823F mutation. We have included the following text in the manuscript:

      To rule out that the lack of enhancement by lgl-1(RNAi) is due to incomplete inactivation of lgl-1, we also re-created the hmp-1(fe4) mutation (S823F) by CRISPR in lgl-1(mib201) mutant animals and wild-type controls. The phenotype of the S823F mutant we created is more severe than that of the original PE97 hmp-1(fe4) strain, with only ~5% of animals becoming fertile adults (Fig. S2F). This likely represents the presence of compensatory changes that have accumulated over time in PE97. Nevertheless, consistent with our RNAi results, the presence of lgl-1(mib201) did not further exacerbate the phenotype of HMP-1(S823F) (Fig. S2E, F). Taken together, the lack of enhancement of hmp-1(S823F) mutants by inactivation of loss of lgl-1 This observation argues against a primary role for lgl-1 in regulating cell junctions.

      • Related to point 4, do pac-1 or lgl-1 null mutants enhance partial knockdown of junction protein DLG-1, or is this effect (of pac-1) specific to HMP-1/AJs?*

      We have attempted to address this point using feeding RNAi against dlg-1. However, we were not able to obtain partial depletion of DLG-1. On RNAi feeding plates, control, pac-1, and lgl-1 animals did not show significant embryonic lethality. We checked RNAi effectiveness with a DLG-1::mCherry strain and found RNAi by feeding to be very ineffective. Since we could not deplete DLG-1 to a level that results in partial embryonic lethality, we were not able to address this question properly.

      Does lgl-1 loss affect PAC-1 protein localization and vice versa?

      It does not. We have added the following text and a figure panel: Loss-of-function mutants that strongly enhance a phenotype are often interpreted as acting in parallel pathways. We therefore examined whether loss of lgl-1 or pac-1 alters the localization of endogenously GFP-tagged LGL-1 or PAC-1. In neither null background did we detect changes in the subcellular localization of the other protein, consistent with LGL-1 and PAC-1 functioning in parallel pathways (Fig. S1D).

      Reviewer #2

      Very little of the imaging data are analyzed quantitatively, and in many cases it is not clear how many embryos were analyzed. While the images that are presented show clear defects, readers cannot determine how reproducible, strong or significant the phenotypes are.

      We completely agree with the reviewer that interpretation of our data requires this information and apologize for the omission in the first manuscript version. The phenotypes are highly penetrant and consistent (timing of arrest, % lethality, junctional defects), and we have now added quantifications throughout the manuscript.

      In particular, the data below should be quantified and, where possible, analyzed statistically:

      • The frequency of the various junctional phenotypes shown in 2C

      We have now quantified the junctional phenotypes. The junctional defects are highly penetrant: >90% of lgl-1; pac-1 embryos have junctional defects (new Fig. 2B). We used airy-scan confocal imaging to analyze the distribution of the different phenotypes (unaffected, spread laterally, and ring-like pattern). The results are shown in Fig. 2G.

      • The expansion of DLG-1::mCherry in pac-1 lgl-1 embryos should be quantified (related to Figure 2B). For example, the percentage of membrane (marked by PH::GFP) occupied by DLG-1 could be quantified.

      We have performed this quantification, shown in Fig. 2D.

      - Similarly, the expansion of the aPKC domain should be quantified (Figure 3A).

      An objective quantification of aPKC signal is difficult due to the relatively weak expression of aPKC::GFP and the lack of a clear demarcating boundary. This is part of the reason we measured tortuosity as a more quantifyable indicator of apical domain expansion. We have now added a qualitative observation table as Figure 3B. In addition, we have expanded the quantification of cell geometry by measuring lateral and basal surfaces. Lateral surfaces were decreased. We added the following text:

      To better understand the reason for the change in geometry, we also measured the lengths of the lateral and basal surfaces (Fig. 3F). We found that the absolute lengths of the apical surfaces were not significantly different between pac-1(RNAi); lgl-1(mib201) and control animals. Instead, the lengths of the lateral domain were reduced (Fig. 3F). Hence, the more dome-shaped appearance of epidermal cells in pac-1; lgl-1 double mutant animals is due to a decrease in lateral domain size, which is consistent with the observed lateral spreading of aPKC.

      • How many embryos were analyzed for each marker shown in Figure 2A, and what proportion showed the described phenotypes? This could be given in the text or in a panel.

      We have added these numbers to panel 2B, and indicated the percentage in the text.

      • The frequency of the various junctional phenotypes shown in 4F.

      To address this, we have changed figure 4F to show three types of phenotype (strong, mild, no phenotype) and added how frequently we observed each to the panels. In rescue experiments, 18/24 embryos showed no junctional defects, while 6/24 showed a mild defect (compared to 100% severe in non-rescued embryos). To make room for this and other quantifications in Figure 4, we moved the demonstration that PAC-1 is depleted by RNAi to supplemental figure S4.

      Because the genetic perturbations used are global (either deletions or RNAi), it is not established whether PAC-1/LGL-1 act in epidermal epithelial cells per se (versus an earlier requirement that manifests in epidermal epithelial cells). While I agree that this is the most likely scenario, other mechanisms are possible.

      Our experiments indeed use global depletion/deletion of lgl-1 and pac-1. We cannot exclude therefore that other tissues do not contribute to the epithelial phenotypes. We assume that other tissues would be affected as well, and in fact have observed abnormal looking pharynx tissue (see our response to reviewer 3 below for examples). As the epidermis is one of the first tissue to develop it is likely the first in which phenotypes become apparent.

      In particular, the overall GFP::aPKC levels appear notably higher in pac-1 lgl-1 embryos in Figure 3A. aPKC levels should be quantified to determine if this is true of pac-1 lgl-1 embryos. If so, couldn't that explain (or at least contribute to) the observed phenotypes?

      Overall higher levels could indeed contribute to the phenotype. However, we have now quantified total aPKC levels in control and pac-1; lgl-1 embryos found no difference between them. We have added the following text to the manuscript: To determine if increased expression of aPKC might explain the broadened apical localization, we measured total intensity levels of aPKC::GFP. However, we detected no differences in fluorescence levels between control and pac-1(RNAi); lgl-1(mib201) animals (Fig. S3B, C).

      Minor

      Figure 4: For completeness, please include the embryonic viability of pac-1 lgl-1 +/- embryos treated with EV and cdc-42(RNAi), as was done for pac-1 lgl-1 pkc-3(ts) in Figure 4E. Presumably the increased proportion of viable embryos with the lgl-1 deletion allele is reflected in an overall increase in embryonic viability.

      The embryonic viability indeed increases, but not as much as one might think because 15% of embryos die from the cdc-42 RNAi itself. The most important rescue argument is that we can obtain adult pac-1; lgl-1 animals with cdc-42 RNAi.

      We have now included the overall rescue and the following text: Overall, cdc-42 RNAi caused a mild increase in embryonic viability (Fig. 4A). However, total embryonic viability may underestimate rescue of pac-1; lgl-1 embryonic lethality, because it also includes the ~15% lethality caused by cdc-42 inactivation itself, even among animals wild type for lgl-1.

      The orientation of the inset images in Figures 2C, 3A and 3D is confusing. An illustration showing how these images are oriented relative to each other would be helpful.

      We have added a figure showing how the junctions are oriented in the figures (Fig. 2E). We have also added supplemental videos S3 and S4 that should illustrate the phenotype more clearly as well.

      For completeness, it would be good to test whether lgl-1(delta) is also synthetically lethal with picc-1(RNAi) (Zilberman 2017).

      We like this idea and had already looked into this. Lgl-1 and picc-1 are not synthetic lethal (see graph in word file submitted). However, PICC-1 is not the only junctional localization signal for PAC-1, as demonstrated by the Nance lab. We find the data interesting but feel that it deserves a more thorough structure/function investigation of PAC-1 than we can provide here. Therefore we would prefer not to include this data.

      Reviewer #3

      We thank the reviewer for their support of our manuscript.

      A few small areas to improve this manuscript:

      p. 6 like 139: "remain" should be "remaining"

      We have fixed this typo.

      Could the authors mention what is the phenotype of the 10% of pac-1 animals that die?

      Yes. They die with pleotropic phenotypes not resembling those of our pac-1; lgl-1 double mutant embryos. We have added examples of these to Figure S1.

      Based on the Supplemental figures, it made me curious to ask: Did the authors notice changes in dorsal epidermal fusions? Cadherin normally disappears in the dorsal hyp7 cells at this time. Did the timing of the fusions change at all?

      We haven't analyzed this in detail but our time-lapse videos show that dorsal fusions still take place and do not seem to be particularly delayed (overall development is slightly delayed but the delay in fusion is consistent with overall delay).

      Again, curiosity driven by the Supplemental figures: did the authors notice defects in apical regions of internal organs, like the pharynx or intestine? The CDC-42 biosensor is asymmetrical in the developing intestine. See: DOI: 10.1242/bio.056911

      We did not pay much attention to the intestine as PAC-1 is barely detectable in this tissue. The pharynx is formed, which we can easily detect in arrested embryos as we use GFP or BFP expressed under the myo-2 promoter to mark the deletion of pac-1. While we did not look closely, we do observe defects in pharynx development.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Jarosinska and colleagues addresses a long-standing mystery in the apical/basal polarity field: why LGL-1 and PAC-1/RhoGAP19D, which are essential in Drosophila, and in some tissue culture contexts, are not essential in C. elegans embryos.

      The authors take an open-ended approach by using genetics, in the form of a genome wide RNAi screen, to find other proteins that enhance the mild phenotypes of lgl-1 mutant embryos. They uncover strong synthetic lethality when they reduce pac-1, a well-documented CDC-42 GAP that supports apical/basal polarity during early embryogenesis, and yet is also only partially required during embryogenesis.

      The phenotypic analysis to understand why the embryos die when missing both lgl-1 and pac-1 leads to a careful analysis of known junctional molecules in C. elegans. Using newly made endogenously tagged junctional proteins, including DLG-1 and AFD, so that they can examine all three C. elegans apical junction complexes, the authors find a penetrant defect in the epidermal junctions as the embryos undergo elongation, an actomyosin dependent contractile event that dramatically reshapes the embryos into long, skinny tubes. With disorganized junctions, the embryos die due to ruptures, or hernias, as shown in the Supplemental Movie 2. In addition, and quite excitingly, the apical domains of the embryos are expanded. These defects are then partially rescued by removing CDC-42 or aPKC using RNAi depletion.

      Major comments:

      The claims and conclusions are supported by the data.

      The data is presented in such a way that it is easy to understand what was done, and how measurements were obtained and evaluated.

      Rigorous documentation of how the strains were built and how the genome wide RNAi screen was conducted is included in the Supplemental files.

      Beautiful use of CRISPR to do the genetics:

      since when they made the deletion of lgl-1 they replaced the coding sequence with GFP, they could use GFP to count the animals carrying the deletion in their double mutant analysis with pac-1 deletion mutants.

      Figures are very nicely done.

      The writing is clear.

      Minor comments:

      A few small areas to improve this manuscript:

      p. 6 like 139: "remain" should be "remaining"

      Could the authors mention what is the phenotype of the 10% of pac-1 animals that die?

      Based on the Supplemental figures, it made me curious to ask: Did the authors notice changes in dorsal epidermal fusions? Cadherin normally disappears in the dorsal hyp7 cells at this time. Did the timing of the fusions change at all?

      Again, curiosity driven by the Supplemental figures: did the authors notice defects in apical regions of internal organs, like the pharynx or intestine? The CDC-42 biosensor is asymmetrical in the developing intestine. See: DOI: 10.1242/bio.056911

      Significance

      This study raises interesting and important questions for the general polarity field. Early embryos have hugely redundant methods to maintain apical/basal polarity, which in C. elegans masked the roles for lgl-1 and pac-1 at earlier events, like compaction, when apical/basal polarity is first established. However, during elongation, when healthy strong junctions are a requirement, the double mutant loss of LGL-1 and PAC-1 results in expanded apical domain, that is lethal.

      The study will be of interest to the broader polarity community, and to developmental biologist interested in how the apical junctions are assembled and strengthened during morphogenesis. The Discussion does a good job of showing what aspects of this study are novel, and which support prior findings that suggested, for example, that PAC-1 may have roles independent of CDC-42. I appreciate the comment that our field needs more and more sensitive biosensors to fully address the changes of key polarity regulators.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary: This study focuses on the polarization of epidermal epithelial cells in C. elegans. Whereas the basolateral polarity protein is LGL-1 is required for epithelial polarity in flies, LGL-1 is dispensable for polarization and viability in C. elegans. Through a whole-genome RNAi screen, Jarosinska et al discover that the depletion of the RhoGAP PAC-1 is synthetically lethal with an lgl-1 deletion mutant. pac-1 lgl-1 double mutants have significant polarity defects in the epidermal epithelial, including mislocalization of junctional markers and expansion of the apical aPKC domain. As a result pac-1 lgl-1 double mutants fail to maintain surface epithelial and arrest development. Genetic interaction data suggest that increased CDC42 and aPKC activity in pac-1 lgl-1 contributes, as least in part, to the polarity defects and resulting embryonic lethality.

      Major comments:

      Very little of the imaging data are analyzed quantitatively, and in many cases it is not clear how many embryos were analyzed. While the images that are presented show clear defects, readers cannot determine how reproducible, strong or significant the phenotypes are. In particular, the data below should be quantified and, where possible, analyzed statistically:

      • The frequency of the various junctional phenotypes shown in 2C
      • The expansion of DLG-1::mCherry in pac-1 lgl-1 embryos should be quantified(related to Figure 2B). For example, the percentage of membrane (marked by PH::GFP) occupied by DLG-1 could be quantified.
      • Similarly, the expansion of the aPKC domain should be quantified (Figure 3A).
      • How many embryos were analyzed for each marker shown in Figure 2A, and what proportion showed the described phenotypes? This could be given in the text or in a panel.
      • The frequency of the various junctional phenotypes shown in 4F.

      Because the genetic perturbations used are global (either deletions or RNAi), it is not established whether PAC-1/LGL-1 act in epidermal epithelial cells per se (versus an earlier requirement that manifests in epidermal epithelial cells). While I agree that this is the most likely scenario, other mechanisms are possible. In particular, the overall GFP::aPKC levels appear notably higher in pac-1 lgl-1 embryos in Figure 3A. aPKC levels should be quantified to determine if this is true of pac-1 lgl-1 embryos. If so, couldn't that explain (or at least contribute to) the observed phenotypes?

      Minor

      Figure 4: For completeness, please include the embryonic viability of pac-1 lgl-1 +/- embryos treated with EV and cdc-42(RNAi), as was done for pac-1 lgl-1 pkc-3(ts) in Figure 4E. Presumably the increased proportion of viable embryos with the lgl-1 deletion allele is reflected in an overall increase in embryonic viability.

      The orientation of the inset images in Figures 2C, 3A and 3D is confusing. An illustration showing how these images are oriented relative to each other would be helpful.

      For completeness, it would be good to test whether lgl-1(delta) is also synthetically lethal with picc-1(RNAi) (Zilberman 2017).

      Significance

      LGL-1 is a conserved polarity protein that is essential for viability in Drosophila. In contrast, lgl-1 mutants are viable and have weak polarity phenotypes in C. elegans. A previous study showed that LGL-1 acts redundantly with the posterior polarity proteins PAR-2 during establishment of anterior/posterior polarity in the one-cell worm embryo. Here, Jarosinska et al show that LGL-1 acts redundantly with another protein, the RhoGAP protein PAC-1, in the polarization of the embryonic epidermal epithelial. The strength of this study is the identification of redundant roles for PAC-1 and LGL-1, the apparent strength of the polarity defects in the double mutant and the broader implication that LGL-1 may act in a range of redundant, cell/tissue specific pathways to regulate polarity. The primary weakness of this study is the lack of quantification. Additionally, the aPKC and CDC42 genetic interaction data hint at potential pathways, but fall short of establishing LGL-1's or PAC-1's mechanism of action.

      Advance: This works identifies a redundant genetic interaction between LGL-1 and PAC-1. While the data require additional quantification, the phenotypes presented appear clear and strong. Although the molecular mechanism by which LGL-1 and PAC-1 act is not well established in the current work, the core observation is significant and should provide a foundation for future studies dissecting the molecular mechanisms.

      Audience: This work will be of interest to a broad audience. LGL-1 is conserved and its role in cell polarization and epithelial polarity is very actively studied, including in mammalian systems.

      Field of expertise. C elegans embryonic development; cell polarity.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Jarosinska and colleagues address the roles of two polarity regulators, pac-1 and lgl-1, in C. elegans epidermal polarity. Loss of function mutations in either of these gene individually does not block polarization, but through a genome-wide RNAi screen, the authors find that pac-1 and lgl-1 enhance each other to cause apical-basal polarity defects and arrest during epidermal morphogenesis. The remainder of the paper focuses on testing genetic interactions between both proteins and AJ proteins (HMP-1) as well as apical proteins (CDC-42, PKC-3). These experiments reveal some interesting differences in how lgl-1 and pac-1 interface with junctional proteins (pac-1 enhances hmp-1 but lgl-1 does not) and apical proteins (lgl-1 suppresses pkc-3 or cdc-42 partial loss but pac-1 does not).

      Minor comments:

      1. Are the lgl-1; pac-1 M-Z- double mutants dead? Only the phenotype of pac-1(M-Z-); lgl-1 (M+Z-) is shown. In figures and text throughout, it should be clear whether mutants are referring to zygotic loss or both maternal and zygotic loss, as this distinction could have major implications on the interpretation of experiments.
      2. In Figure 1C, it would be more appropriate to show a fully elongated WT embryo to contrast with arrested elongation in mutant embryos.
      3. Is the lateral spread of DLG-1 in double mutant embryos a result of failure to polarize DLG-1, or failure to maintain polarity? This should be straightforward to address in higher time resolution movies.
      4. The lack of enhancement of hmp-1(fe4) by lgl-1(RNAi) is quite interesting, given that pac-1 does enhance hmp-1(fe4). To rule out the possibility that this result stems from incomplete lgl-1 RNAi, this experiment should be repeated using the lgl-1 null mutant.
      5. Related to point 4, do pac-1 or lgl-1 null mutants enhance partial knockdown of junction protein DLG-1, or is this effect (of pac-1) specific to HMP-1/AJs?
      6. Does lgl-1 loss affect PAC-1 protein localization and vice versa?

      Significance

      Overall, the manuscript provides additional insights into apical-basal polarization in C. elegans and demonstrates that lgl-1 is likely working in a similar way as in Drosophila, despite the lack of a phenotype in single lgl-1 mutants. I found the experiments to be done rigorously and interpretations of the data appropriate. All of my suggestions on improving the manuscript are minor; suggested experiments should be viewed as optional ways to strengthen the conclusions/impact of the study.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary

      In this paper, Wang and Shu et al. investigate the extent to which the negative binomial (NB) distribution captures the statistical properties of single-cell like count data and the effects of using this model to interpret biophysical parameters. Assuming an underlying telegraph model of transcription, they demonstrate how the NB can produce similar if not equivalent fits to simulated data from various parameter regimes, regimes which can, notably, fall outside of the bursty transcription limit in which the telegraph model is known to have a NB form. The authors then assess how model selection favors the NB or Poisson models over the underlying telegraph model, and how technical noise can lead to greater selection/representation of the NB over the parameter regime. Finally, they demonstrate how the broader applicability of the NB impacts inference of burst size and frequency (commonly inferred from NB fits on single-cell data), preserving relative rather than absolute information.

      The authors use both method of moments and MLE-based approaches to obtain and compare model fits over the same parameter regimes. They also develop the aeBIC metric which balances parametric complexity and distributional similarity to the desired, ground truth distribution, to more quickly approximate the BIC (used for model selection).

      Major comments:

      The likelihood of model fits is used as a main criteria for model selection and comparison (e.g., in the BIC/aeBIC metrics), however it is possible that analysis of the curvature of the likelihood may suggest greater uncertainty/less information about parameter estimates for the different statistical models across the transcriptional regimes tested. Since a major component of this study is to demonstrate to readers that nuanced model selection is important for interpreting single-cell data, it would support these efforts to see if the telegraph versus NB model fits, for example, demonstrate differences in their respective Hessian matrices for the MLE estimates. This would help determine, for those interested in comparing these fits on their data, if there is potential here to distinguish the more optimal/true model or not (i.e., what the extent of the limitations are). The authors describe how in the infinite limit of N_sigma the NB and telegraph models converge to the same distribution, which provides another biological scenario outside transcriptional bursting where the NB can be interpreted as a good statistical model. However, though many parameter regimes are possible not all are observed in real data. Thus for readers to understand how likely these regimes are to be present in the data it would be helpful to discuss in what biological scenarios such a limit may appear and if it is likely to be a common instance, etc (perhaps given the ranges of on/off times observed in the literature https://pmc.ncbi.nlm.nih.gov/articles/PMC10860890/). This would parallel the discussion in the study on the bursty transcription model, often described in the literature as a widespread phenomenon. The p_cap parameter is described as representing technical capture and affords the conclusion in the Discussion that the NB can improve capture of technical noise beyond the biological noise in the system. However, as mentioned later in the Discussion, this effect could also arise from cell to cell differences in transcription rate (extrinsic, biological noise), which cannot be distinguished in this model. This point should be made clearer earlier on, as without use of control genes/spike-ins/etc we cannot distinguish the biological and technical components encompassed by the p_cap term (i.e., whether or not a spread in total UMIs observed over droplets is due to biological or technical capture differences). Since the aeBIC is being presented as a new, faster method in this study, the timing and memory usage in performing these calculations, for each model, should be presented somewhere. The Methods should also have a more explicit description of the steps/tools used to calculate the aeBIC.

      Minor comments:

      Figure S4 mentioned comparison of scRNA-seq with smFISH data to approximate p_cap, however given that smFISH data would have its own technical biases it does not seem exactly clear how a map from smFISH to scRNA-seq would work such as to illuminate the gap incurred by technical bias/capture. Perhaps previous literature/methods doing this can be cited here, or this idea can be fleshed out in the Discussion text for readers interested in better estimating p_cap. In Figure 4 the pink color of the Poisson in c is hard to see, and it may be easier to write the names of the different models in the respective regions that they cover (similarly in Figure 5 c) For Figure 8, it may be easier for the reader to interpret the several plots in a row by repeating the x-axis labels under each set of plots and collating all the legend labels into one box somewhere near the first plots.

      Significance

      General assessment: Overall, the paper is a clear and concise view on the use of the NB in analysis of sparse, transcriptomic count data, the potential effects of technical and biological noise on the pertinence of the NB as the statistical representation, and the impacts on user interpretation of biophysical parameters from these model fits. This study is useful for both biologists and computational scientists looking to gain mechanistic insight from single-cell data.

      The strength of the paper is that the methodology is straightforward and uses simple numerical experiments to demonstrate how and when several common distributions can describe the type of data we encounter in single-cell genomics. They additionally connect these results to common biological interpretations from single-cell measurements and outline regimes in which inferences are likely to be incorrect.

      The paper could benefit from more discussion on the biological interpretations of the findings and regimes analyzed, particularly to help readers interested in how this impacts their data analysis. Supplemental analysis on whether other criteria could potentially distinguish the models in question would also help support the conclusions of model selection/identifiability and if other properties of these model fits can be used for selection or not.

      Advance: The study builds on others in the field by not just fitting several common models to this type of sparse, transcriptomic count data but also describing why these overlapping fits arise and how that affects biological interpretation. Often the focus is more on choosing a sufficient statistical representation without the underlying, mechanistic connections between the models. The results here are thus more technical and mechanistic in nature, describing both the theoretical connections between common single-cell count models and their biophysical interpretations.

      Audience: This result is likely to be of interest to scientists performing data analysis and method development in single-cell genomics, particularly with mechanistic insight in mind. This would be more of interest within the domain of transcriptomics, but it also presents a methodology for studying limitations of identifiability in noisy systems which could be of interest to other biological domains.

      My expertise is in developing representation learning methods and stochastic models of transcription for single-cell biology, which covers the classical models described in this study.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The study is generally well reasoned and thorough, and should be of interest to the community. My only critique relates to the treatment of extrinsic noise and the related discussion: Many studies have concluded that extrinsic noise (e.g., cell-to-cell variability in the transcription rate) is a larger contribution to noise in gene expression than intrinsic noise. (For example, see the seminal review by Raj and van Oudenaarden (PMID: 18957198) and early examples such as Raser and O'Shea: Raser JM, O'Shea EK. Science. 2004;304:1811. doi: 10.1126/science.1098641). For this reason, one must be careful in assuming that the telegraph model by itself fully captures biological variability. I believe this point could be more clearly made in the paper. Did the authors treat a case in which the gene undergoes state switching, but where there is also a significant contribution of extrinsic noise, for example, through variability in the transcription rate and/or other papers? I could not tell for sure if this was explicitly studied. This would be an important scenario to study, because it may be the most likely. I would have thought that this is the most biologically realistic scenario (i.e., strong contributions of both intrinsic and extrinsic noise, along with state switching). My prior assumption has been that the NB model is often empirically indicated because it somehow well captures this combination of intrinsic (including state switching) + extrinsic noise. Could the authors comment on whether this assumption is consistent with their findings? (Neither Case I or Case II in the manuscript captures this scenario). Related to the treatment of extrinsic noise, I was confused by this sentence: "Any variation in the effective transcription rate due to variability in the transcription rate (extrinsic noise on the transcription rate) between cells is indistinguishable from variability in the transcript capture probability and hence is automatically accounted for in our present method. " But doesn't the distribution of transcription rates vary significantly, depending on whether the variation comes from technical noise versus extrinsic biological variability? For example, one source of extrinsic biological variability is differences in RNA polymerase concentrations in different cells. Wouldn't one need to know what kind of distribution to use to capture these effects? In this case, I believe one would need to study various types of compound distributions, depending on the assumptions underlying the biological extrinsic variability.

      Significance

      This paper presents a thorough study of the conditions under which the negative binomial model of transcript distributions can map onto other widely used models, namely the telegraph model of stochastic gene expression. The study is generally well reasoned and thorough, and should be of interest to the community (namely: single cell transcriptomics community, bio mathematicians, biological noise community).

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors present an investigation into the surprising effectiveness of the negative-binomial distribution in modelling transcript counts in single cell RNA sequencing experiments. With experimentally motivated ground-truth models that incorporate transcriptional bursting, they show that when transcription activity is large compared to degradation these distributions coincide. With a novel model selection metric, they indicate the regions of parameter space in which the negative-binomial model is a good approximation to the underlying true model. With this procedure, they also indicate that transcriptional burst parameters are unlikely to be reconstructed by an effective negative-binomial function, but that nevertheless, relative rankings between genes can be identified robustly.

      I would like to commend the authors on an interesting and fairly comprehensive investigation on a topic of considerable importance in the interpretation of single cell RNA sequencing experiments, and on a well written paper. I have no major comments on issues that affect the conclusions of the paper, although I have a few minor suggestions that might aid reader's understanding of the results and their applicability.

      General

      It would be nice to have a comparison with some real data for the burst frequency and size, just to indicate to the reader how important these regions are compared to what might be measured. For example, if most genes are outside of the region that does not accommodate the NB distribution, then the conclusion is quite different than if most real counts are unlikely to accommodated by the NB.

      Inter-cellular variability of transcription dynamics is quite a significant point of interest, so it would be good to have stated earlier that this is not considered, with the mitigation that is noted later. This is particularly important given that in the introduction, the cases mentioned seem to imply that an NB distribution would be more likely with higher inter-cellular variability.

      Introduction

      It would be nice to have a bit more detail here, for example on what UMIs are, and what the parameters of the NB distribution represent in general.

      For smFISH, I would have thought that the more simple explanation is that the NB is often the simplest distribution with some overdispersion that fits the data, and the parameters don't necessarily need to be biologically interpretable?

      It's noted later that the capture probability of modern RNASeq protocols can be ~0.3, which doesn't seem very different compared to 0.7-0.9 of smFISH, so some context here would be good.

      Results

      Eq 1: I don't think you lose anything by giving the Pochammer symbol and Kummer confluent geometric function explicitly here, and it would make it it a lot easier to read. That said, this equation also seems to come out of nowhere, so a reference would be nice.

      I think the moment matching is reasonably convincing, but it might require a little more explicit motivation for a more general audience.

      Thm. 1: Do these converge at similar rates, and if not, does that have any implications for the interpretation of the comparisons (as these are evaluated with specific values)? This might be worth a short comment.

      Fig 3. In the description for this in the text, it would be nice to have an expression of the KL divergence (and what order the arguments are in), for anyone unfamiliar.

      The discussion of the aeBIC seems a bit circuitous. A reasonable prior intention might be to average (or apply a voting function) to individual BIC values, rather than the aeBIC constructed here. And in fact the text goes on to note after the description that this is a good estimate of the expectation of the BIC after all, with some computational advantages. So it might be better to have a more straightforward presentation where this is proposed as an approximation to the expectation of the BIC in the first place.

      Section 2.4: The intro to this section could do with a bit more background of the capture, PCR, sequencing, etc, stages, and what exactly the data generated here represents. Otherwise the discussion of zero inflation and UMIs is a little confusing.

      It would also be nice to have a comment here on the effect of sequencing depth, or similar (compared to capture probability), even if this wouldn't change the interpretation.

      Significance

      The paper provides novel arguments towards the support of the negative-binomial distribution in describing single cell RNA sequencing data, with particular relevance to transcriptional bursting observed in numerous datasets. The paper follows from some notable prior work in the field, and integrates these into a more consistent description, particularly in relation to newer techniques such as UMIs.

      The ubiquity of the negative-binomial distribution means that these arguments will be of relevance to those that perform theoretical or statistical modelling of single cell RNA sequencing data, and theoretically justifies many widely held assumptions. However, the paper does not make any reference to specific reference datasets or commonly observed values, so where in the parameter space data likely lies would still need to be evaluated on a case-by-case basis.

      My expertise is in mathematical modelling and statistics, with some experience of the analysis of single cell RNA sequencing data.

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

      Learn more at Review Commons


      Reply to the reviewers

      The PDF version of point-by-point response includes figures (I, II, III,... IX) that are not included in the manuscript nor in this post but serve to illustrate and clarify our replies to the reviewers' comments.

      Dear Editor,

      Many thanks for forwarding the comments from reviewers #1-#4 regarding our manuscript (Preprint #RC-2025-03087144), entitled "HIV-1 Envelope glycoprotein modulates CXCR4 clustering and dynamics on the T cell membrane", by Quijada-Freire A. et al.

      We have carefully reviewed all reviewer comments and prepared our specific, detailed responses. Alongside this, we have created a revised version of the manuscript to post them on BioRxiv, and we are pleased to announce that we will transfer this new version to an affiliate journal for consideration.

      Reviewer #1

      Thank you very much for considering that our manuscript evaluates an important question and that the reagents used are well prepared and characterized. We also much appreciate that you consider the information generated as potentially useful for those studying HIV infection processes and strategies to prevent infection.

      • While a single particle tracking routine was applied to the data, it's not clear how the signal from a single GFP was defined and if movement during the 100 ms acquisition time impacts this. My concern would be that the routine is tracking fluctuations, and these are related to single particle dynamics, it appears from the movies that the density or the GFP tagged receptors in the cells is too high to allow clear tracking of single molecules. SPT with GFP is very difficult due to bleaching and relatively low quantum yield. Current efforts in this direction that are more successful include using SNAP tags with very photostable organic fluorophores. The data likely does mean something is happening with the receptor, but they need to be more conservative about the interpretation. *

      Some of the paradoxical effects might be better understood through deeper analysis of the SPT data, particularly investigation of active transport and more detailed analysis of "immobile" objects. Comments on early figures illustrate how this could be approached. This would require selecting acquisitions where the GFP density is low enough for SPT and performing a more detailed analysis, but this may be difficult to do with GFP.

      When the authors discuss clusters of 3, how do they calibrate the value of GFP and the impact of diffusion on the measurement. One way to approach this might be single molecules measurements of dilute samples on glass vs in a supported lipid bilayer to map the streams of true immobility to diffusion at >1 µm2/sec.

      We fully understand the reviewer's apprehensions regarding the application of these high-end biophysical techniques, in particular the associated complexity of the data analysis. We provide below extensive explanations on our methodology, which we hope will satisfactorily address all of the reviewer's concerns.

      We would first like to emphasize that the experimental conditions and the quantitative analysis used in our current experiments are similar to the established protocols and methodologies applied by our group previously (Martinez-Muñoz et al. Mol. Cell, 2018; García-Cuesta et al. PNAS, 2022; Gardeta et al. Frontiers in Immunol., 2022; García-Cuesta et al.eLife, 2024; Gardeta et al. Cell. Commun. Signal., 2025) and by others (Calebiro et al. PNAS, 2013; Jaqaman et al. Cell,2011; Mattila et al. Immunity, 2013; Torreno-Pina et al. PNAS, 2014; Torreno-Pina et al. PNAS, 2016).

      As SPT (single-particle tracking) experiments require low-expressing conditions in order to follow individual trajectories (Manzo & García-Parajo Rep. Prog. Phys., 2015), we transiently transfected Jurkat CD4+ cells with CXCR4-AcGFP or CXCR4R334X-AcGFP. At 24 h post-transfection, cells expressing low CXCR4-AcGFP levels were selected by a MoFlo Astrios Cell Sorter (Beckman-Coulter) to ensure optimal conditions for SPT. Using Dako Qifikit (DakoCytomation), we quantified the number of CXCR4 receptors and found ∼8,500 - 22,000 CXCR4-AcGFP receptors/cell, which correspond to a particle density ∼2 - 4.5 particles/mm2 (Figure I, only for review purposes) and are similar to the expression levels found in primary human lymphocytes.

      These cells were resuspended in RPMI supplemented with 2% FBS, NaPyr and L-glutamine and plated on 96-well plates for at least 2 h. Cells were centrifuged and resuspended in a buffer with HBSS, 25 mM HEPES, 2% FBS (pH 7.3) and plated on glass-bottomed microwell dishes (MatTek Corp.) coated with fibronectin (FN) (Sigma-Aldrich, 20 mg/ml, 1 h, 37{degree sign}C). To observe the effect of the ligand, we coated dishes with FN + CXCL12; FN + X4-gp120 or FN + VLPs, as described in material and methods; cells were incubated (20 min, 37{degree sign}C, 5% CO2) before image acquisition.

      For SPT measurements, we use a total internal reflection fluorescence (TIRF) microscope (Leica AM TIRF inverted) equipped with an EM-CCD camera (Andor DU 885-CS0-#10-VP), a 100x oil-immersion objective (HCX PL APO 100x/1.46 NA) and a 488-nm diode laser. The microscope was equipped with incubator and temperature control units; experiments were performed at 37{degree sign}C with 5% CO2. To minimize photobleaching effects before image acquisition, cells were located and focused using the bright field, and a fine focus adjustment in TIRF mode was made at 5% laser power, an intensity insufficient for single-particle detection that ensures negligible photobleaching. Image sequences of individual particles (500 frames) were acquired at 49% laser power with a frame rate of 10 Hz (100 ms/frame). The penetration depth of the evanescent field used was 90 nm.

      We performed automatic tracking of individual particles using a very well established and common algorithm first described by Jaqaman (Jaqaman et al. Nat. Methods, 2008). Nevertheless, we would stress that we implemented this algorithm in a supervised fashion, i.e., we visually inspect each individual trajectory reconstruction in a separate window. Indeed, this algorithm is not able to quantify merging or splitting events.

      We follow each individual fluorescence spot frame-by-frame using a three-by-three matrix around the centroid position of the spot, as it diffuses on the cell membrane. To minimize the effect of photon fluctuations, we averaged the intensity over 20 frames. Nevertheless, to assure the reviewer that most of the single molecule traces last for at least 50 frames (i.e., 5 seconds), we provide the following data and arguments. We currently measure the photobleaching times from individual CD86-AcGFP spots exclusively having one single photobleaching step to guarantee that we are looking at individual CD86-AcGFP molecules. The distribution of the photobleaching times is shown below (Figure II, only for review purposes). Fitting of the distribution to a single exponential decay renders a t0 value of ~5 s. Thus, with 20 frames averaging, we are essentially measuring the whole population of monomers in our experiments. As the survival time of a molecule before photobleaching will strongly depend on the excitation conditions, we used low excitation conditions (2 mW laser power, which corresponds to an excitation power density of ~0.015 kW/cm2 considering the illumination region) and longer integration times (100 ms/frame) to increase the signal-to-background for single GFP detection while minimizing photobleaching.

      To infer the stoichiometry of receptor complexes, we also perform single-step photobleaching analysis of the TIRF trajectories to establish the existence of different populations of monomers, dimers, trimers and nanoclusters and extract their percentage. Some representative trajectories of CXCR4-AcGFP with the number of steps detected are shown in new Supplementary Figure 1.

      The emitted fluorescence (arbitrary units, a.u.) of each spot in the cells is quantified and normalized to the intensity emitted by monomeric CD86-AcGFP spots that strictly showed a single photobleaching step (Dorsch et al. Nat. Methods,2009). We have preferred to use CD86-AcGFP in cells rather than AcGFP on glass to exclude any potential effect on the different photodynamics exhibited by AcGFP when bound directly to glass. We have also previously shown pharmacological controls to exclude CXCL12-mediated receptor clustering due to internalization processes (Martinez-Muñoz et al. Mol. Cell, 2018) that, together with the evaluation of single photobleaching steps and intensity histograms, allow us to exclude the presence of vesicles in our data. Thus, the dimers, trimers and nanoclusters found in our data do correspond to CXCR4 molecules on the cell surface. Finally, distribution of monomeric particle intensities, obtained from the photobleaching analysis, was analyzed by Gaussian fitting, rendering a mean value of 980 {plus minus} 86 a.u. This value was then used as the monomer reference to estimate the number of receptors per particle in both cases, CXCR4-AcGFP and CXCR4R334X-AcGFP (new Supplementary Figure 1).

      • I understand that the CXCL12 or gp120 are attached to the substrate with fibronectin for adhesion. I'm less clear how how that VLPs are integrated. Were these added to cells already attached to FN?*

      For TIRF-M experiments, cells were adhered to glass-bottomed microwell dishes coated with fibronectin, fibronectin + CXCL12, fibronectin + X4-gp120, or fibronectin + VLPs. As for CXCL12 and X4-gp120, the VLPs were attached to fibronectin taking advantage of electrostatic interactions. To clarify the integration of the VLPs in these assays, we have stained the microwell dishes coated with fibronectin and those coated with fibronectin + VLPs with wheat germ agglutinin (WGA) coupled to Alexa647 (Figure III, only for review purposes) and evaluated the staining by confocal microscopy. These results indicate the presence of carbohydrates on the VLPs and are, therefore, indicative of the presence of VLPs on the fibronectin layer.

      Moreover, it is important to remark that the effect of the VLPs on CXCR4 behavior at the cell surface observed by TIRF-M confirmed that the VLPs remained attached to the substrate during the experiment.

      • Fig 1A- The classification of particle tracks into mobile and immobile is overly simplistic description that goes back to bulk FRAP measurements and it not really applicable to single molecule tracking data, where it's rare to see anything that is immobile and alive. An alternative classification strategy uses sub-diffusion, normal diffusion and active diffusion (or active transport) to descriptions and particles can transition between these classes over the tracking period. Fig 1B- this data might be better displayed as histograms showing distributions within the different movement classes.*

      In agreement with the reviewer's commentary, the majority of the particles detected in our TIRF-M experiments were indeed mobile. However, we also detected a variable, and biologically appreciable, percentage of immobile particles depending on the experimental condition analyzed (Figure 1A in the main manuscript). To establish a stringent threshold for identifying these immobile particles under our specific experimental conditions, we used purified monomeric AcGFP proteins immobilized on glass coverslips. Our analysis demonstrated that 95% of these immobilized proteins showed a diffusion coefficient £0.0015 mm2/s; consequently, this value was established as the cutoff to distinguish immobile from mobile trajectories. While the observation of truly immobile entities in a dynamic, living system is rare, the presence of these particles under our conditions is biologically significant. For instance, the detection of large, immobile receptor nanoclusters at the plasma membrane is entirely consistent with facilitating key cellular processes, such as enabling the robust signaling cascade triggered by ligand binding or promoting the crucial events required for efficient viral entry into the cells.

      Regarding the mobile receptors (defined as those with D1-4 values exceeding 0.0015 mm2/s), we observed distinct diffusion profiles derived from mean square displacement (MSD) plots (Figure V) (Manzo & García-Parajo Rep. Prog. Phys., 2015), which were further classified based on motion, using the moment scaling spectrum (MSS) (Ewers et al. PNAS, 2005). Under all experimental conditions, the majority of mobile particles, ∼85%, showed confined diffusion: for example under basal conditions, without ligand addition, ∼90% of mobile particles showed confined diffusion, ∼8.5% showed Brownian-free diffusion and ∼1.5% exhibited directed motion (new Supplementary Figure 5A in the main manuscript). These data have been also included in the revised manuscript to show, in detail, the dynamic parameters of CXCR4.

      Due to the space constraints, it is very difficult to include all the figures generated. However, to ensure comprehensive assessment and transparency (for the purpose of this review), we have included below representative plots of the MSD values as a function of time from individual trajectories, showing different types of motion obtained in our experiments (Figure IV, only for review purposes).

      • Fig 1C,D- It would be helpful to see a plot of D vs MSI at a single particle level. In comparing C and D I'm surprised there is not a larger difference between CXCL12 and X4-gp120. It would also be very important to see the behaviour of X4-gp120 on the CXCR4 deficient Jurkat that would provide a picture of CD4 diffusion. The CXCR4 nanoclustering related to the X4-gp120 could be dominated by CD4 behaviour.*

      As previously described, all analyses were performed under SPT conditions (see previous response to point 1 in this reply). Figure 1C details the percentage of oligomers (>3 receptors/particle) calibrated using Jurkat CD4+ cells electroporated with monomeric CD86-AcGFP (Dorsch et al. Nat. Methods, 2009). The monomer value was determined by analyzing photobleaching steps as described in our previous response to point 1.

      In our experiments, we observed a trend towards a higher number of oligomers upon activation with CXCL12 compared with X4-gp120. This trend was further supported by measurements of Mean Spot Intensity. However, the values are also influenced by the number of larger spots, which represents a minor fraction of the total spots detected.

      The differences between the effect triggered by CXCL12 or X4-gp120 might also be attributed to a combination of factors related to differences in ligand concentration, their structure, and even to the technical requirements of TIRF-M. Both ligands are in contact with the substrate (fibronectin) and the specific nature of this interaction may differ between both ligands and influence their accessibility to CXCR4. Moreover, the requirement of the prior binding of gp120 to CD4 before CXCR4 engagement, in contrast to the direct binding of CXCL12 to CXCR4, might also contribute to the differences observed.

      We previously reported that CXCL12-mediated CXCR4 dynamics are modulated by CD4 co-expression (Martinez-Muñoz et al. Mol. Cell, 2018). We have now detected the formation of CD4 heterodimers with both CXCR4 and CXCR4R334X, and found that these conformations are influenced by gp120-VLPs. In the present manuscript, we did not focus on CD4 clustering as it has been extensively characterized previously (Barrero-Villar et al. J. Cell Sci., 2009; Jiménez-Baranda et al. Nat. Cell. Biol., 2007; Yuan et al. Viruses, 2021). Regarding the investigation of the effects of X4-gp120 on CXCR4-deficient Jurkat cells, which would provide a picture of CD4 diffusion, we would note that a previous report has already addressed this issue using single-molecule super-resolution imaging, and revealed that CD4 molecules on the cell membrane are predominantly found as individual molecules or small clusters of up to 4 molecules, and that the size and number of these clusters increases upon virus binding or gp120 activation (Yuan et al. Viruses, 2021).

      • Fig S1D- This data is really interesting. However, if both the CD4 and the gp120 have his tags they need to be careful as poly-His tags can bind weakly to cells and increasing valency could generate some background. So, they should make the control is fair here. Ideally, using non-his tagged person of sCD4 and gp120 would be needed ideal or they need a His-tagged Fab binding to gp120 that doesn't induce CXCR4 binding.*

      New Supplementary Figure 2D shows that X4-gp120 does not bind Daudi cells (these cells do not express CD4) in the absence of soluble CD4. While the reviewer is correct to state that both proteins contain a Histidine Tag, cell binding is only detected if X4-gp120 binds sCD4. Nonetheless, we have included in the revised Supplementary Figure 2D a control showing the negative binding of sCD4 to Daudi cells in the absence of X4-gp120. Altogether, these results confirm that only sCD4/X4-gp120 complexes bind these cells.

      • Fig S4- Panel D needs a scale bar. I can't figure out what I'm being shown without this.*

      Apologies. A scale bar has been included in this panel (new Supplementary Figure 6D).

      Reviewer #2

      • This study is well described in both the main text and figures. Introduction provides adequate background and cites the literature appropriately. Materials and Methods are detailed. Authors are careful in their interpretations, statistical comparisons, and include necessary controls in each experiment. The Discussion presents a reasonable interpretation of the results. Overall, there are no major weaknesses with this manuscript.*

      We very much appreciate the positive comments of the reviewer regarding the broad interest and strength of our work.

      • NL4-3deltaIN and immature HIV virions are found to have less associated gp120 relative to wild-type particles. It is not obvious why this is the case for the deltaIN particles or genetically immature particles. Can the authors provide possible explanations? (A prior paper was cited, Chojnacki et al Science, 2012 but can the current authors provide their own interpretation.)*

      Our conclusion from the data is actually exactly the opposite. As shown in Figure 2D, the gp120 staining intensity was higher for NL4-3DIN particles (1,786 a.u.) than for gp120-VLPs (1,223 a.u.), indicating lower expression of Env proteins in the latter. Furthermore, analysis of gp120 intensity per particle (Figure 2E) confirmed that gp120-VLPs contained fewer gp120 molecules per particle than NL4-3DIN virions. These levels were comparable with, or even lower than, those observed in primary HIV-1 viruses (Zhu et al. Nature, 2006). This reduction was a direct consequence of the method used to generate the VLPs, as our goal was to produce viral particles with minimal gp120 content to prevent artifacts in receptor clustering that might occur using high levels of Env proteins in the VLPs to activate the receptors.

      This misunderstanding may arise from the fact that we also compared Gag condensation and Env distribution on the surface of gp120-VLPs with those observed in genetically immature particles and integrase-defective NL4-3ΔIN virions, which served as controls. STED microscopy data revealed differences in Env distribution between gp120-VLPs and NL4-3ΔIN virions, supporting the classification of gp120-VLPs as mature particles (Figure 2 A,B).

      Reviewer #3

      We thank the reviewer for considering that our work offers new insights into the spatial organization of receptors during HIV-1 entry and infection and that the manuscript is well written, and the findings significant.

      • For mechanistic basis of gp120-CXCR4 versus CXCL12-CXCR4 differences. Provide additional structural or biochemical evidence to support the claim that gp120 stabilises a distinct CXCR4 conformation compared to CXCL12. If feasible, include molecular modelling, mutagenesis, or cross-linking experiments to corroborate the proposed conformational differences.*

      We appreciate the opportunity to clarify this point. The specific claim that gp120 stabilizes a conformation of CXCR4 that is distinct from the CXCL12-bound state was not explicitly stated in our manuscript, although we agree that our data strongly support this possibility. It is important to consider that CXCL12 binds directly to CXCR4, whereas gp120 requires prior sequential binding to CD4, and its subsequent interaction is with a CXCR4 molecule that is already forming part of the CD4/CXCR4 complex, as demonstrated by our FRET experiments and supported by previous studies (Zaitseva et al. J. Leuk. Biol., 2005; Busillo & Benovic Biochim. Biophys. Acta, 2007; Martínez-Muñoz et al. PNAS, 2014). This difference makes it inherently complex to compare the conformational changes induced by gp120 and CXCL12 on CXCR4.

      However, our findings show that both stimuli induce oligomerization of CXCR4, a phenomenon not observed when mutant CXCR4R334X was exposed to the chemokine CXCL12 (García-Cuesta et al. PNAS, 2022).

      1. CXCL12 induced oligomerization of CXCR4 but did not affect the dynamics of CXCR4R334X (Martinez-Muñoz et al. Mol. Cell, 2018; García-Cuesta et al. PNAS, 2022). By contrast, X4-gp120 and the corresponding VLPs-which require initial binding to CD4 to engage the chemokine receptor-stabilized oligomers of both CXCR4 and CXCR4R334X.

      FRET analysis revealed distinct FRET50 values for CD4/CXCR4 (2.713) and CD4/CXCR4R334X (0.399) complexes, suggesting different conformations for each complex. Consistent with previous reports (Balabanian et al. Blood, 2005; Zmajkovicova et al. Front. Immunol., 2024; García-Cuesta et al. PNAS, 2022), the molecular mechanisms activated by CXCL12 are distinct when comparing CXCR4 with CXCR4R334X. For instance, CXCL12 induces internalization of CXCR4, but not of mutant CXCR4R334X. Conversely, X4-gp120 triggers approximately 25% internalization of both receptors. Similarly, CXCL12 does not promote CD4 internalization in cells co-expressing CXCR4 or CXCR4R334X, whereas X4-gp120 does, although CD4 internalization was significantly higher in cells co-expressing CXCR4.

      These findings suggest that CD4 influences the conformation and the oligomerization state of both co-receptors. To further support this hypothesis, we have conducted new in silico molecular modeling of CD4 in complex with either CXCR4 or its mutant CXCR4R334X using AlphaFold 3.0 (Abramson et al. Nature, 2024). The server was provided with both sequences, and the interaction between the two molecules for each protein was requested. It produced a number of solutions, which were then analyzed using the software ChimeraX 1.10 (Meng et al. Protein Sci., 2023). CXCR4 and its mutant, CXCR4R334X bound to CD4, were superposed using one of the CD4 molecules from each complex, with the aim of comparing the spatial positioning of CD4 molecules when interacting with CXCR4.

      As illustrated in Figure V (only for review purposes), the superposition of the CD4/CXCR4 complexes was complete. However, when CD4/CXCR4 complexes were superimposed with CD4/CXCR4R334X complexes using the same CD4 molecule as a reference, indicated by an arrow in the figure, a clear structural deviation became evident. The main structural difference detected was the positioning of the CD4 transmembrane domains when interacting with either the wild-type or mutant CXCR4. While in complexes with CXCR4, the angle formed by the lines connecting residues E416 at the C-terminus end of CD4 with N196 in CXCR4 was 12{degree sign}, for the CXCR4R334X complex, this angle increased to 24{degree sign}, resulting in a distinct orientation of the CD4 extracellular domain (Figure VI, only for review purposes).

      To further analyze the models obtained, we employed PDBsum software (Laskowski & Thornton Protein Sci., 2021) to predict the CD4/CXCR4 interface residues. Data indicated that at least 50% of the interaction residues differed when the CD4/CXCR4 interaction surface was compared with that of the CD4/CXCR4R334X complex (Figure VII, only for review purposes). It is important to note that while some hydrogen bonds were present in both complex models, others were exclusive to one of them. For instance, whereas Cys394(CD4)-Tyr139 and Lys299(CD4)-Glu272 were present in both CD4/CXCR4 and CD4/CXCR4R334X complexes, the pairs Asn337(CD4)-Ser27(CXCR4R334X) and Lys325(CD4)-Asp26(CXCR4R334X) were only found in CD4/CXCR4R334X complexes.

      These findings, which are consistent with our FRET results, suggest distinct interaction surfaces between CD4 and the two chemokine receptors. Overall, these results are compatible with differences in the spatial conformation adopted by these complexes.

      • For Empty VLP effects on CXCR4 dynamics: Explore potential causes for the observed effects of Env-deficient VLPs. It's valuable to include additional controls such as particles from non-producer cells, lipid composition analysis, or blocking experiments to assess nonspecific interactions. *

      As VLPs are complex entities, we thought that the relevant results should be obtained comparing the effects of Env(-) VLPs with gp120-VLPs. Therefore, we would first remark that regardless of the effect of Env(-) VLPs on CXCR4 dynamics, the most evident finding in this study is the strong effect of gp120-VLPs compared with control Env(-) VLPs. Nevertheless, regarding the effect of the Env(-) VLPs compared with medium, we propose several hypotheses. As several virions can be tethered to the cell surface via glycosaminoglycans (GAGs), we hypothesized that VLPs-GAGs interactions might indirectly influence the dynamics of CXCR4 and CXCR4R334X at the plasma membrane. Additionally, membrane fluidity is essential for receptor dynamics, therefore VLPs interactions with proteins, lipids or any other component of the cell membrane could also alter receptor behavior. It is well known that lipid rafts participate in the interaction of different viruses with target cells (Nayak & Hu Subcell. Biochem., 2004; Manes et al. Nat. Rev. Immunol., 2003; Rioethmullwer et al. Biochim. Biophys. Acta, 2006) and both the lipid composition and the presence of co-expressed proteins modulate ligand-mediated receptor oligomerization (Gardeta et al. Frontiers in Immunol., 2022; Gardeta et al. Cell. Commun. Signal., 2025). We have thus performed Raster Image Correlation Spectroscopy (RICS) analysis to assess membrane fluidity through membrane diffusion measurements on cells treated with Env(-) VLPs.

      Jurkat cells were labeled with Di-4-ANEPPDHG and seeded on FN and on FN + VLPs prior to analysis by RICS on confocal microscopy. The results indicated no significant differences in membrane diffusion under the treatment tested, thereby discarding an effect of VLPs on overall membrane fluidity (Figure VIII, only for review purposes).

      Nonetheless, these results do not rule out other non-specific interactions of Env(-) VLPs with membrane proteins that could affect receptor dynamics. For instance, it has been reported that C-type lectin DC-SIGN acts as an efficient docking site for HIV-1 (Cambi et al. J. Cell. Biol., 2004; Wu & KewalRamani Nat. Rev. Immunol., 2006). However, a detailed investigation of these possible mechanisms is beyond the scope of this manuscript.

      • For Direct link between clustering and infection efficiency - Test whether disruption of CXCR4 clustering (e.g., using actin cytoskeleton inhibitors, membrane lipid perturbants, or clustering-deficient mutants) alters HIV-1 fusion or infection efficiency*.

      Designing experiments using tools that disrupt receptor clustering by interacting with the receptors themselves is difficult and challenging, as these tools bind the receptor and can therefore alter parameters such as its conformation and/or its distribution at the cell membrane, as well as affect some cellular processes such as HIV-1 attachment and cell entry. Moreover, effects on actin polymerization or lipids dynamics can affect not only receptor clustering but also impact on other molecular mechanisms essential for efficient infection.

      Many previous reports have, nonetheless, indirectly correlated receptor clustering with cell infection efficiency. Cholesterol plays a key role in the entry of several viruses. Its depletion in primary cells and cell lines has been shown to confer strong resistance to HIV-1-mediated syncytium formation and infection by both CXCR4- and CCR5-tropic viruses (Liao et al. AIDS Res. Hum. Retrovisruses, 2021). Moderate cholesterol depletion also reduces CXCL12-induced CXCR4 oligomerization and alters receptor dynamics (Gardeta et al. Cell. Commun. Signal., 2025). By restricting the lateral diffusion of CD4, sphingomyelinase treatment inhibits HIV-1 fusion (Finnegan et al. J. Virol., 2007). Depletion of sphingomyelins also disrupts CXCL12-mediated CXCR4 oligomerization and its lateral diffusion (Gardeta et al. Front Immunol., 2022). Additional reports highlight the role of actin polymerization at the viral entry site, which facilitates clustering of HIV-1 receptors, a crucial step for membrane fusion (Serrano et al. Biol. Cell., 2023). Blockade of actin dynamics by Latrunculin A treatment, a drug that sequesters actin monomers and prevents its polymerization, blocks CXCL12-induced CXCR4 dynamics and oligomerization (Martínez-Muñoz et al. Mol. Cell, 2018).

      Altogether, these findings strongly support our hypothesis of a direct link between CXCR4 clustering and the efficiency of HIV-1 infection.

      • CD4/CXCR4 co-endocytosis hypothesis - Support the proposed model with direct evidence from live-cell imaging or co-localization experiments during viral entry. Clarification is needed on whether internalization is simultaneous or sequential for CD4 and CXCR4.*

      When referring to endocytosis of CD4 and CXCR4, we only hypothesized that HIV-1 might promote the internalization of both receptors either sequentially or simultaneously. The hypothesis was based in several findings:

      1) Previous studies have suggested that HIV-1 glycoproteins can reduce CD4 and CXCR4 levels during HIV-1 entry (Choi et al. Virol. J., 2008; Geleziunas et al. FASEB J, 1994; Hubert et al. Eur. J. Immunol., 1995).

      2) Receptor endocytosis has been proposed as a mechanism for HIV-1 entry (Daecke et al. J. Virol., 2005; Aggarwal et al.Traffick, 2017; Miyauchi et al. Cell, 2009; Carter et al. Virology, 2011).

      3) Our data from cells activated with X4-gp120 demonstrated internalization of CD4 and chemokine receptors, which correlated with HIV-1 infection in PBMCs from WHIM patients and healthy donors.

      4) CD4 and CXCR4 have been shown to co-localize in lipid rafts during HIV-1 infection (Manes et al. EMBO Rep., 2000; Popik et al. J. Virol., 2002)

      5) Our FRET data demonstrated that CD4 and CXCR4 form heterocomplexes and that FRET efficiency increased after gp120-VLPs treatment.

      We agree with the reviewer that further experiments are required to test this hypothesis, however, we believe that this is beyond the scope of the current manuscript.

      Minor Comments:

      • The conclusions rely solely on the HXB2 X4-tropic Env. It would strengthen the study to assess whether other X4 or dual-tropic strains induce similar receptor clustering and dynamics.*

      The primary goal of our current study was to investigate the dynamics of the co-receptor CXCR4 during HIV-1 infection, motivated by previous reports showing CD4 oligomerization upon HIV-1 binding and gp120 stimulation (Yuan et al.Viruses, 2021). We initially used a recombinant X4-gp120, a soluble protein that does not fully replicate the functional properties of the native HIV-1 Env. Previous studies have shown that Env consists of gp120 trimers, which redistribute and cluster on the surface of virions following proteolytic Gag cleavage during maturation (Chojnacki et al. Nat. Commun., 2017). An important consideration in receptor oligomerization studies is the concentration of recombinant gp120 used, as it does not accurately reflect the low number of Env trimers present on native HIV-1 particles (Hart et al. J. Histochem. Cytochem., 1993; Zhu et al. Nature, 2006). To address these limitations, we generated virus-like particles (VLPs) containing low levels of X4-gp120 and repeated the dynamic analysis of CXCR4. The use of primary HIV-1 isolates was limited, in this project, to confirm that PBMCs from both healthy donors and WHIM patients were equally susceptible to infection. This result using a primary HIV-1 virus supports the conclusion drawn from our in vitroapproaches. We thus believe that although the use of other X4- and dual-tropic strains may complement and reinforce the analysis, it is far beyond the scope of the current manuscript.

      • Given the observed clustering effects, it would be valuable to explore whether gp120-induced rearrangements alter epitope exposure to broadly neutralizing antibodies like 17b or 3BNC117. This would help connect the mechanistic insights to therapeutic relevance.*

      As 3BNC117, VRC01 and b12 are broadly neutralizing mAbs that recognize conformational epitopes on gp120 (Li et al. J. Virol., 2011; Mata-Fink et al. J. Mol. Biol., 2013), they will struggle to bind the gp120/CD4/CXCR4 complex and therefore may not be ideal for detecting changes within the CD4/CXCR4 complex. The experiment suggested by the reviewer is thus challenging but also very complex. It would require evaluating antibody binding in two experimental conditions, in the absence and in the presence of oligomers. However, our data indicate that receptor oligomerization is promoted by X4-gp120 binding, and the selected antibodies are neutralizing mAbs, so they should block or hinder the binding of gp120 and, consequently, receptor oligomerization. An alternative approach would be to study the neutralizing capacity of these mAbs on cells expressing CD4/CXCR4 or CD4/CXCR4R334X complexes. Variations in their neutralizing activity could be then extrapolated to distinct gp120 conformations, which in turn may reflect differences between CD4/CXCR4 and CD4/CXCR4R334X complexes.

      We thus assessed the ability of the VRC01 and b12, anti-gp120 mAbs, which were available in our laboratory, to neutralize gp120 binding on cells expressing CD4/CXCR4 or CD4/CXCR4R334X. Specifically, increasing concentrations of each antibody were preincubated (60 min, 37ºC) with a fixed amount of X4-gp120 (0.05 mg/ml). The resulting complexes were then incubated with Jurkat cells expressing CD4/CXCR4 or CD4/CXCR4R334X (30 min, 37ºC) and, finally, their binding was analyzed by flow cytometry. Although we did not observe statistically significant differences in the neutralization capacity of b12 or VRC01 for the binding of X4-gp120 depending on the presence of CXCR4 or CXCR4334X, we observed a trend for greater concentrations of both mAbs to neutralize X4-gp120 binding in Jurkat CD4/CXCR4 cells than in Jurkat CD4/CXCR4R334X cells (Figure IX, only for review purposes).

      These slight alterations in the neutralizing capacity of b12 and VRC01 mAbs may thus suggest minimal differences in the conformations of gp120 depending of the coreceptor used. We also detected that X4-gp120 and VLPs expressing gp120, which require initial binding to CD4 to engage the chemokine receptor, stabilized oligomers of both CXCR4 and CXCR4R334X, but FRET data indicated distinct FRET50 values between the partners, (2.713) for CD4/CXCR4 and (0.399) for CD4/CXCR4R334X (Figure 5A,B in the main manuscript). Moreover, we also detected significantly more CD4 internalization mediated by X4-gp120 in cells co-expressing CD4 and CXCR4 than in those co-expressing CD4 and CXCR4R334X (Figure 6 in the main manuscript). Overall these latter data and those included in Figures V, VI and VII of this reply, indicate distinct conformations within each receptor complexes.

      • TIRF imaging limits analysis to the cell substrate interface. It would be useful to clarify whether CXCR4 receptor clustering occurs elsewhere, such as at immunological synapses or during cell-to-cell contact.*

      In recent years, chemokine receptor oligomerization has gained significant research interest due to its role in modulating the ability of cells to sense chemoattractant gradients. This molecular organization is now recognized as a critical factor in governing directed cell migration (Martínez-Muñoz et al. Mol. Cell, 2018; García-Cuesta et al. PNAS, 2022, Hauser et al.Immunity, 2016). In addition, advanced imaging techniques such as single-molecule and super-resolution microscopy have been used to investigate the spatial distribution and dynamic behaviour of CXCR4 within the immunological synapse in T cells (Felce et al. Front. Cell Dev. Biol., 2020). Building on these findings, we are currently conducting a project focused on characterizing CXCR4 clustering specifically within this specialized cellular region.

      • In LVP experiments, it would be useful to report transduction efficiency (% GFP+ cells) alongside MSI data to relate VLP infectivity with receptor clustering functionally.*

      These experiments were designed to validate the functional integrity of the gp120 conformation on the LVPs, confirming their suitability for subsequent TIRF microscopy. Our objective was to establish a robust experimental tool rather than to perform a high-throughput quantification of transduction efficiency. It is for that reason that these experiments were included in new Supplementary Figure S6, which also contains the complete characterization of gp120-VLPs and LVPs. In such experimental conditions, quantifying the percentage of GFP-positive cells relative to the total number of cells plated in each well is very difficult. However, in line with the reviewer's commentary and as we used the same number of cells in each experimental condition, we have included, in the revised manuscript, a complementary graph illustrating the GFP intensity (arbitrary units) detected in all the wells analyzed (new Supplementary Fig. 6E).

      • To ensure that differences in fusion events (Figure 7B) are attributable to target cell receptor properties, consider confirming that effector cells express similar levels of HIV-1 Env. Quantifying gp120 expression by flow cytometry or western blot would rule out the confounding effects of variable Env surface density.*

      In these assays (Figure 7B), we used the same effector cells (cells expressing X4-gp120) in both experimental conditions, ensuring that any observed differences should be attributable solely to the target cells, either JKCD4X4 or JKCD4X4R334X. For this reason, in Figure 7A we included only the binding of X4-gp120 to the target cells which demonstrated similar levels of the receptors expressed by the cells.

      • HIV-mediated receptor downregulation may occur more slowly than ligand-induced internalization. Including a 24-hour time point would help assess whether gp120 induces delayed CD4 or CXCR4 loss beyond the early effects shown and to better capture potential delayed downregulation induced by gp120.*

      The reviewer suggests using a 24-hour time point to facilitate detection of receptor internalization. However, such an extended incubation time may introduce some confounding factors, including receptor degradation, recycling and even de novo synthesis, which could affect the interpretation of the results. Under our experimental conditions, we observed that CXCL12 did not trigger CD4 internalization whereas X4-gp120 did. Interestingly, CD4 internalization depended on the co-receptor expressed by the cells.

      • Increase label font size in microscopy panels for improved readability.*

      Of course; the font size of these panels has been increased in the revised version.

      • Consider adding more references on ligand-induced co-endocytosis of CD4 and chemokine receptors during HIV-1 entry.*

      We have added more references to support this hypothesis (Toyoda et al. J. Virol., 2015; Venzke et al. J. Virol., 2006; Gobeil et al J. Virol., 2013).

      • For Statistical analysis. Biological replicates are adequate, and statistical tests are generally appropriate. For transparency, report n values, exact p-values, and the statistical test used in every figure legend and discussed in the results.*

      Thank you for highlighting the importance of transparency in statistical reporting. We confirm that the n values for all experiments have been included in the figure legends. The statistical tests used for each analysis are also clearly indicated in the figure legends, and the interpretation of these results is discussed in detail in the Results section. Furthermore, the Methods section specifies the tests applied and the thresholds for significance, ensuring full transparency regarding our analytical approach.

      In accordance with established conventions in the field, we have utilized categorical significance indicators (e.g., n.s., *, **, ***) within our figures to enhance readability and focus on biological trends. This approach is widely adopted in high-impact literature to prevent visual clutter. However, to ensure full transparency and reproducibility, we have ensured that the underlying statistical tests and thresholds are clearly defined in the respective figure legends and Methods section.

      Reviewer #4

      We thank the reviewer for considering that this work is presented in a clear fashion, and the main findings are properly highlighted, and for remarking that the paper is of interest to the retrovirology community and possibly to the broader virology community.

      We also agree on the interest that X4-gp120 clusters CXCR4R334X suggests a different binding mechanism for X4-gp120 from that of the natural ligand CXCL12, an aspect that we are now evaluating. These data also indicate that WHIM patients can be infected by HIV-1 similarly to healthy people.

      • The observation that "empty VLPs" reduce CXCR4 diffusivity is potentially interesting. However, it is not supported by the data owing to insufficient controls. The authors correctly discuss the limitations of that observation in the Discussion section (lines 702-704). However, they overinterpret the observation in the Results section (lines 509-512), suggesting non-specific interactions between empty VLPs, CD4 and CXCR4. I suggest either removing the sentence from the Results section or replacing it with a sentence similar to the one in the Discussion section.*

      In accordance with the reviewer`s suggestion, the sentence in the result section has been replaced with one similar to that found in the discussion section. In addition, we have performed Raster Image Correlation Spectroscopy (RICS) analysis using the Di-4-ANEPPDHQ lipid probe to assess membrane fluidity by means of membrane diffusion, and compared the results with those of cells treated with Env(-) VLPs. The results indicated that VLPs did not modulate membrane fluidity (Figure VIII in this reply). Nonetheless, these results do not rule out other potential non-specific interactions of the Env(-) VLPs with other components of the cell membrane that might affect receptor dynamics (see our response to point 2 of reviewer #3 p. 14-15 of this reply).

      • In the case of the WHIM mutant CXCR4-R334X, the addition of "empty VLPs" did not cause a significant change in the diffusivity of CXCR4-R334X (Figure 4B). This result is in contrast with the addition of empty VLPs to WT CXCR4. However, the authors neither mention nor comment on that result in the results section. Please mention the result in the paper and comment on it in relation to the addition of empty VLPs to WT CXCR4.*

      We would remark that the main observation in these experiments should focus on the effect of gp120-VLPs, and the results indicates that gp120-VLPs promoted clustering of CXCR4 and of CXCR4R334X and reduced their diffusion at the cell membrane. The Env(- ) VLPs were included as a negative control in the experiments, to compare the data with those obtained using gp120-VLPs. However, once we observed some residual effect of the Env(-) VLPs, we decided to give a potential explanation, formulated as a hypothesis, that the Env(-) VLPs modulated membrane fluidity. We have now performed a RICS analysis using Di-4-ANEPPDHQ as a lipid probe (Figure IX only for review purposes). The results suggest that Env(-) VLPs do not modulate cell membrane fluidity, although we do not rule out other potential interactions with membrane proteins that might alter receptor dynamics. We appreciate the reviewer's observation and agree that this result can be noted. However, since the main purpose of Figure 4B is to show that gp120-VLPs modulate the dynamics of CXCR4R334X rather than to remark that the Env(-) VLPs also have some effects, we consider that a detailed discussion of this specific aspect would detract from the central finding and may dilute the primary narrative of the study.

      Minor comments

      • It would be helpful for the reader to combine thematically or experimentally linked figures, e.g., Figures 3 and 4.*

      • Figures 3 and 4 are very similar. Please unify the colours in them and the order of the panels (e.g. Figure 3 panel A shows diffusivity of CXCR4, while Figure 4 panel A shows MSI of CXCR4-R334X).*

      While we considered consolidating Figures 3 and 4, we believe that maintaining them as separate entities enhances conceptual clarity. Since Figure 3 establishes the baseline dynamics for wild-type CXCR4 and Figure 4 details the distinct behavior of the CXCR4R334X mutant, keeping them separate allows the reader to fully appreciate the specificities of each system before making a cross-comparison.

      • Some parts of the Discussion section could be shortened, moved to the Introduction (e.g.,lines 648-651), or entirely removed (e.g.,lines 633-635 about GPCRs).*

      In accordance, the Discussion section has been reorganized and shortened to improve clarity.

      • I suggest renaming "empty VLPs" to "Env(−) VLPs" (or similar). The name empty VLPs can mislead the reader into thinking that these are empty vesicles.*

      The term empty VLPs has been renamed to Env(−) VLPs throughout the manuscript to more accurately reflect their composition. Many thanks for this suggestion.

      • Line 492 - please rephrase "...lower expression of Env..." to "...lower expression of Env or its incorporation into the VLPs...".*

      The sentence has been rephrased

      • Line 527 - The data on CXCL12 modulating CXCR4-R334X dynamics and clustering are not present in Figure 4 (or any other Figure). Please add them or rephrase the sentence with an appropriate reference. Make clear which results are yours.*

      • Line 532 - Do the data in the paper really support a model in which CXCL12 binds to CXCR4-R334X? If not, please rephrase with an appropriate reference.*

      Previous studies support the association of CXCL12 with CXCR4R334X (Balabanian et al. Blood, 2005; Hernandez et al. Nat Genet., 2003; Busillo & Benovic Biochim. Biophys. Acta, 2007). In fact, this receptor has been characterized as a gain-of-function variant for this ligand (McDermott et al. J. Cell. Mol. Med., 2011). The revised manuscript now includes these bibliographic references to support this commentary. In any case, our previous data indicate that CXCL12 binding does not affect CXCR4R334X dynamics (García-Cuesta et al. PNAS, 2022).

      • Line 695 - "...lipid rafts during HIV-1 (missing word?) and their ability to..." During what?*

      Many thanks for catching this mistake. The sentence now reads: "Although direct evidence for the internalization of CD4 and CXCR4 as complexes is lacking, their co-localization in lipid rafts during HIV-1 infection (97-99) and their ability to form heterocomplexes (22) strongly suggest they could be endocytosed together."

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

      Learn more at Review Commons


      Referee #4

      Evidence, reproducibility and clarity

      This paper provides new insights into the organisational changes of the X4-tropic HIV-1 co-receptor CXCR4 upon binding of the viral receptor-binding protein X4-gp120, either in its soluble form or when displayed on virus-like particles (VLPs) as Env. The study employs single-particle tracking total internal reflection fluorescence (SPT-TIRF) microscopy to quantify the dynamics and clustering of CXCR4 on CD4+ T cells. The data show that CXCR4 clusters in the presence of X4-gp120 and VLPs, a phenomenon also observed for the primary HIV-1 receptor CD4. The authors also show that a WHIM mutant of CXCR4 (CXCR4-R334X) that does not cluster in the presence of its natural ligand, CXCL12, clusters in the presence of X4-gp120 and VLPs.

      The following points should be clarified or improved prior to publication:

      Major comments:

      1. The observation that "empty VLPs" reduce CXCR4 diffusivity is potentially interesting. However, it is not supported by the data owing to insufficient controls. The authors correctly discuss the limitations of that observation in the Discussion section (lines 702-704). However, they overinterpret the observation in the Results section (lines 509-512), suggesting non-specific interactions between empty VLPs, CD4 and CXCR4. I suggest either removing the sentence from the Results section or replacing it with a sentence similar to the one in the Discussion section.
      2. In the case of the WHIM mutant CXCR4-R334X, the addition of "empty VLPs" did not cause a significant change in the diffusivity of CXCR4-R334X (Figure 4B). This result is in contrast with the addition of empty VLPs to WT CXCR4. However, the authors neither mention nor comment on that result in the results section. Please mention the result in the paper and comment on it in relation to the addition of empty VLPs to WT CXCR4.

      Minor comments:

      1. It would be helpful for the reader to combine thematically or experimentally linked figures, e.g., Figures 3 and 4.
      2. Figures 3 and 4 are very similar. Please unify the colours in them and the order of the panels (e.g. Figure 3 panel A shows diffusivity of CXCR4, while Figure 4 panel A shows MSI of CXCR4-R334X).
      3. Some parts of the Discussion section could be shortened, moved to the Introduction (e.g., lines 648-651), or entirely removed (e.g., lines 633-635 about GPCRs).
      4. I suggest renaming "empty VLPs" to "Env(−) VLPs" (or similar). The name empty VLPs can mislead the reader into thinking that these are empty vesicles.
      5. Line 492 - please rephrase "...lower expression of Env..." to "...lower expression of Env or its incorporation into the VLPs...".
      6. Line 527 - The data on CXCL12 modulating CXCR4-R334X dynamics and clustering are not present in Figure 4 (or any other Figure). Please add them or rephrase the sentence with an appropriate reference. Make clear which results are yours.
      7. Line 532 - Do the data in the paper really support a model in which CXCL12 binds to CXCR4-R334X? If not, please rephrase with an appropriate reference.
      8. Line 695 - "...lipid rafts during HIV-1 (missing word?) and their ability to..." During what?

      Significance

      In summary, the work is presented in a clear fashion, and the main findings are properly highlighted. The paper is of interest to the retrovirology community and possibly to the broader virology community. The findings are not entirely surprising because it has been shown previously that the binding of Env to CD4 mediates CD4 clustering, which would also suggest clustering of the co-receptor. Nonetheless, the paper provides strong evidence that CXCR4 clusters and changes its dynamics in the presence of CD4 and X4-gp120. Moreover, the evidence that X4-gp120 clusters CXCR4-R334X is of high interest because it suggests a different binding mechanism for X4-gp120 from that of the natural ligand CXCL12, raising questions for further research. The diffusivity data with empty VLPs require additional controls to strengthen the evidence. My expertise is in virology and structural biology. I did not comment on the technical aspects of the light-microscopy experiments in the study because these are beyond my expertise.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The author investigates how the HIV-1 Env glycoprotein modulates the nanoscale organisation and dynamics of the CXCR4 co-receptor on CD4⁺ T cells. The author demonstrates that HIV-1 Env induces CXCR4 clustering distinct from that triggered by its natural ligand (CXCL12), implicating spatial receptor organization as a determinant of infection. This study investigates how HIV-1 Env (specifically X4-tropic gp120) alters the membrane organization and dynamics of the chemokine receptor CXCR4 and its WHIM-associated mutant, CXCR4R334X, in a CD4-dependent manner. Using single-particle tracking total internal reflection fluorescence microscopy (SPT-TIRF-M), the authors demonstrate that both soluble gp120 and virus-like particles (VLPs) displaying gp120 induce CXCR4 nanoclustering, reduce receptor diffusivity, and promote immobile nanoclusters of CXCR4 at the membrane of Jurkat T cells and primary CD4⁺ T cell blasts.The work offers new insights into the spatial organisation of receptors during HIV-1 entry and infection. The manuscript is well-written, and the findings are significant.

      Major Comments: 1. For mechanistic basis of gp120-CXCR4 versus CXCL12-CXCR4 differences

      Provide additional structural or biochemical evidence to support the claim that gp120 stabilises a distinct CXCR4 conformation compared to CXCL12.

      If feasible, include molecular modelling, mutagenesis, or cross-linking experiments to corroborate the proposed conformational differences. 2. For Empty VLP effects on CXCR4 dynamics

      Explore potential causes for the observed effects of Env-deficient VLPs. It's valuable to include additional controls such as particles from non-producer cells, lipid composition analysis, or blocking experiments to assess nonspecific interactions. 3. For Direct link between clustering and infection efficiency - Test whether disruption of CXCR4 clustering (e.g., using actin cytoskeleton inhibitors, membrane lipid perturbants, or clustering-deficient mutants) alters HIV-1 fusion or infection efficiency. 4. CD4/CXCR4 co-endocytosis hypothesis - Support the proposed model with direct evidence from live-cell imaging or co-localization experiments during viral entry. Clarification is needed on whether internalization is simultaneous or sequential for CD4 and CXCR4.

      Minor Comments: 1. The conclusions rely solely on the HXB2 X4-tropic Env. It would strengthen the study to assess whether other X4 or dual-tropic strains induce similar receptor clustering and dynamics. 2. Given the observed clustering effects, it would be valuable to explore whether gp120-induced rearrangements alter epitope exposure to broadly neutralizing antibodies like 17b or 3BNC117. This would help connect the mechanistic insights to therapeutic relevance. 3 . TIRF imaging limits analysis to the cell substrate interface. It would be useful to clarify whether CXCR4 receptor clustering occurs elsewhere, such as at immunological synapses or during cell-to-cell contact. 4. In LVP experiments, it would be useful to report transduction efficiency (% GFP+ cells) alongside MSI data to relate VLP infectivity with receptor clustering functionally. 5. To ensure that differences in fusion events (Figure 7B) are attributable to target cell receptor properties, consider confirming that effector cells express similar levels of HIV-1 Env. Quantifying gp120 expression by flow cytometry or western blot would rule out the confounding effects of variable Env surface density 6. HIV-mediated receptor downregulation may occur more slowly than ligand-induced internalization. Including a 24-hour time point would help assess whether gp120 induces delayed CD4 or CXCR4 loss beyond the early effects shown and to better capture potential delayed downregulation induced by gp120. 7. Increase label font size in microscopy panels for improved readability. 8. Consider adding more references on ligand-induced co-endocytosis of CD4 and chemokine receptors during HIV-1 entry. For Statistical analysis. Biological replicates are adequate, and statistical tests are generally appropriate. For transparency, report n values, exact p-values, and the statistical test used in every figure legend and discussed in the results.

      Referee cross-commenting

      Overall, the manuscript provides compelling mechanistic insight into HIV-1 entry by demonstrating Env-induced CXCR4 clustering, including in WHIM mutant receptors. While the core findings are well supported and of high interest, clarifications regarding Env trimer densities, receptor internalization, and the contribution of empty VLPs would further strengthen the work.

      Significance

      Nature and significance of the advance

      This work marks a conceptual and mechanistic breakthrough in understanding HIV-1 entry. It goes beyond the static view of Env-co-receptor interaction to show that nanoscale reorganization of CXCR4, distinct from chemokine-induced clustering, occurs during HIV-1 Env engagement and may be essential for infection Context within existing literature. Previous studies established Env-induced CD4 clustering (Yin et al., 2020) and chemokine-induced CXCR4 nanocluster formation (Martínez-Muñoz et al., 2018), but the exact nanoscale rearrangement of CXCR4 in the context of HIV-1 Env and physiological Env densities remains unquantified. This study addresses this gap using SPT-TIRF, STED microscopy, and functional assays.

      Audience and influence

      The findings will be of interest to researchers in HIV virology, membrane receptor biology, viral entry mechanisms, and therapeutic target development. The receptor-clustering aspect could also influence broader fields of study, such as GPCR organization and immune receptor signalling.

      Reviewer expertise

      I can evaluate HIV-1 entry mechanisms, viral glycoprotein-host-host-host receptor interactions, single-molecule fluorescence microscopy, and membrane protein dynamics. I am less equipped to evaluate the deep structural modelling aspects, though the in silico AlphaFold results are straightforward to interpret in context.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The authors examine the distribution of CXCR4 on the cell surface following exposure to gp120 and HIV virus-like particles (VLPs) using single particle tracking total internal reflection fluorescence (SPT-TIRF) microscopy. They show that gp120 and VLPs promote clustering of wild-type CXCR4 and CXCR4.R334X from a person with WHIM syndrome. The HIV Env-induced clustering involves heterodimeric interactions between CXCR4 and CD4 and spatial distribution and dynamics are distinct from that induced by CXCR4's natural ligand, CXCL12. The authors suggest the CD4-CXCR4 interaction may be targeted to specifically block HIV infection.

      Major comments

      This study is well described in both the main text and figures. Introduction provides adequate background and cites the literature appropriately. Materials and Methods are detailed. Authors are careful in their interpretations, statistical comparisons, and include necessary controls in each experiment. The Discussion presents a reasonable interpretation of the results. Overall, there are no major weaknesses with this manuscript.

      Minor comments

      Ln 477-497. NL4-3deltaIN and immature HIV virions are found to have less associated gp120 relative to wild-type particles. It is not obvious why this is the case for the deltaIN particles or genetically immature particles. Can the authors provide possible explanations? (A prior paper was cited, Chojnacki et al Science, 2012 but can the current authors provide their own interpretation.)

      Significance

      The current study builds on prior works that examined CXCR4 distribution, HIV pseudotyped infection in CXCR4.R334X cells, but goes beyond these studies in resolution and depth of analysis of CXCR4/CD4 nanoclustering, AF3 modeling of CXCR4/CD4 heterodimer, as well as demonstration of replication of HIV in CXCR4.R334X cells.

      Audience:

      Scientists interested in HIV-1, cell biologists and virologists interested in receptor nanoclustering

      Reviewer expertise:

      HIV-1 Envelope glycoproteins and entry assays, HIV broadly neutralizing antibodies, HIV vaccine design

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors investigate the impact of surface bound HIV gp120 and VLPs on CXCR4 dynamics in Jurkat T cells expressing WT or WHIM syndrome mutated CXCR4, which has a defective response to CXCL12. Jurkat cells were transfected with CXCR4-AcGFP. Images were acquired and a single particle tracking routine was applied to generate information about nanoclustering and diffusion, and FRET was used to investigate CD4-CXCR4 proximity. They compare effects of soluble gp120 to immature and mature VLPs, which include varying degrees of gp120 clustering. They find that solid phase gp120 or VLP can increase CXCR4 clustering size and decrease diffusion in Jurkat cells. Surprisingly, VLP lacking gp120 could increase CXCR4 clustering and speed, which is paradoxical as there were no known ligands on the VLPs, but they likely carry many cellular proteins with potential interactions. The impact of CXCL12 and gp120 binding to CXCR4 was different in terms of clustering and receptor down-regulation.

      While a single particle tracking routine was applied to the data, it's not clear how the signal from a single GFP was defined and if movement during the 100 ms acquisition time impacts this. My concern would be that the routine is tracking fluctuations, and these are related to single particle dynamics, it appears from the movies that the density or the GFP tagged receptors in the cells is too high to allow clear tracking of single molecules. SPT with GFP is very difficult due to bleaching and relatively low quantum yield. Current efforts in this direction that are more successful include using SNAP tags with very photostable organic fluorophores. The data likely does mean something is happening with the receptor, but they need to be more conservative about the interpretation.

      Some of the paradoxical effects might be better understood through deeper analysis of the SPT data, particularly investigation of active transport and more detailed analysis of "immobile" objects. Comments on early figures illustrate how this could be approached. This would require selecting acquisitions where the GFP density is low enough for SPT and performing a more detailed analysis, but this may be difficult to do with GFP.

      When the authors discuss clusters of <2 or >3, how do they calibrate the value of GFP and the impact of diffusion on the measurement. One way to approach this might be single molecules measurements of dilute samples on glass vs in a supported lipid bilayer to map the streams of true immobility to diffusion at >1 µm2/sec.

      I understand that the CXCL12 or gp120 are attached to the substrate with fibronectin for adhesion. I'm less clear how how that VLPs are integrated. Were these added to cells already attached to FN? Fig 1A- The classification of particle tracks into mobile and immobile is overly simplistic description that goes back to bulk FRAP measurements and it not really applicable to single molecule tracking data, where it's rare to see anything that is immobile and alive. An alternative classification strategy uses sub-diffusion, normal diffusion and active diffusion (or active transport) to descriptions and particles can transition between these classes over the tracking period. Fig 1B- this data might be better displayed as histograms showing distributions within the different movement classes. Fig 1C,D- It would be helpful to see a plot of D vs MSI at a single particle level. In comparing C and D I'm surprised there is not a larger difference between CXCL12 and X4-gp120. It would also be very important to see the behaviour of X4-gp120 on the CXCR4 deficient Jurkat that would provide a picture of CD4 diffusion. The CXCR4 nanoclustering related to the X4-gp120 could be dominated by CD4 behaviour.

      Fig S1D- This data is really interesting. However, if both the CD4 and the gp120 have his tags they need to be careful as poly-His tags can bind weakly to cells and increasing valency could generate some background. So, they should make the control is fair here. Ideally, using non-his tagged person of sCD4 and gp120 would be needed ideal or they need a His-tagged Fab binding to gp120 that doesn't induce CXCR4 binding.

      Fig S4- Panel D needs a scale bar. I can't figure out what I'm being shown without this.

      Significance

      The strengths are that its an important question and the reagents are well prepared and characterised. They are detecting quantitative effects that will likely be reproducible. The information generated is potentially useful for those studying HIV infection processes and strategies to prevent infection.

      The major weakness is that the conditions for the SPT experiments are not ideal in that the density of particles is too high for SPT and the single molecule basis for assessing nanoclusters is not clear. This means that the data is getting at complex molecules phenomena and less likely be generating pure single molecules measurements.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-03206

      Corresponding author(s____): Teresa M. Przytycka

      General Statements

      We thank all the reviewers for their time and their constructive criticism, based on which we have revised our manuscript. All review comments in are italics. Our responses are indicated in normal font except the excerpts from manuscript which are shown within double quote and in italics. The line numbers indicated here refer to those in the revised manuscript.

      Point-by-point description of the revisions

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

      This paper addresses the interesting question of how cell size may scale with organ size in different tissues. The approach is to mine data from the fly single cell atlas (FCA) which despite its name is a database of gene expression levels in single isolated nuclei. Using this data, they infer cell size based on ribosomal protein gene expression, and based on this approach infer that there are tissue and sex specific differences in scaling, some of which may be driven by differences in ribosomal protein gene expression.

      Response: Indeed, using the FCA dataset, we infer sex-specific differences in both cell size and cell number, which we validated with targeted experiments. We show that Drosophila cell types scale through distinct strategies-via cell size, cell number, or a mix of both-in an allometric rather than uniform fashion. We further propose that these scaling differences are driven, at least in part, by variation in translational activity, reflected in the expression of ribosomal proteins, translation elongation factors, and Myc.

      -----------------------------------------------------------

      I think the idea of mining this database is a clever one, however there a number of concerns about whether the existing data can really be used to draw the conclusions that are stated.

      __Response: __We are pleased to see that the reviewer found the question and our approach interesting.

      -----------------------------------------------------------

      *One concern has to do with the assumption that RP (ribosome protein) expression is a proxy for cell size. It is well established that ribosome abundance scales with cell size, but is there reason to believe that ribosome nuclear gene EXPRESSION correlates with ribosome abundance? *

      I'm not saying that this can't be true, but it seems like a big assumption that needs to be justified with some data. Maybe this is well known in the Drosophila literature, but in that case the relevant literature really needs to be cited.

      __Response: __To avoid any misunderstanding: we use sex-biased RP expression as an indicator of sex differences in cell size only within the same cell type or subtype, as defined by expression-based clustering in the FCA-not as a general estimator of cell size. This measure is applied strictly within the same clusters, never between different ones. To prevent overinterpretation, we replaced the term 'proxy' with 'indicator,' since the earlier wording might have implied that ribosomal gene expression was being used to estimate cell size more broadly.

      We should have begun by providing more background on the well-established link between ribosomal protein gene dosage and cell growth. This context was missing from the introduction, so we have now added a full paragraph outlining what is known about this connection:

      *Added at line 85: *

      "Cell growth, which supports both cell enlargement and cell division, demands elevated protein synthesis, accomplished by boosting translation rates. Indeed, ribosome abundance is known to scale with cell size in many organisms (Schmoller and Skotheim 2015; Cadart and Heald 2022; Serbanescu et al. 2022). Long before it was known that DNA was the carrier of genetic information, Drosophila researchers had identified a large class of mutations known as "Minutes" (Schultz 1929). These were universally haplo-insufficient. A single wild type copy resulted in a tiny slowly growing fly, and the homozygous loss-of-function alleles were lethal. In clones, the Minute cells are clearly smaller and compete poorly with surrounding wild type cells. We now know that most of the Minute loci encode ribosomal proteins (Marygold et al. 2007). Similarly, the Drosophila diminutive locus, also characterized by small flies almost a century ago, is now known to encode the Myc oncogene (Gallant 2013). This is significant as Myc is a regulator of ribosomal protein encoding genes in metazoans, including Drosophila (Grewal et al. 2005). The ribosome is assembled in a specialized nuclear structure called the nucleolus (Ponti 2025). Across species, including Drosophila (Diegmiller et al. 2021) and C. elegans (Ma et al. 2018), nucleolar size scales with cell size and is broadly correlated with growth in cell size and/or cell number, processes that are directly relevant to sex-specific allometry. Collectively, these and many other studies offer compelling evidence that ribosomal biogenesis is positively associated with cell size and growth, underscoring the value of measuring ribosome biogenesis as a metric."

      We understand that the reviewer is asking whether reduced RP mRNA expression directly leads to reduced functional ribosome assembly. We do not have a definitive answer to that specific question. However, we directly measured translation in fat body cells (section: Female bias in ribosomal gene expression in fat body cells leads to sex-biased protein synthesis), and the results show a clear correlation between RP gene expression and biosynthetic activity; even though we did not track every step from transcription to ribosome assembly to polysome loading across all cell types. This would indeed be an excellent direction for future work, including polysome profiling and related assays. Importantly, we did examine the nucleolus (Figure 4), where ribosome assembly occurs, and showed that nucleolar volume scales with RP gene expression. This strongly supports the presence of sex-specific differences in ribosome biogenesis.

      Added at line 115:

      "Building on the earlier studies noted above, as well as our direct measurements of translation bias in the fat body, nucleolar size, and cell size, we used sex-biased expression of ribosomal proteins as an indicator of sex differences in per-nucleus cell size."

      -----------------------------------------------------------

      Second, the interpretation of RP expression as a proxy for cell size seems potentially at odds with the fact that some cells are multi-nucleate. Those cells are big because of multiple nuclei, and so they might not show any increase in ribosome expression per nucleus. presumably for multi-nucleate cells, RP expression if it reflects anything at all would be something to do with cell size PER nucleus.

      Response: Yes, this is a very important point, and this is why we chose multinucleated indirect flight muscles for our direct experimental analysis. We show that in indirect flight muscle cells, adult cell size is greatly influenced by the sex-specific number of nuclei per cell. The female muscle cells are larger and have larger nuclei count per cell. Additionally, they also have higher expression of ribosomal protein coding genes. As the latter data are from the single nucleus sequencing atlas, this already demonstrates what this reviewer is asking for: per nucleus, female muscle cells express more ribosome protein coding mRNAs.

      -----------------------------------------------------------

      *Third, it is well known that many tissues in Drosophila are polyploid or polytene. I don't know enough about the methodology used to produce the FCA to know whether this is somehow normalized. Otherwise, my hypothesis would be that nuclei showing higher RP expression might just be polyploid or polytene. You might say that this could be controlled by asking if all genes are similary upregulated, but that isn't the case since at least in polytene chromosomes it is well known that only a small number of genes are expressed at a given time, while many are silent. *

      Response: Yes, this is an excellent point. As noted above, our study does not distinguish among the different potential causes of sex differences in ribosomal mRNA copy number, as these may vary across cell types. We now explicitly acknowledge it in the discussion (line 327). Importantly, even in the cases when ribosomal gene expression bias primarily reflects differences in DNA content, this still represents a plausible mechanistic route linking ribosomal gene expression to increased nucleolar ribosome biogenesis and, ultimately, larger cell size. This possibility does not alter our main conclusions.

      -----------------------------------------------------------

      Overall, I think a lot more foundational work would need to be done in order to allow the inference of cell size from RP expression. In a way, it is a bit unfortunate that they chose to do this work in Drosophila where so many cells are polyploid, although I gather that even in humans some tissues have this issue, for example large neurons in the brain.

      Response: We acknowledge that we did not clearly reference some of the foundational work in the literature. To address this, we have expanded the introduction to provide additional background and context. We also clarify that our fat body experiment offers independent support for the relationship between ribosomal gene expression bias, nuclear size bias, and corresponding biases in protein synthesis, thereby reinforcing the use of sex-specific ribosomal gene expression as an indicator of sex-specific cell size. Importantly, we assess this bias only within clusters, not between them. These clusters are derived from gene-expression-based clustering and are therefore relatively homogeneous. For example, as discussed in our response to Reviewer #3, the fat body contains several clusters that correspond to expression-defined subtypes of fat body cells. Our previous terminology may have inadvertently implied that we were using ribosomal gene expression to estimate cell size more broadly, which was not our intention.

      As for the choice of the organism, most of the authors are Drosophila researchers and we benefit from the unique, highly replicated data from whole head and whole body of both sexes. Such data is necessary for a non-biased estimation of the differences in nuclear number.

      -----------------------------------------------------------

      *Reviewer #1 (Significance (Required)):

      The idea that gene regulatory networks could "program" differences in scaling by changing levels of ribosomal protein gene expression is a tremendously important one if it can be established, because it would show a simple way for size scaling to be placed under control of developmental regulatory pathways. My original concern when I first looked at the abstract was going to be that yeah the results are interesting but a mechanism is not provided, but as I read it, that concern went away. showing that RP gene expression, which could be programmed by various driving pathways, can affect allometric scaling, would be extremely impactful and really change how we think about scaling, but putting it into the framework of gene expression networks that control other aspects of developmewnht. it would not be necessary to show which pathways actually drive these expression differences, the fact that they are different would be interesting enough to make everyone want to read this paper. But as discussed above I am not, however, convinced by the evidence presented here. So while I think it would be very significant if true, I am not convinced that the conclusion is well supported. This doesn't mean I have a reason to think it is false, just that its not well supported for the reasons I have given.*

      Response: We are grateful to the reviewer for this positive assessment of our findings despite lack of a specific mechanism. We also regret that our initial writing did not clearly situate our work within the foundational literature on the relationship between ribosomal biogenesis and scaling. The key contribution of our study is to demonstrate that sex-biased ribosomal biogenesis plays a role in allometric scaling, providing a basis for future mechanistic exploration. We hope that the revised manuscript now offers clear and compelling support for the conclusion that RP gene expression bias can influence allometric scaling.

      -----------------------------------------------------------

      I hasten to point out that I could be entirely wrong, if the missing bits of logic (i.e. that RP expression matches ribosome abundance and that gene expression in the FCA dataset isn't influenced by ploidy of the nucleus). If suitable references can be provided to support these underlying assumptions, then in fact I think these concerns could be answered with very little effort. Otherwise, I think experiments would be needed to support these assumptions, and that might be non-trivial to do in a reasonable time frame. for that reason, in the next question I have put "cannot tell" for the time estimate.

      Response: While gene expression in some FCA cell types may indeed be influenced by ploidy, our analysis does not depend on distinguishing among the possible sources of gene expression bias, which may vary across cell types. Rather, our key point is that-regardless of its origin-an increase in ribosomal gene expression is associated with enhanced ribosome biogenesis in the nucleolus and, ultimately, larger cell size. Thus, our main conclusions do not rely on any specific mechanism underlying RP gene expression upregulation. We now include additional references supporting the relationship between RP expression bias and cell size bias. We also strengthen the link between ribosomal gene expression and biosynthetic activity by clarifying its relationship with sex-biased Myc expression and the strong correlation with expression bias of EF1. We now include additional references supporting the relationship between RP expression bias and cell size bias. We also strengthen the link between ribosomal gene expression and biosynthetic activity by clarifying its relationship with sex-biased Myc expression and the strong correlation with expression bias of EF1.

      We thank the reviewer for their thoughtful and constructive comments, which have prompted us to clarify both our reasoning and the relevant literature more fully.

      -----------------------------------------------------------

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

      The authors analyzed the FlyAtlas single-nucleus dataset to identify sex differences in gene expression and cell numbers. This led them to focus on muscles, cardiomyocytes, and fat body cells. They then measured cell and nucleolus size across different tissues and showed that reducing Myc function decreases sex differences in fat body cells. Overall, the manuscript provides a characterization of dimorphic differences in cell and organ size across three tissues.*

      Response: This is a nice synopsis of the work.

      -----------------------------------------------------------

      Major Comments: The major claims of the manuscript are well supported by the reported experiments and analyses. While Reviewer #2 considered the major claims of the manuscript to be well supported, by the reported experiments and analysesStatistical analyses appear adequate.

      Response: We agree, and we are glad that the reviewer found our work well supported.

      -----------------------------------------------------------

      *Minor Comments: The following minor issues should be addressed through textual edits:In the Introduction:

      "Disruptions in proportionality, whether due to undergrowth or overgrowth, can lead to reduced fitness or diseases such as cancer." Could the authors provide a reference for this statement, particularly for the claim that disruptions in proportion*

      Response: We apologize for this omission. The following explanation is now included starting at line 39:

      "For example, scaled cell growth is a driver of symmetry in Myc-dependent scaling of bone growth in the skeleton by chondrocyte proliferation (Ota et al. 2007; Zhou et al. 2011). Increased nucleolus size is a well known marker of cancer progression in a histopathological setting (Pianese 1896; Derenzini et al. 1998; Elhamamsy et al. 2022)."

      -----------------------------------------------------------

      *The authors state:

      "This study offers a comprehensive, cellular-resolution analysis of sexual size dimorphism in a model organism, uncovering how differences in cell number and size contribute to sex-specific body plans."*

      The study cannot be considered comprehensive, as not all organs were examined.

      Response: Indeed, "comprehensive" is a loaded word and in the revised manuscript we just omitted it.

      -----------------------------------------------------------

      *The following sentence from the abstract is unclear:

      "By uncovering how a conserved developmental system produces sex-specific proportions through distinct cellular strategies..."*

      * What do the authors mean by a conserved developmental system? Do they refer to a commonly used developmental model, or to a developmental system that is evolutionarily conserved?*

      Response: We acknowledge that the use of the word 'conserved' was inappropriate, and we have therefore removed it from the statement.

      -----------------------------------------------------------

      *Reviewer #2 (Significance (Required)):

      The manuscript presents a relevant exploration of sex-specific differences in cell size and cell number in Drosophila males and females. The limitations of the study are clearly acknowledged in the "Limitations" section. The work does not provide mechanistic insight into the causes or functional consequences of the observed differences. Nonetheless, the study extends our understanding of sexual dimorphism and establishes a foundation for future investigations into the autonomous and systemic mechanistic factors that regulate these differences.*

      Response: Thank you.

      -----------------------------------------------------------

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

      The manuscript by Pal and colleagues addresses an important question: the cellular mechanisms underlying sex differences in organ size. By leveraging single-nucleus transcriptomic data from the adult Drosophila Cell Atlas, the authors show that different cell types adopt distinct strategies to achieve sex differences in organ size-either by increasing cell size or by altering cell number. They then focus on three organs-the indirect flight muscles, the heart, and the fat body-and provide supporting evidence for their transcriptomic analyses.*

      Response: This is a nice summary of the study. Thank you.

      -----------------------------------------------------------

      This study tackles a highly relevant and often overlooked question, as our understanding of the molecular and cellular events driving sex differences remains incomplete. The work presents interesting observations; however, it is largely descriptive, establishing correlations without providing functional evidence or mechanistic insight.

      Response: We agree that this is an often overlooked problem that has been difficult to address experimentally without single-cell genomics. Our work aims to help fill this gap. While the paper does contain descriptive elements, we believe such characterization is important at the early stages of developing a new area of inquiry. The study explores a unique dataset and includes experimental validation to support key observations. We also propose how allometry may be shaped by cell division and cell size, drawing on well-established molecular mechanisms. Thus, the reviewer's comment regarding a lack of mechanistic insight likely pertains to the absence of a direct connection to the sex-determination pathway, which is beyond the scope of the current study.

      -----------------------------------------------------------

      Below are four main points that should be addressed before publication: 1. Introduction and contextualisation of prior work The introduction does not adequately present the current state of knowledge. Several key studies are missing or insufficiently discussed. In particular, the following works should be included and integrated into the introduction: - PMID: 26710087 - shows that the sex determination gene transformer regulates male-female differences in Drosophila body size. - PMID: 28064166 - describes how differences in Myc gene dosage contribute to sex differences in body size. - PMID: 26887495 - demonstrates that the intrinsic sexual identity of adult stem cells can control sex-biased organ size through sex-biased proliferation. - PMID: 28976974 - reveals that Sxl modulates body growth through both tissue-autonomous and non-autonomous mechanisms. - PMID: 39138201 - shows that transformer drives sex differences in organ size and body weight. Incorporating and discussing these references would provide a more comprehensive and up-to-date framework for the study.

      Response: We agree that the literature suggested by the reviewer strengthens the introduction and improves the contextualization of prior work relevant to our study. Although much of it was previously included in the discussion section on cell-autonomous and hormonal regulation, it has now been moved to the introduction, along with the discussion of the papers suggested by the reviewer (beginning at line 58).

      "In Drosophila melanogaster, adult females are substantially larger than males (Fig. 1A1), yet both sexes develop from genetically similar zygotes and share most organs and cell types. In wild type flies, sex is determined by the number of X chromosomes in embryos, with XX flies developing as females and X(Y) flies developing as males due to the activation and stable expression of Sex-lethal only in XX flies (Erickson and Quintero 2007). While it is not entirely clear how sexually dimorphic size is regulated, the sex determination pathway is implicated in size regulation. Sex-reversed flies often show a size based on the X chromosome number rather than sexual morphology. Female Sex-lethal contributes to larger female size independently of sexual identity (Cline 1984), and Sex-lethal expression in insulin producing neurons in the brain also impacts body size (Sawala and Gould 2017). Female-specific Transformer protein is produced as a consequence of female-specific Sex-lethal and also contributes to increased female size (Rideout et al. 2015). This size scaling also applies to individual organs. For example, the Drosophila female gut is longer than the male gut due Transformer activity (Hudry et al. 2016). It has also been suggested that Myc dose (it is X-linked) is a regulator of body size (Mathews et al. 2017), although the failed dosage compensation model proposed has not been demonstrated."

      And again at line 74:

      "These studies show that size is regulated, but they do not address whether scaling is uniform or non-uniform and the mechanism for sexual size differences (SSD). The origins of SSD can, in principle, arise from differences in (i) gene expression, (ii) the presence of sex-specific cell types, (iii) the number of cell-specific nuclei, or (iv) the size (per nucleus) of those cells. Previous research in Drosophila has largely focused on gene expression in sex-specific organs like the gonads (Arbeitman et al. 2002; Parisi et al. 2004; Graveley et al. 2011; Pal et al. 2023), which are governed by a well-characterized sex-determination pathway (Salz and Erickson 2010; Clough and Oliver 2012; Raz et al. 2023) However, whether and how scaling differences in shared, non-sex-specific tissues are achieved via changes in cell size and number remains largely unexamined (Fig. 1A2). These studies show that size is regulated, but they do not address whether scaling is uniform or non-uniform and the mechanism for size differences."

      -----------------------------------------------------------

      2. Use of ribosomal gene expression as a proxy for cell size The authors use ribosomal gene expression levels as a proxy for cell size, but this assumption is not adequately justified. The cited references (refs. 20-22) focus on unicellular organisms (bacteria and yeast) or cleavage divisions in frog embryos, which are fundamentally different from adult Drosophila tissues. The authors should provide evidence that ribosome abundance scales with cell size across the distinct adult Drosophila cell types. Given that most adult fly tissues are post-mitotic, it is more likely that ribosomal gene expression reflects protein synthesis activity rather than cell size, particularly in secretory cell types.

      Response: Reviewer 1 raised a similar point, and we agree. We recognize that the term "proxy" may have been misleading. We use this measure only in the context of sex bias within homogeneous cell clusters, and not between clusters, even when such clusters share the same cell-type annotation. To avoid overinterpretation, we changed "poxy" to "indicator".

      In response to the reviewer's concern, we have expanded our discussion of the relevant supporting literature (additional text starting line 75). We have also directly measured translation in the fat body cells (section: Female bias in ribosomal gene expression in fat body cells leads to sex biased protein synthesis), which clearly demonstrates a correlation between ribosomal protein gene expression and biosynthetic activity. Although, we have not traced the chain of events from expression to ribosome assembly to polysome loading in all cell types, we did examine the nucleolus (Figure 4), where ribosomes are assembled, and we make a strong point that the volume of the nucleolus scales like ribosome protein gene expression. This provides strong evidence for sex-specific ribosome biogenesis contributing to cell size.

      Furthermore, the observation that ribosomal gene expression likely reflects protein synthesis activity is not at odds with increased cell size: biosynthesis increases in larger cells (Schmoller and Skotheim 2015). We have added a panel to Figure 4 showing the relationship between ribosomal gene expression bias and the average expression bias of Eukaryotic Elongation Factor 1 (eEF1).

      -----------------------------------------------------------

      3. Relationship between Myc and sex-biased Rp expression The proposed link between Myc and sex-biased Rp expression is unclear. Panels D and E of Figure 1 show no consistent relationship: some cell types with strong Rp sex bias exhibit either high or low female Myc bias, or even a male bias. The linear regression in Figure 4I (R = 0.07, p = 0.59) confirms the lack of correlation. The authors should clarify this point and adopt a more cautious interpretation regarding Myc as a potential regulator of sex-biased Rp expression and cell size differences. Experimentally, using Myc hypomorph or heterozygous conditions would be more appropriate than complete knockdown to test its role.

      Response: Thank you for noting that the relationship between Myc expression bias and sex-biased RP expression required clarification. This response was prepared in consultation with Myc expert Dr. David Levens.

      We demonstrate that both Myc and RP gene expression exhibit an overall female bias in the body. The absence of a strong correlation across cell clusters does not invalidate this conclusion. Myc is a well-established master regulator of ribosome biogenesis, but its quantitative effects are complex. According to recent models of Myc-mediated gene regulation (Nie et al. 2012; Lin et al. 2012), Myc upregulates all actively transcribed genes. Because this regulation is global, the relationship between changes in Myc expression and corresponding changes in ribosomal protein gene expression depends on cell type. Moreover, (Lorenzin et al. 2016) demonstrated that ribosomal protein genes saturate at relatively low levels of Myc, which helps explain why we observe a correlation in head cell clusters-where Myc expression is lower-but not in body clusters.

      Importantly, on average, the female-specific Myc expression bias is stronger in body cell clusters than in head cell clusters, consistent with the stronger female bias in ribosomal protein gene expression observed in the head relative to the body.

      To make this relationship more transparent, we combined the head and body clusters, which yielded a strong overall correlation (Fig. 4J, replacing the previous Fig. 4H).

      To further strengthen the evidence linking ribosomal gene expression to cell size, we also examined the relationship between ribosomal gene expression bias and Elongation Factor 1 (eEF1) expression bias, a key component of protein biosynthesis during the elongation step of translation. The resulting correlation exceeds 0.9 (new Fig. 4H, added as an additional panel in Fig. 4).

      -----------------------------------------------------------

      4. Conclusions about fat body cell number I have concerns about drawing conclusions on sex differences in fat body cell number from single-nucleus transcriptomic data for two reasons:

      1- Drosophila fat body tissue is heterogeneous, comprising distinct subpopulations (e.g., visceral fat cells, subcuticular fat cells), some of which are sex-specific-such as fat cells associated with the spermathecae in females.

      Response: Thank you for giving us the opportunity to clarify our analysis of the FCA data. Our approach does account for subpopulations within the fat body as well as within other cell types. Based on gene expression profiles, we identify three fat body clusters, all of which are reported in Table S3. One small female-specific cluster (

      When all fat body clusters are combined into a single supercluster, this supercluster still shows a male bias. We have now clarified this point in the manuscript (line 113). Note that both subclusters of fat body are already shown in Fig. 1C and 1D.

      -----------------------------------------------------------

      2- Adult fat body cells can be multinucleated (PMID: 13723227). Apparent sex differences in nucleus number may reflect differences in specific subpopulations or degrees of multinucleation rather than true differences in cell number. To strengthen the conclusions, the analysis should be performed at the level of fat body subpopulations, distinguishing clusters where possible. Additionally, quantifying nuclei relative to actual cell number-as done for muscle tissue-would clarify whether observed sex differences reflect true variation in cell number or differences in nuclear content per cell.

      Response: Yes, some cells can be multinucleate. We specifically address this in the context of muscle cells, where multinucleation is prominent, and we also conducted experimental validation in this tissue. As noted above, our analysis is performed at the subpopulation level, since clusters are defined by expression similarity (Leiden resolution 4.0) rather than by annotation.

      Because our work relies on single-nucleus data, each nucleus is treated as an individual unit of analysis. Nevertheless, we observe genuine nuclear differences within each cluster. Importantly, the presence of multinucleated cells does not alter our conclusions; it simply represents one form of variation in cell number that can be thought of as a subcomponent of cell/nuclei number.

      -----------------------------------------------------------

      Minor corrections/points: 1-The term body size in the title does not accurately reflect the content of the paper. I recommend replacing it with organ size to better align with the study's focus.

      Response: Thank you for the suggestion.

      ----------------------------------------------------------- 2-The term sexual size dimorphism is somewhat inaccurate in this context. Sex differences in size would be more appropriate. The term sexual dimorphism typically refers to traits that exhibit two distinct forms in males and females-such as primary or secondary sexual characteristics like sex organs or sex combs. In contrast, size is a quantitative trait that follows a normal distribution. Although the average female may be larger than the average male, the distributions overlap, making the term dimorphism imprecise.

      Response: Thank you for the suggestion.

      -----------------------------------------------------------

      3-In Figure 2E, there appears to be an inconsistency between the text, figure legend, and the data presented. The text and legend state that the total volume of dorsal longitudinal flight muscle cells was quantified, whereas the graph indicates measurements of nuclear size. This discrepancy should be clarified.

      Response: Thank you for pointing this out. We figured out that Y-axis label in the graph was incorrect and it is now fixed.

      -----------------------------------------------------------

      4-The authors proposed: "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication". Please note that this hypothesis has already been tested functionally in PMID: 39138201 and was shown to be incorrect. Sex differences in body size are completely independent of fat body sexual identity or any intrinsic sex differences within fat cells.

      __Response: __We thank the reviewer for the opportunity to discuss why the data shown in PMID 39138201 (Hérault et al. 2024) do not rule out a model in which the fat body contributes to the sex-specific regulation of body size via interorgan communication. The main reason data in Herault et al cannot rule out such a model is that they use wing size as a proxy for body size. This is in contrast to prior studies, such as (Rideout et al. 2015), in which pupal volume was used to directly measure body size and show a non-autonomous effect of sex determination gene transformer on body size. Measuring body size directly is a more precise readout of growth during the larval stages of development, as opposed to using adult wing area which reflects the growth of a single organ. It is also important to note that the diets used to rear flies in Herault and Rideout differ, which is an important consideration as females do not achieve their maximal size without high dietary protein levels (Millington et al. 2021). To ensure all these points are communicated to readers, we added text to this effect in the revised version of our manuscript.

      Added at line 254:

      "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication (Colombani et al. 2003; Géminard et al. 2009; Rajan and Perrimon 2012; Sano et al. 2015; Koyama and Mirth 2016). Indeed, one study showed the sexual identity of the fat body influenced pupal volume, which is an accurate readout of larval growth (Rideout et al. 2015; Delanoue et al. 2010). While a recent study suggests that male-female differences in body size were regulated independently of fat body sexual identity (Hérault et al. 2024), this study measured the growth of a single organ, the wing, as a proxy for body size. Additional studies are therefore needed to resolve whether fat body protein synthesis plays an important role in regulating sex differences in body size."

      -----------------------------------------------------------

      *5-The authors state: "This demonstrate that Myc plays a key role in regulating the sex difference in nucleolar size." This is an overstatement given the functional data presented. The claim should be toned down to reflect the limited evidence.

      **Referee cross-commenting**

      I completely agree with the main comments of Reviewer 1, as they address the paper's core.*

      Response: We have addressed the comments of Reviewer 1 in the response to reviewer's comments above.

      -----------------------------------------------------------

      *Reviewer #3 (Significance (Required)):

      The main novelty and strongest aspect of this study is its use of single-nucleus transcriptomic data from the adult Drosophila Cell Atlas to investigate how different cell types adopt distinct strategies to generate sex differences in organ size-either by increasing cell size or by altering cell number. Previous studies have largely focused on specific tissues, whereas this work provides a comprehensive, organism-wide view that encompasses all tissues, enabling direct cross-comparison between organs. This represents a clear advance in the field, primarily from a technical perspective, by leveraging organism-wide single-cell transcriptomics. The main limitations lie in the lack of functional experiments and mechanistic insights. Moreover, the proposed mechanism-differences in Myc gene dosage or expression levels-is not entirely novel, as Myc dosage has previously been implicated in contributing to sex differences in body size (PMID: 28064166).*

      Response: We do have some functional testing in the 3 tissues, flight muscle, heart and fat body, however, providing mechanistic insights is beyond the scope of this paper. The paper suggested by the reviewer is an example of one attempt to provide such a mechanism, probably not the only one. We hope that our rich data that we have assembled in this paper provide resources for generating hypotheses and stimulate further research.

      -----------------------------------------------------------

      References

      Cadart, Clotilde, and Rebecca Heald. 2022. "Scaling of Biosynthesis and Metabolism with Cell Size." Molecular Biology of the Cell 33 (9): pe5. https://doi.org/10.1091/mbc.E21-12-0627.

      Diegmiller, Rocky, Caroline A. Doherty, Tomer Stern, Jasmin Imran Alsous, and Stanislav Y. Shvartsman. 2021. "Size Scaling in Collective Cell Growth." Development (Cambridge, England) 148 (18): dev199663. https://doi.org/10.1242/dev.199663.

      Gallant, Peter. 2013. "Myc Function in Drosophila." Cold Spring Harbor Perspectives in Medicine 3 (10): a014324. https://doi.org/10.1101/cshperspect.a014324.

      Grewal, Savraj S., Ling Li, Amir Orian, Robert N. Eisenman, and Bruce A. Edgar. 2005. "Myc-Dependent Regulation of Ribosomal RNA Synthesis during Drosophila Development." Nature Cell Biology 7 (3): 295-302. https://doi.org/10.1038/ncb1223.

      Hérault, Chloé, Thomas Pihl, and Bruno Hudry. 2024. "Cellular Sex throughout the Organism Underlies Somatic Sexual Differentiation." Nature Communications 15 (1): 6925. https://doi.org/10.1038/s41467-024-51228-6.

      Lin, Charles Y., Jakob Lovén, Peter B. Rahl, et al. 2012. "Transcriptional Amplification in Tumor Cells with Elevated C-Myc." Cell 151 (1): 56-67. https://doi.org/10.1016/j.cell.2012.08.026.

      Lorenzin, Francesca, Uwe Benary, Apoorva Baluapuri, et al. 2016. "Different Promoter Affinities Account for Specificity in MYC-Dependent Gene Regulation." eLife 5 (July): e15161. https://doi.org/10.7554/eLife.15161.

      Ma, Tian-Hsiang, Po-Hsiang Chen, Bertrand Chin-Ming Tan, and Szecheng J. Lo. 2018. "Size Scaling of Nucleolus in Caenorhabditis Elegans Embryos." Biomedical Journal 41 (5): 333-36. https://doi.org/10.1016/j.bj.2018.07.003.

      Marygold, Steven J., John Roote, Gunter Reuter, et al. 2007. "The Ribosomal Protein Genes and Minute Loci of Drosophila Melanogaster." Genome Biology 8 (10): R216. https://doi.org/10.1186/gb-2007-8-10-r216.

      Millington, Jason W., George P. Brownrigg, Charlotte Chao, et al. 2021. "Female-Biased Upregulation of Insulin Pathway Activity Mediates the Sex Difference in Drosophila Body Size Plasticity." eLife 10 (January): e58341. https://doi.org/10.7554/eLife.58341.

      Nie, Zuqin, Gangqing Hu, Gang Wei, et al. 2012. "C-Myc Is a Universal Amplifier of Expressed Genes in Lymphocytes and Embryonic Stem Cells." Cell 151 (1): 68-79. https://doi.org/10.1016/j.cell.2012.08.033.

      Ponti, Donatella. 2025. "The Nucleolus: A Central Hub for Ribosome Biogenesis and Cellular Regulatory Signals." International Journal of Molecular Sciences 26 (9): 4174. https://doi.org/10.3390/ijms26094174.

      Rideout, Elizabeth J., Marcus S. Narsaiya, and Savraj S. Grewal. 2015. "The Sex Determination Gene Transformer Regulates Male-Female Differences in Drosophila Body Size." PLOS Genetics 11 (12): e1005683. https://doi.org/10.1371/journal.pgen.1005683.

      Schmoller, Kurt M., and Jan M. Skotheim. 2015. "The Biosynthetic Basis of Cell Size Control." Trends in Cell Biology 25 (12): 793-802. https://doi.org/10.1016/j.tcb.2015.10.006.

      Schultz, J. 1929. "The Minute Reaction in the Development of DROSOPHILA MELANOGASTER." Genetics 14 (4): 366-419. https://doi.org/10.1093/genetics/14.4.366.

      Serbanescu, Diana, Nikola Ojkic, and Shiladitya Banerjee. 2022. "Cellular Resource Allocation Strategies for Cell Size and Shape Control in Bacteria." The FEBS Journal 289 (24): 7891-906. https://doi.org/10.1111/febs.16234.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The manuscript by Pal and colleagues addresses an important question: the cellular mechanisms underlying sex differences in organ size. By leveraging single-nucleus transcriptomic data from the adult Drosophila Cell Atlas, the authors show that different cell types adopt distinct strategies to achieve sex differences in organ size-either by increasing cell size or by altering cell number. They then focus on three organs-the indirect flight muscles, the heart, and the fat body-and provide supporting evidence for their transcriptomic analyses.

      This study tackles a highly relevant and often overlooked question, as our understanding of the molecular and cellular events driving sex differences remains incomplete. The work presents interesting observations; however, it is largely descriptive, establishing correlations without providing functional evidence or mechanistic insight.

      Below are four main points that should be addressed before publication:

      1. Introduction and contextualisation of prior work The introduction does not adequately present the current state of knowledge. Several key studies are missing or insufficiently discussed. In particular, the following works should be included and integrated into the introduction:
        • PMID: 26710087 - shows that the sex determination gene transformer regulates male-female differences in Drosophila body size.
        • PMID: 28064166 - describes how differences in Myc gene dosage contribute to sex differences in body size.
        • PMID: 2688749 - demonstrates that the intrinsic sexual identity of adult stem cells can control sex-biased organ size through sex-biased proliferation.
        • PMID: 28976974 - reveals that Sxl modulates body growth through both tissue-autonomous and non-autonomous mechanisms.
        • PMID: 39138201 - shows that transformer drives sex differences in organ size and body weight. Incorporating and discussing these references would provide a more comprehensive and up-to-date framework for the study.
      2. Use of ribosomal gene expression as a proxy for cell size The authors use ribosomal gene expression levels as a proxy for cell size, but this assumption is not adequately justified. The cited references (refs. 20-22) focus on unicellular organisms (bacteria and yeast) or cleavage divisions in frog embryos, which are fundamentally different from adult Drosophila tissues. The authors should provide evidence that ribosome abundance scales with cell size across the distinct adult Drosophila cell types. Given that most adult fly tissues are post-mitotic, it is more likely that ribosomal gene expression reflects protein synthesis activity rather than cell size, particularly in secretory cell types.
      3. Relationship between Myc and sex-biased Rp expression The proposed link between Myc and sex-biased Rp expression is unclear. Panels D and E of Figure 1 show no consistent relationship: some cell types with strong Rp sex bias exhibit either high or low female Myc bias, or even a male bias. The linear regression in Figure 4I (R = 0.07, p = 0.59) confirms the lack of correlation. The authors should clarify this point and adopt a more cautious interpretation regarding Myc as a potential regulator of sex-biased Rp expression and cell size differences. Experimentally, using Myc hypomorph or heterozygous conditions would be more appropriate than complete knockdown to test its role.
      4. Conclusions about fat body cell number I have concerns about drawing conclusions on sex differences in fat body cell number from single-nucleus transcriptomic data for two reasons:

      1) Drosophila fat body tissue is heterogeneous, comprising distinct subpopulations (e.g., visceral fat cells, subcuticular fat cells), some of which are sex-specific-such as fat cells associated with the spermathecae in females.

      2) Adult fat body cells can be multinucleated (PMID: 13723227). Apparent sex differences in nucleus number may reflect differences in specific subpopulations or degrees of multinucleation rather than true differences in cell number. To strengthen the conclusions, the analysis should be performed at the level of fat body subpopulations, distinguishing clusters where possible. Additionally, quantifying nuclei relative to actual cell number-as done for muscle tissue-would clarify whether observed sex differences reflect true variation in cell number or differences in nuclear content per cell.

      Minor corrections/points:

      1. The term body size in the title does not accurately reflect the content of the paper. I recommend replacing it with organ size to better align with the study's focus.
      2. The term sexual size dimorphism is somewhat inaccurate in this context. Sex differences in size would be more appropriate. The term sexual dimorphism typically refers to traits that exhibit two distinct forms in males and females-such as primary or secondary sexual characteristics like sex organs or sex combs. In contrast, size is a quantitative trait that follows a normal distribution. Although the average female may be larger than the average male, the distributions overlap, making the term dimorphism imprecise.
      3. In Figure 2E, there appears to be an inconsistency between the text, figure legend, and the data presented. The text and legend state that the total volume of dorsal longitudinal flight muscle cells was quantified, whereas the graph indicates measurements of nuclear size. This discrepancy should be clarified.
      4. The authors proposed: "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication". Please note that this hypothesis has already been tested functionally in PMID: 39138201 and was shown to be incorrect. Sex differences in body size are completely independent of fat body sexual identity or any intrinsic sex differences within fat cells.
      5. The authors state: "This demonstrate that Myc plays a key role in regulating the sex difference in nucleolar size." This is an overstatement given the functional data presented. The claim should be toned down to reflect the limited evidence.

      Referee cross-commenting

      I completely agree with the main comments of Reviewer 1, as they address the paper's core.

      Significance

      The main novelty and strongest aspect of this study is its use of single-nucleus transcriptomic data from the adult Drosophila Cell Atlas to investigate how different cell types adopt distinct strategies to generate sex differences in organ size-either by increasing cell size or by altering cell number. Previous studies have largely focused on specific tissues, whereas this work provides a comprehensive, organism-wide view that encompasses all tissues, enabling direct cross-comparison between organs. This represents a clear advance in the field, primarily from a technical perspective, by leveraging organism-wide single-cell transcriptomics. The main limitations lie in the lack of functional experiments and mechanistic insights. Moreover, the proposed mechanism-differences in Myc gene dosage or expression levels-is not entirely novel, as Myc dosage has previously been implicated in contributing to sex differences in body size (PMID: 28064166).

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The authors analyzed the FlyAtlas single-nucleus dataset to identify sex differences in gene expression and cell numbers. This led them to focus on muscles, cardiomyocytes, and fat body cells. They then measured cell and nucleolus size across different tissues and showed that reducing Myc function decreases sex differences in fat body cells. Overall, the manuscript provides a characterization of dimorphic differences in cell and organ size across three tissues.

      Major Comments:

      The major claims of the manuscript are well supported by the reported experiments and analyses. Statistical analyses appear adequate.

      Minor Comments:

      The following minor issues should be addressed through textual edits:In the Introduction:

      "Disruptions in proportionality, whether due to undergrowth or overgrowth, can lead to reduced fitness or diseases such as cancer."

      Could the authors provide a reference for this statement, particularly for the claim that disruptions in proportionality can lead to cancer?

      The authors state:

      "This study offers a comprehensive, cellular-resolution analysis of sexual size dimorphism in a model organism, uncovering how differences in cell number and size contribute to sex-specific body plans."

      The study cannot be considered comprehensive, as not all organs were examined.

      The following sentence from the abstract is unclear:

      "By uncovering how a conserved developmental system produces sex-specific proportions through distinct cellular strategies..."

      What do the authors mean by a conserved developmental system? Do they refer to a commonly used developmental model, or to a developmental system that is evolutionarily conserved?

      Significance

      The manuscript presents a relevant exploration of sex-specific differences in cell size and cell number in Drosophila males and females. The limitations of the study are clearly acknowledged in the "Limitations" section. The work does not provide mechanistic insight into the causes or functional consequences of the observed differences. Nonetheless, the study extends our understanding of sexual dimorphism and establishes a foundation for future investigations into the autonomous and systemic mechanistic factors that regulate these differences.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This paper addresses the interesting question of how cell size may scale with organ size in different tissues. The approach is to mine data from the fly single cell atlas (FCA) which despite its name is a databse of gene expression levels in single isolated nuclei. Using this data, they infer cell size based on ribosomal protein gene expression, and based on this approach infer that there are tissue and sex specific differences in scaling, some of which may be driven by differences in ribosomal protein gene expression.

      I think the idea of mining this database is a clever one, however there a number of concerns about whether the existing data can really be used to draw the conclusions that are stated.

      One concern has to do with the assumption that RP (ribosome protein) expression is a proxy for cell size. It is well established that ribosome abundance sclse with cell size, but is there reason to believe that ribosome nuclear gene EXPRESSION correlates with ribosome abundance? I'm not saying that this can't be true, but it seems like a big assumption that needs to be justified with some data. Maybe this is well known in the Drosophila literature, but in that case the relevant literature really needs to be cited.

      Second, the interpretation of RP expression as a proxy for cell size seems potentially at odds with the fact that some cells are multi-nucleate. those cells are big because of multiple nuclei, and so they might not show any increase in ribosome expression per nucleus. presumably for a multi-nucleate cells, RP expression if it reflects anythnig at all would be something to do with cell size PER nucleus.

      Third, it is well known that many tissues in Drosophila are polyploid or polytene. I don't know enough about the methodology used to produce the FCA to know whether this is somehow normalized. Otherwise, my hypothesis would be that nuclei showing higher RP expression might just be polyploid or polytene. You might say that this could be controlled by asking if all genes are similary upregulated, but that isn't the case since at least in polytene chromosomes it is well known that only a small number of genes are expressed at a given time, while many are silent.

      Overall, I think a lot more foundational work would need to be done in order to allow the inference of cell size from RP expression. In a way, it is a bit unfortunate that they chose to do this work in Drosophila where so many cells are polyploid, although I gather that even in humans some tissues have this issue, for example large neurons in the brain.

      Significance

      The idea that gene regulatory networks could "program" differences in scaling by changing levels of ribosomal protein gene expression is a tremendously important one if it can be established, because it would show a simple way for size scaling to be placed under control of developmental regulatory pathways. My original concern when I first looked at the abstract was going to be that yeah the results are interesting but a mechanism is not provided, but as I read it, that concern went away. showing that RP gene expression, which could be programmed by various driving pathways, can affect allometric scaling, would be extremely impactful and really change how we think about scaling, but putting it into the framework of gene expression networks that control other aspects of developmewnht. it would not be necessary to show which pathways actually drive these expression differences, the fact that they are different would be interesting enough to make everyone want to read this paper. But as discussed above I am not, however, convinced by the evidence presented here. So while I think it would be very significant if true, I am not convinced that the conclusion is well supported. This doesn't mean I have a reason to think it is false, just that its not well supported for the reasons I have given.

      I hasten to point out that I could be entirely wrong, if the missing bits of logic (i.e. that RP expression matches ribosome abundance and that gene expression in the FCA dataset isn't influenced by ploidy of the nucleus). If suitable references can be provided to support these underlying assumptions, then in fact I think these concerns could be answered with very little effort. Otherwise, I think experiments would be needed to support these assumptions, and that might be non-trivial to do in a reasonable time frame. for that reason, in the next question I have put "cannot tell" for the time estimate.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-03091

      Corresponding author(s): Chia-Tsen, Tsai, Liuh-Yow Chen

      1. General Statements [optional]

      We thank the reviewers for their valuable time and constructive feedback on our study, which ultimately improved our manuscript. Herein, we provide a detailed response to each of the reviewers' comments, supported by new data that have been integrated into both the main text and the supplementary figures.

      2. Point-by-point description of the revisions

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

      Summary This manuscript builds upon the authors' prior findings that targeting COUP-TF2 to TRF1 induces ALT-associated phenotypes and G2-mediated synthesis in telomerase-immortalised BJT human fibroblasts. In this study, the authors show that telomere-coupled COUP-TF2 promotes H3K9me3 enrichment in these cells, and that this effect is blocked by TRIM28 depletion. Furthermore, TRIM28 depletion also suppresses the formation of ALT phenotypes in VA13 ALT cells. Given that TRIM28 has been implicated in regulating H3K9me3 deposition via SETDB1, and has been reported to co-purify with TR2 and TR4 (though not previously in the context of ALT telomeres), these findings add mechanistic depth to how heterochromatin regulators contribute to ALT activity. Overall, the manuscript's conclusions are generally supported by the presented data, but several aspects require clarification or additional experimental validation.

      The authors report a modest reduction in telomeric H3K9me3 following COUP-TF2 and TR4 depletion in U-2 OS and VA13 cells (Figure 1B). To strengthen the claim that these orphan receptors specifically regulate H3K9me3, the authors should 1) Assess additional heterochromatic histone marks (e.g., H4K20me3) at telomeres, 2) Normalize telomeric signals to both parental histone levels and input, and 3) Evaluate whether global H3K9me3 levels also decrease upon receptor depletion

      Response: We appreciate the reviewer's suggestion. To address the concern regarding specificity, we assessed H3K27me3 and H4K20me3 levels upon COUP-TF2/TR4 depletion and found no significant changes (Supplementary Fig. 1C). Furthermore, we reprocessed the telomeric ChIP data, normalizing to both input DNA and parental histone levels (Figure 1B). This refined analysis reinforces our original conclusion. Finally, Western blot analysis showed no significant changes in global H3 or H3K9me3 levels upon COUP-TF2/TR4 depletion (Figure 1A). Altogether, these results further support the specificity of COUP-TF2/TR4 for H3K9me3 at telomeres. We have revised the main text (page 3) and updated Figure 1A, 1B, and Supplementary Figure 1C for these changes.

      Most experiments explore chromatin changes in telomerase-positive BJT fibroblasts (Figure 2, Figure 4D). It remains unclear whether similar manipulations in ALT cells yield consistent effects, which would give a broader context for ALT phenotype induction. Are ALT phenotypes similarly induced in ALT cells? Does altered chromatin status affect telomere length or telomerase recruitment/activity? Can these pathways drive ALT phenotypes in non-immortalised cells?

      Response: We appreciate the reviewer's suggestion and have explored chromatin changes in telomerase-negative BJ and IMR90 primary fibroblasts (Supplementary Fig. 2C, D). Consistent to the result in BJ-telomerase cells, we found that VP64-TRF1 decreased telomeric H3, H4, and H3K9me3 levels, whereas KRAB-TRF1 increased these marks. Moreover, expression of either VP64-TRF1 or KRAB-TRF1 was sufficient to induce APB formation and ATDs in BJ and IMR90 cells. These results indicate that the chromatin changes at telomeres can drive ALT phenotypes in both primary and telomerase-immortalized fibroblast cells.

          Additionally, regarding whether chromatin alteration affects telomere length or telomere regulation, we have explored telomere length changes in BJT cells expressing vector, TRF1, KRAB-TRF1 or VP64-TRF1. The result of telomere restriction fragment (TRF) assay showed that the cells of all conditions maintained static telomere lengths through 30 days in culture (data shown below), suggesting that the chromatin alterations may not impact telomerase recruitment or activity. As this result is beyond the scope of current study, this data is only shown here in the rebuttal letter for a reference and is not included in the revised manuscript.
      
          Moreover, according to the reviewer's suggestion, we also carried out VP64-TRF1 or KRAB-TRF1 expression experiments in WI38-VA13/2RA cells that express high TERRA and have altered chromatin structures. Our data revealed that VP64-TRF1 suppresses telomere H3K9me3 and ALT activity, while KRAB-TRF1 increases both (Supplementary Figure 2E), suggesting an association of heterochromatin state with ALT activation in WI38-VA13/2RA cells.
      
          The observation that VP64-TRF1 reduces ALT activity in WI38-2RA/VA13 cells contrasts with findings in BJT cells. It is worth noting that studies from the Azzalian and Linger groups demonstrated that experimentally induced TERRA expression promotes ALT activity in ALT and non-ALT cells (PMID: 36122232, PMID: 40624280). Therefore, we propose that TERRA upregulation by VP64-TRF1 may contribute to the ALT induction observed in BJT cells (Supplementary Figure 2A, B), whereas the ability of VP64-TRF1 to suppress ALT activity in WI38-2RA/VA13 cells could be attributed to the reduction of telomere H3K9me3 and heterochromatin loss. Importantly, KRAB-TRF1 concurrently enhanced histone H3, H4, and H3K9me3 occupancy and ATL activity in both human fibroblasts and ALT cells. Altogether, these results support the notion that heterochromatin formation triggers ALT.
      
          We also examined TRIM28 recruitment to telomeres by telomere-ChIP and found that COUP-TF2LBD-TRF1 promotes TRIM28 telomere enrichment in BJ, IMR90 and U2OS, similar to BJT cells (Supplementary Fig. 5A-D).  Moreover, in ALT cell lines WI38-2RA/VA13, U2OS, and Saos-2, depletion of COUP-TF2 or TR4 reduced TRIM28 telomeric association (Figure 4A, B). Together, the data from human fibroblasts and ALT cells supports a role of orphan NRs in recruiting TRIM28 to ALT telomeres.
      

      We acknowledge the reviewer's suggestions, which allow us to clarify and strengthen the conclusions. The corresponding data are presented in Figure 4A-B and Supplementary Figure 2B-D and 5E-F, and the main text has been modified on page 4-6 in the revised manuscript.

      When referring to Figure 3G, the authors state that that telomeric H3K9me3 was abolished upon depleting TRIM28 from the U2OS and WI38-VA13/2RA cells. Abolished is a strong word for a 50% decrease, and this sentence should be revised. The reduction appears greater than that seen with COUP-TF2/TR4 depletion. Are the effects additive? If so, might TRIM28 act, at least in part, independently of COUP-TF2/TR4?

      Response: We appreciate the reviewer's comments. We have revised the manuscript on page 5, replacing "abolished" with "significantly reduced" to better describe the effect of TRIM28 depletion on telomeric H3K9me3. To further investigate the interplay between TRIM28 and orphan NRs in regulating telomeric H3K9me3, we conducted single and combined knockdown experiments in U2OS and WI38-VA13/2RA cells, followed by telomere-ChIP analysis (Supplementary Figures 4D, E). Our results showed that single depletion of either orphan NRs or TRIM28 lead to a similar decrease in telomeric H3K9me3, and that combined knockdown do not result in any further reduction. These findings support an epistatic interaction between orphan NRs and TRIM28 in the regulation of telomeric H3K9me3. We have expanded on this interpretation in the main text (page 6) and included the relevant data in Supplementary Figures 4D, E.

      VA13 cells consistently exhibit stronger effects than U-2 OS (e.g., Figures 1 and 3). This discrepancy could be linked to the high content of variant repeats in VA13 cells. The authors should assess whether variant repeat content underlies the differential response. Repeating key experiments in additional ALT lines with varied repeat compositions would be informative.

      Response: We appreciate the reviewer's suggestion and have extended our analyses to two additional ALT osteosarcoma cell lines, SAOS-2 and G292. In both lines, depletion of orphan NRs resulted in a consistent decrease in telomeric H3K9me3 levels (Supplementary Figures 1A, B). We also examined the contribution of TRIM28 to telomeric H3K9me3 in these cells. siRNA-mediated depletion of TRIM28 in SAOS-2 and G292 cells similarly caused a significant reduction in telomeric H3K9me3 and ALT phenotypes (Supplementary Figure 4A-C). Together, these results from 4 ALT cell lines confirm that orphan NRs and TRIM28 promote telomeric H3K9me3 formation in ALT cells. We have modified the main text on page 3 and 5-6 for these results.

      In line with the previous point, it would be useful to show whether TRIM28 telomeric enrichment is affected by COUP-TF2/TR4 depletion in U2OS cells (Figure 4C). To improve confidence in these findings, the authors should perform telomeric ChIP assays, especially with the COUP-TF2^LBDΔAF2-TRF1 mutant construct.

      Response: Following the reviewer's suggestion, we performed telomere-ChIP assays to assess TRIM28 enrichment at telomeres upon expression of COUP-TF2LBD-TRF1 and its ΔAF2 mutant in U2OS cells. Consistent with our immunofluorescence results, telomere-ChIP revealed that COUP-TF2LBD-TRF1 expression promotes TRIM28 telomere enrichment, while the AF2 deletion mutant failed to recruit TRIM28 (Supplementary Figure 5D). We have modified the main text on page 6 for this result.

      The immunoprecipitation experiments showing TRIM28 association with orphan receptors should include benzonase treatment to rule out DNA-mediated co-association (Figure 4F-G).

      Response: We appreciate the reviewer's suggestion. To address the possibility of DNA-mediated interactions, we pre-incubated cell lysates with benzonase prior to Co-IP (Page 7). This treatment did not disrupt the association between TRIM28 and COUP-TF2 or TR4 in WI38-VA13/2RA and BJT cells (Supplementary Figures 5E-G), indicating a DNA-independent interaction. We have modified the main text on page 7 for this result.

      The study would benefit from a direct assessment of whether COUP-TF2LBDΔAF2-TRF1 fails to induce ALT phenotypes in BJTfibroblasts.

      Response: We thank the reviewer for this suggestion. As the role of the COUP-TF2 AF2 domain in ALT induction in BJT fibroblasts has recently been thoroughly investigated and published by our group (PMID: 38752489), we have directed the current study towards a more detailed mechanistic question. Specifically, we have carried out experiments to further demonstrate that COUP-TF2 recruits TRIM28 to telomeres via its AF2 domain in both human fibroblasts and ALT cells (Supplementary Figures 5A-D). On Page 6, we have modified the main text for these results and included a citation to our previous publication to provide the necessary background.

      The experiments performed in Figure 5E-H lack a vector-only + siCtrl control.• In Figure 5E, the observation that APB formation is restored in siTRIM28 + Vector-treated cells is unexpected. The authors should address this finding and clarify whether this reflects biological noise or a compensatory effect.

      Response: We thank the reviewer for this suggestion. We have repeated the experiments with a revised design, ensuring a consistent vector background across all groups (Vector + siCtrl, Vector + siTRIM28, TRIM28 WT + siTRIM28, and TRIM28 ΔRBCC + siTRIM28) (Figure 5E-H). This improved design confirms that expression of wild-type TRIM28, but not TRIM28 ΔRBCC, restores APB formation, ATDS, ssTeloC, and telomeric H3K9me3 levels in TRIM28-depleted cells. The updated dataset also resolves the previous unexpected increase in APB formation in the siTRIM28 + Vector condition, which is now excluded. We have modified the main text accordingly on page 8.

      Reviewer #1 (Significance (Required)):

      This work offers valuable mechanistic insight into how COUP-TF2 and TRIM28 coordinate to regulate heterochromatin deposition and ALT phenotype formation. It adds to the growing understanding of chromatin-mediated telomere regulation. What remains unclear is how important this interaction is for ALT maintenance, as H3K9me3 is only moderately altered upon TRIM28 depletion in ALT cells. Depletion of TRIM28 has been shown previously to induce APB formation and telomere elongation in U-2 OS ALT cells (Wang et al., 2021), the opposite to what the authors observed here in VA13 cells (Figure 5E-H). Clarifying whether these differences are variant repeat-dependent, or reflect intrinsic features of specific ALT cell lines, would substantially elevate the study's impact.

      Response: We appreciate the reviewer's recognition of the significance of our work in elucidating the molecular basis of ALT regulation through COUP-TF2-TRIM28-mediated heterochromatin formation. In response to the reviewer's insightful comment regarding the importance of this interaction for ALT maintenance, we have expanded our study. We now include data from three additional primary human fibroblasts and a total of four ALT cancer cell lines (Figure 4, Supplementary Figure 4). These new data further strengthen the conclusion that TRIM28 promotes telomeric H3K9me3 and ALT-associated features. Furthermore, our rescue experiments support the model that the ALT-promoting function of TRIM28 in both fibroblasts and ALT cell lines is mediated through its physical interaction with COUP-TF2 (Supplementary Figure 5). We believe these results provide a solid foundation for demonstrating a cooperative role of COUP-TF2 and TRIM28 in ALT maintenance, and address the reviewer's concern regarding the generalizability of our findings.

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

      Summary This manuscript investigates the role of orphan nuclear receptors (ORs), specifically COUP-TF2 and TR4, in promoting H3K9me3 enrichment at ALT telomeres via recruitment of TRIM28 (KAP1). The authors propose that the AF2 domain of COUP-TF2, located in its ligand-binding domain (LBD), is sufficient to recruit TRIM28 to telomeres. This, in turn, promotes heterochromatinization and induces hallmarks of the Alternative Lengthening of Telomeres (ALT) pathway, including APB formation and telomeric DNA synthesis outside of S-phase. This study addresses one important and unresolved question in the field: by what mechanism is the heterochromatic state established at ALT telomeres? Another timely question, not addressed here is: how is heterochromatin (specifically H3K9me3) functionally linked to ALT? The findings are potentially novel and mechanistically insightful. However, key elements of the study, particularly the central tethering experiments, require stronger quantification and clarity. Additional mechanistic tests and literature adjustments would also improve the manuscript.

      Major Concerns

      Central TRF1-COUP-TF2-LBD result lacks quantification and clarity: the tethering of COUP-TF2's LBD to telomeres via TRF1 is a core result of the paper. This experiment demonstrates that this domain is sufficient to induce weak H3K9me3 enrichment and ALT features (APBs and ATDS). However, the supporting ALT data are presented only in Supplementary Figures S1A and S1B, and are not quantified. These data should be quantified with appropriate statistics and moved to a main figure.

      Response: The current study builds upon our recent publication (PMID: 38752489), which comprehensively analyzed ALT induction (APBs, ATDS, C-circles, T-SCEs) by orphan NR-TRF1 expression (COUP-TF1, COUP-TF2, TR2, and TR4; full-length and LBD) in various human fibroblast cell lines. To avoid potential duplicate publication concerns, particularly regarding APB and ATDS results for COUP-TF2LBD-TRF1 in BJT cells, we have put the data with revised quantification results in Supplementary Figure 1D-E. We will follow the reviewer's suggestion and move this data to the main figures if the editors agree.

      Furthermore, the broader functional implication is not explored. Does this tethering induce a fully functional ALT pathway? For example, can telomerase knockout cells expressing TRF1-COUP-TF2-LBD maintain long-term proliferation? Such evidence would significantly strengthen the impact of the study.

      Response: While COUP-TF2LBD-TRF1 expression rapidly induces key ALT phenotypes, we acknowledge that this alone is insufficient to directly promote telomere lengthening and long-term proliferation of primary fibroblasts, as discussed in Gaela et al., 2024 (PMID: 38752489). However, our ongoing, unpublished studies indicate that COUP-TF2LBD-TRF1 can drive immortalization of primary BJ fibroblasts expressing SV40LT by promoting ALT-mediated telomere elongation (Attached Figure A-C; additional data not shown). These findings suggest that COUP-TF2 may cooperate with additional genetic or epigenetic alterations to facilitate ALT development. We appreciate the reviewer's recognition of this critical aspect. As our immortalization study is still in progress and will be the subject of a separate manuscript, we hope the reviewer understands that the data shown in this letter will not be included in the revised manuscript.

      Chromatin manipulation experiments lead to ambiguous conclusions: the authors propose that telomeric heterochromatin promotes ALT activity, but their own experiments (e.g., Figure 2) show that both heterochromatin-inducing (KRAB-TRF1) and euchromatin-inducing (VP64-TRF1) tethering can trigger ALT-like features. This makes it difficult to conclude that heterochromatin is specifically required.

      To clarify:

      -Did the authors express TRF1-VP64 in an ALT cell line? According to their model, this should suppress ALT activity.

      -More broadly, do chromatin alterations per se (regardless of direction) trigger ALT features? Clarifying these points is important for interpretation.

      Response: In response to the reviewer's suggestion, we expressed VP64-TRF1 and KRAB-TRF1 in WI38-2RA/VA13 cells to investigate telomere chromatin changes and ALT activity. Our data indeed revealed that VP64-TRF1 suppresses telomere H3K9me3 and ALT activity, while KRAB-TRF1 increases both (Supplementary Figure 2E), suggesting that heterochromatin triggers ALT activation.

      The observation that VP64-TRF1 reduces ALT activity in WI38-2RA/VA13 cells contrasts with findings in BJT cells. Of note, studies from the Azzalian and Lingner groups demonstrated that experimentally induced TERRA expression promotes ALT activity in ALT and non-ALT cells (PMID: 36122232, PMID: 40624280). Therefore, we propose that TERRA upregulation may contribute to the ALT induction observed in BJT cells (Figure 2A, Supplementary Figure 2A, B). Given the high basal TERRA expression, expression of VP64-TRF1 and KRAB-TRF1 did not result in a consistent change in TERRA levels (Supplementary Figure 2F). Thus, the ability of VP64-TRF1 to suppress ALT activity in WI38-2RA/VA13 cells could be attributed to the reduction of telomere H3K9me3 and heterochromatin loss. Altogether, our results support the hypothesis that heterochromatin formation, rather than euchromatin triggers ALT.

      We thank the reviewer's insightful comments, which have allowed us to resolve the ambiguity of our results and strengthen the notion that heterochromatin formation promotes ALT. We think that the heterochromatin features and high TERRA expression represent two independent, coexisting mechanisms within ALT cancer cells to guarantee ALT activation. We have modified the main text on page 4-5 accordingly.

      TERRA downregulation contradicts current models: while TERRA upregulation is often observed in ALT cells and is thought to contribute to replication stress and recombination at telomeres, the authors show that TRF1-KAP1 expression induces ALT features while TERRA is downregulated. This observation is not addressed in the manuscript. The authors should at least discuss this discrepancy and propose whether this reflects a cell line-specific phenomenon or a decoupling between TERRA levels and ALT induction in this context.

      Response: We thank the reviewer for the comments. As mentioned above (Major Concerns 2), heterochromatin formation and TERRA expression are two mechanisms that can independently promote ALT. Unlike ALT cell lines that have high TERRA levels, human fibroblasts BJ cells have low TERRA that does not induce ALT phenotypes. Thus, the effect of KRAB-TRF1 on ALT induction in BJ cells could be attributed to the heterochromatin formation, but not reduction of TERRA. We have modified the main text on page 5 to clarify the result.

      Minor Comments

      Introduction (p. 3): The authors cite Episkopou et al. as showing increased H3K9me3 at ALT telomeres. This is incorrect; that paper suggests the opposite. The first study to clearly demonstrate H3K9me3 enrichment at ALT telomeres is Cubiles et al., 2018 and should be cited instead. Results (p. 5, first paragraph): The manuscript should cite Déjardin and Kingston, 2009 as the first to report COUP-TF2 and TR4 localization at ALT telomeres. The studies by Conomos et al., 2012 and Gaela et al., 2024 build on this prior evidence. Please also include this citation in the bibliography.

      Response: We appreciate the reviewer's careful reading and for pointing out these errors. The citation errors on pages 2 and 3 have now been corrected.Broader relevance of TRIM28-OR interaction: TRIM28 is a complex protein with roles in SUMOylation, heterochromatin formation, and transcriptional initiation/elongation regulation.

      The authors should explore whether similar COUP-TF2/TRIM28 interactions occur at other genomic loci. Public ChIP-seq data for COUP-TF2, TR4, and TRIM28 could be mined to investigate whether these factors co-occupy regulatory regions elsewhere in the genome, and how this relates to gene expression states.

      Response: We appreciate the reviewer's insightful suggestion regarding a potential genome-wild functional interaction between TRIM28 and COUP-TF2. To address this, we analyzed public ENCODE ChIP-seq data from K562 cells (TRIM28: ENCSR000BRW; COUP-TF2: ENCSR000BRS). This analysis revealed 3,326 co-binding sites for TRIM28 and COUP-TF2 (Attached Figure A). Interestingly, these co-binding sites were preferentially located within gene bodies (70.7%) and promoter regions (4.3%) (Attached Figures B-D), suggesting a potential cooperative role in gene regulation that aligns with our observation of physical interaction. While the finding is intriguing, a full exploration is beyond the scope of this manuscript, which focuses on ALT telomere regulation. We consider this is an important insight and have briefly noted it in the discussion (p. 9), although the corresponding analyses are not included in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      This work contributes mechanistic insight into how heterochromatin is established at ALT telomeres-an important and timely question in telomere biology and cancer research. It offers a noncanonical recruitment mechanism for TRIM28, independent of KRAB-ZNFs, and highlights the functional role of orphan nuclear receptors in telomeric chromatin regulation. The study has potential implications for understanding ALT regulation and for identifying new intervention points in ALT-positive cancers. The work is conceptually interesting, but the conclusions are currently limited by insufficient quantification, some interpretative ambiguities, and a few overlooked references. Addressing the concerns listed above would significantly enhance the rigor and impact of the manuscript.

      Response: We appreciate the reviewer's recognition of the significance of our work in elucidating the molecular basis of ALT regulation through COUP-TF2-TRIM28-mediated heterochromatin formation. We also thank the reviewer for the valuable feedback, which has significantly strengthened our manuscript.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This manuscript investigates the role of orphan nuclear receptors (ORs), specifically COUP-TF2 and TR4, in promoting H3K9me3 enrichment at ALT telomeres via recruitment of TRIM28 (KAP1). The authors propose that the AF2 domain of COUP-TF2, located in its ligand-binding domain (LBD), is sufficient to recruit TRIM28 to telomeres. This, in turn, promotes heterochromatinization and induces hallmarks of the Alternative Lengthening of Telomeres (ALT) pathway, including APB formation and telomeric DNA synthesis outside of S-phase. This study addresses one important and unresolved question in the field: by what mechanism is the heterochromatic state established at ALT telomeres? Another timely question, not addressed here is: how is heterochromatin (specifically H3K9me3) functionally linked to ALT? The findings are potentially novel and mechanistically insightful. However, key elements of the study, particularly the central tethering experiments, require stronger quantification and clarity. Additional mechanistic tests and literature adjustments would also improve the manuscript.

      Major Concerns

      1. Central TRF1-COUP-TF2-LBD result lacks quantification and clarity: the tethering of COUP-TF2's LBD to telomeres via TRF1 is a core result of the paper. This experiment demonstrates that this domain is sufficient to induce weak H3K9me3 enrichment and ALT features (APBs and ATDS). However, the supporting ALT data are presented only in Supplementary Figures S1A and S1B, and are not quantified. These data should be quantified with appropriate statistics and moved to a main figure. Furthermore, the broader functional implication is not explored. Does this tethering induce a fully functional ALT pathway? For example, can telomerase knockout cells expressing TRF1-COUP-TF2-LBD maintain long-term proliferation? Such evidence would significantly strengthen the impact of the study.
      2. Chromatin manipulation experiments lead to ambiguous conclusions: the authors propose that telomeric heterochromatin promotes ALT activity, but their own experiments (e.g., Figure 2) show that both heterochromatin-inducing (KRAB-TRF1) and euchromatin-inducing (VP64-TRF1) tethering can trigger ALT-like features. This makes it difficult to conclude that heterochromatin is specifically required. To clarify:
      3. Did the authors express TRF1-VP64 in an ALT cell line? According to their model, this should suppress ALT activity.
      4. More broadly, do chromatin alterations per se (regardless of direction) trigger ALT features? Clarifying these points is important for interpretation.
      5. TERRA downregulation contradicts current models: while TERRA upregulation is often observed in ALT cells and is thought to contribute to replication stress and recombination at telomeres, the authors show that TRF1-KAP1 expression induces ALT features while TERRA is downregulated. This observation is not addressed in the manuscript. The authors should at least discuss this discrepancy and propose whether this reflects a cell line-specific phenomenon or a decoupling between TERRA levels and ALT induction in this context.

      Minor Comments

      Introduction (p. 3): The authors cite Episkopou et al. as showing increased H3K9me3 at ALT telomeres. This is incorrect; that paper suggests the opposite. The first study to clearly demonstrate H3K9me3 enrichment at ALT telomeres is Cubiles et al., 2018 and should be cited instead. Results (p. 5, first paragraph): The manuscript should cite Déjardin and Kingston, 2009 as the first to report COUP-TF2 and TR4 localization at ALT telomeres. The studies by Conomos et al., 2012 and Gaela et al., 2024 build on this prior evidence. Please also include this citation in the bibliography. Broader relevance of TRIM28-OR interaction: TRIM28 is a complex protein with roles in SUMOylation, heterochromatin formation, and transcriptional initiation/elongation regulation. The authors should explore whether similar COUP-TF2/TRIM28 interactions occur at other genomic loci. Public ChIP-seq data for COUP-TF2, TR4, and TRIM28 could be mined to investigate whether these factors co-occupy regulatory regions elsewhere in the genome, and how this relates to gene expression states.

      Significance

      This work contributes mechanistic insight into how heterochromatin is established at ALT telomeres-an important and timely question in telomere biology and cancer research. It offers a noncanonical recruitment mechanism for TRIM28, independent of KRAB-ZNFs, and highlights the functional role of orphan nuclear receptors in telomeric chromatin regulation. The study has potential implications for understanding ALT regulation and for identifying new intervention points in ALT-positive cancers.

      The work is conceptually interesting, but the conclusions are currently limited by insufficient quantification, some interpretative ambiguities, and a few overlooked references. Addressing the concerns listed above would significantly enhance the rigor and impact of the manuscript.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This manuscript builds upon the authors' prior findings that targeting COUP-TF2 to TRF1 induces ALT-associated phenotypes and G2-mediated synthesis in telomerase-immortalised BJT human fibroblasts. In this study, the authors show that telomere-coupled COUP-TF2 promotes H3K9me3 enrichment in these cells, and that this effect is blocked by TRIM28 depletion. Furthermore, TRIM28 depletion also suppresses the formation of ALT phenotypes in VA13 ALT cells. Given that TRIM28 has been implicated in regulating H3K9me3 deposition via SETDB1, and has been reported to co-purify with TR2 and TR4 (though not previously in the context of ALT telomeres), these findings add mechanistic depth to how heterochromatin regulators contribute to ALT activity. Overall, the manuscript's conclusions are generally supported by the presented data, but several aspects require clarification or additional experimental validation.

      • The authors report a modest reduction in telomeric H3K9me3 following COUP-TF2 and TR4 depletion in U-2 OS and VA13 cells (Figure 1B). To strengthen the claim that these orphan receptors specifically regulate H3K9me3, the authors should 1) Assess additional heterochromatic histone marks (e.g., H4K20me3) at telomeres, 2) Normalize telomeric signals to both parental histone levels and input, and 3) Evaluate whether global H3K9me3 levels also decrease upon receptor depletion
      • Most experiments explore chromatin changes in telomerase-positive BJT fibroblasts (Figure 2, Figure 4D). It remains unclear whether similar manipulations in ALT cells yield consistent effects, which would give a broader context for ALT phenotype induction. Are ALT phenotypes similarly induced in ALT cells? Does altered chromatin status affect telomere length or telomerase recruitment/activity? Can these pathways drive ALT phenotypes in non-immortalised cells?
      • When referring to Figure 3G, the authors state that that telomeric H3K9me3 was abolished upon depleting TRIM28 from the U2OS and WI38-VA13/2RA cells. Abolished is a strong word for a 50% decrease, and this sentence should be revised. The reduction appears greater than that seen with COUP-TF2/TR4 depletion. Are the effects additive? If so, might TRIM28 act, at least in part, independently of COUP-TF2/TR4?
      • VA13 cells consistently exhibit stronger effects than U-2 OS (e.g., Figures 1 and 3). This discrepancy could be linked to the high content of variant repeats in VA13 cells. The authors should assess whether variant repeat content underlies the differential response. Repeating key experiments in additional ALT lines with varied repeat compositions would be informative.
      • In line with the previous point, it would be useful to show whether TRIM28 telomeric enrichment is affected by COUP-TF2/TR4 depletion in U-2 OS cells (Figure 4C). To improve confidence in these findings, the authors should perform telomeric ChIP assays, especially with the COUP-TF2^LBDΔAF2-TRF1 mutant construct.
      • The immunoprecipitation experiments showing TRIM28 association with orphan receptors should include benzonase treatment to rule out DNA-mediated co-association (Figure 4F-G).
      • The study would benefit from a direct assessment of whether COUP-TF2LBDΔAF2-TRF1 fails to induce ALT phenotypes in BJT fibroblasts.
      • The experiments performed in Figure 5E-H lack a vector-only + siCtrl control.
      • In Figure 5E, the observation that APB formation is restored in siTRIM28 + Vector-treated cells is unexpected. The authors should address this finding and clarify whether this reflects biological noise or a compensatory effect.

      Significance

      This work offers valuable mechanistic insight into how COUP-TF2 and TRIM28 coordinate to regulate heterochromatin deposition and ALT phenotype formation. It adds to the growing understanding of chromatin-mediated telomere regulation. What remains unclear is how important this interaction is for ALT maintenance, as H3K9me3 is only moderately altered upon TRIM28 depletion in ALT cells. Depletion of TRIM28 has been shown previously to induce APB formation and telomere elongation in U-2 OS ALT cells (Wang et al., 2021), the opposite to what the authors observed here in VA13 cells (Figure 5E-H). Clarifying whether these differences are variant repeat-dependent, or reflect intrinsic features of specific ALT cell lines, would substantially elevate the study's impact.

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

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements

      In this study, we mechanistically define a new molecular interaction linking two of the cell's major morphological regulatory pathways-the Rho GTPase and Hippo signaling networks. These two major signaling pathways are both required for life across huge swaths of the tree of life. They are required for the dynamic organization and reorganization of proteins, lipids, and genetic material that occurs in essential cellular processes such as division, motility and differentiation. For decades these pathways have been almost exclusively studied independently, however, they are known to act in concert in cancer to drive cytoskeletal remodeling and morphological changes that promote proliferation and metastasis. However, mechanistic insight into how they are coordinated is lacking.

      Our data reveal a mechanistic model where coordination is mediated by the RhoA GTPase-activating protein ARHGAP18, which forms molecular interactions with both the tumor suppressor Merlin (NF2) and the transcriptional co-regulator YAP (YAP1). Using a combination of state-of-the-art super-resolution microscopy (STORM, SORA-confocal) in cultured human cells, biochemical pulldown assays with purified proteins, and analyses of tissue-derived samples, we characterize ARHGAP18's function from the molecular to the tissue level in both native and cancer model systems.

      Together, these findings establish a previously unrecognized molecular connection between the RhoA and Hippo pathways and culminate in a working model that integrates our current results with prior work from our group and decades of prior studies. This model provides a new conceptual framework for understanding how RhoA and Hippo signaling are coordinated to regulate cell morphology and tumor progression in human cells.

      In this substantially revised manuscript, we have addressed all comments from the expert reviewers described point-by-point below. A shared major comment from the reviewers was the request for direct evidence of the proposed mechanistic model. To address these constructive comments, we've added new experiments, new quantification, new text, new control data, and have added two expert authors, adding super-resolution mouse tissue imaging data for the endogenous study of ARHGAP18 in its native condition. We believe that these additions greatly enhance the manuscript and collectively address the overall message from the reviewer's collective comments.

      2. Point-by-point description of the revisions

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

      This manuscript describes a dual mechanism by which ARHGAP18 regulates the actin cytoskeleton. The authors propose that in addition to the known role for ARHGAP18 in regulating Rho GTPases, it also affects the cytoskeleton through regulation of the Hippo pathway transcriptional regulator YAP. ARHGAP18 knockout Jeg3 cells are were generated and show a clear loss of basal stress fiber like F-actin bundles. The authors further characterize the effects of ARHGAP18 knockout and overexpression. It is also discovered that ARHGAP18 binds to the Hippo pathway regulator Merlin and to YAP. Ultimately it is concluded that ARHGAP18 regulates the F-actin cytoskeleton through dual regulation of RHO GTPases and of YAP. While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

      • *

      *We appreciate the reviewers' constructive comments and have added substantial new data and quantifications to address their concerns. We have focused these new data on directly testing the proposed mechanisms, adding controls, and performing quantitative analysis with statistical testing. Additionally, we have edited our language to make our rationale clearer and to present our conclusions as a more moderate assessment of our experimental results. Below we respond to the specific comments made by the reviewer, followed by a list of additional editorial changes we've made based on the reviewer's overarching comments on clarity and rationale. *

      Specific Comments

      1) The authors make a big point about the effects of ARHGAP18 on myosin light chain phosphorylation. However, this result is not quantified and tested for statistical significance and reproducibility.

      *We thank the reviewer for their comments on our western blotting quantification, which in the original submission version had quantification of RhoA downstream signaling of pCofilin/ Cofilin and pLIMK/ LIMK. We had withheld the pMLC and MLC quantification as the result was previously published with quantification, reproducibility, and statistical significance by our group in our prior manuscript on ARHGAP18 published in Elife in 2024 (Fig. 4E of *

      https://doi.org/10.7554/eLife.83526 ). However, these prior results lacked the new overexpression data. We recognize the need to add these data to this manuscript as requested by the reviewer.

      • *

      *To address the reviewer's comment, we have added quantification of pMLC/MLC (Fig. 1F) *

      2) Along similar lines in Figure 2C they state that overexpression of ARHGAP18 causes cells to invade over the top of their neighbors. This might be true and interesting, but only a single cell is shown and there is no quantification or controls for simply overexpressing something in that cell. The authors also conclude from this image that the overexpression phenotype is independent of its GAP activity on Rho. It is not clear how this conclusion is made based on the data. It would seem like a more definitive experiment would be to see if a similar phenotype was induced by an ARHGAP18 mutant deficient in GAP activity.

      Based on the reviewer's comment, we recognize the qualitative statements made in Figure 2C (now Figure 3) should've been made more quantitative. We have added the control of Jeg 3 WT cells expressed with empty vector flag to show that WT cells do not invade over the top of each other (Fig. 3F). Additionally, we have added the quantification found in Fig. 3E, which shows the % invasive/ non-invasive cells between WT and ARHGAP18 overexpression cells. We have clarified our conclusions to make clear that these data do not directly test if the invasive phenotype derives from a Rho-independent mechanism. The text now states the following conclusion alongside others, which can be seen in our tracked changes:

      • *

      "These data support the conclusion that ARHGAP18 acts to regulate basal and junctional actin. However, it was not clear whether this activity occurred through a Rho-independent or a Rho-dependent mechanism."

      • *

      We have added new data of cells expressing an ARHGAP18 mutant deficient in GAP activity, which is explained in detail in the following response below.

      3) In Figure 3 the authors compare gene expression profiles of ARHGAP18 knockout cells to wild-type cells. They see lots of differences in focal adhesion and cytoskeletal proteins and conclude that this supports their conclusion that ARHGAP18 is not just acting through RHO. The rationale for this in not clear. In addition, they observe changes in expression profiles consistent with changes in YAP activity. They conclude that the effects are direct. This very well might be true. However RHO is a potent regulator of YAP activity and the results seem quite consistent with ARHGAP18 acting through RHO to affect YAP.

      • *

      We thank the reviewer for their comment and believe the revised manuscript now presents direct evidence to support the conclusions made through the editing text and the incorporation of new data.

      • *

      First, the reviewer highlighted that we were not clear in our rationale and explanation of the conclusions made from our RNAseq data in the new Figure 4 (Previously Figure 3). We agree with the reviewer that the RNAseq data alone is not sufficient rationale for the conclusion that ARHGAP18 is acting through YAP directly. In the revised manuscript, the conclusion is now made based on the combination of our multi-faceted investigation of the relationship between ARHGAP18 and YAP (most importantly, new Figure 5). It's important for us to argue that our RNAseq analysis is much more robust and specific than simply reporting a descriptive assay seeing lots of differences in cytoskeletal proteins. We recruited an outside RNAseq expert collaborator; Dr. Yongho Bae, to perform state-of-the-art IPA analysis and a grueling manual curation of the top hit genes to identify the predominant signaling pathways linking the loss of ARHGAP18 to known YAP translational products. We've provided a supplemental table listing each citation supporting the identified YAP pathway associations from this manual curation. We also have added a new discussion paragraph on RNAseq data to clarify our specific RNAseq data results and analysis. In the revised manuscript, we have moderated our language in the results text regarding the RNAseq data to reflect the reviewer's suggestion:

      • *

      "Our RNAseq data alone could not independently confirm if the alterations to transcriptional signaling and expression of actin cytoskeleton proteins were through a Rho-dependent or Rho-independent mechanism."

      • *

      • *

      Second, in this comment and the above, the reviewer highlights the need for a new experiment to directly test the Rho Independent effects of ARHGAP18, which we now provide in the new Figure 5. In this new data, we've applied an experimental design suggested by reviewer 2 regarding the same concern. In short, we've produced and expressed a point mutant variant ARHGAP18(R365A), which abolishes the Rho GAP activity while maintaining the remainder of the protein intact. This construct allows us to directly test the effects of ARHGAP18 independent from its RhoA GAP activity. We find that the GAP-deficient ARHGAP18 is able to fully rescue basal focal adhesions, indicating that the basal actin phenotype is at least in part regulated through a Rho-independent mechanism.

      • *

      • *

      *We believe the revised manuscript, when taken in totality, provides the definitive proof requested by the reviewer. Specifically, the combination of Figure 5, where we show new data using the ARHGAP18(R365A) variant, and the result that ARHGAP18 forms a stable complex with YAP (Fig. 6G) or Merlin (Fig.6A), is supportive of direct Rho-independent molecular interactions between YAP, Merlin, and ARHGAP18. *

      4) In Figure 4A showing Merlin binding to ARHGAP18 there is no control for the amount of Merlin sticking to the column as was done in Figure 4F for binding experiments with YAP. This makes it difficult to determine the significance of the observed binding.

      We have performed the requested control experiment and added the results to Figure 6A.

      5) The images in Figure 4C showing YAP being maintained in the nucleus more in ARHGAP18 knockout cells compared to wild-type. However the images only show a few cells and YAP localization can be highly variable depending on where you look in a field. Images with more cells and some sort of quantification would bolster this result.

      We have provided quantification (Figure 6D) of what was originally Figure 4C (now Figure 6C).

      Reviewer #1 (Significance (Required)):

      While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

      In the above comments, we detail the specific definitive experiments, proper controls, and statistical tests for significance, requested by the reviewer, which we believe greatly strengthen our manuscript.

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

      This manuscript investigates the Rho effector, ARHGAP18 in Jegs cells, a trophoblastic cell line. It presents a number of new pieces of data, which increase our understanding of the importance of this GAP on cell function and explains at a molecular level previous results of other workers in the field. ARHGAP18 was originally given the name "conundrum' and continues to stand apart from the majority of other GAP proteins and their functions. Hence the data here is significant and of high standard.

      The data is clear, and the images are of high quality and extremely impressive in their resolution. It is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli.

      • *

      We appreciate the reviewer's comments and supportive insights.

      The data is based on the use of the cell line Jeg3. Even the authors previous publication in eLife is based only on this cell line. They need to show the conclusions are general and not specific to this line of cells. As an extension of this, is the ARHGAP18 function shown here only in transformed cells? Does the same mechanisms operate in normal cells, which respond to activation to proliferate or migrate?

      • *
      • We respectfully point out that the critical experiments of the prior eLife publication were validated in DLD-1 colorectal cells and not Jeg-3 cells alone (Figure 1-figure supplement 2). Our newly independent lab, established just over a year ago, is unable to perform a full expansion of the manuscript using untransformed cells, however, we agree with the reviewer's perspective and wish to address the comment to the best of our current capability. To answer the reviewers' suggestions, we have recruited Dr. Christine Schaner Tooley, an expert in mouse model system studies. In the revised manuscript, we've added new Super-Resolution SORA confocal images of endogenous ARHGAP18's localization in the intact intestinal villi tissue, and apical junctions of WT mice (Fig.1A-C). These data indicate that endogenous ARHGAP18 is enriched (but not exclusively localized) at the apical plasma membranes of normal WT epithelial cells. This localization, where both Merlin and Ezrin are present at apical membrane/ junctions under normal conditions, is a major component of the working model proposed in Fig. 7. These data also indicate that ARHGAP18 is capable of entering the nucleus in WT cells, another critical aspect of our proposed model. Collectively, our DLD-1 studies published previously and or new studies using WT mice tissue samples support the conclusion that at least some of ARHGAP18's functions described in this manuscript are not limited to Jeg3 cells.*

      In endothelial cells, Lovelace et al 2017 showed localization to microtubules and that depletion of ARHGAP18 resulted in microtubule instability. The authors may like to comment on the differences. Is this a cell type difference or RhoA versus RhoC difference?

      • *

      In our previous publication (Lombardo Elife), we validated the finding that ARHGAP18 forms a complex with microtubules, as we detected tubulin in the ARHGAP18 pulldown experiment (Figure 1- Source Data). However, our data indicate that in Jeg3 cells ARHGAP18 does not localize to the same microtubule associated spheres observed in the Lovelace publication. We now comment on the shared conclusions and differences between this manuscript and the Lovelace et al 2017 in the discussion section.

      • *

      "In endothelial cells, ARHGAP18 has been reported to localize microtubules and plays a role in maintaining proper microtubule stability (Lovelace et al., 2017). In our epithelial cell culture models and WT mouse intestine, we have been unable to detect ARHGAP18 at microtubules suggesting ARHGAP18 may have additional functions is various cell types."

      On pages 7,9 they conclude that MLC and basal and junctional actin are regulated through a GAP independent mechanism. The best way to show this is with overexpression of a GAP mutant.

      We appreciate the reviewer's insight and have produced and expressed a GAP mutant, ARHGAP18(R365A), in our cells, directly testing our conclusion that ARHGAP18 has a GAP-independent function. These data are now presented in revised Figure 5 and explained further in response to reviewer #1.

      There is a huge amount of data presented in Figure 3, but their 2 genes which they focus on, LOP1 and CORO1A, are discussed but no actual data presented in support.

      We now validate the CORO1A by qPCR in Figure 4J.

      • *

      Reviewer #2 (Significance (Required)):

      The data is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli. This manuscript will be of significance to an basic science audience in the field of RhoGTPases and cell migration.

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

      The study by Murray et al explores the effects of ARHGAP18 on the actin cytoskeleton, Rho effector kinases, non-muscle myosin, and transcription. Using super resolution microscopy, they show that in ARHGAP18 KO cells there is a mixed and unexpected cytoskeleton phenotype where myosin phosphorylation appears to be increased, but actin is disorganised with reduced stress fibres, diminished focal adhesions and augmented invasiveness. They conclude that the underlying mechanisms are likely independent from RhoA. Next, they perform RNAseq using the KO cells and identify an array of dysregulated genes, including those that play crucial roles in microvilli (related to previously published findings). Analysis of the data identify gene expression changes that are relevant for altered focal adhesion (integrins). Further analysis reveals that a large cohort of the dysregulated genes are YAP targets. They then show that in ARHGAP18 KO cells YAP nuclear localization, as detected by immunostaining, is augmented; and demonstrate that immobilized ARHGAP18 protein can bind the Hippo regulator merlin as well as YAP itself.

      Major comments:

      1, The premise of the study (that ARHGAP18 is a RhoA effector or may acts independently of RhoA) remains not proven.

      We have added new evidence of direct RhoA independent activity for ARHGAP18 described in the above comments. Specifically, we've added data using a RhoA-GAP dead variant of ARHGAP18 in Figure 5, which we believe addresses this comment.

      • *

      At several places (including in the title) the authors refer to ARHGAP18 as a Rho effector, which would suggest that it is downstream form Rho, but the basis for this is not clear. In fact, their own previous study suggested that ARHGAP is a RhoA regulator, rather than an effector. In general, the connection of the described effects to RhoA remains unclear, and not addressed in this study. The authors seem to go back and forth in their conclusions regarding the connection between ARHGAP18 and RhoA. For example, the first section of results is finished by stating (line 194): "These data support the conclusion that ARHGAP18 acts to regulate basal and junctional actin through Rho-independent mechanism". But the next section starts by stating (line 198): "We hypothesized that the invasive and cytoskeletal phenotypes observed at the basal surface of cells devoid of ARHGAP18 may be a result of changes in regulation at the transcriptional level either directly through RhoA signaling or through an additional mechanism specific to ARHGAP18". The paper would be strengthened by adding data that show whether the effects are indeed downstream, from RhoA or RhoA independent. If there is no sufficient demonstration that ARHGAP18 is downstream of RhoA and is an effector, this needs to be stated explicitly, and the wording should be changed.

      *We now provide new data in Figure 5, which directly tests the RhoA independent functions of ARHGAP18 as recommended by the reviewer. Our understanding of the term effector is 'a molecule that activates, controls, or inactivates a process or action.' Based on this understanding, we used the term to convey ARHGAP18's functional role within the feedback loop, rather than to imply that it acts exclusively downstream. *

      • *

      We seek to clarify our perspective with the reviewer's assertion that we go "back and forth" as to if ARHGAP18 functions in a Rho Dependent or Rho Independent manner. It was our intent to propose a model where ARHGAP 18 acts in two separate circuits that regulate cell signaling. The first circuit involves ARHGAP18's canonical RhoA GAP activity, which involves ERMs and LOK/SLK, and is limited to the apical plasma membrane. This first signaling circuit was characterized in our prior Elife manuscript (Lombardo et al., 2024) and in an earlier JCB manuscript (Zaman and Lombardo et al., 2021). In this newly revised manuscript, we provide a partial mechanistic characterization of the second circuit, which we freely admit is much more complex and will likely require additional study to fully characterize.

      • *

      As both circuits operate as signaling feedback loops, we find the terms 'upstream' and 'downstream' to be of limited value, and we attempt to avoid their use when possible. We retain their use only when referring to the Hippo and ROCK signaling cascades, where these designations are well established. We suggest that the conceptual inconsistencies of Conundrum/ARHGAP18 may have arisen from the tendency to view it in strictly binary terms as upstream or downstream. Here, we propose a third possibility that ARHGAP18 functions as both, participating in a negative feedback loop.

      • *

      *We have edited and added data testing if the effects are Rho independent and discussion text in response to the reviewer's comments and clarify the molecular function of ARHGAP18.

      "Additionally, focal adhesions and basal actin bundles are restored to WT levels when the ARHGAP18(R365A) GAP-ablated mutant is expressed in ARHGAP18 KO cells (Fig. 5A, B). These results represent the strongest argument that ARHGAP18 functions in additional pathways to RhoA/C alone. Our data suggests that at least one of the alternative pathways is through ARHGAP18's interaction with YAP and Merlin. From these data we conclude that ARHGAP18 has important functions in both RhoA signaling through both its GAP activity and in Hippo signaling through its GAP independent binding partners. "*

      • *

      • *

      The study is descriptive and contains a series of observations that are not connected. Because of this, the study's conclusions are not well supported, and key mechanistic insight is limited. The study feels like a set of separate observations, that remain incompletely worked out and have some preliminary feel to them. The model in the last figure also seems to contain hypotheses based on the observations, several of which remains to be proven.

      • *

      *We present our revised manuscript, in which we've more clearly outlined our rationale and conclusions, as detailed in the above responses, to emphasize the overall connectivity of the study. We have also updated the title of Figure 7 to read "__Theoretical __Model of ARHGAP18's coordination of RhoA and Hippo signaling pathways in Human epithelial cells." To make it clear that we are presenting a working model, which has elements that will require additional investigation. Throughout the manuscript, we highlight the unknown elements that remain to be tested or other outstanding questions. Thus, we do not aim to characterize this complex signaling coordination completely. Instead, this manuscript represents the 3rd iteration in our systematic advances to describe this entirely new signaling pathway. We agree that, despite three separate manuscripts (this one included) to date, this work represents an early stage in understanding the system, many additional studies will be needed to characterize this signaling system fully. Figure 7 is presented as a working model that results from a thoughtful combination of our collective data and that of other researchers, derived from numerous species across decades of study. We firmly believe that proposing such integrative models is valuable for advancing the field. We also recognize the importance of clearly indicating which aspects remain hypothetical. We now explicitly note in several places within the discussion which components of the model will require further validation and experimental confirmation. For example, regarding our theoretical mechanism in Figure 7 we state: *

      "Validation of the direct mechanism by which YAP/TAZ transcriptional changes drive basal actin changes in ARHGAP18 KO cells will require further investigation based on predictions from RNAseq results."

      • *

      Addressing any possible connection between key effects of ARHGAP18 KO (changes in actin, focal adhesion, integrins, Yap and merlin binding) could strengthen the manuscript. One such specific question is the whether the changes in integrin expression (RNAseq) are indeed connected to the actin alterations and reduction ion focal adhesions (Fig 1). Staining for these integrins to show they are indeed altered, and/or manipulating any of them to reproduce changes could provide and exciting addition.

      • *

      *We attempted to stain cells for Integrins by purchasing three separate antibodies. However, despite extensive optimization and careful selection of the specific integrins using our RNAseq results we were unable to get any of these antibodies to work in any cell type or condition. We believe that there is a technical challenge to staining for integrins due to their transmembrane and extracellular components, which we were unable to overcome. As an attempt to address the reviewers comment, we alternatively stained cells for paxillin which directly binds the cytoplasmic tails of integrins (Fig. 3&5). *

      Some of the experimental findings are not convincing or lack controls. Fig 1: some of the western blots are not convincing or poor quality. [...] On the same figure, the quality of LIM kinase blots is poor. [...] The signal is weak, and the blot does not appear to support the quantification. The last condition (expression of flag-ARHGAP18) results in a large drop in pLIMK and pcofilin on the blot, which is not reflected by the graph. Addition of *a better blot and the use of strong positive or negative control would boost confidence in these data. *

      • *

      In response to this and other reviewers' comments, we have added new western data and quantification to Figure 1. We now focus on MLC/pMLC data as we believe these data highlight the potential Rho-independent mechanism of ARHGAP18, and we were able to greatly improve the quality of the blots through careful optimization. We hope the reviewer finds these blots and quantifications (Fig. 1E and F) more convincing.

      *We note that phospho-specific Western blotting presents considerably greater technical challenges than conventional blotting. We believe that the appearance of an attractive looking blot does not always correlate to quality or reproducibility and have focused on taking extraordinarily careful steps in the blotting of our phospho-specific antibodies, which at times comes at the cost of the blot's attractiveness in appearance. For example, all phospho-specific antibodies are run using two color fluorescent markers to blot against both the total protein and the phospho-protein on the same blot. This approach often leads to blots that have reduced signal to noise compared to chemiluminescent Westerns. Additionally, we use phospho-specific blocking buffer reagents which do not contain phosphate-based buffers or agents that attract non-specific phospho-staining signals. These blocking buffers are not as effective as non-fat milk in pbs at blocking the background signal, however, they are ultimately cleaner for phospho-specific primary antibodies. We use carefully optimized protocols, from cell treatment to lysis, transfer, and antibody incubation, including methods developed by laboratories where the corresponding author of the manuscript was trained. Nonetheless, despite these efforts, we have now removed the LIMK and cofilin data because we deemed them unnecessary for the main conclusions of this manuscript and were unable to improve their quality to satisfy the reviewer. *

      The changes in pMLC on the western blots are very small, and for any conclusion, these studies require quantification. Further, the expression levels of Flag-ARHGAP18 needs to be shown to support the statement that the protein is expressed, and indeed overexpressed under these conditions (vs just re-expressed).

      In continuation of the above comment, we have made significant effort to improve the quality of our pMLC western blots and now provide quantification in Figure 1. We also now provide the Flag-ARHGAP18 signal as requested by the reviewer.

      Fig 4: the differences in YAP nuclear localization under the various conditions are not well visible. Quantitation of nuclear/cytosolic signal ratio should be provided. Please provide a rationale and more context for using serum starvation and re-addition. What is the expected effect? Serum removal and addition is referred to as nutrient removal and re-addition, but this is inaccurate, as it does not equal nutrient removal, since serum contains a variety of other important components, e.g. growth factors too.

      We have provided new quantification of the nuclear/cytosolic signal ratio in Figure 6D. We have explained our rational for the study through the following new text:

      "Merlin is activated and localized to junctions upon signaling, promoting growth and proliferation; among these signals is the availability of growth factors and other components of serum (Bretscher et al., 2002). We hypothesized that since ARHGAP18 formed a complex with Merlin that ARHGAP18's localization may localize to junctions under conditions which promote Merlin activation."

      • *

      We have altered our use of "nutrient removal" to "serum removal"

      The binding between ARHGAP18 and merlin is interesting, but a key limitation is the use of expressed proteins. Can the binding be shown for the endogenous proteins (IP, colocalization). Another important unaddressed question is the relevance of this binding, and the relation of this to altered YAP nuclear localization.

      • *

      *Our data in Fig. 6G shows binding of a resin bound human ARHGAP18 to endogenous YAP from human cells as suggested by the reviewer. In Fig. 6A, we have selected to use GFP-Merlin as Merlin shares approximately 60% sequence identity with Ezrin, Radixin, and Moesin (ERMs). Their similarity is such that Merlin was named for Moesin-Ezrin-Radixin-Like Protein. In our experience, nearly all Merlin or ERM antibodies have some cross-contaminating signal. Thus, a major concern is that if we were to blot for endogenous Merlin in the pull-down experiment, we may see a band that could in fact be ERMs. To avoid this, we tagged Merlin with GFP to ensure that the product pulled down by ARHGAP18 was Merlin, not an ERM. Regarding the ARHGAP18-resin bound column, our homemade ARHGAP18 antibody is polyclonal. We have extensive experience in pulldown assays and have found that the binding of a polyclonal antibody to the bait protein can produce less accurate results, as the binding site for the antibody is unknown and can sterically hinder attachment of target proteins like Merlin. In our experience, attachment to a flag-tag, which is expressed after a flexible linker at the N- or C-terminus, allows us to overcome this limitation, which we've used in this manuscript. *

      Minor comments:

      Introduction line 99: "When localized to the nucleus, YAP/TAZ promotes the activation of cytoskeletal transcription factors associated with cell proliferation and actin polymerization" Please clarify what you mean by this statement, that is inaccurate in its present for. Did you mean effects on transcription factors that control cytoskeletal proteins, or do you mean that Yap/Taz affect these proteins? Please also provide reference for this.

      We've altered the sentence as suggested by the reviewer, which now reads the following:

      "When localized to the nucleus, YAP/TAZ promotes transcriptional changes associated with cell proliferation and actin polymerization."

      • *

      *The full mechanism for how YAP/TAZ promotes proliferation and actin polymerization is a currently debated issue. We do not think introducing the various current proposed models is required for this manuscript, and we simply intend to convey that when in the nucleus, YAP/TAZ promotes transcriptional changes that drive actin polymerization and cell proliferation. *

      -What is the cell confluence in these experiments? For epithelial cells confluence affects actin structure. Please comment on similarity of confluency across experimental conditions?

      • *

      All cellular experiments are paired where WT and ARHGAP18 KO cells are plated at the same time under identical conditions. For imaging, we plate all cells onto glass coverslips in a 6 well dish so that each condition is literally in the same cell culture plate and gets identical treatment. In our prior Elife paper studying ARHGAP18, we characterized that ARHGAP18 KO cells and WT cells divide at a similar rate and have similar proliferation characteristics. The epithelial cell cultures are maintained for experiments around 70-80% confluency. For the focal adhesion staining experiments, the confluency is slightly lower, between 50-60% to capture the focal adhesions towards the leading edge. We have added the following new text to further describe these methods: "Cell cultures for experiments were maintained at 70%-80% confluency. For focal adhesion experiments, the cell cultures were maintained at 50%-60% confluency."

      -Fig 2 legend: please indicate that the protein detected was non-muscle myosin heavy chain (distinct from the light chain detected in Fig 1).

      • *

      We have altered original Figure 2 (new Figure 3) legend.

      -Line 339-340: please check the syntax of this sentence -Western blot quantification: the comparison of experiments with samples run on different gels/blots requires careful normalization and experimental consistency. Please describe how this was achieved.

      • *

      We have added the following new text to further describe these methods:

      "For blots which required quantification of antibodies that were only rabbit primaries (e.g., pMLC/MLC antibodies listed above), samples were loaded onto a single gel and transferred onto a single membrane at the same time. After transfer, the membrane was cut in half and subsequent steps were done in parallel. All quantified blots were checked for equal loading using either anti-tubulin as a housekeeping protein or total protein as detected by Coomassie staining"

      Reviewer #3 (Significance (Required)):

      Rho signalling is a central regulator of an array of normal and pathological cell functions, and our understanding of the context dependent regulation of this key pathway remains very incomplete. Therefore, new knowledge on the role of specific regulators, such as ARHGAP18, is of interest to a very broad range of researchers. A further exciting aspect of this protein, that despite indications by many studies that it acts as a GAP (inhibitor) for Rho proteins, there are findings in the literature that suggest that its manipulation can affect actin in unexpected (opposite) manner. These point to possible Rho-independent roles, and warranted further in-depth exploration.

      One of the strength of the study is that it explores possible roles of ARHGAP18 beyond RhoA and describes some new and interesting observations, which advance our knowledge. The authors use some excellent tools (e.g. ARHGAP KO cells and re-expression) and approaches (e.g. super resolution microscopy to analyze actin changes, RNAseq and bioinformatics to find genes that may be downstream from ARHGAP18). A key limitation of the study however, is that it is not clear whether the observed findings are indeed independent from RhoA. Further limitation is that potential causal relationships between the described findings are not studied, and therefore the findings are in some cases overinterpreted, and limited mechanistic insights are provided. In some cases the exclusive use of expressed proteins is also a limitation. Finally, some of the experiments also need improvement.

      Reviewer expertise: RhoA signalling, guanine nucleotide exchange factors, epithelial biology, cell migration, intercellular junctions.

      In the above comments, we detail the new experimental data addressing reviewer 3's listed key limitations. We've added new data using the Rho GAP deficient ARHGAP18(R365A) variant which allows for the direct characterization of ARHGAP18's Rho independent activity. We have introduced new data in WT cells studying endogenous proteins to address the limitations from expressed proteins. Finally, we have moderated our language to address overinterpretation. Collectively, we believe that our revised manuscript addresses the constructive reviewer's comments.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The study by Murray et al explores the effects of ARHGAP18 on the actin cytoskeleton, Rho effector kinases, non-muscle myosin, and transcription. Using super resolution microscopy, they show that in ARHGAP18 KO cells there is a mixed and unexpected cytoskeleton phenotype where myosin phosphorylation appears to be increased, but actin is disorganised with reduced stress fibres, diminished focal adhesions and augmented invasiveness. They conclude that the underlying mechanisms are likely independent from RhoA. Next, they perform RNAseq using the KO cells and identify an array of dysregulated genes, including those that play crucial roles in microvilli (related to previously published findings). Analysis of the data identify gene expression changes that are relevant for altered focal adhesion (integrins). Further analysis reveals that a large cohort of the dysregulated genes are YAP targets. They then show that in ARHGAP18 KO cells YAP nuclear localization, as detected by immunostaining, is augmented; and demonstrate that immobilized ARHGAP18 protein can bind the Hippo regulator merlin as well as YAP itself.

      Major comments:

      1. The premise of the study (that ARHGAP18 is a RhoA effector or may acts independently of RhoA) remains not proven. At several places (including in the title) the authors refer to ARHGAP18 as a Rho effector, which would suggest that it is downstream form Rho, but the basis for this is not clear. In fact, their own previous study suggested that ARHGAP is a RhoA regulator, rather than an effector. In general, the connection of the described effects to RhoA remains unclear, and not addressed in this study. The authors seem to go back and forth in their conclusions regarding the connection between ARHGAP18 and RhoA. For example, the first section of results is finished by stating (line 194): "These data support the conclusion that ARHGAP18 acts to regulate basal and junctional actin through Rho-independent mechanism". But the next section starts by stating (line 198): "We hypothesized that the invasive and cytoskeletal phenotypes observed at the basal surface of cells devoid of ARHGAP18 may be a result of changes in regulation at the transcriptional level either directly through RhoA signaling or through an additional mechanism specific to ARHGAP18". The paper would be strengthened by adding data that show whether the effects are indeed downstream, from RhoA or RhoA independent. If there is no sufficient demonstration that ARHGAP18 is downstream of RhoA and is an effector, this needs to be stated explicitly and the wording should be changed.
      2. The study is descriptive and contains a series of observations that are not connected. Because of this, the study's conclusions are not well supported, and key mechanistic insight is limited. The study feels like a set of separate observations, that remain incompletely worked out and have some preliminary feel to them. The model in the last figure also seems to contain hypotheses based on the observations, several of which remains to be proven. Addressing any possible connection between key effects of ARHGAP18 KO (changes in actin, focal adhesion, integrins, Yap and merlin binding) could strengthen the manuscript. One such specific question is the whether the changes in integrin expression (RNAseq) are indeed connected to the actin alterations and reduction ion focal adhesions (Fig 1). Staining for these integrins to show they are indeed altered, and/or manipulating any of them to reproduce changes could provide and exciting addition.
      3. Some of the experimental findings are not convincing or lack controls.

      Fig 1: some of the western blots are not convincing or poor quality. The changes in pMLC on the western blots are very small, and for any conclusion, these studies require quantification. Further, the expression levels of Flag-ARHGAP18 needs to be shown to support the statement that the protein is expressed, and indeed overexpressed under these conditions (vs just re-expressed). On the same figure, the quality of LIM kinase blots is poor. The signal is weak, and the blot does not appear to support the quantification. The last condition (expression of flag-ARHGAP18) results in a large drop in pLIMK and pcofilin on the blot, which is not reflected by the graph. Addition of a better blot and the use of a strong positive or negative control would boost confidence in these data.

      Fig 4: the differences in YAP nuclear localization under the various conditions are not well visible. Quantitation of nuclear/cytosolic signal ratio should be provided. 4. Please provide a rationale and more context for using serum starvation and re-addition. What is the expected effect? Serum removal and addition is referred to as nutrient removal and re-addition, but this is inaccurate, as it does not equal nutrient removal, since serum contains a variety of other important components, e.g. growth factors too. 5. The binding between ARHGAP18 and merlin is interesting, but a key limitation is the use of expressed proteins. Can the binding be shown for the endogenous proteins (IP, colocalization). Another important unaddressed question is the relevance of this binding, and the relation of this to altered YAP nuclear localization.

      Minor comments:

      • Introduction line 99: "When localized to the nucleus, YAP/TAZ promotes the activation of cytoskeletal transcription factors associated with cell proliferation and actin polymerization" Please clarify what you mean by this statement, that is inaccurate in its present for. Did you mean effects on transcription factors that control cytoskeletal proteins, or do you mean that Yap/Taz affect these proteins? Please also provide reference for this.
      • What is the cell confluence in these experiments? For epithelial cells confluence affects actin structure. Please comment on similarity of confluency across experimental conditions?
      • Fig 2 legend: please indicate that the protein detected was non-muscle myosin heavy chain (distinct from the light chain detected in Fig 1).
      • Line 339-340: please check the syntax of this sentence
      • Western blot quantification: the comparison of experiments with samples run on different gels/blots requires careful normalization and experimental consistency. Please describe how this was achieved.

      Significance

      Rho signalling is a central regulator of an array of normal and pathological cell functions, and our understanding of the context dependent regulation of this key pathway remains very incomplete. Therefore, new knowledge on the role of specific regulators, such as ARHGAP18, is of interest to a very broad range of researchers. A further exciting aspect of this protein, that despite indications by many studies that it acts as a GAP (inhibitor) for Rho proteins, there are findings in the literature that suggest that its manipulation can affect actin in unexpected (opposite) manner. These point to possible Rho-independent roles, and warranted further in-depth exploration. One of the strength of the study is that it explores possible roles of ARHGAP18 beyond RhoA and describes some new and interesting observations, which advance our knowledge. The authors use some excellent tools (e.g. ARHGAP KO cells and re-expression) and approaches (e.g. super resolution microscopy to analyze actin changes, RNAseq and bioinformatics to find genes that may be downstream from ARHGAP18). A key limitation of the study however, is that it is not clear whether the observed findings are indeed independent from RhoA. Further limitation is that potential causal relationships between the described findings are not studied, and therefore the findings are in some cases overinterpreted, and limited mechanistic insights are provided. In some cases the exclusive use of expressed proteins is also a limitation. Finally, some of the experiments also need improvement.<br /> Reviewer expertise: RhoA signalling, guanine nucleotide exchange factors, epithelial biology, cell migration, intercellular junctions.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates the Rho effector, ARHGAP18 in Jegs cells, a trophoblastic cell line. It presents a number of new pieces of data, which increase our understanding of the importance of this GAP on cell function and explains at a molecular level previous results of other workers in the field. ARHGAP18 was originally given the name "conundrum' and continues to stand apart from the majority of other GAP proteins and their functions. Hence the data here is significant and of high standard.

      The data is clear, and the images are of high quality and extremely impressive in their resolution. It is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli.

      The data is based on the use of the cell line Jeg3. Even the authors previous publication in eLife is based only on this cell line. They need to show the conclusions are general and not specific to this line of cells. As an extension of this, is the ARHGAP18 function shown here only in transformed cells? Does the same mechanisms operate in normal cells, which respond to activation to proliferate or migrate? In endothelial cells, Lovelace et al 2017 showed localisation to microtubules and that depletion of ARHGAP18 resulted in microtubule instability. The authors may like to comment on the differences. Is this a cell type difference or RhoA versus RhoC difference?

      On pages 7,9 they conclude that MLC and basal and junctional actin are regulated through a GAP independent mechanism. The best way to show this is with overexpression of a GAP mutant.

      There is a huge amount of data presented in Figure 3, but their 2 genes which they focus on, LOP1 and CORO1A, are discussed but no actual data presented in support.

      Significance

      The data is significant and adds a further layer to our understanding of the regulation of cell migration, particularly in the formation and resolution of microvilli.

      This manuscript will be of significance to an basic science audience in the field of RhoGTPases and cell migration.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This manuscript describes a dual mechanism by which ARHGAP18 regulates the actin cytoskeleton. The authors propose that in addition to the known role for ARHGAP18 in regulating Rho GTPases, it also affects the cytoskeleton through regulation of the Hippo pathway transcriptional regulator YAP. ARHGAP18 knockout Jeg3 cells are were generated and show a clear loss of basal stress fiber like F-actin bundles. The authors further characterize the effects of ARHGAP18 knockout and overexpression. It is also discovered that ARHGAP18 binds to the Hippo pathway regulator Merlin and to YAP. Ultimately it is concluded that ARHGAP18 regulates the F-actin cytoskeleton through dual regulation of RHO GTPases and of YAP. While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

      Specific Comments

      1) The authors make a big point about the effects of ARHGAP18 on myosin light chain phosphorylation. However this result is not quantified and tested for statistical significance and reproducibility.

      2) Along similar lines in Figure 2C they state that overexpression of ARHGAP18 causes cells to invade over the top of their neighbors. This might be true and interesting, but only a single cell is shown and there is no quantification or controls for simply overexpressing something in that cell. The authors also conclude from this image that the overexpression phenotype is independent of its GAP activity on Rho. It is not clear how this conclusion is made based on the data. It would seem like a more definitive experiment would be to see if a similar phenotype was induced by an ARHGAP18 mutant deficient in GAP activity.

      3) In Figure 3 the authors compare gene expression profiles of ARHGAP18 knockout cells to wild-type cells. They see lots of differences in focal adhesion and cytoskeletal proteins and conclude that this supports their conclusion that ARHGAP18 is not just acting through RHO. The rationale for this in not clear. In addition, they observe changes in expression profiles consistent with changes in YAP activity. They conclude that the effects are direct. This very well might be true. However RHO is a potent regulator of YAP activity and the results seem quite consistent with ARHGAP18 acting through RHO to affect YAP.

      4) In Figure 4A showing Merlin binding to ARHGAP18 there is no control for the amount of Merlin sticking to the column as was done in Figure 4F for binding experiments with YAP. This makes it difficult to determine the significance of the observed binding.

      5) The images in Figure 4C showing YAP being maintained in the nucleus more in ARHGAP18 knockout cells compared to wild-type. However the images only show a few cells and YAP localization can be highly variable depending on where you look in a field. Images with more cells and some sort of quantification would bolster this result.

      Significance

      While the phenotype of the ARHGAP18 knockout and the association of ARHGAP18 with Merlin and YAP is interesting, I found the authors conclusion that these phenotypes are due to ARHGAP18 regulation of both RHO and YAP to be based on largely correlative evidence and sometimes lacking in controls or tests for significance. In addition the authors often make overly strong conclusions based on the experimental evidence. In some instances, the rationale for how the experimental results support the conclusion is insufficiently articulated, making evaluation challenging. In general although the authors have some interesting observations, more definitive experiments with proper controls and statistical tests for significance and reproducibility are needed to justify their overall conclusions.

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

      Learn more at Review Commons


      Reply to the reviewers

      Dear editor and reviewers,

      We sincerely thank you for your thoughtful comments and constructive suggestions, which have greatly improved the quality and clarity of our manuscript. In response, we have implemented all requested changes, which are highlighted in yellow throughout the revised text, and updated several figures accordingly. Furthermore, we have performed all additional experiments recommended by the reviewers and incorporated the new data into the manuscript. To enhance clarity, we have also included a schematic representation of our proposed model in an additional figure, providing a concise visual summary of our findings.

      We hope that these revisions fully address all concerns raised by the reviewers and meet all the expectations for publication.

      Below, we answer the reviewers point by point (in blue).


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

      In this paper, the authors address the important question of the role of centrosomes during neuronal development. They use Drosophila as an in vivo model. The field is somewhat unclear on the role and importance of centrosomes during neuronal development, although the current data would suggest they are dispensable for axon specification and growth. Early studies in cultured mammalian neurons showed that centrosomes are active and that their microtubules can be cut and transported into the neurites. But a study then showed that centrosomes in these cultured neurons are deactivated relatively early during neuronal development in vitro and that ablating centrosomes even when they are active had no obvious effect on axon specification and growth. Consistent with this, a study in Drosophila provided evidence that centrosomes were not active or necessary in different types of neurons. More recently, a study showed that centrosomal microtubules are dispensable for axon specification and growth in mice in vivo but are required for neuronal migration in the cerebral cortex. However, another study has linked the generation of acetylated microtubules at centrosomes with axon development. In this current study, the authors examine the effect of centrosome loss on various motor and sensory neurons and muscles mainly by examining mutants in essential centriole duplication genes. They associate axonal routing and morphology defects with centrosome loss and provide some evidence that centrosomes could still be active in the developing neurons. Overall, they conclude that centrosomes are active during at least early neuronal development and that this activity is important for proper axonal morphology and routing.

      While I think this study addressing a very interesting and important question, I think as it stands the data is not sufficient to be conclusive on a role for centrosomes during neuronal development. My biggest concern is that most phenotypes have not yet been shown to be cell autonomous, as whole animal mutants have been analysed rather than analysing the effect of cell-specific depletion, and the evidence for active centrosomes needs to be strengthened. If the authors can provide stronger evidence for a role of centrosomes in axonal development then the paper will certainly be of interest to a broad readership.

      We thank the reviewer for the clear and concise summary and fully agree that our study addresses a critical gap in understanding. Centrosomes have long been implicated in morphogenesis, yet their precise contribution to nervous system development has remained unclear. Our findings provide compelling evidence that centrosomes are indispensable for proper nervous system formation and that their absence also triggers muscular defects, highlighting their broader role in tissue organization.

      We acknowledge that the original manuscript lacked some key details; therefore, we have now strengthened our conclusions with additional experiments. Specifically, we demonstrate that these effects are cell-autonomous by using two independent RNAi lines targeted to a subset of motor neurons. Furthermore, we present new data showing that neuronal centrosomes remain active during the early stages of axonal development, emphasising their functional relevance in morphogenesis. All new experiments, figures, and corresponding text revisions are detailed below.

      Major comments 1) The sas-6 transallelic combination shows only 17% embryonic lethality compared to 50% embryonic lethality with sas-4 mutants. Given that both mutants should result in the same degree of centrosome loss (this should be quantified in sas-6 mutants) it would suggest that either sas-4 has other roles away from centrosomes or that the sas-4 mutant chromosome used in the experiment has other mutations that affect viability. The effect of picking up "second-site lethal" mutations on mutant chromosomes is common and so I would not be surprised if this is the reason for the difference in phenotypes. This can be addressed either by "cleaning up" the sas-4 mutant chromosome by backcrossing to wild-type lines, allowing recombination to occur and replace the potential second site mutations, or by using transallelic combinations of sas-4, as they did for sas-6. The "easier" option may just be to analyse all the phenotypes with the sas-6 transallelic combination.

      We appreciate this comment, as it brought to light an issue with the CRISPR line Sas-6-Δa. Upon reanalysing all the data, we determined that this line is embryonic lethal both in homozygosis and when combined with the deficiency uncovering the genomic region, Df(3R)BSC794. In contrast, Sas-6-Δb homozygotes are viable. The inconsistency between these results raised concerns about whether the Δa and Δb Sas-6 mutants carry deletions confined to the Sas-6 coding region. Although this would not hinder our cell biology analysis, it could represent a problem in viability tests. To address this, we repeated all analyses using Sas-6-Δb homozygotes and Sas-6-Δb combined with Df(3R)BSC794. These new results are more consistent and indicate that approximately 50% of Sas-6/Def individuals hatch as adults. Fig. 3 was redone and the manuscript text changed in view of these results.

      2) Using "whole animal" mutants for assessing neuronal morphology is risky due to non-cell-autonomous effects. The authors have carried out some phenotypic analysis of neurons depleted of Sas-4 by cell-specific RNAi, but I feel they need to do this for all of their analysis. This includes embryonic lethality measures, quantification of centrosome numbers, and all axonal phenotypes in Sas-4 RNAi neurons. It would also be prudent to use 2 distinct RNAi lines to help ensure any phenotypes are not off-target effects (and this may help clarify why the authors see some additional phenotypes with RNAi). Indeed, there are relatively weak phenotypes in muscles when using RNAi compared to the mutants and these potential non-cell-autonomous effects could then have a knock-on effect on neuronal morphology. If the authors were concerned that RNAi is not very efficient (explaining any potential weaker phenotypes than in mutants) the authors could examine the effectiveness of RNAi lines by analysing protein depletion by western blotting or mRNA depletion by rt-qPCR (although this has to be done in a different cell type due to the difficulty in obtaining a neuronal extract).

      We have now added a new panel to supplementary Figure 1, showing how the expression of a different Sas-4 RNAi line (2) induces similar nervous system phenotypes when expressed only in aCC, pCC and RP2 pioneer neurons (Sup. Fig. 1 M-O).

      3) When analysing centriole presence or absence it is a good idea to stain with two different centriole markers e.g. Asl and Plp. This helps rule out unspecific staining. It is clear from the images that similar sized foci can be observed outside of the cells (see Figure 5A for example), so clearly some of the foci that appear to be within the cells may also be unspecific staining.

      In a new supplementary figure, we now show that Asl and Plp colocalize and quantify the number of times we find this colocalization in neurons (Supl. Fig 3). In addition, and we apologise for the confusion, but the reason why there are foci outside the marked cells is because these are wholemount embryonic stainings and the anti-Plp antibody marks all centrosomes in all cells in the embryo.

      4) The evidence for active centrosomes is not that convincing. Acetylated tubulin is associated with stable MTs, which are not normally organised by "active" centrosomes that nucleate dynamic microtubules. Moreover, it is plausible that centriole foci happen to overlap with the acetylated tubulin staining by chance. This would explain why not all centrosomes colocalise with acetylated tubulin signal. The authors could better test centrosome activity by performing live imaging with EB1-GFP. If centrosomes are active, it is very easy to observe the many comets produced by the centrosomes.

      We appreciate the reviewer’s comment and agree that acetylated tubulin alone is not an ideal marker for centrosome activity. To address this, we performed live imaging of aCC neurons expressing EB1-GFP together with Asl-Tomato. This was technically challenging because we were imaging only two neurons per segment in live embryos, under significant limitations in fluorescence detection and timing. Despite these constraints, we were able to clearly observe EB1 comets emerging from the centrosome and moving toward the cell periphery, providing direct evidence of microtubule nucleation from centrosomes in neurons.

      Importantly, we complemented this with a microtubule depolymerization/polymerization assay, which provides unequivocal evidence that polymerization initiates at the centrosome. After depolymerization, we observed microtubule regrowth from the centrosome, confirming its role as an active microtubule-organizing centre in these neurons. Together, we hope that these results are enough to demonstrate that neuronal centrosomes are functionally active during early axonal development. These experiments are presented in Figure 6 and corresponding text in the manuscript.

      5) If the authors believe that centrosomes have a role in axon pathfinding in sensory neurons, they should show that these centrosomes are active, at least during early stages (again using EB1-GFP imaging).

      We appreciate the reviewer’s suggestion and agree that EB1-GFP imaging would be the most direct way to assess centrosome activity in sensory neurons. However, performing time-lapse imaging in these neurons is technically very demanding due to their location and accessibility in live embryos, and we did not attempt this approach. Instead, we now provide new evidence showing that sensory neuron centrosomes colocalize with both α-tubulin and γ-tubulin. This strongly supports that these centrosomes are associated with microtubule nucleation machinery and are as likely as motor neuron centrosomes to be active during early stages of axon development. These new data have been included in the revised manuscript (see Figure 5 and corresponding text).

      6) The authors mention in the discussion that "increased JNK activity, can result in axonal wiggliness (Karkali et al, 2023)". I therefore wonder whether centrosome loss may induce JNK activation (the stress response), as this would then indicate an indirect effect of centrosome loss on axonal structure rather than a direct influence of centrosome-generated microtubules. The authors could assess whether the DNK-JNK pathway is activated in neurons lacking centrosomes by expression UAS-Puc-GFP and quantifying the nuclear signal.

      In a new supplementary figure, we now show by using a reporter for JNK signalling, as requested, that Sas-4 neurons do not activate the JNK pathway (Supl. Fig 4).

      7) In Figure 5, the authors claim that they find "a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". I don't think this is a strong correlation. The difference in centriole number between embryos with no defects and those with defects is very small. In contrast, the difference between centriole numbers in control (no defects) and mutant (no defects) is very large. So, there does not appear to be a strong correlation between centrosome number and phenotype.

      We agree and we have corrected this sentence to better explain the results.

      Minor comments

      1) I don't understand Figure 3C - why do the % of surviving homozygotes and heterozygotes add up to 100%? Should the grey boxes not relate to dead and the white to surviving?

      Thank you for pointing this out. Figures 1B and 3C represent only the surviving individuals. The grey boxes correspond to surviving homozygotes, and the white boxes correspond to surviving heterozygotes. The percentages add up to 100% only at embryonic stages because all embryos reach late embryonic stages. The grey and white boxes reflect the proportion of these two genotypes among the survivors, not the total number of embryos including those that died. We have changed the text to convey this.

      2) "In mouse fibroblasts, myoblasts and endothelial cells, centrosome orientation is important for nuclear positioning and cell migration(Chang et al, 2015; Gomes et al, 2005; Kushner et al, 2014)." Do you mean "centrosome position"?

      Yes, text changed, thank you for spotting it.

      3) In the introduction, the authors mention Meka et al. when saying the centrosomal microtubules are important for axonal development, but they should also discuss the counter argument from Vinopal et al., 2023 (Neuron) that showed how centrosomes were required for neuronal migration but not axon growth, which was instead mediated by Golgi-derived microtubules.

      Done, thank you very much.

      4) Lines 228-230 - repeated sentence

      Corrected, thank you very much.

      5) Additionally, we did not detect centrioles in the quadrant opposite the axon exit point (Fig. 2B n=75) - this data is not in Fig 2B

      Correct, it is in figure 4B, thank you very much.

      6) "This significant decrease in the humber of centrioles further supports the critical role of Sas-4 in pioneer neurons of the ventral nerve cord (VNC) during Drosophila embryogenesis". It rather highlights that Sas-4 is required for centriole formation in these neurons. Also, humber = number.

      We agree, and have changed the text, thank you very much.

      7) Result title: Non-ciliated sensory neurons have centrioles. This is kind of obvious. A better title may be "axon phenotypes correlate with centriole numbers in sensory neurons" but unfortunately i don't think there is good evidence for this (See major point above).

      We agree and we have changed. We now believe we have strong evidence to support it. We hope the additional data presented in the revision convincingly demonstrate this point.

      Reviewer #1 (Significance (Required)):

      As mentioned above, the advance will be important if more evidence is provided. In this case, the paper will be interesting to a broad readership. But currently the paper is limited by the lack of evidence for centrosome function and activity in the neurons.

      We hope that reviewer 1, now considers that the manuscript is not limited anymore and that it shows convincing evidence for centrosome function and activity in embryonic neurons.

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

      Summary: In this manuscript, Gonzalez et al. examine the potential function of centrosomes in the neurons and muscle cells of Drosophila embryos. By studying various mutant and RNAi lines in which centriole duplication has been disrupted, they conclude that the loss of centrioles disrupts axonal pathfinding and muscle integrity.

      Major points: 1. Throughout the manuscript, the phenotypes presented are often quite subtle. For this reason, I would really recommend that these experiments are scored blind. Perhaps the authors did this, but I didn't see any mention of this.

      All our phenotypic analyses are performed blind. We apologize for not having originally included this information in the Methods section; it has now been added. Embryos are stained using colorimetric methods (DAB) to label the nervous system, while balancer chromosomes are marked with a fluorescent antibody. This approach allows us to assess and quantify phenotypes using white light without knowing whether the embryos are homozygous mutants or heterozygous, which can only be detected by changing the channels to fluorescence.

      1. The authors conclude that neurons have active centrioles that function as centrosomes (Figure 6), but the data here is confusing. The authors state that in these cells they observe acetylated MTs extending from the centrosomes and these colocalised with g-tubulin. But the authors don't show the overlap between centrosomes, g-tubulin and MTs, as they stain for these separately. This is problematic, as it was not clear from these images that the majority of the MTs really are extending from the centrosome: the centrosome may just associate or be close by to these MT cables (Figure 6A,B). Moreover, the authors show that only a fraction of the centrosomes in these cells associate with g-tubulin, so presumably in cells where the centrosomes lack g-tubulin they would not expect the centrosomes to be associated with the MTs-but they do not show that this is the case. Perhaps the authors can't test this, but an alternative would be to show that these MT arrays are absent in Sas-4 mutants. This would give more confidence that these MTs arise from the centrosomes.

      We agree that the initial data based on acetylated microtubules and γ-tubulin colocalization were not sufficient to conclude that microtubules originate from the centrosome, as these markers can only suggest association. To address this, we have now included additional experiments that provide direct evidence of centrosome activity.

      First, we performed live imaging of aCC neurons expressing EB1-GFP together with Asl-Tomato. Despite the technical challenges of imaging only two neurons per segment in live embryos under strict fluorescence and timing constraints, we were able to clearly observe EB1 comets emerging from the centrosome and moving toward the cell periphery. This demonstrates active microtubule nucleation from centrosomes rather than mere proximity to microtubule bundles.

      Second, we carried out a microtubule depolymerization/polymerization assay, which provides unequivocal evidence that polymerization initiates at the centrosome. After depolymerization, microtubules regrew from the centrosome, confirming its role as an active microtubule-organizing center. These experiments go beyond colocalization and directly address the concern that centrosomes might simply be adjacent to microtubule cables.

      Regarding the suggestion to use Sas-4 mutants, while we did not perform this experiment, the regrowth assay combined with EB1 imaging strongly supports that these microtubules originate from the centrosome. All new data are presented in Figure 6 and the corresponding text in the revised manuscript.

      1. The authors show that muscle cell integrity is compromised by centriole-loss (Figure 2). This is very surprising as it is widely believed that centrosomes are non-functional in muscle cells, and the MTs are instead organised around the nuclear envelope. I'm not aware of the situation in Drosophila muscle cells, but the authors should ideally try to examine if the centrioles are functioning as centrosomes in these cells. At the very least they should discuss how they think centriole-loss is influencing the muscle integrity when it is widely believed they are inactive in these cells.

      We do not claim that centrosomes are active in muscle cells at these developmental stages. The observed muscle defects could result from earlier processes such as cell division, migration, or muscle fusion. We agree that this is an intriguing observation; however, pursuing this question further would go beyond the scope of the current manuscript. As requested by the reviewer, we have now expanded the discussion to consider how centriole loss might impact muscle integrity.

      Regardless of the strength of the supporting data, I think the authors should tone down their conclusions. The title and abstract led me to believe that centriole loss would cause significant problems in axonal pathfinding and muscle integrity. In all the mutant specimens examined (and certainly the low magnification views shown in Figure 1D'-F', Figure 1I'-K' and Figure 2D'-F') the mutants look very similar to the WT. Many readers may not get past the title and abstract, so the authors should make it clearer that these defects are very subtle.

      We have changed the text to convey this idea.

      Minor points: 1. In Figures 4 and 5, CP309 staining is relied on to identify centrioles, but there is quite a background of non-specific dots, making it hard to be certain what is a centriole and what isn't. For example, in Figure 5D' there are lots of dots within some of the cells - are any of these centrioles? How can the authors be certain which dot is a centriole in some of the cells shown in Figure 5C'? Is it possible to use a second marker and only count as centrioles dots that are recognised by both antibodies?

      We thank the reviewer for this suggestion and agree that using a second marker improves confidence in centriole identification. In a new supplementary figure (Supplementary Fig. 3), we now show that Asl and Plp colocalize in neurons and provide a quantification of the frequency of this colocalization. This dual labelling confirms the identity of centrioles and addresses the concern about non-specific background.

      We also apologize for any confusion regarding the presence of foci outside the marked cells. These images are whole-mount embryonic stainings, and the anti-Plp antibody labels all centrosomes in all cells of the embryo, which explains the additional foci observed.

      In the abstract that authors state that traditionally centrosomes have been considered to be non-essential in terminally differentiated cells. I don't think this is correct. In the standard "textbook" view of a cell, the centrosome is normally positioned in the centre of the cell organising an extensive array of MTs that are thought play an important role in organising intracellular transport, the positioning and movement of organelles and the maintenance and establishment of cell polarity. I don't think it is only recent evidence that suggests they play vital roles in terminally differentiated cells.

      We thank the reviewer for this correction and we have changed the text accordingly.

      1. Line 162 the authors state that in the RNAi knockdown lines they observe several additional phenotypes, but then in the same sentence (Line 164) they say that these defects were also observed in the original mutant and mutant/Df lines.

      We apologise for this confusion, we have rearranged the sentence for clearance.

      The sentences in Line281-287 don't reference any of the Figures, so it seems the authors are just stating these results without presenting any data (e.g. "Significantly, we also found a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". If they've tested this correlation, they should show it.

      We have rearranged the sentences for better understanding.

      In Figure 7 I did not understand how the authors measured tortuosity (wiggliness) and could see no description in the methods. This is important as, again the defect seems quite subtle, but perhaps I am not understanding which bits of the axon are being measures. Is it just the small bit of the axons close to the asterixis that is being measured, or the whole FasII track?

      We have now added another quantification and additional descriptions in the methods section.

      Reviewer #2 (Significance (Required)):

      The potential function of centrosomes in axonal outgrowth is quite controversial, so this study is potentially of considerable interest.

      However, several aspects of the data presented here were confusing or not terribly convincing. In its present state, I don't think the main conclusions are strongly enough supported by the data.

      We hope that reviewer 2, now considers that the manuscript is not confusing anymore and that it shows convincing evidence for centrosome function and activity in embryonic neurons.

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

      The manuscript of González et al. entitled "Centriole Loss in Embryonic Development Disrupts Axonal Pathfinding and Muscle Integrity" deals with the role of centrosomes in shaping axonal morphology. To this aim the AA analysed Drosophila Sas-4 mutants that are reported to develop until adult stage without centrioles. Remarkably, the AA observe that 50% of the homozygous mutant embryos fail to hatch as larvae. The present observations suggest that centrosome loss results in axonemal shaping defects and muscle developmental abnormalities. Finally, the AA show the presence of functional centrosomes in neurons. In my opinion, the manuscript is interesting because shows unexpected findings. However, to justify these new findings the AA are required to improve some experimental observations.

      We thank the reviewer for his summary of our work and for considering it interesting. We have taken into account all the comments and believe that these have helped improve our manuscript.

      Major: Abstract- It is unclear in which phenotypic condition the observations of centrosome loss or centrosome presence have been found. Please better explain. l.36. embryos, larvae, adult, from Sas4 or controls? If mutants, the observations are very interesting since Sas4 would be without centrioles. Indeed, Basto et al., show that chemosensory neurons do not develop an axoneme in the absence of centrioles, but extend dendrites toward the sensory bristle.

      We have made clear which refer to wild-type and which are Centriole Loss (CL) conditions. CL conditions refer to mutant and downregulation conditions, whereas targeted downregulation refers to RNAi downregulation only in neurons.

      I do not think appropriate the use of "centriole" in the main title since the centrioles would be localized by true centriolar antigens rather than by centrosomal antigens. This problem occurs throughout the text and some figures where the AA image centrioles by centrosomal material. In Gig. 5A only the AA properly look at Asl localization. The other pictures of presumptive centrioles or centriole quantification report CP309 dots. This localization does not unequivocally reveal centrioles, since CP309 is essentially required for centrosome-mediated Mt nucleation. There are differentiated Drosophila tissues in which centrioles are present, but inactivated, and unable to recruit pericentriolar material. Mt are nucleated by ncMTOCs that contain centrosomal material and gamma-tubulin. Thus, the centrosomal antigens do not colocalize with centrioles.

      We have changed centrioles to centrosomes in the title and most sections in the manuscript. We have also included an extra control, showing that Asl and Plp colocalize and quantify the number of times we find this colocalization in neurons (Supl. Fig 3). Asl is a reliable and widely used marker for centrioles, as it localizes specifically to the centriole structure (Varmark H, Llamazares S, Rebollo E, Lange B, Reina J, Schwarz H, Gonzalez C. Asterless is a centriolar protein required for centrosome function and embryo development in Drosophila. Curr Biol. 2007 Oct 23;17(20):1735-45. doi: 10.1016/j.cub.2007.09.031. PMID: 17935995.)

      Minor: l. 58. The early arrest is mainly due to a checkpoint control. In double mutant for Sas4 and P53 the embryos survive longer, even if their further development is asrrested.

      We thank the reviewer for this comment, and we have changed the text accordingly.

      1. Previous works, also quoted by the AA, reported that in mature neurons the centrosome are inactivated, whereas the present manuscript describes functional centrosomes in Drosophila motor and peripheral nervous system. This is an intriguing observations that needs a better explanation in Discussion section.

      We thank the reviewer for this comment, and we have changed the discussion accordingly.

      l.143-145. I understand that 50% of the Sas4 embryos that reach the adult stage have centrioles. Is it correct? But if it is so, how the AA explain the absence of centrioles in sensory neurons of adult flies as reported by Basto et al. ?

      According to our results they have less centrioles than controls already at embryonic stages. In addition, as reported in Basto et al. they continue losing centrioles during larval stages and metamorphosis, which explains why centrioles are not detected at adult stages.

      l.215. It is unclear for me why the AA analyse Sas6 flies, unless explain the mutant phenotype.

      To strengthen our conclusions with Sas-4 and exclude the possibility that the observed phenotypes arise from a centrosome-independent function of Sas-4. For this reason, we have taken additional steps to confirm that the effects are specifically due to centrosome loss and we used Sas-6 mutants as one of these.

      l.221. How the centrioles have been quantified? What antibody, the AA used.

      We have quantified centrosomes using antibodies agains Plp (CP309) and Asl-YFP expression.

      l.244. and Fig 4C,D. I see high background with CP309. As reported previously I think better to use antibodies against centriolar proteins, such as Sas6, Ana1, Asl, or Sas4 ( if centrioles are present in 50% of mutants as the AA claim, the antibody could be also useful). In addition, I can see some CP309 spots in Fig 4E,F. Are they centrioles?

      Indeed, as we report, Sas-4 mutant embryos are not totally devoid of centrosomes. In addition, and we apologise for the confusion, but the reason why there are foci outside the marked cells in control embryos is because these are wholemount embryonic stainings and the anti-Plp antibody marks all centrosomes in all cells in the embryo, not just in the neurons.

      l.270 and Fig. 5A and Fig.5 C-E. Why the AA localize Cp309 and not Asl (Fig. 5A) to detect centrioles?

      In a new supplementary figure, we now show that Asl and Plp colocalize and quantify the number of times we find this colocalization in neurons (Supl. Fig 3). So, we can use CP309 in neurons to the same effect as Asl-

      L295-296. I cannot see Mts, but only a diffuse staining. I am expecting to see distinct Mt bundles.

      In figure 5 it is now easier to see the MT bundles in the new experiment in Fig. 5F-I , where we performed MT depolymerisation/repolymerisation: Nevertheless, we need to stress out that we are doing these analyses in wholemount embryonic stainings.

      326-327. How the AA explain this different lethality, even if both the proteins are involved in centriole assembly?

      We have now redone all the viability and mutant phenotype analysis using Sas-6 CRISPR mutant over the Deficiency, which is a better way to access the phenotype.

      335-337. In my opinion the quoted publications are not relevant.

      We believe that these references back up our hypothesis because:

      • Metzger et al 2012 stress the importance of nuclear position in muscle development in Drosophila
      • Loh et al 2023, relate centrosomes with nuclear migration in Drosophila
      • Tillery et al 2018, is a review describing MTs in muscle development in Drosophila.

      358-359. Does maternal contribution persist after gastrulation?

      While bulk degradation occurs by midblastula transition, some stable maternal products persist beyond gastrulation. In our case, if centrioles are formed due to the maternal contribution, they will only be diluted by cell division, which explains why we can detect centrioles at late embryonic stages.

      l.366. This is an intriguing point, but as previously observed I have some problem with centriole localization. References. Please uniform Journal abbreviations and control page numbers.

      I hope we have clarified this problem with the new experiments showing MT repolarization from the centrosomes in neurons.

      Reviewer #3 (Significance (Required)):

      The manuscript is potentially interesting for peoples working of cell and molecular biology, and development. However, the paper needs an additional working to be suitable for publication.

      We hope that reviewer 3, considers that the additional work and revision make this manuscript suitable for publication.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The manuscript of González et al. entitled "Centriole Loss in Embryonic Development Disrupts Axonal Pathfinding and Muscle Integrity" deals with the role of centrosomes in shaping axonal morphology. To this aim the AA analysed Drosophila Sas-4 mutants that are reported to develop until adult stage without centrioles. Remarkably, the AA observe that 50% of the homozygous mutant embryos fail to hatch as larvae. The present observations suggest that centrosome loss results in axonemal shaping defects and muscle developmental abnormalities. Finally, the AA show the presence of functional centrosomes in neurons. In my opinion, the manuscript is interesting because shows unexpected findings. However, to justify these new findings the AA are required to improve some experimental observations.

      Major:

      Abstract- It is unclear in which phenotypic condition the observations of centrosome loss or centrosome presence have been found. Please better explain. l.36. embryos, larvae, adult, from Sas4 or controls? If mutants, the observations are very interesting since Sas4 would be without centrioles. Indeed, Basto et al., show that chemosensory neurons do not develop an axoneme in the absence of centrioles, but extend dendrites toward the sensory bristle.

      I do not think appropriate the use of "centriole" in the main title since the centrioles would be localized by true centriolar antigens rather than by centrosomal antigens. This problem occurs throughout the text and some figures where the AA image centrioles by centrosomal material. In Gig. 5A only the AA properly look at Asl localization. The other pictures of presumptive centrioles or centriole quantification report CP309 dots. This localization does not unequivocally reveal centrioles, since CP309 is essentially required for centrosome-mediated Mt nucleation. There are differentiated Drosophila tissues in which centrioles are present, but inactivated, and unable to recruit pericentriolar material. Mt are nucleated by ncMTOCs that contain centrosomal material and gamma-tubulin. Thus, the centrosomal antigens do not colocalize with centrioles.

      Minor:

      l. 58. The early arrest is mainly due to a checkpoint control. In double mutant for Sas4 and P53 the embryos survive longer, even if their further development is asrrested.

      l. 102. Previous works, also quoted by the AA, reported that in mature neurons the centrosome are inactivated, whereas the present manuscript describes functional centrosomes in Drosophila motor and peripheral nervous system. This is an intriguing observations that needs a better explanation in Discussion section.

      l.143-145. I understand that 50% of the Sas4 embryos that reach the adult stage have centrioles. Is it correct? But if it is so, how the AA explain the absence of centrioles in sensory neurons of adult flies as reported by Basto et al. ?

      l.215. It is unclear for me why the AA analyse Sas6 flies, unless explain the mutant phenotype.

      l.221. How the centrioles have been quantified? What antibody, the AA used.

      l.244. and Fig 4C,D. I see high background with CP309. As reported previously I think better to use antibodies against centriolar proteins, such as Sas6, Ana1, Asl, or Sas4 ( if centrioles are present in 50% of mutants as the AA claim, the antibody could be also useful). In addition, I can see some CP309 spots in Fig 4E,F. Are they centrioles?

      l.270 and Fig. 5A and Fig.5 C-E. Why the AA localize Cp309 and not Asl (Fig. 5A) to detect centrioles?

      L295-296. I cannot see Mts, but only a diffuse staining. I am expecting to see distinct Mt bundles.

      L. 326-327. How the AA explain this different lethality, even if both the proteins are involved in centriole assembly?

      l. 335-337. In my opinion the quoted publications are not relevant.

      l. 358-359. Does maternal contribution persist after gastrulation?

      l.366. This is an intriguing point, but as previously observed I have some problem with centriole localization.

      References. Please uniform Journal abbreviations and control page numbers.

      Significance

      The manuscript is potentially interesting for peoples working of cell and molecular biology, and development. However, the paper needs an additional working to be suitable for publication.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary: In this manuscript, Gonzalez et al. examine the potential function of centrosomes in the neurons and muscle cells of Drosophila embryos. By studying various mutant and RNAi lines in which centriole duplication has been disrupted, they conclude that the loss of centrioles disrupts axonal pathfinding and muscle integrity.

      Major points:

      1. Throughout the manuscript, the phenotypes presented are often quite subtle. For this reason, I would really recommend that these experiments are scored blind. Perhaps the authors did this, but I didn't see any mention of this.
      2. The authors conclude that neurons have active centrioles that function as centrosomes (Figure 6), but the data here is confusing. The authors state that in these cells they observe acetylated MTs extending from the centrosomes and these colocalised with g-tubulin. But the authors don't show the overlap between centrosomes, g-tubulin and MTs, as they stain for these separately. This is problematic, as it was not clear from these images that the majority of the MTs really are extending from the centrosome: the centrosome may just associate or be close by to these MT cables (Figure 6A,B). Moreover, the authors show that only a fraction of the centrosomes in these cells associate with g-tubulin, so presumably in cells where the centrosomes lack g-tubulin they would not expect the centrosomes to be associated with the MTs-but they do not show that this is the case. Perhaps the authors can't test this, but an alternative would be to show that these MT arrays are absent in Sas-4 mutants. This would give more confidence that these MTs arise from the centrosomes.
      3. The authors show that muscle cell integrity is compromised by centriole-loss (Figure 2). This is very surprising as it is widely believed that centrosomes are non-functional in muscle cells, and the MTs are instead organised around the nuclear envelope. I'm not aware of the situation in Drosophila muscle cells, but the authors should ideally try to examine if the centrioles are functioning as centrosomes in these cells. At the very least they should discuss how they think centriole-loss is influencing the muscle integrity when it is widely believed they are inactive in these cells.
      4. Regardless of the strength of the supporting data, I think the authors should tone down their conclusions. The title and abstract led me to believe that centriole loss would cause significant problems in axonal pathfinding and muscle integrity. In all the mutant specimens examined (and certainly the low magnification views shown in Figure 1D'-F', Figure 1I'-K' and Figure 2D'-F') the mutants look very similar to the WT. Many readers may not get past the title and abstract, so the authors should make it clearer that these defects are very subtle.

      Minor points:

      1. In Figures 4 and 5, CP309 staining is relied on to identify centrioles, but there is quite a background of non-specific dots, making it hard to be certain what is a centriole and what isn't. For example, in Figure 5D' there are lots of dots within some of the cells - are any of these centrioles? How can the authors be certain which dot is a centriole in some of the cells shown in Figure 5C'? Is it possible to use a second marker and only count as centrioles dots that are recognised by both antibodies?
      2. In the abstract that authors state that traditionally centrosomes have been considered to be non-essential in terminally differentiated cells. I don't think this is correct. In the standard "textbook" view of a cell, the centrosome is normally positioned in the centre of the cell organising an extensive array of MTs that are thought play an important role in organising intracellular transport, the positioning and movement of organelles and the maintenance and establishment of cell polarity. I don't think it is only recent evidence that suggests they play vital roles in terminally differentiated cells.
      3. Line 162 the authors state that in the RNAi knockdown lines they observe several additional phenotypes, but then in the same sentence (Line 164) they say that these defects were also observed in the original mutant and mutant/Df lines.
      4. The sentences in Line281-287 don't reference any of the Figures, so it seems the authors are just stating these results without presenting any data (e.g. "Significantly, we also found a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". If they've tested this correlation, they should show it.
      5. In Figure 7 I did not understand how the authors measured tortuosity (wiggliness) and could see no description in the methods. This is important as, again the defect seems quite subtle, but perhaps I am not understanding which bits of the axon are being measures. Is it just the small bit of the axons close to the asterixis that is being measured, or the whole FasII track?

      Significance

      The potential function of centrosomes in axonal outgrowth is quite controversial, so this study is potentially of considerable interest.

      However, several aspects of the data presented here were confusing or not terribly convincing. In its present state, I don't think the main conclusions are strongly enough supported by the data.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this paper, the authors address the important question of the role of centrosomes during neuronal development. They use Drosophila as an in vivo model. The field is somewhat unclear on the role and importance of centrosomes during neuronal development, although the current data would suggest they are dispensable for axon specification and growth. Early studies in cultured mammalian neurons showed that centrosomes are active and that their microtubules can be cut and transported into the neurites. But a study then showed that centrosomes in these cultured neurons are deactivated relatively early during neuronal development in vitro and that ablating centrosomes even when they are active had no obvious effect on axon specification and growth. Consistent with this, a study in Drosophila provided evidence that centrosomes were not active or necessary in different types of neurons. More recently, a study showed that centrosomal microtubules are dispensable for axon specification and growth in mice in vivo but are required for neuronal migration in the cerebral cortex. However, another study has linked the generation of acetylated microtubules at centrosomes with axon development. In this current study, the authors examine the effect of centrosome loss on various motor and sensory neurons and muscles mainly by examining mutants in essential centriole duplication genes. They associate axonal routing and morphology defects with centrosome loss and provide some evidence that centrosomes could still be active in the developing neurons. Overall, they conclude that centrosomes are active during at least early neuronal development and that this activity is important for proper axonal morphology and routing.

      While I think this study addressing a very interesting and important question, I think as it stands the data is not sufficient to be conclusive on a role for centrosomes during neuronal development. My biggest concern is that most phenotypes have not yet been shown to be cell autonomous, as whole animal mutants have been analysed rather than analysing the effect of cell-specific depletion, and the evidence for active centrosomes needs to be strengthened. If the authors can provide stronger evidence for a role of centrosomes in axonal development then the paper will certainly be of interest to a broad readership.

      Major comments

      1. The sas-6 transallelic combination shows only 17% embryonic lethality compared to 50% embryonic lethality with sas-4 mutants. Given that both mutants should result in the same degree of centrosome loss (this should be quantified in sas-6 mutants) it would suggest that either sas-4 has other roles away from centrosomes or that the sas-4 mutant chromosome used in the experiment has other mutations that affect viability. The effect of picking up "second-site lethal" mutations on mutant chromosomes is common and so I would not be surprised if this is the reason for the difference in phenotypes. This can be addressed either by "cleaning up" the sas-4 mutant chromosome by backcrossing to wild-type lines, allowing recombination to occur and replace the potential second site mutations, or by using transallelic combinations of sas-4, as they did for sas-6. The "easier" option may just be to analyse all the phenotypes with the sas-6 transallelic combination.
      2. Using "whole animal" mutants for assessing neuronal morphology is risky due to non-cell-autonomous effects. The authors have carried out some phenotypic analysis of neurons depleted of Sas-4 by cell-specific RNAi, but I feel they need to do this for all of their analysis. This includes embryonic lethality measures, quantification of centrosome numbers, and all axonal phenotypes in Sas-4 RNAi neurons. It would also be prudent to use 2 distinct RNAi lines to help ensure any phenotypes are not off-target effects (and this may help clarify why the authors see some additional phenotypes with RNAi). Indeed, there are relatively weak phenotypes in muscles when using RNAi compared to the mutants and these potential non-cell-autonomous effects could then have a knock-on effect on neuronal morphology. If the authors were concerned that RNAi is not very efficient (explaining any potential weaker phenotypes than in mutants) the authors could examine the effectiveness of RNAi lines by analysing protein depletion by western blotting or mRNA depletion by rt-qPCR (although this has to be done in a different cell type due to the difficulty in obtaining a neuronal extract).
      3. When analysing centriole presence or absence it is a good idea to stain with two different centriole markers e.g. Asl and Plp. This helps rule out unspecific staining. It is clear from the images that similar sized foci can be observed outside of the cells (see Figure 5A for example), so clearly some of the foci that appear to be within the cells may also be unspecific staining.
      4. The evidence for active centrosomes is not that convincing. Acetylated tubulin is associated with stable MTs, which are not normally organised by "active" centrosomes that nucleate dynamic microtubules. Moreover, it is plausible that centriole foci happen to overlap with the acetylated tubulin staining by chance. This would explain why not all centrosomes colocalise with acetylated tubulin signal. The authors could better test centrosome activity by performing live imaging with EB1-GFP. If centrosomes are active, it is very easy to observe the many comets produced by the centrosomes.
      5. If the authors believe that centrosomes have a role in axon pathfinding in sensory neurons, they should show that these centrosomes are active, at least during early stages (again using EB1-GFP imaging).
      6. The authors mention in the discussion that "increased JNK activity, can result in axonal wiggliness (Karkali et al, 2023)". I therefore wonder whether centrosome loss may induce JNK activation (the stress response), as this would then indicate an indirect effect of centrosome loss on axonal structure rather than a direct influence of centrosome-generated microtubules. The authors could assess whether the DNK-JNK pathway is activated in neurons lacking centrosomes by expression UAS-Puc-GFP and quantifying the nuclear signal.
      7. In Figure 5, the authors claim that they find "a correlation between axonal guidance phenotypes and the numbers of centrioles per embryo". I don't think this is a strong correlation. The difference in centriole number between embryos with no defects and those with defects is very small. In contrast, the difference between centriole numbers in control (no defects) and mutant (no defects) is very large. So, there does not appear to be a strong correlation between centrosome number and phenotype.

      Minor comments

      1. I don't understand Figure 3C - why do the % of surviving homozygotes and heterozygotes add up to 100%? Should the grey boxes not relate to dead and the white to surviving?
      2. "In mouse fibroblasts, myoblasts and endothelial cells, centrosome orientation is important for nuclear positioning and cell migration(Chang et al, 2015; Gomes et al, 2005; Kushner et al, 2014)." Do you mean "centrosome position"?
      3. In the introduction, the authors mention Meka et al. when saying the centrosomal microtubules are important for axonal development, but they should also discuss the counter argument from Vinopal et al., 2023 (Neuron) that showed how centrosomes were required for neuronal migration but not axon growth, which was instead mediated by Golgi-derived microtubules.
      4. Lines 228-230 - repeated sentence
      5. Additionally, we did not detect centrioles in the quadrant opposite the axon exit point (Fig. 2B n=75) - this data is not in Fig 2B
      6. "This significant decrease in the humber of centrioles further supports the critical role of Sas-4 in pioneer neurons of the ventral nerve cord (VNC) during Drosophila embryogenesis". It rather highlights that Sas-4 is required for centriole formation in these neurons. Also, humber = number.
      7. Result title: Non-ciliated sensory neurons have centrioles. This is kind of obvious. A better title may be "axon phenotypes correlate with centriole numbers in sensory neurons" but unfortunately i don't think there is good evidence for this (See major point above).

      Significance

      As mentioned above, the advance will be important if more evidence is provided. In this case, the paper will be interesting to a broad readership. But currently the paper is limited by the lack of evidence for centrosome function and activity in the neurons.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      A previous study by Komada et al. demonstrated that MAP7 is expressed in both Sertoli and germ cells, and that Map7 gene-trap mutant mice display disrupted microtubule bundle formation in Sertoli cells, accompanied by defects in spermatid manchettes and germ cell loss. In the current study, Kikuchi et al. investigated the role of MAP7 in the formation of the Sertoli cell apical domain during the first wave of spermatogenesis. They generated a GFP-tagged MAP7 mouse line and demonstrated that the endogenous MAP7 protein localizes to the apical microtubules in Sertoli cells and to the manchette microtubules in step 9-11 spermatids. They also generated a new Map7 knockout (KO) mouse line in a genetic background distinct from the one used in the previous study. Focusing on stages before the emergence of step 9-11 spermatids, the authors aimed to isolate defects caused by the function of MAP7 in Sertoli cells. They report that loss of MAP7 impairs Sertoli cell polarity and apical domain formation, accompanied by the microtubule remodeling defect. Using the GFP-tagged MAP7 line, they performed immunoprecipitation-mass spectrometry and identified several MAP7-interacting proteins in the testis, including MYH9. They further observed that MAP7 deletion alters the distribution of MYH9. Single-cell RNA sequencing revealed that the loss of MAP7 in Sertoli cells resulted in slight transcriptomic shifts but had no significant impact on their functional differentiation. Single-cell RNA sequencing analysis also showed delayed meiotic progression in the MAP7-deficient testis. Overall, while the study provides some interesting discoveries of early Sertoli cell defects in MAP7-deficient testes, some conclusions are premature and not fully supported by the presented data. The mechanistic investigations remain limited in depth.

      Response: We thank the reviewer for this insightful summary. We agree that some of our initial interpretations were speculative and have revised the relevant sections to more accurately reflect the limitations of the current data. We also acknowledge that further mechanistic studies will be important to strengthen our conclusions, and we have outlined these plans in the individual responses below.

      Major comments:

      Although the infertility phenotype of the Map7 gene-trap mutant mice has been reported previously, it remains essential to assess fertility in this newly generated MAP7 knockout line. While the authors present testis size and histological differences between WT and KO mice (Extended Fig. 2e and 2f), there is no corresponding description or interpretation in the main text regarding fertility outcomes.

      Response: We thank the reviewer for raising this point. Although we had presented the differences in testis size and histology between wild-type and Map7-/- mice, we agree that a description of the corresponding fertility outcomes was missing from the main text. We have now revised the relevant part of the Results section as follows: “Consistent with observations in Map7 gene-trap mice, Map7-/- males exhibited reduced testis size and spermatogenic defects (Supplemental Fig. 2E, F). Notably, the cauda epididymis of Map7-/- males contained no mature spermatozoa (Supplemental Fig. 2F), indicating male infertility.” (page 5, line 33–page 6, line 2)

      • In Figure 2C, the authors identified Sertoli cells, spermatogonia cells, and spermatocytes using SEM, based on their cell morphology and adhesion to the basement membrane. Given that the loss of MAP7 disrupts the polarity and architecture of Sertoli cells, the position of germ cells will be affected, making this identification criterion less reliable.

      Response: We appreciate the reviewer’s comment. While the reviewer notes that cell identification was based on cell morphology and adhesion to the basement membrane, we clarify that nuclear morphology was also considered, as described in the original manuscript. Specifically, germ cells have spherical nuclei, whereas Sertoli cell nuclei are irregularly shaped (representative segmentation results can be provided as an additional Supplemental Figure upon request). Round spermatids at P21 can be distinguished from spermatocytes by their smaller nuclear size. In addition, spermatogonia remain attached to the basement membrane even in Map7-/- testes, as confirmed by GFRα1-positive spermatogonial stem cells (Figure 6A). Together, these features ensure reliable identification of each cell type, independent of the altered polarity observed in Map7-deficient Sertoli cells.

      • In Figure 2e, the number of Sox9-positive Sertoli cells in MAP7 knockout mice appears higher than that in the control at P17. Quantification of total Sox9-positive cells should be done to determine whether MAP7 deletion increases Sertoli cell numbers.

      Response: As suggested by the reviewer, we will quantify the density of SOX9-positive Sertoli cells per unit area of seminiferous tubule at P10 and P17 in Map7+/- and Map7-/- testes, and include the results in the revised manuscript.

      • To determine whether MAP7's role in regulating Sertoli cell polarity relies on germ cells, the authors treated mice with busulfan at P28 to delete germ cells, a stage after Sertoli cell polarity defect has developed in MAP7 knockout mice. This data is insufficient to support the conclusion that MAP7 regulates Sertoli cell polarity independently of the presence of germ cells. Germ cell deletion should be done before the Sertoli cell defect develops to address this question.

      Response: We appreciate the reviewer’s thoughtful comment regarding the interpretation of the busulfan experiments. While depletion of germ cells at P28 enabled us to assess Sertoli cell polarity in the absence of postnatal spermatogonia, these experiments do not definitively determine whether MAP7 regulates Sertoli cell polarity independently of germ cells. Neonatal germ-cell depletion would more directly test germ cell–independent effects; however, systemic busulfan administration at early developmental stages is highly toxic, often causing bone marrow failure and multi-organ damage, which precludes survival and confounds analysis of testis-specific effects. Although germ cell ablation could, in principle, be achieved using transgenic approaches that exploit the natural resistance of mice to diphtheria toxin (DTX) (reviewed in Smith et al., Andrology, 2015), these strategies require multiple transgenes and show minor variability in efficiency, making them impractical for our current experiments. Generating the necessary genetic combinations would require considerable time. We therefore plan to pursue alternative genetic approaches in future work.

      In the revised manuscript, we have modified the relevant section to more accurately reflect the limitations of the current experiments, as follows: “Busulfan was administered at P28, and testes were analyzed 6 weeks later, after complete elimination of germ cell lineages. Following treatment, Map7+/- mice showed testis-to-body weight ratios comparable to untreated Map7-/- mice (Supplemental Fig. 3D), and hematoxylin-eosin (HE) staining confirmed germ cell depletion (Fig. 2F; Supplemental Fig. 3E). In Map7+/- testes, most Sertoli nuclei remained basally positioned, indicating that once apical–basal polarity is established, it is stably maintained even in the absence of germ cells. In contrast, Map7-/- Sertoli nuclei were frequently misoriented toward the lumen under the same conditions (Fig. 2F; Supplemental Fig. 3E), suggesting that polarity defects in Map7-deficient Sertoli cells occur independently of germ cell presence.” (page 7, lines 20–28)

      In addition, we have added the following sentences to the Discussion section to highlight the implication of these findings: “In addition, even after germ cell depletion by busulfan treatment, Map7-deficient Sertoli cells failed to reestablish basal nuclear positioning, indicating that loss of MAP7 causes an intrinsic polarity defect. These findings suggest that MAP7 acts as a cell-autonomous regulator of Sertoli cell polarity, rather than mediating effects indirectly through germ cell–Sertoli cell interactions.” (page 15, lines17–21)

      • The resolution of the SEM images in Figure 3c is insufficient to evaluate tight and adherens junctions clearly. As such, these images do not convincingly support the claim that adherens junctions are absent in the KO testes.

      Response: We thank the reviewer for this insightful comment. Tight junctions can be reliably identified in SEM images as dense intercellular structures accompanied by endoplasmic reticulum aligned along the cell boundaries. The region immediately apical to the tight junctions likely corresponds to adherens junctions, which are also associated with the endoplasmic reticulum. Unlike tight junctions, these regions exhibit wider intercellular spaces, consistent with the looser membrane apposition characteristic of adherens junctions, although they cannot be unambiguously distinguished from gap junctions or desmosomes based on morphology alone. In the original figure, 2× binning reduced image resolution, which may have contributed to the reviewer’s concern.

      In the revised manuscript, we have re-acquired the SEM images in high-resolution mode, focusing on the relevant regions. The new high-resolution images have replaced the original panels in revised Figure 3C, providing clearer visualization of junctional structures at P10 and P21 in Map7+/- and Map7-/- testes. The original Figure 3C images have been moved to Supplemental Figure 4B for reference.

      The corresponding section in the Results has been revised as follows in the updated manuscript: “We then performed SEM to examine the effects of Map7 KO. In P21 Map7+/- testes, electron-dense regions along the basal side of Sertoli–Sertoli junctions corresponded to tight junctions closely associated with the endoplasmic reticulum, consistent with previous reports (Luaces et al. 2023) (Fig. 3C; Supplemental Fig. 4B). The region immediately apical to the tight junctions likely represents adherens junctions, which were also associated with the endoplasmic reticulum. Unlike tight junctions, these regions displayed wider intercellular spaces, reflecting the looser membrane apposition typical of adherens junctions, though they could not be definitively distinguished from gap junctions or desmosomes based on morphology alone (Fig. 3C; Supplemental Fig. 4B). At P10, both Map7+/- and Map7-/- testes lacked clearly defined tight junctions and adherens junction–like structures (Fig. 3C; Supplemental Fig. 4B). In P21 Map7-/- mice, Sertoli cells formed expanded basal tight junctions but failed to establish adherens junction–like structures (Fig. 3C; Supplemental Fig. 4B).” (page 8, line 34–page 9, line 12)

      • GFP-tagged reporter mice and HeLa cells were used for immunoprecipitation-mass spectrometry to identify proteins that interact with MAP7. Given that the authors aimed to elucidate the mechanism by which MAP7 regulates Sertoli cell cytoskeleton organization, the rationale for including HeLa cells is unclear and should be better justified or reconsidered.

      Response: We thank the reviewer for this comment. MAP7-egfpKI HeLa cells were used as a complementary system to identify MAP7-associated proteins, providing sufficient material and a controlled environment for robust detection. By comparing IP-MS results from MAP7-egfpKI HeLa cells and P17–P20 Map7-egfpKI testes, we can distinguish proteins that are specific to polarized Sertoli cells: proteins detected exclusively in P17–P20 testes may be involved in Sertoli cell polarization, whereas proteins detected in both systems likely represent general MAP7-associated factors that are not specific to Sertoli cell polarity.

      This rationale has been clarified in the revised manuscript by adding the following sentence to the Results section: “MAP7-egfpKI HeLa cells were used as a complementary system, providing sufficient material and a controlled environment for robust detection of MAP7-associated proteins. Comparison of IP-MS results between MAP7-egfpKI HeLa cells and P17–P20 Map7-egfpKI testes allows identification of MAP7-associated proteins that are specific to polarized Sertoli cells, whereas proteins detected in both systems likely represent general MAP7-associated proteins.” (page 9 lines 27-32)

      • The authors observed that MYH9, one of the MAP7-interacting proteins, does not colocalize with ectopic microtubule and F-actin structures in MAP7 KO testes and concluded that MAP7 facilitates the integration of microtubules and F-actin via interaction with NMII heavy chains. This conclusion is speculative and not adequately supported by the presented data.

      Response: We thank the reviewer for this insightful comment. We agree that our initial conclusion was speculative and have revised the relevant section to more accurately reflect the limitations of the current data. The revised text now reads as follows: “These findings indicate that MYH9 localization at the luminal interface depends on MAP7, and suggest that MAP7 helps coordinate microtubules and F-actin, potentially via its association with NMII heavy chains.” (page 10, lines 13–15)

      To further elucidate this mechanism, we will perform biochemical domain-mapping to define the MAP7 region responsible for MYH9 complex formation. We have already established a series of human MAP7 deletion mutants (as reported previously, EMBO Rep., 2018) and will conduct co-immunoprecipitation assays in HEK293 cells to identify the specific MAP7 domain required for complex formation with MYH9. Based on these results, we plan to use AlphaFold3 to predict the three-dimensional structure of the MAP7–MYH9 complex. These analyses will help clarify how MAP7 associates with the actomyosin network and provide additional mechanistic insights that complement our in vivo observations of MYH9 mislocalization in Map7-/- testes.

      • The authors used Spearman correlation coefficients to analyze six Sertoli cell clusters and generated a minimum spanning tree to infer differentiation trajectories. However, details on the method used for constructing the tree are lacking. Moreover, relying solely on Spearman correlation to define differentiation topology is oversimplified.

      Response: We appreciate the reviewer’s valuable feedback. We agree that Spearman correlation alone is insufficient to infer differentiation topology. In response, we reanalyzed the data using Monocle3, which implements branch-aware pseudotime inference to capture both cluster continuity and differentiation directionality. This reanalysis provides a more accurate reconstruction of differentiation trajectories among the six Sertoli cell clusters. Although the overall trajectories appeared different and a higher proportion of Map7-/- Sertoli cells exhibited very low pseudotime values, comparison of the control and Map7-/- trajectories revealed that the average node degree was nearly identical, indicating that the local graph structure—reflecting the connectivity among neighboring cells—was largely preserved. The numbers of branch points and the graph diameter differed slightly, likely due to differences in sample size (311 control vs. 434 Map7-/- Sertoli cells) and distribution bias rather than major topological changes. Accordingly, Figures 5C and 5D have been replaced with the updated Monocle3-based trajectory analysis, and the corresponding text in the Results section and figure legend have been revised as follows:

      “To reconstruct differentiation trajectories among the six Sertoli cell clusters, we reanalyzed the datasets using Monocle3, which incorporates branch-aware pseudotime inference. Cluster C1 was selected as the root based on shared specificity and entropy scores, consistent with its metabolically active and transcriptionally diverse profile (Fig. 5B, C; Supplemental Fig. 7). While the overall trajectories appeared altered, the proportion of Map7-/- Sertoli cells with very low pseudotime values was only modestly increased (Fig. 5D). Comparison with controls showed that the average node degree was nearly identical (Fig. 5C), indicating that the local graph structure, reflecting connectivity among neighboring cells, remained largely intact. Minor differences in branch points and graph diameter likely reflect inherent variability in the data rather than major topological changes (Supplemental Fig. 6B). Consistent with this, the relative proportions of the six clusters showed only modest shifts, suggesting that the overall architecture of Sertoli cell differentiation is largely preserved in the absence of MAP7.” (page 11, lines 7-18)

      “(C) Control and Map7-/- Sertoli cells were visualized separately using UMAPs constructed in Seurat. Using the same datasets, pseudotime trajectories were inferred with Monocle3. For root selection, shared_score (cluster overlap), specificity_score (cluster uniqueness), and entropy_score (transcriptional diversity) were computed, resulting in cluster 1 being selected as the root. The numbers of nodes, edges, branch points, average degree, and diameter of each trajectory are shown below the corresponding UMAPs. (D) Parallel comparison of pseudotime distributions between control and Map7-/- populations.” (page 30, lines 5-12)

      Minor comments:

      • Several extended data figures are redundant with main figures and do not provide additional value (e.g., Fig. 2d vs. Extended Data Fig. 3a; Fig. 2f vs. Extended Data Fig. 3d; Fig. 2C vs. Extended Data Fig. 4b; Fig. 3d vs. Extended Data Fig. 4c). The authors should consolidate or remove duplicates.

      Response: Regarding the concerns about redundancy between main and Supplemental figures, we would like to clarify the rationale for retaining certain Supplemental figures.

      Fig. 2D vs. Supplemental Fig. 3A: Due to space limitations in the main figure, only the merged three-color image was shown. We believe that the single-color grayscale images in Supplemental Fig. 3A provide additional clarity, allowing easier visualization of SOX9-positive Sertoli cell distribution and differences in F-actin structure.

      Fig. 2F vs. Supplemental Fig. 3E: In the main figure, only the high-magnification image was shown due to space constraints. The lower-magnification image in Supplemental Fig. 3E demonstrates that the selected field was not chosen arbitrarily, providing context for the observed structures. In addition, Supplemental Fig. 3E includes both low- and high-magnification images of age-matched busulfan (-) testes as a control for the busulfan (+) condition, further supporting the validity of the comparison.

      For the above-mentioned cases (Fig. 2D vs. Supplemental. 3A; Fig. 2F vs. Supplemental Fig. 3E), as well as other potentially overlapping figures (e.g., Fig. 3D vs. Supplemental Fig. 4C), we believe that the additional single-channel and lower-magnification images provide important context that cannot be fully conveyed in the main figures due to space limitations. Nevertheless, to address the reviewer’s concern, we will (i) clearly state the purpose of each Supplemental figure in the corresponding legends, and (ii) re-evaluate all figures to consolidate or remove any truly redundant panels. Our goal is to ensure that all figures collectively convey the data in the most concise and informative manner.

      • Figure citations in the main text do not consistently match figure content. For example, on page 7 (lines 5-6), the text refers to Extended Data Fig. 4a for SOX9 staining. Yet, it is the extended Data Fig. 3a that contains the relevant data. Similarly, the reference to Extended Data Fig. 4b and 4c on page 7 (lines 7-8) for adult defects is inaccurate.

      Response: We thank the reviewer for drawing attention to these inconsistencies. We have carefully checked all figure citations throughout the main text and corrected them so that they consistently match the figure content. The revised manuscript reflects these corrections.

      • In Figure 2e, percentages of Sertoli cells across three layers are shown. The figure legend should specify which layer(s) show statistically significant differences between WT and KO.

      Response: We are grateful to the reviewer for highlighting this point. Statistical comparisons were performed between Map7+/- and Map7-/- mice within each corresponding layer at P17. Statistical significance was assessed using Student’s t-test, and all three layers showed significant differences between Map7+/- and Map7-/- (P < 2.20 × 10⁻⁴). The figure legend has been revised accordingly as follows: “Statistical comparisons between Map7+/- and Map7-/- mice were performed for each corresponding layer at P17 using Student’s t-test. All three layers showed significant differences between Map7+/- and Map7-/- mice (*, P<2.20 × 10⁻⁴).” (page 28, lines 5-8)

      • The current color scheme for F-actin and TUBB3 in Figure 3 lacks sufficient contrast. Adjusting to more distinguishable colors would improve readability.

      Response: Response: We thank the reviewer for this helpful suggestion. In the original merged images, four channels (DNA, TUBB3, F-actin, and β-catenin) were displayed together, which reduced contrast between cytoskeletal signals. To improve clarity, we generated new merged images showing only TUBB3 and F-actin, allowing better visual distinction between these components. In addition, β-catenin and DNA are now displayed together as a separate merged image (β-catenin in yellow and DNA in blue) in the final column, highlighting the altered localization of β-catenin in Map7-/- testes.

      • Since multiple scale bars with different units are present within the same figures, adding units directly above or beside each scale bar would improve readability.

      Response: We thank the reviewer for the suggestion. Following this recommendation, we have added units directly above each scale bar in all figures to improve readability.

      • It is recommended to directly mark Sertoli cells, spermatogonia, and spermatocytes on the SEM images in Figure 2C for clearer visualization.

      Response: We thank the reviewer for the suggestion. We will follow this recommendation by performing segmentation and directly marking Sertoli cells, spermatogonia, and spermatocytes on the SEM images in Figure 2C to improve visualization.

      • The quantification of Sertoli cell positioning shown in Fig. 2C is already described in the main text and is unnecessary in the figure.

      Response: We appreciate the reviewer’s comment regarding the quantification of Sertoli cell positioning. Although the results are described in the main text, we believe that the visual presentation in Figure 2C is essential for conveying the spatial distribution pattern in an intuitive and comparative manner. To address the concern about redundancy, we have slightly revised the figure legend (page 27, lines 28–29) to clarify that this panel provides a visual summary of the quantitative data described in the text, thereby improving clarity without unnecessary duplication.

      _Referee cross-commenting_

      I concur with Reviewer 2 that the Map7-eGFP mouse model is a valuable tool for the research community. I also agree that performing MAP7-MYH9 double immunofluorescence staining to demonstrate their colocalization would further strengthen the authors' conclusions regarding their interaction. My overall assessment of the manuscript remains unchanged: the study represents an incremental advance that extends previous findings on MAP7 function but provides limited new mechanistic insight.

      Reviewer #1 (Significance):

      This study investigates the role of the microtubule-associated protein MAP7 in Sertoli cell polarity and apical domain formation during early stages of spermatogenesis. Using GFP-tagged and MAP7 knockout mouse models, the authors show that MAP7 localizes to apical microtubules and is required for Sertoli cell cytoskeletal organization and germ cell development. While the study identifies early Sertoli cell defects and candidate MAP7-interacting proteins, the mechanistic insights remain limited, and several conclusions require stronger experimental support. Overall, the discovery represents an incremental advance that extends prior findings on MAP7 function, providing additional but modest insights into the role of MAP7 in cytoskeletal regulation in male reproduction.

      Response: We thank the reviewer for their constructive comments and thoughtful evaluation of our manuscript. We appreciate the positive feedback regarding the value of the Map7-egfpKI mouse model for the research community. We also thank the reviewer for the suggestion to perform MAP7–MYH9 double immunofluorescence staining to demonstrate colocalization, which we agree will further strengthen the mechanistic support.

      We would like to clarify that several aspects of our findings represent novel contributions within a field where the mechanisms of microtubule remodeling during apical domain formation have remained largely unresolved. In particular, our study provides evidence that MAP7 is asymmetrically enriched at the apical microtubule network in Sertoli cells and contributes to the directional organization of these microtubules—an aspect of Sertoli cell polarity that has not been previously characterized. Our results further indicate that dynamic microtubule turnover, rather than stabilization alone, is required for proper apical domain formation, addressing a gap in current understanding of how microtubules are reorganized during early polarity establishment. In addition, the data support a role for MAP7 in coordinating microtubule and actomyosin organization, suggesting a scaffolding function that links these cytoskeletal systems. We also observe that Sertoli cell polarity can be functionally separated from cell identity and that disruptions in apical domain architecture precede delays in germ cell developmental progression. Taken together, these observations provide mechanistic insight that expands upon previous studies of MAP7 function at the cellular level.

      The conclusions are supported by multiple, complementary lines of evidence, including knockout and Map7-egfpKI mouse models, high-resolution electron microscopy, immunoprecipitation–mass spectrometry, and single-cell RNA sequencing. While we agree that further experiments, such as MAP7–MYH9 double staining, will strengthen the mechanistic framework, we will also perform complementary biochemical analyses to provide additional insight. Specifically, we plan to conduct domain-mapping experiments to identify the MAP7 region required for MYH9 complex formation, coupled with co-immunoprecipitation assays in cultured cells to validate this association.

      Although generating new mutant mouse lines is not feasible within the scope of this revision, and no in vitro system fully recapitulates Sertoli cell polarization, these complementary approaches will provide further mechanistic support. We believe that these planned experiments, together with the current dataset, will clarify the underlying mechanisms and reinforce the significance of our findings, while appropriately acknowledging the current limits of experimental evidence.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In this manuscript the authors evaluate the role of Microtubule Associated Protein 7 (MAP7) in postnatal Sertoli cell development. The authors build two novel transgenic mouse lines (Map7-eGFP, Map7 knockout) which will be useful tools to the community. The transgenic mouse lines are used in paired advanced sequencing experiments and advanced imaging experiments to determine how Sertoli cell MAP7 is involved in the first wave of spermatogenesis. The authors identify MAP7 as an important regulator of Sertoli cell polarity and junction formation with loss of MAP7 disrupting intracellular microtubule and F-actin arrangement and Sertoli cell morphology. These structural issues impact the first wave of spermatogenesis causing a meiotic delay that limits round spermatid numbers. The authors also identify possible binding partners for MAP7, key among those MYH9.

      The authors did a great job building a complex multi-modal project that addressed the question of MAP7 function from many angles. The is an excellent balance of using many advanced methods while still keeping the project narrowed, to use only tools to address the real questions. The lack of quality testing on the germ cells outside of TUNEL is disappointing, but the Conclusion section implies that this sort of work is being done currently so the omission in this manuscript is acceptable. However, there is an issue with the imaging portion of the work on MYH9. The conclusions from the MYH9 data is currently overstated, super-resolution imaging of Map7 knockouts with microtubule and F-actin stains, and imaging that uses MYH9 with either Map7-eGFP or anti-MAP7 are also needed to both support the MAP7-MYH9 interaction normally and lack of interaction with failure of MYH9 to localize to microtubules and F-actin in knockouts. Since a Leica SP8 was used for the imaging, using either Leica LIGHTNING or just higher magnification will likely be the easiest solution.

      Response: We sincerely appreciate the reviewer’s thorough and positive evaluation of our study. We are encouraged that the reviewer recognized the overall strength of our multi-modal approach and the scientific value of the Map7-egfp knock-in and Map7 knockout genome-edited mouse models that we generated. We also thank the reviewer for highlighting the balance between methodological breadth and focused, hypothesis-driven investigation in our work.

      Regarding the reviewer’s valuable comments on the imaging data, we have addressed them as follows. We improved the cytoskeletal imaging data as described in response to the reviewer’s minor comments. Specifically, in the revised Figure 3B, we replaced the original images with higher-resolution confocal images to provide a clearer view of cytoskeletal organization. In addition, following Reviewer #1’s suggestion, we modified the panel layout to enlarge each field and enhance the contrast between TUBB3 and F-actin channels, allowing better visualization of their altered localization in Map7-/- testes.

      We agree that super-resolution imaging comparing control and Map7-/- testes stained for TUBB3 and F-actin would further strengthen the analysis. If the current resolution is still considered insufficient, we plan to perform additional imaging using a Carl Zeiss Airyscan or Leica Stellaris 5 system to further improve spatial resolution and confirm the observed cytoskeletal phenotypes. Finally, we will perform co-imaging of MYH9 with MAP7 to validate their spatial relationship under normal conditions, complementing the existing data obtained from Map7-/- testes.

      This manuscript is nicely organized with almost all of the results spelled out very clearly and almost always paired with figures that make compelling and convincing support for the conclusions. There are minor revision suggestions for improving the manuscript listed below. These include synching up Figure and Supplemental Figure reference mismatches. There are also many minor, but important, details that need to be added to the Methods section including many catalog numbers and some references.

      - Some of the imaging, especially Fig4F could benefit and be more convincing with super-resolution imaging in the 150nm range (SIM, Airyscan, LIGHTNING, SoRa) possibly even just imaging with a higher magnification objective (60x or 100x)

      Response: We appreciate the reviewer’s suggestion to improve the resolution of the imaging data. In addition to revising Figure 3B as described above, we have also replaced the images in Figure 4F with higher-resolution confocal images to provide a clearer view of MYH9 localization relative to microtubules and F-actin. These revised images highlight that MYH9 specifically accumulates at apical regions where microtubules and F-actin intersect, forming the apical ES, but is not localized to the basal ES-associated F-actin structures. To retain spatial context and allow readers to appreciate the overall distribution pattern, the original lower-magnification images from Figure 4F have been moved to Supplemental Figure 5.

      - SuppFig1D: Please add context in the legend to the meaning of the Yellow Stars and "O->U" labels. The latter would seem to be to indicate the Ovarian and Uterine sides of the image

      Response: In response to this comment, we revised the figure legend to clarify the annotations. The legend now states: “O, ovary side; U, uterus side. Asterisks indicate secretory cells that lack planar cell polarity.”

      - Pg6Line7: up to P23 or up to P35?

      Response: We appreciate the reviewer’s attention to this detail. The text has been revised for clarity as follows: “To examine the temporal dynamics of Sertoli cell polarity establishment, we analyzed seminiferous tubule morphology across the first wave of spermatogenesis, from postnatal day (P)10 to P35. To specifically assess the role of MAP7 in Sertoli cells while minimizing contributions from germ cells, our analysis focused on stages up to P23, before MAP7 expression becomes detectable in step 9–11 spermatids (Fig. 1), to exclude potential secondary effects resulting from MAP7 loss in germ cells.” (page 6, lines 5-10)

      - SuppFig4B: Does SuppFig4B reference back to Fig3B or Fig3C? If the latter please update this in the legend.

      - Pg7Line21-23: Is SuppFig3D,E meant to be referenced and not SuppFig5A,B?

      - Pg8Line22-25: Is SuppFig4A meant to be reference and not SuppFig5?

      - Pg8Line34-Pg9Line: Is SuppFig4B meant to be reference and not SuppFig5B?

      Response: We appreciate the reviewer’s careful reading. All mismatches in Supplemental figure references have been corrected, ensuring that each reference in the text now accurately corresponds to the appropriate data.

      - Pg9Line28-33: Would the authors be willing to rework this figure to include images that more closely match the reported findings? The current version does not strongly support the idea that MYH9 fails to localize to microtubule and F-actin domains in Map7 knockout P17 seminiferous tubules. This could also just be a matter of acquiring these images at a higher magnification or with a lower-end (150nm range) super-resolution system (SIM, Airyscan, LIGHTNING, SoRa etc)

      Response: Following the reviewer’s recommendation, we replaced the images in Figure 4F with higher-resolution confocal images to better visualize MYH9 localization relative to microtubules and F-actin in Map7+/- and Map7-/- testes. These revised images demonstrate that MYH9 specifically accumulates at apical regions where microtubules and F-actin intersect, but not at the basal ES-associated F-actin structures. To preserve spatial context, the original low-magnification images have been moved to Supplemental Figure 5. If additional resolution is required, we are prepared to acquire further images using an Airyscan or Stellaris 5 system.

      - SuppFig7A: The legend notes these are P23 samples but the image label says 8W. Please update this to whichever is the correct age.

      Response: We thank the reviewer for pointing out this discrepancy. The figure legend for Supplemental Figure 7A (now revised as Supplemental Figure 8A) has been corrected to indicate that the samples are from 8-week-old mice, consistent with the image label.

      - Pg16Line4-5: Please include in the text the vendor and catalog number for the C57BL/6 mice

      Response: The text now specifies: “C57BL/6NJcl mice were purchased from CLEA Japan (Tokyo, Japan)” (page 17, line 4). CLEA Japan does not assign catalog numbers to mouse strains.

      - Pg16Line18-19: Please include in the text the catalog number for the DMEM

      - Pg16Line19-20: Please include in the text the vendor and catalog number for the FBS

      - Pg16Line20: Please include in the text the vendor and catalog number for the Pen-Strep

      Response: We have added vendor and catalog information as follows: “Wild-type and MAP7-EGFPKI HeLa cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, 043-30085; Fujifilm Wako Pure Chemical, Osaka, Japan) supplemented with 10% fetal bovine serum (FBS, 35-015-CV; Corning, Corning, NY, USA) and penicillin–streptomycin (26253-84; Nacalai, Kyoto, Japan) at 37 °C in a humidified atmosphere containing 5% CO₂ 18.” (page 17, lines 18-22)

      - Pg17Line6-12: Thank you for including organized and detailed information about the primers, please also define the PCR protocol used including temperatures, timing, and cycles for Map7 knockout genotyping

      - Pg17Line20-27: Thank you for including organized and detailed information about the primers, please also define the PCR protocol used including temperatures, timing, and cycles for Map7-eGFP genotyping

      Response: The text has been updated to include the PCR conditions used for genotyping as follows: “Genotyping PCR was routinely performed as follows. Genomic DNA was prepared by incubating a small piece of the cut toe in 180 µL of 50 mM NaOH at 95 °C for 15 min, followed by neutralization with 20 µL of 1 M Tris-HCl (pH 8.0). After centrifugation for 20 min, 1 µL of the resulting DNA solution was used as the PCR template. Each reaction (8 µL total volume) contained 4 µL of Quick Taq HS DyeMix (DTM-101; Toyobo, Osaka, Japan) and a primer mix. PCR cycling conditions were as follows: 94 °C for 2 min; 35 cycles of 94 °C for 30 s, 65 °C for 30 s, and 72 °C for 1 min; followed by a final extension at 72 °C for 2 min and a hold at 4 °C. PCR products were analyzed using agarose gel electrophoresis. This protocol was also applied to other mouse lines and alleles generated in this study.” (page 18, lines 17–25)

      - Pg17Line30: Please include in the text the vendor and catalog number for the Laemmli sample buffer

      Response: We clarified that the buffer was prepared in-house.

      - Pg17Line32&SuppTable1: Thank you for including an organized and detailed table for the primary antibodies used, please also make either a similar table or expand the current table to include secondary antibody information

      - Pg17Line32: Please note in the text which primary antibodies and secondary antibodies from Supp Table 1

      Response: Supplementary Table 1 has been updated to include both primary and HRP-conjugated secondary antibodies. In the Immunoblotting section of the Materials and Methods, we specified the antibodies used: “The following primary antibodies were used: mouse anti-Actin (C4, 0869100-CF; MP Biomedicals, Irvine, CA, USA), mouse anti-Clathrin heavy chain (610500; BD Biosciences, Franklin Lakes, NJ, USA), rat anti-GFP (GF090R; Nacalai, 04404-84), rabbit anti-MAP7 (SAB1408648; Sigma-Aldrich, St. Louis, MO, USA), rabbit anti-MAP7 (C2C3, GTX120907; GeneTex, Irvine, CA, USA), and mouse anti-α-tubulin (DM1A, T6199; Sigma-Aldrich). Corresponding HRP-conjugated secondary antibodies were used for detection: goat anti-mouse IgG (12-349; Sigma-Aldrich), goat anti-rabbit IgG (12-348; Sigma-Aldrich), and goat anti-rat IgG (AP136P; Sigma-Aldrich). Detailed information for all primary and secondary antibodies is provided in Supplementary Table 1.” (page 19, lines 14-22)

      - Pg18Line2: Please include in the text the vendor and catalog number for the Bouin's

      Response: The text has been updated to indicate that Bouin’s solution was prepared in-house

      - Pg18Line3: Please include in the text the catalog number for the CREST-coated glass slides

      - Pg18Line7: Please include in the text the catalog number for the OCT compound

      - Pg18Line11: Please include in the text the vendor and catalog number for the Donkey Serum

      - Pg18Line11: Please include in the text the vendor and catalog number for the Goat Serum

      Response: The text now includes vendor and catalog information for all these reagents, including CREST-coated slides (SCRE-01; Matsunami Glass, Osaka, Japan), OCT compound (4583; Sakura Finetechnical, Tokyo, Japan), donkey serum (017-000-121; Jackson ImmunoResearch Laboratories, PA, USA), and goat serum (005-000-121; Jackson ImmunoResearch Laboratories).

      - Pg18Line13: Thank you for including an organized and detailed table for the primary antibodies used, please also make either a similar table or expand the current table to include secondary antibody information

      Response: We thank the reviewer for the suggestion. Supplementary Table 1 already includes information for the antibodies used for immunoblotting, and we have now added information for the Alexa Fluor-conjugated secondary antibodies used for immunofluorescence in this study.

      - Pg18Line18: Please include in the text the vendor and catalog number for the DAPI

      Response: The text has been updated to include the vendor and catalog number for DAPI (D9542; Sigma-Aldrich).

      - Pg18Line19: Please also include information about the objectives used including catalog numbers, detectors used (PMT vs HyD)

      Response: We thank the reviewer for the suggestion. The following information has been added to the Histological analysis section in Materials and Methods: “Objectives used were HC PL APO 40×/1.30 OIL CS2 (11506428; Leica) and HC PL APO 63×/1.40 OIL CS2 (11506350; Leica), with digital zoom applied as needed for high-magnification imaging. DAPI was detected using PMT detectors, while Alexa Fluor 488, 594, and 647 signals were captured using HyD detectors. Images were acquired in sequential mode with detector settings adjusted to prevent signal bleed-through.” (page 20, lines 13-17)

      - Pg18Line23: Please cite in the text the reference paper for Fiji (Schindelin et al. 2012 Nature Methods PMID: 22743772) and note the version of Fiji used

      - Pg18Line24: Please note the version of Aivia used

      Response: We have revised the text accordingly by citing the reference paper for Fiji (Schindelin et al., 2012, Nature Methods, PMID: 22743772) and noting the version used (v.2.16/1.54p). In addition, we have added the version of Aivia used in this study (version 14.1).

      - Pg18Line25: If possible, please use a more robust and reliable system than Microsoft Excel to do statistics (Graphpad Prism, Stata, R, etc), if this is not possible please note the version of Microsoft Excel used

      Response: We appreciate the reviewer’s suggestion. For basic statistical analyses such as the Student’s t-test, we used Microsoft Excel (Microsoft Office LTSC Professional Plus 2021), which has been sufficient for these standard calculations. For more advanced analyses, including ANOVA and single-cell RNA-seq analyses, we used R. These details have now been added to the text.

      - Pg18Line25: Please cite in the text the reference paper for R (R Core Team 2021 R Foundation for Statistical Computing "R: A Language and Environment for Statistical Computing") and note the version of R used

      - Pg18Line25: Please note the specific R package with version used to do ANOVA, and cite in the text the reference for this package

      Response: We have cited the reference for R (R Core Team, 2021. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria) and noted the version used (version 4.4.0) in the text. In addition, regarding ANOVA, we have added the following description: “For ANOVA analysis, linear models were fitted using the base stats package (lm function), and analysis of variance was conducted with the anova function.” (page 20, lines 23-25)

      - Pg18Line25: Please clarify, was a R package called "AVNOVA" used to do ANOVA or is this a typo?

      Response: We thank the reviewer for pointing this out. It was a typographical error — the correct term is “ANOVA”. The text has been corrected accordingly.

      - Pg18Line32: Please include in the text the catalog number for the EPON 812 Resin

      - Pg19Line3: Please include the version number for Stacker Neo

      - Pg19Line5: Please include the vendor and version number for Amira 2022

      - Pg19Line5: Please include the version number for Microscopy Image Browser

      - Pg19Line5: Please include the version number for MATLAB that was used to run Microscopy Image Browser

      Response: We added the catalog number for the EPON 812 resin and the vendor and version information for the software used. The following details have been included in the revised text:

      EPON 812 resin: TAAB Embedding Resin Kit with DMP-30 (T004; TAAB Laboratory and Microscopy, Berks, UK)

      Stacker Neo: version 3.5.3.0; JEOL

      Amira 2022: version 2022.1; Thermo Fisher Scientific

      Microscopy Image Browser: version 2.91

      Note that although Microscopy Image Browser is written in MATLAB, we used the standalone version that does not require a separate MATLAB installation.

      - Pg19Line: 9-10: Please include in the text the catalog number for the complete protease inhibitor

      - Pg19Line14: Please include in the text the catalog number for the Magnetic Agarose Beads

      - Pg19Line16: Please include in the text the catalog number for the GFP-Trap Magnetic Agarose Beads

      Response: We have added the catalog numbers for the complete protease inhibitor (4693116001), control magnetic agarose beads (bmab), and GFP-Trap magnetic agarose beads (gtma).

      - Pg19Line21: Please note in the text which primary antibodies and secondary antibodies from Supp Table 1

      - Pg19Line21-22: Please include in the text the catalog number for the ECL Prime

      Response: We thank the reviewer for the helpful suggestions. The description regarding immunoblotting (“Eluted samples were separated by SDS–PAGE, transferred to PVDF membranes…”) was reorganized: overlapping content has been removed, and the necessary information has been integrated into the “Immunoblotting” section, where details of the primary and secondary antibodies (listed in Supplementary Table 1) are already provided. In addition, the information for ECL Prime has been updated to “Amersham ECL Prime (RPN2236; Cytiva, Tokyo, Japan)”.

      - Pg20Line2: Please include the version number for Xcalibur

      Response: The version of Xcalibur used in this study (version 4.0.27.19) has been added to the text.

      - Pg20Line5: Please cite in the text the reference paper for SWISS-PROT (Bairoch and Apweiler 1999 Nucleic Acid Research PMID: 9847139)

      Response: The reference paper for SWISS-PROT (Bairoch and Apweiler, 1999, Nucleic Acids Research, PMID: 9847139) has been cited in the text.

      - Pg19Line26: Please include in the text the catalog number for the NuPAGE gels

      - Pg19Line28: Please include in the text the catalog number for the SimpleBlue SafeStain

      Response: Both catalog numbers have been added in the Mass spectrometry section as follows: 4–12% NuPAGE gels (NP0321PK2; Thermo Fisher Scientific) and SimplyBlue SafeStain (LC6060; Thermo Fisher Scientific).

      - Pg20Line26: Please include in the text the catalog number for the Chromium Singel Cell 3' Reagent Kits v3

      Response: The catalog number for the Chromium Single Cell 3′ Reagent Kits v3 (PN-1000075; 10x Genomics) has been added to the text.

      - Pg21Line3: Please cite in the text the reference paper for R (R Core Team 2021 R Foundation for Statistical Computing "R: A Language and Environment for Statistical Computing")

      Response: The reference for R (R Core Team, 2021. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria) has already been cited in the “Histological analysis” section, where ANOVA analysis is described.

      - Pg21Line3 Please cite in the text the reference for RStudio (Posit team (2025). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.)

      Response: The reference for RStudio (Posit team, 2025. RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA, USA. URL: http://www.posit.co/) has been added to the text.

      - Pg21Line23: Please include the version number for Metascape

      Response: The version of Metascape used in this study (v3.5.20250701) has been added to the text.

      - SuppFig12: please update the legend to include a description after the title and update the figure labeling to correspond to the legend. Also, this figure is currently not referenced anywhere in the text.

      Response: We have updated the legend for Supplemental Figure 12 (Supplemental Figure 13) to include a descriptive sentence after the title and have adjusted the figure labeling to match the legend. The revised legend now reads: “Full-scan images of the agarose gels shown in Supplemental Figs. 1B and 2C are displayed in the upper and lower left panels, respectively, while the corresponding full-scan images of the immunoblots shown in Supplemental Figs. 1C and 2D are presented in the upper and lower right panels, respectively.”

      As these images serve as source data, they are not referenced directly in the main text.

      _Referee cross-commenting_

      I generally agree with Reviewer 1 and specifically concur related to adding details about fertility assessment of the Map7 Knockout line, and enhancing the SEM imaging.

      Response: As noted in our response to Reviewer #1, we have re-acquired the SEM images in high-resolution mode, focusing on the relevant regions. The new high-resolution images have replaced the original panels in revised Figure 3C, providing clearer visualization of junctional structures at P10 and P21 in Map7+/- and Map7-/- testes. The original Figure 3C images have been moved to Supplemental Figure 4B for reference.

      Reviewer #2 (Significance):

      There are mouse lines, and datasets that will be useful resources to the field. This work also advances our understanding of a period in Sertoli cell development that is critical to fertility but very understudied.

      Response: We thank the reviewer for the positive comments and for recognizing the potential value of our mouse lines and datasets to the field, as well as the significance of our work in advancing the understanding of this critical but understudied period in Sertoli cell development.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript the authors evaluate the role of Microtubule Associated Protein 7 (MAP7) in postnatal Sertoli cell development. The authors build two novel transgenic mouse lines (Map7-eGFP, Map7 knockout) which will be useful tools to the community. The transgenic mouse lines are used in paired advanced sequencing experiments and advanced imaging experiments to determine how Sertoli cell MAP7 is involved in the first wave of spermatogenesis. The authors identify MAP7 as an important regulator of Sertoli cell polarity and junction formation with loss of MAP7 disrupting intracellular microtubule and F-actin arrangement and Sertoli cell morphology. These structural issues impact the first wave of spermatogenesis causing a meiotic delay that limits round spermatid numbers. The authors also identify possible binding partners for MAP7, key among those MYH9.

      The authors did a great job building a complex multi-modal project that addressed the question of MAP7 function from many angles. The is an excellent balance of using many advanced methods while still keeping the project narrowed, to use only tools to address the real questions. The lack of quality testing on the germ cells outside of TUNEL is disappointing, but the Conclusion section implies that this sort of work is being done currently so the omission in this manuscript is acceptable. However, there is an issue with the imaging portion of the work on MYH9. The conclusions from the MYH9 data is currently overstated, super-resolution imaging of Map7 knockouts with microtubule and F-actin stains, and imaging that uses MYH9 with either Map7-eGFP or anti-MAP7 are also needed to both support the MAP7-MYH9 interaction normally and lack of interaction with failure of MYH9 to localize to microtubules and F-actin in knockouts. Since a Leica SP8 was used for the imaging, using either Leica LIGHTNING or just higher magnification will likely be the easiest solution.

      This manuscript is nicely organized with almost all of the results spelled out very clearly and almost always paired with figures that make compelling and convincing support for the conclusions. There are minor revision suggestions for improving the manuscript listed below. These include synching up Figure and Supplemental Figure reference mismatches. There are also many minor, but important, details that need to be added to the Methods section including many catalog numbers and some references.

      • Some of the imaging, especially Fig4F could benefit and be more convincing with super-resolution imaging in the 150nm range (SIM, Airyscan, LIGHTNING, SoRa) possibly even just imaging with a higher magnification objective (60x or 100x)
      • SuppFig1D: Please add context in the legend to the meaning of the Yellow Stars and "O->U" labels. The latter would seem to be to indicate the Ovarian and Uterine sides of the image
      • Pg6Line7: ¿up to P23 or up to P35?
      • SuppFig4B: ¿Does SuppFig4B reference back to Fig3B or Fig3C? If the latter please update this in the legend.
      • Pg7Line21-23: ¿Is SuppFig3D,E meant to be referenced and not SuppFig5A,B?
      • Pg8Line22-25: ¿Is SuppFig4A meant to be reference and not SuppFig5?
      • Pg8Line34-Pg9Line: ¿Is SuppFig4B meant to be reference and not SuppFig5B?
      • Pg9Line28-33: ¿Would the authors be willing to rework this figure to include images that more closely match the reported findings? The current version does not strongly support the idea that MYH9 fails to localize to microtubule and F-actin domains in Map7 knockout P17 seminiferous tubules. This could also just be a matter of acquiring these images at a higher magnification or with a lower-end (150nm range) super-resolution system (SIM, Airyscan, LIGHTNING, SoRa etc)
      • SuppFig7A: The legend notes these are P23 samples but the image label says 8W. Please update this to whichever is the correct age.
      • Pg16Line4-5: Please include in the text the vendor and catalog number for the C57BL/6 mice
      • Pg16Line18-19: Please include in the text the catalog number for the DMEM
      • Pg16Line19-20: Please include in the text the vendor and catalog number for the FBS
      • Pg16Line20: Please include in the text the vendor and catalog number for the Pen-Strep
      • Pg17Line6-12: Thank you for including organized and detailed information about the primers, please also define the PCR protocol used including temperatures, timing, and cycles for Map7 knockout genotyping
      • Pg17Line20-27: Thank you for including organized and detailed information about the primers, please also define the PCR protocol used including temperatures, timing, and cycles for Map7-eGFP genotyping
      • Pg17Line30: Please include in the text the vendor and catalog number for the Laemmli sample buffer
      • Pg17Line32&SuppTable1: Thank you for including an organized and detailed table for the primary antibodies used, please also make either a similar table or expand the current table to include secondary antibody information
      • Pg17Line32: Please note in the text which primary antibodies and secondary antibodies from Supp Table 1
      • Pg18Line2: Please include in the text the vendor and catalog number for the Bouin's
      • Pg18Line3: Please include in the text the catalog number for the CREST-coated glass slides
      • Pg18Line7: Please include in the text the catalog number for the OCT compound
      • Pg18Line11: Please include in the text the vendor and catalog number for the Donkey Serum
      • Pg18Line11: Please include in the text the vendor and catalog number for the Goat Serum
      • Pg18Line13: Thank you for including an organized and detailed table for the primary antibodies used, please also make either a similar table or expand the current table to include secondary antibody information
      • Pg18Line18: Please include in the text the vendor and catalog number for the DAPI
      • Pg18Line19: Please also include information about the objectives used including catalog numbers, detectors used (PMT vs HyD)
      • Pg18Line23: Please cite in the text the reference paper for Fiji (Schindelin et al. 2012 Nature Methods PMID: 22743772) and note the version of Fiji used
      • Pg18Line24: Please note the version of Aivia used
      • Pg18Line25: If possible, please use a more robust and reliable system than Microsoft Excel to do statistics (Graphpad Prism, Stata, R, etc), if this is not possible please note the version of Microsoft Excel used
      • Pg18Line25: Please cite in the text the reference paper for R (R Core Team 2021 R Foundation for Statistical Computing "R: A Language and Environment for Statistical Computing") and not ethe version of R used
      • Pg18Line25: ¿Please clarify, was a R package called "AVNOVA" used to do ANOVA or is this a typo?
      • Pg18Line25: Please note the specific R package with version used to do ANOVA, and cite in the text the reference for this package
      • Pg18Line32: Please include in the text the catalog number for the EPON 812 Resin
      • Pg19Line3: Please include the version number for Stacker Neo
      • Pg19Line5: Please include the vendor and version number for Amira 2022
      • Pg19Line5: Please include the version number for Microscopy Image Browser
      • Pg19Line5: Please include the version number for MATLAB that was used to run Microscopy Image Browser
      • Pg19Line: 9-10: Please include in the text the catalog number for the complete protease inhibitor
      • Pg19Line14: Please include in the text the catalog number for the Magnetic Agarose Beads
      • Pg19Line16: Please include in the text the catalog number for the GFP-Trap Magnetic Agarose Beads
      • Pg19Line21: Please note in the text which primary antibodies and secondary antibodies from Supp Table 1
      • Pg19Line21-22: Please include in the text the catalog number for the ECL Prime
      • Pg20Line2: Please include the version number for Xcalibur
      • Pg20Line5: Please cite in the text the reference paper for SWISS-PROT (Bairoch and Apweiler 1999 Nucleic Acid Research PMID: 9847139)
      • Pg19Line26: Please include in the text the catalog number for the NuPAGE gels
      • Pg19Line28: Please include in the text the catalog number for the SimpleBlue SafeStain
      • Pg20Line26: Please include in the text the catalog number for the Chromium Singel Cell 3' Reagent Kits v3
      • Pg21Line3: Please cite in the text the reference paper for R (R Core Team 2021 R Foundation for Statistical Computing "R: A Language and Environment for Statistical Computing")
      • Pg21Line3 Please cite in the text the reference for RStudio (Posit team (2025). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.)
      • Pg21Line23: Please include the version number for Metascape
      • SuppFig12: please update the legend to include a description after the title and update the figure labeling to correspond to the legend. Also, this figure is currently not referenced anywhere in the text.

      Referee cross-commenting

      I generally agree with Reviewer 1 and specifically concur related to adding details about fertility assessment of the Map7 Knockout line, and enhancing the SEM imaging.

      Significance

      There are mouse lines, and datasets that will be useful resources to the field. This work also advances our understanding of a period in Sertoli cell development that is critical to fertility but very understudied.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      A previous study by Komada et al. demonstrated that MAP7 is expressed in both Sertoli and germ cells, and that Map7 gene-trap mutant mice display disrupted microtubule bundle formation in Sertoli cells, accompanied by defects in spermatid manchettes and germ cell loss. In the current study, Kikuchi et al. investigated the role of MAP7 in the formation of the Sertoli cell apical domain during the first wave of spermatogenesis. They generated a GFP-tagged MAP7 mouse line and demonstrated that the endogenous MAP7 protein localizes to the apical microtubules in Sertoli cells and to the manchette microtubules in step 9-11 spermatids. They also generated a new Map7 knockout (KO) mouse line in a genetic background distinct from the one used in the previous study. Focusing on stages before the emergence of step 9-11 spermatids, the authors aimed to isolate defects caused by the function of MAP7 in Sertoli cells. They report that loss of MAP7 impairs Sertoli cell polarity and apical domain formation, accompanied by the microtubule remodeling defect. Using the GFP-tagged MAP7 line, they performed immunoprecipitation-mass spectrometry and identified several MAP7-interacting proteins in the testis, including MYH9. They further observed that MAP7 deletion alters the distribution of MYH9. Single-cell RNA sequencing revealed that the loss of MAP7 in Sertoli cells resulted in slight transcriptomic shifts but had no significant impact on their functional differentiation. Single-cell RNA sequencing analysis also showed delayed meiotic progression in the MAP7-deficient testis. Overall, while the study provides some interesting discoveries of early Sertoli cell defects in MAP7-deficient testes, some conclusions are premature and not fully supported by the presented data. The mechanistic investigations remain limited in depth.

      Major comments:

      • Although the infertility phenotype of the Map7 gene-trap mutant mice has been reported previously, it remains essential to assess fertility in this newly generated MAP7 knockout line. While the authors present testis size and histological differences between WT and KO mice (Extended Fig. 2e and 2f), there is no corresponding description or interpretation in the main text regarding fertility outcomes.
      • In Figure 2C, the authors identified Sertoli cells, spermatogonia cells, and spermatocytes using SEM, based on their cell morphology and adhesion to the basement membrane. Given that the loss of MAP7 disrupts the polarity and architecture of Sertoli cells, the position of germ cells will be affected, making this identification criterion less reliable.
      • In Figure 2e, the number of Sox9-positive Sertoli cells in MAP7 knockout mice appears higher than that in the control at P17. Quantification of total Sox9-positive cells should be done to determine whether MAP7 deletion increases Sertoli cell numbers.
      • To determine whether MAP7's role in regulating Sertoli cell polarity relies on germ cells, the authors treated mice with busulfan at P28 to delete germ cells, a stage after Sertoli cell polarity defect has developed in MAP7 knockout mice. This data is insufficient to support the conclusion that MAP7 regulates Sertoli cell polarity independently of the presence of germ cells. Germ cell deletion should be done before the Sertoli cell defect develops to address this question.
      • The resolution of the SEM images in Figure 3c is insufficient to evaluate tight and adherens junctions clearly. As such, these images do not convincingly support the claim that adherens junctions are absent in the KO testes.
      • GFP-tagged reporter mice and HeLa cells were used for immunoprecipitation-mass spectrometry to identify proteins that interact with MAP7. Given that the authors aimed to elucidate the mechanism by which MAP7 regulates Sertoli cell cytoskeleton organization, the rationale for including HeLa cells is unclear and should be better justified or reconsidered.
      • The authors observed that MYH9, one of the MAP7-interacting proteins, does not colocalize with ectopic microtubule and F-actin structures in MAP7 KO testes and concluded that MAP7 facilitates the integration of microtubules and F-actin via interaction with NMII heavy chains. This conclusion is speculative and not adequately supported by the presented data.
      • The authors used Spearman correlation coefficients to analyze six Sertoli cell clusters and generated a minimum spanning tree to infer differentiation trajectories. However, details on the method used for constructing the tree are lacking. Moreover, relying solely on Spearman correlation to define differentiation topology is oversimplified.

      Minor comments:

      • Several extended data figures are redundant with main figures and do not provide additional value (e.g., Fig. 2d vs. Extended Data Fig. 3a; Fig. 2f vs. Extended Data Fig. 3d; Fig. 2C vs. Extended Data Fig. 4b; Fig. 3d vs. Extended Data Fig. 4c). The authors should consolidate or remove duplicates.
      • Figure citations in the main text do not consistently match figure content. For example, on page 7 (lines 5-6), the text refers to Extended Data Fig. 4a for SOX9 staining. Yet, it is the extended Data Fig. 3a that contains the relevant data. Similarly, the reference to Extended Data Fig. 4b and 4c on page 7 (lines 7-8) for adult defects is inaccurate.
      • In Figure 2e, percentages of Sertoli cells across three layers are shown. The figure legend should specify which layer(s) show statistically significant differences between WT and KO.
      • The current color scheme for F-actin and TUBB3 in Figure 3 lacks sufficient contrast. Adjusting to more distinguishable colors would improve readability.
      • Since multiple scale bars with different units are present within the same figures, adding units directly above or beside each scale bar would improve readability.
      • It is recommended to directly mark Sertoli cells, spermatogonia, and spermatocytes on the SEM images in Figure 2C for clearer visualization.
      • The quantification of Sertoli cell positioning shown in Fig. 2C is already described in the main text and is unnecessary in the figure.

      Referee cross-commenting

      I concur with Reviewer 2 that the Map7-eGFP mouse model is a valuable tool for the research community. I also agree that performing MAP7-MYH9 double immunofluorescence staining to demonstrate their colocalization would further strengthen the authors' conclusions regarding their interaction. My overall assessment of the manuscript remains unchanged: the study represents an incremental advance that extends previous findings on MAP7 function but provides limited new mechanistic insight.

      Significance

      This study investigates the role of the microtubule-associated protein MAP7 in Sertoli cell polarity and apical domain formation during early stages of spermatogenesis. Using GFP-tagged and MAP7 knockout mouse models, the authors show that MAP7 localizes to apical microtubules and is required for Sertoli cell cytoskeletal organization and germ cell development. While the study identifies early Sertoli cell defects and candidate MAP7-interacting proteins, the mechanistic insights remain limited, and several conclusions require stronger experimental support. Overall, the discovery represents an incremental advance that extends prior findings on MAP7 function, providing additional but modest insights into the role of MAP7 in cytoskeletal regulation in male reproduction.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary:

      The manuscript titled "Unravelling the Progression of the Zebrafish Primary Body Axis with Reconstructed Spatiotemporal Transcriptomics" presents a comprehensive analysis of the development of the primary body axis in zebrafish by integrating bulk RNA-seq, 3D images, and Stereo-Seq. The authors first clearly demonstrate the application of Palette for integrating RNA-seq and Stereo-Seq using published spatial transcriptomics data of Drosophila embryos. Subsequently, they produced serial bulk RNA-seq data for certain developmental stages of Danio rerio embryos and utilized published Stereo-Seq data. Through robust validation, the authors observe the molecular network involved in AP axis formation. While the authors show that integrating bulk RNA-seq data with Stereo-Seq improves spatial resolution, additional proof is required to demonstrate the extent of this improvement.

      Response: We thank the reviewer for the positive feedback on our Palette pipeline, zSTEP construction and analysis of primary body axis development. We appreciate the constructive suggestions provided, which we can implement to improve our manuscript. As pointed out by the reviewer, some analysis procedures were not described in sufficient detail. To address this, we have added more explanatory texts and additional schematic diagrams to make the methods clearer and more understandable. We also thank the reviewer for the meticulous reading and for reminding us to include parameters, references and essential texts, which significantly improve the manuscript quality and make the manuscript more rigorous. Furthermore, as suggested by the reviewer, the extent of the improvement on the spatial resolution was not clearly demonstrated in the manuscript. Therefore, we have provided an additional figure to show the original expression on the stacked Stereo-seq slices and 3D live image compared to the expression from zSTEP, and the results indicate that zSTEP provides better, more continuous expression patterns. We still have two remaining tasks that are expected to be completed within the next month. We hope our responses have address the concerns raised by the reviewer, and we are pleased to provide any additional proof as needed.

      Major Comments:

      1. Lines 66-68: Discuss the limitations of existing tools and explicitly state the advantages of using Palette.

      Response: We thank the reviewer for the valuable suggestion. We have added the following new texts after line 68 to emphasize the features and advantages of Palette.

      "Newly developed tools are committed to integrating bulk and/or scRNA-seq data with ST data to enhance spatial resolution, focusing on expression at the spot level. However, gene expression patterns are closely correlated to the biological functions and are more critical for understanding biological processes. Therefore, a tool focusing on inferring spatial gene expression patterns would be desirable."

      1. Body Pattern Genes Analysis: For both Drosophila and Danio rerio, it would be valuable to examine body pattern genes in Stereo-Seq and apply Palette to determine if the resolution of the segments improves or merges. The resolution of the A-P axis is convincing, but further evidence for other segments would be beneficial.

      Response: We thank the reviewer for the suggestions. For the Drosophila data, we only used two adjacent slices for Palette performance assessment, and thus were only able to evaluate the expression patterns within the slice.

      For the zebrafish data, although we have construct zSTEP as a 3D transcriptomic atlas, we have to admit that the left-right (LR) and dorsal-ventral (DV) patterning is not satisfactory enough. Here we show a section from the dorsal part of 16 hpf zSTEP that displays a relatively well-defined left-right pattern (Fig. 2). Along the left-right axis, the notochord cells are centrally located, flanked by somite cells on either side, with the outermost cells being pronephros.

      One reason for the limited LR and DV patterning is that the original annotation of the ST data does not clearly distinguish all the cell types. Another reason is likely due to the disordered cell positions when stacking ST slices. Thus, our zSTEP is most suitable for investigating the AP patterns, while the performances on LR and DV patterns may not achieve the same level of accuracy.

      See response letter for the figure.

      1. Figure 2d: Include the A-P line for which the intensity profile was plotted in the main figure, rather than just in the supplementary material. Additionally, consider simplifying the plot by not combining three lines into one, as it complicates the interpretation of observations.

      Response: We thank the reviewer for the helpful suggestions. We have updated Figure 2d and Figure S1b by adding a A-P line on each subfigure (Fig. 3). Additionally, as the reviewer suggested, we have separated the intensity plots so that each subfigure now includes a dedicated intensity plot along A-P axis.

      See response letter for the figure.

      1. Drosophila Data Analysis: While the alignment and validation of Danio rerio sections are clearly explained, the analysis and validation of Drosophila data are insufficiently detailed. Provide a more thorough explanation of how the intensity profiles between BDGP in situ data and Stereo-Seq data are adjusted.

      Response: We thank the reviewer for raising this issue. To make the analysis procedure clearer, we have updated Figure 2a (Fig. 4) and added explanatory texts in the figure legends to describe the processing procedure for the Drosophila ST data.

      See response letter for the figure.

      Additionally, the following sentences have been added into the Methods section to describe the generation of the intensity profiles.

      "The intensity plot profiles along AP axis were generated through the following steps: The expression pattern plot images or in situ hybridization images were imported into ImageJ and converted to grayscale. The colour was then inverted, and a line of a certain width (here set as 10) was drawn across from the anterior part to the posterior part (Fig. S1a). The signal intensities along the width of the line were measured and imported into R for generating intensity plots."

      1. Figure 3d: Present a plot with the expected expression profiles of the three genes if the embryo is aligned as anticipated.

      Response: We thank the reviewer for this helpful suggestion, which improves the clarity of our manuscript. We have added the following subfigure in as Figure 3d (Fig. 5) to show the expected expression profiles of the three midline genes along left-right axis.

      See response letter for the figure.

      1. Analysis Without Palette: Between lines 277-438, the outcome of using Palette with bulk RNA-seq and Stereo-Seq is convincing. However, consider the following:

      o What would be the observations if the analysis were conducted solely with Stereo-Seq data, without incorporating bulk RNA-seq data and employing Palette?

      Response: We thank the reviewer for raising this important question. Here we show the comparison of ST expression on stacked Stereo-seq slices, ST expression projected on 3D live images, and the Palette-inferred expression (Fig. 6). The stacked ST slices do not fully reflect the zebrafish morphology, and the gene expression appears sparse, making it look massive (the first row). While after projecting ST expression onto the live image, the expression patterns can be observed on zebrafish morphology, but the expression is still sparsely distributed in spots (the second row). However, the expression patterns captured by Palette in zSTEP show more continuous expression patterns (the third row), which are more similar to the observations in in situ hybridization images (the fourth row). We are considering put these analyses into the supplementary figure.

      See response letter for the figure.

      o This study uses only Stereo-Seq as the spatial transcriptomics reference. It would strengthen the argument to use at least one other spatial transcriptomics method, such as Visium or MERFISH, in conjunction with bulk RNA-seq and Palette, to demonstrate whether Palette consistently improves gene expression resolution.

      Response: We thank the reviewer for raising this professional question. To demonstrate a broad application of Palette, it would be necessary to test Palette performance using different types of ST references. We plan to perform extra analyses to evaluate Palette performance using Visium and MERFISH data as ST references, respectively. Additionally, our Palette pipeline only takes the overlapped genes for inference. As only hundreds of genes can be detected by MERFISH, Palette can only infer the expression patterns of these genes. As mentioned in the work of Liu et al. (2023), MERFISH can independently resolve distinct cell types and spatial structures, and thus we believe Palette will also show great performance when using MERFISH as ST reference. We've already started the analyses and expect to accomplish it within the next month. And we will update the analyses as separated tutorials to the GitHub repository.

      Reference:

      Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci Alliance 6 (2023).

      1. PDAC Data Analysis: Provide a more detailed explanation of the PDAC data analysis and use appropriate colors in the tissue images to clearly distinguish cell types.

      Response: We thank the reviewer for the suggestions. We have updated the colours used in the tissue images to be consistent to the colours in tissue clustering analysis. Additionally, we have added an additional subfigure in supplementary figure (Fig. 7) with more explanatory texts in the figure legends to provide a more thorough explanation for the analysis.

      See response letter for the figure.

      1. Comparison with Other Methods: State the limitations of not using STitch3D and Spateo for alignment and explain why these methods were not employed.

      Response: We thank the reviewer for raising this constructive comment. We fully agree with you that the introduction of published alignment algorithms would be helpful in our analysis. Currently, the slice alignment is adjusted manually, and thus the main limitation of not using these tools is that manual operation may induce bias compared to the alignment generated by computational algorithm. Unfortunately, STitch3D and Spateo are not included in this study because of two reasons. First, these two newly developed tools have been recently posted, and our analyses were largely completed before that. Therefore, we only mentioned these tools in the Discussion section. Second, we do not want to embed too many external tools into our analysis, which may increase the difficulties for researchers' operation. Specifically, STitch3D and Spateo are configured to run in Python environment, while Palette is based on R packages. Moreover, without these tools, our current manual alignment also achieves desired performance. However, we value this enlightening suggestion by the reviewer and therefore plan to further compare the performance of manual alignment versus the mentioned two alignment tools. At present, we have a preliminary comparison scheme and collected relevant datasets. Hopefully, we will complete this analysis within the next 1 to 2 weeks.

      Minor Comments:

      1. References: Add references to the statements in lines 51-53.

      Response: We thank the reviewer for reminding us of the missing references. We have added the works of Junker et al. (2014), Liu et al. (2022), Chen et al. (2022), Wang et al. (2022), Shi et al. (2023) and Satija et al. (2015) as references in line 53 as follows.

      "Thus, great efforts are ongoing to construct gene expression maps of these models with higher resolution, depth, and comprehensiveness1-6."

      References:

      1. Junker, J.P. et al. Genome-wide RNA Tomography in the zebrafish embryo. Cell 159, 662-675 (2014).
      2. Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell 57, 1284-1298 e1285 (2022).
      3. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777-1792 e1721 (2022).
      4. Wang, M. et al. High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae. Dev Cell 57, 1271-1283 e1274 (2022).
      5. Shi, H. et al. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 622, 552-561 (2023).
      6. Satija, R. et al. Spatial reconstruction of single-cell gene expression data. Nature biotechnology 33, 495-502 (2015)
      1. Scientific Name Consistency: Ensure consistency in using either "Danio rerio" or "zebrafish" throughout the manuscript.

      Response: We thank the reviewer for this suggestion. We have changed "Danio rerio" to "zebrafish" to make "zebrafish" consistent throughout the manuscript.

      1. Related References: Include the following relevant references:

      o https://academic.oup.com/bib/article/25/4/bbae316/7705532

      o https://www.life-science-alliance.org/content/6/1/e202201701

      Response: We thank the reviewer for bringing these two relevant works to us. Baul et al. (2024) presented STGAT leveraging Graph Attention Networks for integrating spatial transcriptomics and bulk RNA-seq, and Liu et al. (2023) demonstrated the concordance of MERFISH ST with bulk and single-cell RNA-seq. Both are excellent works and relevant to our work. We have added these two references in line 61 and line 68, respectively.

      References:

      Baul, S. et al. Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks. Brief Bioinform 25 (2024).

      Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci Alliance 6 (2023).

      1. Figure 1a: In the Venn diagram, include the number of genes in the bulk and Stereo-Seq datasets, as well as the number of overlapping genes.

      Response: We thank the reviewer reminding us to include these important numbers. And in our current manuscript, we have added the following sentences in the Methods section to provide the gene numbers (Fig. 8). While the Venn diagram in Figure 1a serves as a schematic representation, so we did not include the gene numbers, as these may vary depending on the actual data.

      "Palette was performed on the aligned slices using the overlapped genes. For the 10 hpf embryo, there were 24,658 genes in the bulk data, 18,698 genes in the Stereo-seq data, and 16,601 overlapped genes. For the 12 hpf embryo, there were 23,018 genes in the bulk data, 18,948 genes in the Stereo-seq data, and 16,401 overlapped genes. For the 16 hpf embryo, there were 24,357 genes in the bulk data, 23,110 genes in the Stereo-seq data, and 19,539 overlapped genes."

      See response letter for the figure.

      1. Figure 1 Improvement: Enlarge Figure 1 and reduce repetitive elements, such as parts of the deconvolution and Figure 1b.

      Response: We thank the reviewer for the helpful suggestion. We agree with the reviewer that the deconvolution sections appear repetitive. We have updated Figure 1 (Fig. 9) by replacing these repetitive elements with a clearer and simpler diagram.

      See response letter for the figure.

      1. Figure 3f: Explain the black discontinuous line in the plot.

      Response: We thank the reviewer for the reminder. We are sorry about the lack of the explanation. We have added the below explanation for the black discontinuous line in the legend of Figure 3 (Fig. 10) as follows.

      See response letter for the figure.

      1. Line 610: State the percentage of unpaired imaging spots.

      Response: We thank the review for the reminder. We are sorry about not including the paired and unpaired spot number. We have added the number of paired spots with the percentage in the total spots in the Method section as follows.

      "The numbers of mapped spots for the 10 hpf, 12 hpf and 16 hpf embryos are 15,379 (69.4% of the total spots), 14,697 (70.5% of the total spots) and 21,605 (77.2% of the total spots), respectively."

      1. Lines 616-618: Specify the unit for the spot diameter.

      Response: We thank the reviewer for the reminder. Again, we are sorry about not including the spot diameter information in our previous version of manuscript. We have added the spot diameter in Method section as follows.

      "In the Stereo-seq data, each spot contained 15 × 15 DNA nanoball (DNB) spots (The diameter of each spot is near 10 μm)."

      Reviewer #1 (Significance):

      This algorithm will be useful not only for the field of developmental biology but also for wider applications in spatial omics. Although I have expertise in spatial omics technology development, my understanding of computational biology is limited, which restricts my ability to fully evaluate the Palette algorithm presented in this paper.

      Response: We thank the reviewer for recognizing our work, and we greatly appreciate the constructive suggestions from the reviewer. Although the reviewer acknowledged limited expertise in computational biology, the comments from the reviewer are highly professional and valuable. Following the suggestions from the reviewer, we have not only included more explanatory texts and figures to make the analysis procedures clearer and more understandable, but also supplemented the important parameters that were missing in our previous manuscript. We also provided extra figure to demonstrate the improvements of zSTEP on gene expression patterns. We believe that our work is now more scientific and more understandable, and we will continue working to solve the remaining issues as planned. We express our thanks for the reviewer again.

      Reviewer #2 (Evidence, reproducibility and clarity):

      The authors of the study introduce the Palette method, a novel approach designed to infer spatial gene expression patterns from bulk RNA-sequencing (RNA-seq) data. This method is complemented by the development of the DreSTEP 3D spatial gene expression atlas of zebrafish embryos, establishing a comprehensive resource for visualizing gene expression and investigating spatial cell-cell interactions in developmental biology.

      Response: We sincerely appreciate the reviewer's positive feedback on our Palette pipeline and the zSTEP 3D spatial expression atlas of zebrafish embryos. We also thank the reviewer for the professional comments and constructive suggestions. The reviewer raised the concerns from the aspect of algorithm design and computational biology, which we did not address well in our previous manuscript. We agree with the reviewer that we did not clarify the selection criteria of the parameters in detail, and we are now working on the additional analyses to address this issue.

      We also agree with the reviewer that we did not provide enough discussion of the strategies used in the pipeline, the features of Palette and the application scenarios of Palette and zSTEP. For wide use of our tools, it is significantly important to state these aspects. In this revised version, we have added more paragraphs in the Discussion section to address this issue. Additionally, we acknowledge that we did not adequately demonstrate the computational efficacy and computational requirements, which are important for researchers. We are also working on the additional analyses to address this issue.

      Finally, we thank the reviewer again for the professional and constructive suggestions. These suggestions are addressable, and by following them, we believe our manuscript will see a significant improvement, especially in the Palette pipeline part, making the pipeline more rigorous and easier to access. We are confident that we can complete the planned additional tasks within the next 1-2 months.

      1. The efficacy of the Palette method may be compromised by its dependency on the quality of the reference spatial transcriptomics data. As highlighted in the study, variations in data quality can lead to significant challenges in reconstructing accurate spatial expression patterns from bulk data. This underscores the necessity of evaluating quality parameters, such as the number of gene detections and spatial resolution, to ensure reliable outcomes. Additional studies should rigorously assess how these quality factors influence the accuracy and efficiency of the algorithm in various data contexts, particularly under diverse conditions of gene detection.

      Response: We thank the reviewer for this valuable suggestion. We agree with the reviewer that the quality of the reference ST data may greatly influence the performance and efficacy of the Palette, and we have added paragraphs in the Discussion section to further discuss the impact of ST data quality on Palette performance. As mentioned by the reviewer, gene detections and spatial resolution are two important parameters that can influence the Palette performance. Low gene detection may impact the clustering process, making the cell types of spots not distinguished well. To evaluate the performance of Palette when ST data shows low gene detection, we plan to applied Palette using MERFISH data as the ST reference, which only captures hundreds of genes. Moreover, we will also investigate the impact of spatial resolution on Palette performance by merging ST spots to simulate lower resolution scenarios, as well as the impact of gene detection by randomly reducing detected genes. Through the comparison among the inferred expression patterns with ST data of different spatial resolutions or different numbers of detected genes, we can better access the performance of Palette and provide guidance to researchers on the appropriate ST data requirements for optimal performance. These analyses will take another one month to accomplish after this round of revision due to the limited response time.

      1. The methodology raises pertinent questions regarding how the clustering results from different algorithms may affect the reconstructions by the Palette method. The authors would better provide a detailed discussion/comparison of clustering processes that optimize the reconstruction of spatial patterns, ensuring precision in the downstream analyses.

      Response: We thank the reviewer for the constructive comments. We agree with the reviewer that the differences in clustering results would impact the inference of the Palette. In our Palette pipeline, rather than develop a new methodology for clustering, we employ the BayesSpace for spot clustering, which considers both spot transcriptional similarity and neighbouring structure for clustering. In this case, researchers may adjust the parameters in the BayesSpace package to achieve optimal clustering results. Actually, in most cases, the spot identities were achieved through UMAP analysis, which only considers the transcriptional differences but does not consider the spatial information. This kind of clustering strategy will potentially lead to an intricate arrangement of spots belonging to different clusters, and may result in sparse gene expression in Palette outcome, which is different from the patterns in bona fide tissues. Therefore, a suitable clustering strategy will definitely help capture the local patterns.

      Moreover, our Palette pipeline also can use the clustering results from the tissue histomorphology. Using tissue histomorphology for clustering would be a good choice, as it is closer to the real case. The following Figure (Fig. 11) displays the Palette performance on PDAC datasets using both spatial clustering and histomorphology clustering strategies. The result using histomorphology clustering captures the weak pattern (indicated by the red circle) that were missed when using the spatial clustering (Fig. 11d).

      See response letter for the figure.

      1. The choice to utilize only highly expressed genes in the initial stages of the Palette algorithm also warrants further exploration. Addressing the criteria for determining which genes qualify as "highly expressed" and outlining robust cutoff will enhance the algorithm's rigor and applicability. Similarly, in the iterative estimation of gene expression across spatial spots, establishing optimal iteration conditions is crucial. Implementing a loss function may offer a systematic method for concluding iterations, thus refining computational efficiency.

      Response: We thank the reviewer for the professional suggestions. As pointed out by the reviewer, the selection of highly expressed genes and the iteration times are two important parameters in our pipeline. The definition of highly expressed genes and the number of highly expressed genes are important for achieving a satisfactory clustering performance. We tested the impact of different numbers of highly expressed genes on cluster performance in our preliminary analyses, while we did not summarize these tests and specify the parameters. Therefore, we plan to include a supplementary figure showing the clustering performances under different definitions of highly expressed genes and different numbers of highly expressed genes. Additionally, for the iteration conditions, we have tested different iteration numbers to find out a suitable iteration number to achieve a stable expression in each spot. The following figure (Fig. 1) shows the results after performing Palette with different iteration times. We randomly selected 20 cells and compared their expression across tests with varying iteration times. The results indicate that for a ST dataset with 819 spots, the expression in each spot becomes nearly stable after 5000 iteration times. We previously did not consider the computational efficiency, while here the reviewer raises a valuable and professional suggestion to implement a loss function to determine the optimal number of iterations. We greatly appreciate this suggestion, and plan to apply a loss function to summarize the optimal iteration times for ST datasets of different sizes. This will provide guidance for potential researchers in selecting iteration times and enhance computational efficiency.

      See response letter for the figure.

      1. Performance metrics relating to processing speed and computational demands remain inadequately addressed in the current framework. Understanding how the Palette method scales across varying gene counts and bulk RNA-seq datasets will be essential for potential applications in larger biological contexts. Notably, the quantitative demands of analyzing 20,000 genes when processing 10, 100, or 1,000 bulk RNA profiles must be articulated to guide researchers in planning accordingly.

      Response: We thank the reviewer for this valuable and professional suggestion. In our previous analyses, we did not consider the computation efficiency, processing speed and computational demands, which are important information for potential researchers. To address this issue, we will list our computer configuration first. And under this configuration, we plan to run Palette on datasets with different numbers of overlapped genes or ST references with varying spot numbers, and then summarize the running times into a metrics table. This will help researchers estimate the running time for their datasets and guide them in planning the analyses. We will begin the analyses soon and expect to complete the analysis within the next 1 to 2 months.

      Minor opinions:

      1. Despite the promising advances offered by the zebrafish 3D reconstruction, there is a lack of details regarding numbers of the spatial transcriptomics (ST) data utilized, and the number of bulk RNA-seq data employed in the analyses. These parameters need to be clarified.

      Response: We thank the reviewer for reminding us of these parameters. We are sorry for not including these parameters in our previous manuscript. We have now included the numbers of bulk, ST and overlap genes in the Methods section as follows (Fig. 12).

      "Palette was performed on the aligned slices using the overlapped genes. For the 10 hpf embryo, there were 24,658 genes in the bulk data, 18,698 genes in the Stereo-seq data, and 16,601 overlapped genes. For the 12 hpf embryo, there were 23,018 genes in the bulk data, 18,948 genes in the Stereo-seq data, and 16,401 overlapped genes. For the 16 hpf embryo, there were 24,357 genes in the bulk data, 23,110 genes in the Stereo-seq data, and 19,539 overlapped genes."

      See response letter for the figure.

      1. Issues regarding spatial cell-cell communication, especially concerning interactions over longer distances, necessitate careful consideration. Introducing spatial distance constraints could help formulate more realistic models of cellular interactions, a vital aspect of embryonic development.

      Response: We thank the reviewer for this essential comment. We agree with the reviewer that the spatial distance is an essential factor to investigate in vivo cell-cell communication during embryonic development. Therefore, in our analyses, we employed CellChat for spatial cell-cell communication analysis, which can be used to infer and visualize spatial cell-cell communication network for ST datasets, considering the spatial distance as constrains of the computed communication probability. However, during our analyses, we observed that there were interactions between cell types over longer distances, as mentioned by the reviewer. We then investigated how these interactions of longer distances occurred. Here, we show the FGF interaction between tail bud and neural crest cells from our spatial cell-cell analysis as an example, and the distance between these two cell types appears quite significant (Fig. 13). We labelled tail bud cells and neural crest cells on the selected midline section and observed that, although most neural crest cells are distributed anteriorly, a small number of neural crest cells are located at tail, close to the tail bud cells. Therefore, the observed interaction between tail bud and neural crest cells is likely due to their adjacent distribution in the tail region, while the anteriorly distributed of neural crest spot in spatial cell-cell communication analysis reflects the anterior positioning of most neural crest cells. As a result, the distances shown on the spatial cell-cell communication analysis are not the real distance between two cell types.

      In most cases in our spatial cell-cell communication analyses, the observed interactions over longer distances are likely influenced by this visualization strategy. Additionally, pre-processing the dataset may enhance the performance of the analyses. Here we performed systematic analyses of the entire embryo, which can make the interactions between cell types appear massive. To investigate specific biological questions, researchers can subset cell types of interest or categorize them into different subtypes based on their positions.

      See response letter for the figure.

      1. Evaluation metrics such as the Adjusted Rand Index (ARI) and Root Mean Square Error (RMSE) represent critical tools for systematically measuring the similarity of inferred spatial patterns, yet their specific application within this context should be elaborated.

      Response: We thank the reviewer for recommending these two tools. We have applied them to evaluate the similarity between the expression patterns (Fig. 14). The inclusion of these statistical values makes our comparisons of expression patterns more scientific and convincing. And we have added the following texts in the Methods section to describe the calculation of these two values.

      "The Adjusted Rand Index (ARI) and Root Mean Square Error (RMSE) were used to evaluate the similarity of the expression patterns. The expression patterns of in situ hybridization images were considered as the expected values, and the expression patterns of ST data and inferred expression patterns were compared to the expected values. Common positions along the AP axis within all three expression profiles were used, and the RMSE were calculated based on the scaled intensity of these positions. Values greater than the threshold were set to 1; otherwise, they were set to 0, and the ARI was then calculated based on the intensity category. Higher ARI and lower RMSE indicate greater similarity."

      See response letter for the figure.

      1. The study's limitations surrounding ST data quality cannot be overstated. Discussing scenarios where only limited or poor-quality ST data are available will be crucial for guiding future studies. Furthermore, a clear explanation of how enhanced specificity and accuracy translate into tangible biological insights is essential for demystifying the underlying mechanisms driving developmental processes.

      Response: We thank the reviewer for raising this essential suggestion. We have realized that in our previous manuscript, our discussion on the advantages and limitations of Palette and zSTEP was neither broad nor detailed enough.

      Therefore, in our revised manuscript, we have added the following paragraphs to further discuss the advantages and limitations of Palette and zSTEP, as well as the potential application of zSTEP in developmental biology.

      In this section, we have emphasized again the impact of ST data quality on the performance of Palette and zSTEP, and then compared Palette with the strategy that uses well-established marker genes to infer spatial information. We demonstrated that although Palette cannot achieve single cell resolution, it captures the major expression patterns, which are closely correlated to biological functions and critical for embryonic development. Furthermore, we further discussed that zSTEP is not only a valuable tool for investigating gene expression patterns, but also has the potential in evaluating the reaction-diffusion model to investigate the complicated and well-choreographed pattern formation during embryonic development.

      As here we have provided a more comprehensive discussion about Palette and zSTEP, we think that the potential researchers will better understand the application scenarios of our inference pipeline and our datasets. We hope our study can assist and inspire further research in the field of spatial transcriptomics and developmental biology.

      "Thirdly, the performance of Palette and zSTEP heavily relied on the quality of ST data. If the quality of ST data is not of sufficient quality, the low-expression genes may not be detected or only appear in very few scattered spots, and the performance of spot clustering could also be affected. Moreover, in this study, for example, the Stereo-seq data of 12 hpf zebrafish embryo had fewer slices on the right side (Fig. S3b), resulting in more blank spots in the right part of zSTEP for the 12 hpf embryo. However, with the ongoing advancements in spatial resolution and data quality, the performance of Palette is expected to be enhanced and demonstrate even greater potential for analysing spatiotemporal gene expression.

      On the other hand, compared to the brilliant strategy that infers spatial information of scRNA-seq data from well-established genes, our Palette pipeline cannot achieve single cell resolution. However, our Palette pipeline is based on the ST reference, and thus preserves the real positional relationships between spots. Furthermore, the focus of our pipeline is to infer the gene expression patterns, which are closely correlated to biological functions and critical for embryonic development, rather than the sparse expression within individual spots. In this regard, our Palette pipeline can be advantageous, as it allows for reconstruction of the major expression profiles, which are often more relevant for understanding developmental processes. Additionally, our Palette can be applied to serial sections, enabling the construction of 3D ST atlas.

      Finally, while the current analyses demonstrated that zSTEP can serve as a valuable tool for identifying genes having specific patterns at certain developmental stages, the exploration of zSTEP is still limited. During animal development, pattern formation is always one of the most important developmental issues. As demonstrated by the reaction-diffusion (RD) model, morphogen molecules are produced at specific regions of the embryo, forming morphogen gradients to guide cell specification, while interactions between different morphogens instruct more complicated and well-choreographed pattern formation. Our Palette constructed zSTEP, as a comprehensive transcriptomic expression pattern during development, could be leveraged to evaluate and prove the RD model during development, including AP patterning. Moreover, the investigation of gene expression patterns should not be limited to morphogens and TFs, and further investigation of their roles in AP patterning is desirable. Additionally, here a random forest model may be sufficient for investigating the most essential morphogens and TFs for AP axis refinement, while more sophisticated machine learning models may be required for addressing more specific biological questions."

      Reviewer #2 (Significance):

      The Palette pipeline demonstrates a marked improvement in specificity and accuracy when predicting spatial gene expression patterns. Evaluative studies on Drosophila and zebrafish datasets affirm its enhanced performance compared to existing methodologies. By effectively reconstructing spatial information from bulk transcriptomic data, the Palette method innovatively merges the philosophy of leveraging single-cell transcriptomic data for deconvolution analyses. This integration is pivotal, advancing traditional bulk RNA-seq approaches while laying the groundwork for future research.

      One of the notable achievements in this work is the construction of the DreSTEP atlas, which integrates serial bulk RNA-seq data with advanced 3D imaging techniques. This resource grants researchers unprecedented access to the visualization of gene expression patterns across the zebrafish embryo, facilitating the investigation of spatial relationships and cell-cell interactions critical for developmental processes. Such capabilities are invaluable for understanding the intricate dynamics of embryogenesis and the distinct roles of individual cell types.

      Response: We thank the reviewer for the positive evaluation of our work, either the Palette pipeline or zSTEP. The reviewer has strong expertise in algorithm development and computational biology, and the concerns and suggestions from the reviewer are significantly precious and valuable for us. Regarding the bioinformatics tool development, we did not have extensive experiences, and thus we did not thoroughly address the selection criteria or clarify the parameters used in the pipeline, which may influence the application by other researchers. Therefore, we sincerely appreciate the professional suggestions from the reviewer, which we can follow to address these issues, improve our manuscript and make our work more impactful for researchers. Additionally, we did not consider computation efficiency, processing speed and computational demands, which would be important factors for other researchers to use Palette. We would like to add extra analyses to address these aspects.

      Currently, based on the suggestions from the reviewer, we have added extra texts discussing the clustering strategy in Palette pipeline, the advantages and limitations of Palette, and the potential application of zSTEP in developmental biology. We believe that readers will now have a clearer understanding of the performance of Palette and the application scenarios of both Palette and zSTEP. We have not fully addressed the comments raised by the reviewer yet, while we are working on the planned additional analyses and expect to complete all these tasks within the next 1-2 months. We sincerely thank the reviewer for the professional and valuable suggestions, which definitely improve our work and will make it accessible for a wide range of researchers.

      Finally, through this review process, we have learned a lot about the important considerations and requirements when designing bioinformatics tools, and we benefit a lot from the thoughtful guidance. We express our thanks to the reviewer again for the guidance, and we will try our best to address the remaining issues to further improve our manuscript.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Evidence, reproducibility and clarity

      In this study, Dong and colleagues developed a computational pipeline to use spatial transcriptomics (ST) datasets as a reference to infer the spatial patterns of gene expression from bulk RNA sequencing data. This approach aims to overcome the low read depth and limited gene detection capabilities in current ST datasets, while exploiting its ability to provide highly resolved spatial information. By combining bulk RNA-seq datasets from 3 developmental stages during early zebrafish development with previously available ST and imaging datasets, the authors build DreSTEP (Danio rerio spatiotemporal expression profiles). Using this approach, they go on to identify the morphogens and transcription factors involved in anteroposterior patterning.

      The paper is well written, and the pipeline presented in this study is likely to be useful beyond the case studies included in this study. There are a few questions that, in my view, would be important to clarify to increase the impact of this work:

      Response: We sincerely appreciate the positive feedback from the reviewer on the Palette pipeline and zebrafish spatiotemporal expression profiles zSTEP. We thank the reviewer for the constructive suggestions, which have inspired us to think deeply about application and advantages of Palette and zSTEP for future studies.

      We fully agree with the reviewer that we do not sufficiently clarify the advantages and limitations of our inference pipeline in the original manuscript. The questions raised by the reviewer are very insightful. For example, while the inference expression patterns may closely resemble the in situ hybridization observation, which we consider as good performance, the reviewer pointed out that we should consider whether weak, yet real expression may have been removed. These questions have motivated us to think more deeply about the underlying principles and assumptions of our inference pipeline. Following the reviewer's questions, we have expanded our discussion on the application of zSTEP in developmental biology and the features of Palette compared to the existing strategies.

      We believe that after incorporating the revisions, our current manuscript now demonstrates the application scenario of Palette clearer and suggested the application of zSTEP for investigating biological questions in developmental biology. We are grateful for the reviewer's guidance, which helps us increase the impact of our work.

      1. The authors mention that they used a variable factor to adjust expression differences between the ST and bulk RNA-seq datasets. It would be important for the authors to comment on how much overlap in gene expression is necessary between the datasets for an accurate calculation of this variable factor? Can this be directly tested, for instance, by testing how their conclusions vary if expression is adjusted by a variable factor calculated from only a smaller set of genes?

      Response: We thank the reviewer for the professional questions. We are sorry about not including the gene numbers in our previous manuscript. And now we have provided the numbers of genes in bulk and ST data and the numbers of the overlapped genes (Fig. 15).

      "Palette was performed on the aligned slices using the overlapped genes. For the 10 hpf embryo, there were 24,658 genes in the bulk data, 18,698 genes in the Stereo-seq data, and 16,601 overlapped genes. For the 12 hpf embryo, there were 23,018 genes in the bulk data, 18,948 genes in the Stereo-seq data, and 16,401 overlapped genes. For the 16 hpf embryo, there were 24,357 genes in the bulk data, 23,110 genes in the Stereo-seq data, and 19,539 overlapped genes."

      See response letter for the figure.

      For Palette implementation, we took all the overlapped genes. To calculate the variable factor, we aggregated the expression of each gene in the ST data, and then used the expression of the bulk data to divide the aggregated expression for variable factor calculation. As a result, each overlapped gene was assigned a variable factor to adjust its expression, based on its difference between bulk and ST data. The rationale behind this approach is that by considering the ST data as a whole, we can effectively reduce the variations among individual spots. This allows the variable factors to provide reasonable adjustment to gene expression.

      Above all, the variable factors can be directly calculated. Currently Palette only can infer the expression patterns of overlapped genes. It means when the number of overlapped genes is small, such as MERFISH only detecting hundreds of genes, Palette can only infer the expression patterns of these genes. However, if the MERFISH data have good quality, which enable resolving distinct cell types, we believe Palette will also show good performance when using MERFISH as ST reference. Additionally, we plan to perform Palette using MERFISH as ST reference to further demonstrate its broad application when using different ST references.

      1. Palette gives rise to highly spatially precise patterns, which closely match those found in ISH. However, the smoothening of the expression can also remove weak, yet real, local expression patterns, as shown for idgf6 in Fig. 2a. Can the authors test this more extensively for other genes?

      Response: We thank the reviewer for this essential question. We agree with the reviewer that weak, yet real expression might be removed in our Palette inference pipeline. The weak, sparse expression may be due to the ST technique itself or the variations in samples. However, that sparse gene expression may not have biological meaning, and the focus of our pipeline in to capture the expression patterns, which are closely correlated with functions and crucial for embryonic development. Therefore, our algorithm considers spot characteristics and emphasize cluster-specific expression, resulting in spatial-specific expression patterns. In most cases, the main gene expression patterns can be captured, which can help understand gene functions and roles in embryonic development. We have updated Supplementary Figure S1a (Fig. 16) to include more gene patterns to demonstrate this point.

      See response letter for the figure.

      1. Using adjacent slices for ST and "bulk RNA-seq" may provide better results than those obtained when comparing two independent datasets. Could the authors also extend the analysis of Palette's functionalities by using separate, previously available but independent datasets, for ST and bulk RNA-seq in Drosophila as well?

      Response: We thank the reviewer for the valuable question. We agree with the reviewer that using adjacent slices may provide better results. The idea here is that the inferred spatial expression patterns from pseudo bulk RNA-seq can be used to compare with the real expression of ST to evaluate Palette performance. We have updated our Figure 2a (Fig. 17) to illustrate the analysis clearer.

      See response letter for the figure.

      To demonstrate the Palette's functionalities, we have used Palette to infer zebrafish bulk RNA-seq slice (Junker et al., 2014) using Stereo-seq slice (Liu et al., 2022) as ST reference, and these two datasets are separate and independent. We agree with the reviewer that it would be good to use separate datasets to test in Drosophila to further demonstrate the Palette's functionalities. However, unfortunately, we did not find the Drosophila serial bulk RNA-seq data along left-right axis of the corresponding stages, and thus we might be unable to perform the extra analyses using independent Drosophila datasets.

      References:

      Junker, J.P. et al. Genome-wide RNA Tomography in the zebrafish embryo. Cell 159, 662-675 (2014).

      Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell 57, 1284-1298 e1285 (2022).

      1. The DreSTEP analysis in zebrafish embryos is interesting and validates well-established observations in the field. Can the authors also discuss whether and how their dataset allows them to refine our understanding of the spatial or temporal pattern of the morphogens and TFs involved in AP patterning? This would further validate their approach.

      Response: We appreciate the reviewer for recognition of our zSTEP and raising this valuable question, which has inspired us to think more deeply about the potential application of zSTEP in developmental biology. As the reviewer noted, our zSTEP analyses have validated well-established observations in the field. Rather than focusing on the sparse expression detected in ST data, zSTEP emphasizes the gene expression patterns that are closely correlated with biological functions and critical for embryonic development. Therefore, zSTEP can serve as a valuable tool for identifying the genes having specific patterns at certain developmental stages.

      Pattern formation is one of the most important developmental issues for all animals. The reaction-diffusion (RD) model is a widely recognized theoretical framework used to explain self-regulated pattern formation in developing animal embryos (Kondo & Miura, 2010). Morphogen molecules are produced at specific regions of the embryo, forming morphogen gradients to guide cell specification. Most importantly, interactions between different morphogens instruct more complicated and well-choreographed pattern formation. Our Palette-constructed zSTEP provides a comprehensive transcriptomic expression pattern, including all morphogens and TFs, across the whole embryo during development. These valuable resources, in our opinion, could be leveraged to evaluate and prove the RD model during development, including AP patterning. In our current zSTEP analyses, we have already identified genes that exhibit specific expression patterns along AP axis, some of which have not been fully characterized. These genes could be potential targets for further investigation into their roles in AP patterning, although they are not the primary focus of this study. Additionally, our analyses only focused on morphogens and TFs, but zSTEP can be used to investigate the expression patterns of other genes as well. Moreover, we employed a random forest model to investigate the most essential morphogens and TFs for AP axis refinement, which is one of the basic applications of zSTEP. To investigate specific biological questions of interest, it would be worth exploring the use of more sophisticated machine learning models.

      We have added the following paragraph in the Discussion section to discuss the potential application of zSTEP in future studies.

      "Finally, while the current analyses demonstrated that zSTEP can serve as a valuable tool for identifying genes having specific patterns at certain developmental stages, the exploration of zSTEP is still limited. During animal development, pattern formation is always one of the most important developmental issues. As demonstrated by the reaction-diffusion (RD) model, morphogen molecules are produced at specific regions of the embryo, forming morphogen gradients to guide cell specification, while interactions between different morphogens instruct more complicated and well-choreographed pattern formation. Our Palette constructed zSTEP, as a comprehensive transcriptomic expression pattern during development, could be leveraged to evaluate and prove the RD model during development, including AP patterning. Moreover, the investigation of gene expression patterns should not be limited to morphogens and TFs, and further investigation of their roles in AP patterning is desirable. Additionally, here a random forest model may be sufficient for investigating the most essential morphogens and TFs for AP axis refinement, while more sophisticated machine learning models may be required for addressing more specific biological questions."

      Reference

      Kondo, S. & Miura, T. Reaction-Diffusion model as a framework for understanding biological pattern formation. Science 329, 1616-1620 (2010).

      1. Can the authors comment on the limits of this inference pipeline? And how it performs as compared to single-cell RNA sequencing datasets where spatial information is inferred from well-established marker genes?

      Response: We appreciate the reviewer for this insightful question, which has inspired us to further explore the advantages and limitations of the Palette pipeline in comparison with other inference strategies. As mentioned in the Discussion section, a key limitation of the inference pipeline is its heavy reliance on the quality of ST data. It is obvious that if the quality of ST data is not of sufficient quality, the low-expression genes may not be detected or only appear in very few scattered spots. We think it is a common issue for any inference tools using ST data as the reference. However, with the ongoing advancements in spatial resolution and data quality, the performance of Palette is expected to be improved.

      As a comparison, the single-cell RNA sequencing datasets where spatial information is inferred from well-established marker genes do not face this limitation. The ground-breaking work by Satija et al. (2015) used such a strategy that combined scRNA-seq and in situ hybridizations of well-established marker genes to infer spatial location, enabling single cell resolution, as it maintains the high read depth and gene detection. One advantages of this scRNA-seq-based strategy is that it provides the transcriptomics of individual cells, rather than a combination of cell within a ST spot, although the positional relationships between cells are not real.

      However, compared to the inference from ST data, the positional relationships between cells are not directly captured. On the other hand, as the embryonic development progresses, more cell types will be specified, and the body patterning becomes more complex. In this scenario, using well-established marker gene to infer spatial information would be much more challenging. Additionally, there are not many scRNA-seq datasets of serial sections, and thus this strategy may not be used to construct 3D ST atlas.

      In contrast, our Palette inference pipeline is based on the ST data, which preserves the real positional relationships between spots. Although our inference pipeline cannot achieve single cell resolution, it focuses on the gene expression patterns rather than the sparse expression within individual spots. By applying Palette to paired serial sections, we were able to generated a 3D spatial expression atlas of zebrafish embryos, which has showed promising performance for investigating gene expression patterns and their involvement in AP patterning.

      Reference

      Satija, R. et al. Spatial reconstruction of single-cell gene expression data. Nature biotechnology 33, 495-502 (2015)

      We have updated the following paragraphs to further demonstrating the limitation of the inference pipeline in details in the Discussion section.

      "Thirdly, the performance of Palette and zSTEP heavily relied on the quality of ST data. If the quality of ST data is not of sufficient quality, the low-expression genes may not be detected or only appear in very few scattered spots, and the performance of spot clustering could also be affected. Moreover, in this study, for example, the Stereo-seq data of 12 hpf zebrafish embryo had fewer slices on the right side (Fig. S3b), resulting in more blank spots in the right part of zSTEP for the 12 hpf embryo. However, with the ongoing advancements in spatial resolution and data quality, the performance of Palette is expected to be enhanced and demonstrate even greater potential for analysing spatiotemporal gene expression.

      On the other hand, compared to the brilliant strategy that infers spatial information of scRNA-seq data from well-established genes, our Palette pipeline cannot achieve single cell resolution. However, our Palette pipeline is based on the ST reference, and thus preserves the real positional relationships between spots. Furthermore, the focus of our pipeline is to infer the gene expression patterns, which are closely correlated to biological functions and critical for embryonic development, rather than the sparse expression within individual spots. In this regard, our Palette pipeline can be advantageous, as it allows for reconstruction of the major expression profiles, which are often more relevant for understanding developmental processes. Additionally, our Palette can be applied to serial sections, enabling the construction of 3D ST atlas."

      Reviewer #3 (Significance):

      This study tackles an important challenge in biology - the difficult to resolve gene expression patterns with high spatial precision and in a high-throughput manner. By integrating sequencing datasets from previously published studies, as well as newly-generated datasets, the authors provide evidence that their novel inference pipeline enables them to obtain high-quality spatial information simply from bulk RNA-seq datasets, using ST as a reference. The development of this pipeline - Palette - is a major part of this manuscript and its applicability is validated using datasets from Drosophila and zebrafish embryos. This in an important advance for the field, but it would be nice for the authors to further comment on i) the validity of some of their approaches and how they may influence the quality of their inference, as well as, ii) potential pitfalls/limitations of this approach as compared to others available in the field. This would synthetize both previous and current findings into a conceptual and technological framework that would have a strong impact well beyond cell and developmental biology.

      Audience: This study would be relevant for a broad audience of biologists, interested in morphogen signaling, gene regulatory networks and cell fate specification.

      Expertise in zebrafish development, gastrulation, morphogen signaling and morphogenesis.

      Response: We thank the reviewer for providing the positive feedback, arising these valuable questions, which have motivated us to deeply consider the design concept and further application of Palette and zSTEP. Based on the insightful questions from the reviewer, we have added two extra paragraphs in the Discussion section to further discuss the potential application of zSTEP in developmental biology and application scenarios of the Palette pipeline. Specially, we have demonstrated that the performance of the inference pipeline relies on the spatial resolution and data quality of the ST data. We have then compared the advantages and limitations of Palette with the existing brilliant spatial inference strategy, which infers spatial information of scRNA-seq from well-established marker genes. Although our inference pipeline cannot achieve single cell resolution, it can capture the major expression patterns, which are closely correlated to functions and critical for embryonic development. We believe this will help readers gain a clearer understanding of the advantage and limitations of our pipeline compared to other tools, as well as the tasks for which Palette and our constructed zSTEP can be utilized. We express our thanks to the reviewer again for the valuable comments.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In this study, Dong and colleagues developed a computational pipeline to use spatial transcriptomics (ST) datasets as a reference to infer the spatial patterns of gene expression from bulk RNA sequencing data. This approach aims to overcome the low read depth and limited gene detection capabilities in current ST datasets, while exploiting its ability to provide highly resolved spatial information. By combining bulk RNAseq datasets from 3 developmental stages during early zebrafish development with previously available ST and imaging datasets, the authors build DreSTEP (Danio rerio spatiotemporal expression profiles). Using this approach, they go on to identify the morphogens and transcription factors involved in anteroposterior patterning.

      The paper is well written, and the pipeline presented in this study is likely to be useful beyond the case studies included in this study. There are a few questions that, in my view, would be important to clarify to increase the impact of this work:

      1. The authors mention that they used a variable factor to adjust expression differences between the ST and bulk RNAseq datasets. It would be important for the authors to comment on how much overlap in gene expression is necessary between the datasets for an accurate calculation of this variable factor? Can this be directly tested, for instance, by testing how their conclusions vary if expression is adjusted by a variable factor calculated from only a smaller set of genes?
      2. Palette gives rise to highly spatially precise patterns, which closely match those found in ISH. However, the smoothening of the expression can also remove weak, yet real, local expression patterns, as shown for idgf6 in Fig. 2a. Can the authors test this more extensively for other genes?
      3. Using adjacent slices for ST and "bulk RNAseq" may provide better results than those obtained when comparing two independent datasets. Could the authors also extend the analysis of Palette's functionalities by using separate, previously available but independent datasets, for ST and bulk RNAseq in Drosophila as well?
      4. The DreSTEP analysis in zebrafish embryos is interesting and validates well-established observations in the field. Can the authors also discuss whether and how their dataset allows them to refine our understanding of the spatial or temporal pattern of the morphogens and TFs involved in AP patterning? This would further validate their approach.
      5. Can the authors comment on the limits of this inference pipeline? And how it performs as compared to single-cell RNA sequencing datasets where spatial information is inferred from well-established marker genes?

      Significance

      This study tackles an important challenge in biology - the difficult to resolve gene expression patterns with high spatial precision and in a high-throughput manner. By integrating sequencing datasets from previously published studies, as well as newly-generated datasets, the authors provide evidence that their novel inference pipeline enables them to obtain high-quality spatial information simply from bulk RNAseq datasets, using ST as a reference. The development of this pipeline - Palette - is a major part of this manuscript and its applicability is validated using datasets from Drosophila and zebrafish embryos. This in an important advance for the field, but it would be nice for the authors to further comment on i) the validity of some of their approaches and how they may influence the quality of their inference, as well as, ii) potential pitfalls/limitations of this approach as compared to others available in the field. This would synthetize both previous and current findings into a conceptual and technological framework that would have a strong impact well beyond cell and developmental biology.

      Audience: This study would be relevant for a broad audience of biologists, interested in morphogen signaling, gene regulatory networks and cell fate specification.

      Expertise in zebrafish development, gastrulation, morphogen signaling and morphogenesis.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The authors of the study introduce the Palette method, a novel approach designed to infer spatial gene expression patterns from bulk RNA-sequencing (RNA-seq) data. This method is complemented by the development of the DreSTEP 3D spatial gene expression atlas of zebrafish embryos, establishing a comprehensive resource for visualizing gene expression and investigating spatial cell-cell interactions in developmental biology.

      Major concerns:

      1. The efficacy of the Palette method may be compromised by its dependency on the quality of the reference spatial transcriptomics data. As highlighted in the study, variations in data quality can lead to significant challenges in reconstructing accurate spatial expression patterns from bulk data. This underscores the necessity of evaluating quality parameters, such as the number of gene detections and spatial resolution, to ensure reliable outcomes. Additional studies should rigorously assess how these quality factors influence the accuracy and efficiency of the algorithm in various data contexts, particularly under diverse conditions of gene detection.
      2. The methodology raises pertinent questions regarding how the clustering results from different algorithms may affect the reconstructions by the Palette method. The authors would better provide a detailed discussion/comparison of clustering processes that optimize the reconstruction of spatial patterns, ensuring precision in the downstream analyses.
      3. The choice to utilize only highly expressed genes in the initial stages of the Palette algorithm also warrants further exploration. Addressing the criteria for determining which genes qualify as "highly expressed" and outlining robust cutoff will enhance the algorithm's rigor and applicability. Similarly, in the iterative estimation of gene expression across spatial spots, establishing optimal iteration conditions is crucial. Implementing a loss function may offer a systematic method for concluding iterations, thus refining computational efficiency.
      4. Performance metrics relating to processing speed and computational demands remain inadequately addressed in the current framework. Understanding how the Palette method scales across varying gene counts and bulk RNA-seq datasets will be essential for potential applications in larger biological contexts. Notably, the quantitative demands of analyzing 20,000 genes when processing 10, 100, or 1,000 bulk RNA profiles must be articulated to guide researchers in planning accordingly.

      Minor opinions:

      1. Despite the promising advances offered by the zebrafish 3D reconstruction, there is a lack of details regarding numbers of the spatial transcriptomics (ST) data utilized, and the number of bulk RNA-seq data employed in the analyses. These parameters need to be clarified.
      2. Issues regarding spatial cell-cell communication, especially concerning interactions over longer distances, necessitate careful consideration. Introducing spatial distance constraints could help formulate more realistic models of cellular interactions, a vital aspect of embryonic development.
      3. Evaluation metrics such as the Adjusted Rand Index (ARI) and Root Mean Square Error (RMSE) represent critical tools for systematically measuring the similarity of inferred spatial patterns, yet their specific application within this context should be elaborated.
      4. The study's limitations surrounding ST data quality cannot be overstated. Discussing scenarios where only limited or poor-quality ST data are available will be crucial for guiding future studies. Furthermore, a clear explanation of how enhanced specificity and accuracy translate into tangible biological insights is essential for demystifying the underlying mechanisms driving developmental processes.

      Significance

      The Palette pipeline demonstrates a marked improvement in specificity and accuracy when predicting spatial gene expression patterns. Evaluative studies on Drosophila and zebrafish datasets affirm its enhanced performance compared to existing methodologies. By effectively reconstructing spatial information from bulk transcriptomic data, the Palette method innovatively merges the philosophy of leveraging single-cell transcriptomic data for deconvolution analyses. This integration is pivotal, advancing traditional bulk RNA-seq approaches while laying the groundwork for future research.

      One of the notable achievements in this work is the construction of the DreSTEP atlas, which integrates serial bulk RNA-seq data with advanced 3D imaging techniques. This resource grants researchers unprecedented access to the visualization of gene expression patterns across the zebrafish embryo, facilitating the investigation of spatial relationships and cell-cell interactions critical for developmental processes. Such capabilities are invaluable for understanding the intricate dynamics of embryogenesis and the distinct roles of individual cell types.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The manuscript titled "Unravelling the Progression of the Zebrafish Primary Body Axis with Reconstructed Spatiotemporal Transcriptomics" presents a comprehensive analysis of the development of the primary body axis in zebrafish by integrating bulk RNA-seq, 3D images, and Stereo-Seq. The authors first clearly demonstrate the application of Palette for integrating RNA-seq and Stereo-Seq using published spatial transcriptomics data of Drosophila embryos. Subsequently, they produced serial bulk RNA-seq data for certain developmental stages of Danio rerio embryos and utilized published Stereo-Seq data. Through robust validation, the authors observe the molecular network involved in AP axis formation. While the authors show that integrating bulk RNA-seq data with Stereo-Seq improves spatial resolution, additional proof is required to demonstrate the extent of this improvement.

      Major Comments:

      1. Lines 66-68: Discuss the limitations of existing tools and explicitly state the advantages of using Palette.
      2. Body Pattern Genes Analysis: For both Drosophila and Danio rerio, it would be valuable to examine body pattern genes in Stereo-Seq and apply Palette to determine if the resolution of the segments improves or merges. The resolution of the A-P axis is convincing, but further evidence for other segments would be beneficial.
      3. Figure 2d: Include the A-P line for which the intensity profile was plotted in the main figure, rather than just in the supplementary material. Additionally, consider simplifying the plot by not combining three lines into one, as it complicates the interpretation of observations.
      4. Drosophila Data Analysis: While the alignment and validation of Danio rerio sections are clearly explained, the analysis and validation of Drosophila data are insufficiently detailed. Provide a more thorough explanation of how the intensity profiles between BDGP in situ data and Stereo-Seq data are adjusted.
      5. Figure 3d: Present a plot with the expected expression profiles of the three genes if the embryo is aligned as anticipated.
      6. Analysis Without Palette: Between lines 277-438, the outcome of using Palette with bulk RNA-seq and Stereo-Seq is convincing. However, consider the following:<br /> o What would be the observations if the analysis were conducted solely with Stereo-Seq data, without incorporating bulk RNA-seq data and employing Palette?<br /> o This study uses only Stereo-Seq as the spatial transcriptomics reference. It would strengthen the argument to use at least one other spatial transcriptomics method, such as Visium or MERFISH, in conjunction with bulk RNA-seq and Palette, to demonstrate whether Palette consistently improves gene expression resolution.
      7. PDAC Data Analysis: Provide a more detailed explanation of the PDAC data analysis and use appropriate colors in the tissue images to clearly distinguish cell types.
      8. Comparison with Other Methods: State the limitations of not using STitch3D and Spateo for alignment and explain why these methods were not employed.

      Minor Comments:

      1. References: Add references to the statements in lines 51-53.
      2. Scientific Name Consistency: Ensure consistency in using either "Danio rerio" or "zebrafish" throughout the manuscript.
      3. Related References: Include the following relevant references:
      4. https://academic.oup.com/bib/article/25/4/bbae316/7705532
      5. https://www.life-science-alliance.org/content/6/1/e202201701
      6. Figure 1a: In the Venn diagram, include the number of genes in the bulk and Stereo-Seq datasets, as well as the number of overlapping genes.
      7. Figure 1 Improvement: Enlarge Figure 1 and reduce repetitive elements, such as parts of the deconvolution and Figure 1b.
      8. Figure 3f: Explain the black discontinuous line in the plot.
      9. Line 610: State the percentage of unpaired imaging spots.
      10. Lines 616-618: Specify the unit for the spot diameter.

      Significance

      This algorithm will be useful not only for the field of developmental biology but also for wider applications in spatial omics. Although I have expertise in spatial omics technology development, my understanding of computational biology is limited, which restricts my ability to fully evaluate the Palette algorithm presented in this paper.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      Reply to the Reviewers

      I thank the Referees for their...

      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This is a good manuscript, well performed and well presented. I have several suggestions/questions to enhance the clarity of the concept, as technically the work is rather well performed.

      1. I suggest that the authors explain better the mesenchymal-to-epithelial (MET) transition in reprogramming. Perhaps, explaining that epithelial gene acquisition (e.g., CDH1) and epidermal cell fate are not exactly the same. This approach could also be used to divide the genes they study further in their analyses.
      2. KLF4 is both a repressor and an activator in different cell contexts including reprogramming. Does HIC2 act only as repressor? Is it possible that HIC2 is repressing KLF4-activated genes bad for reprogramming (including epidermal genes) and activating KLF4-suppressed genes ncessary for reprogramming? This should not be too difficult to explore with their current dataset and they also could look at available datasets for histone modifications in reprogramming.
      3. Does HIC2 bind to genes related to somatic cell identify that need to be suppressed in reprogramming before the MET phase takes place?
      4. Does HIC2 influence proliferation during reprogramming?

      Referee cross-commenting

      Comments by the other reviewers are sound and will help improve the manuscript.

      Significance

      In this manuscript, Kaji and colleagues perform a CRISPR/Cas9 screen to identify genes involved in mouse somatic cell reprogramming, identifying HIC2 as a target that they further validate. They conclude that HIC2 acts by repressing the epidermal/epithelial program induced by KLF4 during reprogramming. Studying the complex role of transcription factor interactions in the context of cell fate conversions (of any kind and not just somatic cell reprogramming) is highly relevant. This work helps clarify such complexity in a specific context but the work has wider conceptual implications.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The study by Beniazza et al. aims to address the inefficiencies associated with OSKM-mediated reprogramming. Through a genome-wide CRISPR/Cas9 knockout screen, the authors identified 14 genes essential for iPSC reprogramming but dispensable for ESC self-renewal. Among these, HIC2 significantly enhanced reprogramming efficiency, yielding approximately a tenfold increase compared to standard conditions. scRNA-seq analyses revealed that HIC2-overexpressing cells follow a more direct trajectory toward pluripotency, bypassing the KLF4-dependent activation of keratinocyte and epidermal gene programs. ChIP-seq profiling further demonstrated that HIC2 and KLF4 co-occupy approximately 60% of their genomic targets, indicating substantial regulatory overlap. Notably, this co-binding and its functional effects are dose-dependent on KLF4, as shown by experiments comparing high KLF4 expression systems (standard OSKM and STEMCCA+9 constructs) with low KLF4 conditions (STEMCCA cassette lacking additional KLF4). The authors conclude that HIC2's modulatory effect occurs specifically under high KLF4 levels.

      Major Comments

      Figure 1D: What is the efficiency of gRNA library transduction into MEFs? What percentage of MEF cells were successfully knocked out? Figures 2B/C: To rule out the possibility that the observed variability in reprogramming efficiency among the tested factor combinations stems from differences in MKOS expression levels, the authors should provide evidence showing that the expression levels of all MKOS factors are comparable across samples. Figures 2D/E: To rule out a fibroblast-specific effect, can the authors show whether the epidermal gene signature is also upregulated during NSC reprogramming and whether Hic2 overexpression suppresses this signature? Figure 2H: Are the 13 signature genes that distinguish MKOS-Hic2-iPSCs from MKOS-iPSCs consistently identified across independent Hic2-iPSC lines, or does each reprogramming event produce a distinct gene set? If the signature is consistent, this is an important observation and should be further addressed and discussed. Figure 3K: Can the authors show the expression levels of MKOS and Hic2 transgenes in all samples? The same concern applies to Figure 4I. The reviewer wishes to be confident that the reduction in epidermal gene expression observed in MEFs is not due to variable transgene expression caused by multiple vector introductions (e.g., KLF4 alone versus KLF4 + Hic2), which could potentially lead to lower KLF4 expression through co-transfection competition. Does KLF4 overexpression in Hic2-knockdown MEFs lead to greater upregulation of the epidermal gene signature compared to the wild-type control? Figure 4C: It appears that only about half of the Hic2 binding sites overlap with KLF4 sites. What are the characteristics of the other Hic2-specific sites, and how might they contribute to reprogramming, if at all? Can the authors perform a reprogramming experiment using a combination that lacks KLF4 (e.g., replacing KLF4 with Esrrb or BMP4, as shown in PMID: 19136965 and PMID: 21135873) and test the effect of Hic2 under these conditions? Do KLF4 and HIC2 physically interact? The authors should perform a co-immunoprecipitation assay to address this question. What is the effect of Hic2 during human reprogramming? Does it play a similar regulatory role?

      Minor Comments

      • Typographical errors should be checked and avoided; for example, on page 10, the word 'colonies' was misspelled.
      • Some blank squares appear in the Methods section; please correct these formatting errors.

      Referee cross-commenting

      All suggestions are feasible within a relatively short time frame and will improve the manuscript.

      Significance

      Overall, this study is of significant interest to the stem cell community and presents a well-designed and carefully executed experimental framework. However, several concerns remain that should be addressed prior to publication.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary: This work identified 14 genes essential for iPSC reprogramming but not essential for ESC maintenance and MEF proliferation by analyzing three CRISPR/Cas9-mediated genome-wide KO screens. Among them, they found that overexpression of the Hic2 gene can greatly promote OSKM-driven reprogramming. By using scRNA-seq in time points of the reprogramming process, they found that Hic2 can bypass the epidermal gene expressing state during reprogramming. Then, using ChIP-seq, they found that HIC2 and KLF4 have common binding sites on epidermal genes. Finally, by expressing KLF4 alone or KLF4 and HIC2 together, they demonstrated that HIC2 can inhibit KLF4-driven epidermal gene expression.

      Major comments: The claims and conclusions are well-supported by the data and do not require additional experiments or analysis. The data and methods are presented in a reproducible way.

      Minor comments: There seem to be some typos. For example, "we selected 30 genes with low FDRs in ESC maintenance" may be "high depletion FDR," since you want nonessential genes for ESC maintenance. The "log10(-FDR)" may be "-log10(FDR)." Some figures lack P values. Perhaps it would be useful to analyze whether Hic2 reduces reprogramming heterogeneity. Validation experiments, such as trilineage differentiation, could be considered to demonstrate that Hic2 does not affect the pluripotency and differentiation capacity of iPSCs.

      Significance

      General Assessment: This work is based on three CRISPR/Cas9-mediated genome-wide KO screens, which makes it comprehensive and reliable. They discovered that HIC2 and OSKM can drive reprogramming without an epidermal gene expression intermediate. They also found extensive common binding sites of HIC2 and KLF4 at target genes. This work not only enables more efficient reprogramming but also expands our understanding of the reprogramming process. Among HIC2 and KLF4 common target genes, some are repressed while others are activated, and it will be very interesting to study the mechanism of this selective function.

      Advance: Compared to natural `embryonic development, OSKM-driven reprogramming is very inefficient, and our understanding of the mechanisms of efficient reprogramming remains poor. The specific role of the epidermal gene expression state in the reprogramming process remains unclear. This work strongly supports the idea that repression of the epidermal gene expression state can promote iPSC generation. Moreover, previous studies on Hic2 are limited, and this work enriches our understanding of its mechanisms and functions.

      Audience: This study may be of interest to those interested in basic research on reprogramming mechanisms or Hic2, as well as those developing efficient reprogramming technologies.

      My field: Reprogramming, stem cells, aging, transcription factors.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      In this manuscript, Xiong and colleagues investigate the mechanisms operating downstream to TRIM32 and controlling myogenic progression from proliferation to differentiation. Overall, the bulk of the data presented is robust. Although further investigation of specific aspects would make the conclusions more definitive (see below), it is an interesting contribution to the field of scientists studying the molecular basis of muscle diseases.

      We thank the Reviewer for appreciating our work and for their valuable suggestions to improve our manuscript. We have carefully addressed some of the concerns raised, as detailed here, while others, which require more experimental efforts, will be addressed as detailed in the Revision Plan.

      In my opinion, a few aspects would improve the manuscript. Firstly, the conclusion that Trim32 regulates c-Myc mRNA stability could be expanded and corroborated by further mechanistic studies:

      1. Studies investigating whether Tim32 binds directly to c-Myc RNA. Moreover, although possibly beyond the scope of this study, an unbiased screening of RNA species binding to Trim32 would be informative. Authors’ response. This point will be addressed as detailed in the Revision Plan

      If possible, studies in which the overexpression of different mutants presenting specific altered functional domains (NHL domain known to bind RNAs and Ring domain reportedly involved in protein ubiquitination) would be used to test if they are capable or incapable of rescuing the reported alteration of Trim32 KO cell lines in c-Myc expression and muscle maturation.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      An optional aspect that might be interesting to explore is whether the alterations in c-Myc expression observed in C2C12 might be replicated with primary myoblasts or satellite cells devoid of Trim32.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      I also have a few minor points to highlight:

        • It is unclear if the differences highlighted in graphs 5G, EV5D, and EV5E are statistically significant.*

      Authors’ response. We thank the Reviewer for raising this point. We now indicated the statistical analyses performed on the data presented in the mentioned figures (according also to a point of Reviewer #3). According to the conclusion that Trim32 is necessary for proper regulation of c-Myc transcript stability, using 2-way-ANOVA, the data now reported as Figure 5G show the statistically significant effect of the genotype at 6h (right-hand graph) but not at D0 (left-hand graph). In the graphs of Fig. EV5 D and E at D0 no significant changes are observed whereas at 6h the data show significant difference at the 40 min time point. We included this info in the graphs and in the corresponding legends.

      - On page 10, it is stated that c-Myc down-regulation cannot rescue KO myotube morphology fully nor increase the differentiation index significantly, but the corresponding data is not shown. Could the authors include those quantifications in the manuscript?

      Authors’ response. As suggested, we included the graph showing the differentiation index upon c-Myc silencing in the Trim32 KO clones and in the WT clones, as a novel panel in Figure 6 (Fig. 6D). As already reported in the text, a partial recovery of differentiation index is observed but the increase is not statistically significant. In contrast, no changes are observed applying the same silencing in the WT cells. Legend and text were modified accordingly.

      Reviewer #1 (Significance (Required)):

      The manuscript offers several strengths. It provides novel mechanistic insight by identifying a previously unrecognized role for Trim32 in regulating c-Myc mRNA stability during the onset of myogenic differentiation. The study is supported by a robust methodology that integrates CRISPR/Cas9 gene editing, transcriptomic profiling, flow cytometry, biochemical assays, and rescue experiments using siRNA knockdown. Furthermore, the work has a disease relevance, as it uncovers a mechanistic link between Trim32 deficiency and impaired myogenesis, with implications for the pathogenesis of LGMDR8. * * At the same time, the study has some limitations. The findings rely exclusively on the C2C12 myoblast cell line, which may not fully represent primary satellite cell or in vivo biology. The functional rescue achieved through c-Myc knockdown is only partial, restoring Myogenin expression but not the full differentiation index or morphology, indicating that additional mechanisms are likely involved. Although evidence supports a role for Trim32 in mRNA destabilization, the precise molecular partners-such as RNA-binding activity, microRNA involvement, or ligase function-remain undefined. Some discrepancies with previous studies, including Trim32-mediated protein degradation of c-Myc, are acknowledged but not experimentally resolved. Moreover, functional validation in animal models or patient-derived cells is currently lacking. Despite these limitations, the study represents an advancement for the field. It shifts the conceptual framework from Trim32's canonical role in protein ubiquitination to a novel function in RNA regulation during myogenesis. It also raises potential clinical implications by suggesting that targeting the Trim32-c-Myc axis, or modulating c-Myc stability, may represent a therapeutic strategy for LGMDR8. This work will be of particular interest to muscle biology researchers studying myogenesis and the molecular basis of muscle disease, RNA biology specialists investigating post-transcriptional regulation and mRNA stability, and neuromuscular disease researchers and clinicians seeking to identify new molecular targets for therapeutic intervention in LGMDR8. * * The Reviewer expressing this opinion is an expert in muscle stem cells, muscle regeneration, and muscle development.

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

      Summary: * * In this study, the authors sought to investigate the molecular role of Trim32, a tripartite motif-containing E3 ubiquitin ligase often associated with its dysregulation in Limb-Girdle Muscular Dystrophy Recessive 8 (LGMDR8), and its role in the dynamics of skeletal muscle differentiation. Using a CRISPR-Cas9 model of Trim32 knockout in C2C12 murine myoblasts, the authors demonstrate that loss of Trim32 alters the myogenic process, particularly by impairing the transition from proliferation to differentiation. The authors provide evidence in the way of transcriptomic profiling that displays an alteration of myogenic signaling in the Trim32 KO cells, leading to a disruption of myotube formation in-vitro. Interestingly, while previous studies have focused on Trim32's role in protein ubiquitination and degradation of c-Myc, the authors provide evidence that Trim32-regulation of c-Myc occurs at the level of mRNA stability. The authors show that the sustained c-Myc expression in Trim32 knockout cells disrupts the timely expression of key myogenic factors and interferes with critical withdrawal of myoblasts from the cell cycle required for myotube formation. Overall, the study offers a new insight into how Trim32 regulates early myogenic progression and highlights a potential therapeutic target for addressing the defects in muscular regeneration observed in LGMDR8.

      We thank the Reviewer for valuing our work and for their appreciated suggestions to improve our manuscript. We have carefully addressed some of the concerns raised as detailed here, while others, which require more laborious experimental efforts, will be addressed as reported in the Revision Plan.

      Major Comments:

      The work is a bit incremental based on this:

      https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030445 * * And this:

      https://www.nature.com/articles/s41418-018-0129-0 * * To their credit, the authors do cite the above papers.

      Authors’ response. We thank the Reviewer for this careful evaluation of our work against the current literature and for recognising the contribution of our findings to the understanding of myogenesis complex picture in which the involvement of Trim32 and c-Myc, and of the Trim32-c-Myc axis, can occur at several stages and likely in narrow time windows along the process, thus possibly explaining some reports inconsistencies.

      The authors do provide compelling evidence that Trim32 deficiency disrupts C2C12 myogenic differentiation and sustained c-Myc expression contributes to this defective process. However, while knockdown of c-Myc does restore Myogenin levels, it was not sufficient to normalize myotube morphology or differentiation index, suggesting an incomplete picture of the Trim32-dependent pathways involved. The authors should qualify their claim by emphasizing that c-Myc regulation is a major, but not exclusive, mechanism underlying the observed defects. This will prevent an overgeneralization and better align the conclusions with the author's data.

      Authors’ response. We agree with the Reviewer and we modified our phrasing that implied Trim32-c-Myc axis as the exclusive mechanism by explicitly indicated that other pathways contribute to guarantee proper myogenesis, in the Abstract and in Discussion.

      The Abstract now reads: … suggesting that the Trim32–c-Myc axis may represent an essential hub, although likely not the exclusive molecular mechanism, in muscle regeneration within LGMDR8 pathogenesis.”

      The Discussion now reads: “Functionally, we demonstrated that c-Myc contributes to the impaired myogenesis observed in Trim32 KO clones, although this is clearly not the only factor involved in the Trim32-mediated myogenic network; realistically other molecular mechanisms can participate in this process as also suggested by our transcriptomic results.”

      The authors provide a thorough and well-executed interrogation of cell cycle dynamics in Trim32 KO clones, combining phosphor-histone H3 flow cytometry of DNA content, and CFSE proliferation assays. These complementary approaches convincingly show that, while proliferation states remain similar in WT and KO cells, Trim32-deficient myoblasts fail in their normal withdraw from the cell cycle during exposure to differentiation-inducing conditions. This work adds clarity to a previously inconsistent literature and greatly strengthens the study.

      Authors’ response. We thank the Reviewer for appreciating our thorough analyses on cell cycle dynamics in proliferation conditions and at the onset of the differentiation process.

      The transcriptomic analysis (detailed In the "Transcriptomic analysis of Trim32 WT and KO clones along early differentiation" section of Results) is central to the manuscript and provides strong evidence that Trim32 deficiency disrupts normal differentiation processes. However, the description of the pathway enrichment results is highly detailed and somewhat compressed, which may make it challenging for readers to following the key biological 'take-homes'. The narrative quickly moves across their multiple analyses like MDS, clustering, heatmaps, and bubble plots without pausing to guide the reader through what each analysis contributes to the overall biological interpretation. As a result, the key findings (reduced muscle development pathways in KO cells and enrichment of cell cycle-related pathways) can feel somewhat muted. The authors may consider reorganizing this section, so the primary biological insights are highlighted and supported by each of their analyses. This would allow the biological implications to be more accessible to a broader readership.

      Authors’ response. We thank the Reviewer for raising this point and apologise for being too brief in describing the data, leaving indeed some points excessively implicit. As suggested, we now reorganised this session and added the lists of enriched canonical pathways relative to WT vs KO comparisons at D0 and D3 (Fig. EV3B) as well as those relative to the comparison between D0 and D3 for both WT and Trim32 KO samples (Fig. EV3C), with their relative scores. We changed the Results section “Transcriptomic analysis of Trim32 WT and Trim32 KO clones along early differentiationas reported here below and modified the legends accordingly.

      The paragraph now reads: Based on our initial observations, the absence of Trim32 already exerts a significant impact by day 3 (D3) of C2C12 myogenic differentiation. To investigate how Trim32 influences early global transcriptional changes during the proliferative phase (D0) and early differentiation (D3), we performed an unbiased transcriptomic profiling of WT and Trim32 KO clones (Fig. 2A). Multidimensional Scaling (MDS) analysis revealed clear segregation of gene expression profiles based on both time of differentiation (Dim1, 44% variance) and Trim32 genotype (Dim2, 16% variance) (Fig. 2A). Likewise, hierarchical clustering grouped WT and Trim32 KO clones into distinct clusters at both timepoints, indicating consistent genotype-specific transcriptional differences (Fig. EV3A). Differentially Expressed Genes (DEGs) were detected in the Trim32 KO transcriptome relative to WT, at both D0 and D3. In proliferating conditions, 72 genes were upregulated and 189 were downregulated whereas at D3 of differentiation, 72 genes were upregulated and 212 were downregulated. Ingenuity Pathway Analysis of the DEGs revealed the top 10 Canonical Pathways displayed in Fig. EV3B as enriched at either D0 or D3 (Fig. EV3B). Several of these pathways can underscore relevant Trim32-mediated functions though most of them represent generic functions not immediately attributable to the observed myogenesis defects.

      Notably, the transcriptional divergence between WT and Trim32 KO cells is more pronounced at D3, as evidenced by a greater separation along the MSD Dim2 axis, suggesting that Trim32-dependent transcriptional regulation intensifies during early differentiation (Fig. 2A). Given our interest in the differentiation process, we therefore focused our analyses comparing the changes occurring from D0 to D3 in WT (WT D3 vs. D0) and in Trim32 KO (KO D3 vs. D0) RNAseq data.

      Pathway enrichment analysis of D3 vs. D0 DEGs allowed the selection of the top-scored pathways for both WT and Trim32 KO data. We obtained 18 top-scored pathways enriched in each genotype (-log(p-value) ³ 9 cut-off): 14 are shared while 4 are top-ranked only in WT and 4 only in Trim32 KO (Fig. EV3C). For the following analyses, we employed thus a total of 22 distinct pathways and to better mine those relevant in the passage from the proliferation stage to the early differentiation one and that are affected by the lack of Trim32, we built a bubble plot comparing side-by-side the scores and enrichment of the 22 selected top-scored pathways above in WT and Trim32 KO (Fig. 2B). A heatmap of DEGs included within these selected pathways confirms the clustering of the samples considering both the genotypes and the timepoints highlighting gene expression differences (Fig. 2C). These pathways are mainly related to muscle development, cell cycle regulation, genome stability maintenance and few other metabolic cascades.

      As expected given the results related to Figure 1, moving from D0 to D3 WT clones showed robust upregulation of key transcripts associated with the Inactive Sarcomere Protein Complex, a category encompassing most genes in the “Striated Muscle Contraction” pathway, while in Trim32 KO clones this pathway was not among those enriched in the transition from D0 to D3 (Fig. EV3C). Detailed analyses of transcripts enclosed within this pathway revealed that on the transition from proliferation to differentiation, WT clones show upregulation of several Myosin Heavy Chain isoforms (e.g., MYH3, MYH6, MYH8), α-Actin 1 (ACTA1), α-Actinin 2 (ACTN2), Desmin (DES), Tropomodulin 1 (TMOD1), and Titin (TTN), a pattern consistent with previous reports, while these same transcripts were either non-detected or only modestly upregulated in Trim32 KO clones at D3 (Fig. 2D). This genotype-specific disparity was further confirmed by gene set enrichment barcode plots, which demonstrated significant enrichment of these muscle-related transcripts in WT cells (FDR_UP = 0.0062), but not in Trim32 KO cells (FDR_UP = 0.24) (Fig. EV3D). These findings support an early transcriptional basis for the impaired myogenesis previously observed in Trim32 KO cells.

      In addition to differences in muscle-specific gene expression, we observed that also several pathways related to cell proliferation and cell cycle regulation were more enriched in Trim32 KO cells compared to WT. This suggests that altered cell proliferation may contribute to the distinct differentiation behavior observed in Trim32 KO versus WT (Fig. 2B). Given that cell cycle exit is a critical prerequisite for the onset of myogenic differentiation and considering that previous studies on Trim32 role in cell cycle regulation have reported inconsistent findings, we further examined cell cycle dynamics under our experimental conditions to clarify Trim32 contribution to this process

      The work would be greatly strengthened by the conclusion of LGMDR8 primary cells, and rescue experiments of TRIM32 to explore myogenesis.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      Also, EU (5-ethynyl uridine) pulse-chase experiments to label nascent and stable RNA coupled with MYC pulldowns and qPCR (or RNA-sequencing of both pools) would further enhance the claim that MYC stability is being affected.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      "On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025)." Also address and discuss the following, as what is currently written is not entirely accurate: https://www.embopress.org/doi/full/10.1038/s44319-024-00299-z and https://journals.physiology.org/doi/prev/20250724-aop/abs/10.1152/ajpcell.00528.2025

      Authors’ response. We thank the Reviewer for bringing to our attention these two publications, that indeed, add important piece of data to recapitulate the in vivo complexity of c-Myc role in myogenesis. We included this point in our Discussion.

      The Discussion now reads: “On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025). Other reports, instead, demonstrated the implication of c-Myc periodic pulses, mimicking resistance-exercise, in muscle growth, a role that cannot though be observed in our experimental model (Edman et al., 2024; Jones et al., 2025).”

      Minor Comments:

      Z-score scale used in the pathway bubble plot (Figure 2C) could benefit from alternative color choices. Current gradient is a bit muddy and clarity for the reader could be improved by more distinct color options, particularly in the transition from positive to negative Z-score.

      Authors’ response. As suggested, we modified the z-score-representing colors using a more distinct gradient especially in the positive to negative transition in Figure 2B.

      Clarification on the rationale for selecting the "top 18" pathways would be helpful, as it is not clear if this cutoff was chosen arbitrarily or reflects a specific statistical or biological threshold.

      Authors’ response. As now better explained (see comment regarding Major point: Transcriptomics), we used a cut-off of -log(p-value) above or equal to 9 for pathways enriched in DEGs of the D0 vs D3 comparison for both WT and Trim32 KO. The threshold is now included in the Results section and the pathways (shared between WT and Trim32 KO and unique) are listed as Fig. EV3C.

      The authors alternates between using "Trim 32 KO clones" and "KO clones" throughout the manuscript. Consistent terminology across figures and text would improve readability.

      Authors’ response. We thank the Reviewer for this remark, and we apologise for having overlooked it. We amended this throughout the manuscript by always using for clarity “Trim32 KO clones/cells”.

      Cell culture methodology does not specify passage number or culture duration (only "At confluence") before differentiation. This is important, as C2C12 differentiation potential can drift with extended passaging.

      Authors’ response. We agree with the Reviewer that C2C12 passaging can reduce the differentiation potential of this myoblast cell lines; this is indeed the main reason why we decided to employ WT clones, which underwent the same editing process as those that resulted mutated in the Trim32 gene, as reference controls throughout our study. We apologise for not indicating the passages in the first version of the manuscript that now is amended as per here below in the Methods section:

      The C2C12 parental cells used in this study were maintained within passages 3–8. All clonal cell lines (see below) were utilized within 10 passages following gene editing. In all experiments, WT and Trim32 KO clones of comparable passage numbers were used to ensure consistency and minimize passage-related variability.

      Reviewer #2 (Significance (Required)):

      General Assessment:

      This study provides a thorough investigation of Trim32's role the processes related to skeletal muscle differentiation using a CRISPR-Cas9 knockout C2C12 model. The strengths of this study lie in the multi-layered experimental approach as the authors incorporated transcriptomics, cell cycle profiling, and stability assays which collectively build a strong case for their hypothesis that Trim32 is a key factor in the normal regulation of myogenesis. The work is also strengthened by the use of multiple biological and technical replicates, particularly the independent KO clones which helps address potential clonal variation issues that could occur. The largest limitation to this study is that, while the c-Myc mechanism is well explored, the other Trim32-dependent pathways associated with the disruption (implicated by the incomplete rescue by c-Myc knockdown) are not as well addressed. Overall however, the study convincingly identifies a critical function for Trim32 during skeletal muscle differentiation. * * Advance: * * To my knowledge, this is the first study to demonstrate the mRNA stability level of c-Myc regulation by Trim32, rather than through the ubiquitin-mediated protein degradation. This work will advance the current understanding and provide a more complete understanding of Trim32's role in c-Myc regulation. Beyond c-Myc, this work highlights the idea that TRIM family proteins can influence RNA stability which could implicate a broader role in RNA biology and has potential for future therapeutic targeting. * * Audience: * * This research will be of interest to an audience that focuses on broad skeletal muscle biology but primarily to readers with more focused research such as myogenesis and neuromuscular disease (LGMDR8 in particular) where the defined Trim32 governance over early differentiation checkpoints will be of interest. It will also provide mechanistic insights to those outside of skeletal muscle that study TRIM family proteins, ubiquitin biology, and RNA regulation. For translational/clinical researchers, it identifies the Trim32/c-Myc axis as a potential therapeutic target for LGMDR8 and related muscular dystrophies.

      Expertise: * * My expertise lies in skeletal muscle biology, gene editing, transgenic mouse models, and bioinformatics. I feel confident evaluating the data and conclusions as presented.

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

      • In this paper, the authors examine the role of TRIM32, implicated in limb girdle muscular dystrophy recessive 8 (LGMDR8), in the differentiation of C2C12 mouse myoblasts. Using CRISPR, they generate mutant and wild-type clones and compare their differentiation capacity in vitro. They report that Trim32-deficient clones exhibit delayed and defective myogenic differentiation. RNA-seq analysis reveals widespread changes in gene expression, although few are validated by independent methods. Notably, Trim32 mutant cells maintain residual proliferation under differentiation conditions, apparently due to a failure to downregulate c-Myc. Translation inhibition experiments suggest that TRIM32 promotes c-Myc mRNA destabilization, but this conclusion is insufficiently substantiated. The authors also perform rescue experiments, showing that c-Myc knockdown in Trim32-deficient cells alleviates some differentiation defects. However, this rescue is not quantified, was conducted in only two of the three knockout lines, and is supported by inappropriate statistical analysis of gene expression. Overall, the manuscript in its current form has substantial weaknesses that preclude publication. Beyond statistical issues, the major concerns are: (1) exclusive reliance on the immortalized C2C12 line, with no validation in primary/satellite cells or in vivo, (2) insufficient mechanistic evidence that TRIM32 acts directly on c-Myc mRNA, and (3) overinterpretation of disease relevance in the absence of supporting patient or in vivo data. Please find more details below:*

      We thank the Reviewer for the in-depth assessment of our work and precious suggestions to improve the manuscript. We have carefully addressed some of the concerns raised, as detailed here, while others, which require more experimental efforts, will be addressed as detailed in the Revision Plan.

      - TRIM32 complementation / rescue experiments to exclude clonal or off-target CRISPR effects and show specificity are lacking.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      - The authors link their in vitro findings to LGMDR8 pathogenesis and propose that the Trim32-c-Myc axis may serve as a central regulator of muscle regeneration in the disease. However, LGMDR8 is a complex disorder, and connecting muscle wasting in patients to differentiation assays in C2C12 cells is difficult to justify. No direct evidence is provided that the proposed mRNA mechanism operates in patient-derived samples or in mouse satellite cells. Moreover, the partial rescue achieved by c-Myc knockdown (which does not fully restore myotube morphology or differentiation index) further suggests that the disease connection is not straightforward. Validation of the TRIM32-c-Myc axis in a physiologically relevant system, such as LGMD patient myoblasts or Trim32 mutant mouse cells, would greatly strengthen the claim.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      -Some gene expression changes from the RNA-seq study in Figure 2 should be validated by qPCR

      Authors’ response. We thank the reviewer for this suggestion. This point will be addressed as detailed in the Revision Plan. We have selected several transcripts that will be evaluated in independent samples in order to validate the RNAseq results.

      - The paper shows siRNA knockdown of c-Myc in KO restores Myogenin RNA/protein but does not fully rescue myotube morphology or differentiation index. This suggests that Trim32 controls additional effectors beyond c-Myc; yet the authors do not pursue other candidate mediators identified in the RNA-seq. The manuscript would be strengthened by systematically testing whether other deregulated transcripts contribute to the phenotype.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      - There are concerns with experimental/statistical issues and insufficient replicate reporting. The authors use unpaired two-tailed Student's t-test across many comparisons; multiple testing corrections or ANOVA where appropriate should be used. In Figure EV5B and Figure 6B, the authors perform statistical analyses with control values set to 1. This method masks the inherent variability between experiments and artificially augments p values. Control sample values need to be normalized to one another to have reliable statistical analysis. Myotube morphology and differentiation index quantifications need clear description of fields counted, blind analysis, and number of biological replicates.

      Authors’ response. We thank the Reviewer for raising this point.

      Regarding the replicates, we clarified in the Methods and Legends that the Trim32 KO experiments have been performed on 3 biological replicates (independent clones) and the same for the reference control (3 independent WT clones), except for the Fig. 6 experiments that were performed on 2 Trim32 KO and 2 WT clones. All the Western Blots, immunofluorescence, qPCR data are representative of the results of at least 3 independent experiments unless otherwise stated. We reported the number and type of replicates as well as the microscope fields analyzed.

      We repeated the statistical analyses of the data in Figure 5G, EV5D, EV5E, employing more appropriately the 2-way-ANOVA test, as suggested, and we now reported this info in the graphs and legends.

      We thank the Reviewer for raising this point, we agree and substituted the graphs in Fig. EV5B and 6B showing the control values normalised as suggested. The statistical analyses now reflect this change.

      -Some English mistakes require additional read-throughs. For example: "Indeed, Trim32 has no effect on the stability of c-Myc mRNA in proliferating conditions, but upon induction of differentiation the stability of c-Myc mRNA resulted enhanced in Trim32 KO clones (Fig. 5G, Fig. EV5D and 5E)."

      Authors’ response. We re-edited this revised version of the manuscript as suggested.

      -Results in Figure 5A should be quantified

      Authors’ response. We amended this point by quantifying the results shown in Fig. 5A, we added the graph of the quantification of 3 experimental replicates to the Figure. Quantification confirms that no statistically significant difference is observed. The Figure and the relative legend are modified accordingly.

      -Based on the nuclear marker p84, the separation of cytoplasmic and nuclear fractions is not ideal in Figure 5D

      Authors’ response. We agree with the Reviewer that the presence of p84 also in the cytoplasmic fraction is not ideal. Regrettably, we observed this faint p84 band in all the experiments performed. We think however, that this is not impacting on the result that clearly shows that c-Myc and Trim32 are never detected in the same compartment.

      -In Figure 6, it is not appropriate to perform statistical analyses on only two data points per condition.

      Authors’ response. We agree with the Reviewer and we now show the graph of the results of the 3 technical replicates for 2 biological replicates and do not indicate any statistics (Fig. 6B). The graph was also modified according to a previous point raised.

      -The nuclear MYOG phenotype is very interesting; could this be related to requirements of TRIM32 in fusion?

      Authors’ response. We agree with the Reviewer that Trim32 might also be necessary for myoblast fusion. This point is however beyond the scope of the present study and will be addressed in future work.

      - The hypothesis that TRIM32 destabilizes c-Myc mRNA is intriguing but requires stronger mechanistic support. This would be more convincing with RNA immunoprecipitation to test direct association with c-Myc mRNA, and/or co-immunoprecipitation to identify interactions between TRIM32 and proteins involved in mRNA stability. The study would also be strengthened by reporter assays, such as c-Myc 3′UTR luciferase constructs in WT and KO cells, to directly demonstrate 3′UTR-dependent regulation of mRNA stability.

      Authors’ response. This point will be addressed as detailed in the Revision Plan

      Reviewer #3 (Significance (Required)):

      The manuscript presents a minor conceptual advance in understanding TRIM32 function in myogenic differentiation. Its main limitation is that all experiments were performed in C2C12 cells. While C2C12 are a classical system to study muscle differentiation, they are an immortalized, long-cultured, and genetically unstable line that represents a committed myoblast stage rather than bona fide satellite cells. They therefore do not fully model the biology of early regenerative responses. Several TRIM32 phenotypes reported in the literature differ between primary satellite cells and cell lines, and the authors themselves note such discrepancies. Extrapolating these findings to LGMDR8 pathogenesis without validation in primary human myoblasts, satellite cell assays, or in vivo regeneration models is therefore not justified. Previous work has already established clear roles for TRIM32 in mouse satellite cells in vivo and in patient myoblasts in vitro, whereas this study introduces a novel link to c-Myc regulation during differentiation. In addition, without mechanistic evidence, the central claim that TRIM32 regulates c-Myc mRNA stability remains descriptive and incomplete. Nevertheless, the results will be of interest to researchers studying LGMD and to those exploring TRIM32 biology in broader contexts. I review this manuscript as a muscle biologist with expertise in satellite cell biology and transcriptional regulation.

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

      Reply to the Reviewers

      I thank the Referees for their...

      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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

      Reply to the Reviewers

      I thank the Referees for their...

      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In this paper, the authors examine the role of TRIM32, implicated in limb girdle muscular dystrophy recessive 8 (LGMDR8), in the differentiation of C2C12 mouse myoblasts. Using CRISPR, they generate mutant and wild-type clones and compare their differentiation capacity in vitro. They report that Trim32-deficient clones exhibit delayed and defective myogenic differentiation. RNA-seq analysis reveals widespread changes in gene expression, although few are validated by independent methods. Notably, Trim32 mutant cells maintain residual proliferation under differentiation conditions, apparently due to a failure to downregulate c-Myc. Translation inhibition experiments suggest that TRIM32 promotes c-Myc mRNA destabilization, but this conclusion is insufficiently substantiated. The authors also perform rescue experiments, showing that c-Myc knockdown in Trim32-deficient cells alleviates some differentiation defects. However, this rescue is not quantified, was conducted in only two of the three knockout lines, and is supported by inappropriate statistical analysis of gene expression. Overall, the manuscript in its current form has substantial weaknesses that preclude publication. Beyond statistical issues, the major concerns are: (1) exclusive reliance on the immortalized C2C12 line, with no validation in primary/satellite cells or in vivo, (2) insufficient mechanistic evidence that TRIM32 acts directly on c-Myc mRNA, and (3) overinterpretation of disease relevance in the absence of supporting patient or in vivo data. Please find more details below:

      • TRIM32 complementation / rescue experiments to exclude clonal or off-target CRISPR effects and show specificity are lacking.
      • The authors link their in vitro findings to LGMDR8 pathogenesis and propose that the Trim32-c-Myc axis may serve as a central regulator of muscle regeneration in the disease. However, LGMDR8 is a complex disorder, and connecting muscle wasting in patients to differentiation assays in C2C12 cells is difficult to justify. No direct evidence is provided that the proposed mRNA mechanism operates in patient-derived samples or in mouse satellite cells. Moreover, the partial rescue achieved by c-Myc knockdown (which does not fully restore myotube morphology or differentiation index) further suggests that the disease connection is not straightforward. Validation of the TRIM32-c-Myc axis in a physiologically relevant system, such as LGMD patient myoblasts or Trim32 mutant mouse cells, would greatly strengthen the claim. -Some gene expression changes from the RNA-seq study in Figure 2 should be validated by qPCR
      • The paper shows siRNA knockdown of c-Myc in KO restores Myogenin RNA/protein but does not fully rescue myotube morphology or differentiation index. This suggests that Trim32 controls additional effectors beyond c-Myc; yet the authors do not pursue other candidate mediators identified in the RNA-seq. The manuscript would be strengthened by systematically testing whether other deregulated transcripts contribute to the phenotype.
      • There are concerns with experimental/statistical issues and insufficient replicate reporting. The authors use unpaired two-tailed Student's t-test across many comparisons; multiple testing corrections or ANOVA where appropriate should be used. In Figure EV5B and Figure 6B, the authors perform statistical analyses with control values set to 1. This method masks the inherent variability between experiments and artificially augments p values. Control sample values need to be normalized to one another to have reliable statistical analysis. Myotube morphology and differentiation index quantifications need clear description of fields counted, blind analysis, and number of biological replicates. -Some English mistakes require additional read-throughs. For example: "Indeed, Trim32 has no effect on the stability of c-Myc mRNA in proliferating conditions, but upon induction of differentiation the stability of c-Myc mRNA resulted enhanced in Trim32 KO clones (Fig. 5G, Fig. EV5D and 5E)." -Results in Figure 5A should be quantified -Based on the nuclear marker p84, the separation of cytoplasmic and nuclear fractions is not ideal in Figure 5D -In Figure 6, it is not appropriate to perform statistical analyses on only two data points per condition. -The nuclear MYOG phenotype is very interesting; could this be related to requirements of TRIM32 in fusion?
      • The hypothesis that TRIM32 destabilizes c-Myc mRNA is intriguing but requires stronger mechanistic support. This would be more convincing with RNA immunoprecipitation to test direct association with c-Myc mRNA, and/or co-immunoprecipitation to identify interactions between TRIM32 and proteins involved in mRNA stability. The study would also be strengthened by reporter assays, such as c-Myc 3′UTR luciferase constructs in WT and KO cells, to directly demonstrate 3′UTR-dependent regulation of mRNA stability.

      Significance

      The manuscript presents a minor conceptual advance in understanding TRIM32 function in myogenic differentiation. Its main limitation is that all experiments were performed in C2C12 cells. While C2C12 are a classical system to study muscle differentiation, they are an immortalized, long-cultured, and genetically unstable line that represents a committed myoblast stage rather than bona fide satellite cells. They therefore do not fully model the biology of early regenerative responses. Several TRIM32 phenotypes reported in the literature differ between primary satellite cells and cell lines, and the authors themselves note such discrepancies. Extrapolating these findings to LGMDR8 pathogenesis without validation in primary human myoblasts, satellite cell assays, or in vivo regeneration models is therefore not justified. Previous work has already established clear roles for TRIM32 in mouse satellite cells in vivo and in patient myoblasts in vitro, whereas this study introduces a novel link to c-Myc regulation during differentiation. In addition, without mechanistic evidence, the central claim that TRIM32 regulates c-Myc mRNA stability remains descriptive and incomplete. Nevertheless, the results will be of interest to researchers studying LGMD and to those exploring TRIM32 biology in broader contexts. I review this manuscript as a muscle biologist with expertise in satellite cell biology and transcriptional regulation.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors sought to investigate the molecular role of Trim32, a tripartite motif-containing E3 ubiquitin ligase often associated with its dysregulation in Limb-Girdle Muscular Dystrophy Recessive 8 (LGMDR8), and its role in the dynamics of skeletal muscle differentiation. Using a CRISPR-Cas9 model of Trim32 knockout in C2C12 murine myoblasts, the authors demonstrate that loss of Trim32 alters the myogenic process, particularly by impairing the transition from proliferation to differentiation. The authors provide evidence in the way of transcriptomic profiling that displays an alteration of myogenic signaling in the Trim32 KO cells, leading to a disruption of myotube formation in-vitro. Interestingly, while previous studies have focused on Trim32's role in protein ubiquitination and degradation of c-Myc, the authors provide evidence that Trim32-regulation of c-Myc occurs at the level of mRNA stability. The authors show that the sustained c-Myc expression in Trim32 knockout cells disrupts the timely expression of key myogenic factors and interferes with critical withdrawal of myoblasts from the cell cycle required for myotube formation. Overall, the study offers a new insight into how Trim32 regulates early myogenic progression and highlights a potential therapeutic target for addressing the defects in muscular regeneration observed in LGMDR8.

      Major Comments:

      The work is a bit incremental based on this: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030445 And this: https://www.nature.com/articles/s41418-018-0129-0 To their credit, the authors do cite the above papers.

      The authors do provide compelling evidence that Trim32 deficiency disrupts C2C12 myogenic differentiation and sustained c-Myc expression contributes to this defective process. However, while knockdown of c-Myc does restore Myogenin levels, it was not sufficient to normalize myotube morphology or differentiation index, suggesting an incomplete picture of the Trim32-dependent pathways involved. The authors should qualify their claim by emphasizing that c-Myc regulation is a major, but not exclusive, mechanism underlying the observed defects. This will prevent an overgeneralization and better align the conclusions with the author's data. The authors provide a thorough and well-executed interrogation of cell cycle dynamics in Trim32 KO clones, combining phosphor-histone H3 flow cytometry of DNA content, and CFSE proliferation assays. These complementary approaches convincingly show that, while proliferation states remain similar in WT and KO cells, Trim32-deficient myoblasts fail in their normal withdraw from the cell cycle during exposure to differentiation-inducing conditions. This work adds clarity to a previously inconsistent literature and greatly strengthens the study.

      The transcriptomic analysis (detailed In the "Transcriptomic analysis of Trim32 WT and KO clones along early differentiation" section of Results) is central to the manuscript and provides strong evidence that Trim32 deficiency disrupts normal differentiation processes. However, the description of the pathway enrichment results is highly detailed and somewhat compressed, which may make it challenging for readers to following the key biological 'take-homes'. The narrative quickly moves across their multiple analyses like MDS, clustering, heatmaps, and bubble plots without pausing to guide the reader through what each analysis contributes to the overall biological interpretation. As a result, the key findings (reduced muscle development pathways in KO cells and enrichment of cell cycle-related pathways) can feel somewhat muted. The authors may consider reorganizing this section, so the primary biological insights are highlighted and supported by each of their analyses. This would allow the biological implications to be more accessible to a broader readership.

      The work would be greatly strengthened by the conclusion of LGMDR8 primary cells, and rescue experiments of TRIM32 to explore myogenesis. Also, EU (5-ethynyl uridine) pulse-chase experiments to label nascent and stable RNA coupled with MYC pulldowns and qPCR (or RNA-sequencing of both pools) would further enhance the claim that MYC stability is being affected.

      "On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025)." Also address and discuss the following, as what is currently written is not entirely accurate: https://www.embopress.org/doi/full/10.1038/s44319-024-00299-z and https://journals.physiology.org/doi/prev/20250724-aop/abs/10.1152/ajpcell.00528.2025

      Minor Comments:

      Z-score scale used in the pathway bubble plot (Figure 2C) could benefit from alternative color choices. Current gradient is a bit muddy and clarity for the reader could be improved by more distinct color options, particularly in the transition from positive to negative Z-score.

      Clarification on the rationale for selecting the "top 18" pathways would be helpful, as it is not clear if this cutoff was chosen arbitrarily or reflects a specific statistical or biological threshold.

      The authors alternates between using "Trim 32 KO clones" and "KO clones" throughout the manuscript. Consistent terminology across figures and text would improve readability.

      Cell culture methodology does not specify passage number or culture duration (only "At confluence") before differentiation. This is important, as C2C12 differentiation potential can drift with extended passaging.

      Significance

      General Assessment:

      This study provides a thorough investigation of Trim32's role the processes related to skeletal muscle differentiation using a CRISPR-Cas9 knockout C2C12 model. The strengths of this study lie in the multi-layered experimental approach as the authors incorporated transcriptomics, cell cycle profiling, and stability assays which collectively build a strong case for their hypothesis that Trim32 is a key factor in the normal regulation of myogenesis. The work is also strengthened by the use of multiple biological and technical replicates, particularly the independent KO clones which helps address potential clonal variation issues that could occur. The largest limitation to this study is that, while the c-Myc mechanism is well explored, the other Trim32-dependent pathways associated with the disruption (implicated by the incomplete rescue by c-Myc knockdown) are not as well addressed. Overall however, the study convincingly identifies a critical function for Trim32 during skeletal muscle differentiation.

      Advance:

      To my knowledge, this is the first study to demonstrate the mRNA stability level of c-Myc regulation by Trim32, rather than through the ubiquitin-mediated protein degradation. This work will advance the current understanding and provide a more complete understanding of Trim32's role in c-Myc regulation. Beyond c-Myc, this work highlights the idea that TRIM family proteins can influence RNA stability which could implicate a broader role in RNA biology and has potential for future therapeutic targeting.

      Audience:

      This research will be of interest to an audience that focuses on broad skeletal muscle biology but primarily to readers with more focused research such as myogenesis and neuromuscular disease (LGMDR8 in particular) where the defined Trim32 governance over early differentiation checkpoints will be of interest. It will also provide mechanistic insights to those outside of skeletal muscle that study TRIM family proteins, ubiquitin biology, and RNA regulation. For translational/clinical researchers, it identifies the Trim32/c-Myc axis as a potential therapeutic target for LGMDR8 and related muscular dystrophies.

      Expertise:

      My expertise lies in skeletal muscle biology, gene editing, transgenic mouse models, and bioinformatics. I feel confident evaluating the data and conclusions as presented.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Xiong and colleagues investigate the mechanisms operating downstream to TRIM32 and controlling myogenic progression from proliferation to differentiation. Overall, the bulk of the data presented is robust. Although further investigation of specific aspects would make the conclusions more definitive (see below), it is an interesting contribution to the field of scientists studying the molecular basis of muscle diseases. In my opinion, a few aspects would improve the manuscript.

      Firstly, the conclusion that Trim32 regulates c-Myc mRNA stability could be expanded and corroborated by further mechanistic studies:

      1. Studies investigating whether Tim32 binds directly to c-Myc RNA. Moreover, although possibly beyond the scope of this study, an unbiased screening of RNA species binding to Trim32 would be informative.
      2. If possible, studies in which the overexpression of different mutants presenting specific altered functional domains (NHL domain known to bind RNAs and Ring domain reportedly involved in protein ubiquitination) would be used to test if they are capable or incapable of rescuing the reported alteration of Trim32 KO cell lines in c-Myc expression and muscle maturation. An optional aspect that might be interesting to explore is whether the alterations in c-Myc expression observed in C2C12 might be replicated with primary myoblasts or satellite cells devoid of Trim32.

      I also have a few minor points to highlight:

      • It is unclear if the differences highlighted in graphs 5G, EV5D, and EV5E are statistically significant.
      • On page 10, it is stated that c-Myc down-regulation cannot rescue KO myotube morphology fully nor increase the differentiation index significantly, but the corresponding data is not shown. Could the authors include those quantifications in the manuscript?

      Significance

      The manuscript offers several strengths. It provides novel mechanistic insight by identifying a previously unrecognized role for Trim32 in regulating c-Myc mRNA stability during the onset of myogenic differentiation. The study is supported by a robust methodology that integrates CRISPR/Cas9 gene editing, transcriptomic profiling, flow cytometry, biochemical assays, and rescue experiments using siRNA knockdown. Furthermore, the work has a disease relevance, as it uncovers a mechanistic link between Trim32 deficiency and impaired myogenesis, with implications for the pathogenesis of LGMDR8. At the same time, the study has some limitations. The findings rely exclusively on the C2C12 myoblast cell line, which may not fully represent primary satellite cell or in vivo biology. The functional rescue achieved through c-Myc knockdown is only partial, restoring Myogenin expression but not the full differentiation index or morphology, indicating that additional mechanisms are likely involved. Although evidence supports a role for Trim32 in mRNA destabilization, the precise molecular partners-such as RNA-binding activity, microRNA involvement, or ligase function-remain undefined. Some discrepancies with previous studies, including Trim32-mediated protein degradation of c-Myc, are acknowledged but not experimentally resolved. Moreover, functional validation in animal models or patient-derived cells is currently lacking.

      Despite these limitations, the study represents an advancement for the field. It shifts the conceptual framework from Trim32's canonical role in protein ubiquitination to a novel function in RNA regulation during myogenesis. It also raises potential clinical implications by suggesting that targeting the Trim32-c-Myc axis, or modulating c-Myc stability, may represent a therapeutic strategy for LGMDR8. This work will be of particular interest to muscle biology researchers studying myogenesis and the molecular basis of muscle disease, RNA biology specialists investigating post-transcriptional regulation and mRNA stability, and neuromuscular disease researchers and clinicians seeking to identify new molecular targets for therapeutic intervention in LGMDR8.

      The Reviewer expressing this opinion is an expert in muscle stem cells, muscle regeneration, and muscle development.

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

      Learn more at Review Commons


      Reply to the reviewers

      We are grateful to the reviewers for their thoughtful and constructive evaluations of our manuscript. Their comments helped us clarify key aspects of the study and strengthen both the presentation and interpretation of our findings. The central goal of this work is to dissect how the opposing activities of GATA4 and CTCF coordinate chromatin topology and transcriptional timing during human cardiomyogenesis. The reviewers’ feedback has allowed us to refine this message and better contextualize our results within the broader framework of chromatin regulation and cardiac development.

      In response to the reviews, in our preliminary revision we have already implemented substantial improvements to the manuscript, including additional analyses, clearer data visualization, and revisions to the text to avoid overinterpretation. These refinements enhance the robustness of our conclusions without altering the overall scope of the study. A small number of additional analyses and experiments are ongoing and will be added to the full revision, as detailed below.

      We believe that the revised manuscript, together with the planned updates, fully addresses the reviewers’ concerns and substantially strengthens the contribution of this work to the field.

      Reviewer 1 – Point 1:

      In the datasets you are examining, what are the relative percentages in each of the four groups relating compartmentalization change to expression change (A→B, expression up; A→B, down; B→A, up; B→A, down)?

      We quantified compartment–expression relationships using Hi-C and bulk RNA-seq from H9 ESCs and CMs. The percentages for each category are shown below and incorporated into updated Figure S2H.

      Group

      Downregulated in CM

      Upregulated in CM

      A-to-A

      11.92%

      8.44%

      A-to-B

      18.20%

      2.79%

      B-to-A

      7.96%

      18.07%

      B-to-B

      14.36%

      6.44%

      A chi-squared test comparing observed vs. expected distributions (based on gene density across bins) confirmed a strong association between compartment dynamics and transcriptional behavior. B-to-A genes are significantly enriched among genes upregulated in CMs, while A-to-B genes are enriched among those downregulated (updated Figure S2H).

      We next assessed with GSEA how these gene classes respond to GATA4 and CTCF knockdown. In 2D CMs, GATA4 knockdown reduces expression of CM-upregulated B-to-A genes and increases expression of CM-downregulated A-to-B genes, whereas CTCF knockdown produces the opposite pattern (updated Figure 2F).

      Applying the same analysis to cardioid bulk RNA-seq (updated Figure 4E) revealed the strongest effects in SHF-RV organoids, consistent with monolayer data. In SHF-A organoids, only GATA4 knockdown had a measurable impact on CM-upregulated B-to-A and CM-downregulated A-to-B genes. Because the subsets of CM-downregulated B-to-A and CM-upregulated A-to-B genes were very small and showed no consistent trends, Figure 4 focuses on the two informative categories only. The full classification is provided in Reviewer Figure 1 below.

      (The figure cannot be rendered in this text-only format)

      Reviewer Figure 1. GSEA for CM-upregulated B-to-A and CM-downregulated A-to-B genes. p-values by Adaptive Monte-Carlo Permutation test.

      Reviewer 1 – Point 2

      This phrase in the abstract is imprecise: ‘whereas premature CTCF depletion accelerates yet confounds cardiomyocyte maturation.’


      The abstract has been revised to: “whereas premature CTCF depletion accelerates yet alters cardiomyocyte maturation.” (lines 29-30).

      Reviewer 1 – Point 3

      Regarding this statement: "Disruption of [3D chromatin architecture] has been linked to genetic dilated cardiomyopathy (DCM) caused by lamin A/C mutations8,9, and mutations in chromatin regulators are strongly enriched in de novo congenital heart defects (CHD)10, underscoring their pathogenic relevance11." The first studies to implicate chromatin structural changes in heart disease, including the role of CTCF in that process, were PMID: 28802249, a model of acquired, rather than genetic, disease.

      We added the following sentence to the paragraph introducing CTCF: “Moreover, depletion of CTCF in the adult cardiomyocytes leads to heart failure28,29.” (line 72)

      Reviewer 1 – Point 4

      Can you quantify this statement: ‘the compartment switch coincided with progressive reduction of promoter–gene body interactions’?

      We quantified promoter–gene body contacts by calculating the area under the curve (AUC) of the virtual 4C signal derived from H9 Hi-C data across differentiation. As a result of this analysis we added the following sentence: “Quantitatively, interactions between the TTN promoter and its gene body decreased by ~55% from the pluripotent stage to day 80 cardiomyocytes.” (lines 89-91).


      Reviewer 1 – Point 5

      Regarding this statement: "six regions became less accessible in CMs, correlating with ChIP-seq signal for the ubiquitous architectural protein CTCF." I don't see 6 ATAC peaks in either TTN trace in Figure 1A.

      We corrected the text as it follows: “TTN experienced clear changes in chromatin accessibility during CM differentiation: ATAC-seq identified two CM-specific peaks that correlated with ChIP-seq signal for the cardiac pioneer TF GATA4 at the two promoters, one driving full length titin and the other the shorter cronos isoform. In contrast, two regions became less accessible in CMs, correlating with two of the six ChIP-seq peaks for the ubiquitous architectural protein CTCF” (lines 93-97). We attribute the differences between ChIP-seq and ATAC-seq profiles to methodological sensitivity and/or biological variability between datasets generated in different laboratories and cell batches.

      Reviewer 1 – Point 6

      Western blots need molecular weight markers.

      We edited the relevant panels accordingly (updated Figures 1E and 2B).

      Reviewer 1 – Point 7

      Regarding this statement: "The decrease in CTCF protein levels may explain its selective detachment from TTN during cardiomyogenesis." At face value, these findings suggest the opposite: i.e. that a massive downregulation of CTCF at protein level should affect its binding across the genome, which is not tested and is hard to evaluate between ChIP-seq studies from different groups and from different developmental timeframes.

      We revised the text to avoid implying selective detachment and performed a genome-wide analysis of CTCF occupancy using ENCODE ChIP-seq datasets generated by the same laboratory with matched protocols in hESCs and hESC-derived CMs. This analysis shows that 43.2% of CTCF sites present in ESCs are lost in CMs, whereas only 5.7% are gained, confirming a broad reduction in CTCF binding during differentiation. These results are now included in__ updated Figure 1B__.

      Reviewer 1 – Point 8a

      A couple thoughts on the FISH experiments in Figure 2. A claim of 'impaired B-A transition' would be more convincing if you show, by FISH, that the relative distance of TTN from lamin B increases with differentiation.

      Although prior work from us and others has established that TTN transitions from the nuclear periphery in hESCs to a more internal position during cardiomyogenesis (Poleshko et al. 2017; Bertero et al. 2019a), we are reproducing this trajectory in WTC11 hiPSCs as part of the FISH experiments for the full revision.

      __Reviewer 1 – Point 8b __

      In the [FISH] images: are you showing a total projection of all z planes? One assumes the quantitation is relative to a 3D reconstruction in which the lamin B signal is restricted to the periphery. Have you shown this? __

      Quantification was performed on full 3D reconstructions from Z-stacks, as detailed in the Methods (lines 721-727). While the original submission displayed maximum-intensity projections, updated Figure 2D and Figure S2E now show representative single optical sections, which more clearly highlight the spatial relationship between the TTN locus and the nuclear lamina.

      Reviewer 1 – Point 8c

      Lastly, these data are very interesting and important, provoking reexamination of your interpretation of the results in Figure 1. Figure 1 was interpreted to show that less CTCF binding led to decreased lamina (and thus B compartment) association during development. Figure 2 shows that depleting CTCF does not change association of TTN with lamina.

      Our interpretation is that by day 25 of hiPSC-CM differentiation the TTN locus may have reached its maximal radial repositioning even in control cells, limiting the ability to detect earlier effects of CTCF depletion. To test whether CTCF knockdown accelerates lamina detachment at earlier stages, we are repeating the FISH analysis for the inducible CTCF knockdown line at multiple time points during differentiation.

      Reviewer 1 – Point 9

      A thought about this statement: "Altogether, these results suggest that GATA4 and CTCF function as positive and negative regulators of B-to-A compartment switching, likely acting through global and local chromatin remodeling, respectively." GATA4 induces TTN expression and its knockdown prevents TTN expression-the evidence that GATA4 affects compartmentalization is unclear. By activating the gene, GATA4 may shift TTN to B classification.

      Our current data do not allow us to disentangle whether GATA4-driven transcriptional activation precedes or follows the B-to-A compartment shift. We have therefore removed the mechanistic speculation from this sentence to avoid overinterpretation. Nevertheless, the analyses in updated Figure 2F, discussed in the response to Reviewer 1 - Point 1, show that GATA4 knockdown preferentially reduces expression of CM-upregulated B-to-A genes, while CTCF knockdown has the opposite effect, supporting the conclusion that both factors influence the transcriptional programs associated with B-to-A transitions.

      Reviewer 1 – Point 10

      __I'm not sure what I am looking at in Figure 3C. Are those traces integration of interactions over a defined window? "Each [mutant is] clearly different from WT" is not obvious from the presentation. The histograms are plotting AUC of what? Interactions of those peaks with the mutated region? I genuinely appreciate how laborious this experiment must have been and encourage you to explain better what you are showing. __

      We revised the main text to avoid overstating the differences (“clearly” “in a similar manner”, line 192) and expanded the l__egends of updated Figures 3C–D__ to clarify what is being shown: “(C) 4C-seq in hiPSCs using the promoter-proximal region of TTN as viewpoint. The top panel shows raw interaction profiles. The lower panels plot pairwise differences between conditions to reveal subtle changes. A schematic indicating the 4C viewpoint is included for clarity. Right inset: zoom of the CBS4–5 region. Mean of n = 3 cultures. (D) AUC of the differential 4C-seq signal for defined intervals (panel C). p-values by one-sample t-test against μ = 0.”. We also added a visual cue in updated Figure 3C indicating the 4C viewpoint to facilitate interpretation.

      Reviewer 1 – Point 11

      Again acknowledging how challenging these experiments are: when you mutant a locus, you change CTCF binding but you also change the DNA. Thus, attributing the changes in interactions to presence/absence of CTCF binding is difficult, because the DNA substrate itself has changed. Perhaps you are presenting all of this as a negative result, given the modest effect on transcription, which is as important as a positive result, given the assumptions usually made about such things. But the results are not clearly described and your interpretation seems to go between implying the structural change causative and being agnostic.

      We recognize that deleting a genomic region can affect both CTCF binding and the DNA substrate itself. For this reason, we implemented two parallel genome-editing strategies:

      (1) a straightforward Cas9-mediated deletion of ~100 bp centered on each CBS, and

      (2) a more precise HDR approach replacing only the 20 bp core CTCF motif.

      Because the HDR strategy succeeded, all downstream analyses were carried out on these minimal edits, which substantially limit disruption of other transcription factor motifs and reduce the likelihood of sequence-dependent polymer effects unrelated to CTCF.

      Nevertheless, to avoid implying unwarranted causality in the absence of more conclusive evidence, we added a paragraph to the Discussion outlining these limitations, including the sentence: “Our study also reflects general challenges in separating chromatin-architectural and transcriptional mechanisms. Although the CBS edits were restricted to the core CTCF motifs, additional sequence-dependent effects cannot be fully excluded, and we therefore interpret the resulting changes as consistent with—but not exclusively due to—loss of CTCF binding.” (lines 365-368)

      Reviewer 1 - Point 12.

      Figure 4C: since you have RNA-seq data, a much more objective way to present these data would be to show all data (again, A-B, up; A-B, down; B-A, up; B-A, down) and the effects of CTCF or GATA4. Regardless, you can still focus on the cardiac specific genes. But my guess is if you examine all genes, the pattern you show in panel C will not be present in the majority of cases. Furthermore, if this hypothesis is wrong, such an analysis will allow you to identify other genes affected by the mechanisms you describe and your analysis will test whether these mechanisms are in fact conserved at different loci.

      As outlined in our response to Point 1, we extended the analysis to all genes undergoing compartment changes and incorporated this into the cardioid RNA-seq dataset. This revealed a clear and consistent relationship between GATA4 or CTCF knockdown and the expression of B-to-A and A-to-B gene classes (updated Figure 4E).

      Reviewer 2 - Point 1.1

      1. CTCF regulation at TTN locus:

      (1) Figure 1A: The claim of the authors about convergent CTCF sites and transcriptional activation of TTN is quite simplistic. This claim is only valid when we know where cohesin is loaded. If cohesin is loaded at then intragenic GATA4 binding site, then the only important CTCF sites is at the promoter of TTN. I suggest that the authors read few more publications which may help the authors to better understand how cohesin and CTCF team up to regulate transcription, such as Hsieh et al., Nature Genetics, 2022; Liu et al., Nature Genetics, 2021; Rinzema et al., Nature Structural and Molecular Biology, 2022.

      __Suggestion: The authors should add cohesin (RAD21/SMC1A) and NIPBL ChIP-seq for better interpretation. __

      In line with the reviewer’s insightful suggestion, we integrated cohesin ChIP-seq data into updated Figure 1A. Specifically, we added a RAD21 ChIP-seq track from hESCs, which provides direct evidence of cohesin occupancy across the TTN locus. RAD21 binding closely parallels CTCF binding at five sites within the gene body, supporting a model in which promoter-proximal CTCF anchors cohesin to stabilize repressive loops at this locus. This analysis substantially strengthens the mechanistic framework and is consistent with the studies recommended by the reviewer, which we have now cited (lines 68 and 104).

      Reviewer 2 - Point 1.2. (2) Figure 3B: If delta2CBS only has heterozygenous deletion of CBS6, why we would expect the binding will be weaken to 50%. However, the CTCF binding is reduced to around 1/10 in the ChIP-qPCR. How do the authors explain this?

      Sequencing of the Δ2CBS line shows that one CBS6 allele carries the intended EcoRI replacement, while the second allele contains a 2-bp deletion within the core CTCF motif (Figure S3C). Remarkably, this small deletion is sufficient to abolish CTCF binding, resulting in complete loss of occupancy at CBS6 despite heterozygosity. We clarified this in the text as follows: “CTCF ChIP-qPCR in hiPSCs confirmed complete loss of CTCF binding at the targeted sites, including CBS6 in the Δ2CBS line, indicating that the 2-bp deletion sufficed to disrupt CTCF binding while occupancy at other CBSs remained unaffected.” (lines 187–189).

      Reviewer 2 - Point 1.3a (3) Figure 3C: There are two problems with the 4C experiments: (a) The changes are really mild. In fact, none of the p-values in Figure 3D are significant.

      The effect of deleting CBS1 is indeed modest, consistent with reports that individual CTCF binding sites often show functional redundancy (i.e., Rodríguez-Carballo et al. 2017; Barutcu et al. 2018; Kang et al. 2021). Nevertheless, our 4C-seq experiments have reproducibly shown the same directional trend across biological replicates. To increase statistical power and more rigorously assess the robustness of this effect, we are generating additional 4C replicates as part of the full revision.

      Reviewer 2 - Point 1.3b [In the 4C experiments] (b) The authors should also consider a model that CTCF directly serves as a repressor. In this way, 3D genome may not be involved. B-A switch is simply caused by the activation of the locus.

      We now explicitly acknowledge this possibility in the Discussion. The revised text states: “Moreover, our data cannot unambiguously separate CTCF’s architectural role from potential direct repressive activity. Both mechanisms could contribute to the observed effects, and our findings likely reflect the combined influence of CTCF on chromatin topology and gene regulation.” (lines 368–371).

      Reviewer 2 - Point 2.1a 2. __(CTCF) detachment: The authors mentioned few times "detachment". In the context of this manuscript, the authors indicate detachment from nuclear lamina. However, the authors haven't provide convincing evidence about this. __

      In the two instances where we used the term “detachment,” we intended it to refer exclusively to reduced CTCF binding to DNA, not to lamina repositioning. To avoid ambiguity, we have replaced “detachment” with “reduced binding” in both locations (lines 123 and 329). We do not use this term to describe TTN–lamina positioning.

      Reviewer 2 - Point 2.1b (1) Figure 1D: I doubt whether such changes of CTCF protein abundance will lead to LAD detachment. Suggest the authors read van Schaik et al., Genome Biology, 2022. With the full depletion of CTCF, the effects on LADs are still very restricted.

      We agree that the observed correlation between reduced CTCF levels and the relocation of TTN away from a LAD does not establish causality. As outlined in our response to Reviewer 1 – Point 8c, we are performing additional FISH experiments at earlier differentiation stages in the CTCF inducible knockdown line to directly assess whether partial CTCF depletion is sufficient to alter the timing of TTN–lamina separation.

      Reviewer 2 - Point 2.2 (2) Figure 2D: Lamin B1 should be mostly at nuclear periphery. I have few questions: (1) is the antibody specific? (2) do these cells carry mutation in LMNB1 gene? (3) is the staining actually LMNA?

      As also clarified in response to Reviewer 1 – Point 8b, the original images displayed maximum-intensity projections of Z-stacks, which obscured the peripheral distribution of LMNB1. We have updated Figure 2D and Figure S2E to show representative individual optical sections, which more clearly display the expected peripheral LMNB1 signal. We also confirm that the antibody used is specific for LMNB1 and previously validated (Bertero et al. 2019b), and that the WTC11-derived lines used in this study carry no mutation in LMNB1.

      Reviewer 2 - Point 3

      3. Opposite functions of GATA4 and CTCF: These data in Figure 5E-H argues the opposite role of GATA4 and CTCF in transcriptional regulation. Would it be that CTCF KD just affected cell proliferation, which is actually known for many cell types, rather than affect CM differentiation process? If this is the reason, inversed correlation between CTCF KD and GATA4 KD in Figure 4D could also be explained by opposite effects on cell cycle.

      We directly evaluated this possibility. In FHF–LV cardioids, cell cycle profiling in Figure 6C and Figure S6C (now S7C) showed that CTCF knockdown does not alter the distribution of CMs across G1/S/G2–M phases, in contrast to the marked increase in proliferation observed with GATA4 knockdown.

      Because this comment referred specifically to the SHF data, we also analyzed mitotic gene expression in the SHF–RV bulk RNA-seq dataset using GSEA. CTCF knockdown did not significantly enrich any cell cycle–related gene sets, whereas GATA4 knockdown produced a strong enrichment for mitotic cell cycle terms, in line with FHF-LV data (Reviewer Figure 2).

      These results are summarized in updated Figure S5C, reporting also the results of the broader GSEA analysis, and together indicate that the transcriptional divergence between CTCF and GATA4 knockdown is not simply explained by opposing effects on proliferation.

      (The figure cannot be rendered in this text-only format)

      Reviewer Figure 2. GSEA for mitotic cell cycle in SHF-RV after inducible knockdown of CTCF (left) or GATA4 (right). p-values by Adaptive Monte-Carlo Permutation test.

      Reviewer 2 - Point 4 4. In discussion, the authors suggested that CTCF is a local chromatin remodeller. In my view, association with local chromatin compaction doesn't qualify CTCF as a chromatin remodeler. To my knowledge, CTCF does not have an enzymatic domain, then how does it remodel chromatin?

      Our intended meaning was that CTCF shapes 3D chromatin architecture through its role in organizing intergenic looping, not that it remodels chromatin enzymatically. To avoid confusion, we have removed the original sentence from the Discussion.

      Reviewer 2 - Point 5. 5. Some conclusions are drawn based on insignificant p-values, e.g. Figure 2F, Figure 3D, etc. The authors should be careful about their conclusion, and tone down their statement for the observations have borderline significance.

      The conclusions based on bulk RNA-seq have been revised in response to Reviewer 1 – Point 1 (updated Figure 2F). By subsetting B-to-A and A-to-B genes according to their expression dynamics, this analysis now yields clearer and statistically significant differences between conditions.

      Regarding the 4C-seq data, as acknowledged in Reviewer 2 – Point 3a, the observed effects are modest. We are generating additional biological replicates to increase statistical power. In the meantime, we have adjusted the text to avoid overstating these findings. The revised manuscript now states: “While the difference did not reach significance, these trends suggest …” (lines 199–200).

      Reviewer 2 - Minor comment 1. Minor comments: 1. Figure 1A: (1) I suggest to label two promoters in the gene model. It's unclear in the figure in the current version; (2) I was a bit confused with the way how the authors labeled CTCF directionality. I thought there are a lot of promoters. Why didn't they use triangles?

      We updated Figure 1A to label both TTN promoters and indicate their orientation. For CTCF sites, we now clearly display the motif direction and core binding region as determined by FIMO analysis of the CTCF ChIP-seq peaks, improving consistency and interpretability.

      Reviewer 2 - Minor comment 2. 2. Figure 2C: I think the drastical reduction of titin-mEGFP levels is only due to the way how the authors analyze their FACS data. Can the author quantify on median fluorescence intensity?

      The gating strategy for titin-mEGFP⁺ cells was defined using a reporter-negative control, and cells lacking TNNT2 expression showed no detectable titin-mEGFP signal, confirming the specificity of the gate. To complement this analysis, we also quantified the median fluorescence intensity (MFI) of titin-mEGFP⁺ cells. The MFI analysis corroborates the original findings, showing a significant decrease in GATA4 knockdown and an increase in CTCF knockdown (updated Figure S2D).

      __Reviewer 2 - Minor comment 3. 3. Figure S2G: P value should be -log10, I assume. Please label it accurately. __

      We appreciate the reviewer pointing out this labeling error. In the revised manuscript, this panel has been removed to accommodate the updated compartment–expression analysis now presented in updated Figure 2H (see response to Reviewer 1 – Point 1), and the issue is no longer applicable.

      References

      Barutcu AR, Maass PG, Lewandowski JP, Weiner CL, Rinn JL. 2018. A TAD boundary is preserved upon deletion of the CTCF-rich Firre locus. Nat Commun 9: 1444.

      Bertero A, Fields PA, Ramani V, Bonora G, Yardımcı GG, Reinecke H, Pabon L, Noble WS, Shendure J, Murry CE. 2019a. Dynamics of genome reorganization during human cardiogenesis reveal an RBM20-dependent splicing factory. Nature communications 10: 1538.

      Bertero A, Fields PA, Smith AS, Leonard A, Beussman K, Sniadecki NJ, Kim D-H, Tse H-F, Pabon L, Shendure J, et al. 2019b. Chromatin compartment dynamics in a haploinsufficient model of cardiac laminopathy. Journal of Cell Biology 218: 2919–44.

      Kang J, Kim YW, Park S, Kang Y, Kim A. 2021. Multiple CTCF sites cooperate with each other to maintain a TAD for enhancer–promoter interaction in the β-globin locus. The FASEB Journal 35: e21768.

      Poleshko A, Shah PP, Gupta M, Babu A, Morley MP, Manderfield LJ, Ifkovits JL, Calderon D, Aghajanian H, Sierra-Pagán JE, et al. 2017. Genome-Nuclear Lamina Interactions Regulate Cardiac Stem Cell Lineage Restriction. Cell 171: 573–587.

      Rodríguez-Carballo E, Lopez-Delisle L, Zhan Y, Fabre PJ, Beccari L, El-Idrissi I, Huynh THN, Ozadam H, Dekker J, Duboule D. 2017. The HoxD cluster is a dynamic and resilient TAD boundary controlling the segregation of antagonistic regulatory landscapes. Genes Dev 31: 2264–2281.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Becca et al. characterized the functions of GATA4 and CTCF in the context of cardiomyogenesis. The authors aim to establish a link between 3D genome changes (A/B compartment and long-range chromatin interactions) and activation of cardiac specific genes such as TTN. They showed opposite effects of GATA4 and CTCF in regulating these genes as well as phenotypical traits. I have the following suggestions and questions:

      Major comments:

      1. CTCF regulation at TTN locus:

      (1) Figure 1A: The claim of the authors about convergent CTCF sites and transcriptional activation of TTN is quite simplistic. This claim is only valid when we know where cohesin is loaded. If cohesin is loaded at then intragenic GATA4 binding site, then the only important CTCF sites is at the promoter of TTN. I suggest that the authors read few more publications which may help the authors to better understand how cohesin and CTCF team up to regulate transcription, such as Hsieh et al., Nature Genetics, 2022; Liu et al., Nature Genetics, 2021; Rinzema et al., Nature Structural and Molecular Biology, 2022.

      Suggestion: The authors should add cohesin (RAD21/SMC1A) and NIPBL ChIP-seq for better interpretation. (2) Figure 3B: If delta2CBS only has heterozygenous deletion of CBS6, why we would expect the binding will be weaken to 50%. However, the CTCF binding is reduced to around 1/10 in the ChIP-qPCR. How do the authors explain this?

      (3) Figure 3C: There are two problems with the 4C experiments: (a) The changes are really mild. In fact, none of the p-values in Figure 3D are significant; (b) The authors should also consider a model that CTCF directly serves as a repressor. In this way, 3D genome may not be involved. B-A switch is simply caused by the activation of the locus. 2. (CTCF) detachment: The authors mentioned few times "detachment". In the context of this manuscript, the authors indicate detachment from nuclear lamina. However, the authors haven't provide convincing evidence about this.

      (1) Figure 1D: I doubt whether such changes of CTCF protein abundance will lead to LAD detachment. Suggest the authors read van Schaik et al., Genome Biology, 2022. With the full depletion of CTCF, the effects on LADs are still very restricted.

      (2) Figure 2D: Lamin B1 should be mostly at nuclear periphery. I have few questions: (1) is the antibody specific? (2) do these cells carry mutation in LMNB1 gene? (3) is the staining actually LMNA? 3. Opposite functions of GATA4 and CTCF: These data in Figure 5E-H argues the opposite role of GATA4 and CTCF in transcriptional regulation. Would it be that CTCF KD just affected cell proliferation, which is actually known for many cell types, rather than affect CM differentiation process? If this is the reason, inversed correlation between CTCF KD and GATA4 KD in Figure 4D could also be explained by opposite effects on cell cycle. 4. In discussion, the authors suggested that CTCF is a local chromatin remodeller. In my view, association with local chromatin compaction doesn't qualify CTCF as a chromatin remodeler. To my knowledge, CTCF does not have an enzymatic domain, then how does it remodel chromatin? 5. Some conclusions are drawn based on insignificant p-values, e.g. Figure 2F, Figure 3D, etc. The authors should be careful about their conclusion, and tone down their statement for the observations have borderline significance.

      Minor comments:

      1. Figure 1A: (1) I suggest to label two promoters in the gene model. It's unclear in the figure in the current version; (2) I was a bit confused with the way how the authors labeled CTCF directionality. I thought there are a lot of promoters. Why didn't they use triangles?
      2. Figure 2C: I think the drastical reduction of titin-mEGFP levels is only due to the way how the authors analyze their FACS data. Can the author quantify on median fluorescence intensity?
      3. Figure S2G: P value should be -log10, I assume. Please label it accurately.

      Significance

      Strengths and limitations:

      I feel that single-cell analysis and functional analysis of GATA4 and CTCF using cardiac organoid model are elegant. However, the weak part of the manuscript is the link between 3D genome and activation of TTN. I also think the authors should include more possible explanations for the interpretation of some genome organization data (CTCF site deletion, 4C, etc).

      Advance: The study does provide useful information to understand transcriptional regulation during cardiac lineage specification. The link between 3D genome and cardiac lineage specification is conceptually nice but needs more data to support.

      Audience: developmental biologists who is interested in heart development and molecular biologists with specific interests in gene regulation.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Becca and others examines the relationship between GATA4 and CTCF in chromatin organization and cardiac maturation. There are several very interesting observations that lead to potentially new insights into the relationship between genome folding, gene expression and the relationship between transcription factors and chromatin structural proteins. To better justify their interpretations and provide a more objective analysis of the data, the authors may consider the following:

      In the datasets you are examining, what are the relative percentages in each of the four groups relating compartmentalization change to expression change (A to B, expression up; A-B, down; B-A, up; B-A, down)?

      This phrase in the abstract is imprecise: "whereas premature CTCF depletion accelerates yet confounds cardiomyocyte maturation."

      Regarding this statement: "Disruption of [3D chromatin architecture] has been linked to genetic dilated cardiomyopathy (DCM) caused by lamin A/C mutations8,9, and mutations in chromatin regulators are strongly enriched in de novo congenital heart defects (CHD)10, underscoring their pathogenic relevance11." The first studies to implicate chromatin structural changes in heart disease, including the role of CTCF in that process, were PMID: 28802249, a model of acquired, rather than genetic, disease.

      Can you quantify this statement: "the compartment switch coincided with progressive reduction of promoter-gene body interactions"?

      Regarding this statement: "six regions became less accessible in CMs, correlating with ChIP-seq signal for the ubiquitous architectural protein CTCF." I don't see 6 ATAC peaks in either TTN trace in Figure 1A.

      Western blots need molecular weight markers.

      Regarding this statement: "The decrease in CTCF protein levels may explain its selective detachment from TTN during cardiomyogenesis." At face value, these findings suggest the opposite: i.e. that a massive downregulation of CTCF at protein level should affect its binding across the genome, which is not tested and is hard to evaluate between ChIP-seq studies from different groups and from different developmental timeframes.

      A couple thoughts on the FISH experiments in Figure 2. A claim of 'impaired B-A transition' would be more convincing if you show, by FISH, that the relative distance of TTN from lamin B increases with differentiation. In the images: are you showing a total projection of all z planes? One assumes the quantitation is relative to a 3D reconstruction in which the lamin B signal is restricted to the periphery. Have you shown this? Lastly, these data are very interesting and important, provoking reexamination of your interpretation of the results in Figure 1. Figure 1 was interpreted to show that less CTCF binding led to decreased lamina (and thus B compartment) association during development. Figure 2 shows that depleting CTCF does not change association of TTN with lamina.

      A thought about this statement: "Altogether, these results suggest that GATA4 and CTCF function as positive and negative regulators of B-to-A compartment switching, likely acting through global and local chromatin remodeling, respectively." GATA4 induces TTN expression and its knockdown prevents TTN expression-the evidence that GATA4 affects compartmentalization is unclear. By activating the gene, GATA4 may shift TTN to B classification.

      I'm not sure what I am looking at in Figure 3C. Are those traces integration of interactions over a defined window? "Each [mutant is] clearly different from WT" is not obvious from the presentation. The histograms are plotting AUC of what? Interactions of those peaks with the mutated region? I genuinely appreciate how laborious this experiment must have been and encourage you to explain better what you are showing.

      Again acknowledging how challenging these experiments are: when you mutant a locus, you change CTCF binding but you also change the DNA. Thus, attributing the changes in interactions to presence/absence of CTCF binding is difficult, because the DNA substrate itself has changed. Perhaps you are presenting all of this as a negative result, given the modest effect on transcription, which is as important as a positive result, given the assumptions usually made about such things. But the results are not clearly described and your interpretation seems to go between implying the structural change causative and being agnostic.

      Figure 4C: since you have RNA-seq data, a much more objective way to present these data would be to show all data (again, A-B, up; A-B, down; B-A, up; B-A, down) and the effects of CTCF or GATA4. Regardless, you can still focus on the cardiac specific genes. But my guess is if you examine all genes, the pattern you show in panel C will not be present in the majority of cases. Furthermore, if this hypothesis is wrong, such an analysis will allow you to identify other genes affected by the mechanisms you describe and your analysis will test whether these mechanisms are in fact conserved at different loci.

      Significance

      This manuscript by Becca and others examines the relationship between GATA4 and CTCF in chromatin organization and cardiac maturation. There are several very interesting observations that lead to potentially new insights into the relationship between genome folding, gene expression and the relationship between transcription factors and chromatin structural proteins.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1

      1. First, the authors have not convincingly shown that skin cells, or more specifically skin ECs, are a major source of circulating G-CSF in the psoriasis model as stated in the title and abstract. The data in Figure 4 show selective upregulation of Csf3 gene in skin ECs and their ability to secrete G-CSF upon IMQ treatment in vitro. However, the provided data do not address to what degree the skin EC-derived G-CSF contributes to the elevated level of circulating G-CSF. Additional experiments to selectively deplete G-CSF in skin ECs, or at least in skin cells of the affected site, are warranted to support the authors' claim. Does intradermal injection of G-CSF neutralizing antibody into the psoriatic skin reduce circulating levels of G-CSF?

      Author's response:

      Thank you for reviewer's comment. We agree with the Reviewer#1 that it is important to directly block G-CSF to the skin via intradermal injection and measure the G-CSF level in the serum afterwards. Therefore, we will perform intradermal injection of IgG-isotype or anti-G-CSF antibody into the IMQ-induced psoriatic mice.

      Another concern is insufficient demonstration of G-CSF-mediated emergency granulopoiesis in the psoriasis model. All data in Figure 5 were obtained from experiments with only n=3, and adding more replicates, in particular to those in Figure 5B, which show quite some variation in MPP numbers, is recommended. The relatively small reduction of BM granulocyte numbers (Figure 5C) compared to greater depletion of circulating granulocytes (Figure S5A) raises the possibility that it is the mobilization effect rather than granulopoiesis-stimulating effect that skin-derived G-CSF exerts to promote supply of circulating neutrophils that eventually infiltrate into the affected skin. This could also explain the negligible effect of IL-1blockade (Figure S4), which selectively shut off myelopoiesis-stimulating effect of IL-1 (Pietras et al. Nat Cell Biol 2016, PMID: 27111842). Are the HSPCs in the psoriasis model more cycling? Do they show myeloid-skewed differentiation when cultured ex vivo or upon transplantation?

      Author's response: Thank you for these critical comments. We agree to do the following experiments to address them:

      1) HSPCs quantification in Figure 5 especially the MPPs will be added with more replicates.

      2) We will assess cycling status of HSPCs by flow cytometric analysis of Ki67and Propidium Iodide to characterize G0, G1 and G2/M cell cycle phase.

      3) To test myeloid-skewed differentiation, Lin- c-Kit+ Sca-1+ cells containing HSPCs will be isolated from bone marrow of Vas/IMQ-treated mice and transplanted into lethally irradiated syngeneic mice.

      The authors' claim that skin-derived G-CSF "induces" neutrophil infiltration warrants further clarification. Alternative explanation is that the upregulated neutrophil-attracting chemokines (Figure S1D) could induce infiltration, whereas G-CSF increase the number of neutrophils to circulate in the vessels near the psoriatic skin. This notion seems supported elsewhere (Moos et al. J Invest Dermatol. 2019, PMID: 30684554). Can the infiltration be inhibited by systemically injecting neutralizing antibody of their receptor, CXCR2?

      Author's response: The manuscript focuses on the skin-derived G-CSF function as a long-distance signal for emergency granulopoiesis in the bone marrow upon psoriasis, not the chemoattractant property of it. The sentence of interest is "We found that upon psoriasis induction, skin-resident endothelial cells are activated to produce G-CSF which activates emergency granulopoiesis in bone marrow and induces cutaneous infiltration and accumulation of neutrophil that are functionally inflammatory." in line 28-30. In agreement with point #2 from Reviewer#2, the fact that neutrophil recruitment factors (CXCL1, CXCL2, and CXCL5) were upregulated in psoriatic skin (Figure S1D), suggesting a CXCL-mediated neutrophil recruitment. The sentence of concern need to be changed to "We found that upon psoriasis induction, skin-resident endothelial cells are activated to produce G-CSF which activates emergency granulopoiesis in bone marrow, leading to cutaneous accumulation of neutrophil that are functionally inflammatory.". This revised sentence has omitted the proposal that G-CSF directly dictates neutrophils mobilization to the skin, which is not the key message of the study. Therefore, we found that the CXCR2 (CXCLs receptor) blockade experiment may be of the benefit of future studies.

      It remains unclear how skin-derived G-CSF accumulates pathogenic neutrophils. The authors state "pathogenic granulopoiesis," but are the circulating neutrophils in the psoriatic mice already "pathogenic" or do they acquire pathogenic phenotype after cutaneous infiltration? Additional RNA-seq to compare circulating and infiltrated neutrophils would answer this question.

      Author's response: We appreciate this valuable comment. We will perform RNA-seq with the peripheral blood-circulating neutrophils (CD45+ CD11b+ Ly6G+ Ly6Cmid) versus skin-infiltrating neutrophils from both Vas/IMQ mice.

      In addition, how the accumulated pathogenic neutrophils exacerbate the psoriatic changes remains obscure. Although the authors have attempted to correlate Il17a gene expression in infiltrated neutrophils with psoriatic skin changes, the data do not address to what degree it contributes to cutaneous IL-17A protein levels. The data that cutaneous neutrophil depletion leads to subtle decrease in skin IL-17A expression (Figure 2H) rather supports alternative possibilities. For instance, as indicated elsewhere, IL-17A cutaneous tone could be enhanced by neutrophil-mediated augmentation of Th17 or gamma/delta T cell function (Lambert et al. J Invest Dermatol. 2019, PMID: 30528823). Does neutrophil depletion or G-CSF neutralization alter cell numbers or function of cutaneous Th17 and gamma/delta T cells?

      Author's response: Thank you for this insightful comment. To better understand the relative contribution of neutrophils to the cutaneous IL-17A tone in the psoriatic skin, we will perform flowcytometric analysis of Th17 and gamma/delta T cells which are widely known as the major source of IL-17 in psoriatic skin of IMQ-induced mice following injection of isotype-matched or anti-Ly6G antibody.

      Finally, as the above conclusions rely solely on the IMQ-induced acute psoriasis model, it would be informative if they could be derived from another psoriasis model. IMQ is known to induce unintended systemic inflammation due to grooming-associated ingestion (Gangwar et al. J Invest Dermatol. 2022, PMID: 34953514), and "pathological crosstalk between skin and BM in psoriatic inflammation" could be strengthened by an intradermal injection model.

      Author's response: We appreciate the reviewer for bringing this important point. Regarding the systemic inflammation upon psoriasis, the above-cited study reported increased IFN-B expression in the intestines of IMQ-ingested animal (Grine L et al. Sci Rep. 2016, PMID: 26818707 in Gangwar et al. J Invest Dermatol. 2022, PMID: 34953514). We examined several pro-inflammatory cytokines including IFN-b, IFN-g, and IL-6 and in contrast, found no systemic increase in all these cytokines, except for IFN-g downregulation (Explanation Figure 1), which suggests no evidence of grooming-associated ingestion.

      We also examined the Csf3 expression across several distinctively located tissues which showed a selective upregulation in the skin (Figure 4C), suggesting a skin-restricted perturbation. In addition, one study showed that IMQ-ingestion didn't alter number of gut injury-associated CXCR3+ macrophages nor did it aggravate skin inflammation (Pinget et al. Cell Reports. 2022, PMID: 35977500). Together, these findings support that IMQ-induced psoriasis by topical cutaneous application used in our study elicit a local inflammation but not systemic inflammation.

      The authors, however, realize that testing alternative psoriasis model such as intradermal injection of IL-23 (Chan et al. J Exp Med. 2006, PMID: 17074928) will strengthen the skin-local insults within the psoriasis model employed, and should be tested in the future.

      Minor comments

      Figure 1E shows multiple elongated Ly6G+ structures in d0-2 control and d0 IMQ skins that do not appear to be neutrophils.

      Author's response: We appreciate the Reviewer#1 pointing this issue. As mentioned by the Reviewer#1, the elongated structures detected in the intravital microscopy are not neutrophils, but autofluorescence from the skin bulge regions (Wun et al. J Invest Dermatol. 2005, PMID: 15816847). We have eliminated these unspecific signals from the transformation and quantification (Figure 1F, S1G, and S1H). We will also add an explanatory sentence in Materials and Methods section "Of note, the fluorescent signal with elongated structures resembling hair bulge were autofluorescence and thus removed from further analysis." to be more precise about our methods.

      In Figure 2C, the bottom GSEA seems to be showing type II IFN response, not type I IFN, according to the text.

      Author's response: Thank you for the comment, we will correct this misspelling.

      Author's response: We appreciate that Reviewer#1 bring up this point. We examined the kinetics of the bone marrow cellularity and GMPs across 4 days of psoriasis induction in mice. The bone marrow cell number was lowered along that span with lowermost count at 2 days. Consistent to the BM-cellularity, the GMP number was also lowered about one-third in the first 2 days of psoriasis. This kinetic is consistent with the previous report showing a rapid reduction of GMPs in the bone marrow within 2 days following systemic G-CSF administration driven emergency granulopoiesis (Hirai et al. Nat. Immunol. 2006, PMID: 16751774). From 2 days to 4 days, the GMP number rapidly increased to slightly above basal number (Explanation Figure 2). This timely coordinated expansion suggests a significant supply of GMPs from the differentiating upstream myeloid progenitors (Figure 3B).

      When the psoriatic mice with elevated G-CSF is injected with anti-G-CSF or IgG-isotype antibody, the bone marrow cellularity and GMP numbers at 4 days were (Explanation Figure 3). Firstly, as psoriasis reduced bone marrow cellularity (Explanation Figure 2), the unchanged number after anti-G-CSF injection indicates that administration of 10µg/day for 4 days does not significantly affect mobilization of psoriatic bone marrow cells. Secondly, the similar GMP numbers at 4 days psoriasis is plausibly due to snapshot analysis when it has already in the numerical recovery period (Explanation Figure 2). Importantly, the notion that anti-G-CSF injection to psoriatic mice reduced granulocytes in the bone marrow, peripheral blood, and skin suggesting G-CSF as a key mediator in psoriatic driven emergency granulopoiesis on top of unlikely case of ineffective anti-G-CSF treatment.

      Taken together, these data suggest a G-CSF mediated emergency granulopoiesis occurrence in the IMQ-induced psoriasis. We will put these data into a revised Figure.

      In Figures 6B, in which cluster of human skin cells IL-17A expression would be enriched?

      Author's response: Thank you for this important point. The IL-17A expression is found in the T-cell cluster (Explanation Figure 4). We also expected to see IL-17A contribution from other cell subset(s), in particular neutrophil. However, due to the fragile nature of neutrophils and thereby, technical difficulty to get their sequencing reads, this dataset (GSE173706) doesn't contain neutrophils, but rather monocytes, macrophages, and dendritic cells among the myeloid subset (Explanation Figure 5). With this, it leaves open the question on what potential contribution of IL-17A produced by neutrophils is in human psoriasis (Reich et al. Exp. Dermatol. 2015, PMID: 25828362).

      Figure 1E shows multiple elongated Ly6G+ structures in d0-2 control and d0 IMQ skins that do not appear to be neutrophils.

      Author's response: We appreciate the Reviewer#1 pointing this issue. As mentioned by the Reviewer#1, the elongated structures detected in the intravital microscopy are not neutrophils, but autofluorescence from the skin bulge regions (Wun et al. J Invest Dermatol. 2005, PMID: 15816847). We have eliminated these unspecific signals from the transformation and quantification (Figure 1F, S1G, and S1H). We will also add an explanatory sentence in Materials and Methods section "Of note, the fluorescent signal with elongated structures resembling hair bulge were autofluorescence and thus removed from further analysis." to be more precise about our methods.

      In Figure 2C, the bottom GSEA seems to be showing type II IFN response, not type I IFN, according to the text.

      Author's response: Thank you for the comment, we will correct this misspelling.

      Reviewer#2

      1. Interpretation of neutrophil transcriptomic changes (Figure 2)

      The RNA-seq analysis reveals substantial downregulation of several canonical pro inflammatory pathways in neutrophils from psoriatic skin, including IL-6, IL-1, and type II interferon signaling. The authors should discuss the functional relevance of this unexpected transcriptional repression. For example, does this indicate a shift toward specialized effector functions rather than classical cytokine responsiveness? More importantly, the most striking transcriptional change is the upregulation of NADPH oxidase-related genes (e.g., Nox1, Nox3, Nox4, Enox2). This suggests an oxidative stress-driven pathogenic mechanism, potentially more relevant than IL-17A production. Yet this aspect is not explored in the manuscript. Assessing ROS levels or oxidative neutrophil effector functions in this model would considerably strengthen the mechanistic link. Conversely, although IL-17A is upregulated in neutrophils, neutrophil depletion reduces total Il17a expression in skin only partially. This indicates that neutrophils are unlikely to be the dominant IL-17A source in the lesion. The authors' focus on neutrophil-derived IL 17A therefore seems overstated. A more rigorous assessment-e.g., conditional deletion of Il17a specifically in neutrophils-would be required to establish its true contribution. Taken together, the data suggest that oxidative programs, rather than IL-17A production, may represent the principal pathogenic axis downstream of neutrophils, and this deserves deeper discussion.

      Author's response: Thank you for raising this valuable views. We have agreed to address these critical points by the following approaches:

      1) To address the changes in NADPH oxidase-related gene signature, we will measure ROS production in the neutrophils from skin and peripheral blood with DHR123.

      2) Responding to the IL17A contribution by neutrophils, we will flow cytometrically assess the Th17 and gamma/delta T cell population in the skin of psoriatic mice treated with anti-Ly6G or isotype-matched antibody as was suggested by Reviewer#1.

      3) We will discuss downregulation of the canonical pro inflammatory and IL-17 pathways in the psoriatic neutrophils in the discussion.

      Human data reanalysis (Figure 6):

      The re-analysis of bulk and single-cell RNA-seq datasets is valuable but incomplete. Several mechanistically relevant questions could be addressed with the available data:

      2.1. GM-CSF (CSF2) is also strongly upregulated in psoriatic lesions (bulk RNA-seq). It would be informative to determine whether endothelial cells also express CSF2 in the scRNA-seq dataset, as this would suggest coordinated regulation of myeloid-supporting cytokines.

      2.2. Myeloid cell subsets should be examined more closely. A comparison of human myeloid transcriptomes with the mouse neutrophil RNA-seq would clarify whether similar IL-17A-related or NADPH oxidase-related signatures occur in human disease. In particular, which cell types express IL17A in human lesions?

      2.3. Chemokine production should be attributed to specific cell types. Bulk RNA-seq confirms strong induction of CXCL1, CXCL2, CXCL5, but the scRNA-seq dataset allows determining whether these chemokines originate from endothelial cells or other stromal/immune populations. This information is important for defining whether endothelial cells coordinate both neutrophil recruitment and granulopoiesis.

      Addressing these points would make the human-mouse comparison substantially stronger.

      Author's response: Thank you for pointing these important issues. By reanalyzing the dataset, we found several points regarding the comments, as follows:

      2.1) CSF2 is expressed by T-cell cluster in the human skin dataset (Explanation Figure 4), in agreement with previous murine study (Hartwig et al. Cell Reports. 2018, PMID: 30590032). We will add this data in the revised manuscript.

      2.2) In line with point#10 from Reviewer#1, the dataset clearly shows T-cell cluster as the main IL17A source (Explanation Figure 4 above). The dataset, however, doesn't contain phenotypic neutrophils (CEACAM (CD66b) and PGLYRP1) but monocytes, macrophages, and dendritic cells (Explanation Figure 5 above). This loss was probably due to a technical limitation given the difficulty in capturing sequencing reads from fragile neutrophils. Therefore, it is no longer possible to reanalyze IL-17 expression in the absence of neutrophils in the datapool.

      2.3) Reanalysis of CXCLs in the human scRNAseq dataset (GSE173706) clarified their secretion dynamics and cellular sources under normal and psoriatic condition. In normal skin, all examined cell subsets show only low CXCLs expression. In contrast, psoriatic skin exhibits significant CXCLs upregulation with distinct cell subsets clearly showing dramatic upregulation, potentially being the major CXCLs source. CXCL1 is markedly upregulated in fibroblasts, myeloid cells, and melanocyte and nerve cells. CXCL2 is strikingly upregulated to myeloid cells, while CXCL5 is hugely increased in fibroblasts, myeloid cells, and mast cells (Explanation Figure 7). Taken together, these results suggest that CXCLs upregulation in the psoriatic skin is coordinatively executed by both stromal and immune compartments. Of note, the endothelial cells show minimal changes in CXCLs expression, even downregulate CXCL2 in psoriasis, indicating that they are unlikely to be the major contributor to CXCL-mediated neutrophil recruitment.

      **Referees cross-commenting**

      I agree with Reviewer 1 that the contribution of EC-derived G-CSF to circulating G-CSF levels and to emergency myelopoiesis requires additional genetic or neutralization experiments to be fully established.

      Author's response: We appreciate that Reviewer#2 raised this key point. In addition to examining the serum G-CSF upon intradermal anti-G-CSF administration in point#1 from Reviewer#1 above, we will also examine the emergency myelopoiesis signs in vivo.

      Minor points

      1. Line 319: the text likely refers to Figure S4, not S3.

      Author's response: Thank you, we will correct the nomenclature.

      Line 338: "psoriatic" is misspelled.

      Author's response: Thank you, we will change this to "psoriatic".

      Reviewer #3

      • Place the work in the context of the existing literature (provide references, where appropriate).

      Psoriasis is extensively studied, a good recent reference- https://doi.org/10.1016/j.mam.2024.101306

      Author's response: Thank you for Reviewer#3's suggestion. The referenced study highlights the current paradigm that largely focus on skin-restricted mechanism and overlook potential cross-organ interaction in the psoriasis inflammation. Our findings provide a new insight into the skin-bone marrow crosstalk in the disease context. In addition, the suggested reference underscores the key roles of diverse innate immune cells including neutrophils, eosinophils, dendritic cells, etc. which is fundamental for our study and might also guide future exploration of additional innate cell subsets beyond neutrophils. We will therefore include the mentioned reference to our revised manuscript.

      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      It is all good. May add graphical-abstract.

      Author's response: Thank you for the reviewer's input, we agree that a graphical-abstract will help the readers more clearly grasp the key messages of our manuscript. We will include it in the revised manuscript.

      Major comments:

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      No. It is very solid.

      Author's response: We appreciate the reviewer's view that the claims in our paper are solid.

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Such a discovery clearly opens many options, and it is fascinating to suggest additional experiments for future studies. It is a complete study, best to publish as-is and let many to read and proceed with this new concept.

      Author's response: We thank the reviewer for noting that the current experimental evidence is complete that no additional experiments are necessary at this stage. We agree that the discovery opens prospective directions for future studies.

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      N/A - I suggest no additional experiments at this point. Get it published and see how many will follow this new direction!

      Author's response: We thank the reviewer for recognizing that the experimental data has been sufficient to be a foundation for the future research.

      • Are the data and the methods presented in such a way that they can be reproduced?

      Yes.

      Author's response: We thank the reviewer for recognizing that our methods are reproducible.

      • Are the experiments adequately replicated, and is the statistical analysis adequate?

      Yes. The data are of very high quality.

      Author's response: We are grateful that the reviewer view our replication strategy and statistical analysis are of a high quality.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      None. It is good as-is. One may always suggest minor things- but this one is better published so many laboratories may rush for this new direction. I think it will be interesting studying some long-term impacts, and changes not only of neutrophils but also of other innate cells, such as DCs, Macrophages, and Eosinophils - so it is best to let laboratories that focus on these cells know of the discovery and pursue independent studies.

      Author's response: We appreciate the reviewer's assessment that our paper is already well set for the community to explore the newly proposed direction.

      • Are the text and figures clear and accurate?

      Yes.

      Author's response: We thank the reviewer's evaluation. We have ensured that the text and figures in our manuscript are clear and accurate. Once again, we thank the reviewer for the encouraging and constructive appraisal. We are pleased that the reviewer find the manuscript has already been strong and suitable for publication.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      Study titled: "Skin-derived G-CSF activates pathological granulopoiesis upon psoriasis" by Kosasih and Takizawa. Paper show establishment of psoriasis model in C57BL/6 mice. They focus on neutrophils infiltration following the Imiquimod cream induction. Importantly, authors show that the induction of psoriasis in the skin cause a robust enhancement of granulopoiesis in the bone marrow. Mechanistically, G-CSF is produced in the skin, especially by endothelial cells. Blocking of G-CSF gained clear inhibition of psoriatic pathology. They further add human data showing that patient with psoriasis have more neutrophils and more G-CSF in their skin endothelial cells.

      Parts of the study are simply in line with previous knowledge (e.g.- neutrophils infiltration into psoriatic skin, IL17a). authors show some data that largely confirm the model used. Major discovery: skin endothelial cells are secreting G-CSF that induce granulopoiesis in the bone-marrow. This is a conceptual advancement of this study: psoriatic skin not only recruit neutrophils from the blood, but also enhance the generation of new neutrophils in the bone-marrow. That a major- psoriasis at the level of the model used must not be considered as a confined-pathology. It affect systematically, and might also benefit new systemic treatments. There are plenty of follow-up experiments to pursue now, so it is critical to publish this finding and let many laboratories to know of this new direction. I expect this study to attract high interest and many citations.

      Major comments:

      • Are the key conclusions convincing?

      Yes. The study has excellent data, with good quantification, and very solid support for the discovery and interpretations. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      No. It is very solid. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Such a discovery clearly opens many options, and it is fascinating to suggest additional experiments for future studies. It is a complete study, best to publish as-is and let many to read and proceed with this new concept. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      N/A - I suggest no additional experiments at this point. Get it published and see how many will follow this new direction! - Are the data and the methods presented in such a way that they can be reproduced?

      Yes. - Are the experiments adequately replicated, and is the statistical analysis adequate?

      Yes. The data are of very high quality.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      None. It is good as-is. One may always suggest minor things- but this one is better published so many laboratories may rush for this new direction. I think it will be interesting studying some long-term impacts, and changes not only of neutrophils but also of other innate cells, such as DCs, Macrophages, and Eosinophils - so it is best to let laboratories that focus on these cells know of the discovery and pursue independent studies. - Are prior studies referenced appropriately?

      Yes. I may suggest adding a recent review by Park and Jung, 2024, https://doi.org/10.1016/j.mam.2024.101306 to cover current concepts of innate immunity in psoriasis. - Are the text and figures clear and accurate?

      Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      It is all good. May add graphical-abstract.

      Significance

      • Describe the nature and significance of the advance (e.g., conceptual, technical, clinical) for the field.

      Conceptual advancement - discovery of a major impact of psoriasis on bone-marrow granulopoiesis. Explicit finding of endothelial-cells G-CSF as a major communication moiety.

      Neutrophil recruitment and IL17A are well established. G-CSF of endothelial cells brings the conceptual advancement- psoriasis at the level induced by IMQ develops local pathology, but is tightly linked to systemic changes. The impact on bone-marrow granulopoiesis may have many implications. So far, it was largely considered that chronic inflammation may affect hematopoiesis, but this study reveals an acute and specific communication between skin and bone marrow. The neutrophils are not only recruited from blood- they are made anew, so the disease is enhanced significantly! This discovery led to a novel basic understanding and suggests novel therapeutic options. - State what audience might be interested in and influenced by the reported findings.

      Dermatologist, immunologist, haematologist - this one goes for a broad audience. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Immunology and hematology. I am not an expert of dermatology.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      General assessment

      This is a well-written and carefully executed study that identifies skin-derived G-CSF as a key driver of pathological emergency granulopoiesis in an imiquimod-induced mouse model of psoriasis. The authors convincingly show that endothelial cells are the dominant source of G-CSF in inflamed skin, and that this cytokine mediates systemic hematopoietic skewing and neutrophil accumulation, ultimately aggravating psoriatic pathology. The eanalysis of human transcriptomic datasets strengthens the translational relevance of the findings. Overall, the conclusions are well supported by the data. However, several mechanistically relevant aspects remain underexplored, particularly regarding the functional state of psoriatic neutrophils and the human data integration. Addressing these points would substantially enhance the impact of the study.

      Major points

      1. Interpretation of neutrophil transcriptomic changes (Figure 2)

      The RNA-seq analysis reveals substantial downregulation of several canonical proinflammatory pathways in neutrophils from psoriatic skin, including IL-6, IL-1, and type II interferon signaling. The authors should discuss the functional relevance of this unexpected transcriptional repression. For example, does this indicate a shift toward specialized effector functions rather than classical cytokine responsiveness? More importantly, the most striking transcriptional change is the upregulation of NADPH oxidase-related genes (e.g., Nox1, Nox3, Nox4, Enox2). This suggests an oxidativestress-driven pathogenic mechanism, potentially more relevant than IL-17A production. Yet this aspect is not explored in the manuscript. Assessing ROS levels or oxidative neutrophil effector functions in this model would considerably strengthen the mechanistic link.

      Conversely, although IL-17A is upregulated in neutrophils, neutrophil depletion reduces total Il17a expression in skin only partially. This indicates that neutrophils are unlikely to be the dominant IL-17A source in the lesion. The authors' focus on neutrophil-derived IL17A therefore seems overstated. A more rigorous assessment-e.g., conditional deletion of Il17a specifically in neutrophils-would be required to establish its true contribution. Taken together, the data suggest that oxidative programs, rather than IL-17A production, may represent the principal pathogenic axis downstream of neutrophils, and this deserves deeper discussion. 2. Human data reanalysis (Figure 6):

      The re-analysis of bulk and single-cell RNA-seq datasets is valuable but incomplete.

      Several mechanistically relevant questions could be addressed with the available data:

      2.1. GM-CSF (CSF2) is also strongly upregulated in psoriatic lesions (bulk RNA-seq). It would be informative to determine whether endothelial cells also express CSF2 in the scRNA-seq dataset, as this would suggest coordinated regulation of myeloid-supporting cytokines.

      2.2. Myeloid cell subsets should be examined more closely. A comparison of human myeloid transcriptomes with the mouse neutrophil RNA-seq would clarify whether similar IL-17A-related or NADPH oxidase-related signatures occur in human disease. In particular, which cell types express IL17A in human lesions?

      2.3. Chemokine production should be attributed to specific cell types. Bulk RNA-seq confirms strong induction of CXCL1, CXCL2, CXCL5, but the scRNA-seq dataset allows determining whether these chemokines originate from endothelial cells or other stromal/immune populations. This information is important for defining whether endothelial cells coordinate both neutrophil recruitment and granulopoiesis. Addressing these points would make the human-mouse comparison substantially stronger.

      Minor points

      1. Line 319: the text likely refers to Figure S4, not S3.
      2. Line 338: "psoriatic" is misspelled.

      Referees cross-commenting

      I agree with Reviewer 1 that the contribution of EC-derived G-CSF to circulating G-CSF levels and to emergency myelopoiesis requires additional genetic or neutralization experiments to be fully established.

      Significance

      The study is solid and potentially impactful, particularly for audiences working in inflammation and hematopoiesis, as it uncovers a cross-organ mechanism linking skinderived G-CSF to emergency granulopoiesis in psoriasis. My expertise lies in inflammation and hematopoiesis, and from this perspective several essential mechanistic issues remain insufficiently addressed. In particular, the neutrophil transcriptomic data highlight strong induction of NADPH oxidase-related pathways, which appears more biologically meaningful than the modest Il17a upregulation emphasized by the authors. Likewise, the human RNA-seq reanalyses leave open key questions regarding CSF2 expression, myeloid heterogeneity, and chemokine cellular sources. These issues affect the strength and interpretation of the central claims. For these reasons, I recommend major revision before the manuscript can be considered further.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      A role of neutrophils in psoriasis pathogenesis has been highlighted by several past studies; however, how the neutrophils are recruited to the affected skin has not been fully understood. The work by Kosasih et al. tackles a relevant question and has investigated the effect of psoriatic skin inflammation on BM myelopoiesis. Using an IMQ-induced acute psoriasis mouse model, the authors derive 3 major conclusions: (1) skin ECs secrete G-CSF into circulation in response to psoriatic stress, (2) skin EC-derived G-CSF stimulates emergency granulopoiesis, and (3) skin EC-derived G-CSF induces infiltration and accumulation of pathogenic neutrophils in the affected skin. The authors provide many pieces of interesting data, but most of them remain correlative and insufficient to support the conclusions. Many of the experiments were performed in a small number of samples or mice (mostly with n=3), leaving the story still preliminary.

      Major comments:

      1. First, the authors have not convincingly shown that skin cells, or more specifically skin ECs, are a major source of circulating G-CSF in the psoriasis model as stated in the title and abstract. The data in Figure 4 show selective upregulation of Csf3 gene in skin ECs and their ability to secrete G-CSF upon IMQ treatment in vitro. However, the provided data do not address to what degree the skin EC-derived G-CSF contributes to the elevated level of circulating G-CSF. Additional experiments to selectively deplete G-CSF in skin ECs, or at least in skin cells of the affected site, are warranted to support the authors' claim. Does intradermal injection of G-CSF neutralizing antibody into the psoriatic skin reduce circulating levels of G-CSF?
      2. Another concern is insufficient demonstration of G-CSF-mediated emergency granulopoiesis in the psoriasis model. All data in Figure 5 were obtained from experiments with only n=3, and adding more replicates, in particular to those in Figure 5B, which show quite some variation in MPP numbers, is recommended. The relatively small reduction of BM granulocyte numbers (Figure 5C) compared to greater depletion of circulating granulocytes (Figure S5A) raises the possibility that it is the mobilization effect rather than granulopoiesis-stimulating effect that skin-derived G-CSF exerts to promote supply of circulating neutrophils that eventually infiltrate into the affected skin. This could also explain the negligible effect of IL-1blockade (Figure S4), which selectively shut off myelopoiesis-stimulating effect of IL-1 (Pietras et al. Nat Cell Biol 2016, PMID: 27111842). Are the HSPCs in the psoriasis model more cycling? Do they show myeloid-skewed differentiation when cultured ex vivo or upon transplantation?
      3. The authors' claim that skin-derived G-CSF "induces" neutrophil infiltration warrants further clarification. Alternative explanation is that the upregulated neutrophil-attracting chemokines (Figure S1D) could induce infiltration, whereas G-CSF increase the number of neutrophils to circulate in the vessels near the psoriatic skin. This notion seems supported elsewhere (Moos et al. J Invest Dermatol. 2019, PMID: 30684554). Can the infiltration be inhibited by systemically injecting neutralizing antibody of their receptor, CXCR2?
      4. It remains unclear how skin-derived G-CSF accumulates pathogenic neutrophils. The authors state "pathogenic granulopoiesis," but are the circulating neutrophils in the psoriatic mice already "pathogenic" or do they acquire pathogenic phenotype after cutaneous infiltration? Additional RNA-seq to compare circulating and infiltrated neutrophils would answer this question.
      5. In addition, how the accumulated pathogenic neutrophils exacerbate the psoriatic changes remains obscure. Although the authors have attempted to correlate Il17a gene expression in infiltrated neutrophils with psoriatic skin changes, the data do not address to what degree it contributes to cutaneous IL-17A protein levels. The data that cutaneous neutrophil depletion leads to subtle decrease in skin IL-17A expression (Figure 2H) rather supports alternative possibilities. For instance, as indicated elsewhere, IL-17A cutaneous tone could be enhanced by neutrophil-mediated augmentation of Th17 or gamma/delta T cell function (Lambert et al. J Invest Dermatol. 2019, PMID: 30528823). Does neutrophil depletion or G-CSF neutralization alter cell numbers or function of cutaneous Th17 and gamma/delta T cells?
      6. Finally, as the above conclusions rely solely on the IMQ-induced acute psoriasis model, it would be informative if they could be derived from another psoriasis model. IMQ is known to induce unintended systemic inflammation due to grooming-associated ingestion (Gangwar et al. J Invest Dermatol. 2022, PMID: 34953514), and "pathological crosstalk between skin and BM in psoriatic inflammation" could be strengthened by an intradermal injection model.

      Minor comments:

      1. Figure 1E shows multiple elongated Ly6G+ structures in d0-2 control and d0 IMQ skins that do not appear to be neutrophils.
      2. In Figure 2C, the bottom GSEA seems to be showing type II IFN response, not type I IFN, according to the text.
      3. For the BM analysis in Figures 3, 5, S3, and S5, it would be informative if BM cellularity and numbers of committed myeloid progenitors (e.g., GMPs) are shown.
      4. In Figures 6B, in which cluster of human skin cells IL-17A expression would be enriched?

      Significance

      Although quite a few studies have reported various examples of emergency myelopoiesis (Swann et al. Nat Rev Immunol. 2024, PMID: 38467802), there is limited evidence on its occurrence and involvement in locally restricted disease, such as periodontitis (Li et al. Cell 2022, PMID: 35483374; 35483374). As an HSC biologist, I see this study is conceptually interesting as it could extend the above concept to psoriasis, a non-infectious, local inflammatory disease in the skin, and describes a potential causal link between skin-derived G-CSF and emergency myelopoiesis. That said, as detailed in the first section, the conclusions, especially that related to emergency myelopoiesis driven by skin-derived G-CSF, need to be more convincingly supported before taking its value. The findings offer additional understanding of how psoriasis is developed in concert with aberrant hematopoiesis and will be relevant to those working in the field of dermatology, immunology, and hematology.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      Summary The manuscript by Aarts et al. explores the role of GRHL2 as a regulator of the progesterone receptor (PR) in breast cancer cells. The authors show that GRHL2 and PR interact in a hormone-independent manner and based on genomic analyses, propose that they co-regulate target genes via chromatin looping. To support this model, the study integrates both newly generated and previously published datasets, including ChIP-seq, CUT&RUN, RNA-seq, and chromatin interaction assays, in breast cancer cell models (T47DS and T47D).

      Major comments: R1.1 Novelty of GRHL2 in steroid receptor biology The role of GRHL2 as a co-regulator of steroid hormone receptors has previously been described for ER (J Endocr Soc. 2021;5(Suppl 1):A819) and AR (Cancer Res. 2017;77:3417-3430). In the ER study, the authors also employed a GRHL2 ΔTAD T47D cell model. Therefore, while this manuscript extends GRHL2 involvement to PR, the contribution appears incremental rather than conceptual.

      We are fully aware of the previously described role of GRHL2 as a co-regulator of steroid hormone receptors, particularly ER and AR. As acknowledged in our introduction (lines 104-108), we explicitly state: "Grainyhead-like 2 (GRHL2) has recently emerged as a potential pioneer factor in hormone receptor-positive cancers, including breast cancer21. However, nearly all studies to date have focused on GRHL2 in the context of ER and estrogen signaling, leaving its role in PR- and progesterone-mediated regulation unexplored22-26".

      As for the specific publications that the reviewer refers to: The first refers to an abstract from an annual meeting of the Endocrine Society. As we have been unable to assess the original data underpinning the abstract - including the mentioned GRHL2 DTAD model - we prefer not to cite this particular reference. We do cite other work by the same authors (Reese et al. 2022, our ref. 25). We also cite the AR study mentioned by the reviewer (our ref. 55) in our discussion. As such, we think we do give credit to prior work done in this area.

      By characterizing GRHL2 as a co-regulator of the progesterone receptor (PR), we expand on the current understanding of GRHL2 as a common transcriptional regulator within the broader context of steroid hormone receptor biology. Given that ER and PR are frequently co-expressed and active within the same breast cancer cells, our findings raise the important possibility that GRHL2 may actively coordinate or modulate the balance between ER- and PR-driven transcriptional programs, as postulated in the discussion paragraph.

      Importantly, we also functionally link PR/GRHL2-bound enhancers to their target genes (Fig5), providing novel insights into the downstream regulatory networks influenced by this interaction. These results not only offer a deeper mechanistic understanding of PR signaling in breast cancer but also lay the groundwork for future comparative analyses between GRHL2's role in ER-, AR-, and PR-mediated gene regulation.

      As such, we respectfully suggest that our work offers more than an incremental advance in our knowledge and understanding of GRHL2 and steroid hormone receptor biology.

      R1.2 Mechanistic depth The study provides limited mechanistic insight into how GRHL2 functions as a PR co-regulator. Key mechanistic questions remain unaddressed, such as whether GRHL2 modulates PR activation, the sequential recruitment of co-activators/co-repressors, engages chromatin remodelers, or alters PR DNA-binding dynamics. Incorporating these analyses would considerably strengthen the mechanistic conclusions.

      Although our RNA-seq data demonstrate that GRHL2 modulates the expression of PR target genes, and our CUT&RUN experiments show that GRHL2 chromatin binding is reshaped upon R5020 exposure, we acknowledge that we have not further dissected the molecular mechanisms by which GRHL2 functions as a PR co-regulator.

      We did consider several follow-up experiments to address this, including PR CUT&RUN in GRHL2 knockdown cells, CUT&RUN for known co-activators such as KMT2C/D and P300, as well as functional studies involving GRHL2 TAD and DBD mutants. However, due to technical and logistical challenges, we were unable to carry out these experiments within the timeframe of this study.

      That said, we fully recognize that such approaches would provide deeper mechanistic insight into the interplay between PR and GRHL2. We have therefore explicitly acknowledged this limitation in our limitations of the study section (line 502-507) and mention this as an important avenue for future investigation.

      R1.3 Definition of GRHL2-PR regulatory regions (Figure 2) The 6,335 loci defined as GRHL2-PR co-regulatory regions are derived from a PR ChIP-seq performed in the presence of hormone and a GRHL2 ChIP-seq performed in its absence. This approach raises doubts about whether GRHL2 and PR actually co-occupy these regions under ligand stimulation. GRHL2 ChIP-seq experiments in both hormone-treated and untreated conditions are necessary to provide stronger support for this conclusion.

      Although bulk ChIP-seq cannot definitively demonstrate simultaneous binding of PR and GRHL2 at the same genomic regions, we agree that the ChIP-seq experiments we present do not provide a definitive answer on if GRHL2 and PR co-occupy these regions under ligand stimulation. As a first step to address this, we performed CUT&RUN experiments for both GRHL2 and PR under untreated and R5020-treated conditions. These experiments revealed a subset of overlapping PR and GRHL2 binding sites (approximately {plus minus}5% of the identified PR peaks under ligand stimulation).

      We specifically chose CUT&RUN to minimize artifacts from crosslinking and sonication, thereby reducing background and enabling the mapping of high-confidence direct DNA-binding events: Given that a fraction of GRHL2 physically interacts with PR (Fig1D), it is possible that ChIP-seq detects indirect binding of GRHL2 at PR-bound sites and vice versa. CUT&RUN, by contrast, allows us to identify direct binding sites with higher confidence.

      Nonetheless, although outside the scope of the current manuscript, we agree that a dedicated GRHL2 ChIP with and without ligand stimulation would provide additional insight, and we have accordingly added this suggestion to the discussion (line 502-507).

      R1.4 Cell model considerations The manuscript relies heavily on the T47DS subclone, which expresses markedly higher PR levels than parental T47D cells (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Kalkhoven et al., Int J Cancer 1995). This raises concerns about physiological relevance. Key findings, including co-IP and qPCR-ChIP experiments, should be validated in additional breast cancer models such as parental T47D, BT474, and MCF-7 cells to generalize the conclusions. Furthermore, data obtained from T47D (PR ChIP-seq, HiChIP, CTCF and Rad21 ChIP-seq) and T47DS (RNA-seq, CUT&RUN) are combined along the manuscript. Given the substantial differences in PR expression between these cell lines, this approach is problematic and should be reconsidered.

      We agree that physiological relevance is important to consider. Here, all existing model systems have some limitations. In our experience, it is technically challenging to robustly measure gene expression changes in parental T47D cells (or MCF7 cells, for that matter) in response to progesterone stimulation (Aarts et al., J Mammary Gland Biol Neoplasia 2023). As we set out to integrate PR and GRHL2 binding to downstream target gene induction, we therefore opted for the most progesterone responsive model system (T47DS cells). We agree that observations made in T47D and T47DS cells should not be overinterpreted and require further validation. We have now explicitly acknowledged this and added it to the discussion (line 507-509).

      As for the reviewer's suggestion to use MCF7 cells: apart from its suboptimal PR-responsiveness, this cell line is also known to harbor GRHL2 amplification, resulting in elevated GRHL2 levels (Reese et al., Endocrinology2019). By that line of reasoning, the use of MCF7 cells would also introduce concerns about physiological relevance. That being said, and as noted in the discussion (line 390-391), the study by Mohammed et al. which identified GRHL2 as a PR interactor using RIME, was performed in both MCF7 and T47D cells. This further supports the notion that the PR-GRHL2 interaction is not limited to a single cell line.

      R1.5 CUT&RUN vs ChIP-seq data The CUT&RUN experiments identify fewer than 10% of the PR binding sites reported in the ChIP-seq datasets. This discrepancy likely results from methodological differences (e.g., absence of crosslinking, potential loss of weaker binding events). The overlap of only 158 sites between PR and GRHL2 under hormone treatment (Figure 3B) provides limited support for the proposed model and should be interpreted with greater caution.

      We acknowledge the discrepancy between the number of binding sites between ChIP-seq and CUT&RUN. Indeed, methodological differences likely contribute to the differences in PR binding sites reported between the ChIP-seq and CUT&RUN datasets. As the reviewer correctly notes, the absence of crosslinking and sonication in CUT&RUN reduces detection of weaker binding events. However, it also reduces the detection of indirect binding events which could increase the reported number of peaks in ChIPseq data (e.g. the common presence of "shadow peaks").

      As also discussed in our response to R1.3, we deliberately chose the CUT&RUN approach to enable the identification of high-confidence direct DNA-binding events. Since GRHL2 physically interacts with PR, ChIP-seq could potentially capture indirect binding of GRHL2 at PR-bound sites, and vice versa. By contrast, CUT&RUN primarily captures direct DNA-protein interactions, offering a more specific binding profile. Thus, while the number of CUT&RUN binding sites is much smaller than previously reported by ChIP-seq, we are confident that they represent true, direct binding events.

      We would also like to emphasize that the model presented in figure 6 does not represent a generic or random gene, but rather a specific gene that is co-regulated by both GRHL2 and PR. In this specific case, regulation is proposed to occur via looping interactions from either individual TF-bound sites (e.g., PR-only or GRHL2-only) or shared GRHL2/PR sites. We do not propose that only shared sites are functionally relevant, nor do we assume that GRHL2 and PR must both be directly bound to DNA at these shared sites. Therefore, overlapping sites identified by ChIP-seq-potentially reflecting indirect binding events-could indeed be missed by CUT&RUN, yet still contribute to gene regulation. To clarify this, we have revised the main text (line 331-334) and the legend of Figure 6 to explicitly state that the model refers to a gene with established co-regulation by both GRHL2 and PR.

      R1.6 Gene expression analyses (Figure 4) The RNA-seq analysis after 24 hours of hormone treatment likely captures indirect or secondary effects rather than the direct PR-GRHL2 regulatory program. Including earlier time points (e.g., 4-hour induction) in the analysis would better capture primary transcriptional responses. The criteria used to define PR-GRHL2 co-regulated genes are not convincing and may not reflect the regulatory interactions proposed in the model. Strong basal expression changes in GRHL2-depleted cells suggest that much of the transcriptional response is PR-independent, conflicting with the model (Figure 6). A more straightforward approach would be to define hormone-regulated genes in shControl cells and then examine their response in GRHL2-depleted cells. Finally, integrating chromatin accessibility and histone modification datasets (e.g., ATAC-seq, H3K27ac ChIP-seq) would help establish whether PR-GRHL2-bound regions correspond to active enhancers, providing stronger functional support for the proposed regulatory model.

      We thank the reviewer for pointing this out. We now recognize that our criteria for selecting PR/GRHL2 co-regulated genes were not clearly described. To address this, we have revised our approach as per the reviewer's suggestion: we first identified early and sustained PR target genes based on their response at 4 and 24 hours of induction and subsequently overlaid this list with the gene expression changes observed in GRHL2-depleted cells. This revised approach reduced the amount of PR-responsive, GRHL2 regulated target genes from 549 to 298 (46% reduction). We consequently updated all following analyses, resulting in revised figures 4 and 5 and supplementary figures 2,3 and 4. As a result of this revised approach, the number of genes that are transcriptionally regulated by GRHL2 and PR (RNAseq data) that also harbor a PR loop anchor at or near their TSS after 30 minutes of progesterone stimulation (PR HiChIP data) dropped from 114 to 79 (30% reduction). We thank the reviewer for suggesting this more straightforward approach and want to emphasize that our overall conclusions remain unaltered.

      As above in our response to R1.3, we want to emphasize that the model presented in figure 6 does not depict a generic or randomly chosen gene, but a gene that is specifically co-regulated by both GRHL2 and PR. We also want to emphasize that the majority of GRHL2's transcriptional activity is PR-independent. This is consistent with the limited fraction of GRHL2 that co-immunoprecipitated with PR (Figure 1D), and with the well-established roles of GRHL2 beyond steroid receptor signaling. In fact, the overall importance of GRHL2 for cell viability in T47D(S) cells is underscored by our inability to generate a full knockout (multiple failed attempts of CRISPR/Cas mediated GRHL2 deletion in T47D(S) and MCF7 cells), and by the strong selection we observed against high-level GRHL2 knockdown using shRNA.

      As for the reviewer's suggestion to assess whether GRHL2/PR co-bound regions correspond to active enhancers by integrating H3K27ac and ATAC-seq data: We have re-analyzed publicly available H3K27ac and ATAC-seq datasets from T47D cells (references 42 and 43). These analyses are now added to figure 2 (F and G). The H3K27Ac profile suggests that GRHL2-PR overlapping sites indeed correspond to more active enhancers (Figure 2F), with a proposed role for GRHL2 since siGRHL2 affects the accessibility of these sites (Figure 2G).

      Minor comments Page 19: The statement that "PR and GRHL2 trigger extensive chromatin reorganization" is not experimentally supported. ATAC-seq would be an appropriate method to test this directly.

      We agree with the reviewer and have removed this sentence, as it does not contribute meaningfully to the flow of the manuscript.

      Prior literature on GRHL2 as a steroid receptor co-regulator should be discussed more thoroughly.

      We now added additional literature on GRHL2 as a steroid hormone receptor co-regulator in the discussion (line 397-401) and we cite the papers suggested by R1 in R1.1 (references 25 and 54).

      Reviewer #1 (Significance (Required)):

      The identification of novel PR co-regulators is an important objective, as the mechanistic basis of PR signaling in breast cancer remains incompletely understood. The main strength of this study lies in highlighting GRHL2 as a factor influencing PR genomic binding and transcriptional regulation, thereby expanding the repertoire of regulators implicated in PR biology.

      That said, the novelty is limited, given the established roles of GRHL2 in ER and AR regulation. Mechanistic insight is underdeveloped, and the reliance on an engineered T47DS model with supra-physiological PR levels reduces the general impact. Without validation in physiologically relevant breast cancer models and clearer separation of direct versus indirect effects, the overall advance remains modest.

      The manuscript will be of interest to a specialized audience in the fields of nuclear receptor signaling, breast cancer genomics, and transcriptional regulation. Broader appeal, including translational or clinical relevance, is limited in its current form.

      We have addressed all of these points in our response above and agree that with our implemented changes, this study should reach (and appeal to) an audience interested in transcriptional regulation, chromatin biology, hormone receptor signaling and breast cancer.

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

      The authors present a study investigating the role of GRHL2 in hormone receptor signaling. Previous research has primarily focused on GRHL2 interaction with estrogen receptor (ER) and androgen receptor (AR). In breast cancer, GRHL2 has been extensively studied in relation to ER, while its potential involvement with the progesterone receptor (PR) remains largely unexplored. This is the rationale of this study to investigate the relation between PR and GRHL2. The authors demonstrate an interaction between GRHL2 and PR and further explore this relationship at the level of genomic binding sites. They also perform GRHL2 knockdown experiments to identify target genes and link these transcriptional changes back to GRHL2-PR chromatin occupancy. However, several conceptual and technical aspects of the study require clarification to fully support the authors' conclusions.

      R2.1 Given the high sequence similarity among GRHL family members, this raises questions about the specificity of the antibody used for GRHL2 RIME. The authors should address whether the antibody cross-reacts with GRHL1 or GRHL3. For example, GRHL1 shows a higher log fold change than GRHL2 in the RIME data.

      Indeed, GRHL1, GRHL2, and GRHL3 are structurally related. They share a similar domain organization and are all {plus minus}70kDa in size. Sequence similarity is primarily confined to the DNA-binding domain, with GRHL2 and GRHL3 showing 81% similarity in this region, and GRHL1 showing 63% similarity to GRHL2/3 (Ming, Nucleic Acids Res 2018).

      The antibody used, sourced from the Human Protein Atlas, is widely used in the field. It targets an epitope within the transactivation domain (TAD) of GRHL2-a region with relatively low sequence similarity to the corresponding domains in GRHL1 and GRHL3.

      We assessed the specificity of the antibody using western blotting (Supplementary Figure 2A) in T47DS wild-type and GRHL2 knockdown cells. As expected, GRHL2 protein levels were reduced in the knockdown cells providing convincing evidence that the antibody recognizes GRHL2. The remaining signal in shGRHL2 knockdown cells could either be due to remaining GRHL2 protein or due to the antibody detecting GRHL1/3. Furthermore, the observed high log-fold enrichment of GRHL1 in our RIME may reflect known heterodimer formation between GRHL1 and GRHL2, rather dan antibody cross-reactivity. As such, we cannot formally rule out cross-reactivity and have mentioned this in the limitations section (line 497-501).

      R2.2 In addition, in RIME experiments, one would typically expect the bait protein to be among the most highly enriched proteins compared to control samples. If this is not the case, it raises questions about the efficiency of the pulldown, antibody specificity, or potential technical issues. The authors should comment on the enrichment level of the bait protein in their data to reassure readers about the quality of the experiment.

      We agree with the reviewer that this information is crucial for assessing the quality of the experiment. We have therefore added the enrichment levels (log₂ fold change of IgG control over pulldown) to the methods section (line 592).

      As the reviewer notes, GRHL2 was not among the top enriched proteins in our dataset. This is due to unexpectedly high background binding of GRHL2 to the IgG control antibody/beads, for which we currently have no explanation. As a result, although we detected many unique GRHL2 peptides, observed high sequence coverage (>70%), and GRHL2 ranked among the highest in both iBAQ and LFQ values, its relative enrichment was reduced due to the elevated background. During our RIME optimization, Coomassie blue staining of input and IP samples revealed a band at the expected molecular weight of GRHL2 in the pull down samples that was absent in the IgG control (see figure 1 for the reviewer below, 4 right lanes), supporting the conclusion that GRHL2 is specifically enriched in our GRHL2 RIME samples. Combined with enrichment of some of the expected interacting proteins (e.g. KMT2C and KMT2D), we are convinced that the experiment of sufficient quality to support our conclusions.

      Figure 1 for reviewer: Coomassie blue staining of input and IP GRHL2 and IgG RIME samples. NT = non-treated, T = treated.

      R2.3 The authors report log2 fold changes calculated using iBAQ values for the bait versus IgG control pulldown. While iBAQ provides an estimate of protein abundance within samples, it is not specifically designed for quantitative comparison between samples without appropriate normalization. It would be helpful to clarify the normalization strategy applied and consider using LFQ intensities.

      We understand the reviewer's concern. Due to the high background observed in the IgG control sample (see R2.2), the LFQ-based normalization did not accurately reflect the enrichment of GRHL2, which was clearly supported by other parameters such as the number of unique peptides (see rebuttal Table 1). After discussions with our Mass Spectrometry facility, we decided to consider the iBAQ values-which reflect the absolute protein abundance within each sample-as a valid and informative measure of enrichment. In the context of elevated background levels, iBAQ provides an alternative and reliable metric for assessing protein enrichment and was therefore used for our interactor analysis.

      Unique peptides

      IBAQ GRHL2

      IBAQ IgG

      LFQ GRHL2

      LFQ IgG

      GRHL2

      52

      1753400.00

      155355.67

      5948666.67

      3085700.00

      GRHL1

      23

      56988.33

      199.03

      334373.33

      847.23

      *Table 1. Unique peptide, IBAQ and LFQ values of the GRHL2 and IgG pulldowns for GRHL2 and GRHL1 *

      R2.4 Other studies have reported PR RIME, which could be a valuable source to investigate whether GRHL proteins were detected.

      We thank the reviewer for pointing this out. We are aware of the PR RIME, generated by Mohammed et al., which we refer to in the discussion (lines 390-391). This study indeed identified GRHL2 as a PR-interacting protein in MCF7 and T47D cells. Although they do not mention this interaction in the text, the interaction is clearly indicated in one of the figures from their paper, which supports our findings. To our knowledge, no other PR RIME datasets in MCF7 or T47D cells have been published to date.

      R2.5 In line 137, the term "protein score" is mentioned. Could the authors please clarify what this means and how it was calculated.

      We agree that this point was not clearly explained in the original text. The scores presented reflect the MaxQuant protein identification confidence, specifically the sum of peptide-level scores (from Andromeda), which indicates the relative confidence in protein detection. We have now added this clarification to line 137 and to the legend of Figure 1.

      R2.6 In line 140-141. The fact that GRHL2 interacts with chromatin remodelers does not by itself prove that GRHL2 acts as a pioneer factor or chromatin modulator. Demonstrating pioneer function typically requires direct evidence of chromatin opening or binding to closed chromatin regions (e.g., ATAC-seq, nucleosome occupancy assays). I recommend revising this statement or providing supporting evidence.

      We agree that the fact that GRHL2 interacts with chromatin remodelers does not by itself prove that GRHL2 acts as a pioneer factor or chromatin modulator. However, a previous study (Jacobs et al, Nature genetics, 2018) has shown directly that the GRHL family members (including GRHL2) have pioneering function and regulate the accessibility of enhancers. We adapted line 140-141 to state this more clearly. In addition, our newly added data in Figure 2G also support the fact that GRHL2 has a role in regulating chromatin accessibility in T47D cells.

      R2.7 The pulldown Western blot lacks an IgG control in panel D.

      This is correct. As the co-IP in Figure 1D served as a validation of the RIME and was specifically aimed at determining the effect of hormone treatment on the observed PR/GRHL2 interaction, we did not perform this control given the scale of the experiment. However, during RIME optimization, we performed GRHL2 staining of the IgG controls by western blot, shown in figure 2 for the reviewer below. As stated above, some background GRHL2 signal was observed in the IgG samples, but a clear enrichment is seen in the GRHL2 IP.

      Taken together, we believe that the well-controlled RIME, combined with the co-IP presented, provides strong evidence that the observed signal reflects a genuine GRHL-PR interaction.

      Figure 2 for reviewer: WB of input and IP GRHL2 and IgG RIME samples stained for GRHL2. NT = non-treated, T = treated

      R2.8 Depending on the journal and target audience, it may be helpful to briefly explain what R5020 is at its first mention (line 146).

      Thank you. We have adapted this accordingly.

      R2.9 The authors state that three technical replicates were performed for each experimental condition. It would be helpful to clarify the expected level of overlap between biological replicates of RIME experiments. This clarification is necessary, especially given the focus on uniquely enriched proteins in untreated versus treated cells, and the observation that some identified proteins in specific conditions are not chromatin-associated. Replicates or validations would strengthen the findings.

      We use the term technical rather than biological replicates because for cell lines, defining true biological replicates is challenging, as most variability arises from experimental rather than biological differences. To introduce some variation, we split our T47DS cells into three parallel dishes 5 days prior to starting the treatment. We purposely did this, to minimize to minimize the likelihood that proteins identified as uniquely enriched are artifacts. Each of the three technical replicates comes from one of these three parallel splits (so equal passage numbers but propagated in parallel dishes for 5 days before the start of the experiment).

      To generate the three technical replicates for our RIME, we plated cells from the parallel grown splits. Treatments for the three replicates were performed per replicate. Samples were crosslinked, harvested and lysed for subsequent RIME analysis, the three replicates were processed in parallel, for technical and logistical reasons. To clarify the experimental setup, we have updated the methods section accordingly (lines 566-568).

      As for the detection of non-chromatin-associated proteins: We cannot rule out that these are artifacts, as they may arise from residual cytosolic lysate during nuclear extraction. Alternatively, they could reflect a more dynamic subcellular localization of these proteins than currently annotated or appreciated.

      R2.10 The volcano plot for the RIME experiment appears to show three distinct clusters of proteins on the right, which is unusual for this type of analysis. The presence of these apparent groupings may suggest an artifact from the data processing, such as imputation. Can the authors clarify the origin of these groupings? If it is due to imputation or missing values, I recommend applying a stricter threshold, such as requiring detection in all three replicates (3/3) to improve the robustness of the enrichment analysis and increase confidence in the identified interactors.

      We thank the reviewer for pointing this out. As suggested, we re-evaluated the imputation and applied a stricter threshold, requiring detection in all three replicates. Indeed, the separate clusters were due to missing values, therefore we now revised the imputation method by imputing values based on the normal distribution. Using this revised analysis, we identify 2352 GRHL2 interactors instead of 1140, but the number of interacting proteins annotated as transcription factors or chromatin-associated/modifying proteins was still 103. Figure 1B, 1E, and Supplementary Figure 4A have been updated accordingly. We also revised the methods section to reflect this change. We think this suggestion has improved our analysis of the data and we thank the reviewer for pointing this out.

      R2.11 The statement that "PR and GRHL2 frequently overlap" may be overstated given that only ~700 overlapping sites are reported (cut&run).

      We have replaced "frequently overlap" by "can overlap" (line 229-230).

      R2.12 The model in Figure 6 suggests limited chromatin occupancy of PR and GRHL2 in hormone-depleted conditions, consistent with the known requirement of ligand for stable PR-DNA binding. However, Figure 1 shows no major difference in GRHL2-PR interaction between untreated and hormone-treated cells. This raises questions about where and how this interaction occurs in the absence of hormone. Since PR binding to chromatin is typically minimal without ligand, can the authors clarify this given that RIME data reflect chromatin-bound interactions.

      Indeed, the model in figure 6 suggests limited chromatin occupancy of PR and GRHL2 under hormone-depleted conditions. It is, however, important to note that the locus shown represents a gene regulated by both PR and GRHL2 - and not just any gene. We recognize that this was not sufficiently clear in the original version, and we have now clarified this in both the main text (line 331-334) and the figure legend.

      We propose that PR and GRHL2 bind or become enriched at enhancer sites associated with their target genes upon ligand stimulation. This is consistent with the known requirement of ligand for stable PR-DNA binding and with our observation that PR/GRHL2 overlapping peaks are detected only in the ligand-treated condition of the CUT&RUN experiment. Given the broader role of GRHL2, it also binds chromatin independently of progesterone and the progesterone receptor, which is why we included-but did not focus on-GRHL2-only binding events in our model.

      We would also like to clarify that, although RIME includes a nuclear enrichment step that enriches for chromatin-associated proteins, the pulldown is performed on nuclear lysates. Therefore, it captures both chromatin-bound protein complexes and freely soluble nuclear complexes, which unfortunately cannot be distinguished. GRHL2 is well established as a nuclear protein (Zeng et al., Cancers 2024; Riethdorf et al., International Journal of Cancer 2015), and although PR is classically described as translocating to the nucleus upon hormone stimulation, several studies-including our own-have shown that PR is continuously present in the nucleus (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Frigo et al., Essays Biochem. 2021).

      We therefore propose that PR and GRHL2 may already interact in the nucleus without directly binding to chromatin. Given our observation that GRHL2 binding sites on the chromatin are redistributed upon R5020 mediated signaling activation, we hypothesize that such pre-formed PR-GRHL2 nuclear complexes may assist the rapid recruitment of GRHL2 to progesterone-responsive chromatin regions.

      We have expanded the discussion to include a dedicated section addressing this point (line 376-388).

      R2.13 It would be of interest to assess the overlap between the proteins identified in the RIME experiment and the motif analysis results.

      In the discussion section of our original manuscript, we highlighted some overlapping proteins in the RIME and motif analysis, including STAT6 and FOXA1. However, we had not yet systematically analyzed overlap in both analyses. To address this, we now compared all enriched motifs (so not only the top 5 as displayed in our figures) under GRHL2, PR, and GRHL2/PR shared sites from both the CUT&RUN and ChIP-seq datasets with the proteins identified as GRHL2 interactors in our RIME. Although we identified numerous GRHL2-associated proteins, relatively few of them were transcription factors whose binding motifs were also enriched under GRHL2 peaks.

      In our revised manuscript we have added a section in the discussion highlighting our systematic overlap of the results of our RIME experiment and the motif enrichment of the ChIP-seq and CUT&RUN analysis (line 415-436).

      R2.14 The authors chose CUT&RUN to assess chromatin binding of PR and GRHL2. Given that RIME is also based on chromatin immunoprecipitation - ChIP protocol, it would be helpful to clarify why CUT&RUN was selected over ChIP-seq for the DNA-binding assays. What is the overlap with published data?

      As also mentioned in our response to R1.3 and R1.5, we deliberately chose the CUT&RUN approach to minimize artifacts introduced by crosslinking and sonication, thereby reducing background and allowing the identification of high-confidence, direct DNA-binding events. Since GRHL2 physically interacts with PR, ChIP-seq could potentially capture indirect binding of GRHL2 at PR-bound sites (and vice versa). In contrast, CUT&RUN primarily detects direct DNA-protein interactions, providing a more specific and accurate binding profile. Additionally, CUT&RUN serves as an independent validation method for data obtained using ChIP-like protocols.

      Since CUT&RUN, similar to ChIP, can show limited reproducibility (Nordin et al., Nucleic Acids Research, 2024), and to our knowledge few PR CUT&RUN and no GRHL2 CUT&RUN datasets are currently available, it is challenging to directly compare our data with published datasets. Nevertheless, studies performing PR or ER CUT&RUN (Gillis et al., Cancer Research, 2024; Reese et al., Molecular and Cellular Biology, 2022) report a comparable number of peaks-in the same range of thousands-as observed in our data. This suggests that a single CUT&RUN experiment in general may detect fewer events than a single ChIP-seq experiment, but that the peaks that are found are likely to reflect direct binding events.

      Reviewer #2 (Significance (Required)):

      General Assessment: This study investigates the role of the transcription factor GRHL2 in modulating PR function, using RIME and CUT&RUN to explore protein-protein and protein-chromatin interactions. GRHL2 have been implicated in epithelial biology and transcriptional regulation and interaction with steroid hormone receptors has been reported. This study extends the field by showing a functional link between GRHL2 and PR, which has implications for understanding hormone-dependent gene regulation.

      The research will primarily interest a specialized audience in transcriptional regulation, chromatin biology, and hormone receptor signaling.

      Key words for this reviewer: chromatin biology, transcription factor function, epigenomics, and proteomics.

      We agree that with our implemented changes, this study should reach (and appeal to) an audience interested in transcriptional regulation, chromatin biology, hormone receptor signaling and breast cancer.

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

      This study explores the important transcriptional coordination role of Grainyhead-like 2 (GRHL2) on the transcriptional regulatory function of progesterone receptor (PR). In this paper, the authors start with their recruitment characteristics, take into account their regulatory effects on downstream genes and their effects on the occurrence and development of breast cancer, and further clarify the coordination between them in three-dimensional space. The interaction between GRHL2 and PR, and the subsequent important influence on the co-regulated genes by GRHL2 and PR are analyzed. The overall framework of this study is mainly by RNA seq and CUT-TAG analysis, the molecular mechanism underlying the association between GRHL2 and PR and regulation function of two proteins in breast cancer is not clearly clarified. Some details need to be further improved:

      Major comments: R3.1 For Fig.1D, the molecular weight of each protein should be marked in the diagram, and the expression of GRHL2 in the input group should be supplemented.

      We apologize for not including molecular weights in our initial submission. We are not entirely clear what the reviewer means with their statement that "the expression of GRHL2 in the input group should be supplemented". The blot depicted in Figure 1D shows both the input signal and the IP. For the reviewer's information, the full Western blot is depicted below.

      Figure 3 for reviewer: Full WBs of input and IP GRHL2 samples stained for GRHL2 or PR. NT = non-treated, T = treated

      R3.2 In Fig.2B and Fig 5C, it should be describe well whether GRHL2 recruitment is in the absence or presence of R5020? How about the co-occupancy of PR and GRHL2 region, Promoter or enhancer region? It would be better to show histone marks such as H3K27ac and H3K4me1 to annotate the enhancer region.

      As also stated in our response to R1.3, we acknowledge that the ChIP-seq experiments cannot definitively determine whether GRHL2 and PR co-occupy genomic regions under ligand-stimulated conditions, since the GRHL2 dataset was generated in the absence of progesterone stimulation (as indicated in lines 167-169). To clarify this, we have now specified this detail in the legend of figure 2 by noting "untreated GRHL2 ChIP." To directly assess GRHL2 chromatin binding under progesterone-stimulated conditions, we performed CUT&RUN experiments for both GRHL2 and PR under untreated and R5020-treated conditions. These experiments revealed a subset of overlapping PR and GRHL2 binding sites (approximately 5% of all identified PR peaks.

      In our original manuscript, we performed genomic annotation of the GRHL2, PR, and GRHL2/PR overlapping peaks (Figure 2E) and found that most of these sites were located in intergenic regions, where enhancers are typically found, with ~20% located in promoter regions. We appreciate the reviewer's suggestion to further overlap the ChIP-seq peaks with histone marks such as H3K27ac and H3K4me1. We have now incorporated publicly available ATAC-seq and H3K27ac ChIP datasets in our revised manuscript (as also suggested by Reviewer 1) and find that shared GRHL2/PR sites are indeed located in active enhancer regions marked by H3K27ac (see Figure 2F). Additionally, as expected, we find that GRHL2/PR overlapping sites are enriched at open chromatin (Figure 2G).

      R3.3 What is the biological function analysis by KEGG or GO analysis for the overlapping genes from VN plots of RNA-seq with CUT-TAG peaks. The genes co-regulated by GRH2L and PR are further determined.

      For us, it is not entirely clear what reviewer 3 is asking here, but we can explain the following: as it is challenging to integrate HiChIP with CUT&RUN, due to the fundamentally different nature of the two techniques, we chose not to directly assign genes to CUT&RUN peaks. However, we did carefully link the GRHL2, PR, and GRHL2/PR ChIP-seq peaks to their target genes by integrating chromatin looping data from a PR HiChIP analysis. The result from this analysis is depicted in Figure 4B.

      As suggested by this reviewer, we also performed a GO-term analysis on the 79 genes that are regulated by both GRHL2 and PR (we now have 79 genes after the re-analysis as suggested in R1.6). The corresponding results are provided for the reviewer in figure 3 of this rebuttal (below). As this additional analysis does not provide further biological insight beyond what is already presented in Figure 4C, we decided to not include this figure in the manuscript.

      Figure 4 for reviewer: GO-term analysis on the 79 GRHL2-PR co-regulated genes that are transcriptionally regulated by GRHL2 and PR and that also harbor a PR HiChIP loop anchor at or near their TSS

      R3.4 Western blotting should be performed to determine the protein levels of downstream genes co-regulated genes by GRH2L and PR in the absence or presence of R5020.

      We agree that determining the response of co-regulated is important. Therefore, in Figure 4D, we present three representative examples of genes that are directly co-regulated by GRHL2 and PR-specifically, genes that are differentially expressed after 4 hours of R5020 exposure. Although protein levels of the targets are of functional importance, GRHL2 and PR are of transcription factors whose immediate effects are primarily exerted at the level of gene transcription. Therefore, in our opinion, changes in mRNA abundance provide the most direct and mechanistically relevant readout of their regulatory activity.

      R3.5 The author mentioned that this study positions that GRHL2 acts as a crucial modulator of steroid hormone receptor function, while the authors do not provide the evidences that how does GRHL2 regulate PR-mediated transactivation, and how about these two proteins subcellular distribution in breast cancer cells.

      We agree that while our RNA-seq data demonstrate that GRHL2 modulates the expression of PR target genes, and our CUT&RUN experiments show that GRHL2 chromatin binding is reshaped upon R5020 exposure, we have not yet further dissected the molecular mechanism by which GRHL2 functions as a PR co-regulator.

      As also mentioned in our response to R1.2, we did consider several follow-up experiments to address this, including PR CUT&RUN in GRHL2 knockdown cells, CUT&RUN for known co-activators such as KMT2C/D and P300, as well as functional studies involving GRHL2 TAD and DBD mutants. However, due to technical and logistical challenges, we were unable to carry out these experiments within the timeframe of this study.

      That said, we fully recognize that such approaches would provide deeper mechanistic insight into the interplay between PR and GRHL2. We have therefore explicitly acknowledged this limitation in our limitations of the study section (lines 502-507) and consider it an important avenue for future investigation.

      Regarding the subcellular distribution in breast cancer cells: As also mentioned in our response to R2.12, GRHL2 is well established as a nuclear protein (Zeng et al., Cancers 2024; Riethdorf et al., International Journal of Cancer 2015), and although PR is classically described as translocating to the nucleus upon hormone stimulation, several studies-including our own-have shown that PR is continuously present in the nucleus (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Frigo et al., Essays Biochem. 2021). Thus, both proteins mostly reside in the nucleus in breast (cancer) cells both in the absence and presence of hormone stimulation, but dynamic subcellular shuttling is likely to occur.

      Minor comments: Please describe in more detail the relationship between PR and GRHL2 binding independent of the hormone in the discussion section.

      As also mentioned in our response to R2.12, we have expanded the discussion to include a dedicated section addressing this point (lines 376-388).

      Reviewer #3 (Significance (Required)):

      Advance: Compare the study to existing published knowledge, it fills a gap. The authors provide RNA seq and CUT-TAG sequence analysis to show the recruitment of GRHL2 and PR and the co-regulated genes in the absence or presence of progesterone.

      Audience: breast surgery will be interested, and the audiences will cover clinical and basic research.

      My expertise is focused on the epigenetic modulation of steroid hormone receptors in the related cancers, such as breast cancer, prostate cancer, and endometrial carcinoma.

      We agree that with our implemented changes, this study should reach (and appeal to) an audience interested in transcriptional regulation, chromatin biology, hormone receptor signaling and breast cancer.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This study explores the important transcriptional coordination role of Grainyhead-like 2 (GRHL2) on the transcriptional regulatory function of progesterone receptor (PR). In this paper, the authors start with their recruitment characteristics, take into account their regulatory effects on downstream genes and their effects on the occurrence and development of breast cancer, and further clarify the coordination between them in three-dimensional space. The interaction between GRHL2 and PR, and the subsequent important influence on the co-regulated genes by GRHL2 and PR are analyzed. The overall framework of this study is mainly by RNA seq and CUT-TAG analysis, the molecular mechanism underlying the association between GRHL2 and PR and regulation function of two proteins in breast cancer is not clearly clarified. Some details need to be further improved:

      Major comments:

      1. For Fig.1D, the molecular weight of each protein should be marked in the diagram, and the expression of GRHL2 in the input group should be supplemented.
      2. In Fig.2B and Fig 5C, it should be describe well whether GRHL2 recruitment is in the absence or presence of R5020? How about the co-occupancy of PR and GRHL2 region, Promoter or enhancer region? It would be better to show histone marks such as H3K27ac and H3K4me1 to annotate the enhancer region.
      3. What is the biological function analysis by KEGG or GO analysis for the overlapping genes from VN plots of RNA-seq with CUT-TAG peaks. The genes co-regulated by GRH2L and PR are further determined.
      4. Western blotting should be performed to determine the protein levels of downstream genes co-regulated genes by GRH2L and PR in the absence or presence of R5020.
      5. The author mentioned that this study positions that GRHL2 acts as a crucial modulator of steroid hormone receptor function, while the authors do not provide the evidences that how does GRHL2 regulate PR-mediated transactivation, and how about these two proteins subcellular distribution in breast cancer cells.

      Minor comments:

      Please describe in more detail the relationship between PR and GRHL2 binding independent of the hormone in the discussion section.

      Significance

      Advance: Compare the study to existing published knowledge, it fills a gap. The authors provide RNA seq and CUT-TAG sequence analysis to show the recruitment of GRHL2 and PR and the co-regulated genes in the absence or presence of progesterone.

      Audience: breast surgery will be interested, and the audiences will cover clinical and basic research.

      My expertise is focused on the epigenetic modulation of steroid hormone receptors in the related cancers, such as breast cancer, prostate cancer, and endometrial carcinoma.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The authors present a study investigating the role of GRHL2 in hormone receptor signaling. Previous research has primarily focused on GRHL2 interaction with estrogen receptor (ER) and androgen receptor (AR). In breast cancer, GRHL2 has been extensively studied in relation to ER, while its potential involvement with the progesterone receptor (PR) remains largely unexplored. This is the rational of this study to investigate the relation between PR and GRHL2. The authors demonstrate an interaction between GRHL2 and PR and further explore this relationship at the level of genomic binding sites. They also perform GRHL2 knockdown experiments to identify target genes and link these transcriptional changes back to GRHL2-PR chromatin occupancy. However, several conceptual and technical aspects of the study require clarification to fully support the authors' conclusions.

      1. Given the high sequence similarity among GRHL family members, this raises questions about the specificity of the antibody used for GRHL2 RIME. The authors should address whether the antibody cross-reacts with GRHL1 or GRHL3. For example, GRHL1 shows a higher log fold change than GRHL2 in the RIME data.
      2. In addition, in RIME experiments, one would typically expect the bait protein to be among the most highly enriched proteins compared to control samples. If this is not the case, it raises questions about the efficiency of the pulldown, antibody specificity, or potential technical issues. The authors should comment on the enrichment level of the bait protein in their data to reassure readers about the quality of the experiment.
      3. The authors report log2 fold changes calculated using iBAQ values for the bait versus IgG control pulldown. While iBAQ provides an estimate of protein abundance within samples, it is not specifically designed for quantitative comparison between samples without appropriate normalization. It would be helpful to clarify the normalization strategy applied and consider using LFQ intensities.
      4. Other studies have reported PR RIME, which could be a valuable source to investigate whether GRHL proteins were detected.
      5. In line 137, the term "protein score" is mentioned. Could the authors please clarify what this means and how it was calculated.
      6. In line 140-141. The fact that GRHL2 interacts with chromatin remodelers does not by itself prove that GRHL2 acts as a pioneer factor or chromatin modulator. Demonstrating pioneer function typically requires direct evidence of chromatin opening or binding to closed chromatin regions (e.g., ATAC-seq, nucleosome occupancy assays). I recommend revising this statement or providing supporting evidence.
      7. The pulldown Western blot lacks an IgG control in panel D.
      8. Depending on the journal and target audience, it may be helpful to briefly explain what R5020 is at its first mention (line 146).
      9. The authors state that three technical replicates were performed for each experimental condition. It would be helpful to clarify the expected level of overlap between biological replicates of RIME experiments. This clarification is necessary, especially given the focus on uniquely enriched proteins in untreated versus treated cells, and the observation that some identified proteins in specific conditions are not chromatin-associated. Replicates or validations would strengthen the findings.
      10. The volcano plot for the RIME experiment appears to show three distinct clusters of proteins on the right, which is unusual for this type of analysis. The presence of these apparent groupings may suggest an artifact from the data processing, such as imputation. Can the authors clarify the origin of these groupings? If it is due to imputation or missing values, I recommend applying a stricter threshold, such as requiring detection in all three replicates (3/3) to improve the robustness of the enrichment analysis and increase confidence in the identified interactors.
      11. The statement that "PR and GRHL2 frequently overlap" may be overstated given that only ~700 overlapping sites are reported (cut&run).
      12. The model in Figure 6 suggests limited chromatin occupancy of PR and GRHL2 in hormone-depleted conditions, consistent with the known requirement of ligand for stable PR-DNA binding. However, Figure 1 shows no major difference in GRHL2-PR interaction between untreated and hormone-treated cells. This raises questions about where and how this interaction occurs in the absence of hormone. Since PR binding to chromatin is typically minimal without ligand, can the authors clarify this given that RIME data reflect chromatin-bound interactions.
      13. It would be of interest to assess the overlap between the proteins identified in the RIME experiment and the motif analysis results.
      14. The authors chose CUT&RUN to assess chromatin binding of PR and GRHL2. Given that RIME is also based on chromatin immunoprecipitation - ChIP protocol, it would be helpful to clarify why CUT&RUN was selected over ChIP-seq for the DNA-binding assays. What is the overlap with published data?

      Significance

      General Assessment:

      This study investigates the role of the transcription factor GRHL2 in modulating PR function, using RIME and CUT&RUN to explore protein-protein and protein-chromatin interactions. GRHL2 have been implicated in epithelial biology and transcriptional regulation and interaction with steroid hormone receptors has been reported. This study extends the field by showing a functional link between GRHL2 and PR, which has implications for understanding hormone-dependent gene regulation.

      The research will primarily interest a specialized audience in transcriptional regulation, chromatin biology, and hormone receptor signaling.

      Key words for this reviewer: chromatin biology, transcription factor function, epigenomics, and proteomics.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The manuscript by Aarts et al. explores the role of GRHL2 as a regulator of the progesterone receptor (PR) in breast cancer cells. The authors show that GRHL2 and PR interact in a hormone-independent manner and, based on genomic analyses, propose that they co-regulate target genes via chromatin looping. To support this model, the study integrates both newly generated and previously published datasets, including ChIP-seq, CUT&RUN, RNA-seq, and chromatin interaction assays, in breast cancer cell models (T47DS and T47D).

      Major comments:

      1. Novelty of GRHL2 in steroid receptor biology The role of GRHL2 as a co-regulator of steroid hormone receptors has previously been described for ER (J Endocr Soc. 2021;5(Suppl 1):A819) and AR (Cancer Res. 2017;77:3417-3430). In the ER study, the authors also employed a GRHL2 ΔTAD T47D cell model. Therefore, while this manuscript extends GRHL2 involvement to PR, the contribution appears incremental rather than conceptual.
      2. Mechanistic depth The study provides limited mechanistic insight into how GRHL2 functions as a PR co-regulator. Key mechanistic questions remain unaddressed, such as whether GRHL2 modulates PR activation, the sequential recruitment of co-activators/co-repressors, engages chromatin remodelers, or alters PR DNA-binding dynamics. Incorporating these analyses would considerably strengthen the mechanistic conclusions.
      3. Definition of GRHL2-PR regulatory regions (Figure 2) The 6,335 loci defined as GRHL2-PR co-regulatory regions are derived from a PR ChIP-seq performed in the presence of hormone and a GRHL2 ChIP-seq performed in its absence. This approach raises doubts about whether GRHL2 and PR actually co-occupy these regions under ligand stimulation. GRHL2 ChIP-seq experiments in both hormone-treated and untreated conditions are necessary to provide stronger support for this conclusion.
      4. Cell model considerations The manuscript relies heavily on the T47DS subclone, which expresses markedly higher PR levels than parental T47D cells (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Kalkhoven et al., Int J Cancer 1995). This raises concerns about physiological relevance. Key findings, including co-IP and qPCR-ChIP experiments, should be validated in additional breast cancer models such as parental T47D, BT474, and MCF-7 cells to generalize the conclusions. Furthermore, data obtained from T47D (PR ChIP-seq, HiChIP, CTCF and Rad21 ChIP-seq) and T47DS (RNA-seq, CUT&RUN) are combined along the manuscript. Given the substantial differences in PR expression between these cell lines, this approach is problematic and should be reconsidered.
      5. CUT&RUN vs ChIP-seq data The CUT&RUN experiments identify fewer than 10% of the PR binding sites reported in the ChIP-seq datasets. This discrepancy likely results from methodological differences (e.g., absence of crosslinking, potential loss of weaker binding events). The overlap of only 158 sites between PR and GRHL2 under hormone treatment (Figure 3B) provides limited support for the proposed model and should be interpreted with greater caution.
      6. Gene expression analyses (Figure 4) The RNA-seq analysis after 24 hours of hormone treatment likely captures indirect or secondary effects rather than the direct PR-GRHL2 regulatory program. Including earlier time points (e.g., 4-hour induction) in the analysis would better capture primary transcriptional responses. The criteria used to define PR-GRHL2 co-regulated genes are not convincing and may not reflect the regulatory interactions proposed in the model. Strong basal expression changes in GRHL2-depleted cells suggest that much of the transcriptional response is PR-independent, conflicting with the model (Figure 6). A more straightforward approach would be to define hormone-regulated genes in shControl cells and then examine their response in GRHL2-depleted cells. Finally, integrating chromatin accessibility and histone modification datasets (e.g., ATAC-seq, H3K27ac ChIP-seq) would help establish whether PR-GRHL2-bound regions correspond to active enhancers, providing stronger functional support for the proposed regulatory model.

      Minor comments

      Page 19: The statement that "PR and GRHL2 trigger extensive chromatin reorganization" is not experimentally supported. ATAC-seq would be an appropriate method to test this directly.

      Prior literature on GRHL2 as a steroid receptor co-regulator should be discussed more thoroughly.

      Significance

      The identification of novel PR co-regulators is an important objective, as the mechanistic basis of PR signaling in breast cancer remains incompletely understood. The main strength of this study lies in highlighting GRHL2 as a factor influencing PR genomic binding and transcriptional regulation, thereby expanding the repertoire of regulators implicated in PR biology. That said, the novelty is limited, given the established roles of GRHL2 in ER and AR regulation. Mechanistic insight is underdeveloped, and the reliance on an engineered T47DS model with supra-physiological PR levels reduces the general impact. Without validation in physiologically relevant breast cancer models and clearer separation of direct versus indirect effects, the overall advance remains modest.

      The manuscript will be of interest to a specialized audience in the fields of nuclear receptor signaling, breast cancer genomics, and transcriptional regulation. Broader appeal, including translational or clinical relevance, is limited in its current form.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors report the construction and validation of a novel SOX2-TBXT dual-reporter mouse embryonic stem cell (mESC) line, a tool enabling the simultaneous, live visualization of SOX2 and TBXT expression. Using this line, they established an in vitro differentiation system that generates populations of SOX2/TBXT co-expressing cells which mimic neuromesodermal progenitors (NMPs). The authors combined clonal analysis to assess the fate potential of these cells and applied gastruloid models to dissect the functional consequences of gene expression dynamics on axis elongation. The central finding is that the level of TBXT expression predicts and directs lineage potential. This work identifies specific expression thresholds that bias progenitors toward a mesodermal fate.

      Major comments

      The study's conclusions would be substantially strengthened by more direct validation in an embryonic context. Could the authors quantify endogenous SOX2 and TBXT protein levels of the NMPs in the mouse embryos with immunofluorescence? This would test whether a similar heterogeneity in TBXT expression exists in vivo and whether it correlates with the cells' spatial position.

      The role of SOX2 in the quantitative model seems underdeveloped. To justify the authors' claim, could the authors analyze their existing imaging data in more detail to disentangle if the absolute TBXT level is essential rather than the ratio of Sox2 to Tbxt is the driving factor for determining NMP fate?

      It seems that SOX2 expression also appears heterogeneous from both in vitro differentiation and gastruloid models. Quantifications, and a discussion of this heterogeneity and whether it influences the fate-decision process would be helpful.

      To support the authors' claim that the reporter line recapitulates endogenous protein expression in vivo (lines 121-128), please include a control immunostaining of wild-type embryo for SOX2 and TBXT to compare expression patterns side-by-side with those embryos shown in Figure 1B, Suppl. Fig. 1C, D. To substantiate the claims regarding cellular expression patterns within the embryo (line 125-128), the use of higher-resolution imaging, such as confocal microscopy, is recommended.

      The differentiation trajectory described culminates in a double-negative (Sox2-mCherry-negative, Tbxt-GFP-negative) population (lines 246-247). To provide a more complete picture of this fate progression, could the authors perform qPCR for relevant lineage markers to validate the molecular identity of this terminal population?

      Minor comments

      Scale bars for micrographs are missing in Figure 1B, Suppl. Fig. 1C, and D. The claims regarding the dynamics of TBXT and SOX2 expression in gastruloids following WNT/NOTCH inhibition (Figure 4B, 4D, 4E) would be more compelling if the authors include supplementary videos of the time-lapse imaging.

      In lines 322-324, the authors conclude that Tbxt-cells are the driving cells. Please elaborate on the interpretation that this is a cell-autonomous effect driven by TBXT levels. The observation that Sox2 levels increase ~10-15 hours after WNT/NOTCH inhibition is interesting (Figure 4D, 4E). Could the authors discuss this upregulation?

      Significance

      General Assessment

      In the development of a novel dual Sox2/Tbxt reporter cell line, which provides a powerful tool for quantitatively understanding the dynamics of cell fate specification during gastrulation and potentially in other developmental contexts. However, a key limitation is the study's primary focus on in vitro models. The findings will require further validation in an in vivo context.

      Advance

      This study provides a technical advance that provides a new resource available to the field for stem cell and developmental biology.

      Audience

      This paper will primarily interest a specialized audience, particularly developmental and stem cell biologists who study the fundamental mechanisms of embryogenesis, cell fate specification, and axis elongation.

      Field of expertise

      Stem cell biology and developmental biology. I do not have the expertise to evaluate their mathematical modeling.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, the authors generate a novel reporter mouse ESC line to track SOX2 and TBXT dynamics in neuromesodermal progenitors. The authors leverage multiple systems (in vitro differentiation, chimeric embryos and gastruloids) to address how SOX2/TBXT levels impact the contribution of neuromesodermal progenitors towards neural versus mesodermal fates. They show that the levels of TBXT can predict differentiation outcomes, whereby certain thresholds of TBXT influence the differentiation towards a mesodermal identity. In gastruloids, perturbing either WNT or NOTCH signalling coincides with diminished axial elongation. In vitro, the bias mediated by TBXT is also somewhat influenced by the substrate.

      Major comments:

      There are some key items to clarify that are important to resolve the interpretation of the results and clarify the main advances for a broader readership.

      With respect to the mESC differentiation into NMPs (using an established protocol from Gouti et al 2014), after Day 3, cells are cultured in conditions where they are exposed to both CHIR and FGF for a further two days. As these extended CHIR/FGF conditions don't appear to be characterised by Gouti et al., 2014, what proportions and progenitors are generated in the dish under these conditions? The loss of SOX2 by day 5 suggests mesodermal progenitors are the main derivatives but further characterization (eg Meox1/Msgn1) would be needed to verify this claim.

      Further validation of the generated derivatives would also be useful in the re-plating experiments (Figure 3) to test whether the double negative cells are transitioning to a mesodermal (eg Meox1/Msgn1) or neural derivative (eg Sox1/Pax6). Similarly, at day 5 and 6 of the differentiation, there appears to be a loss of Sox2 expression in some of the replated cells from the Sox2-positive population (see Figure 3D). Could the authors please clarify whether the double negative cells represent neural progenitors, and/or alternative cell types? Do the replated cells transiently adopt Tbxt? This would be possible by staining with neural (SOX1), or (pre)somitic mesoderm genes (MSGN1,MEOX1) or adjusting the text to reflect the uncertainty.

      At line 172 "The cells posterior to the node expressed only TBXT." Do these cells have low SOX2 expression that is hard to detect? Are these TBXT-positive cells derived from the primitive streak? Would staining with the primitive streak marker TBX6 enable visualization of these distinct cell types, and/or could the authors please label the figure in more detail.

      Could the authors please comment on the design of the reporter system in a bit more detail? For example, please clarify the necessity to generate a TBXT reporter that includes a H2B-GFP, unlike Sox2, which does not include a H2B. Can the authors distinguish between an increase in the threshold of TBXT, versus an increase detected due to the stability of the H2B-GFP? The low versus high TBXT cells may reflect early versus late TBXT expressing cells. Are the changes in TBXT expression (eg Supplementary Figure 2) significantly changed between the low versus high GFP populations? Additionally, why is the level of GFP similar across low and high GFP populations in Sup Fig 2? Could the in vivo data be used to quantify differences in TBXT (similar to what has been shown previously in Ivanovitch et al., 2021 PMID: 33999917)?

      Optional

      • Altering extrinsic cues (such as RA/CHIR) could clarify how reversible the high TBXT state is as cells progress towards mesoderm. Can you redirect the TBXT-high cells to a neural fate or are they already irreversibly committed?
      • Line 283: Flk1 is also expressed in TBXT-low cells. It would be interesting to test whether the TBXT-low cells and the SOX2neg/TBXTpos can generate lateral plate mesoderm or whether this competence to generate lateral plate mesoderm is limited to TBXT-high cells.
      • Line 324: The lack of elongation in gastruloids following the inhibition of WNT/NOTCH is clear. Do the authors expect that the reintroduction of TBXT would rescue axis elongation in the WNT/NOTCH inhibited gastruloids?

      Minor comments:

      Figure 3B - The SOX2+/TBXT- population only shows a moderate level of SOX1 by RT-qPCR. Using pre-neural markers (e.g. NKX1-2) might be helpful here to show progression towards a neural progenitor identity.

      Line 310 - Can you comment on the general efficiency in generating elongating gastruloids compared to WT cells and/or previous literature?

      Line 236 - Add "at constant SOX2 levels" (when comparing orange and yellow populations)

      Figure 5

      Fig5C and E take time to understand. Potentially expanding the figure legend slightly could be helpful to the reader.

      Supplementary Figure 2

      Line 864 - refer to Fig3A

      Line 350 - The link with testing the different substrates is a bit abrupt. Please can you make it clearer by modifying the text to explain the hypothesis being tested here.

      Line 363 - Could you comment in relation to what happens in the embryo to future mesoderm progenitors that seem to have a more motile phenotype compared to neural progenitors (eg Romanos et al 2021 PMID: 34607629)?

      Significance

      In this work, the authors have explored how the dynamics of SOX2/TBXT impact the decision process of neuromesodermal progenitors (NMPs). They have engineered and validated a novel dual fluorescent reporter ESC line to track SOX2 and TBXT. The work combines in vitro, in vivo and modelling approaches to understand how NMPs make decisions - a highly relevant and important question for multiple fields spanning developmental and quantitative biology, and the engineering of cell types in vitro from pluripotent cells. The authors propose a critically important finding: that discrete thresholds of TBXT influence the outcome of NMPs. However, further clarification is required to solidify these claims (discussed above).

      The data generated from this study also suggests that in contrast to Tbxt, the level of Sox2 does not appear to impact the NMP fate decision. While these are interesting and important findings, it is not entirely clear in this version of the manuscript how these advances relate with previous studies that have highlighted critical roles mediated by Sox2 and its level of expression (including the work of Koch et al 2017 - PMID: 28826820 and Blassberg et al PMID: 35550614). We expect that a broader discussion will in turn broaden the general interest and value of the work to a wider readership.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The work of Binagui-Casas, Granes and colleagues, investigates in a rigorous way the origin and potential of murine NMPs. The authors first validated a dual reporter system, which monitors Sox2 and Tbxt expression. Next, they identified the location in the embryo and the sequence of gene activation (Sox2 first, then Tbxt) leading to NMPs specification. Importantly, the in vitro model faithfully mimics the in vivo ontogeny of NMPs. Among Sox2+ NMCs, the authors observed different levels of Tbxt, which they proposed mark different stages of mesoderm maturation, with high Tbxt corresponding to more mature mesodermal state. Through sorting and replating of different populations, the authors convincingly showed that Sox2+/Tbxt-low cells are still bipotent, while Sox2+/Tbxt-high are committed towards the mesoderm lineage. Using a gastruloid model, the authors then showed that Tbxt expression correlates with axis elongation, as both are reduced upon inhibition of either WNT or NOTCH Finally, single-cell sorting followed by differentiation in FGF/CHIR and high throughput microscopy confirmed that double Sox2/Tbxt positive cells behave as NMPs and that high levels of Tbxt predispose cells towards mesoderm differentiation. These conclusions were further supported by mathematical modelling. The manuscript is easy to read, and figures are very clear. I have only minor suggestions, as I find the manuscript quite solid and complete.

      Minor points:

      1. The reporter system is based on stable fluorescent proteins, whose half-life is generally much longer than the endogenous proteins. This could generate a discrepancy between expression of reporters and the endogenous proteins. I find this relevant for Tbxt, as it is very clear that Tbxt reporter levels dictates the differentiation propensity, but I wonder whether, Tbxt low cells actually express TBXT protein or not. It might be the case that only a fraction of Tbxt low cells actually express TBXT protein, or none. It would enough to sort the populations showed in Figure 3A and perform immunostaining for endogenous SOX2 and TBXT. This could reveal even better correlations between their levels and cell behaviour.
      2. In the experiments in which IWP2 and LY411575 are used, I would suggest to asses cell viability, as the two inhibitors could induce toxicity. Staining for cleaved caspase-3 or a TUNEL assay would be enough. It would also be important to confirm that IWP2 blocks WNT signalling (by looking at WNT target genes or staining for active beta-cateinin) and that LY411575 blocks NOTCH signalling.
      3. I would define in the figure legends what the black line in figure 5E represents.

      Referees cross-commenting

      I agree with Reviewer #2 comment about "Further validation of the generated derivatives" by staining for additional markers.

      I also made a comment related to GFP stability, as Reviewer #2 did (i.e. The low versus high TBXT cells may reflect early versus late TBXT expressing cells ).

      Significance

      Overall, the manuscript uses an elegant approach and address an important question about NMPs behaviour. The results presented are an important advance in knowledge of NMP biology. I am confident that both stem cell and developmental biologist would be interested in this manuscript. I am an expert of pluripotency, signaling and models of early mammalian development.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-03174

      Corresponding author(s): Cristina, Tocchini and Susan, Mango

      1. General Statements

      We thank the reviewers for their thoughtful and constructive comments. We were pleased that the reviewers found our study “rigorous”, “well presented”, “technically strong”, and “novel”. We are also grateful for their recognition that our work identifies a function for a HOT region in gene regulation and provides new insights into the role of the uHOT in controlling dlg-1 expression.

      Point-by-point description of the revisions

      We have addressed the reviewers’ concerns by clarifying and refining the text, particularly regarding the intron 1 results, improving the quantitation and statistical analyses, and making adjustments and additions to text and figures.

      Specific responses to each point are provided below in blue.

      Reviewer #1

        • The results fully support the authors conclusions regarding the significant role of the upstream HOT region ("uHOT") with strong fluorescence activity and substantial phenotypic effects (i.e., the animals have very low brood sizes and rarely progress through hatching). This data is well presented and technically well done.* Thank you.
      1. In my view, their conclusions regarding the intronic HOT region are speculative and unconvincing. See below for main criticisms.*

      We agree, and have made changes throughout the manuscript to make this point clearer. Specifically, we contextualize the role of intron 1 as a putative enhancer in reporter assays, but not in endogenous, physiological conditions. Some examples are:

      Abstract: “(…) In contrast, the intronic region displays weak enhancer-like activity when tested in transcriptional reporter assays but is dispensable in transcriptional control when studied at the endogenous locus. Our findings reveal how HOT regions contribute to gene regulation during animal development and illustrate how regulatory potential identified in isolated contexts can be selectively deployed or buffered within the native genomic architecture.”

      Background: “(…) The HOT region in the first intron possesses weak transcriptional capabilities that are restricted to epidermal cells as observed in transcriptional reporters, but seem to not be employed in physiological contexts.” As it will become clear reading this updated version of the manuscript, we cannot exclude at present a functional role during non-physiological conditions (e.g., stress)

      Results and discussion: “(…) This is in contrast with what the reporter experiments showed, where intron 1 alone was permissive for transcription and slightly enhanced the FL transgene expression levels (Figure 1F,G and S4). (…)”

      Other changes can be found highlighted in yellow in the manuscript.

      • Furthermore, their conclusions about interactions between the two tested regions is speculative and they show no strong evidence for this claim.*

      We thank the reviewer for raising this concern. To avoid overstating our conclusions, we now frame the potential interaction between the two studied HOT regions strictly in the context of previously published ARC-C data (Huang et al., 2022). We clarify in the revised text that these interactions have been observed in earlier work during larval stages (Huang et al., 2022), but remain to be validated during embryogenesis, and we present them solely as contextual information rather than as a central conclusion.

      In Results and discussion section we wrote: “(…) Although the presence of a fountain at this locus remains to be confirmed during embryogenesis, Accessible Region Conformation Capture (ARC-C), a method that maps chromatin contacts anchored at accessible regulatory elements, showed that the putative HOT region interacts with other DNA sequences, including the first intron of dlg-1 (1). (…)”

      * The authors claim that not all the phenotypic effects seen from deleting the uHOT region are specific to the dlg-1 gene. This is an interesting model, but the authors show essentially no data to support this or any explanation of what other gene might be regulated.*

      We appreciate the reviewer’s comment and have revised the manuscript to ensure that the possibility of additional regulatory effects from the uHOT region is presented as a hypothesis rather than a claim. Our study was designed to investigate HOT-region–based transcriptional regulation rather than chromatin interactions, and we now make this scope more explicit in the text. The revised discussion highlights that, although ARC-C data suggest the uHOT region may contact other loci, the idea that these interactions contribute to the observed phenotypes remains speculative and will require dedicated future work.

      In Results and discussion section we wrote: “(…) Because, as previously shown, the upstream HOT region exhibits chromatin interactions with other genomic loci (1), its depletion might affect gene expression of beyond dlg-1 alone. An intriguing hypothesis is that these phenotypes do not arise only from the reduction in dlg-1 mRNA and DLG-1 protein levels, but also from synergistic, partial loss-of-function phenotypes involving other genes (24). (…)”

      * Finally, some of the hypotheses in the text could be more accurately framed by the authors. They claim HOT regions are often considered non-functional (lines 189-191). Also, they claim that correct expression levels and patterning is usually regulation by elements within a few hundred basepairs of the CDS (lines 78-80). These claims are not generally accepted in the field, despite a relatively compact genome. Notably, both claims were tested and disproven by Chen et al (2014), Genome Research, where the authors specifically showed strong transcriptional activity from 10 out of 10 HOT regions located up to 4.7 kb upstream of their nearest gene. Chen et al. 2014 is cited by Tocchini et al. and it is, therefore, surprisingly inconsistent with the claims in this manuscript.*

      We thank the reviewer for this comment and have revised the text to clarify our intended meaning and avoid framing discussion points as absolute claims. We changed “often” to “frequently” in both sentences so that they better reflect general trends rather than universal rules.

      The revised text now reads: “Controversially, C. elegans sequences that dictate correct expression levels and patterning are frequently located within a few hundred base-pairs (bp) (maximum around 1,000–1,500 bp) from a gene’s CDS (3,13–15),”;

      And: “HOT regions in C. elegans, as well as other systems, have been predominantly associated with promoters and were frequently considered non-functional or simply reflective of accessible chromatin (25).”

      Regarding the comparison to Chen et al., 2014, we note that their reporters did not include a reference baseline for “strong” transcriptional activity, and only five of the ten tested HOT regions were located more than 1.5 kb from the nearest TSS. Therefore, our phrasing is consistent with their findings while describing general trends observed in the C. elegans genome rather than absolute rules. We have also ensured that these sentences are presented as discussion points rather than definitive claims. We hope these revisions make the framing and context clearer to the reader. The fluorescence expression from the intronic HOT region is not visible by eye and the quantification shows very little expression, suggestive of background fluorescence. Although the authors show statistical significance in Figure 1G, I would argue this is possibly based on inappropriate comparisons and/or a wrong choice statistical test. The fluorescence levels should be compared to a non-transgenic animal and/or to a transgenic animal with the tested region shuffled but in an equivalent

      We understand the reviewer’s concern regarding the low fluorescence levels observed for the intronic HOT reporter. To address this, we have now included a Figure S4 with higher-exposure versions of the embryos shown in Figure 1. These panels confirm that the nuclear signal is genuine: embryos without a functional transcriptional transgene do not display any comparable fluorescence, aside from the characteristic cytoplasmic granules associated with embryonic autofluorescence. Similar reference images have also been added to Figure S3 to clarify the appearance of autofluorescence under the same imaging conditions.

      Regarding the quantitation analyses, as suggested by the reviewers, we now consistently quantify fluorescence by calculating the mean intensity for each embryo (biological replicates) and performing statistical analyses on these values. This approach ensures that the statistical tests are applied to independent biological measurements.

      * I would suggest the authors remove their claims about the intronic enhancer and the interaction between the two regions. And I would suggest softening the claims about the uHOT regulation of another putatitive gene.*

      We have revised the manuscript to avoid definitive claims regarding the presence of an interaction between the two studied HOT regions. These points are now presented strictly as hypotheses within the discussion, suggested by previously published ARC-C data rather than by our own experimental evidence. Likewise, we have softened our statements regarding the possibility that the uHOT region may regulate additional gene(s). This idea is now framed as a speculative model that will require dedicated future studies, rather than as a conclusion of the present work. Quotes can be found in the previous points (#3 and #4) raised by Reviewer 1.

      * The authors would need to demonstrate several things to support their current claims. The major experiments necessary are:*

        • Insert single-copy transgene with a minimal promoter and the intronic sequence scrambled to generate a proper baseline control. It is very possible that the intronic sequence does drive some expression, but the current control is not appropriate for statistical comparison (e.g., only the transgene with intron 1 contains the minimal promoter from pes-10, which may have baseline transcriptional activity even without the intron placed in front of the transgene).* We thank the reviewer for this suggestion. We agree that a scrambled-sequence control can be informative in some contexts; however, in this case we believe the existing data already address the concern. In our dataset, all uHOT reporter constructs—each containing the same minimal promoter—show consistent background levels in the absence of regulatory input, providing an internal baseline for comparison. For this reason, we consider the current controls sufficient to interpret the effects of the intronic region in reporter assays.

      In general, the minimal Δpes-10 promoter is specifically designed to have negligible basal transcriptional activity on its own, and this property has been extensively validated in previous studies (reference included in the revised manuscript).

      * It is not very clear why the authors did not test intron 1 within the H2B of the transgene and just the minimal promoter in front of the transgene, but only in the context of the full-length promoter. The authors show a minor difference in expression levels for the full-length (FL) and full-length with intron 1 (FL-INT1) but show a large statistical differnce. The authors use an inappropriate statistical test (T-test) for this experiment and treat many datapoints from the same embryo as independent, which is clearly not the case. Even minor differences in staging, transgene silencing in early development, or variability would potentially bias their data collection.*

      We thank the reviewer for this comment. Our goal was to assess the potential contribution of intron 1 in two complementary contexts: (i) on its own, upstream of a minimal promoter, to test whether it can in principle support transcription, and (ii) within the full-length promoter construct, which more closely reflects the endogenous configuration. For this reason, we did not generate an additional construct placing intron 1 within the H2B reporter driven only by the minimal promoter, as we considered this redundant with the information provided by the existing INT1 and FL-INT1 reporters.

      Regarding the statistical analysis, we agree that treating multiple measurements from the same embryo as independent is not appropriate. In the revised manuscript, we now use the mean fluorescence intensity per embryo as a single biological replicate and perform all statistical tests on these independent values. This approach avoids pseudo-replication and ensures that the analysis is robust to variability in staging or transgene behavior. The conclusions remain the same.

      * The authors claim, based on ARC-C data previously published by their lab (Huang et al. 2022) that the dlg-1 HOT region interacts with "other" genomic regions. This is potentially interesting but the evidence for this should be included in the manuscript itself, perhaps by re-analyzing data from the 2022 manuscript?*

      We thank the reviewer for this suggestion. The chromatin-interaction data referred to in the manuscript originate from the work of Huang et al., 2022, published by the Ahringer lab. As these ARC-C datasets are already publicly available and thoroughly analyzed in the original publication, we felt that reproducing them in our manuscript was not necessary for supporting the limited contextual point we make. Our intent is simply to note that previous work reported contacts between the uHOT region and additional loci. To address the reviewer’s concern, we have revised the manuscript to make clear that we are referencing previously published ARC-C observations and that we do not present these interactions as new findings from our study.

      For example, in Results and discussion section we wrote: “(…) Because, as previously shown, the upstream HOT region exhibits chromatin interactions with other genomic loci (1), its depletion might affect gene expression beyond dlg-1 alone. An intriguing hypothesis is that these phenotypes do not arise only from the reduction in dlg-1 mRNA and DLG-1 protein levels, but also from a synergistic, partial loss-of-function phenotypes involving other genes (24). (…)”

      * The fluorescence quantification is difficult to interpret from the attached data file (Table S1). For the invidividual values, it is unclear how many indpendent experiments (different embryos) were conducted. The authors should clarify if every data value is from an independent embryo or if they used several values from the same embryo. If they did use several values from the same embryo, how did they do this? Did they take very cell? Or did they focus on specific cells? How did they ensure embryo staging?*

      We thank the reviewer for pointing this out. To clarify the quantification procedure, we have expanded the description in the Methods section (“Live imaging: microscopy, quantitation, and analysis”). The revised text now specifies that each data point represents the normalized fluorescence value obtained from three nuclei (or five junctions, depending on the construct), all taken from the same anatomical positions across embryos. Two independent biological replicates were performed for each experiment, with each embryo contributing a single averaged value.

      As noted in the figure legends, the specific nuclei used for quantification are indicated in each panel (with dashed outlines), and a reference nucleus marked with an asterisk allows unambiguous identification of the same positions across all conditions. We are happy to further refine this description if additional clarification is needed.

      * The authors also do not describe how they validated single-copy insertions (partial transgene deletions in integrants are not infrequent and they only appear to use a single insertion for each strain). This should be described and or added as a caveat if no validation was performed.*

      The authors also do not describe any validation for the CRISPR alleles, either deletions or insertion of the synthetic intron into dlg-1. How were accurate gene edits verified.

      We thank the reviewer for highlighting the importance of validating the genetic constructs. We have now clarified this more explicitly in the revised Methods section and in Table S1. All single-copy transgene insertions and all CRISPR-generated alleles were verified by genotyping and Sanger sequencing to confirm correct integration and the absence of unintended rearrangements.

      • *

      I am not convinced the statistical analysis of the fluorescence data is correct. Unless the authors show that every datapoint in the fluorescence quantification is independent, then I would argue they vastly overestimate the statistical significance. Even small differences are shown to have "***" levels of significance, which does not appear empirically plausible.

      We thank the reviewer for highlighting this point. To ensure that each data point represents an independent measurement, we now calculate the mean fluorescence per embryo (from three nuclei or five junctions) and use these per-embryo means as biological replicates for statistical testing. Two independent experiments were performed for each condition. Statistical differences were evaluated using a one-tailed t-test on the per-embryo means, as indicated in the revised Methods section.

      After this adjustment, the differences remain statistically significant, although less extreme than in the initial analysis (now p * *

      This study is so closely related to the Chen et al study, that I believe this study should be discussed in more detail to put the data into context.

      We thank the reviewer for this suggestion. While we refer to Chen et al., 2014 as a relevant prior study for context, we believe that our work addresses distinct questions and experimental approaches. Specifically, our study focuses on HOT region-based transcriptional regulation in the dlg-1 locus and its functional dissection in vivo, which is conceptually and methodologically different from the scope of Chen et al., 2014 where the author tested the functionality of HOT region-containing promoters in the context of single-copy integrated transcriptional reporters. We hope this is clearer to the reader in the revised manuscript.

      * Add H2B to the mNG in Figure 1 in order to understand where the first intron was inserted.*

      We thank the reviewer for this suggestion. A schematic representation of the transgene is already provided above the corresponding images to indicate the location of the first intron.

      For additional clarity, we have now added the following sentence in the main text: “In the other, intron 1 was inserted in the FL transgene within the H2B coding sequence (at position 25 from the ATG), preserving the canonical splice junctions with AG at the end of the first exon and a G at the beginning of the second exon, so that it acted as a bona fide intron (FL-INT1) (Figure 1F).”

      This should help readers understand the placement of the intron without requiring modifications to the figure itself.__ __

      Reviewer #2

      1) The authors suggest that the region upstream of the dlg-1 gene is a HOT region. Although they highlight that other broad studies pick up this region as a HOT region, it would be good that the authors dive into the HOT identity of the region and characterize it, as it is a major part of their study. In addition to multiple TFs binding to the site, there are different criteria by which a region would be considered a HOT region. E.g. is there increased signal on this region in the IgG ChIP-seq tracks? Is the area CpG dense?

      We thank the reviewer for this suggestion. In the manuscript and Figure S1, we show several features of HOT regions, including transcription factor binding and chromatin marks. To further characterize the dlg-1 uHOT region, we have added the following sentence to the text: “The conserved region is positioned approximately four Kb from the CDS of dlg-1 in a CpG-dense sequence (2), and is overlapping and bordered by chromatin marks typically found in enhancers (5,16).”

      This addition provides additional evidence supporting the identity of the region as a HOT region, complementing the features already presented.

      * 2) When describing the HOT region, they refer to Pol II binding as 'confirming its role as a promoter': non-promoter regions can also have Pol II binding, especially enhancers. Having binding of Pol II does not confirm its role as promoter. On the contrary, seeing the K27ac and K4me1 would point towards it being an enhancer.*

      The sentence has been revised to clarify the interpretation of Pol II binding: “This HOT site also contains RNA Pol II peaks during embryogenesis (Figure S1C), supporting its role as a promoter or enhancer (9).” This wording avoids overinterpreting Pol II binding alone, while acknowledging that the HOT region may have both promoter and enhancer characteristics.

      We would like to note that the relevant chromatin marks (H3K27ac and H3K4me1), which are indicative of enhancer activity, are described in the text: “(…) Specifically, it is enriched in acetylated lysine 27 (H3K27ac) and mono- and di-methylated lysine 4 of histone H3 (H3K4me1/2), and depleted from tri-methylated lysine 4 of histone H3 (H3K4me3) (Figure S1D) (5,16). (…)”

      These changes clarify that the HOT region may have enhancer characteristics and avoid overinterpreting the Pol II signal.

      * 3) In S1B, the authors show TF binding tracks. They also have a diagram of the region subsets (HOT1-4) that were later tested. What is their criteria for dividing the HOT region into those fragments? From looking at Fig S1, the 'proper' HOT region (ie. Where protein binding occurs) seems to be divided into two (one chunk as part of HOT3 and one chunk as part of HOT4). Can the authors comment on the effects of this division?*

      To clarify the criteria for dividing the HOT region into subregions, we have added the following sentence to the main text: “The subregions were chosen taking into account (i) enrichment of putative TF binding sites (uHOT1 for PHA-4, uHOT2 for YAP-1 and NHR-25, uHOT3 for ELT-3, and uHOT4 for PHA-4 and others (e.g., ELT-1 and ELT-3)), (ii) Pol II binding peaks, and (iii) histone modification peaks (Fig. S1C,D).”

      This description explains the rationale behind the division and clarifies why the HOT region was split into these four fragments for functional testing.

      * 4) For the reporter experiments, the first experiments carry the histone H2B sequence and the second set of experiments (where the HOT region is dissected) carry a minimal promoter Δ*pes-10 (MINp). The results could be affected by the addition of these sequences. Is there a reason for this difference? Can the authors please justify it?

      The difference in reporter design reflects the distinct goals of the two sets of experiments. The H2B sequence, coupled to mNG, is used as a coding sequence throughout the first part of the study (reporter analysis). This is commonly used to (i) concentrate the fluorescence signal (mNG) into nuclei (H2B) and (ii) be able to identify specific cells more accurately for quantitation reasons (intensity and consistency). The Δpes-10 promoter is instead used to analyze whether specific sequences possess enhancer potential: this promoter alone possesses the sequences that can allow transcription only in the presence of transcription factors that bind to the studied sequence placed upstream it.

      To clarify this distinction in the manuscript, we have added the following sentence: “(…) Each region was paired with the minimal promoter Δpes-10 (MINp) (Figure 1D) and generated four transcriptional reporters. Δpes-10 is commonly used to generate transcriptional reporter aimed at assessing candidate regulatory enhancer sequences (20). The minimal promoter drives expression only when transcription factors bind to the tested upstream sequence and test enhancer activity. (…)”

      5) Regarding the H2B sequence: ' 137: first intron [...] inserted in the FL transgene within the H2B sequence, acting as an actual intron (FL-INT1)': how was the location of the insertion chosen? Does it disrupt H2B? can it be that the H2B sequence contributed to dampening down the expression of mNG and disrupting it makes it stronger? It would be important to run the first experiments with minimal promoters and not with the H2B sequence.

      The location of the intron insertion within the H2B coding sequence was chosen to preserve proper splicing and avoid disrupting H2B protein. We added the following sentence to clarify this point: “(…) In the other, the intron was inserted in the FL transgene within the H2B coding sequence (at position 25 from the ATG), preserving the canonical splice junctions with AG at the end of the first exon and a G at the beginning of the second exon, so that it acted as a bona fide intron (FL-INT1) (Figure 1F). (…)

      * 6) Have the authors explored the features of the sequences underlying the different HOT subregions? (e.g. running a motif enrichment analysis)? Is there anything special about HOT3 that could make it a functional region? It would be good to compare uHOT3 vs the others that do not drive the correct pattern. Since it's a HOT region, it may not have a special feature, but it is important to look into it.*

      We thank the reviewer for this suggestion. To clarify the rationale for dividing the HOT region into four subregions, we have added the following sentence to the main text: “(…) The subregions were chosen taking into account (i) enrichment of putative TF binding sites (uHOT1 for PHA-4, uHOT2 for YAP-1 and NHR-25, uHOT3 for ELT-3, and uHOT4 for PHA-4 and others (e.g., ELT-1 and ELT-3)), (ii) Pol II binding peaks, and (iii) histone modification peaks (Fig. S1C,D). (…)”

      While uHOT3 does not appear to possess unique sequence features beyond these general HOT-region characteristics, this approach allowed us to systematically test which fragments contribute to transcriptional activity and patterning.

      7) For comparisons, the authors run t-tests. Is the data parametric? Otherwise, it would be more suitable to use a non-parametric test.

      To ensure that each data point represents an independent biological replicate, we now calculate the mean fluorescence intensity per embryo and perform statistical tests on these per-embryo means. The data meet the assumptions of parametric tests, and we use a one-tailed t-test as indicated in the Methods.

      * 1) The authors work with C. elegans embryos at comma stage, according to the methods section. It would be good if the authors mentioned it in the main text so that the reader is informed.*

      Thanks for this suggestion. We added this sentence in the main text: “(…) Live imaging and quantitation analyses on embryos at the comma stage (used throughout the study for consistency purposes) showed (…)”.

      * 2) 'Notably, the upstream HOT region is located more than four kilo-bases (Kb) away the CDS, and the one in the first intron contains enhancer sites, too.': what do they mean by 'enhance sites, too'. Is the region known as a functional enhancer? If so, could you please provide the reference?*

      Here the clarification from the revised text: “(…) Notably, the upstream HOT region is located more than four kilo-bases (Kb) away the CDS, and the one in the first intron does not only contain two TSS but also three enhancer sites (8). (…)”

      * 3) 'We hypothesized the upstream HOT region is the main driver of dlg-1 transcriptional regulation.': this sentence needs more reasoning. What led to this hypothesis? Is it the fact of seeing multiple TFs binding there? The chromatin marks?*

      The reasoning behind the hypothesis is described in the preceding paragraph, and to make this connection clearer, we have revised the sentence to begin with: “Considering all of this information, we hypothesized the upstream HOT region is the main driver of dlg-1 transcriptional regulation. (…)”.

      This change explicitly links the hypothesis to the observed TF binding and chromatin marks described above.

      * 4) The labels of S1B are too wide, as if they have stretched the image. Could the authors please correct this?*

      Yes, we agree with Reviewer 2. We corrected this.

      * 5) This sentence does not flow with the rest of the text '84 - cohesins have been shown to organize the DNA in a way that active enhancers make contacts in the 3D space forming "fountains" detectable in Hi-C data (17,18).': is there a reason to explain this? I would remove it if not, as it can confuse the reader.*

      We thank the reviewer for this comment. We agree that the sentence could potentially interrupt the flow; however, it is important for introducing the concept of “fountains” in 3D genome organization, which is necessary to understand the subsequent statement: “(…) Although the presence of a fountain at this locus remains to be confirmed during embryogenesis, Accessible Region Conformation Capture (ARC-C), a method that maps chromatin contacts anchored at accessible regulatory elements, showed that the putative HOT region interacts with other DNA sequences, including the first intron of dlg-1 (1). (…)”.

      Therefore, we have retained this sentence to provide the necessary background for readers.

      * 6) The authors mentioned that 'ARC-C data showed the putative HOT region interacts with other DNA sequences, including the first intron of dlg': have the authors analysed the data from the previous paper? A figure with the relevant data could illustrate this interaction so that the reader knows which specific region has been shown to interact with which. This would also bring clarity as to why they chose intron1 for additional experiments.*

      We thank the reviewer for this suggestion. We have examined the relevant ARC-C data from the previous publication (Huang et al., 2022). However, as these results are already published, we do not feel it is necessary to reproduce them in our manuscript. The mentioning of these interactions is intended only to introduce the concept for discussion and to provide context for why intron 1 was considered in subsequent experiments

      * 7) 'two deletion sequences spanning from the beginning (uHOT) or the end (Short) of the HOT region until the dlg-1 CDS': From the diagrams of the figure, I understand that uHOT has the distal region deleted, and the short HOT has the distal and the upstream regions deleted. Is this correct? Could you clarify this in the text? E.g. 'we designed two reporters - one containing the sequence starting at the HOT region and ending at the dlg-1 CDS, and the other without the HOT region, but rather starting downstream of it until the dlg-1 CDS'.*

      To clarify the design of the reporters, we have revised the text as follows: “(…) To test this idea, we generated three single-copy, integrated transcriptional reporters carrying a histone H2B sequence fused to an mNeon-Green (mNG) fluorescent protein sequence under the transcriptional control of the following dlg-1 upstream regions: (i) a full-length sequence (“FL” = Distal + uHOT + Proximal sequences), (ii) one spanning from the beginning of the HOT region to the dlg-1 CDS (“uHOT” = uHOT + Proximal sequences), and (iii) one starting at the end of the HOT region and ending at the dlg-1 CDS (“Short” = Proximal sequence) (Figure 1A-C). (…)”

      This description clarifies which parts of the upstream region are included in each reporter and matches the schematics in Figure 1.

      * 8) 'Specifically, it spanned from bp 5,475,070 to 5,475,709 on chromosome X and removed HOT2 and HOT2 sequences' - this is unclear to me. What sequences are removed? HOT2 and 3?*

      Thanks for spotting this typo. It has now been corrected.

      * 9) 'ARC-C' is not introduced. Please spell out what this is. Accessible Region Conformation Capture (ARC-C). It would be helpful to include a sentence of what it is, as it will not be known by many readers.*

      You are right, we changed into: “(…) Although the presence of a fountain at this locus remains to be confirmed during embryogenesis, Accessible Region Conformation Capture (ARC-C), a method that maps chromatin contacts anchored at accessible regulatory elements, showed that the putative HOT region interacts with other DNA sequences, including the first intron of dlg-1 (1). (...)”

      * 10) Fig 1 B, diagram on the right: the H2B sequence is missing. I see that is indicated in the legend as part of mNG but this can be misleading. Could the authors add it to the diagram for clarification?*

      Yes, you are right. We added this in the figure.__ __

      Reviewer #3

      The authors' claims are generally supported by the data, thoug the last sentence of the abstract was a bit overstated. They state that they "reveal the function of HOT regions in animals development...."; it would be more accurate to state that they linked the role of an upstream HOT region to dlg-1 regulation, and their findings hint that this element could have additional regulatory functions. The authors can either temper their conclusions or try RNA-seq experiments to find additional genes that are misregulated by the delta-uHOT deletion allele. [OPTIONAL]. Another [OPTIONAL] experiment that would strengthen the claims is to perform RNAi knockdown or DLG-1 protein depletion and link that to phenotype to show that the dlg-1 mRNA and DLG-1 protein changes seen in the uHOT mutant do not explain the lethality observed.

      We thank the reviewer for this comment. We have studied HOT region function in the context of a model organism, C. elegans; therefore, we believe that describing our findings as revealing a function of HOT regions in animal development is accurate. The sentence aims at noting that these observations may provide broader insights into HOT region regulation. We changed the last sentence of the abstract into: “(…) Our findings reveal how HOT regions contribute to gene regulation during animal development and illustrate how regulatory potential identified in isolated contexts can be selectively deployed or buffered within the native genomic architecture. (…)”.

      We note that RNA-seq is beyond the scope of this study; our discussion of potential effects on other genes is intended only as a hypothesis for future work. RNAi of dlg-1 has been previously reported and is cited in the manuscript, providing context for the phenotypes observed and discussed.

      1. * When printed out I cannot read what the tracks are in Fig S1. Adding larger text to indicate what those tracks are is necessary.* Yes, you are right. We changed this in the figure.

      2. *

      3. Line 79. I would change the word "usually" to "frequently" in the discussion about regulatory element position. While promoters ranging from a few hundred to 2000 basepairs are frequently used, there are numerous examples where important enhancers can be further away.*

      Corrected.

      * Line 93-95. The description of the reporters was very confusing. When referring to the deletion sequences it sounds like that is what is missing rather than what is included. Rather, if I understand correctly the uHOT is the sequence from the start of the uHOT to the CDS and Short starts at the end of uHOT (omitting it). Adding the promoter fragments to the figure would improve clarity.*

      To clarify the design of the reporters, we have revised the text as follows: “(…) To test this idea, we generated three single-copy, integrated transcriptional reporters carrying a histone H2B sequence fused to an mNeon-Green (mNG) fluorescent protein sequence under the transcriptional control of the following dlg-1 upstream regions: (i) a full-length sequence (“FL” = Distal + uHOT + Proximal sequences), (ii) one spanning from the beginning of the HOT region to the dlg-1 CDS (“uHOT” = uHOT + Proximal sequences), and (iii) one starting at the end of the HOT region and ending at the dlg-1 CDS (“Short” = Proximal sequence) (Figure 1A-C). (…)”

      This description clarifies which parts of the upstream region are included in each reporter and matches the schematics in Figure 1.

      * Line 108. Re-work the phrase "increase majorly". Majorly increase would be better.*

      We thank the reviewer for this suggestion. The verb is used here as an infinitive (“to increase majorly”), and in standard English the infinitive is usually not split. Therefore, we have kept the phrasing as it currently appears in the manuscript.

      * Line 153-154. The deletion indicates that HOT2 and HOT2 were removed. Was one supposed to be HOT3?*

      Thanks for spotting this typo. It has now been corrected.

      * In the figure legends the number of animals scored and the number of biological repeats is missing.*

      Added.

      * Figure 1 title in the legend. Should read "main driver" not "man driver".*

      Thanks for spotting this typo. It has now been corrected.

      * The references need to be gone through carefully and cleaned up. There are numerous gene and species names that are not italicized. There are also extra elements added by the reference manager such as [Internet].*

      Thanks for pointing it out. We used Zotero and the requested formatting from the journal of our choice. We will discuss with their team how to go through this issue.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      High occupancy target (HOT) regions are genomic sequences in C. elegans that are bound by large numbers of transcription factors and emerged from systematic ChIP-seq studies. Whether they play physiologically important roles in gene regulation is not clear. in In this manuscript, Tocchini et al. examine the function of two HOT regions using a combination of promoter reporters, genome editing, and smFISH. One HOT region is upstream of the dlg-1 gene and other is in the first intron of dlg-1.

      The claims about the impact of the upstream HOT region on dlg-1 expression are convincing. Omitting the sequence in a promoter reporter reduces expression, the element is sufficient to drive expression from a MINp::mNG reporter, and deletion of the element reduces dlg-1 expression and causes developmental defects. The claims about the intronic HOT region need to be tempered slightly. The element drives weak expression in a MINp::mNG reporter but the replacement of the dlg-1 first intron with a syntron had no effect on expression, limiting the claims that be made about this regulatory element. The authors' claims are generally supported by the data, thoug the last sentence of the abstract was a bit overstated. They state that they "reveal the function of HOT regions in animals development...."; it would be more accurate to state that they linked the role of an upstream HOT region to dlg-1 regulation, and their findings hint that this element could have additional regulatory functions. The authors can either temper their conclusions or try RNA-seq experiments to find additional genes that are misregulated by the delta-uHOT deletion allele. [OPTIONAL]. Another [OPTIONAL] experiment that would strengthen the claims is to perform RNAi knockdown or DLG-1 protein depletion and link that to phenotype to show that the dlg-1 mRNA and DLG-1 protein changes seen in the uHOT mutant do not explain the lethality observed.

      There are elements of the manuscript that must be improved for clarity/accuracy.

      1. When printed out I cannot read what the tracks are in Fig S1. Adding larger text to indicate what those tracks are is necessary.
      2. Line 79. I would change the word "usually" to "frequently" in the discussion about regulatory element position. While promoters ranging from a few hundred to 2000 basepairs are frequently used, there are numerous examples where important enhancers can be further away.
      3. Line 93-95. The description of the reporters was very confusing. When referring to the deletion sequences it sounds like that is what is missing rather than what is included. Rather, if I understand correctly the uHOT is the sequence from the start of the uHOT to the CDS and Short starts at the end of uHOT (omitting it). Adding the promoter fragments to the figure would improve clarity.
      4. Line 108. Re-work the phrase "increase majorly". Majorly increase would be better.
      5. Line 153-154. The deletion indicates that HOT2 and HOT2 were removed. Was one supposed to be HOT3?
      6. In the figure legends the number of animals scored and the number of biological repeats is missing.
      7. Figure 1 title in the legend. Should read "main driver" not "man driver",
      8. The references need to be gone through carefully and cleaned up. There are numerous gene and species names that are not italicized. There are also extra elements added by the reference manager such as [Internet].

      Referee cross-commenting

      I agree with the comments from the previous reviewers. The suggested experiments are reasonable. Reviewer 1's point about the Chen et al 2014 Genome Res paper is really important. I put the revision as unknown as it depended on whether they did the optional experiments I suggested. If they revise their text, tempering claims, adjusting statistical analyses, then that could be 1-3 months. If they did the RNA-seq that I suggested, that would be a longer timeline.

      Significance

      The study is generally rigorously done. Strengths are that this work finds a function for a HOT region in gene regulation. Limitations are that the work is currently very thorough regulatory element bashing. They convincingly demonstrate the role of uHOT in regulating dlg-1 and suggest that the reduction of DLG-1 levels does not explain the phenotype. This finding is of interest to basic researchers in gene regulation. Without going into that discrepancy more, the significance is limited. Linking HOT regions to novel regulatory mechanisms controlling multiple genes would be broadly interesting to the gene regulation and developmental biology.

      I am a C. elegans molecular biologist with expertise in gene regulatory networks.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors investigate the functionality of a HOT region located upstream of the dlg-1 gene in Caenorhabditis elegans. This region is bound by multiple proteins and enriched for H3K27ac and H3K4me1, features characteristic of enhancers. Using reporter assays, they dissect the region and identify a sub-fragment, HOT3, as responsible for driving gene expression in epidermis, with a pattern similar to that of dlg-1 itself. Deletion of this region leads to downregulation of dlg-1 and lethality before or shortly after hatching, in contrast to complete dlg-1 knockouts, which die at mid-embryogenesis. They further examine the role of the gene's first intron, previously reported to physically interact with the HOT region. Incorporating intron 1 into the reporter construct slightly increases expression, suggesting an additive regulatory effect. However, replacing intron 1 with a synthetic sequence at the endogenous locus does not cause major changes. Overall, this study demonstrates that HOT regions can play a functional role in gene regulation, challenging the prevailing view that they are largely non-functional.

      Major comments:

      Overall, the paper lacks to explain their reasoning on choosing certain conditions and it also lacks on discussions on relevant topics, highlighted below.

      1) The authors suggest that the region upstream of the dlg-1 gene is a HOT region. Although they highlight that other broad studies pick up this region as a HOT region, it would be good that the authors dive into the HOT identity of the region and characterize it, as it is a major part of their study. In addition to multiple TFs binding to the site, there are different criteria by which a region would be considered a HOT region. E.g. is there increased signal on this region in the IgG ChIP-seq tracks? Is the area CpG dense?

      2) When describing the HOT region, they refer to Pol II binding as 'confirming its role as a promoter': non-promoter regions can also have Pol II binding, especially enhancers. Having binding of Pol II does not confirm its role as promoter. On the contrary, seeing the K27ac and K4me1 would point towards it being an enhancer.

      3) In S1B, the authors show TF binding tracks. They also have a diagram of the region subsets (HOT1-4) that were later tested. What is their criteria for dividing the HOT region into those fragments? From looking at Fig S1, the 'proper' HOT region (ie. Where protein binding occurs) seems to be divided into two (one chunk as part of HOT3 and one chunk as part of HOT4). Can the authors comment on the effects of this division?

      4) For the reporter experiments, the first experiments carry the histone H2B sequence and the second set of experiments (where the HOT region is dissected) carry a minimal promoter Δpes-10 (MINp). The results could be affected by the addition of these sequences. Is there a reason for this difference? Can the authors please justify it?

      5) Regarding the H2B sequence: ' 137: first intron [...] inserted in the FL transgene within the H2B sequence, acting as an actual intron (FL-INT1)': how was the location of the insertion chosen? Does it disrupt H2B? can it be that the H2B sequence contributed to dampening down the expression of mNG and disrupting it makes it stronger? It would be important to run the first experiments with minimal promoters and not with the H2B sequence.

      6) Have the authors explored the features of the sequences underlying the different HOT subregions? (e.g. running a motif enrichment analysis)? Is there anything special about HOT3 that could make it a functional region? It would be good to compare uHOT3 vs the others that do not drive the correct pattern. Since it's a HOT region, it may not have a special feature, but it is important to look into it.

      7) For comparisons, the authors run t-tests. Is the data parametric? Otherwise, it would be more suitable to use a non-parametric test.

      Minor comments:

      1) The authors work with C. elegans embryos at comma stage, according to the methods section. It would be good if the authors mentioned it in the main text so that the reader is informed.

      2) 'Notably, the upstream HOT region is located more than four kilo-bases (Kb) away the CDS, and the one in the first intron contains enhancer sites, too.': what do they mean by 'enhance sites, too'. Is the region known as a functional enhancer? If so, could you please provide the reference?

      3) 'We hypothesized the upstream HOT region is the main driver of dlg-1 transcriptional regulation.': this sentence needs more reasoning. What led to this hypothesis? Is it the fact of seeing multiple TFs binding there? The chromatin marks?

      4) The labels of S1B are too wide, as if they have stretched the image. Could the authors please correct this?

      5) This sentence does not flow with the rest of the text '84 - cohesins have been shown to organize the DNA in a way that active enhancers make contacts in the 3D space forming "fountains" detectable in Hi-C data (17,18).': is there a reason to explain this? I would remove it if not, as it can confuse the reader.

      6) The authors mentioned that 'ARC-C data showed the putative HOT region interacts with other DNA sequences, including the first intron of dlg': have the authors analysed the data from the previous paper? A figure with the relevant data could illustrate this interaction so that the reader knows which specific region has been shown to interact with which. This would also bring clarity as to why they chose intron1 for additional experiments.

      7) 'two deletion sequences spanning from the beginning (uHOT) or the end (Short) of the HOT region until the dlg-1 CDS': From the diagrams of the figure, I understand that uHOT has the distal region deleted, and the short HOT has the distal and the upstream regions deleted. Is this correct? Could you clarify this in the text? E.g. 'we designed two reporters - one containing the sequence starting at the HOT region and ending at the dlg-1 CDS, and the other without the HOT region, but rather starting downstream of it until the dlg-1 CDS'.

      8) 'Specifically, it spanned from bp 5,475,070 to 5,475,709 on chromosome X and removed HOT2 and HOT2 sequences' - this is unclear to me. What sequences are removed? HOT2 and 3?

      9) 'ARC-C' is not introduced. Please spell out what this is. Accessible Region Conformation Capture (ARC-C). It would be helpful to include a sentence of what it is, as it will not be known by many readers.

      10) Fig 1 B, diagram on the right: the H2B sequence is missing. I see that is indicated in the legend as part of mNG but this can be misleading. Could the authors add it to the diagram for clarification?

      Significance

      HOT regions are thought to be artifacts from ChIP-seq experiments. This study provides evidence that at least some HOT regions can have a functional role in gene regulation, emphasizing that they should not be dismissed outright.

      The findings will be of interest to researchers investigating the biological nature of HOT regions, as well as to those who have encountered HOT regions in their own sequencing datasets. In addition, researchers studying the regulation of dlg-1 in C. elegans may find this work particularly relevant. I work on gene regulation during embryonic development and my technical expertise is omics and fluorescence microscopy. Since I do not work in C. elegans, I cannot evaluate if the patterns/location of the signal is where they claim it to be, I do not know if the cells marked are epidermal cells.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      In this manuscript, Tocchini et al. characterize two enhancer regions, one distal and one intronic, of the gene dlg-1 in C. elegans. The two enhancers are termed high-occupancy target (HOT) regions as defined by their binding of most transcription factors, as identified by the modENCODE project. The authors test transcriptional activity of the two HOT regions using single-copy transgene assays and assay their functional relevance by deleting the regions using CRISPR/Cas9 genome editing. The authors observe robust transcriptional activity and functional effects of the distal regulatory element and little evidence for enhancer activity from the intronic enhancer. From these assays, the authors conclude that the distal and intronic enhancers coordinate to fine tune gene expression in a cell-type specific manner.

      Major comments:

      • Are the key conclusions convincing?

      • The results fully support the authors conclusions regarding the significant role of the upstream HOT region ("uHOT") with strong fluorescence activity and substantial phenotypic effects (i.e., the animals have very low brood sizes and rarely progress through hatching). This data is well presented and technically well done.

      • In my view, their conclusions regarding the intronic HOT region are speculative and unconvincing. See below for main criticisms.
      • Furthermore, their conclusions about interactions between the two tested regions is speculative and they show no strong evidence for this claim.
      • The authors claim that not all the phenotypic effects seen from deleting the uHOT region are specific to the dlg-1 gene. This is an interesting model, but the authors show essentially no data to support this or any explanation of what other gene might be regulated.
      • Finally, some of the hypotheses in the text could be more accurately framed by the authors. They claim HOT regions are often considered non-functional (lines 189-191). Also, they claim that correct expression levels and patterning is usually regulation by elements within a few hundred basepairs of the CDS (lines 78-80). These claims are not generally accepted in the field, despite a relatively compact genome. Notably, both claims were tested and disproven by Chen et al (2014), Genome Research, where the authors specifically showed strong transcriptional activity from 10 out of 10 HOT regions located up to 4.7 kb upstream of their nearest gene. Chen et al. 2014 is cited by Tocchini et al. and it is, therefore, surprisingly inconsistent with the claims in this manuscript.

      The fluorescence expression from the intronic HOT region is not visible by eye and the quantification shows very little expression, suggestive of background fluorescence. Although the authors show statistical significance in Figure 1G, I would argue this is possibly based on inappropriate comparisons and/or a wrong choice statistical test. The fluorescence levels should be compared to a non-transgenic animal and/or to a transgenic animal with the tested region shuffled but in an equivalent - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      Yes, I would suggest the authors remove their claims about the intronic enhancer and the interaction between the two regions. And I would suggest softening the claims about the uHOT regulation of another putatitive gene. - 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, the authors would need to demonstrate several things to support their current claims. The major experiments necessary are:

      1. Insert single-copy transgene with a minimal promoter and the intronic sequence scrambled to generate a proper baseline control. It is very possible that the intronic sequence does drive some expression, but the current control is not appropriate for statistical comparison (e.g., only the transgene with intron 1 contains the minimal promoter from pes-10, which may have baseline transcriptional activity even without the intron placed in front of the transgene).
      2. It is not very clear why the authors did not test intron 1 within the H2B of the transgene and just the minimal promoter in front of the transgene, but only in the context of the full-length promoter. The authors show a minor difference in expression levels for the full-length (FL) and full-length with intron 1 (FL-INT1) but show a large statistical differnce. The authors use an inappropriate statistical test (T-test) for this experiment and treat many datapoints from the same embryo as independent, which is clearly not the case. Even minor differences in staging, transgene silencing in early development, or variability would potentially bias their data collection.
      3. The authors claim, based on ARC-C data previously published by their lab (Huang et al. 2022) that the dlg-1 HOT region interacts with "other" genomic regions. This is potentially interesting but the evidence for this should be included in the manuscript itself, perhaps by re-analyzing data from the 2022 manuscript?
      4. 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.

      These experiments are not costly (two transgenes inserted by single-copy transgenesis) nor particularly time-consuming. With cloning, injection, and microscopy, these experiments can be conducted in 6 weeks with relatively few "hands on" hours. The cost should be very reasonably (reagents surely less than €500). - Are the data and the methods presented in such a way that they can be reproduced?

      The data are not entirely clear and could benefit from additional details. This is a partial list but shows the general concern.

      The fluorescence quantification is difficult to interpret from the attached data file (Table S1). For the invidividual values, it is unclear how many indpendent experiments (different embryos) were conducted. The authors should clarify if every data value is from an independent embryo or if they used several values from the same embryo. If they did use several values from the same embryo, how did they do this? Did they take very cell? Or did they focus on specific cells? How did they ensure embryo staging?

      The authors also do not describe how they validated single-copy insertions (partial transgene deletions in integrants are not infrequent and they only appear to use a single insertion for each strain). This should be described and or added as a caveat if no validation was performed.

      The authors also do not describe any validation for the CRISPR alleles, either deletions or insertion of the synthetic intron into dlg-1. How were accurate gene edits verified. - Are the experiments adequately replicated and statistical analysis adequate?

      I am not convinced the statistical analysis of the fluorescence data is correct. Unless the authors show that every datapoint in the fluorescence quantification is independent, then I would argue they vastly overestimate the statistical significance. Even small differences are shown to have "***" levels of significance, which does not appear empirically plausible.

      Minor comments:

      • Specific experimental issues that are easily addressable.
      • Are prior studies referenced appropriately?

      This study is so closely related to the Chen et al study, that I believe this study should be discussed in more detail to put the data into context. - Are the text and figures clear and accurate?

      Yes, the text and figurea are clear - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      Add H2B to the mNG in Figure 1 in order to understand where the first intron was inserted.

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
      • Place the work in the context of the existing literature (provide references, where appropriate).
      • State what audience might be interested in and influenced by the reported findings.
      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      This manuscript shows an incremental advance in our understanding of HOT regions in C. elegans. The authors replicate similar data presented previously (enhancer assays on HOT regions, PMID: 24653213). Importantly, the authors funcationally validate their data with smFISH and CRISPR-mediated deletion of two enhancers (including the substitution of the intron for a synthetic intron), which is, to my knowledge, novel and advances the field. As such, the data presented validate and increase our confidence in prior results on HOT regions. Unfortunately, the more interesting conclusions about HOT region interactions and synergy to direct expression are less well supported. The work will likely be mainly of interest to C. elegans researchers working on transcriptional regulation. My own field of expertise is C. elegans gene regulation and my lab frequently uses transcriptional transgene assays to determine gene expression.

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

      Learn more at Review Commons


      Reply to the reviewers

      __Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

      This study explores chromatin organization around trans-splicing acceptor sites (TASs) in the trypanosomatid parasites Trypanosoma cruzi, T. brucei and Leishmania major. By systematically re-analyzing MNase-seq and MNase-ChIP-seq datasets, the authors conclude that TASs are protected by an MNase-sensitive complex that is, at least in part, histone-based, and that single-copy and multi-copy genes display differential chromatin accessibility. Altogether, the data suggest a common chromatin landscape at TASs and imply that chromatin may modulate transcript maturation, adding a new regulatory layer to an unusual gene-expression system.

      I value integrative studies of this kind and appreciate the careful, consistent data analysis the authors implemented to extract novel insights. That said, several aspects require clarification or revision before the conclusions can be robustly supported. My main concerns are listed below, organized by topic/result section.

      TAS prediction * Why were TAS predictions derived only from insect-stage RNA-seq data? Restricting TAS calls to one life stage risks biasing predictions toward transcripts that are highly expressed in that stage and may reduce annotation accuracy for lowly expressed or stage-specific genes. Please justify this choice and, if possible, evaluate TAS robustness using additional transcriptomes or explicitly state the limitation.

      TAS predictions derived only from insect-stage RNA-seq data because in a previous study it was shown that there are no significant differences between stages in the 5'UTR procesing in T. cruzi life stages (https://doi.org/10.3389/fgene.2020.00166) We are not testing an additional transcriptome here, because the robustness of the software was already probed in the original article were UTRme was described (Radio S, 2018 doi:10.3389/fgene.2018.00671).

      Results - "There is a distinctive average nucleosome arrangement at the TASs in TriTryps": * You state that "In the case of L. major the samples are less digested." However, Supplementary Fig. S1 suggests that replicate 1 of L. major is less digested than the T. brucei samples, while replicate 2 of L. major looks similarly digested. Please clarify which replicates you reference and correct the statement if needed.

      The reviewer has a good point. We made our statement based on the value of the maximum peak of the sequenced DNA molecules, which in general is a good indicative of the extension of the digestion achieved by the sample (Cole H, NAR, 2011).

      As the reviewer correctly points, we should have also considered the length of the DNA molecules in each percentile. However, in this case both, T. brucei's and L major's samples were gel purified before sequencing and it is hard to know exactly what fragments were left behind in each case. Therefore, it is better not to over conclude on that regard.

      We have now comment on this in the main manuscript, and we have clarified in the figure legends which data set we used in each case in the figure legends and in Table S1.

      * It appears you plot one replicate in Fig. 1b and the other in Suppl. Fig. S2. Please indicate explicitly which replicate is in each plot. For T. brucei, the NDR upstream of the TAS is clearer in Suppl. Fig. S2 while the TAS protection is less prominent; based on your digestion argument, this should correspond to the more-digested replicate. Please confirm.

      The replicates used for the construction of each figure are explicitly indicated in Table S1. Although we have detailed in the table the original publication, the project and accession number for each data set, the reviewer is correct that in this case it was still not completely clear to which length distribution heatmap was each sample associated with. To avoid this confusion, we have now added the accession number for each data set to the figure legends and also clarified in Table S1. Regarding the reviewer's comment on the correspondence between the observed TAS protection and the extent of samples digestion, he/she is correct that for a more digested sample we would expect a clearer NDR. In this case, the difference in the extent of digestion between these two samples is minor, as observed the length of the main peak in the length distribution histogram for sequenced DNA molecules is the same. These two samples GSM5363006, represented in Fig1 b, and GSM5363007, represented in S2, belong to the same original paper (Maree et al 2017), and both were gel purified before sequencing. Therefore, any difference between them could not only be the result of a minor difference in the digestion level achieved in each experiment but could be also biased by the fragments included or not during gel purification. Therefore, I would not over conclude about TAS protection from this comparison. We have now included a brief comment on this, in the figure discussion

      * The protected region around the TAS appears centered on the TAS in T. brucei but upstream in L. major. This is an interesting difference. If it is technical (different digestion or TAS prediction offset), explain why; if likely biological, discuss possible mechanisms and implications.

      We appreciate the reviewer suggestion. We cannot assure if it is due to technical or biological reasons, but there is evidence that L. major 's genome has a different dinucleotide content and it might have an impact on nucleosome assembly. We have now added a comment about this observation in the final discussion of the manuscript.

      Additionally, we analyzed DRIP-seq data for L. major, recently published doi: 10.1038/s41467-025-56785-y, and we observed that the R-loop footprint co-localized with the MNase-protected region upstream of the TAS (new S5 Fig), suggesting that the shift is not related to the MNase-seq technique.

      Results - "An MNase sensitive complex occupies the TASs in T. brucei": * The definition of "MNase activity" and the ordering of samples into Low/Intermediate/High digestion are unclear. Did you infer digestion levels from fragment distributions rather than from controlled experimental timepoints? In Suppl. Fig. S3a it is not obvious how "Low digestion" was defined; that sample's fragment distribution appears intermediate. Please provide objective metrics (e.g., median fragment length, fraction 120-180 bp) used to classify digestion levels.

      As the reviewer suggests, the ideal experiment would be to perform a time course of MNase reaction with all the samples in parallel, or to work with a fixed time point adding increasing amounts of MNase. However, even when making controlled experimental timepoints, you need to check the length distribution histogram of sequenced DNA molecules to be sure which level of digestion you have achieved.

      In this particular case, we used public available data sets to make this analysis. We made an arbitrary definition of low, intermediate and high level of digestion, not as an absolute level of digestion, but as a comparative output among the tested samples. We based our definition on the comparison of __the main peak in length distribution heatmaps because this parameter is the best metric to estimate the level of digestion of a given sample. It represents the percentage of the total DNA sequenced that contains the predominant length in the sample tested. __Hence, we considered:

      low digestion: when the main peak is longer than the expected protection for a nucleosome (longer than 150 bp). We expect this sample to contain additional longer bands that correspond to less digested material.

      intermediate digestion, when the main peak is the expected for the nucleosome core-protection (˜146-150bp).

      high digestion, when the main peak is shorter than that (shorter than 146 bp). This case, is normally accompanied by a bigger dispersion in fragment sizes.

      To do this analysis, we chose samples that render different MNase protection of the TAS when plotting all the sequenced DNA molecules relative to this point and we used this protection as a predictor of the extent of sample digestion (Figure 2). To corroborate our hypothesis, that the degree of TAS protection was indeed related to the extent of the MNase digestion of a given sample, we looked at the length distribution histogram of the sequenced DNA molecules in each case. It is the best measurement of the extent of the digestion achieved, especially, when sequencing the whole sample without any gel purification and representing all the reads in the analysis as we did. The only caveat is with the sample called "intermediate digestion 1" that belongs to the original work of Mareé 2017, since only this data set was gel purified. To avoid this problem, we decided to remove this data from figures 2 and S3. In summary, the 3 remaining samples comes from the same lab, and belong to the same publication (Mareé 2022). These sample are the inputs of native MNase ChIp-seq, obtain the same way, totally comparable among each other.

      * Several fragment distributions show a sharp cutoff at ~100-125 bp. Was this due to gel purification or bioinformatic filtering? State this clearly in Methods. If gel purification occurred, that can explain why some datasets preserve the MNase-sensitive region.

      The sharp cutoff is neither due to gel purification or bioinformatic filtering, it is just due to the length of the paired-end read used in each case. In earlier works the most common was to sequence only 50bp, with the improvement of technologies it went up to 75,100 or 125 bp. We have now clarified in Table S1 the length of the paired-reads used in each case when possible.

      * Please reconcile cases where samples labeled as more-digested contain a larger proportion of >200 bp fragments than supposedly less-digested samples; this ordering affects the inference that digestion level determines the loss/preservation of TAS protection. Based on the distributions I see, "Intermediate digestion 1" appears most consistent with an expected MNase curve - please confirm and correct the manuscript accordingly.

      As explained above, it's a common observation in MNase digestion of chromatin that more extensive digestion can still result in a broad range of fragment sizes, including some longer fragments. This seemingly counter-intuitive result is primarily due to the non-uniform accessibility of chromatin and the sequence preference of the MNase enzyme, which has a preference for AT reach sequences.

      The rationale of this is as follows: when you digest chromatin with MNase and the objective is to map nucleosomes genome-wide, the ideal situation would be to get the whole material contained in the mononucleosome band. Given that MNase is less efficient to digest protected DNA but, if the reaction proceeds further, it always ends up destroying part of it, the result is always far from perfect. The better situation we can get, is to obtain samples were ˜80% of the material is contained in the mononucloesome band. __And here comes the main point: __even in the best scenario, you always get some additional longer bands, such as those for di or tri nucleosomes. If you keep digesting, you will get less than 80 % in the nucleosome band and, those remaining DNA fragments that use to contain di and tri nucleosomes start getting digested as well, originating a bigger dispersion in fragments sizes. How do we explain persistence of Long Fragments? The longest fragments (di-, tri-nucleosomes) that persist in a highly digested sample are the ones that were originally most highly protected by proteins or higher-order structure, or by containing a poor AT sequence content, making their linker DNA extremely resistant to initial cleavage. Once the majority of the genome is fragmented, these few resistant longer fragments become a more visible component of the remaining population, contributing to a broader size dispersion. Hence, you end up observing a bigger dispersion in length distributions in the final material. Bottom line, it is not a good practice to work with under or over digested samples. Our main point, is to emphasize that especially when comparing samples, it important to compare those with comparable levels of digestion. Otherwise, a different sampling of the genome will be represented in the remaining sequenced DNA.

      Results - "The MNase sensitive complexes protecting the TASs in T. brucei and T. cruzi are at least partly composed of histones": * The evidence that histones are part of the MNase-sensitive complex relies on H3 MNase-ChIP signal in subnucleosomal fragment bins. This seems to conflict with the observation (Fig. 1) that fragments protecting TASs are often nucleosome-sized. Please reconcile these points: are H3 signals confined to subnucleosomal fragments flanking the TAS while the TAS itself is depleted of H3? Provide plots that compare MNase-seq and H3 ChIP signals stratified by consistent fragment-size bins to clarify this.

      What we learned from other eukaryotic organisms that were deeply studied, such as yeast, is that NDRs are normally generated at regulatory points in the genome. In this sense, yeast tRNA genes have a complex with a bootprint smaller than a nucleosome formed by TFIIIC-TFIIB (Nagarajavel, doi: 10.1093/nar/gkt611). On the other hand, many promotor regions have an MNase-sensitive complex with a nucleosome-size footprint, but it does not contain histones (Chereji, et al 2017, doi:10.1016/j.molcel.2016.12.009). The reviewer is right that from Figure 1 and S2 we could observe that the footprint of whatever occupies the TAS region, especially in T. brucei, is nucleosome-size. However, it only shows the size, but it doesn't prove the nature of its components. Nevertheless, those are only MNase-seq data sets. Since it does not include a precipitation with specific antibodies, we cannot confirm the protecting complex is made up by histones. In parallel, a complementary study by Wedel 2017, from Siegel's lab, shows that using a properly digested sample and further immunoprecipitating with a-H3 antibody, the TAS is not protected by nucleosomes at least not when analyzing nucleosome size-DNA molecules. Besides, Briggs et. al 2018 (doi: 10.1093/nar/gky928) showed that at least at intergenic regions H3 occupancy goes down while R-loops accumulation increases. We have now added a new figure 4 replotting R-loops and MNase-ChIP-seq for H3 relative to our predicted TAS showing this anti-correlation and how it partly correlates with MNase protection as well. As a control we show that Rpb9 trends resembles H3 as Siegel's lab have shown in Wedel 2018. Moreover, we analyzed redate from a recently published paper (doi: 10.1038/s41467-025-56785-y) added a new supplemental figure 5 showing that a similar correlation between MNase protection and R-loop footprint occurs in L. major (S5 Fig).

      * Please indicate which datasets are used for each panel in Suppl. Fig. S4 (e.g., Wedel et al., Maree et al.), and avoid calling data from different labs "replicates" unless they are true replicates.

      In most of our analysis we used real replicated experiments. Such is the case MNase-seq data used in Figure 1, with the corresponding replicate experiments used in Figure S2; T. cruzi MNase-ChIP-seq data used in Figure 3b and 4a with the respective replicate used in Figures S4 and S5 (now S6 in the revised manuscript). The only case in which we used experiments coming from two different laboratories, is in the case of MNase-ChIP-seq for H3 from T. brucei. Unfortunately, there are only two public data sets coming each of them from different laboratories. The samples used in Fig 3 (from Siegel's lab) whether the IP from H3 represented in S4 and S5 (S6 n the updated version) comes from another lab (Patterton's). To be more rigorous, we now call them data 1 and 2 when comparing these particular case.

      The reviewer is right that in this particular case one is native chromatin (Pattertons') while the other one is crosslinked (Siegel's). We have now clarified it in the main text that unfortunately we do not count on a replicate but even under both condition the result remains the same, and this is compatible with my own experience, were crosslinking does not affect the global nucleosome patterns (compared nucleosome organization from crosslinked chromatin MNAse-seq inputs Chereji, Mol Cell, 2017 doi: 10.1016/j.molcel.2016.12.009 and native MNase-seq from Ocampo, NAR, 2016 doi: 10.1093/nar/gkw068).

      * Several datasets show a sharp lower bound on fragment size in the subnucleosomal range (e.g., ~80-100 bp). Is this a filtering artifact or a gel-size selection? Clarify in Methods and, if this is an artifact, consider replotting after removing the cutoff.

      We have only filtered adapter dimmer or overrepresented sequences when needed. In Figures 2 and S3 we represented all the sequenced reads. In other figures when we sort fragments sizes in silico, such as nucleosome range, dinucleosome or subnucleosome size, we make a note in the figure legends. What the reviewer points is related to the length of the sequence DNA fragment in each experiment. As we explained above, the older data-sets were performed with 50 bp paired-end reads, the newer ones are 75, 100 or 125bp. This is information is now clarified in Table S1.

      __Results - "The TASs of single and multi-copy genes are differentially protected by nucleosomes": __

      __ __* Please include T. brucei RNA-seq data in Suppl. Fig. S5b as you did for T. cruzi.

      We have shown chromatin organization for T. brucei in previous S5b to illustrate that there is a similar trend. Unfortunately, we did not get a robust list of multi-copy genes for T. brucei as we did get for T. cruzi, therefore we do not want to over conclude showing the RNA-seq for these subsets of genes. The limitation is related to the fact that UTRme restrict the search and is extremely strict when calling sites at repetitive regions. Additionally, attending to the request of one reviewer we have now changed the UTR predictions for T. brucei using a different RNA-seq data set from Lister 427(detail in method section). Given that with the new predictions it was even harder to obtain the list of multicopy genes for T. brucei, we decided to remove that figure in the updated version of the manuscript.

      * Discuss how low or absent expression of multigene families affects TAS annotation (which relies on RNA-seq) and whether annotation inaccuracies could bias the observed chromatin differences.

      The mapping of occurrence and annotations that belong to repetitive regions has great complexity. UTRme is specially designed to avoid overcalling those sites. In other words, there is a chance that we could be underestimating the number of predicted TASs at multi-copy genes. Regarding the impact on chromatin analysis, we cannot rule out that it might have an impact, but the observation favors our conclusion, since even when some TASs at multi-copy genes can remain elusive, we observe more nucleosome density at those places.

      * The statement that multi-copy genes show an "oscillation" between AT and GC dinucleotides is not clearly supported: the multi-copy average appears noisier and is based on fewer loci. Please tone down this claim or provide statistical support that the pattern is periodic rather than noisy.

      We have fixed this now in the preliminary revised version

      * How were multi-copy genes defined in T. brucei? Include the classification method in Methods.

      This classification was done the same way it was explained for T. cruzi. However, decided to remove the supplemental figure that included this sorting.

      Genomes and annotations: * If transcriptomic data for the Y strain was used for T. cruzi, please explain why a Y strain genome was not used (e.g., Wang et al. 2021 GCA_015033655.1), or justify the choice. For T. brucei, consider the more recent Lister 427 assembly (Tb427_2018) from TriTrypDB. Use strain-matched genomes and transcriptomes when possible, or discuss limitations.

      The most appropriate way to analyze high throughput data, is to aline it to the same genome were the experiments were conducted. This was clearly illustrated in a previous publication from our group were we explained how should be analyzed data from the hybrid CL Brener strain. A common practice in the past was to use only Esmeraldo-like genome for simplicity, but this resulted in output artifacts. Therefore, we aligned it to CL Brener genome, and then focused the main analysis on the Esmeraldo haplotype (Beati Plos ONE, 2023). Ideally, we should have counted on transcriptomic data for the same strain (CL Brener or Esmeraldo). Since this was not the case at that moment, we used data from Y strain that belongs to the same DTU with Esmeraldo.

      In the case of T. brucei, when we started our analysis and the software code for UTRme was written, the previous version of the genome was available. Upon 2018 version came up, we checked chromatin parameters and observed that it did not change the main observations. Therefore, we continue working with our previous setups.

      Reproducibility and broader integration: * Please share the full analysis pipeline (ideally on GitHub/Zenodo) so the results are reproducible from raw reads to plots.

      We are preparing a full pipeline in GitHub. We will make it available before manuscript full revision

      * As an optional but helpful expansion, consider including additional datasets (other life stages, BSF MNase-seq, ATAC-seq, DRIP-seq) where available to strengthen comparative claims.

      We are now including a new figure 4 and a supplemental figure 5 including DRIP-seq and Rp9 ChIP-seq for T. brucei (revised Fig 4) and DRIP-seq for L. major (S5 Fig). Additionally, we added FAIRE-seq data to previous Fig 4 now Fig 5 (revised Fig 5C).

      We are analyzing ATAC-seq data for T. brucei.

      Regarding BSF MNase-seq, the original article by Mareé 2017 claims that there is not significant difference for average chromatin organization between the two life forms; therefore, is not worth including that analysis.

      Optional analyses that would strengthen the study: * Stratify single-copy genes by expression (high / medium / low) and examine average nucleosome occupancy at TASs for each group; a correlation between expression and NDR depth would strengthen the functional link to maturation.

      We have now included a panel in suplemental figure 5 (now revised S6), showing the concordance for chromatin organization of stratified genes by RNA-seq levels relative to TAS.

      __Minor / editorial comments: __ * In the Introduction, the sentence "transcription is initiated from dispersed promoters and in general they coincide with divergent strand switch regions" should be qualified: such initiation sites also include single transcription start regions.

      We have clarified this in the preliminary revised version

      * Define the dotted line in length distribution plots (if it is not the median, please clarify) and consider placing it at 147 bp across plots to ease comparison.

      The dotted line is just to indicate where the maximum peak is located. It is now clarified in figure legends.

      * In Suppl. Fig. 4b "Replicate2" the x-axis ticks are misaligned with labels - please fix.

      We have now fixed the figure. Thanks for noticing this mistake.

      * Typo in the Introduction: "remodellingremodeling" → "remodeling

      Thanks for noticing this mistake, it is fixed in the current version of the manuscript

      **Referee cross-commenting** Comment 1: I think Reviewer #2 and Reviewer #3 missed that they authors of this manuscript do cite and consider the results from Wedel at al. 2017. They even re-analysed their data (e.g. Figure 3a). I second Reviewer #2 comment indicating that the inclusion of a schematic figure to help readers visualize and better understand the findings would be an important addition.

      Comment 2: I agree with Reviewer #3 that the use of different MNase digestion procedures in the different datasets have to be considered. On the other hand, I don't think there is a problem with figure 1 showing an MNase-protected TAS for T. brucei as it is based on MNase-seq data and reproduces the reported results (Maree et al. 2017). What the Siegel lab did in Wedel et al. 2017 was MNase-ChIPseq of H3 showing nucleosome depletion at TAS, but both results are not necessary contradictory: There could still be something else (which does not contain H3) sitting on the TAS protecting it from MNase digestion.

      Reviewer #1 (Significance (Required)):

      This study provides a systematic comparative analysis of chromatin landscapes at trans-splicing acceptor sites (TASs) in trypanosomatids, an area that has been relatively underexplored. By re-analyzing and harmonizing existing MNase-seq and MNase-ChIP-seq datasets, the authors highlight conserved and divergent features of nucleosome occupancy around TASs and propose that chromatin contributes to the fidelity of transcript maturation. The significance lies in three aspects: 1. Conceptual advance: It broadens our understanding of gene regulation in organisms where transcription initiation is unusual and largely constitutive, suggesting that chromatin can still modulate post-transcriptional processes such as trans-splicing. 2. Integrative perspective: Bringing together data from T. cruzi, T. brucei and L. major provides a comparative framework that may inspire further mechanistic studies across kinetoplastids. 3. Hypothesis generation: The findings open testable avenues about the role of chromatin in coordinating transcript maturation, the contribution of DNA sequence composition, and potential interactions with R-loops or RNA-binding proteins. Researchers in parasitology, chromatin biology, and RNA processing will find it a useful resource and a stimulus for targeted experimental follow-up.

      My expertise is in gene regulation in eukaryotic parasites, with a focus on bioinformatic analysis of high-throughput sequencing data

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

      Siri et al. perform a comparative analysis using publicly available MNase-seq data from three trypanosomatids (T. brucei, T. cruzi, and Leishmania), showing that a similar chromatin profile is observed at TAS (trans-splicing acceptor site) regions. The original studies had already demonstrated that the nucleosome profile at TAS differs from the rest of the genome; however, this work fills an important gap in the literature by providing the most reliable cross-species comparison of nucleosome profiles among the tritryps. To achieve this, the authors applied the same computational analysis pipeline and carefully evaluated MNase digestion levels, which are known to influence nucleosome profiling outcomes.

      In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms. The manuscript could be improved with some clarifications and adjustments:

      1. The authors state from the beginning that available MNase data indicate altered nucleosome occupancy around the TAS. However, they could also emphasize that the conclusions across the different trypanosomatids are inconsistent and even contradictory: NDR in T. cruzi versus protection-in different locations-in T. brucei and Leishmania.

      We start our manuscript by referring to the first MNase-seq data sets publicly available for each TriTryp and we point that one of the main observations, in each of them, is the occurrence of a change in nucleosome density or occupancy at intergenic regions. In T. cruzi, in a previous publication from our group, we stablished that this intergenic drop in nucleosome density occurs near the trans-splicing acceptor site. In this work, we extend our study to the other members of TriTryps: T. brucei and L. major.

      In T. brucei the papers from Patterton's lab and Siegel's lab came out almost simultaneously in 2017. Hence, they do not comment on each other's work. The first one claims the presence of a well-positioned nucleosome at the TAS by using MNase-seq, while the second one, shows an NDR at the TAS by using MNase-ChIP-seq. However, we do not think they are contradictory, or they have inconsistency. We brought them together along the manuscript because we think these works can provide complementary information.

      On one hand, we infer data from Pattertons lab is slightly less digested than the sample from Siegel's lab. Therefore, we discuss that this moderate digestion must be the reason why they managed to detect an MNase protecting complex sitting at the TAS (Figure 1). On the other hand, Sigel's lab includes an additional step by performing MNase-ChIP-seq, showing that when analyzing nucleosome size fragments, histones are not detected at the TAS. Here, we go further in this analysis on figure 3, showing that only when looking at subnucleosome-size fragments, we can detect histone H3. And this is also true for T. cruzi.

      By integrating every analysis in this work and the previous ones, we propose that TASs are protected by an MNase-sensitive complex (proved in Figure 2). This complex most likely is only partly formed by histones, since only when analyzing sub-nucleosomes size DNA molecules we can detect histone H3 (Figure 3). To be sure that the complex is not entirely made up by histones, future studies should perform an MNse-ChIP-seq with less digested samples. However, it was previously shown that R-loops are enriched at those intergenic NDRs (Briggs, 2018 doi: 10.1093/nar/gky928) and that R-loops have plenty of interacting proteins (Girasol, 2023 10.1093/nar/gkad836). Therefore, most likely, this MNase-sensitive complexed have a hybrid nature made up by H3 and some other regulatory molecules, possibly involved in trans-splicing. We have now added a new figure 4 showing R-loop co-localization with the NDR.

      Regarding the comparison between different organisms, after explaining the sensitivity to MNase of the TAS protecting complex, we discuss that when comparing equally digested samples T. cruzi and T. brucei display a similar chromatin landscape with a mild NDR at the TAS (See T. cruzi represented in Figure 1 compared to T. brucei represented in Intermediate digestion 2 in Figure 2, intermediate digestion in the revised manuscript). Unfortunately, we cannot make a good comparison with L. major, since we do not count on a similar level of digestion. However, by analyzing a recently published DRIP-seq data-set for L. major we show that R-loop signal co localize with MNase-protection in a similar way (new S5 Fig).

      Another point that requires clarification concerns what the authors mean in the introduction and discussion when they write that trypanosomes have "...poorly organized chromatin with nucleosomes that are not strikingly positioned or phased." On the other hand, they also cite evidence of organization: "...well-positioned nucleosome at the spliced-out region.. in Leishmania (ref 34)"; "...a well-positioned nucleosome at the TASs for internal genes (ref37)"; "...a nucleosome depletion was observed upstream of every gene (ref 35)." Aren't these examples of organized chromatin with at least a few phased nucleosomes? In addition, in ref 37, figure 4 shows at least two (possibly three to four) nucleosomes that appear phased. In my opinion, the authors should first define more precisely what they mean by "poorly organized chromatin" and clarify that this interpretation does not contradict the findings highlighted in the cited literature.

      For a better understanding of nucleosome positioning and phasing I recommend the review: Clark 2010 doi:10.1080/073911010010524945, Figure 4. Briefly, in a cell population there are different alternative positions that a given nucleosome can adopt. However, some are more favorable. When talking about favorable positions, we refer to the coordinates in the genome that are most likely covered by a nucleosome and are predominant in the cell population. Additionally, nucleosomes could be phased or not. This refers not only the position in the genome, but to the distance relative to a given point. In yeast, or in highly transcribed genes of more complex eukaryotes, nucleosomes are regularly spaced and phased relative to the transcription start site (TSS) or to the +1 nucleosome (Ocampo, NAR, 2016, doi:10.1093/nar/gkw068). In trypanosomes, nucleosomes have some regular distribution when making a browser inspection but, given that they are not properly phased with respect to any point, it is almost impossible to make a spacing estimation from paired-end data. This is also consistent with a chromatin that is transcribed in an almost constitutive manner.

      As the reviewer mention, we do site evidence of organization. We think the original observations are correct, but we do not fully agree with some of the original statements. In this manuscript our aim is to take the best we learned from their original works and to make a constructive contribution adding to the original discussions. In this regard, in trypanosomes there are some conserved patterns in the chromatin landscape, but their nucleosomes are far from being well-positioned or phased. For a better understanding, compare the variations observed in the y axis when representing av. nucleosome occupancy in yeast with those observed in trypanosomes and you will see that the troughs and peaks are much more prominent in yeast than the ones observed in any TryTryp member.

      Following the reviewer's suggestion we have now clarified this in the main text.

      The paper would also benefit from the inclusion of a schematic figure to help readers visualize and better understand the findings. What is the biological impact of having nucleosomes, di-nucleosomes, or sub-nucleosomes at TAS? This is not obvious to readers outside the chromatin field. For example, the following statement is not intuitive: "We observed that, when analyzing nucleosome-size (120-180 bp) DNA molecules or longer fragments (180-300 bp), the TASs of either T. cruzi or T. brucei are mostly nucleosome-depleted. However, when representing fragments smaller than a nucleosome-size (50-120 bp) some histone protection is unmasked (Fig. 3 and Fig. S4). This observation suggests that the MNase sensitive complex sitting at the TASs is at least partly composed of histones." Please clarify.

      We appreciate the reviewer's suggestion to make a schematic figure. We have now added a new Figure 6.

      Regarding the biological impact of having mono, di or subnucleosome fragments, it is important to unveil the fragment size of the protected DNA to infer the nature of the protecting complex. In the case of tRNA genes in yeast, at pol III promoters they found footprints smaller than a nucleosome size that ended up being TFIIB-TFIIC (Nagarajavel, doi: 10.1093/nar/gkt611). Therefore, detecting something smaller than a nucleosome might suggest the binding of trans-acting factors different than histones or involving histones in a mixed complex. These mixed complexes are also observed, and that is the case of the centromeric nucleosome which has a very peculiar composition (Ocampo and Clark, Cells Reports, 2015). On the other hand, if instead we detect bigger fragments, it could be indicative of the presence of bigger protecting molecules or that those regions are part of higher order chromatin organization still inaccessible for MNase linker digestions.

      Here we show on 2Dplots, that complex or components protecting the TAS have nucleosome size, but we cannot assure they are entirely made up by histones, since, only when looking at subnucleosome-size fragments, we are able to detect histone H3. We have now added part of this explanation to the discussion.

      By integrating every analysis in this work and the previous ones, we propose that the TAS is protected by an MNase-sensitive complex (Figure 2). This complex most likely is only partly formed by histones, since only when analyzing sub-nucleosomes size DNA molecules we can detect histone H3 (Figure 3). As explained above, to be sure that the complex is not entirely made up by histones, future studies should perform an MNse-ChIP-seq with less digested samples. However, it was previously shown that R-loops are enriched at those intergenic NDRs (Briggs 2018) and that R-loops have plenty of interacting proteins (Girasol, 2023). Therefore, most likely, this MNase-sensitive complexed have a hybrid nature made up by H3 and some other regulatory molecules. We have now added a new figure 4 showing R-loop partial co-localization with MNase protection.

      Some references are missing or incorrect:

      we will make a thorough revision

      "In trypanosomes, there are no canonical promoter regions." - please check Cordon-Obras et al. (Navarro's group). Thank you for the appropiate suggestion.

      Thank you for the appropriate suggestion. We have now added this reference

      Please, cite the study by Wedel et al. (Siegel's group), which also performed MNase-seq analysis in T. brucei.

      We understand that reviewer number 2# missed that we cited this reference and that we did used the raw data from the manuscript of Wedel et. al 2017 form Siegel's group. We used the MNase-ChIP-seq data set of histone H3 in our analysis for Figures 3, S4 and S6 (in the revised version), also detailed in table S1. To be even more explicit, we have now included the accession number of each data set in the figure legends.

      Figure-specific comments: Fig. S3: Why does the number of larger fragments increase with greater MNase digestion? Shouldn't the opposite be expected?

      This a good observation. As we also explained to reviewer#1:

      It's a common observation in MNase digestion of chromatin that more extensive digestion can still result in a broad range of fragment sizes, including some longer fragments. This seemingly counter-intuitive result is primarily due to the non-uniform accessibility of chromatin and the sequence preference of the MNase enzyme.

      The rationale of this is as follows: when you digest chromatin with MNase and the objective is to map nucleosomes genome-wide, the ideal situation would get the whole material contained in the mononucleosome band. Given that MNase is less efficient to digest protected DNA but, if the reaction proceeds further, it always ends up destroying part of it, the result is always far from perfect. The better situation we can get, is to obtain samples were ˜80% of the material is contained in the mononucloesome band. __And here comes the main point: __even in the best scenario, you always have some additional longer bands, such as those for di or tri nucleosomes. If you keep digesting, you will get less than 80 % in the nucleosome band and, those remaining DNA fragments that use to contain di and tri nucleosomes start getting digested as well originating a bigger dispersion in fragments sizes. How do we explain persistence of Long Fragments? The longest fragments (di-, tri-nucleosomes) that persist in a highly digested sample are the ones that were originally most highly protected by proteins or higher-order structure, making their linker DNA extremely resistant to initial cleavage. Once most of the genome is fragmented, these few resistant longer fragments become a more visible component of the remaining population, contributing to a broader size dispersion. Hence, there you end up having a bigger dispersion in length distributions in the final material. Bottom line, it is not a good practice to work with under or overdirected samples. Our main point is to emphasize that especially when comparing samples, it important to compare those with comparable levels of digestion. Otherwise, a different sampling of the genome will be represented in the remaining sequenced DNA.

      Minor points:

      There are several typos throughout the manuscript.

      Thanks for the observation. We will check carefully.

      Methods: "Dinucelotide frecuency calculation."

      We will add a code in GitHub

      Reviewer #2 (Significance (Required)):

      In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms. Audience: basic science and specialized readers.

      Expertise: epigenetics and gene expression in trypanosomatids.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

      The authors analysed publicly accessible MNase-seq data in TriTryps parasites, focusing on the chromatin structure around trans-splicing acceptor sites (TASs), which are vital for processing gene transcripts. They describe a mild nucleosome depletion at the TAS of T. cruzi and L. major, whereas a histone-containing complex protects the TASs of T. brucei. In the subsequent analysis of T. brucei, they suggest that a Mnase-sensitive complex is localised at the TASs. For single-copy versus multi-copy genes, the authors show different di-nucleotide patterns and chromatin structures. Accordingly, they propose this difference could be a novel mechanism to ensure the accuracy of trans-splicing in these parasites.

      Before providing an in- depth review of the manuscript, I note that some missing information would have helped in assessing the study more thoroughly; however, in the light of the available information, I provide the following comments for consideration.

      The numbering of the figures, including the figure legends, is missing in the PDF file. This is essential for assessing the provided information.

      We apologized for not including the figure numbers in the main text, although they are located in the right place when called in the text. The omission was unwillingly made when figure legends were moved to the bottom of the main text. This is now fixed in the updated version of the manuscript.

      The publicly available Mnase- seq data are manyfold, with multiple datasets available for T. cruzi, for example. It is unclear from the manuscript which dataset was used for which figure. This must be clarified.

      This was detailed in Table S1. We have now replaced the table by an improved version, and we have also included the accession number of each data set used in the figure legends.

      Why do the authors start in figure 1 with the description of an MNase- protected TAS for T.brucei, given that it has been clearly shown by the Siegel lab that there is a nucleosome depletion similar to other parasites?

      We did not want to ignore the paper from Patterton's lab because it was the first one to map nucleosomes genome-wide in T. brucei and the main finding of that paper claimed the existence of a well-positioned nucleosome at intergenic regions, what we though constitutes a point worth to be discussed. While Patterton's work use MNase-seq from gel-purified samples and provides replicated experiments sequenced in really good depth; Siegel's lab uses MNase-ChIP-seq of histone H3 but performs only one experiment and its input was not sequenced. So, each work has its own caveats and provides different information that together contributes to make a more comprehensive study. We think that bringing up both data sets to the discussion, as we have done in Figures 1 and 3, helps us and the community working in the field to enrich the discussion.

      If the authors re- analyse the data, they should compare their pipeline to those used in the other studies, highlighting differences and potential improvements.

      We are working on this point. We will provide a more detail description in the final revision.

      Since many figures resemble those in already published studies, there seems little reason to repeat and compare without a detailed comparison of the pipelines and their differences.

      Following the reviewer advice, we are now working on highlighting the main differences that justify analyzing the data the way we did and will be added in the finally revised method section.

      At a first glance, some of the figures might look similar when looking at the original manuscripts comparing with ours. However, with a careful and detailed reading of our manuscripts you can notice that we have added several analyses that allow to unveil information that was not disclosed before.

      First, we perform a systematic comparison analyzing every data set the same way from beginning to end, being the main difference with previous studies the thorough and precise prediction of TAS for the three organisms. Second, we represent the average chromatin organization relative to those predicted TASs for TriTryps and discuss their global patterns. Third, by representing the average chromatin into heatmaps, we show for the very first time, that those average nucleosome landscape are not just an average, they keep a similar organization in most of the genome. These was not done in any of the previous manuscripts except for our own (Beati, PLOS One 2023). Additionally, we introduce the discussion of how the extension of MNase reaction can affect the output of these experiments and we show 2D-plots and length distribution heatmaps to discuss this point (a point completely ignored in all the chromatin literature for trypanosomes). Furthermore, we made a far-reaching analysis by considering the contributions of each publish work even when addressed by different techniques. Finally, we discuss our findings in the context of a topic of current interest in the field, such as TriTryp's genome compartmentalization.

      Several previous Mnase- seq analysis studies addressing chromatin accessibility emphasized the importance of using varying degrees of chromatin digestion, from low to high digestion (30496478, 38959309, 27151365).

      The reviewer is correct, and this point is exactly what we intended to illustrate in figure number 2. We appreciate he/she suggests these references that we are now citing in the final discussion. Just to clarify, using varying degrees of chromatin digestion is useful to make conclusions about a given organism but when comparing samples, strains, histone marks, etc. It is extremely important to do it upon selection of similar digested samples.

      No information on the extent of DNA hydrolysis is provided in the original Mnase- seq studies. This key information can not be inferred from the length distribution of the sequenced reads.

      The reviewer is correct that "No information on the extent of DNA hydrolysis is provided in the original Mnase-seq studies" and this is another reason why our analysis is so important to be published and discussed by the scientific community working in trypanosomes. We disagree with the reviewer in the second statement, since the level of digestion of a sequenced sample is actually tested by representing the length distribution of the total DNA sequenced. It is true that before sequencing you can, and should, check the level of digestion of the purified samples in an agarose gel and/or in a bioanalyzer. It could be also tested after library preparation, but before sequencing, expecting to observe the samples sizes incremented in size by the addition of the library adapters. But, the final test of success when working with MNase digested samples is to analyze length of DNA molecules by representing the histograms with length distribution of the sequenced DNA molecules. Remarkably, on occasions different samples might look very similar when run in a gel, but they render different length distribution histograms and this is because the nucleosome core could be intact but they might have suffered a differential trimming of the linker DNA associated to it or even be chewed inside (see Cole Hope 2011, section 5.2, doi: 10.1016/B978-0-12-391938-0.00006-9, for a detailed explanation).

      As the input material are selected, in part gel- purified mono- nucleosomal DNA bands. Furthermore the datasets are not directly comparable, as some use native MNase, while others employ MNase after crosslinking; some involve short digestion times at 37 {degree sign} C, while others involve longer digestion at lower temperatures. Combining these datasets to support the idea of an MNase- sensitive complex at the TAS of T. brucei therefore may not be appropriate, and additional experiments using consistent methodologies would strengthen the study's conclusions.

      In my opinion, describing an MNase- sensitive complex based solely on these data is not feasible. It requires specifically designed experiments using a consistent method and well- defined MNase digestion kinetics.

      As the reviewer suggests, the ideal experiment would be to perform a time course of MNase reaction with all the samples in parallel, or to work with a fix time point adding increasing amounts of MNase. However, the information obtained from the detail analysis of the length distribution histogram of sequenced DNA molecules the best test of the real outcome. In fact, those samples with different digestion levels were probably not generated on purpose.

      The only data sets that were gel purified are those from Mareé 2017 (Patterton's lab), used in Figures 1, S1 and S2 and those from L. major shown in Fig 1. It was a common practice during those years, then we learned that is not necessary to gel purify, since we can sort fragment sizes later in silico when needed.

      As we explained to reviewer #1, to avoid this conflict, we decided to remove this data from figures 2 and S3. In summary, the 3 remaining samples comes from the same lab, and belong to the same publication (Mareé 2022). These sample are the inputs of native MNase ChIp-seq, obtain the same way, totally comparable among each other.

      Reviewer #3 (Significance (Required)):

      Due to the lack of controlled MNase digestion, use of heterogeneous datasets, and absence of benchmarking against previous studies, the conclusions regarding MNase-sensitive complexes and their functional significance remain speculative. With standardized MNase digestion and clearly annotated datasets, this study could provide a valuable contribution to understanding chromatin regulation in TriTryps parasites.

      As we have explained in the previous point our conclusions are valid since we do not compare in any figure samples coming from different treatments. The only exception to this comment could be in figure 3 when talking about MNase-ChIP-seq. We have now added a clear and explicit comment in the section and the discussion that despite having subtle differences in experimental procedures we arrive to the same results. This is the case for T. cruzi IP, run from crosslinked chromatin, compared to T. brucei's IP, run from native chromatin.

      Along the years it was observed in the chromatin field that nucleosomes are so tightly bound to DNA that crosslinking is not necessary. However, it is still a common practice specially when performing IPs. In our own hands, we did not observe any difference at the global level neither in T. cruzi (unpublished) nor in my previous work with yeast (compared nucleosome organization from crosslinked chromatin MNAse-seq inputs Chereji, Mol Cell, 2017 doi:10.1016/j.molcel.2016.12.009 and native MNase-seq from Ocampo, NAR, 2016 doi: 10.1093/nar/gkw068).

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The authors analysed publicly accessible MNase-seq data in TriTryps parasites, focusing on the chromatin structure around trans-splicing acceptor sites (TASs), which are vital for processing gene transcripts. They describe a mild nucleosome depletion at the TAS of T. cruzi and L. major, whereas a histone-containing complex protects the TASs of T. brucei. In the subsequent analysis of T. brucei, they suggest that a Mnase-sensitive complex is localised at the TASs. For single-copy versus multi-copy genes, the authors show different di-nucleotide patterns and chromatin structures. Accordingly, they propose this difference could be a novel mechanism to ensure the accuracy of trans-splicing in these parasites.

      Before providing an in- depth review of the manuscript, I note that some missing information would have helped in assessing the study more thoroughly; however, in the light of the available information, I provide the following comments for consideration.

      The numbering of the figures, including the figure legends, is missing in the PDF file. This is essential for assessing the provided information. The publicly available Mnase- seq data are manyfold, with multiple datasets available for T. cruzi, for example. It is unclear from the manuscript which dataset was used for which figure. This must be clarified. Why do the authors start in figure 1 with the description of an MNase- protected TAS for T.brucei, given that it has been clearly shown by the Siegel lab that there is a nucleosome depletion similar to other parasites? If the authors re- analyse the data, they should compare their pipeline to those used in the other studies, highlighting differences and potential improvements. Since many figures resemble those in already published studies, there seems little reason to repeat and compare without a detailed comparison of the pipelines and their differences. Several previous Mnase- seq analysis studies addressing chromatin accessibility emphasised the importance of using varying degrees of chromatin digestion, from low to high digestion (30496478, 38959309, 27151365). No information on the extent of DNA hydrolysis is provided in the original Mnase- seq studies. This key information can not be inferred from the length distribution of the sequenced reads. As the input material are selected, in part gel- purified mono- nucleosomal DNA bands. Furthermore the datasets are not directly comparable, as some use native MNase, while others employ MNase after crosslinking; some involve short digestion times at 37 {degree sign} C, while others involve longer digestion at lower temperatures. Combining these datasets to support the idea of an MNase- sensitive complex at the TAS of T. brucei therefore may not be appropriate, and additional experiments using consistent methodologies would strengthen the study's conclusions. In my opinion, describing an MNase- sensitive complex based solely on these data is not feasible. It requires specifically designed experiments using a consistent method and well- defined MNase digestion kinetics.

      Significance

      Due to the lack of controlled MNase digestion, use of heterogeneous datasets, and absence of benchmarking against previous studies, the conclusions regarding MNase-sensitive complexes and their functional significance remain speculative. With standardized MNase digestion and clearly annotated datasets, this study could provide a valuable contribution to understanding chromatin regulation in TriTryps parasites.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Siri et al. perform a comparative analysis using publicly available MNase-seq data from three trypanosomatids (T. brucei, T. cruzi, and Leishmania), showing that a similar chromatin profile is observed at TAS (trans-splicing acceptor site) regions. The original studies had already demonstrated that the nucleosome profile at TAS differs from the rest of the genome; however, this work fills an important gap in the literature by providing the most reliable cross-species comparison of nucleosome profiles among the tritryps. To achieve this, the authors applied the same computational analysis pipeline and carefully evaluated MNase digestion levels, which are known to influence nucleosome profiling outcomes.

      In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms. The manuscript could be improved with some clarifications and adjustments:

      1. The authors state from the beginning that available MNase data indicate altered nucleosome occupancy around the TAS. However, they could also emphasize that the conclusions across the different trypanosomatids are inconsistent and even contradictory: NDR in T. cruzi versus protection-in different locations-in T. brucei and Leishmania.
      2. Another point that requires clarification concerns what the authors mean in the introduction and discussion when they write that trypanosomes have "...poorly organized chromatin with nucleosomes that are not strikingly positioned or phased." On the other hand, they also cite evidence of organization: "...well-positioned nucleosome at the spliced-out region.. in Leishmania (ref 34)"; "...a well-positioned nucleosome at the TASs for internal genes (ref37)"; "...a nucleosome depletion was observed upstream of every gene (ref 35)." Aren't these examples of organized chromatin with at least a few phased nucleosomes? In addition, in ref 37, figure 4 shows at least two (possibly three to four) nucleosomes that appear phased. In my opinion, the authors should first define more precisely what they mean by "poorly organized chromatin" and clarify that this interpretation does not contradict the findings highlighted in the cited literature.
      3. The paper would also benefit from the inclusion of a schematic figure to help readers visualize and better understand the findings. What is the biological impact of having nucleosomes, di-nucleosomes, or sub-nucleosomes at TAS? This is not obvious to readers outside the chromatin field. For example, the following statement is not intuitive: "We observed that, when analyzing nucleosome-size (120-180 bp) DNA molecules or longer fragments (180-300 bp), the TASs of either T. cruzi or T. brucei are mostly nucleosome-depleted. However, when representing fragments smaller than a nucleosome-size (50-120 bp) some histone protection is unmasked (Fig. 3 and Fig. S4). This observation suggests that the MNase sensitive complex sitting at the TASs is at least partly composed of histones." Please clarify. Some references are missing or incorrect:

      "In trypanosomes, there are no canonical promoter regions." - please check Cordon-Obras et al. (Navarro's group).

      Please, cite the study by Wedel et al. (Siegel's group), which also performed MNase-seq analysis in T. brucei.

      Figure-specific comments:

      Fig. S3: Why does the number of larger fragments increase with greater MNase digestion? Shouldn't the opposite be expected?

      Fig. S5B: Why not use MNase conditions under which T. cruzi and T. brucei display comparable profiles at TAS? This would facilitate interpretation.

      Minor points:

      There are several typos throughout the manuscript.

      Methods: "Dinucelotide frecuency calculation."

      Significance

      In my view, the main conclusion is that the profiles are indeed similar-even when comparing T. brucei and T. cruzi. This was not clear in previous studies (and even appeared contradictory, reporting nucleosome depletion versus enrichment) largely due to differences in chromatin digestion across these organisms.

      Audience: basic science and specialized readers.

      Expertise: epigenetics and gene expression in trypanosomatids.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This study explores chromatin organization around trans-splicing acceptor sites (TASs) in the trypanosomatid parasites Trypanosoma cruzi, T. brucei and Leishmania major. By systematically re-analyzing MNase-seq and MNase-ChIP-seq datasets, the authors conclude that TASs are protected by an MNase-sensitive complex that is, at least in part, histone-based, and that single-copy and multi-copy genes display differential chromatin accessibility. Altogether, the data suggest a common chromatin landscape at TASs and imply that chromatin may modulate transcript maturation, adding a new regulatory layer to an unusual gene-expression system.

      I value integrative studies of this kind and appreciate the careful, consistent data analysis the authors implemented to extract novel insights. That said, several aspects require clarification or revision before the conclusions can be robustly supported. My main concerns are listed below, organized by topic/result section.

      TAS prediction:

      • Why were TAS predictions derived only from insect-stage RNA-seq data? Restricting TAS calls to one life stage risks biasing predictions toward transcripts that are highly expressed in that stage and may reduce annotation accuracy for lowly expressed or stage-specific genes. Please justify this choice and, if possible, evaluate TAS robustness using additional transcriptomes or explicitly state the limitation.

      Results

      • "There is a distinctive average nucleosome arrangement at the TASs in TriTryps":
      • You state that "In the case of L. major the samples are less digested." However, Supplementary Fig. S1 suggests that replicate 1 of L. major is less digested than the T. brucei samples, while replicate 2 of L. major looks similarly digested. Please clarify which replicates you reference and correct the statement if needed.
      • It appears you plot one replicate in Fig. 1b and the other in Suppl. Fig. S2. Please indicate explicitly which replicate is in each plot. For T. brucei, the NDR upstream of the TAS is clearer in Suppl. Fig. S2 while the TAS protection is less prominent; based on your digestion argument, this should correspond to the more-digested replicate. Please confirm. The protected region around the TAS appears centered on the TAS in T. brucei but upstream in L. major. This is an interesting difference. If it is technical (different digestion or TAS prediction offset), explain why; if likely biological, discuss possible mechanisms and implications.

      Results

      • "An MNase sensitive complex occupies the TASs in T. brucei":
      • The definition of "MNase activity" and the ordering of samples into Low/Intermediate/High digestion are unclear. Did you infer digestion levels from fragment distributions rather than from controlled experimental timepoints? In Suppl. Fig. S3a it is not obvious how "Low digestion" was defined; that sample's fragment distribution appears intermediate. Please provide objective metrics (e.g., median fragment length, fraction 120-180 bp) used to classify digestion levels.
      • Several fragment distributions show a sharp cutoff at ~100-125 bp. Was this due to gel purification or bioinformatic filtering? State this clearly in Methods. If gel purification occurred, that can explain why some datasets preserve the MNase-sensitive region.
      • Please reconcile cases where samples labeled as more-digested contain a larger proportion of >200 bp fragments than supposedly less-digested samples; this ordering affects the inference that digestion level determines the loss/preservation of TAS protection. Based on the distributions I see, "Intermediate digestion 1" appears most consistent with an expected MNase curve - please confirm and correct the manuscript accordingly. Results - "The MNase sensitive complexes protecting the TASs in T. brucei and T. cruzi are at least partly composed of histones":
      • The evidence that histones are part of the MNase-sensitive complex relies on H3 MNase-ChIP signal in subnucleosomal fragment bins. This seems to conflict with the observation (Fig. 1) that fragments protecting TASs are often nucleosome-sized. Please reconcile these points: are H3 signals confined to subnucleosomal fragments flanking the TAS while the TAS itself is depleted of H3? Provide plots that compare MNase-seq and H3 ChIP signals stratified by consistent fragment-size bins to clarify this.
      • Please indicate which datasets are used for each panel in Suppl. Fig. S4 (e.g., Wedel et al., Maree et al.), and avoid calling data from different labs "replicates" unless they are true replicates.
      • Several datasets show a sharp lower bound on fragment size in the subnucleosomal range (e.g., ~80-100 bp). Is this a filtering artifact or a gel-size selection? Clarify in Methods and, if this is an artifact, consider replotting after removing the cutoff. Results - "The TASs of single and multi-copy genes are differentially protected by nucleosomes":
      • Please include T. brucei RNA-seq data in Suppl. Fig. S5b as you did for T. cruzi.
      • Discuss how low or absent expression of multigene families affects TAS annotation (which relies on RNA-seq) and whether annotation inaccuracies could bias the observed chromatin differences.
      • The statement that multi-copy genes show an "oscillation" between AT and GC dinucleotides is not clearly supported: the multi-copy average appears noisier and is based on fewer loci. Please tone down this claim or provide statistical support that the pattern is periodic rather than noisy.
      • How were multi-copy genes defined in T. brucei? Include the classification method in Methods.

      Genomes and annotations:

      • If transcriptomic data for the Y strain was used for T. cruzi, please explain why a Y strain genome was not used (e.g., Wang et al. 2021 GCA_015033655.1), or justify the choice. For T. brucei, consider the more recent Lister 427 assembly (Tb427_2018) from TriTrypDB. Use strain-matched genomes and transcriptomes when possible, or discuss limitations.

      Reproducibility and broader integration:

      • Please share the full analysis pipeline (ideally on GitHub/Zenodo) so the results are reproducible from raw reads to plots.
      • As an optional but helpful expansion, consider including additional datasets (other life stages, BSF MNase-seq, ATAC-seq, DRIP-seq) where available to strengthen comparative claims. Optional analyses that would strengthen the study:
      • Stratify single-copy genes by expression (high / medium / low) and examine average nucleosome occupancy at TASs for each group; a correlation between expression and NDR depth would strengthen the functional link to maturation.

      Minor / editorial comments:

      • In the Introduction, the sentence "transcription is initiated from dispersed promoters and in general they coincide with divergent strand switch regions" should be qualified: such initiation sites also include single transcription start regions.
      • Define the dotted line in length distribution plots (if it is not the median, please clarify) and consider placing it at 147 bp across plots to ease comparison.
      • In Suppl. Fig. 4b "Replicate2" the x-axis ticks are misaligned with labels - please fix.
      • Typo in the Introduction: "remodellingremodeling" → "remodeling."

      Referee cross-commenting

      Comment 1: I think Reviewer #2 and Reviewer #3 missed that they authors of this manuscript do cite and consider the results from Wedel at al. 2017. They even re-analysed their data (e.g. Figure 3a). I second Reviewer #2 comment indicating that the inclusion of a schematic figure to help readers visualize and better understand the findings would be an important addition.

      Comment 2: I agree with Reviewer #3 that the use of different MNase digestion procedures in the different datasets have to be considered. On the other hand, I don't think there is a problem with figure 1 showing an MNase-protected TAS for T. brucei as it is based on MNase-seq data and reproduces the reported results (Maree et al. 2017). What the Siegel lab did in Wedel et al. 2017 was MNase-ChIPseq of H3 showing nucleosome depletion at TAS, but both results are not necessary contradictory: There could still be something else (which does not contain H3) sitting on the TAS protecting it from MNase digestion.

      Significance

      This study provides a systematic comparative analysis of chromatin landscapes at trans-splicing acceptor sites (TASs) in trypanosomatids, an area that has been relatively underexplored. By re-analyzing and harmonizing existing MNase-seq and MNase-ChIP-seq datasets, the authors highlight conserved and divergent features of nucleosome occupancy around TASs and propose that chromatin contributes to the fidelity of transcript maturation.

      The significance lies in three aspects:

      1. Conceptual advance: It broadens our understanding of gene regulation in organisms where transcription initiation is unusual and largely constitutive, suggesting that chromatin can still modulate post-transcriptional processes such as trans-splicing.
      2. Integrative perspective: Bringing together data from T. cruzi, T. brucei and L. major provides a comparative framework that may inspire further mechanistic studies across kinetoplastids.
      3. Hypothesis generation: The findings open testable avenues about the role of chromatin in coordinating transcript maturation, the contribution of DNA sequence composition, and potential interactions with R-loops or RNA-binding proteins. Researchers in parasitology, chromatin biology, and RNA processing will find it a useful resource and a stimulus for targeted experimental follow-up.

      My expertise is in gene regulation in eukaryotic parasites, with a focus on bioinformatic analysis of high-throughput sequencing data

    1. 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 #4

      Evidence, reproducibility and clarity

      Major criticisms

      The manuscript by Chapa-y-Lazo et al. is confusing. It does not provide precise information about the three photostable monomers developed by different research groups. Please read the review (ref. 17) carefully. The monomeric version analyzed in this study was developed by Ivorra-Molla et al. and should be referred to as StayGold-E138D. This variant excels in dispersibility (monomericity), photostability, and molecular brightness (the product of the molar extinction coefficient and the fluorescence quantum yield). However, when analyzed in animal cells, StayGold-E138D is practically dim, and its brightness is poor. This can be seen in Figures 2, 3, S5, and S6 of the manuscript. The maturation efficiency of the chromophore is not so good in fly embryos. On the other hand, Ando et al. independently developed a monomeric version of StayGold called mStayGold at FPbase and Addgene. Therefore, I think that the authors should acknowledge that their analysis of StayGold monomer behavior is still incomplete. Additionally, the evolution tree of StayGold shown in Figure S2 is incorrect. The side-by side comparison of the three monomeric variants of StayGold, including StayGold-E138D and mStayGold, is documented in a recent preprint. Comparison of monomeric variants of StayGold | bioRxiv

      Minor comments

      Line 84 z-stacks were acquired using a spinning disc confocal microscope. Line 100 we collected a z-stack through each embryo. Line373 We analyzed the slices from 7 µm to 20.5 µm depth. Line 390 Depth 9 µm to 21 µm was analyzed. It is not clear what "z-stack" means in these sentences.

      Significance

      Nothing in particular.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Chapa-y-Lazo and colleagues report the detailed characterization of a number of different genetically-encoded fluorescent proteins in Drosophila embryos. The screening and selection of an appropriate fluorescent protein for imaging tasks is an important and often neglected part of experimental design, and datasets such as this one will be extremely useful in guiding decision making for other users. The manuscript is well-written and carefully controlled for different developmental stages and nicely compares the most pertinent properties of FPs such as brightness, photobleaching, and folding time. There would be a couple of additional experiments that would be nice to see but are not strictly necessary for improving the paper as-is, but might be helpful points to include in the discussion.

      Comments:

      1) All fluorophores in this study were fused to H2Av, at the same insertion site, which makes for a nice and easy comparison between lines. However, histone-binding proteins can sometimes behave unpredictably when tagged with different things and in addition it would be interesting to see if the fusion protein affects the FP properties in anyway. I.e. would sfGFP be brighter than mEmerald when bound to a CAAX sequence or some other organelle? It would be impractical for this study to re-do all the FPs, but the top two hits could be interesting and would potentially be quite interesting if there is a significant difference in behaviour between FPs when bound to different proteins/cellular compartments. Else maybe a mention in the discussion?

      2) Another way to compare the fluorophore folding time would be to selectively bleach a portion of the embryo at the same developmental stage and measure the time it takes for each FP to recover to the same intensity as the rest of the embryo. This could potentially control for any delay for developmental reasons.

      3) Some of the lines in the figure plots could be a bit thicker - purple and pink when overlapping are hard to distinguish.

      Significance

      This manuscript will be quite useful for those who are deciding between which fluorescent protein or combination to use for their live-imaging work, and additionally has created a number of useful fly strains in the process. It will hopefully also start a discussion about proper characterization and quantification of fluorescent reporters under different conditions, ideally before all the effort to generate an entirely new genetically modified animal is performed.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript Saunders and colleagues benchmark the brightness, folding speed and photostability of a variety of red (8 versions) and green fluorescent proteins (9 versions), which have been widely used for in vivo imaging. They fused each protein to histone2Av, cloned the fusion into attP constructs and inserted them in the Drosophila genome at the same genetic location. Thus, expression levels can be compared. Nuclei at embryonic cycle 14 were imaged, segmented and fluorescence was quantified. At this early stage the maturation kinetics of the fluorophore can particularly influence its fluorescence intensity.

      Additionally, stage 15-16 embryos were imaged at the dorsal side to quantify brightness. As the histone promoter is active in all cells, the fluorescence in the nuclei of all cell types can be quantified. Brightness differences between the different proteins vary a bit between both experiments, likely taking folding versus brightness into account. Generally, sfGFP, mEGFP, mEmerald as well as mStrawberry and mScarlet are the brightest. Next, developmental movies were recorded starting at gastrulation to estimate the folding rates of the different proteins. No large differences of the relative fluorescence increase over time were reported. To estimate photostability, embryos were imaged ventrally shortly before the onset of gastrulation for 2 or 4 hours with high laser intensity and the fluorescence intensity was recorded. Consistent with data in the literature, StayGold is the most photostable green protein, although it is not the brightest from the start, likely to also slower folding. From the red proteins mRFP and mCherry are good choices for long-term imaging.

      In summary, these results do not bring huge surprises but are still valuable for future choice of protein tagging for imaging. Best green proteins are mEGFP, mNeonGreen, mStayGold with differences in brightness vs stability. For red, no protein is the clear winner, mScarlet-I is good in folding and brightness but others are better for photostability.

      Major comments:

      1. Form the methods, it is not clear which promoter is used to drive expression of the histone2Av fusions. I assume this is not UAS but the histone promotor/enhancer. Please clarify.
      2. From text is not always what the purpose of the experiment is. For example, it is not mentioned that developmental movies were recorded for the data related to Figure 3 to calculate folding, while bleaching was measured in the movies related to Figure 4. In contrast to simple single time points in Figures 1 and 2.

      Minor comments:

      1. Please add time to movie 2 and rotate it such that anterior is to the left and dorsal it up.
      2. Lines 141 - 144 should refer to Figure 3D not 4D.
      3. Movies 3 and 4, please insert time.

      Significance

      Experiments are well performed and the finding are useful to guide the future choice of fluorophores in Drosophila and possibly other model organisms. Results are not very surprising, as the major finding that StayGold is photostable (but not the brightest) is not entirely new but still reassuring. It is particularly nice to have the differences confirmed by well controlled side-by-side measurements in Drosophila. This will likely guide many Drosophila researchers to tag their favourite protein with StayGold in the future.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This is an outstanding study of high practical value, which provided the systematic performance evaluation of in vivo fluorophores under the same condition in the field of Drosophila developmental biology. By site-integrating 17 green and red fluorescent proteins into the same genomic locus and evaluating their fluorescent intensity (early/late embryos), folding time and photostability on the same imaging platform, this provides a powerful database for researchers.

      Major comments:

      Q1: All fluorescent proteins are fused with histone H2Av. Will this nuclear localization expression pattern mask the performance differences of fluorescent proteins in other subcellular structures (such as cell membranes, cytoplasm, and cytoskeleton)?

      Q2: How do the authors ensure precise developmental synchrony among different embryos to avoid the influence of developmental time differences on fluorescence intensity and folding curves?

      Q3: In this study, the authors conduct a quantitative screen of nine green and eight red fluorophore lines in Drosophila. A logical and valuable extension of this work would be a systematic evaluation of newer fluorescent proteins, including promising candidates like mBaoJin, mScarlet3, and mScarlet3-H.

      Q4:The study does not discuss the potential for fluorescent proteins to interfere with biological function. Although the proteins were expressed from the identical genomic location, variations in their size, structure, or fusion design may influence the target protein's localization or activity.

      Lines 145-147 "The only profile that did not fit well to this phenomenological function was mStayGold which did not display a clear reduction in its rate of intensity increase". What is the reason that causes mStayGold to fail to fit well? Is it related to the unique structure?

      Lines 154-156 "For the red fluorophores, the intensity profiles were more varied (Fig. 3E). They could not be reduced to a single curve, unlike the green fluorophores (Fig. S7B). The phenomenological function I(t) did not fit the curves well". Compared with green fluorophores, the intensity profiles of red fluorophores vary greatly, what's the major factors drive this difference?

      Lines 206 "Fluorophores including mEGFP and mEmerald displayed a secondary peak in intensity around an hour after experiment initiation. This is consistent with a change in the rate of protein production". What is the mechanism behind the secondary peak, and why is it distinctly observed only in mEGFP and mEmerald?

      Minor comments:

      Line 143 "curve I(t) = I0 tanh (t-tin/ts) (Fig. 4D, Methods)". It's not Fig. 4D, but Fig. 3D.

      Line 145 "time is smallest for Superfolder GFP and longest for mNeonGreen (Fig. 4D)". Not Fig. 4D, but Fig. 3D.

      Line159 "mScarlet" must be replaced with "mScarlet-I".

      Significance

      The systematic performance evaluation of in vivo fluorophores under the same condition will give a comprehensive guidence when choosing fluorescent proteins in the field of Drosophila developmental biology.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-03195R

      Point-by-Point Response to Reviewers

      We thank the reviewers for their thoughtful and constructive evaluations, which have helped us substantially improve the clarity, rigor, and balance of our manuscript. We are grateful for their recognition that our integrated ATAC-seq and RNA-seq analyses provide a valuable and technically sound contribution to understanding soxB1-2 function and regenerative neurogenesis in planarians.

      We have carefully addressed the reviewers' major points as follows:

      1. Direct versus indirect regulation by SoxB1-2:____ In the revision, we explicitly acknowledge the limitations of inferring direct regulation from our current datasets and have revised statements throughout the Results and Discussion to emphasize that our findings are correlative.
      2. Evidence for pioneer activity:____ Although the pioneer role of SoxB1 transcription factors in well established in other systems, we agree that additional binding or motif data would be required to formally demonstrate SoxB1-2 pioneer function. Accordingly, we performed motif analysis and revised the text throughout to frame SoxB1-2's proposed role as consistent with, rather than demonstrating transcriptional activator activity.
      3. Motif enrichment and downstream regulatory interactions:____ In response to Reviewer #1's suggestion, we have included a new motif enrichment analysis in the supplement to contextualize possible co-regulators within the SoxB1-2 network.
      4. Data reproducibility and peak-calling consistency:____ We have included sample correlations ____and peak overlaps for ATAC-seq samples in the revision, providing a clearer assessment of reproducibility.
      5. Clarification of co-expression and downstream targets:____ We included co-expression plots for soxB1-2 with mecom and castor in the supplemental materials. These plots were generated from previously published scRNA-seq data and demonstrate that cells expressing soxB1-2 also express mecom and __ __We appreciate the reviewers' recognition that our methods are rigorous and our data accessible. We have incorporated all major revisions suggested and believe have strengthened the manuscript's precision, interpretations, and conclusions. Below, we respond to each comment in detail.

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

      Summary

      The authors of this interesting study take the approach of combining RNAi, RNA-seq and ATAC-seq to try to build a regulatory network surrounding the function of a planarian SoxB1 ortholog, broadly required for neural specification during planarian regeneration. They find a number of chromatin regions that differentially accessible (measured by ATAC-seq), associate these with potential genes by proximity to the TSS. They then compare this set of genes with those that are differentially regulated (using RNA-seq), after SoxB1 RNAi mediated knockdown. This allows them the authors some focus on potential directly regulated targets of the planarian SoxB1. Two of these downstream targets, the mecom and castor transcription factors are then studied in greater detail.

      Major Comments

      I have no suggestions for new experiments that fit sensibly with the scope of the current work. There are other analyses that could be appropriate with the ATAC-seq data, but may not make sense in the content of SoxB1 acting as pioneer factor.

      I would like to see motif enrichment analysis under the set of peaks to see if SoxB1 is opening chromatin for a restricted set of other transcription factors to then bind. Much of this could be taken from Neiro et al, eLife 2022 (which also used ATAC-seq) and matched planarians TF families to likely binding motifs. This could add some breadth to the regulatory network. It could be revealing for example if downstream TF also help regulate other targets that SoxB1 makes available, this is pattern often seen for cell specification (as I am sure the authors are aware). Alternatively, it may reveal other candidate regulators.

      Thank you for this suggestion. We agree with the reviewers that this analysis should be done. We ran the motif enrichment analysis using the same methods as outlined in Neiro et al. eLife, 2022. We have included a new motif enrichment analysis in the supplement to contextualize possible co-regulators within the SoxB1-2 network.

      Overall peak calling consistency with ATAC-sample would be useful to report as well, to give readers an idea of noise in the data. What was the correlation between samples?

      __Excellent point. In response to this comment, we ran a Pearson correlation test on replicates within gfp and soxB1-2 RNAi replicates to get an idea of overall correlation between replicates. Additionally, we calculated percent overlap of peaks for biological replicates and between treatment groups. __

      While it is logical to focus on downregulated genes, it would also be interesting to look at upregulated genes in some detail. In simple terms would we expect to see the representation of an alternate set of fate decisions being made by neoblast progeny?

      This is also an important point that we considered but initially did not pursue it due to the lack of tools to test upregulated gene function. However, the reviewer is correct that this is straightforward to perform computationally. Thus, we have performed Gene Ontology analysis on the upregulated genes in all RNA-seq datasets (soxB1-2 RNAi, mecom RNAi, and castor RNAi). Both mecom and castor datasets did not reveal enrichment within the upregulated portion of the dataset. Genes upregulated after soxB1-2 RNAi were enriched for metabolic, xenobiotic detoxification, potassium homeostasis, and endocytic programs. Rather than indicating a shift toward alternative lineages, including non-ectodermal fates, these signatures are consistent with stress-responsive and homeostatic programs activated following loss of soxB1-2. We did not detect enrichment patterns strongly associated with alternative cell fates. We conclude that this analysis does not formally exclude potential shifts in lineage-specific transcriptional programs, but does support our hypothesis that soxB1-2 functions as a transcriptional activator.

      Can the authors be explicit about whether they have evidence for co-expression of SoxB1/castor and SoxB1/mecom? I could find this clearly and it would be important to be clear whether this basic piece of evidence is in place or not at this stage.

      We included co-expression plots for soxB1-2 with mecom and castor in the supplemental material. These plots were generated from previously published scRNA-seq data and demonstrate that cells expressing soxB1-2 also express mecom and castor. We have not done experiments showing co-expression via in situ at this time.

      Minor comments

      Formally loss of castor and mecom expression does mean these cells are absent, strictly the cell absence needs an independent method. It might be useful to clarify this with the evidence of be clear that cells are "very probably" not produced.

      We agree that loss of castor and mecom expression does not formally demonstrate the physical absence of these cells, and that independent methods would be required to definitively confirm their loss. In response, we have revised our wording to indicate that castor- and mecom-expressing cells are very likely not being produced, rather than stating that they are absent.

      Reviewer #1 (Significance (Required)):

      Significance

      Strengths and limitations.

      The precise exploitation of the planarian system to identify potential targets, and therefore regulatory mechanisms, mediated by SoxB1 is an interesting contribution to the fi eld. We know almost nothing about the regulatory mechanisms that allow regeneration and how these might have evolved, and this work is well-executed step in that direction.

      Advance

      The paper makes a clear advance in our understanding of an important process in animals (neural specification) and how this happens in the context in the context during an example of animal regeneration. The methods are state-of-the-art with respect to what is possible in the planarian system.

      Audience

      This will be of wide interest to developmental biologists, particularly those studying regeneration in planarians and other regenerative systems,and those who study comparative neurodevelopment.

      Expertise

      I have expertise in functional genomics in the context of stem cells and regeneration, particularly in the planarian model system

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

      Review - Cathell, et al (RC-2025-03195)

      Summary and Significance:

      Understanding regenerative neurogenesis has been difficult due to the limited amount of neurogenesis that occurs after injury in most animal species. Planarians, with their adult neurogenesis and robust post-injury response, allow us to get a glimpse into regenerative neurogenesis. The Zayas laboratory previously revealed a key role for SoxB1-2 in maintenance and regeneration of a broad set of sensory and peripheral neurons in the planarian body. SoxB1-2 also has a role in many epidermal fates. Their previous work left open the tempting possibility that SoxB1-2 acts as a very upstream regulator of epidermal and neuronal fates, potentially acting as a pioneer transcription factor within these lineages. In the manuscript currently under review, Cathell and colleagues use ATAC-Seq and RNA-Seq to investigate chromatin changes after SoxB1-2(RNAi). With the experimental limitations in planarians, this is a strong first step toward testing their hypothesis that SoxB1-2acts as a pioneer within a set of planarian lineages. Beyond these cell types, this work is also important because planarian cell fates often rely on a suite of transcription factors, but the nature of transcription factor cooperation has been much less well understood. Indeed, the authors do show that loss of SoxB1-2 by RNAi causes changes in a number of accessible regions of the genome; many of these chromatin changes correspond to changes in gene expression of genes nearby these peaks. The authors also examine in more detail two genes that have genomic and transcriptomic changes after SoxB1-2(RNAi), mecom and castor. The authors completed RNA-Seq on mecom(RNAi) and castor(RNAi) animals, identifying genes downregulated after loss of either factor that are also seen in SoxB1-2(RNAi). The results in this paper are rigorous and very well presented. I will share two major limitations of the study and some suggestions for addressing them, but this work may also be acceptable without those changes at some journals.

      Limitation 1:

      The paper aims to test the hypothesis that SoxB1-2 is a pioneer transcription factor. Observation that SoxB1-2(RNAi) leads to loss of many accessible regions in the chromatin supports the hypothesis. However, an alternate possibility is that SoxB1-2 leads to transcription of another factor that is a pioneer factor or a chromatin remodeling enzyme; in either of these cases, the accessibility peak changes may not be due to SoxB1-2 directly but due to another protein that SoxB1-2 promotes. The authors describe how they can address this limitation in the future; in the meantime, is it known what the likely binding for SoxB1-2 would be (experimentally or based on homology)? If so, could the authors examine the relative abundance of SoxB1-2 binding sites in peaks that change after SoxB1-2(RNAi)? This could be compared to the abundance of the same binding sequence in non-changing peaks. Enrichment of SoxB1-2 binding sites in ATAC peaks that change after its RNAi would support the argument that chromatin changes are directly due to SoxB1-2.

      We appreciate the feedback and agree that distinguishing between direct SoxB1-2 pioneer activity and indirect effects mediated through downstream regulators is an important consideration. While we did not perform a direct abundance analysis of potential chromatin-remodeling cofactors, we conducted a motif enrichment analysis following the approach of Neiro et al. (eLife, 2022), comparing control and soxB1-2(RNAi) peak sets. This analysis revealed that Sox-family motifs, particularly SoxB1-like motifs, were among the most enriched in regions that remain accessible in control animals relative to soxB1-2(RNAi) animals, consistent with a model in which SoxB1-2 directly contributes to establishing or maintaining accessibility at these loci. We have now included this analysis in the supplemental materials to further contextualize potential co-regulators and transcriptional partners within the SoxB1-2 regulatory network. We agree and acknowledge in the report that future studies assessing chromatin remodeling factor expression and abundance will be valuable to definitively separate direct and indirect pioneer activity.

      Limitation 2:

      The characterization of mecom and castor is somewhat preliminary relative to the deep work in the rest of the paper. I think this could be addressed with a few experiments. The authors could validate RNA-seq findings with ISH to show that cells are lost after reduction of either TF (this would support the model figure). The authors could also try to define whether loss of either TF causes behavioral phenotypes that might be similar to SoxB1-2(RNAi); this would be a second line of evidence that the TFs are downstream of key events in the SoxB1-2

      pathway.

      Thank you for this suggestion. We agree that additional validation of the mecom and castor RNA-seq results and further phenotypic characterization would strengthen this section. We are currently conducting in situ hybridization experiments to validate transcriptional changes in mecom and castor using the same experimental framework applied to soxB1-2 downstream candidates. We anticipate completing these studies within the next three months and will incorporate the results into future work.

      Regarding behavioral phenotypes, we performed preliminary screening for robust behavioral responses, including mechanosensory responses, but did not observe overt defects. However, the lack of established, standardized behavioral assays in planarians presents a current limitation; such assays need to be developed de novo, and predicting specific behavioral phenotypes in advance remains challenging. We fully agree that functional behavioral assays represent an important next step and are actively exploring strategies to systematically develop and implement them going forward.

      Other questions or comments for the authors:

      Is it known how other Sox factors work as pioneer TFs? Are key binding partners known? I wondered if it would be possible to show that SoxB1-2 is co-expressed with the genes that encode these partners and/or if RNAi of these factors would phenocopy SoxB1-2. This is likely beyond the scope of this paper, but if the authors wanted to further support their argument about SoxB1-2 acting as a pioneer in planarians, this might be an additional way to do it.

      In other systems, Sox pioneer factors often act together with POU family transcription factors (for example, Oct4 and Brn2) and PAX family members such as Pax6. In planarians, a POU homolog (pou-p1) is expressed in neoblasts and may represent an interesting candidate co-factor for future investigation in the context of SoxB1-2 pioneer activity. We have also previously examined the relationship between SoxB1-2 and the POU family transcription factors pou4-1 and pou4-2. Although RNAi of these factors does not fully phenocopy soxB1-2 knockdown, pou4-2(RNAi) results in loss of mechanosensation, suggesting that downstream POU factors may contribute to aspects of neural function regulated by SoxB1-2 (McCubbin et al. eLife 2025). We agree that co-expression and functional interaction studies with these candidates would be highly informative, and we view this as an exciting future direction beyond the scope of the current manuscript.

      This paper is one of few to use ATAC-Seq in planarians. First, I think the authors should make a bigger deal of their generation of a dataset with this tool! Second, it would be great to know whether the ATAC-Seq data (controls and/or RNAi) will be browsable in any planarian databases or in a new website for other scientists. I believe that in addition to the data being used to test hypotheses about planarians, the data could also be a huge hypothesis generating resource in the planarian community, so I would encourage the authors to both self-promote their contribution and make plans to share it as widely and usably as possible.

      Thank you very much for this encouraging feedback. We appreciate the suggestion and have strengthened the text to emphasize the significance of generating this ATAC-seq resource for the planarian field. We agree that these datasets represent a valuable community resource and are committed to making all control and soxB1-2(RNAi) ATAC-seq data publicly accessible.

      Reviewer #2 (Significance (Required)):

      This paper's strengths are that it addresses an important problem in regenerative biology in a rigorous manner. The writing and presentation of the data are excellent. The paper also provides excellent datasets that will be very useful to other researchers in the fi eld. Finally, the work is one of, if not the first to examine how the action of one transcription factor in planarians leads to changes in the cellular and chromatin environment that could then be acted upon by subsequent factors. This is an important contribution to the planarian fi eld, but also one that will be useful for other developmental neuroscientists and regenerative biologists.

      I described a couple of limitations in the review above, but the strengths outweigh the weaknesses.

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

      The authors investigated the role of soxB1-2 in planarian neural and epidermal lineage specification. Using ATAC-seq and RNA-seq from head fragments after soxB1-2 RNAi, they identified regions of decreased chromatin accessibility and reduced gene expression, demonstrating that soxB1-2 induces neural and sensory programs. Integration of the datasets yielded 31 overlapping candidate targets correlating ATAC-seq and RNA-seq. Downstream analyses of transcription factors that had either/or differentially accessible regulatory region or showed differential expression (castor and mecom) implicated these transcription factors in mechanosensory and ciliary modules. The authors combined additional techniques, such as in situ hybridization to support the observations based on the ATACseq/RNAseq data. The manuscript is clearly written as well as data presentation in the main and supplementary figures. The major claim of the manuscript is that SoxB1-2 is likely a pioneer transcription factor that alters the accessibility of the chromatin, which if true, would be one of the first demonstrations of direct transcriptional regulation in planarians. As described below, I am not certain that this interpretation of the data is more valid than alternative interpretations.

      Major comments

      1. Direct vs. indirect regulation. The current analysis does not distinguish between direct and indirect soxB1-2 targets, therefore, this analysis cannot indicate whether soxB1-2 functions as a pioneer transcription. ATAC-seq and RNA-seq, as performed here, do not determine whether reduced accessibility or downregulation of gene expression represents a change within existing cells or a reduction in the proportion of specific cell types in the libraries produced. This limitation should be explicitly recognized where causal statements are made. In fact, several pieces of information strongly suggest that indirect effects are abundant in the data: (1) the observed loss of accessibility and gene expression in late epidermal progenitors likely represent indirect effects, indicating that within the timeframe of the experiment, it is impossible (using these techniques) to distinguish between the scenarios. (2) The finding that castor knockdown reduces soxB1-2 expression likely reflects population loss rather than direct regulation, given overlapping expression domains. This further illustrates the difficulty in inferring directionality from such datasets. In order to provide evidence for a more direct association between soxB1-2 and the differentially accessible chromatin regions, a sequence(e.g., motif) analysis would be required. Other approaches to infer direct regulation would have been useful, but they are not available in planarians to the best of my knowledge.

      We agree that distinguishing between direct SoxB1-2 pioneer activity and indirect chromatin changes mediated by downstream factors is an important consideration. As suggested, examining the enrichment of SoxB1-2 binding motifs in regions that lose accessibility following soxB1-2(RNAi) can provide supporting evidence for direct regulation.

      While we did not conduct a direct abundance analysis of all potential chromatin-remodeling cofactors, we performed a motif enrichment analysis following the methodology of Neiro et al. (eLife, 2022), comparing control-specific and soxB1-2(RNAi)-specific accessible peak sets. Consistent with a direct role for SoxB1-2 in chromatin regulation, Sox-family motifs, particularly SoxB1-like motifs, were among the most significantly enriched in regions that maintain accessibility in control animals relative to soxB1-2(RNAi) animals.

      Evidence for pioneer activity. The authors correctly acknowledge that they do not present direct evidence of soxB1-2 binding or chromatin opening. However, the section title in the Discussion could be interpreted as implying otherwise. The claim of pioneer activity should remain explicitly tentative until supported (at least) by motif or binding data.

      We have performed suggested motif analysis and changed the language in this section to better fit the data.

      Replication and dataset comparability. Both ATAC-seq and soxB1-2 RNA-seq were performed on head fragments, but the number of replicates differ between assays (ATAC-seq n=2 per group, RNA-seq n=4-6). This is of course acceptable, but when interpreting the results, it should be taken into consideration that the statistical power is different when using data collected using different techniques and having a varied number of replicates.

      Thank you for raising this important point regarding replication and comparability across datasets. We agree that the differing number of biological replicates between the ATAC-seq and RNA-seq experiments results in different statistical power across assays. We have now clarified this consideration in the manuscript text.

      Minor comments

      "Thousands of accessible chromatin sites". Please state the number of peaks and the thresholds for calling them. Ensure consistency between text (264 DA peaks) and Figure 1 legend (269 DA peaks).

      __We have clarified specific peak numbers and will include the calling parameters in the methods section. Additionally, we will fix the discrepancies between differential peaks. __

      Specify the y-axis normalization units in all coverage plots.

      We have specified this across plots.

      Clarify replicate numbers consistently in the text and figure legends.

      We have identified and corrected discrepancies in the figure legends vs text and correct them and ensured they are included consistently across datasets.

      Referees cross commenting

      The reviews are highly consistent. They recognize the value of the work, and raise similar points. The main shared view is that the current data do not distinguish direct from indirect effects, and claims about pioneer activity should be softened, and further analysis of the differentially accessible peaks could strengthen the link between SoxB1-2 and the chromatin changes.

      -I don't think that it's necessary to further characterize experimentally mecom or castor (as suggested), but of course that it could have value.

      We thank all three reviewers for their positive assessment of the value of our work aiming to elucidate mechanisms by which SoxB1-2 programs planarian stem cells. In the revision, we have improved the presentation and carefully edited conclusions about the function of SoxB1-2. Performing motif analysis and GO annotation of upregulated genes has strengthened our observation that SoxB1-2 acts as an activator and has revealed putative binding sites.

      The preliminary revision does not yet include further characterization of mecom and castor downstream genes. In response to Reviewer #2, we appreciate that additional validation of the mecom and castor RNA-seq results and further phenotypic characterization would strengthen this section. Although we are currently conducting in situ hybridization experiments to validate transcriptional changes in mecom and castor using the same experimental framework applied to soxB1-2 downstream candidates, we also reconsidered, as we did in our first revision, whether this is necessary or better suited for future investigations.

      In the revision, we noted that our Discussion points were not balanced and that we emphasized the mecom and castor results in a manner that distracted from the major focus of the work, likely contributing to the impression that additional experimental evidence was required. Therefore, we have revised the section accordingly and streamlined the Discussion to avoid repetitive statements and to focus on the insights gained into the mechanism of SoxB1-2 function in planarian neurogenesis. We remain open to including these additional experiments if the reviewers or handling editors consider them essential; however, we agree that their inclusion is not absolutely necessary.

      Reviewer #3 (Significance (Required)):

      General assessment. The study offers valuable observations by combining chromatin and transcriptional analysis of planarian neural differentiation. The integration with in situ validation convincingly demonstrates effects on neural tissues and provides a solid resource for future functional work. However, mechanistic interpretation remains limited, partly because of technical limitations of the system. The data support an important role for soxB1-2 in neural and epidermal lineage regulation, but not direct binding or chromatin-opening activity. The authors have previously published analysis of soxB1-2 in planarians, so the addition of ATAC-seq data contributes to solving another piece of the puzzle.

      __Advance. __

      This is one of the first studies to couple ATAC-seq and RNA-seq in planarian tissue to dissect regulatory logic during regeneration. It identifies new candidate regulators of sensory and epidermal differentiation and identifies soxB1-2 as a likely upstream factor in ectodermal lineage networks. The work extends previous studies on soxB1-2 activity and neural cell production by integrating chromatin and transcriptional layers. In that respect the results are very solid, although the study remains correlative at the mechanistic level.

      Audience.

      This work will potentially interest researchers interested in regeneration and transcriptional networks. The datasets and gene lists will be valuable references for follow-up studies on planarian ectodermal lineages, and therefore will appeal to this community.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The authors investigated the role of soxB1-2 in planarian neural and epidermal lineage specification. Using ATAC-seq and RNA-seq from head fragments after soxB1-2 RNAi, they identified regions of decreased chromatin accessibility and reduced gene expression, demonstrating that soxB1-2 induces neural and sensory programs. Integration of the datasets yielded 31 overlapping candidate targets correlating ATAC-seq and RNA-seq. Downstream analyses of transcription factors that had either/or differentially accessible regulatory region or showed differential expression (castor and mecom) implicated these transcription factors in mechanosensory and ciliary modules. The authors combined additional techniques, such as in situ hybridization to support the observations based on the ATACseq/RNAseq data. The manuscript is clearly written as well as data presentation in the main and supplementary figures. The major claim of the manuscript is that SoxB1-2 is likely a pioneer transcription factor that alters the accessibility of the chromatin, which if true, would be one of the first demonstrations of direct transcriptional regulation in planarians. As described below, I am not certain that this interpretation of the data is more valid than alternative interpretations.

      Major comments

      1. Direct vs. indirect regulation. The current analysis does not distinguish between direct and indirect soxB1-2 targets, therefore, this analysis cannot indicate whether soxB1-2 functions as a pioneer transcription. ATAC-seq and RNA-seq, as performed here, do not determine whether reduced accessibility or downregulation of gene expression represents a change within existing cells or a reduction in the proportion of specific cell types in the libraries produced. This limitation should be explicitly recognized where causal statements are made. In fact, several pieces of information strongly suggest that indirect effects are abundant in the data: (1) the observed loss of accessibility and gene expression in late epidermal progenitors likely represent indirect effects, indicating that within the timeframe of the experiment, it is impossible (using these techniques) to distinguish between the scenarios. (2) The finding that castor knockdown reduces soxB1-2 expression likely reflects population loss rather than direct regulation, given overlapping expression domains. This further illustrates the difficulty in inferring directionality from such datasets. In order to provide evidence for a more direct association between soxB1-2 and the differentially accessible chromatin regions, a sequence (e.g., motif) analysis would be required. Other approaches to infer direct regulation would have been useful, but they are not available in planarians to the best of my knowledge.
      2. Evidence for pioneer activity. The authors correctly acknowledge that they do not present direct evidence of soxB1-2 binding or chromatin opening. However, the section title in the Discussion could be interpreted as implying otherwise. The claim of pioneer activity should remain explicitly tentative until supported (at least) by motif or binding data.
      3. Replication and dataset comparability. Both ATAC-seq and soxB1-2 RNA-seq were performed on head fragments, but the number of replicates differ between assays (ATAC-seq n=2 per group, RNA-seq n=4-6). This is of course acceptable, but when interpreting the results, it should be taken into consideration that the statistical power is different when using data collected using different techniques and having a varied number of replicates.

      Minor comments

      "Thousands of accessible chromatin sites". Please state the number of peaks and the thresholds for calling them. Ensure consistency between text (264 DA peaks) and Figure 1 legend (269 DA peaks). Specify the y-axis normalization units in all coverage plots. Clarify replicate numbers consistently in the text and figure legends.

      Referees cross commenting

      The reviews are highly consistent. They recognize the value of the work, and raise similar points. The main shared view is that the current data do not distinguish direct from indirect effects, and claims about pioneer activity should be softened, and further analysis of the differentially accessible peaks could strengthen the link between SoxB1-2 and the chromatin changes.

      • I don't think that it's necessary to further characterize experimentally mecom or castor (as suggested), but of course that it could have value.

      Significance

      General assessment. The study offers valuable observations by combining chromatin and transcriptional analysis of planarian neural differentiation. The integration with in situ validation convincingly demonstrates effects on neural tissues and provides a solid resource for future functional work. However, mechanistic interpretation remains limited, partly because of technical limitations of the system. The data support an important role for soxB1-2 in neural and epidermal lineage regulation, but not direct binding or chromatin-opening activity. The authors have previously published analysis of soxB1-2 in planarians, so the addition of ATAC-seq data contributes to solving another piece of the puzzle.

      Advance. This is one of the first studies to couple ATAC-seq and RNA-seq in planarian tissue to dissect regulatory logic during regeneration. It identifies new candidate regulators of sensory and epidermal differentiation and identifies soxB1-2 as a likely upstream factor in ectodermal lineage networks. The work extends previous studies on soxB1-2 activity and neural cell production by integrating chromatin and transcriptional layers. In that respect the results are very solid, although the study remains correlative at the mechanistic level.

      Audience. This work will potentially interest researchers interested in regeneration and transcriptional networks. The datasets and gene lists will be valuable references for follow-up studies on planarian ectodermal lineages, and therefore will appeal to this community.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Review - Cathell, et al (RC-2025-03195)

      Summary and Significance:

      Understanding regenerative neurogenesis has been difficult due to the limited amount of neurogenesis that occurs after injury in most animal species. Planarians, with their adult neurogenesis and robust post-injury response, allow us to get a glimpse into regenerative neurogenesis. The Zayas laboratory previously revealed a key role for SoxB1-2 in maintenance and regeneration of a broad set of sensory and peripheral neurons in the planarian body. SoxB1-2 also has a role in many epidermal fates. Their previous work left open the tempting possibility that SoxB1-2 acts as a very upstream regulator of epidermal and neuronal fates, potentially acting as a pioneer transcription factor within these lineages. In the manuscript currently under review, Cathell and colleagues use ATAC-Seq and RNA-Seq to investigate chromatin changes after SoxB1-2(RNAi). With the experimental limitations in planarians, this is a strong first step toward testing their hypothesis that SoxB1-2 acts as a pioneer within a set of planarian lineages. Beyond these cell types, this work is also important because planarian cell fates often rely on a suite of transcription factors, but the nature of transcription factor cooperation has been much less well understood. Indeed, the authors do show that loss of SoxB1-2 by RNAi causes changes in a number of accessible regions of the genome; many of these chromatin changes correspond to changes in gene expression of genes nearby these peaks. The authors also examine in more detail two genes that have genomic and transcriptomic changes after SoxB1-2(RNAi), mecom and castor. The authors completed RNA-Seq on mecom(RNAi) and castor(RNAi) animals, identifying genes downregulated after loss of either factor that are also seen in SoxB1-2(RNAi). The results in this paper are rigorous and very well presented. I will share two major limitations of the study and some suggestions for addressing them, but this work may also be acceptable without those changes at some journals.

      Limitation 1:

      The paper aims to test the hypothesis that SoxB1-2 is a pioneer transcription factor. Observation that SoxB1-2(RNAi) leads to loss of many accessible regions in the chromatin supports the hypothesis. However, an alternate possibility is that SoxB1-2 leads to transcription of another factor that is a pioneer factor or a chromatin remodeling enzyme; in either of these cases, the accessibility peak changes may not be due to SoxB1-2 directly but due to another protein that SoxB1-2 promotes. The authors describe how they can address this limitation in the future; in the meantime, is it known what the likely binding for SoxB1-2 would be (experimentally or based on homology)? If so, could the authors examine the relative abundance of SoxB1-2 binding sites in peaks that change after SoxB1-2(RNAi)? This could be compared to the abundance of the same binding sequence in non-changing peaks. Enrichment of SoxB1-2 binding sites in ATAC peaks that change after its RNAi would support the argument that chromatin changes are directly due to SoxB1-2.

      Limitation 2:

      The characterization of mecom and castor is somewhat preliminary relative to the deep work in the rest of the paper. I think this could be addressed with a few experiments. The authors could validate RNA-seq findings with ISH to show that cells are lost after reduction of either TF (this would support the model figure). The authors could also try to define whether loss of either TF causes behavioral phenotypes that might be similar to SoxB1-2(RNAi); this would be a second line of evidence that the TFs are downstream of key events in the SoxB1-2 pathway.

      Other questions or comments for the authors:

      Is it known how other Sox factors work as pioneer TFs? Are key binding partners known? I wondered if it would be possible to show that SoxB1-2 is co-expressed with the genes that encode these partners and/or if RNAi of these factors would phenocopy SoxB1-2. This is likely beyond the scope of this paper, but if the authors wanted to further support their argument about SoxB1-2 acting as a pioneer in planarians, this might be an additional way to do it. This paper is one of few to use ATAC-Seq in planarians. First, I think the authors should make a bigger deal of their generation of a dataset with this tool! Second, it would be great to know whether the ATAC-Seq data (controls and/or RNAi) will be browsable in any planarian databases or in a new website for other scientists. I believe that in addition to the data being used to test hypotheses about planarians, the data could also be a huge hypothesis generating resource in the planarian community, so I would encourage the authors to both self-promote their contribution and make plans to share it as widely and usably as possible.

      Significance

      This paper's strengths are that it addresses an important problem in regenerative biology in a rigorous manner. The writing and presentation of the data are excellent. The paper also provides excellent datasets that will be very useful to other researchers in the field. Finally, the work is one of, if not the first to examine how the action of one transcription factor in planarians leads to changes in the cellular and chromatin environment that could then be acted upon by subsequent factors. This is an important contribution to the planarian field, but also one that will be useful for other developmental neuroscientists and regenerative biologists.

      I described a couple of limitations in the review above, but the strengths outweigh the weaknesses.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The authors of this interesting study take the approach of combing RNAi, RNA-seq and ATAC-seq to try to build a regulatory network surrounding the function of a planarian SoxB1 ortholog, broadly required for neural specification during planarian regeneration. They find a number of chromatin regions that differentially accessible (measured by ATAC-seq), associate these with potential genes by promity to the TSS. They then compare this set of genes with those that are differentially regulated (using RNA-seq), after SoxB1 RNAi mediated knockdown. This allows them the authors some focus on potential directly regulated targets of the planarian SoxB1. Two of these downstream targets, the mecom and castor transcription factors are then studied in greater detail.

      Major Comments.

      I have no suggestions for new experiments that fit sensibly with the scope of the current work. There are other analyses that could be appropriate with the ATAC-seq data, but may not make sense in the content of SoxB1 acting as pioneer factor.

      I would like to see motif enrichment analysis under the set of peaks to see if SoxB1 is opening chromatin for a restricted set of other transcription factors to then bind. Much of this could be taken from Neiro et al, eLife 2022 (which also used ATAC-seq) and matched planarians TF families to likely binding motifs. This could add some breadth to the regulatory network. It could be revealing for example if downstream TF also help regulate other targets that SoxB1 makes available, this is pattern often seen for cell specification (as I am sure the authors are aware). Alternatively, it may reveal other candidate regulators. Overall peak calling consistency with ATAC-sample would be useful to report as well, to give readers an idea of noise in the data. What was the correlation between samples? While it is logical to focus on downregulated genes, it would also be interesting to look at upregulated genes in some detail. In simple terms would we expect to see the representation of an alternate set of fate decisions being made by neoblast progeny? Can the authors be explicit about whether they have evidence for co-expression of SoxB1/castor and SoxB1/mecom? I could find this clearly and it would be important to be clear whether this basic piece of evidence is in place or not at this stage.

      Summary

      The authors of this interesting study take the approach of combing RNAi, RNA-seq and ATAC-seq to try to build a regulatory network surrounding the function of a planarian SoxB1 ortholog, broadly required for neural specification during planarian regeneration. They find a number of chromatin regions that differentially accessible (measured by ATAC-seq), associate these with potential genes by promity to the TSS. They then compare this set of genes with those that are differentially regulated (using RNA-seq), after SoxB1 RNAi mediated knockdown. This allows them the authors some focus on potential directly regulated targets of the planarian SoxB1. Two of these downstream targets, the mecom and castor transcription factors are then studied in greater detail.

      Major Comments.

      N suggestions for new experiments that fit sensibly with the scope of the current work. There are other analyses that could be appropriate with the ATAC-seq data but may not make sense in the content of SoxB1 acting as pioneer factor. Overall, the study is executed very well, methods are sound, data and analysis well-presented and narrated, and the results placed in context. The experiments are clearly reproducible and can be built on, all data is accessible to others. Motif enrichment analysis under the set of peaks to see if SoxB1 is opening chromatin for a restricted set of other transcription factors to then bind. Much of this could be taken from Neiro et al, eLife 2022 (which also used ATAC-seq) and matched planarians TF families to likely binding motifs. This could add some breadth to the regulatory network. It could be revealing for example if downstream TF also help regulate other targets that SoxB1 makes available, this is pattern often seen for cell specification (as I am sure the authors are aware). Alternatively, it may reveal other candidate regulators. Overall peak calling consistency with ATAC-sample would be useful to report as well, to give readers an idea of noise in the data. What was the correlation between samples? While it is logical to focus on downregulated genes, it would also be interesting to look at upregulated genes in some detail. In simple terms would we expect to see the representation of an alternate set of fate decisions being made by neoblast progeny? Can the authors be explicit about whether they have evidence for co-expression of SoxB1/castor and SoxB1/mecom? I could find this clearly and it would be important to be clear whether this basic piece of evidence is in place, or not, at this stage.

      Minor comments.

      Formally loss of castor and mecom expression does mean these cells are absent, strictly the cell absence needs an independent method. It might be useful to clarify this with the evidence of be clear that cells are "very probably" not produced.

      Significance

      Strengths and limitations.

      The precise exploitation of the planarian system to identify potential targets, and therefore regulatory mechanisms, mediated by SoxB1 is an interesting contribution to the field. We know almost nothing about the regulatory mechanisms that allow regeneration and how these might have evolved, and this work is well-executed step in that direction.

      Advance

      The paper makes a clear advance in our understanding of an important process in animals (neural specification) and how this happens in the context in the context during an example of animal regeneration. The methods are state-of-the-art with respect to what is possible in the planarian system.

      Audience

      This will be of wide interest to developmental biologists, particularly those studying regeneration in planarians and other regenerative systems, and those who study comparative neurodevelopment.

      Expertise

      I have expertise in functional genomics in the context of stem cells and regeneration, particularly in the planarian model system

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

      Learn more at Review Commons


      Reply to the reviewers

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

      In this manuscript, the authors employed fast MAS NMR spectroscopy to investigate the gel aggregation of longer repeat (48×) RNAs, revealing inherent folding structures and interactions (i.e., G-quadruplex and duplex). The dynamic structure of the RNA gel was not resolved at high resolution, and only the structural features-namely, the coexistence of G-quadruplexes and duplexes-were inferred. The 1D and 2D NMR spectra were not assigned to specific atomic positions within the RNA, which makes it difficult to perform molecular dynamics (MD) modeling to elucidate the dynamic nature of the RNA gel. The following comments are provided for the authors' consideration:

      Reviewer #1, Comment 1:

      Figure 2E and Figure 3A: The data suggest that Ca²⁺ promotes stronger G-quadruplex formation within the RNA gel compared with Mg²⁺. This observation is somewhat puzzling, as Mg²⁺ is generally known to stabilize G-quadruplex structures. The authors should clarify this discrepancy.

      __Response: __Mg2+ is also a stabilizer of double-stranded RNA. In most cases, Mg²⁺ stabilizes RNA duplexes more significantly than it stabilizes G-quadruplexes. When Mg2+ is removed and replaced for Ca2+, RNA duplex is destabilized more than G4 structures. We have added a clarification regarding that to the Conclusions section.

      Reviewer #1, Comment 2:

      Figures 2 and 3: The authors use the chemical shift at δN 144.1 ppm to distinguish between G-quadruplex and duplex structures. How was the reliability of this assignment evaluated? Chemical shifts of RNA atoms can be influenced by various factors such as intermolecular interactions, conformational stress, and local chemical environment, not only by higher-order structures. This point should be substantiated by citing relevant references or by analyzing additional RNA structures exhibiting δN 144.1 ppm signals using NMR spectroscopy.

      Response: The assignment was made by comparing the chemical shifts with published data and by comparing the obtained spectra with existing datasets in the lab. We have added an explanation to the Results section and cited the literature. The 144.1 ppm was an illustrative value selected for guiding the discussion and we noted that it could sound too specific. We modified Figure 2 to outline the regions of chemical shifts in accordance with our interpretation of spectra.

      Reviewer #1, Comment 3:

      The authors state that "Our findings demonstrate that fast MAS NMR spectroscopy enables atomic-resolution monitoring of structural changes in GGGGCC repeat RNA of physiological lengths." This claim appears overstated, as no molecular model was constructed to define atomic coordinates based on NMR restraints.

      Response: We agree and we have rewritten the conclusions to be more precise in wording. The new text does not mention “atomic-resolution” anymore.

      Reviewer #1, Comment 4: Figure 3B: The experiment using nuclear extracts supplemented with Mg²⁺ to study RNA aggregation via 2D NMR may not accurately reflect intracellular conditions. It would be informative to perform a parallel experiment using nuclear extracts without additional Mg²⁺ to better simulate the native environment for RNA folding.

      __Response: __We agree that we have not yet approached physiological conditions and that it would be interesting to obtain data for conditions at physiological Mg2+ concentrations in the range between 0.5 mM – 1 mM. The buffer of purchased nuclear extracts does not contain MgCl2, so some MgCl2 would still need to be added. In our opinion, nuclear extracts are actually not the optimal way to move forward, since they still differ from real in cell environment with the caveat that their composition is not well controlled. Full reconstitution with recombinant proteins might be a better approach because stoichiometry can be better regulated.

      __Reviewer #1 (Significance (Required)): __ In this manuscript, the authors employed fast MAS NMR spectroscopy to investigate the gel aggregation of longer repeat (48×) RNAs, revealing inherent folding structures and interactions (i.e., G-quadruplex and duplex). The dynamic structure of the RNA gel was not resolved at high resolution, and only the structural features-namely, the coexistence of G-quadruplexes and duplexes-were inferred. The 1D and 2D NMR spectra were not assigned to specific atomic positions within the RNA, which makes it difficult to perform molecular dynamics (MD) modeling to elucidate the dynamic nature of the RNA gel.

      Response: We agree that constraints for molecular dynamics cannot be derived from these data. The focus of this work is methodological: to demonstrate how 1H-15N 2D correlation spectra can be used to characterize G-G pairing in RNA gels directly. Such spectra could be used to study effects of small molecules or interacting proteins for example.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ The manuscript by Kragelj et al. has the potential to become a valuable study demonstrating the role and power of modern solid-state NMR spectroscopy in investigating molecular assemblies that are otherwise inaccessible to other structural biology techniques. However, due to poor experimental execution and incomplete data interpretation, the manuscript requires substantial revision before it can be considered for publication in any journal.

      __Reviewer #2, Major Concern __Inspection of the analytical gels of the transcribed RNA clearly shows that the desired RNA product constitutes only about 10% of the total crude transcript. The RNA must therefore be purified, for example by preparative PAGE, before performing any NMR or other biophysical studies. As it stands, all spectra shown in the figures represent a combined signal of all products in the crude mixture rather than the intended 48 repeat RNA. Consequently, all analyses and conclusions currently refer to a heterogeneous mixture of transcripts rather than the specific target RNA.

      Response: The estimate of 10% 48xG4C2 on the gel is an overstatement. While multiple bands are visible, they correspond to dimers or multimers of the 48xG4C2 RNA. Transcripts that are longer than 48xG4C2 cannot occur in our transcription conditions. Bands at lower masses than expected are folded RNA. The high repeat length and the presence of Mg²⁺ during transcription promote multimerization, which is not fully reversed by denaturation in urea. If shorter transcripts had arisen from early termination they would be still substantially longer than 24 repeats based of what is visible on the gel and would thus remain within the pathological length range. Therefore, the observed NMR spectra primarily report on 48 repeat lengths.

      __Reviewer #2, Specific Comments 1: __The statements: "We show that a technique called NMR spectroscopy under fast Magic Angle Spinning (fast MAS NMR) can be used to obtain structural information on GGGGCC repeat RNAs of physiological lengths. Fast MAS NMR can be used to obtain structural information on biomolecules regardless of their size." on page 1 are not entirely correct. Firstly, not only fast MAS NMR but MAS NMR in general can provide structural information on biomolecules regardless of their size. Fast MAS primarily allows for ¹H-detected experiments, improves spectral resolution, and reduces the required sample amount. Conventional ¹³C-detected solid-state MAS NMR can provide very similar structural information. A more thorough review of relevant literature could help address this issue.

      Response: We have clarified the distinction between MAS NMR and Fast MAS NMR in the introduction.

      __Reviewer #2, Specific Comments 2: __Secondly, MAS NMR has already been applied to systems of comparable complexity - for instance, the (CUG)₉₇ repeat studied by the Goerlach group as early as 2005. That work provided a comprehensive structural characterization of a similar molecular assembly. The authors are strongly encouraged to cite these studies (e.g., Riedel et al., J. Biomol. NMR, 2005; Riedel et al., Angew. Chem., 2006).

      Response: We added a mention of that study in the introduction.

      Reviewer #2, Experimental Description 1: The experimental details are poorly documented and need to be described in sufficient detail for reproducibility. Specifically: 1. What was the transcription scale? What was the yield (e.g., xx mg RNA per 1 mL transcription reaction)?

      Response: Between 3.5 mg and 4.5 mg per 10 ml transcription reaction. We’ve added this information to the methods.

      Reviewer #2, Experimental Description 2: 2. Why was the transcription product not purified? Dialysis only removes small molecules, while all macromolecular impurities above the cutoff remain. What was the dialysis cutoff used?

      Response: RNA was purified using dialysis and phenol-chloroform precipitation. We have added the information about molecular weight cutoff for dialysis membranes to the methods.

      Reviewer #2, Experimental Description 3: 3. How much RNA was used for each precipitation experiment? Were the amounts normalized? For example, if 10 mg of pellet were obtained, what fraction of that mass corresponded to RNA? Was this ratio consistent across all samples?

      Response: In the test gel formations, we used 180.0 µg per condition. We used 108.0 µg of RNA for gelation test in the presence of nuclear extracts. We have not determined the water content in the gels. We added this information to methods and results section.

      Reviewer #2, Experimental Description 4: 4. Why is there a smaller amount of precipitate when nuclear extract (NE) or CaCl₂ is added?

      Response: The apparent difference in pellet size may reflect variations in water content rather than RNA quantity. While the Figure 1 might entice to directly compare pellet weights across different ion series tests, our primary goal was to determine the minimal divalent-ion concentrations required to reproducibly obtain gels. We have added a clarification in the Results section and in the Figure 1 caption regarding the comparability of conditions

      Reviewer #2, Experimental Description 5: 5. The authors should describe NE addition in more detail: What is the composition of NE? What buffer was used (particularly Mg²⁺ and salt concentrations)? Was a control performed with NE buffer-type alone (without NE)?

      Response: We have added the full description of NE buffer to the methods section. Its composition is: 40 mM Tris pH 8.0, 100 mM KCl, 0.2 mM EDTA, 0.5 mM PMSF, 0.5 mM DTT, 25 % glycerol. After mixing the nuclear extract with RNA, the target buffer was: 20 mM Tris pH 8.0, 90 mM KCl, 0.1 mM EDTA, 0.25 mM PMSF, 0.75 mM DTT, 12.5% glycerol, and 10 mM MgCl2.

      We have not performed a control with NE buffer-type alone but we confirmed separately that glycerol does not affect gel formation.

      Reviewer #2, Experimental Description 6: 6. How much pellet/RNA material was actually packed into each MAS rotor?

      Response: Starting with a 5 mg pellet, we packed a rotor with a volume of 3 µl. We added this information to the methods section.

      Reviewer #2, Additional Clarifications: P5. What is meant by "selective" in the phrase "We recorded a selective 1D-¹H MAS NMR spectrum of 48×G₄C₂ RNA gels"?

      Response: That was a typo. We meant imino-selective. It is now corrected.

      __Reviewer #2, Additional Clarifications: __ There are also several contradictions between statements in the text and the corresponding figures. For example: • Page 4: The authors write that "The addition of at least 5 mM Mg²⁺ was required for significant 48×G₄C₂ aggregation." However, Figure 1E shows significant aggregation already at 3 mM MgCl₂ (NE−), and in samples containing NE, aggregation appears even at 1 mM MgCl₂. Was aggregation already present in the sample containing NE but without any added MgCl₂?

      Response: We changed text in the results section to more closely align with what’s depicted on the figure. There was some aggregation present in the nuclear extracts but it was of different quantity and quality. We clarified this in the results section.

      __Reviewer #2 (Significance (Required)): __ The manuscript by Kragelj et al. has the potential to become a valuable study demonstrating the role and power of modern solid-state NMR spectroscopy in investigating molecular assemblies that are otherwise inaccessible to other structural biology techniques.

      In its current form, tthe manuscript has significant experimental concerns - particularly the lack of RNA purification and inadequate description of materials and methods. The data therefore cannot support the conclusions presented. I recommend extensive revision and repetition of the experiments using purified RNA material before further consideration for publication.

      __Response: __We’ve addressed the concerns about RNA purification within the response to the first comment (Major concern).

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ This is an interesting manuscript reporting evidence for formation of both hairpins and G-quadruplexes within RNA aggregates formed by ALS expansion repeats (GGGGCC)n. This is in line with literature but never directly confirmed. Given the novelty of the method (NMR magic angle) and of the data (NMR on aggregate), I believe this manuscript should be considered for publication. I also trust the methods are appropriately reported and reproducible.

      Below are my main points:

      Major points:

      __Reviewer #3, Comment 1: __ 1) RNA aggregation of the GGGGCCn repeat has been reported for expansion as short as 6-8 repeats (see Raguseo et al. Nat Commun 2023), so the authors might not see aggregation under the conditions they use for these shorter repeats but this can happen under physiological conditions . The ionic strengths and the conditions used can vary heavily the phase diagram and the authors therefore should tone down significantly their conclusions. They characterise one aggregate that is likely to contain both secondary structures under the conditions used (in terms of ion and pHs). However, it has been shown in Raguseo et al that aggregates can arise by both intermolecular G4s and hairpins (or a mixture of them) depending on the ionic conditions used. This means that what the authors report might not be necessarily relevant in cells, which should be caveated in the manuscript.

      __Response: __We toned down our statements regarding aggregation of shorter repeats in the introduction. We added the citation to Raguseo et al. Nat Commun 2023, which indeed provides useful insights about aggregation of GGGGCC repeats. In Supplementary Figure 1, we had data on gel formation with 8x and 24x repeats which showed these repeat lengths form gels to some extent. We oversimplified our conclusion and said there were no aggregates which needs correction, especially considering other studies reported in the literature have observed in vitro aggregation of these repeat lengths. We modified the results section to reflect this nuance.

      __Reviewer #3, Comment 2: __ 2) It would be important to perform perturbation experiments that might promote/disrupt formation of the G4 or hairpin and see if this affect RNA aggregation, which has been already reported by Raguseo et al, and wether this can be appreciated spectroscopically in their assay. This can be done by taking advantage of some of the experiments reported in the manuscript mentioned above, such as: PDS treatment (favouring monomolecular G4s and preventing aggregation), Li vs K treatment (favouring hairpin over G4s), NMM photo-oxidation (disassembling G4s) or addition of ALS relevant RNA binding proteins (i.e. TDP-43). Not all of these controls need to be performed but it would be good to reconcile how the fraction of G4 vs hairpin reflect aggregates' properties, since the authors offer such a nice technique to measure this.

      Response: We appreciate the reviewer’s suggestions and we would be eager to do the perturbation experiments in the future. However, these experiments would require additional optimization and waiting for approval and availability of measurement time on a high-field NMR spectrometer. Given that the primary goal of this manuscript is reporting on the methodological approach, we think the current data adequately demonstrate the technique’s utility.

      __Reviewer #3, Comment 3: __ 3) I disagree with the speculation of the monomolecular G4 being formed within the condensates, as the authors have no evidence to support this. It has been shown that n=8 repeat forms multimolecular G4s that are responsible of aggregation, so the authors need to provide direct evidence to support this hypothesis if they want to keep it in the manuscript, as it would clash with previous reports (Raguseo et al Nat Commun 2023)

      Response: We agree that multimolecular G4s contribute to aggregation in our 48xG4C2 gels. We also realized, after reading this comment, that the original presentation of data and schematics may have unintentionally suggested the presence of monomolecular G4 in our RNA gels. To address this, we have added a clarification to the results section, we modified Figure 2 and 3, and we included a new Supplementary Figure 4. For clarification, both multimolecular and monomolecular G4s in model oligonucleotides produce imino 1H and 15N chemical shifts in the same region and cannot be distinguished by the experiments used in our study. Based on the observations reported in the literature, we believe that G4s in 48xG4C2 form primarily intermolecularly, although direct experimental proof is not available with the present data.

      Minor points:

      __Reviewer #3, Comment 4: __ 4) An obvious omission in the literature is Raguseo et al Nat Commun 2023, extensively mentioned above. Given the relevance of the findings reported in this manuscript for this study, this should be appropriately referenced for clarity.

      Response: We’ve added the citation to Raguseo et al Nat Commun 2023 to the introduction where in vitro aggregation is discussed.

      __Reviewer #3, Comment 5: __ 5) The schematic in Figure 3 is somehow confusing and the structures reported and how they relate to aggregate formation is not clear. Given that in structural studies presentation and appearance is everything, I would strongly recommend to the authors to improve the clarity of the schematic for the benefit of the readers.

      Response: We thank you for your comment. We’ve modified the figure, and we hope it is now clearer.

      Providing that the authors can address the criticisms raised, I would be supportive of publication of this fine study.

      Reviewer #3 (Significance (Required)):

      The main strength of this paper is to provide direct evidence of DNA secondary structure formation within aggregates, which is something that has not been done before. This is important as it reconcile with the relevance of hairpin formation for the disease (reported by Disney and co-workers) and the relevance of G4-formation in the process of aggregation through multimolecular G4-formation (reported by Di Antonio and co-workers). Given the significance of the findings in this context and the novelty of the method applied to the study of RNA aggregation, this reviewer is supportive for publication of this manuscript and of its relevance to the field. I would be, however, more careful in the conclusions reported and would add additional controls to strengthen the conclusions.

      Response: We thank the reviewer for the comment. In the conclusion section, we have added a statement highlighting the potential roles of both double-stranded and G4 structures in gel formation, in line with what has been reported in previous studies.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This is an interesting manuscript reporting evidence for formation of both hairpins and G-quadruplexes within RNA aggregates formed by ALS expansion repeats (GGGGCC)n. This is in line with literature but never directly confirmed. Given the novelty of the method (NMR magic angle) and of the data (NMR on aggregate), I believe this manuscript should be considered for publication. I also trust the methods are appropriately reported and reproducible.

      Below are my main points:

      Major points:

      1) RNA aggregation of the GGGGCCn repeat has been reported for expansion as short as 6-8 repeats (see Raguseo et al. Nat Commun 2023), so the authors might not see aggregation under the conditions they use for these shorter repeats but this can happen under physiological conditions . The ionic strengths and the conditions used can vary heavily the phase diagram and the authors therefore should tone down significantly their conclusions. They characterise one aggregate that is likely to contain both secondary structures under the conditions used (in terms of ion and pHs). However, it has been shown in Raguseo et al that aggregates can arise by both intermolecular G4s and hairpins (or a mixture of them) depending on the ionic conditions used. This means that what the authors report might not be necessarily relevant in cells, which should be caveated in the manuscript.

      2) It would be important to perform perturbation experiments that might promote/disrupt formation of the G4 or hairpin and see if this affect RNA aggregation, which has been already reported by Raguseo et al, and wether this can be appreciated spectroscopically in their assay. This can be done by taking advantage of some of the experiments reported in the manuscript mentioned above, such as: PDS treatment (favouring monomolecular G4s and preventing aggregation), Li vs K treatment (favouring hairpin over G4s), NMM photo-oxidation (disassembling G4s) or addition of ALS relevant RNA binding proteins (i.e. TDP-43). Not all of these controls need to be performed but it would be good to reconcile how the fraction of G4 vs hairpin reflect aggregates' properties, since the authors offer such a nice technique to measure this.

      3) I disagree with the speculation of the monomolecular G4 being formed within the condensates, as the authors have no evidence to support this. It has been shown that n=8 repeat forms multimolecular G4s that are responsible of aggregation, so the authors need to provide direct evidence to support this hypothesis if they want to keep it in the manuscript, as it would clash with previous reports (Raguseo et al Nat Commun 2023)

      Minor points:

      4) An obvious omission in the literature is Raguseo et al Nat Commun 2023, extensively mentioned above. Given the relevance of the findings reported in this manuscript for this study, this should be appropriately referenced for clarity.

      5) The schematic in Figure 3 is somehow confusing and the structures reported and how they relate to aggregate formation is not clear. Given that in structural studies presentation and appearance is everything, I would strongly recommend to the authors to improve the clarity of the schematic for the benefit of the readers.

      Providing that the authors can address the criticisms raised, I would be supportive of publication of this fine study.

      Significance

      The main strength of this paper is to provide direct evidence of DNA secondary structure formation within aggregates, which is something that has not been done before. This is important as it reconcile with the relevance of hairpin formation for the disease (reported by Disney and co-workers) and the relevance of G4-formation in the process of aggregation through multimolecular G4-formation (reported by Di Antonio and co-workers). Given the significance of the findings in this context and the novelty of the method applied to the study of RNA aggregation, this reviewer is supportive for publication of this manuscript and of its relevance to the field. I would be, however, more careful in the conclusions reported and would add additional controls to strengthen the conclusions.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Kragelj et al. has the potential to become a valuable study demonstrating the role and power of modern solid-state NMR spectroscopy in investigating molecular assemblies that are otherwise inaccessible to other structural biology techniques. However, due to poor experimental execution and incomplete data interpretation, the manuscript requires substantial revision before it can be considered for publication in any journal.

      Major Concern

      Inspection of the analytical gels of the transcribed RNA clearly shows that the desired RNA product constitutes only about 10% of the total crude transcript. The RNA must therefore be purified, for example by preparative PAGE, before performing any NMR or other biophysical studies. As it stands, all spectra shown in the figures represent a combined signal of all products in the crude mixture rather than the intended 48× repeat RNA. Consequently, all analyses and conclusions currently refer to a heterogeneous mixture of transcripts rather than the specific target RNA.

      Specific Comments

      The statements: "We show that a technique called NMR spectroscopy under fast Magic Angle Spinning (fast MAS NMR) can be used to obtain structural information on GGGGCC repeat RNAs of physiological lengths. Fast MAS NMR can be used to obtain structural information on biomolecules regardless of their size." on page 1 are not entirely correct. Firstly, not only fast MAS NMR but MAS NMR in general can provide structural information on biomolecules regardless of their size. Fast MAS primarily allows for ¹H-detected experiments, improves spectral resolution, and reduces the required sample amount. Conventional ¹³C-detected solid-state MAS NMR can provide very similar structural information. A more thorough review of relevant literature could help address this issue. Secondly, MAS NMR has already been applied to systems of comparable complexity - for instance, the (CUG)₉₇ repeat studied by the Goerlach group as early as 2005. That work provided a comprehensive structural characterization of a similar molecular assembly. The authors are strongly encouraged to cite these studies (e.g., Riedel et al., J. Biomol. NMR, 2005; Riedel et al., Angew. Chem., 2006).

      Experimental Description

      The experimental details are poorly documented and need to be described in sufficient detail for reproducibility. Specifically:

      1. What was the transcription scale? What was the yield (e.g., xx mg RNA per 1 mL transcription reaction)?
      2. Why was the transcription product not purified? Dialysis only removes small molecules, while all macromolecular impurities above the cutoff remain. What was the dialysis cutoff used?
      3. How much RNA was used for each precipitation experiment? Were the amounts normalized? For example, if 10 mg of pellet were obtained, what fraction of that mass corresponded to RNA? Was this ratio consistent across all samples?
      4. Why is there a smaller amount of precipitate when nuclear extract (NE) or CaCl₂ is added?
      5. The authors should describe NE addition in more detail: What is the composition of NE? What buffer was used (particularly Mg²⁺ and salt concentrations)? Was a control performed with NE buffer-type alone (without NE)?
      6. How much pellet/RNA material was actually packed into each MAS rotor? Additional Clarifications P5. What is meant by "selective" in the phrase "We recorded a selective 1D-¹H MAS NMR spectrum of 48×G₄C₂ RNA gels"? There are also several contradictions between statements in the text and the corresponding figures. For example:

      7. Page 4: The authors write that "The addition of at least 5 mM Mg²⁺ was required for significant 48×G₄C₂ aggregation." However, Figure 1E shows significant aggregation already at 3 mM MgCl₂ (NE−), and in samples containing NE, aggregation appears even at 1 mM MgCl₂. Was aggregation already present in the sample containing NE but without any added MgCl₂?

      Significance

      The manuscript by Kragelj et al. has the potential to become a valuable study demonstrating the role and power of modern solid-state NMR spectroscopy in investigating molecular assemblies that are otherwise inaccessible to other structural biology techniques.

      In its current form, tthe manuscript has significant experimental concerns - particularly the lack of RNA purification and inadequate description of materials and methods. The data therefore cannot support the conclusions presented. I recommend extensive revision and repetition of the experiments using purified RNA material before further consideration for publication.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, the authors employed fast MAS NMR spectroscopy to investigate the gel aggregation of longer repeat (48×) RNAs, revealing inherent folding structures and interactions (i.e., G-quadruplex and duplex).

      The dynamic structure of the RNA gel was not resolved at high resolution, and only the structural features-namely, the coexistence of G-quadruplexes and duplexes-were inferred. The 1D and 2D NMR spectra were not assigned to specific atomic positions within the RNA, which makes it difficult to perform molecular dynamics (MD) modeling to elucidate the dynamic nature of the RNA gel. The following comments are provided for the authors' consideration:

      1. Figure 2E and Figure 3A: The data suggest that Ca²⁺ promotes stronger G-quadruplex formation within the RNA gel compared with Mg²⁺. This observation is somewhat puzzling, as Mg²⁺ is generally known to stabilize G-quadruplex structures. The authors should clarify this discrepancy.
      2. Figures 2 and 3: The authors use the chemical shift at δN 144.1 ppm to distinguish between G-quadruplex and duplex structures. How was the reliability of this assignment evaluated? Chemical shifts of RNA atoms can be influenced by various factors such as intermolecular interactions, conformational stress, and local chemical environment, not only by higher-order structures. This point should be substantiated by citing relevant references or by analyzing additional RNA structures exhibiting δN 144.1 ppm signals using NMR spectroscopy.
      3. The authors state that "Our findings demonstrate that fast MAS NMR spectroscopy enables atomic-resolution monitoring of structural changes in GGGGCC repeat RNA of physiological lengths." This claim appears overstated, as no molecular model was constructed to define atomic coordinates based on NMR restraints.
      4. Figure 3B: The experiment using nuclear extracts supplemented with Mg²⁺ to study RNA aggregation via 2D NMR may not accurately reflect intracellular conditions. It would be informative to perform a parallel experiment using nuclear extracts without additional Mg²⁺ to better simulate the native environment for RNA folding.

      Significance

      In this manuscript, the authors employed fast MAS NMR spectroscopy to investigate the gel aggregation of longer repeat (48×) RNAs, revealing inherent folding structures and interactions (i.e., G-quadruplex and duplex).

      The dynamic structure of the RNA gel was not resolved at high resolution, and only the structural features-namely, the coexistence of G-quadruplexes and duplexes-were inferred. The 1D and 2D NMR spectra were not assigned to specific atomic positions within the RNA, which makes it difficult to perform molecular dynamics (MD) modeling to elucidate the dynamic nature of the RNA gel.

  4. Nov 2025
    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

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

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

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

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

      Major comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      Reviewer #1 (Significance (Required)):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

      Reviewer #3 (Significance (Required)):

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

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

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

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

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

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

      Major points:

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

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

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

      Minor comments:

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

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

      Significance

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

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

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

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

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

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

      Major comments:

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

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

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

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

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

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

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

      Minor comments:

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

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

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

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

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

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

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

      Significance

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

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

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

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

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

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

      Major comments

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

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

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

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

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

      Minor comments

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

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

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

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

      Significance

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

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

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

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

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

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC -2025-03175

      Corresponding author(s): Gernot Längst

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

      • *

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

      1. General Statements [optional]

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

      2. Point-by-point description of the revisions

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

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

      With best regards

      The authors of the manuscript

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

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

      Major Comments:

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

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

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

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

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

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

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

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

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

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

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

      __ Lack of Quality Control in the Pipeline __

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

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

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

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

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

      • __Lack of biological complexity: __simulated data often fails to capture the full complexity of biological systems including the heterogeneity, variability, and subtle dependencies present in real-world data. Simplifications and omissions in simulation models can result in test datasets that are more tractable but less realistic, causing software to appear robust or accurate under idealized conditions, while underperforming on actual experimental data.
      • __Risks of Overfitting: __Software may be tuned to perform well on simulated datasets leading to overfitting and falsely inflated performance metrics. This undermines the predictive or diagnostic value of the results for real biological data
      • Poor Model Fidelity and Hidden Assumptions: The authenticity of simulated data is bounded by the fidelity of the underlying models. If those models are inaccurate or make untested assumptions, the generated data may not reflect real experimental or clinical scenarios. This can mask software shortcomings or bias validation toward specific, perhaps irrelevant, scenarios. Therefore, we decided to validate the performance of the pipeline in the biological context of the analyzed data:

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

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

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

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

      __ Use of Mono-nucleosomes Only __

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

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

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

      nucDetective:

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

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

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

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

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

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

      __ Reference Nucleosome Numbers __

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

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

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

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

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

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

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

      __ Figure 1B and Nucleosome Spacing __

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

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

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

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

      __ Genome-wide Occupancy Claims __

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

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

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

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

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

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

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

      Dependence on DANPOS

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

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

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

      __ Control Data Usage __

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

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

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

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

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

      __ Lack of Statistical Power for Time-Series Analyses __

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

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

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

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

      Reproducibility of Methods

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

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

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

      The Zenodo repository contains:

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

      The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

      Nucleosome coverage tracks, annotation of nucleosome positions and dynamic nucleosomes are deposited additonally in the folder "Pf_nucleosome_annotation_of_nucDetective".

      To make this clearer we added following text to Material and Methods in ”The nucDetective pipeline” section:

      Changes in the manuscript text (L518-519):

      The code, software and annotations used to run the nucDetective pipeline along with the output have been deposited on Zenodo (https://doi.org/10.5281/zenodo.16779899).

      __ Supplementary Tables __

      Including supplementary tables showing pipeline outputs (e.g., nucleosome scores, heatmaps, TSS extraction) would help readers understand the input-output structure and support figure interpretations.

      See comments above.

      We included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

      The repository contains:

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

      The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

      Minor Comments:

      The authors should moderate claims such as "no studies have reported a well-positioned +1 nucleosome" in P. falciparum, as this contradicts existing literature. Similarly, avoid statements like "poorly understood chromatin architecture of Pf," which undervalue extensive prior work (e.g., discovery of histone lactylation in Plasmodium, Merrick et al., 2023).

      We would like to clarify that we neither wrote that ““no studies have reported a well-positioned +1 nucleosome”” in P. falciparum nor did we intend to imply such thing. However, we acknowledge that our original wording may have been unclear. To address this, we have revised the manuscript to explicitly acknowledge prior studies on chromatin organization and highlight our contribution.

      In the Abstract L26-L30: Contrary to the current view of irregular chromatin, we demonstrate for the first time regular phased nucleosome arrays downstream of TSSs, which, together with the established +1 nucleosome and upstream nucleosome-depleted region, reveal a complete canonical eukaryotic promoter architecture in Pf.

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

      Results L180-L183: These new Pf nucleosome maps reveal a nucleosome organisation at transcription start sites (TSS) reminiscent of the general eukaryotic chromatin structure, featuring a reported well-positioned +1 nucleosome , an upstream nucleosome-free region (NFR, Bunnik et al. 2014, Kensche et al. 2016), and shown for the first time in Pf, a phased nucleosome array downstream of the TSS.

      Discussion L412-L421: Previous analyses of Pf chromatin have identified +1 nucleosomes and NFRs (Bunnik et al 2014, Kensche et al. 2016). Here we extend this understanding by demonstrating phased nucleosome array structures throughout the genome. This finding provides evidence for a spatial regulation of nucleosome positioning in Pf, challenging the notion that nucleosome positioning is relatively random in gene bodies (Bunnik et al. 2014, Kensche et al. 2016). Consequently our results contribute to the understanding that Pf exhibits a typical eukaryotic chromatin structure, including well-defined nucleosome positioning at the TSS and regularly spaced nucleosome arrays (Schones et al. 2008; Yuan et al. 2005).

      The phrase “poorly understood chromatin architecture” has been modified to “underexplored chromatin architecture” in order to more accurately reflect the potential for further analyses and contributions to the field, while avoiding any potential misinterpretation of an attempt to undervalue previous work.

      Track labels in figures (e.g., Figure 5B) are too small to be legible.

      We made the labels bigger.

      Several figures (e.g., Figure 5B, S4B) lack statistical significance tests. Are the differences marked with stars statistically significant or just visually different?

      We added statistics to S4B.

      Differences in 5B were identified by visual inspection. To clarify this, we exchanged the asterisks to arrows in Fig.5B and changed the text in the legend:

      Arrows mark descriptive visual differences in nucleosome occupancy.

      Figure S3 includes a small black line on top of the table. Is this an accidental crop?

      We checked the figure carefully; however, the black line does not appear in our PDF viewer or on the printed paper

      The authors should state the weaknesses and limitations of this pipeline.

      We added a limitation section in discussion, see comments above

      Reviewer #1 (Significance (Required)):

      The proposed pipeline is useful and timely. It can benefit research groups willing to analyse MNase-Seq data of complex genomes such as P. falciparum. The tool requires users to have extensive experience in coding as the authors didn't include any clear and explicit codes on how to start processing the data from raw files. Nevertheless, there are multiple tool that can detect nucleosome occupancy and that are not cited by the authors not mention. I have included for the authors a link where a large list of tools for analysis of nucleosome positioning experiments tools/pipelines were developed for (Software to analyse nucleosome positioning experiments - Gene Regulation - Teif Lab). I think it would be useful for the authors to direct the reference this.

      We appreciate the reviewer’s valuable suggestion. We included a citation to the comprehensive database of nucleosome analysis tools curated by the Teif lab (Shtumpf et al., 2022). We chose to reference only selected tools in addition to this resource rather than listing all individual tools to maintain clarity and avoid overloading the manuscript with numerous citations.

      Despite valid, I still believe that controlling their pipeline by filtering out false positives and including more QC steps at the Inspector stage is strongly needed. That would boost the significance of this pipeline.

      We thank the reviewer for the assessment of our study and for recognizing that our MNase-seq analysis pipeline nucDetective can be a useful tool for the chromatin community utilizing MNase-Seq in complex settings.

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

      In this manuscript, Holzinger and colleagues have developed a new pipeline to assess chromatin organization in linear space and time. They used this pipeline to reevaluate nucleosome organization in the malaria parasite, P. falciparum. Their analysis revealed typical arrangement of nucleosomes around the transcriptional start site. Furthermore, it further strengthened and refined the connection between specific nucleosome dynamics and epigenetic marks, transcription factor binding sites or transcriptional activity.

      Major comments

      • I am wondering what is the main selling point of this manuscript is. If it is the development of the nucDetective pipeline, perhaps it would be best to first benchmark it and directly compare it to existing tools on a dataset where nucleosome fussiness, shifting and regularity has been analyzed before. If on the other hand, new insights into Plasmodium chromatin biology is the primary target validation of some of the novel findings would be advantageous (e.g. refinement of TSS positions, relevance of novel motifs, etc).

      NucDetective presents a novel pipeline to identify dynamic nucleosome properties within different datasets, like time series or developmental stages, as analysed for the erythrocytic cycle in this manuscript. As such kind of a pipeline, allowing direct comparisons, does not exist for MNase-Seq data, we used the existing analysis and high quality dataset of Kensche et al., to visualize the strong improvements of this kind of analysis. Accordingly, we combined the pipeline development and the reasearch of chromatin structure analysis, being able to showcase the utility of this new pipeline.

      • The authors identify a strong positioning of +1 nucleosome by searching for a positioned nucleosomes in the vicinity of the assigned TSS. Given the ill-defined nature of TSSs, this approach sounds logic at first glance. However, given the rather broad search space from -100 till +300bp, I am wondering whether it is a sort of "self-fulfilling prophecy". Conversely, it would be good to validate that this approach indeed helps to refine TSS positions.

      We thank the reviewer for raising this important point. We would like to clarify that we do not claim to redefine or precisely determine TSS positions in our study. Instead, we use annotated TSS coordinates as a reference to identify nucleosomes that correspond to the +1 nucleosome, based on their proximity to the TSS.

      We selected the search window from -100 to +300 bp to account for known variability in Pf TSS annotation. For example, dominant transcription start sites identified by 5'UTR-seq tag clusters can differ by several hundred base pairs within a single time point (Chappell et al., 2020). The broad window thus allows us to capture the principal nucleosome positions near a TSS, even when the TSS itself is imprecise or heterogeneous. Based on the TSS centered plots (Figure 2C and Figure S1B), we reasoned that a window of -100 to +300 is sufficient to capture the majority of the +1 nucleosomes, which would have been missed by using smaller window sizes. This strategy aligns with well-established conventions in yeast chromatin biology, where the +1 nucleosome is defined relative to the TSS (Jiang and Pugh, 2009; Zhang et al. 2011) and commonly used as an anchor point to visualize downstream phased nucleosome arrays and upstream nucleosome-depleted regions (Rossi et al., 2021; Oberbeckmann et al., 2019; Krietenstein et al., 2016 and many more). Accordingly, our approach leverages these accepted standards to interpret nucleosome positioning without re-defining TSS annotations.

      • Figure 1C: I am wondering how should the reader interpret the changes in nucleosomal repeat length changes throughout the cycle. Is linker DNA on average 10 nucleotides shorter at T30 compared to T5 timepoint? If so how could such "dramatic reorganization" be achieved at the molecular level in absence of a known linker DNA-binding protein. More importantly is this observation supported by additional evidence (e.g. dinucleosomal fragment length) or could it be due to slightly different digestion of the chromatin at the different stages or other technical variables?

      We thank the reviewer for this insightful question regarding the interpretation of NRL changes across the cell cycle. The reviewer is right in her or his interpretation – linker DNA is on average ~10 bp shorter at T30 than at T5.

      To address concerns about additional evidence and potential MNase digestion variability, we now analyzed MNase-seq fragment sizes by shifting mononucleosome peaks of each time point to the canonical 147 bp length, to correct for MNase digestion differences. After this normalisation, dinucleosome fragment length distributions revealed the shortest linker lengths at T30 and T35, whereas T5 and T10 showed longer DNA linkers. These results confirm our previous NRL measurements based on mononucleosomal read distances while controlling for MNase digestion bias.

      The molecular basis of this reorganization, is still unclear. While linker histone H1 is considered absent in Plasmodium falciparum, presence of an uncharacterized linker DNA–binding protein or alternative factors fulfilling a similar role can not be excluded (Gill et al. 2010). However, H1 absence across all developmental stages, fails to explain stage-specific chromatin changes. We hypothesize that Apicomplexans evolved specialized chromatin remodelers to compensate for the missing H1, which may also drive the dynamic NRL changes observed. The low NRL coincides with high transcriptional activity in Pf during trophozoite stage is consistent with previous reports linking elevated transcription to reduced NRL in other eukaryotes (Baldi et al. 2018). In addition, the schizont stage involves multiple rounds of DNA replication requiring large histone supplies being produced during that time. It may well be that a high level of histone synthesis and DNA amplification, results in a short time period with increased nucleosome density and shorter NRL, until the system reaches again equilibrium (Beshnova et al. 2014). Although speculative we suggest a model wherein increased transcription promotes elevated nucleosome turnover and re-assembly by specialized remodeling enzymes, combined with high abundance of histones, resulting in higher nucleosome density and decreased NRL. Unfortunately, absolute quantification of nucleosome levels from this MNase-seq dataset is not possible without spike-in controls, which makes it infeasible to test the hypothesis with the available data set (Chen et al. 2016).

      Minor comments

      • I am wondering whether fuzziness and occupancy changes are truly independent categories. I am asking as both could lead to reduction of the signal at the nucleosome dyad and because they show markedly similar distribution in relation to the TSS and associate with identical epigenetic features (Figure 2B-D). Figure 2A indicates minimal overlap between them, but this could be due to the fact that the criteria to define these subtypes is defined such to place nucleosomes to one or the other category, but at the end they represent two flavors of the same thing.

      Indeed, changes in occupancy and fuzziness can appear related because both features may reduce signal intensity at the nucleosome dyad and both are connected to “poor nucleosome positioning”. However, their definitions and measurements are clearly distinct and technically independent. Occupancy reflects the peak height at the nucleosome dyad, while fuzziness quantifies the spread of reads around the peak, measured as the standard deviation of read positions within each nucleosome peak (Jiang and Pugh, 2009; Chen et al., 2013). Although a reduction in occupancy can contribute to increased fuzziness by diminishing the dyad axis signal, fuzziness primarily arises from increased variability in the flanking regions around the nucleosome position center. While this distinction is established in the field, it is also often confused by the concept of well (high occupancy, low fuzziness) and poorly (high fuzziness, low occupancy) positioned nucleosomes, where both of these features are considered.

      • Do the authors detect spatial relationship between fuzzy and repositioned/evicted nucleosomes at the level of individual nucleosomes pairs. With other words, can fuzziness be the consequence of repositioning/eviction of the neighboring nucleosome?

      In Figure 2A we analyse the spatial overlap of all features to each other. The analysis clearly shows that fuzziness, occupancy changes and position changes occur mostly at distinct spatial sites (overlaps between 3 and 10%, Fig. 2A). Therefore, we suggest that the features correspond to independent processes. Likewise, we do observe an overlap between occupancy and ATAC-seq peaks, but not nucleosome positioning shifts, clearly discriminating different processes.

      • Figure 4: enrichment values and measure of statistical significance for the different motifs are missing. Also have there been any other motifs identified.

      This information is present in Supplemental Figure S3. Here we show the top 3 hits in each cluster. In the figure legend of Figure 4 we reference to Fig. S3:

      L1054 –1055:

      “Additional enriched motifs along with the significance of motif enrichment and the fraction of motifs at the respective nucleosome positions are shown in Figure S3”

      • The M&M would benefit from some more details, e.g. settings in the piepline, or which fragment sizes were used to map the MNase-seq data?

      We included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

      The repository contains:

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

      The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

      Nucleosome coverage tracks, annotation of nucleosome positions and dynamic nucleosomes are deposited additonally in the folder "Pf_nucleosome_annotation_of_nucDetective".

      To make this clearer we added following text to Material and Methods in ”The nucDetective pipeline” section:

      Changes in the manuscript (L518-519):

      The code, software and annotations used to run the nucDetective pipeline along with the output have been deposited on Zenodo (https://doi.org/10.5281/zenodo.16779899).

      which fragment sizes were used to map the MNase-seq data?

      The default setting in nucDetective is to use fragment sizes of 140 – 200 bp, which corresponds to the main mono-nucleosome fraction in standard MNase-seq experiments. However, the correct selection of fragment sizes may vary depending on the organism and the variations in MNase-seq protocols. Therefore, the pipeline offers the option of changing the cutoff parameter (--minLen; --maxLen), accordingly. Kensche et al thoroughly tested the best selection of the fragment sizes for the data set, which is used in this manuscript. We agree with their selection and used the same cutoffs (75-175 bp).

      This is stated in line 535-536:

      The fragments are further filtered to mono-nucleosome sized fragments (here we used 75 – 175 bp)

      We changed the text:

      The fragments are further filtered to mono-nucleosome sized fragments (default setting 140-200 bp; changed in this study to 75 – 175 bp)

      We highlighted other parameters used in this study in the material and methods part.

      Reviewer #2 (Significance (Required)):

      Overall, the manuscript is well written and findings are clearly and elegantly presented. The manuscript describes a new pipeline to map and analyze MNase-seq data across different stages or conditions, though the broader applicability of the pipeline and advancements over existing tools could be better demonstrated. Importantly, the manuscript make use of this pipeline to provide a refined and likely more accurate view on (the dynamics of) nucleosome positioning over the AT-rich genome of P. falciparum. While these observations make sense they remain rather descriptive/associative and lack further experimental validation. Overall, this manuscript could be interest to both researchers working on chromatin biology and Plasmodium gene-regulation.

      We thank the reviewer for the assessment of our study and for recognizing that the results of our MNase-seq analysis pipeline nucDetective contribute to a better understanding of Pf chromatin biology.

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

      The manuscript "Deciphering chromatin architecture and dynamics in Plasmodium 2 falciparum using the nucDetective pipeline" describes computational analysis of previously published data of P falciparum chromatin. This work corrects the prevailing view that this parasitic organism has an unusually disorganized chromatin organization, which had been attributed to its high genomic AT content, lack of histone H1, and ancient derivation. The authors show that instead P falciparum has a very typical chromatin organization. Part of the refinement is due to aligning data on +1 nucleosome positions instead of TSSs, which have been poorly mapped. The computational tools corral some useful features, for querying epigenomic structure that make visualization straightforward, especially for fuzzy nucleosomes.

      Reviewer #3 (Significance (Required)):

      As a computational package this is a nice presentation of fairly central questions. The assessment and display of fuzzy nucleosomes is a nice feature.

      We thank the reviewer for the assessment of our study and are pleased that the reviewer acknowledges the value and usability of our pipeline.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The manuscript "Deciphering chromatin architecture and dynamics in Plasmodium 2 falciparum using the nucDetective pipeline" describes computational analysis of previously published data of P falciparum chromatin. This work corrects the prevailing view that this parasitic organism has an unusually disorganized chromatin organization, which had been attributed to its high genomic AT content, lack of histone H1, and ancient derivation. The authors show that instead P falciparum has a very typical chromatin organization. Part of the refinement is due to aligning data on +1 nucleosome positions instead of TSSs, which have been poorly mapped. The computational tools corral some useful features, for querying epigenomic structure that make visualization straightforward, especially for fuzzy nucleosomes.

      Significance

      As a computational package this is a nice presentation of fairly central questions. The assessment and display of fuzzy nucleosomes is a nice feature.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Holzinger and colleagues have developed a new pipeline to assess chromatin organization in linear space and time. They used this pipeline to reevaluate nucleosome organization in the malaria parasite, P. falciparum. Their analysis revealed typical arrangement of nucleosomes around the transcriptional start site. Furthermore, it further strengthened and refined the connection between specific nucleosome dynamics and epigenetic marks, transcription factor binding sites or transcriptional activity.

      Major comments

      • I am wondering what is the main selling point of this manuscript is. If it is the development of the nucDetective pipeline, perhaps it would be best to first benchmark it and directly compare it to existing tools on a dataset where nucleosome fussiness, shifting and regularity has been analyzed before. If on the other hand, new insights into Plasmodium chromatin biology is the primary target validation of some of the novel findings would be advantageous (e.g. refinement of TSS positions, relevance of novel motifs, etc).
      • The authors identify a strong positioning of +1 nucleosome by searching for a positioned nucleosomes in the vicinity of the assigned TSS. Given the ill-defined nature of TSSs, this approach sounds logic at first glance. However, given the rather broad search space from -100 till +300bp, I am wondering whether it is a sort of "self-fulfilling prophecy". Conversely, it would be good to validate that this approach indeed helps to refine TSS positions.
      • Figure 1C: I am wondering how should the reader interpret the changes in nucleosomal repeat length changes throughout the cycle. Is linker DNA on average 10 nucleotides shorter at T30 compared to T5 timepoint? If so how could such "dramatic reorganization" be achieved at the molecular level in absence of a known linker DNA-binding protein. More importantly is this observation supported by additional evidence (e.g. dinucleosomal fragment length) or could it be due to slightly different digestion of the chromatin at the different stages or other technical variables?

      Minor comments

      • I am wondering whether fuzziness and occupancy changes are truly independent categories. I am asking as both could lead to reduction of the signal at the nucleosome dyad and because they show markedly similar distribution in relation to the TSS and associate with identical epigenetic features (Figure 2B-D). Figure 2A indicates minimal overlap between them, but this could be due to the fact that the criteria to define these subtypes is defined such to place nucleosomes to one or the other category, but at the end they represent two flavors of the same thing.
      • Do the authors detect spatial relationship between fuzzy and repositioned/evicted nucleosomes at the level of individual nucleosomes pairs. With other words, can fuzziness be the consequence of repositioning/eviction of the neighboring nucleosome?
      • Figure 4: enrichment values and measure of statistical significance for the different motifs are missing. Also have there been any other motifs identified.
      • The M&M would benefit from some more details, e.g. settings in the piepline, or which fragment sizes were used to map the MNase-seq data?

      Significance

      Overall, the manuscript is well written and findings are clearly and elegantly presented. The manuscript describes a new pipeline to map and analyze MNase-seq data across different stages or conditions, though the broader applicability of the pipeline and advancements over existing tools could be better demonstrated. Importantly, the manuscript make use of this pipeline to provide a refined and likely more accurate view on (the dynamics of) nucleosome positioning over the AT-rich genome of P. falciparum. While these observations make sense they remain rather descriptive/associative and lack further experimental validation. Overall, this manuscript could be interest to both researchers working on chromatin biology and Plasmodium gene-regulation.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

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


      Major Comments:

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

      • Clarification of +1 Nucleosome Positioning in P. falciparum The authors should acknowledge that +1 nucleosomes have been previously reported in P. falciparum. For example, Kensche et al. (2016) used MNase-seq to map ~2,278 TSSs (based on enriched 5′-end RNA data) and found that the +1 nucleosome is positioned directly over the TSS in most genes: "Analysis of 2278 start sites uncovered positioning of a +1 nucleosome right over the TSS in almost all analysed regions" (Figure 3A). They also described a nucleosome-depleted region (NDR) upstream of the TSS, which varies in size, while the +1 nucleosome frequently overlaps the TSS. The authors should nuance their claims accordingly. Nevertheless, I do agree that the +1 positioning in P. falciparum may be fuzzier as compared to yeast or mammals. Moreover, the correlation between +1 nucleosome occupancy and gene expression is often weak and that several genes show similar nucleosome profiles regardless of expression level. This raises my question: did the authors observe any of these patterns in their new data?
      • Lack of Quality Control in the Pipeline The authors claim (lines 152-153) that QC is performed at every stage, but this is not supported by the implementation. On the GitHub page (GitHub - uschwartz/nucDetective), QC steps are only marked at the Profiler stage using standard tools (FastQC, MultiQC). The Inspector stage, which is crucial for validating nucleosome detection, lacks QC entirely. The authors should implement additional steps to assess the quality of nucleosome calls. For example, how are false positives managed? ROC curves should be used to evaluate true positive vs. false positive rates when defining dynamic nucleosomes. How sequencing biases are addressed?
      • Use of Mono-nucleosomes Only The authors re-analyze the Kensche et al. (2016) dataset using only mono-nucleosomes and claim improved nucleosome profiles, including identification of tandem arrays previously unreported in P. falciparum. Two key issues arise:
      • Is the apparent improvement due simply to focusing on mono-nucleosomes (as implied in lines 342-346)?
      • How does the pipeline perform with di- or tri-nucleosomes, which are also biologically relevant (Kensche et al., 2016 and others)? Furthermore, the limitation to mono-nucleosomes is only mentioned in the methods, not in the results or discussion, which could mislead readers.
      • Reference Nucleosome Numbers The authors identify 49,999 reference nucleosome positions. How does this compare to previous analyses of similar datasets? This should be explicitly addressed.
      • Figure 1B and Nucleosome Spacing The authors claim that Figure 1B shows developmental stage-specific variation in nucleosome spacing. However, only T35 shows a visible upstream change at position 0. In A4, A6, and A8 (Figure S4), no major change is apparent. Statistical tests are needed to validate whether the observed differences are significant and should be described in the figure legends and main text.
      • Genome-wide Occupancy Claims The claim that nucleosomes are "evenly distributed throughout the genome" (Figure S2A) is questionable. Chromosomes 3 and 11 show strong peaks mid-chromosome, and chromosome 14 shows little to no signal at the ends. This should be discussed. Subtelomeric regions, such as those containing var genes, are known to have unique chromatin features. For instance, Lopez-Rubio et al. (2009) show that subtelomeric regions are enriched for H3K9me3 and HP1, correlating with gene silencing. Should these regions not display different nucleosome distributions? Do you expect the Plasmodium genome (or any genome) to have uniform nucleosome distribution?
      • Dependence on DANPOS The authors criticize the DANPOS pipeline for its limitations but use it extensively within nucDetective. This contradiction confuses the reader. Is nucDetective an original pipeline, or a wrapper built on existing tools?
      • Control Data Usage The authors should clarify whether gDNA controls were used throughout the analysis, as done in Kensche et al. (2016). Currently, this is mentioned only in the figure legend for Figure 5, not in the methods or results.
      • Lack of Statistical Power for Time-Series Analyses Although the pipeline is presented as suitable for time-series data, it lacks statistical tools to determine whether differences in nucleosome positioning or fuzziness are significant across conditions. Visual interpretation alone is insufficient. Statistical support is essential for any differential analysis.
      • Reproducibility of Methods The Methods section is not sufficient to reproduce the results. The GitHub repository lacks the necessary code to generate the paper's figures and focuses on an exemplary yeast dataset. The authors should either:
        • Update the repository with relevant scripts and examples,
        • Clearly state the repository's purpose, or
        • Remove the link entirely. Readers must understand that nucDetective is dedicated to assessing nucleosome fuzziness, occupancy, shift, and regularity dynamics-not downstream analyses presented in the paper.
      • Supplementary Tables Including supplementary tables showing pipeline outputs (e.g., nucleosome scores, heatmaps, TSS extraction) would help readers understand the input-output structure and support figure interpretations.

      Minor Comments:

      • The authors should moderate claims such as "no studies have reported a well-positioned +1 nucleosome" in P. falciparum, as this contradicts existing literature. Similarly, avoid statements like "poorly understood chromatin architecture of Pf," which undervalue extensive prior work (e.g., discovery of histone lactylation in Plasmodium, Merrick et al., 2023).
      • Track labels in figures (e.g., Figure 5B) are too small to be legible.
      • Several figures (e.g., Figure 5B, S4B) lack statistical significance tests. Are the differences marked with stars statistically significant or just visually different?
      • Figure S3 includes a small black line on top of the table. Is this an accidental crop?
      • The authors should state the weaknesses and limitations of this pipeline.

      Significance

      • The proposed pipeline is useful and timely. It can benefit research groups willing to analyse MNase-Seq data of complex genomes such as P. falciparum. The tool requires users to have extensive experience in coding as the authors didn't include any clear and explicit codes on how to start processing the data from raw files. Nevertheless, there are multiple tool that can detect nucleosome occupancy and that are not cited by the authors not mention. I have included for the authors a link where a large list of tools for analysis of nucleosome positioning experiments tools/pipelines were developed for (Software to analyse nucleosome positioning experiments - Gene Regulation - Teif Lab). I think it would be useful for the authors to direct the reference this.
      • Despite valid, I still believe that controlling their pipeline by filtering out false positives and including more QC steps at the Inspector stage is strongly needed. That would boost the significance of this pipeline.
    1. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The study has carefully controlled and rigorous data. For the most part, the results are consistent with their claims. Except for a few modifications, it should be published. My suggestions are:

      1. Fig 2A. I cannot see the red line in the plot that is mentioned in the legend. Please add it.
      2. Fig 2A. The Manhattan plot shows a number of loci in the genome that have peaks of significant SNPs, not just the locus encompassing Malt-A. It might be worth highlighting the loci or peaks better in the plot. It is pretty minimalist as is.
      3. Linkage disequilibrium is a problem in Drosophila. Many SNPs are hitchikers riding along with a single causative SNP due to infrequent recombination between hitchiker and causative SNPs. How many SNPs are significant and please list the SNPs or intervals considered significant in the GWAS. The text is vague and brief. The plot in Fig 2A is problematic by being overly minimal.
      4. Regarding the GWAS loci they found. It would be worth comparing these regions of the genome with significant GWAS scores to those regions identified in an earlier study. In 2013, Cassidy et al performed artificial selection on Drosophila populations using the same trait (scutellar bristle number) as this study. They did whole genome sequencing of the population before and after selection, and found loci in the genome that exhibited signs of selection through having altered allele frequencies at some loci. Are some of the loci identified in that study the same as in this GWAS study? Are some of the genes implicated in that study the same? The old data is publicly available and so could be easily mined.
      5. Tables 1 is cut apart in its format. Please format properly.
      6. Across the work, there is a lack of statistical testing of significance in bristle number between treated groups. These phenotypes need testing. The number of animals assayed in each experiment are listed but no tests for statistical significance are presented. A chi square or better yet, a fishers exact test would be appropriate. Some of the sample numbers seem low for the claims made, i.e. 8 animals scored for UAS-MalA1 control group.. This testing should be done for all data in Table 1, Fig 2C, Supp Fig 2 A, Fig 4E and any others I might have missed.
      7. Fig 3A, are the individual datapoints single replicates of metabolomic samples? The description of what PCA was done is minimal and needs more description. I assume they performed PCA using metabolites as variables. They did not say. Nor did they explain how PCA was performed except for the software. They "normalized" the data to the median. Did they center the matrix of variable values to the median before doing PCA - is that what they mean? Why not center to the mean values? Typically one calculates the mean value for a given variable, ie a single metabolite, across all samples, and then calculates the difference between the measured value from one sample and the mean value for that variable. That needs to be done. It is not standard to center to the median. They should also normalize the data to eliminate biasing in the PCA results because of variance due to very abundant metabolites, The variables with large values (ie abundant metabolites) overly contribute to the explanatory variance in a PCA analysis unless one normalizes. This normalization is typically done by taking the difference between measured and mean values (as described above), and dividing that difference by the standard deviation of the variable's measurements. Think of it as a Z-score. The matrix data then is centered around zero for each variable, and each variable's values range from -5 to +5. Then perform PCA. Otherwise highly abundant metabolites bias the analysis. Again, this type of normalization is standard for PCA.
      8. How many metabolites were measured? What were they, ie the list. Provide please
      9. Results described in Fig 5A are the weakest in the manuscript and really could be supplemental. It is weakly circumstantal evidence for the claim being made. Temperature affects so many things, it could be coincidence that dilp levels change and this change correlates with bristle number. Many things change with temperature. Definitely they should not end the results section with such weak data,
      10. Carthew and colleagues showed that IPC ablation suppressed the scutellar bristle phenotypes of miR9a and scute mutants. Does Mal-A1 knockdown have similar effects on these mutants? One would predict yes.
      11. The authors mention the 2019 paper by Cassidy et al and some of the results therein regarding inhibiting carbohydrate metabolism and phenotype suppression (robustness). But not only miR-9a and scutellar bristles were tested in that paper but a wide variety of mutations in TFs, signaling proteins and other miRNAs. All their results were consistent with the findings of the current ms. The authors could discuss this more in depth. Also, Cassidy et al put forth a quantitative model that explained how limiting glucose metabolsm could provide robustness for a wide variety of developmental decisions. It might be worth discussing this model in light of their results.

      Significance

      This manuscript describes an interesting study of developmental robustness and its intersection with organismal metabolism. It builds upon prior papers that have addressed the link between metabolism and development. It describes an ingenious approach to the problem and uncovers maltose metabolism in Drosophila as one such connection to sensory organ development and patterning. The important take home message for me is that they found natural genetic variants from the wild that confer greater robustness to the fly's morphological development, and these genetic variants are found in an enzyme that broadly metabolizes maltose, a simple sugar. Whereas previous studies used genetic manipulation to impact metabolism, this study shows that genetic variants in the wild exhibit effects on robustness. It suggests there might be a tradeoff between more vigorous carbohydrate metabolism and fidelity in morphological development.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors performed GWAS to identify associations between the mean bristle number in Drosophila melanogaster adults and different SNPs present in 95 lines of the DGRP panel rear at 18C. They selected genes harboring those SNPs linked to bristle number that also had a moderate or high expression at the third insta larva stage to perform an RNAi screen. This RNAi screen, which included 43 genes, identified Maltase-A1 (Mal-A1) as a contributor to bristle number. Therefore, the authors then focus on investigating possible metabolic and transcriptional changes underlying the effect of Mal-A1 knockdown on bristle number. After whole-body knockdown using the da-gal4 driver, the authors identified decreased glucose in whole body and hemolymph, and decreased dilp3 mRNA expression in whole body, intestine, and insulin producing cells (IPC) in the larva brain. Similar to a whole-body Mal-A1 knockdown, a gut epithelial cell-specific gal4 driver (NP1) also decreased dilp3 mRNA expression in the whole body and larva brain. The authors suggest that Mal-A1 activity in the intestine may affect bristle number through lowering available glucose in the intestine, which decreases circulating glucose levels in the hemolymph, and in turn decreases dilp3 mRNA expression in the larva brain, leading to decreased bristle number. Finally, to validate the influence of bristle number via dilp3-mediated insulin signaling in the brain, the authors reared larvae at 18C, which they showed increased bristle number. Supporting their proposed model, rearing larvae at 18C increased dilp3 mRNA expression in the brain, which correlated with increased bristle number.

      Major comments:

      1. The main finding of this paper is the identification of Mal-A1 gene as a regulator of bristle number in Drosophila adults. However, the authors do not to show clear phenotypes which could stem from a lack of experimental rigor. As an example in Fig. 2C (source data not provided) the UAS-Mal-A1-RNAi line V15789 in the absence of GAL4 shows 5% abnormal bristle number compared with 2% upon knockdown. If I'm understanding the data provided, this means that abnormal bristle number was observed in 2 flies (out of 40) in the UAS-line alone compared with ~2 flies (out of 111) in the presence of GAL4. For line V106220, 2% (n=56) showed abnormal bristles compared with 0% (n=37) upon in the presence of GAL4. In absolute numbers this would mean that abnormal bristle number was observed in ~1 fly (out of 56) in the UAS-line alone compared with 0 flies (out of 37) upon knockdown. All of these experiments do not use sufficient n, which according to the reviewers calculations (to show a 3% increase, with 80% confidence the n should be around 750-800). In addition no information on statistical tests or whether biological replicates were performed is included. Due to the main finding heavily relying on this phenotype of abnormal bristle number, this reviewer is not confident that the conclusions of the manuscript are supported. This problem also applies to other experiments presented in the manuscript, which suffer from low n, significantly decreasing the enthusiasm for the presented results.
      2. The authors do not to show that Drosophila insulin- like peptide 3 (dilp3) level affects the SOPs in a nonautonomous manner. The only experiments included are showing indirect effects.
      3. There are important statistical details missing in some of the figures (see comments below)
      4. Important details are missing from the methods for results or analysis to be reproduced. For example, the method section for GWAS analysis is lacking details, a script should be provided as supplemental information, as well as a table similar to the one provided for the RNAi screen.

      Minor comments

      • There are some typos like referring to 'using w118 male mice' in the 'Phenotypic Analysis of Maltase Knockdown; (1) Bristle number count'
      • Details in methods. For GWAS experiments, could the authors define what their cutoffs were for selecting genes harboring SNPs linked to bristle number? How many base pairs from a gene? or enhancer? They selected only those gene with moderate or high expression, but what does it mean?
      • In Fig. 2A, could the authors provide all significant SNPs identified by their GWAS analysis as supplemental material?
      • In Fig. 2A, it is stated in the legend " and the red line represents the significance threshold calculated using Bonferroni correction...". This might be a problem with the pdf document but I did not find the red line in the Manhattan plot that the authors refer to.
      • In Fig. 4E, could the authors provide the n number as in other figures?
      • Check citations. Some references have missing parts. For example; Ref 5 is missing the last 2 words of the title. In Manuscript it reads: "Trehalose metabolism confers developmental robustness and stability in Drosophila by regulating.". It should be "Trehalose metabolism confers developmental robustness and stability in Drosophila by regulating glucose homeostasis."

      Significance

      While the significance of identifying a novel regulatory mechanism for developmental robustness in Drosophila melanogaster is high and would be interesting for a broad audience, the authors do not present convincing experimental evidence to support their hypothesis. This is due to the insufficient number of replicates as well as the lack of experiments showing a direct role of insulin signaling.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this article, the authors identified a gut-expressed enzyme, maltase-A1, which regulates the developmental robustness of SSO. Through a GWAS analysis of bristle phenotypes using the DGRP lines, maltase-A1 was revealed as a significant regulator of bristle number. Knockdown of maltase-A1 suppressed the increase in bristle count. Metabolite profiling of key carbohydrates showed a reduction in several sugar levels, potentially leading to decreased ilp3 release from the CNS. Furthermore, the authors demonstrated that cold temperature induces higher expression of both ilp2 and ilp3, which may contribute to the observed increase in bristle number.

      The findings of this study offer valuable insights into how nutrient metabolism may influence developmental robustness. However, a key limitation lies in the correlative nature of the observations. Further experimental validation is needed to establish a direct causal relationship. 1. To further support the hypothesis that Maltase-A1 mutation reduces bristle variance by lowering hemolymph glucose levels and consequently decreasing insulin secretion, it would be essential to test whether providing a higher concentration of dietary glucose to Maltase-A1 mutant larvae can rescue the mutant phenotype. 2. To further substantiate the claim that ilp3 acts as a downstream effector in the Maltase-A1 regulatory pathway, it would be important to perform ilp3 knockdown and overexpression experiments in both wild-type and Maltase-A1 mutant backgrounds. This approach could help determine whether altered ilp3 expression levels directly contribute to the bristle phenotype associated with Maltase-A1 dysfunction.

      Significance

      How nutrients regulate developmental processes is an intriguing question in developmental biology. This study employs GWAS to identify an unexpected regulator of bristle development, offering new insights into how nutrient metabolism may influence developmental robustness. I believe this article will be of great interest to audiences in developmental biology.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

      The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

      Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division. Overall, I have only a few minor suggestions.

      We appreciate the reviewers’ support of our study.

      1) In wild-type - Pds1 levels are high during M1 and A1, but low in MII. Can the authors comment on this? In line 217, what is meant by "slightly attenuated? Can the authors comment on how anaphase occurs in presence of high Pds1? There is even a low but significant level in MII.

      The higher levels of Pds1 in meiosis I compared to meiosis II has been observed previously using immunofluorescence and live imaging1–3. Although the reasons are not completely clear, we speculate that there is insufficient time between the two divisions to re-accumulate Pds1 prior to separase re-activation.

      We agree “slightly attenuated” was confusing and we have re-worded this sentence to read “Addition ABA at the time of prophase release resulted in Pds1securin stabilisation throughout the time course, consistent with delays in both metaphase I and II”.

      We do not believe that either anaphase I or II occur in the presence of high Pds1. Western blotting represents the amount of Pds1 in the population of cells at a given time point. The time between meiosis I and II is very short even when treated with ABA. For example, in Figure 2B, spindle morphology counts show that the anaphase I peak is around 40% at its maxima (105 min) and around 40% of cells are in either metaphase I or metaphase II, and will be Pds1 positive. In contrast, due to the better efficiency of meiosis II, anaphase II hardly occurs at all in these conditions, since anaphase II spindles (and the second nuclear division) are observed at very low frequency (maximum 10%) from 165 minutes onwards. Instead, metaphase II spindles partially or fully breakdown, without undergoing anaphase extension. Taking Pds1 levels from the western blot and the spindle data together leads to the conclusion that at the end of the time-course, these cells are biochemically in metaphase II, but unable to maintain a robust spindle. Spindle collapse is also observed in other situations where meiotic exit fails, and potentially reflects an uncoupling of the cell cycle from the programme governing gamete differentiation3–5. We will explain this point in a revised version while referring to representative images that from evidence for this, as also requested by the reviewer below.

      2) The figures with data characterizing the system are mostly graphs showing time course of MI and MII. There is no cytology, which is a little surprising since the stage is determined by spindle morphology. It would help to see sample sizes (ie. In the Figure legends) and also representative images. It would also be nice to see images comparing the same stage in the SynSAC cells versus normal cells. Are there any differences in the morphology of the spindles or chromosomes when in the SynSAC system?

      This is an excellent suggestion and will also help clarify the point above. We will provide images of cells at the different stages. For each timepoint, 100 cells were scored. We have already included this information in the figure legends

      3) A possible criticism of this system could be that the SAC signal promoting arrest is not coming from the kinetochore. Are there any possible consequences of this? In vertebrate cells, the RZZ complex streams off the kinetochore. Yeast don't have RZZ but this is an example of something that is SAC dependent and happens at the kinetochore. Can the authors discuss possible limitations such as this? Does the inhibition of the APC effect the native kinetochores? This could be good or bad. A bad possibility is that the cell is behaving as if it is in MII, but the kinetochores have made their microtubule attachments and behave as if in anaphase.

      In our view, the fact that SynSAC does not come from kinetochores is a major advantage as this allows the study of the kinetochore in an unperturbed state. It is also important to note that the canonical checkpoint components are all still present in the SynSAC strains, and perturbations in kinetochore-microtubule interactions would be expected to mount a kinetochore-driven checkpoint response as normal. Indeed, it would be interesting in future work to understand how disrupting kinetochore-microtubule attachments alters kinetochore composition (presumably checkpoint proteins will be recruited) and phosphorylation but this is beyond the scope of this work. In terms of the state at which we are arresting cells – this is a true metaphase because cohesion has not been lost but kinetochore-microtubule attachments have been established. This is evident from the enrichment of microtubule regulators but not checkpoint proteins in the kinetochore purifications from metaphase I and II. While this state is expected to occur only transiently in yeast, since the establishment of proper kinetochore-microtubule attachments triggers anaphase onset, the ability to capture this properly bioriented state will be extremely informative for future studies. We appreciate the reviewers’ insight in highlighting these interesting discussion points which we will include in a revised version.

      Reviewer #1 (Significance (Required)):

      These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

      The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

      Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division.

      We appreciate the reviewer’s enthusiasm for our work.

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

      The manuscript submitted by Koch et al. describes a novel approach to collect budding yeast cells in metaphase I or metaphase II by synthetically activating the spinde checkpoint (SAC). The arrest is transient and reversible. This synchronization strategy will be extremely useful for studying meiosis I and meiosis II, and compare the two divisions. The authors characterized this so-named syncSACapproach and could confirm previous observations that the SAC arrest is less efficient in meiosis I than in meiosis II. They found that downregulation of the SAC response through PP1 phosphatase is stronger in meiosis I than in meiosis II. The authors then went on to purify kinetochore-associated proteins from metaphase I and II extracts for proteome and phosphoproteome analysis. Their data will be of significant interest to the cell cycle community (they compared their datasets also to kinetochores purified from cells arrested in prophase I and -with SynSAC in mitosis).

      I have only a couple of minor comments:

      1) I would add the Suppl Figure 1A to main Figure 1A. What is really exciting here is the arrest in metaphase II, so I don't understand why the authors characterize metaphase I in the main figure, but not metaphase II. But this is only a suggestion.

      This is a good suggestion, we will do this in our full revision.

      2) Line 197, the authors state: ...SyncSACinduced a more pronounced delay in metaphase II than in metaphase I. However, line 229 and 240 the auhtors talk about a "longer delay in metaphase Thank you for pointing this out, this is indeed a typo and we have corrected it.

      3) The authors describe striking differences for both protein abundance and phosphorylation for key kinetochore associated proteins. I found one very interesting protein that seems to be very abundant and phosphorylated in metaphase I but not metaphase II, namely Sgo1. Do the authors think that Sgo1 is not required in metaphase II anymore? (Top hit in suppl Fig 8D).

      This is indeed an interesting observation, which we plan to investigate as part of another study in the future. Indeed, data from mouse indicates that shugoshin-dependent cohesin deprotection is already absent in meiosis II in mouse oocytes6, though whether this is also true in yeast is not known. Furthermore, this does not rule out other functions of Sgo1 in meiosis II (for example promoting biorientation). We will include this point in the discussion.

      Reviewer #2 (Significance (Required)):

      The technique described here will be of great interest to the cell cycle community. Furthermore, the authors provide data sets on purified kinetochores of different meiotic stages and compare them to mitosis. This paper will thus be highly cited, for the technique, and also for the application of the technique.

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

      In their manuscript, Koch et al. describe a novel strategy to synchronize cells of the budding yeast Saccharomyces cerevisiae in metaphase I and metaphase II, thereby facilitating comparative analyses between these meiotic stages. This approach, termed SynSAC, adapts a method previously developed in fission yeast and human cells that enables the ectopic induction of a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC components upon addition of the plant hormone abscisic acid (ABA). This is a valuable tool, which has the advantage that induces SAC-dependent inhibition of the anaphase promoting complex without perturbing kinetochores. Furthermore, since the same strategy and yeast strain can be also used to induce a metaphase arrest during mitosis, the methodology developed by Koch et al. enables comparative analyses between mitotic and meiotic cell divisions. To validate their strategy, the authors purified kinetochores from meiotic metaphase I and metaphase II, as well as from mitotic metaphase, and compared their protein composition and phosphorylation profiles. The results are presented clearly and in an organized manner.

      We are grateful to the reviewer for their support.

      Despite the relevance of both the methodology and the comparative analyses, several main issues should be addressed: 1.- In contrast to the strong metaphase arrest induced by ABA addition in mitosis (Supp. Fig. 2), the SynSAC strategy only promotes a delay in metaphase I and metaphase II as cells progress through meiosis. This delay extends the duration of both meiotic stages, but does not markedly increase the percentage of metaphase I or II cells in the population at a given timepoint of the meiotic time course (Fig. 1C). Therefore, although SynSAC broadens the time window for sample collection, it does not substantially improve differential analyses between stages compared with a standard NDT80 prophase block synchronization experiment. Could a higher ABA concentration or repeated hormone addition improve the tightness of the meiotic metaphase arrest?

      For many purposes the enrichment and extended time for sample collection is sufficient, as we demonstrate here. However, as pointed out by the reviewer below, the system can be improved by use of the 4A-RASA mutations to provide a stronger arrest (see our response below). We did not experiment with higher ABA concentrations or repeated addition since the very robust arrest achieved with the 4A-RASA mutant deemed this unnecessary.

      2.- Unlike the standard SynSAC strategy, introducing mutations that prevent PP1 binding to the SynSAC construct considerably extended the duration of the meiotic metaphase arrests. In particular, mutating PP1 binding sites in both the RVxF (RASA) and the SILK (4A) motifs of the Spc105(1-455)-PYL construct caused a strong metaphase I arrest that persisted until the end of the meiotic time course (Fig. 3A). This stronger and more prolonged 4A-RASA SynSAC arrest would directly address the issue raised above. It is unclear why the authors did not emphasize more this improved system. Indeed, the 4A-RASA SynSAC approach could be presented as the optimal strategy to induce a conditional metaphase arrest in budding yeast meiosis, since it not only adapts but also improves the original methods designed for fission yeast and human cells. Along the same lines, it is surprising that the authors did not exploit the stronger arrest achieved with the 4A-RASA mutant to compare kinetochore composition at meiotic metaphase I and II.

      We agree that the 4A-RASA mutant is the best tool to use for the arrest and going forward this will be our approach. We collected the proteomics data and the data on the SynSAC mutant variants concurrently, so we did not know about the improved arrest at the time the proteomics experiment was done. Because very good arrest was already achieved with the unmutated SynSAC construct, we could not justify repeating the proteomics experiment which is a large amount of work using significant resources. However, we will highlight the potential of the 4A-RASA mutant more prominently in our full revision.

      3.- The results shown in Supp. Fig. 4C are intriguing and merit further discussion. Mitotic growth in ABA suggest that the RASA mutation silences the SynSAC effect, yet this was not observed for the 4A or the double 4A-RASA mutants. Notably, in contrast to mitosis, the SynSAC 4A-RASA mutation leads to a more pronounced metaphase I meiotic delay (Fig. 3A). It is also noteworthy that the RVAF mutation partially restores mitotic growth in ABA. This observation supports, as previously demonstrated in human cells, that Aurora B-mediated phosphorylation of S77 within the RVSF motif is important to prevent PP1 binding to Spc105 in budding yeast as well.

      We agree these are intriguing findings that highlight key differences as to the wiring of the spindle checkpoint in meiosis and mitosis and potential for future studies, however, currently we can only speculate as to the underlying cause. The effect of the RASA mutation in mitosis is unexpected and unexplained. However, the fact that the 4A-RASA mutation causes a stronger delay in meiosis I compared to mitosis can be explained by a greater prominence of PP1 phosphatase in meiosis. Indeed, our data (Figure 4A) show that the PP1 phosphatase Glc7 and its regulatory subunit Fin1 are highly enriched on kinetochores at all meiotic stages compared to mitosis.

      We agree that the improved growth of the RVAF mutant is intriguing and points to a role of Aurora B-mediated phosphorylation, though previous work has not supported such a role 7.

      We will include a discussion of these important points in a revised version.

      4.- To demonstrate the applicability of the SynSAC approach, the authors immunoprecipitated the kinetochore protein Dsn1 from cells arrested at different meiotic or mitotic stages, and compared kinetochore composition using data independent acquisition (DIA) mass spectrometry. Quantification and comparative analyses of total and kinetochore protein levels were conducted in parallel for cells expressing either FLAG-tagged or untagged Dsn1 (Supp. Fig. 7A-B). To better detect potential changes, protein abundances were next scaled to Dsn1 levels in each sample (Supp. Fig. 7C-D). However, it is not clear why the authors did not normalize protein abundance in the immunoprecipitations from tagged samples at each stage to the corresponding untagged control, instead of performing a separate analysis. This would be particularly relevant given the high sensitivity of DIA mass spectrometry, which enabled quantification of thousands of proteins. Furthermore, the authors compared protein abundances in tagged-samples from mitotic metaphase and meiotic prophase, metaphase I and metaphase II (Supp. Fig. 7E-F). If protein amounts in each case were not normalized to the untagged controls, as inferred from the text (lines 333 to 338), the observed differences could simply reflect global changes in protein expression at different stages rather than specific differences in protein association to kinetochores.

      While we agree with the reviewer that at first glance, normalising to no tag makes the most sense, in practice there is very low background signal in the no tag sample which means that any random fluctuations have a big impact on the final fold change. This approach therefore introduces artefacts into the data rather than improving normalisation.

      To provide reassurance that our kinetochore immunoprecipitations are specific, and that the background (no tag) signal is indeed very low, we will provide a new supplemental figure showing the volcanos comparing kinetochore purifications at each stage with their corresponding no tag control. These volcano plots show very clearly that the major enriched proteins are kinetochore proteins and associated factors, in all cases.

      It is also important to note that our experiment looks at relative changes of the same protein over time, which we expect to be relatively small in the whole cell lysate. We previously documented proteins that change in abundance in whole cell lysates throughout meiosis8. In this study, we found that relatively few proteins significantly change in abundance, supporting this view.

      Our aim in the current study was to understand how the relative composition of the kinetochore changes and for this, we believe that a direct comparison to Dsn1, a central kinetochore protein which we immunoprecipitated is the most appropriate normalisation.

      5.- Despite the large amount of potentially valuable data generated, the manuscript focuses mainly on results that reinforce previously established observations (e.g., premature SAC silencing in meiosis I by PP1, changes in kinetochore composition, etc.). The discussion would benefit from a deeper analysis of novel findings that underscore the broader significance of this study.

      We strongly agree with this point and we will re-frame the discussion to focus on the novel findings, as also raised by the other reviewers.

      Finally, minor concerns are: 1.- Meiotic progression in SynSAC strains lacking Mad1, Mad2 or Mad3 is severely affected (Fig. 1D and Supp. Fig. 1), making it difficult to assess whether, as the authors state, the metaphase delays depend on the canonical SAC cascade. In addition, as a general note, graphs displaying meiotic time courses could be improved for clarity (e.g., thinner data lines, addition of axis gridlines and external tick marks, etc.).

      We will generate the data to include a checkpoint mutant +/- ABA for direct comparison. We will take steps to improve the clarity of presentation of the meiotic timecourse graphs, though our experience is that uncluttered graphs make it easier to compare trends.

      2.- Spore viability following SynSAC induction in meiosis was used as an indicator that this experimental approach does not disrupt kinetochore function and chromosome segregation. However, this is an indirect measure. Direct monitoring of genome distribution using GFP-tagged chromosomes would have provided more robust evidence. Notably, the SynSAC mad3Δ mutant shows a slight viability defect, which might reflect chromosome segregation defects that are more pronounced in the absence of a functional SAC.

      Spore viability is a much more sensitive way of analysing segregation defects that GFP-labelled chromosomes. This is because GFP labelling allows only a single chromosome to be followed. On the other hand, if any of the 16 chromosomes mis-segregate in a given meiosis this would result in one or more aneuploid spores in the tetrad, which are typically inviable. The fact that spore viability is not significantly different from wild type in this analysis indicates that there are no major chromosome segregation defects in these strains, and we therefore do not plan to do this experiment.

      3.- It is surprising that, although SAC activity is proposed to be weaker in metaphase I, the levels of CPC/SAC proteins seem to be higher at this stage of meiosis than in metaphase II or mitotic metaphase (Fig. 4A-B).

      We agree, this is surprising and we will point this out in the revised discussion. We speculate that the challenge in biorienting homologs which are held together by chiasmata, rather than back-to-back kinetochores results in a greater requirement for error correction in meiosis I. Interestingly, the data with the RASA mutant also point to increased PP1 activity in meiosis I, and we additionally observed increased levels of PP1 (Glc7 and Fin1) on meiotic kinetochores, consistent with the idea that cycles of error correction and silencing are elevated in meiosis I.

      4.- Although a more detailed exploration of kinetochore composition or phosphorylation changes is beyond the scope of the manuscript, some key observations could have been validated experimentally (e.g., enrichment of proteins at kinetochores, phosphorylation events that were identified as specific or enriched at a certain meiotic stage, etc.).

      We agree that this is beyond the scope of the current study but will form the start of future projects from our group, and hopefully others.

      5.- Several typographical errors should be corrected (e.g., "Knetochores" in Fig. 4 legend, "250uM ABA" in Supp. Fig. 1 legend, etc.)

      Thank you for pointing these out, they have been corrected.

      Reviewer #3 (Significance (Required)):

      Koch et al. describe a novel methodology, SynSAC, to synchronize budding yeast cells in metaphase I or metaphase II during meiosis, as well and in mitotic metaphase, thereby enabling differential analyses among these cell division stages. Their approach builds on prior strategies originally developed in fission yeast and human cells models to induce a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC proteins upon addition of abscisic acid (ABA). The results from this manuscript are of special relevance for researchers studying meiosis and using Saccharomyces cerevisiae as a model. Moreover, the differential analysis of the composition and phosphorylation of kinetochores from meiotic metaphase I and metaphase II adds interest for the broader meiosis research community. Finally, regarding my expertise, I am a researcher specialized in the regulation of cell division.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In their manuscript, Koch et al. describe a novel strategy to synchronize cells of the budding yeast Saccharomyces cerevisiae in metaphase I and metaphase II, thereby facilitating comparative analyses between these meiotic stages. This approach, termed SynSAC, adapts a method previously developed in fission yeast and human cells that enables the ectopic induction of a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC components upon addition of the plant hormone abscisic acid (ABA). This is a valuable tool, which has the advantage that induces SAC-dependent inhibition of the anaphase promoting complex without perturbing kinetochores. Furthermore, since the same strategy and yeast strain can be also used to induce a metaphase arrest during mitosis, the methodology developed by Koch et al. enables comparative analyses between mitotic and meiotic cell divisions. To validate their strategy, the authors purified kinetochores from meiotic metaphase I and metaphase II, as well as from mitotic metaphase, and compared their protein composition and phosphorylation profiles. The results are presented clearly and in an organized manner. Despite the relevance of both the methodology and the comparative analyses, several main issues should be addressed:

      1.- In contrast to the strong metaphase arrest induced by ABA addition in mitosis (Supp. Fig. 2), the SynSAC strategy only promotes a delay in metaphase I and metaphase II as cells progress through meiosis. This delay extends the duration of both meiotic stages, but does not markedly increase the percentage of metaphase I or II cells in the population at a given timepoint of the meiotic time course (Fig. 1C). Therefore, although SynSAC broadens the time window for sample collection, it does not substantially improve differential analyses between stages compared with a standard NDT80 prophase block synchronization experiment. Could a higher ABA concentration or repeated hormone addition improve the tightness of the meiotic metaphase arrest? 2.- Unlike the standard SynSAC strategy, introducing mutations that prevent PP1 binding to the SynSAC construct considerably extended the duration of the meiotic metaphase arrests. In particular, mutating PP1 binding sites in both the RVxF (RASA) and the SILK (4A) motifs of the Spc105(1-455)-PYL construct caused a strong metaphase I arrest that persisted until the end of the meiotic time course (Fig. 3A). This stronger and more prolonged 4A-RASA SynSAC arrest would directly address the issue raised above. It is unclear why the authors did not emphasize more this improved system. Indeed, the 4A-RASA SynSAC approach could be presented as the optimal strategy to induce a conditional metaphase arrest in budding yeast meiosis, since it not only adapts but also improves the original methods designed for fission yeast and human cells. Along the same lines, it is surprising that the authors did not exploit the stronger arrest achieved with the 4A-RASA mutant to compare kinetochore composition at meiotic metaphase I and II. 3.- The results shown in Supp. Fig. 4C are intriguing and merit further discussion. Mitotic growth in ABA suggest that the RASA mutation silences the SynSAC effect, yet this was not observed for the 4A or the double 4A-RASA mutants. Notably, in contrast to mitosis, the SynSAC 4A-RASA mutation leads to a more pronounced metaphase I meiotic delay (Fig. 3A). It is also noteworthy that the RVAF mutation partially restores mitotic growth in ABA. This observation supports, as previously demonstrated in human cells, that Aurora B-mediated phosphorylation of S77 within the RVSF motif is important to prevent PP1 binding to Spc105 in budding yeast as well. 4.- To demonstrate the applicability of the SynSAC approach, the authors immunoprecipitated the kinetochore protein Dsn1 from cells arrested at different meiotic or mitotic stages, and compared kinetochore composition using data independent acquisition (DIA) mass spectrometry. Quantification and comparative analyses of total and kinetochore protein levels were conducted in parallel for cells expressing either FLAG-tagged or untagged Dsn1 (Supp. Fig. 7A-B). To better detect potential changes, protein abundances were next scaled to Dsn1 levels in each sample (Supp. Fig. 7C-D). However, it is not clear why the authors did not normalize protein abundance in the immunoprecipitations from tagged samples at each stage to the corresponding untagged control, instead of performing a separate analysis. This would be particularly relevant given the high sensitivity of DIA mass spectrometry, which enabled quantification of thousands of proteins. Furthermore, the authors compared protein abundances in tagged-samples from mitotic metaphase and meiotic prophase, metaphase I and metaphase II (Supp. Fig. 7E-F). If protein amounts in each case were not normalized to the untagged controls, as inferred from the text (lines 333 to 338), the observed differences could simply reflect global changes in protein expression at different stages rather than specific differences in protein association to kinetochores. 5.- Despite the large amount of potentially valuable data generated, the manuscript focuses mainly on results that reinforce previously established observations (e.g., premature SAC silencing in meiosis I by PP1, changes in kinetochore composition, etc.). The discussion would benefit from a deeper analysis of novel findings that underscore the broader significance of this study.

      Finally, minor concerns are:

      1.- Meiotic progression in SynSAC strains lacking Mad1, Mad2 or Mad3 is severely affected (Fig. 1D and Supp. Fig. 1), making it difficult to assess whether, as the authors state, the metaphase delays depend on the canonical SAC cascade. In addition, as a general note, graphs displaying meiotic time courses could be improved for clarity (e.g., thinner data lines, addition of axis gridlines and external tick marks, etc.). 2.- Spore viability following SynSAC induction in meiosis was used as an indicator that this experimental approach does not disrupt kinetochore function and chromosome segregation. However, this is an indirect measure. Direct monitoring of genome distribution using GFP-tagged chromosomes would have provided more robust evidence. Notably, the SynSAC mad3Δ mutant shows a slight viability defect, which might reflect chromosome segregation defects that are more pronounced in the absence of a functional SAC. 3.- It is surprising that, although SAC activity is proposed to be weaker in metaphase I, the levels of CPC/SAC proteins seem to be higher at this stage of meiosis than in metaphase II or mitotic metaphase (Fig. 4A-B). 4.- Although a more detailed exploration of kinetochore composition or phosphorylation changes is beyond the scope of the manuscript, some key observations could have been validated experimentally (e.g., enrichment of proteins at kinetochores, phosphorylation events that were identified as specific or enriched at a certain meiotic stage, etc.). 5.- Several typographical errors should be corrected (e.g., "Knetochores" in Fig. 4 legend, "250uM ABA" in Supp. Fig. 1 legend, etc.)

      Significance

      Koch et al. describe a novel methodology, SynSAC, to synchronize budding yeast cells in metaphase I or metaphase II during meiosis, as well and in mitotic metaphase, thereby enabling differential analyses among these cell division stages. Their approach builds on prior strategies originally developed in fission yeast and human cells models to induce a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC proteins upon addition of abscisic acid (ABA). The results from this manuscript are of special relevance for researchers studying meiosis and using Saccharomyces cerevisiae as a model. Moreover, the differential analysis of the composition and phosphorylation of kinetochores from meiotic metaphase I and metaphase II adds interest for the broader meiosis research community. Finally, regarding my expertise, I am a researcher specialized in the regulation of cell division.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The manuscript submitted by Koch et al. describes a novel approach to collect budding yeast cells in metaphase I or metaphase II by synthetically activating the spinde checkpoint (SAC). The arrest is transient and reversible. This synchronization strategy will be extremely useful for studying meiosis I and meiosis II, and compare the two divisions. The authors characterized this so-named syncSACapproach and could confirm previous observations that the SAC arrest is less efficient in meiosis I than in meiosis II. They found that downregulation of the SAC response through PP1 phosphatase is stronger in meiosis I than in meiosis II. The authors then went on to purify kinetochore-associated proteins from metaphase I and II extracts for proteome and phosphoproteome analysis. Their data will be of significant interest to the cell cycle community (they compared their datasets also to kinetochores purified from cells arrested in prophase I and -with SynSAC in mitosis).

      I have only a couple of minor comments:

      1) I would add the Suppl Figure 1A to main Figure 1A. What is really exciting here is the arrest in metaphase II, so I don't understand why the authors characterize metaphase I in the main figure, but not metaphase II. But this is only a suggestion.

      2) Line 197, the authors state: ...SyncSACinduced a more pronounced delay in metaphase II than in metaphase I. However, line 229 and 240 the auhtors talk about a "longer delay in metaphase <i compared to metaphase II"... this seems to be a mix-up.

      3) The authors describe striking differences for both protein abundance and phosphorylation for key kinetochore associated proteins. I found one very interesting protein that seems to be very abundant and phosphorylated in metaphase I but not metaphase II, namely Sgo1. Do the authors think that Sgo1 is not required in metaphase II anymore? (Top hit in suppl Fig 8D).

      Significance

      The technique described here will be of great interest to the cell cycle community. Furthermore, the authors provide data sets on purified kinetochores of different meiotic stages and compare them to mitosis. This paper will thus be highly cited, for the technique, and also for the application of the technique.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

      The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

      Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division. Overall, I have only a few minor suggestions.

      1) In wild-type - Pds1 levels are high during M1 and A1, but low in MII. Can the authors comment on this? In line 217, what is meant by "slightly attenuated? Can the authors comment on how anaphase occurs in presence of high Pds1? There is even a low but significant level in MII.

      2) The figures with data characterizing the system are mostly graphs showing time course of MI and MII. There is no cytology, which is a little surprising since the stage is determined by spindle morphology. It would help to see sample sizes (ie. In the Figure legends) and also representative images. It would also be nice to see images comparing the same stage in the SynSAC cells versus normal cells. Are there any differences in the morphology of the spindles or chromosomes when in the SynSAC system?

      3) A possible criticism of this system could be that the SAC signal promoting arrest is not coming from the kinetochore. Are there any possible consequences of this? In vertebrate cells, the RZZ complex streams off the kinetochore. Yeast don't have RZZ but this is an example of something that is SAC dependent and happens at the kinetochore. Can the authors discuss possible limitations such as this? Does the inhibition of the APC effect the native kinetochores? This could be good or bad. A bad possibility is that the cell is behaving as if it is in MII, but the kinetochores have made their microtubule attachments and behave as if in anaphase.

      Significance

      These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

      The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

      Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-03160

      Corresponding author(s) Padinjat, Raghu

      [The “revision plan” should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

      • *

      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

      • *

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

      1. General Statements [optional]

      We thank all three reviewers for appreciating the novelty of our analysis of CERT function in a physiological context in vivo. While many studies have been published on the biochemistry and function of CERT in cultured cells, there are limited studies, if any, relating the impact of CRT function at the biochemical level to its function on a physiological process, in our case the electrical response to light.

      We also that all reviewers for commenting on the importance of our rescue of dcert mutants with hCERT and the scientific insights raised by this experiment. All reviewers have also noted the importance of strengthening our observation that hCERT, in these cells, is localized at ER-PM MCS rather that the more widely reported localization at the Golgi. We highlight that many excellent studies which have localized CERT at the Golgi are performed in cultured, immortalized, mammalian cells. There are limited studies on the localization of this protein in primary cells, neurons or in polarized cells. With the additional experiments we have proposed in the revision for this aspect of the manuscript, we believe the findings will be of great novelty and widespread interest.

      We believe we can address almost all points raised by reviewers thereby strengthening this exciting manuscript.

      2. Description of the planned revisions

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

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

      This manuscript dissects the physiological function of ceramide transfer protein (CERT) by studying the phenotype of CERT null Drosophila.

      dCERT null animals have a reduced electrical response to light in their photoreceptors, reduced baseline PIP2 accumulation in the cells and delayed re-synthesis of PIP2 and its precursor, PI4P after light stimulation. There are also reduced ER:PM contact sites at the rhabdomere and a corresponding reduction in the localization of PI/PA exchange protein, RDGB at this site. Therefore, the animals seem to have an impaired ability for sustaining phototransduction, which is nonetheless milder than that seen after loss of RDGB, for example. In terms of biochemical function, there is no overall change in ceramides, with some minor increases in specific short chain pools. There is however a large decrease in PE-ceramide species, again selective for a few molecular species. Curiously, decreasing ceramides with a mutant in ceramide synthesis is able to partially rescue both the electrical response and RDGB localization in dCERT flies, implying the increased ceramide species contribute to the phenotype. In addition, a mutation in PE-ceramide synthase largely phenocopies the dCERT null, exhiniting both increases ceramides and decreased PE-ceramide.

      In addition, dCERT flies were shown to have reduced localization of some plasma membrane proteins to detergent-resistant membrane fractions, as well as up regulation of the IRE1 and PERK stress-response pathways. Finally, dCERT nulls could be rescued with the human CERT protein, demonstrating conservation of core physiological function between these animals. Surprisingly, CERT is reported to localize to the ER:PM junctions at rhabdomeres, as opposed to the expected ER:Golgi contact sites. Specific areas where the manuscript could be strengthened include:

      Figure 2 studies the phototransduction system. Although clear changes in PI4P and PIP2 are seen, it would be interesting to see if changed PA accumulation occur in the dCERT animals, since RDGB localization is disrupted: this is expected to cause PM PA accumulation along with reduced PIP2 synthesis.

      It is an important question raised by the reviewer to check PA levels. In the present study we have noticed that localization of RDGB at the base of the rhabdomere in dcert1 is reduced but not completely removed. Consequently, one may consider the situation on dcert1 as a partial loss of function of RDGB and consistent with this, the delay in PI4P and PI(4,5)P2 resynthesis is not as severe as in rdgB9 which is a strong hypomorph (PMID: 26203165).

      rdgB9 mutants also show an elevation in PA levels and the reviewer is right that one might expect changes in PA levels too as RDGB is a PI/PA transfer protein. We expect that if measured, there will be a modest elevation in PA levels. However, previous work has shown that elevation of PA levels at the or close to the rhabdomere lead to retinal degeneration Specifically, elevated PA levels by dPLD overexpression disrupts rhabdomere biogenesis and leads to retinal degeneration (PMID: 19349583). Similarly, loss of the lipid transfer protein RDGB leads to photoreceptor degeneration (PMID: 26203165). In this study, we report that retinal degeneration is not a phenotype of dcert1. Thus measurements of PA levels though interesting may not be that informative in the context of the present study. However, if necessary, we can measure PA levels in dcert1.

      Lines 228-230 state: "These findings suggest an important contribution for reduced PE - Cer levels in the eye phenotypes of dcert". Does it not also suggest a contribution of the elevated ceramide species, since these are also observed in the CPES animals?

      We agree with the reviewer that not only reduced PE-Ceramide but also elevated ceramide levels in GMR>CPESi could contribute to the eye phenotype. This statement will be revised to reflect this conclusion.

      Figure 6D is a key finding that human CERT localized to the rhabdomere at ER:PM contact sites, though the reviewer was not convinced by these images. Is the protein truly localized to the contact sites, or simply have a pool of over-expressed protein localized to the surrounding cytoplasm? It also does not rule out localization (and therefore function) at ER:PM contact sites.

      Since hCERT completely rescued eye phenotype of dcert1 the localization we observe for hCERT must be at least partly relevant. We will perform additional IHC experiments to

      • Co-localize hCERT with an ER-PM MCS marker, e.g RDGB in wild type flies
      • Co-localize hCERT with VAP-A that is enriched at the ER-PM MCS. This should help to determine if there are MCS and non-MCS pools of hCERT in these cells. marker, e.g RDGB in wild type flies
      • Test if there is a pool of hCERT, in these cells that also localizes (or not) with the Golgi marker Golgin 84. These will be included in the revision to strengthen this important point.

      Statistics: There are a large number of t-tests employed that do not correct for multiple comparisons, for example in figures 3B, 3D, 3H, 4C, 6C, S2A, S2B, S3B and S3C.

      We will performed multiple comparisons with mentioned data and incorporate in the revised manuscript.

      There are two Western blotting sections in the methods.

      The first Western blotting methods is for general blots in the paper. The second western blotting section is related to the samples from detergent resistant membrane (DRM) fractions. We will clearly explain this information in the methods section of the manuscript.

      Reviewer #1 (Significance (Required)):

      Overall, the manuscript is clearly and succinctly written, with the data well presented and mostly convincing. The paper demonstrates clear phenotypes associated with loss of dCERT function, with surprising consequences for the function of a signaling system localized to ER:PM contact sites. To this reviewer, there seem to be three cogent observations of the paper: (i) loss of dCERT leads to accumulation of ceramides and loss of PE-ceramide, which together drive the phenotype. (ii) this ceramide alteration disrupts ER:PM contact sites and thus impairs phototransduction and (iii) rescue by human CERT and its apparent localization to ER:PM contact sites implies a potential novel site of action. Although surprising and novel, the significance of these observations are a little unclear: there is no obvious mechanism by which the elevated ceramide species and decreased PE-ceramide causes the specific failure in phototrasnduction, and the evidence for a novel site of action of CERT at the ER:PM contact sites is not compelling. Therefore, although an interesting and novel set of observations, the manuscript does not reveal a clear mechanistic basis for CERT physiological function.

      We thank reviewer for appreciating the quality of our manuscript while also highlighting points through which its impact can be enhanced. To our knowledge this is one of the first studies to tackle the challenging problem of a role for CERT in physiological function. We would like to highlight two points raised:

      • We do understand that the localisation of hCERT at ER-PM MCS is unusual compared to the traditional reported localization to ER-Golgi sites. This is important for the overall interpretation of the results in the paper on how dCERT regulates phototransduction. As indicated in response to an earlier comment by the reviewer we will perform additional experiments to strengthen our conclusion of the localization of hCERT.
      • With regard to how loss of dCERT affects phototransduction, we feel to likely mechanisms contribute. If the localization of hCERT to ER-PM MCS is verified through additional experiments (see proposal above) then it is important to note that ER-PM MCS in these cells includes the SMC (smooth endoplasmic reticulum) the major site of lipid synthesis. It is possible that loss of dCERT leads to ceramide accumulation in the smooth ER and disruption of ER-PM contacts. That may explain why reducing the levels of ceramide at this site partially rescues the eye phenotype.

      The multi-protein INAD-TRP-NORPA complex, central to phototransduction have previously been shown to localise to DRMs in photoreceptors. PE-Ceramides are important contributors to the formation of plasma membrane DRMs and we have presented biochemical evidence that the formation of these DRMs are reduced in the dcert1. This may be a mechanism contributing to reduced phototransduction. This latter mechanism has been proposed as a physiological function of DRMs but we think our data may be the first to show it in a physiological model.

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

      Summary Non-vesicular lipid transfer by lipid transfer proteins regulates organelle lipid compositions and functions. CERT transfers ceramide from the ER to Golgi to produce sphingomyelin, although CERT function in animal development and physiology is less clear. Using dcert1 (a protein-null allele), this paper shows a disruption of the sole Drosophila CERT gene causes reduced ERG amplitude in photoreceptors. While the level and localization of phototransduction machinery appears unaffected, the level of PIP2 and the localization of RDGB are perturbed. Collectively, these observations establish a novel link between CERT and phospholipase signaling in phototransduction. To understand the molecular mechanism further, the authors performed lipid chromatography and mass spec to characterize ceramide species in dcert1. This analysis reveals that whereas the total ceramide remains unaffected, most PE-ceramide species are reduced. The authors use lace mutant (serine palmitoyl transferase) and CPES (ceramide phosphoethanolamine synthase) RNAi to distinguish whether it is the accumulation of ceramide in the ER or the reduction of sphingolipid derivates in the Golgi that is the cause for the reduced ERG amplitude. Mutating one copy of lace reduces ceramide level by 50% and partially rescues the ERG defect, suggesting that the accumulation of ceramide in the ER is a cause. CPES RNAi phenocopies the reduced ERG amplitude, suggesting the production of certain sphingolipid is also relevant.

      Major comments: 1. By showing the reduced PIP2 level, the decreased SMC sites at the base of rhabdomeres, and the diffused RDGB localization in dcert1, the authors favor the model, in which the disruption of ceramide metabolism affects PIP transport. However, it is unclear if the reduced PIP2 level (i.e., reduced PH-PLCd::GFP staining) is specific to the rhabdomeres. It should be possible to compare PH-PLCd::GFP signals in different plasma membranes between wildtype and dcert1. If PH-PLCd::GFP signal is specifically reduced at the rhabdomeres, this conclusion will be greatly strengthened. In addition, the photoreceptor apical plasma membrane includes rhabdomere and stalk membrane. Is the PH-PLCd::GFP signal at the stalk membrane also affected?

      Due to the physical organization of optics in the fly eye, the pseudopupil imaging method used in this study collects the signal for the PIP2 probe (PH-PLCd::GFP) mainly from the apical rhabdomere membrane of photoreceptors in live imaging experimental mode. Therefore, the PIP2 signal from these experiments cannot be used to interpret the level of PIP2 either at the stalk membrane or indeed the basolateral membrane.

      The point raised by the reviewer, i.e whether CERT selectively controls PIP2 levels at the rhabdomere membrane or not, is an interesting one. To do this, we will need to fix fly photoreceptors and determine the PH-PLCd::GFP signal using single slice confocal imaging. When combined with a stalk marker such as CRUMBS, it should be possible to address the question of which are the membrane domains at which dCERT controls PIP2 levels. If the sole mechanism of action of dCERT is via disruption of ER-PM MCS then only the apical rhabdomere membrane PIP2 should be affected leaving the stalk membrane and basolateral membrane unaffected.

      Thank you very much for raising this specific point.

      The analysis of RDGB localization should be done in mosaic dcert1 retinas, which will be more convincing with internal control for each comparison. In addition, the phalloidin staining in Figure 2J shows distinct patterns of adherens junctions, indicating that the wildtype and dcert1 were imaged at different focal planes.

      We understand that having mosaics is an alternative an elegant way to perform a a side by side analysis of control and mutant. However this would require significant investment of time and effort, perhaps beyond the scope of this study. If we were to perform a mosaic analysis, this would compromise our ERG analysis since ERG is an extracellular recording We feel that this is beyond the scope of this study and perhaps may not be necessary as such (see below).

      In the revision we will present equivalent sections of control and dcert1 taken from the nuclear plane of the photoreceptor. This should resolve the reviewer’s concerns.

      The significance of ceramide species levels in dcert1 and GMR>CPESRNAi needs to be explained better. Do certain alterations represent accumulation of ceramides in the ER?

      Species level analysis of changes in ceramides reveal that elevations in dcert1 are seen mainly in the short chain ceramides (14 and 16 carbon chains). These most likely represent the short chain ceramides synthesised in the ER and accumulating due to the block in further metabolism to PE-Cer due to depletion in CPES.

      Species level analysis of changes in ceramides reveal that in dcert1 there is a ceramide transport related defect leading to elevation, primarily, in the short chain ceramides (14 and 16 carbon chains), and this selective supply defect leads to a reduction in PE-Cer levels, with a maximum change in the ratio of short-chain Cer:PE Cer (Figure 3A-D). Though there is no apparent change in the total ceramide level the species specific elevation in the ceramides disturb the fine -balance between the short-chain ceramides and the long and very-long chain ceramides. As the function of long and very-long chain ceramides are implicated in dendrite development and neuronal morphology (doi: 10.1371/journal.pgen.1011880), therefore this alteration in the fine balance between different ceramide species probably impacts the integrity and fluidity of the membrane environment. On the other hand it leads to a possibility of a defined function of the short-chain ceramides in electrical responses to light signalling in the eye, especially with respect to the PE-ceramides that are reduced by around 50%.

      In contrast the GMR>CPESRNAi leads to more of a substrate accumulation showing ceramide increase (14, 16, 18, 20 carbon chains) and decrease in PE-Cer levels (Figure 4D, E). In this case Cer accumulation is due to the block in further metabolism to PE-Cer arising from depletion in CPES.

      We will include this in the discussion of a revised version.

      The suppression by lace is interpreted as evidence that the reduced ERG amplitude in dcert1 is caused by ceramide accumulation in the ER. This interpretation seems preliminary as lace may interact with dcert genetically by other mechanisms.

      The dcert1 mutant exhibits increased levels of short-chain ceramides (Fig 3B), whereas the lace heterozygous mutant (laceK05305/+) displays reduced short-chain ceramide levels (Supp Fig 2B). In the laceK05305/+; dcert1 double mutant, ceramide levels are lower than those observed in the dcert1 mutant alone (Supp Fig 2B), indicating a partial genetic rescue of the elevated ceramide phenotype.

      Furthermore, through multiple independent genetic manipulations that modulate ceramide metabolism (alterations of dcert, cpes and lace), we consistently observe that increased ceramide levels correlate with a reduction in ERG amplitude, suggesting that ceramide accumulation negatively impacts photoreceptor function. Taken together, these observations indicate that the reduction in ceramide levels in the laceK05305/+; dcert1 double mutant likely contributes to the suppression of the ERG defect observed in the dcert1 mutant.

      The authors show that ERG amplitude is reduced in GMR>CPESRNAi. While this phenocopying is consistent with the reduced ERG amplitude in dcert1 being caused by reduced production of PE-ceramide, GMR>CPESRNAi also shows an increase in total ceramide level. Could this support the hypothesis that reduced ERG amplitude is caused by an accumulation of ceramide elsewhere? In addition, is the ERG amplitude reduction in GMR>CPESRNAi sensitive to lace?

      We agree that in addition to reduced PE-Ceramide, the elevated ceramide levels in GMR>CPESi could contribute to the eye phenotype. We will introduce lace heterozygous mutant in the GMR>CPESi background to test the contribution of elevated ceramide levels in the *GMR>CPESi * background and incorporate the data in the revision. Thank you for this suggestion.

      Along the same line, while the total ceramide level is significantly reduced in lace heterozygotes, is the PE-ceramide level also reduced? If yes, wouldn't this be contradictory to PE-ceramide production being important for ERG amplitude?

      Mass spec measurements show that levels of PE-Cer were not reduced in lacek05305/+ compared to wild type. This data will be included in the revised manuscript. However, the ERG amplitude of these flies and also in those with lace depletion using two independent RNAi lines were not reduced.

      What is the explanation and significance for the age-dependent deterioration of ERG amplitude in dcert1? Likewise, the significance of no retinal degeneration is not clearly presented.

      There could be multiple reasons for the age dependent deterioration of the ERG amplitude, in the absence of retinal degeneration. Drosophila phototransduction cascade depends heavily on ATP production. The age dependent reduction in ATP synthesis could lead to deterioration in the ERG amplitude. These may include instability of the DRMs due to reduced PE-Cer, lower ATP levels due to mitochondrial dysfunction, an perhaps others. A previous study has shown that ATP production is highly reduced along with oxidative stress and metabolic dysfunction in dcert1 flies aged to 10 days and beyond (PMID: 17592126). The same study has also found no neuronal degeneration in dcert1 that phenocopies absence of photoreceptor degeneration in the present study. We will attempt a few experiments to rule in or rule out the these and revise the discussion accordingly.

      The rescue of dcert1 phenotype by the expression of human CERT is a nice result. In addition to demonstrating a functional conservation, it allows a determination of CERT protein localization. However, the quality of images in Figure 6D should be improved. The phalloidin staining was rather poor, and the CNX99A in the lower panel was over-exposed, generating bleed-through signals at the rhabdomeres. In addition, the localization of hCERT should be explored further. For instance, does hCERT colocalize with RDGB? Is the hCERT localization altered in lace or GMR>CPESRNAi background?

      As indicated in response to reviewer 1:

      We will perform additional IHC experiments to

      • Co-localize hCERT with an ER-PM MCS marker, e.g RDGB in wild type flies
      • Co-localize hCERT with VAP-A that is enriched at the ER-PM MCS. This should help to determine if there are MCS and non-MCS pools of hCERT in these cells. marker, e.g RDGB in wild type flies
      • Test if there is a pool of hCERT, in these cells that also localizes (or not) with the Golgi marker Golgin 84. These will be included in the revision to strengthen this important point.

      We will also attempt to perform hCERT localization in lace or GMR>CPESRNAi background

      Minor comments: 1. In Line 128, Df(732) should be Df(3L)BSC732.

      Changes will be incorporated in the main manuscript.

      GMR-SMSrRNAi shows an increase in ERG peak amplitude. Is there an explanation for this?

      GMR-SMSrRNAi did show slight increase in ERG peak amplitude but was not statistically significant.

      Reviewer #2 (Significance (Required)):

      Significance As CERT mutations are implicated in human learning disability, a better understanding of CERT function in neuronal cells is certainly of interest. While the link between ceramide transport and phospholipase signaling is novel and interesting, this paper does not clearly explain the mechanism. In addition, as the ERG were measured long after the retinal cells were deficient in CERT or CPES, it is difficult to assess whether the observed phenotype is a primary defect. Furthermore, the quality of some images needs to be improved. Thus, I feel the manuscript in its current form is too preliminary.

      We thank reviewer for highlighting the importance and significance of our work in the light of recent studies of CERT function in ID. As with all genetic studies it is difficult to completely disentangle the role of a gene during development from a role only in the adult. However, we will attempt to perhaps use the GAL80ts system to uncouple these two potential components of CERT function in photoreceptors. The goal will be to determine if CERT has a specific role only in adult photoreceptors or if this is coupled to a developmental role. Since ID is as a neurodevelopmental disorder, a developmental role for CERT would be equally interesting.

      As previously indicated images will be improved bearing in mind the reviewer comments.

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

      Summary: Lipid transfer proteins (LTPs) shuttle lipids between organelle membranes at membrane contact sites (MCSs). While extensive biochemical and cell culture studies have elucidated many aspects of LTP function, their in vivo physiological roles are only beginning to be understood. In this manuscript, the authors investigate the physiological role of the ceramide transfer protein (CERT) in Drosophila adult photoreceptors-a model previously employed by this group to study LTP function at ER-PM contact sites under physiological conditions. Using a combination of genetic, biochemical, and physiological approaches, they analyze a protein-null mutant of dcert. They show that loss of dcert causes a reduction in electrical response to light with progressive decrease in electroretinogram (ERG) amplitude with age but no retinal degeneration. Lipidomic analysis shows that while the total levels of ceramides are not changed in dcert mutants, they do observe significant change in certain species of ceramides and depletion of downstream metabolite phosphoethanolamine ceramide (PE-Cer). Using fluorescent biosensors, the authors demonstrate reduced PIP2 levels at the plasma membrane, unchanged basal PI4P levels and slower resynthesis kinetics of both lipids following depletion. Electron microscopy and immunolabeling further reveal a reduced density of ER-PM MCSs and mislocalization of the MCS-resident lipid transfer protein RDGB. Genetic interaction studies with lace and RNAi-mediated knockdown of CPES support the conclusion that both ER ceramide accumulation and PM PE-Cer depletion contribute to the observed defects in dcert mutants. In addition, detergent-resistant membrane fractionation indicates altered plasma membrane organization in the absence of dcert. The study also reports upregulation of unfolded protein response transcripts, including IRE1 and PERK, suggesting increased ER stress. Finally, expression of human CERT rescues the reduced electrical response, demonstrating functional conservation across species. Overall the manuscript is well written that builds on established work and experiments are technically rigorous. The results are clearly presented and provide valuable insights into the physiological role of CERT.

      Major comments: 1.The reduced ERG amplitude appears to be the central phenotype associated with the loss of dcert, and most of the experiments in this manuscript effectively build a mechanistic framework to explain this observation. However, the experiments addressing detergent-resistant membrane domains (DRMs) and the unfolded protein response (UPR) seem somewhat disconnected from the main focus of the study. The DRM and UPR data feel peripheral and could benefit from few experiments for functional linkage to the ERG defect or should be moved to supplementary.

      We agree with the reviewer that further experiments are needed to link the DRM data to the ERG defects. That would need specific biochemical alteration at the PM to modulate PE-Cer species and their effect on scaffolding proteins required for phototransduction (that is beyond the scope of the present study). We will consider moving these to the supplementary section as suggested by the reviewer.

      2.The changes in ceramide species and reduction in PE-Cer are key findings of the study. These results should be further validated by performing a genetic rescue using the BAC or hCERT fly line to confirm that the lipidomic changes are specifically due to loss of CERT function.

      Thank you for this comment. We will include this in the revised manuscript.

      3.Figure 2B-C and 2E-F: Representative images corresponding to the quantified data should be included to illustrate the changes in PIP2 and PI4P reporters. Given that the fluorescence intensity of the PIP2 reporter at the PM is reduced in the dcert mutant relative to control, the authors should also verify that the reporter is expressed at comparable levels across genotypes.

      • As mentioned by the reviewer we will include representative images alongside our quantified data both of the basal ones and that of the kinetic study.
      • Western blot of reporters (PH-PLCd::GFP and P4M::GFP) across genotypes will be added to the revised manuscript. 4.Figure 2J-K: The partial mislocalization of RDGB represents an important observation that could mechanistically explain the reduced resynthesis of PI4P and PIP2 and consequently, the decreased ERG amplitude in dcert mutants. However, this result requires further validation. First, the authors should confirm whether this mislocalization is specific to RDGB by performing co-staining with another ER-PM MCS marker, such as VAP-A, to assess whether overall MCS organization is disrupted. Second, the quantification of RDGB enrichment at ER-PM MCSs should be refined. From the representative images, RDGB appears redistributed toward the photoreceptor cell body, but the presented quantification does not clearly reflect this shift. The authors should therefore include an analysis comparing RDGB levels in the cell body versus the submicrovillar region across genotypes. This analysis should be repeated for similar experiments across the study. Additionally, the total RDGB protein level should be quantified and reported. Finally, since RDGB mislocalization could directly contribute to the decreased ERG amplitude, it would be valuable to test whether overexpression of RDGB in dcert mutants can rescue the ERG phenotype.

      • In our ultrastructural studies (Fig. 2H, 2I and Sup. Fig. 1A, 1B) we did see reduction in PM-SMC MCS that was corroborated with RDGB staining.

      • Comparative ratio analysis of RDGB localisation at ER-PM MCS vs cell body will be included in the manuscript for all RDGB staining.
      • We have done western analysis for total RDGB protein level in ROR and dcert1. This data will be included in the revised manuscript.
      • This is a very interesting suggestion and we will test if RDGB overexpression can rescue ERG phenotype in dcert1.

      5.Figure 3F and I-J: Inclusion of appropriate WT and laceK05205/+ controls is necessary to allow proper interpretation of the results. These controls would strengthen the conclusions regarding the functional relationship between dcert and lace.

      Changes will be incorporated as per the suggestion.

      6.Figure 5C: The representative images shown here appear to contradict the findings described in Figure 2A. In Figure 5C, Rhodopsin 1 levels seem markedly reduced in the dcert mutants, whereas the text states that Rh1 levels are comparable between control and mutant photoreceptors. The authors should replace or reverify the representative images to ensure that they accurately reflect the conclusions presented in the text.

      We will reverify the representative images and changes will be accordingly incorporated.

      7.Figure 6D: The reported localization of hCERT to ER-PM MCSs is a key and potentially insightful observation, as it suggests the subcellular site of dcert activity in photoreceptors. However, the representative images provided are not sufficiently conclusive to support this claim. The authors should validate hCERT localization by co-staining with established markers like RDGB for ER-PM CNX99A for the ER and a Golgi marker since mammalian CERT is classically localized to ER-Golgi interfaces. Optionally, the authors could also quantify the relative distribution of hCERT among these compartments to provide a clearer assessment of its subcellular localization.

      As indicated in response to reviewer 1:

      We will perform additional IHC experiments to

      • Co-localize hCERT with an ER-PM MCS marker, e.g RDGB in wild type flies
      • Co-localize hCERT with VAP-A that is enriched at the ER-PM MCS. This should help to determine if there are MCS and non-MCS pools of hCERT in these cells. marker, e.g RDGB in wild type flies
      • Test if there is a pool of hCERT, in these cells that also localizes (or not) with the Golgi marker Golgin 84. These will be included in the revision to strengthen this important point.

      Minor comments: 1.In the first paragraph of introduction, authors should consider citing few of the key MCS literature.

      Additional literature will be included as per the suggestion.

      2.Line 132: data not shown is not acceptable. Authors should consider presenting the findings in the supplemental figure.

      Data will be added in supplement as per the suggestion.

      3.The authors should include a comprehensive table or Excel sheet summarizing all statistical analyses. This should include the sample size, type of statistical test used and exact p-values. Providing this information will improve the transparency, reproducibility and overall rigor of the study.

      We will provide all the statistical analyses in mentioned format as per the suggestion.

      4.The materials and methods section can be reorganized to include citation for flystocks which do not have stock number or RRIDs if the stocks were previously described but are not available from public repositories. They should expand on the details of various quantification methods used in the study. Finally including a section of Statistical analyses would further enhance transparency and reproducibility

      • Stock details will be added wherever missing as per the suggestion.
      • Statistical analyses section will be included in the material and methods. **Referee cross-commenting**

      1.I concur with Reviewer 1 regarding the need for more detailed reporting of statistical analyses.

      We will perform multiple comparisons with mentioned data and incorporate in the revised manuscript.

      2.I also agree with Reviewer 3 that the discussion should be expanded to address the age-dependent deterioration of ERG amplitude observed in the dcert mutants. This progressive decline could provide valuable insight into the long-term requirement of CERT function and signaling capacity at the photoreceptor membrane.

      Expanded discussion on the age dependent ERG amplitude decline will be incorporated in the discussion as per the suggestion.

      Reviewer #3 (Significance (Required)):

      This study explores the physiological function of CERT, a LTP localized at MCSs in Drosophila photoreceptors and uncovers a novel role in regulating plasma membrane PE-Cer levels and GPCR-mediated signaling. These findings significantly advances our understanding of how CERT-mediated lipid transport regulates G-protein coupled phospholipase C signaling in vivo. This work also highlights Drosophila photoreceptors as a powerful system to analyze the physiological significance of lipid-dependent signaling processes. This work will be of interest to researchers in neuronal cell biology, membrane dynamics and lipid signaling community. This review is based on my expertise in neuronal cell biology.

      We thank the reviewer for appreciating the significance of our work from a neuroscience perspective.

      • *

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

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

      • *

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

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

      • *

      We can address all reviewer points in the revision. However, we will not be able to perform a mosaic analysis of the impact of dcert1 mutant in the retina. We feel this is beyond the scope of this revision. In our response, we have highlighted how controls included in the revision offset the need for a mosaic analysis at this stage.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Lipid transfer proteins (LTPs) shuttle lipids between organelle membranes at membrane contact sites (MCSs). While extensive biochemical and cell culture studies have elucidated many aspects of LTP function, their in vivo physiological roles are only beginning to be understood. In this manuscript, the authors investigate the physiological role of the ceramide transfer protein (CERT) in Drosophila adult photoreceptors-a model previously employed by this group to study LTP function at ER-PM contact sites under physiological conditions. Using a combination of genetic, biochemical, and physiological approaches, they analyze a protein-null mutant of dcert. They show that loss of dcert causes a reduction in electrical response to light with progressive decrease in electroretinogram (ERG) amplitude with age but no retinal degeneration. Lipidomic analysis shows that while the total levels of ceramides are not changed in dcert mutants, they do observe significant change in certain species of ceramides and depletion of downstream metabolite phosphoethanolamine ceramide (PE-Cer). Using fluorescent biosensors, the authors demonstrate reduced PIP2 levels at the plasma membrane, unchanged basal PI4P levels and slower resynthesis kinetics of both lipids following depletion. Electron microscopy and immunolabeling further reveal a reduced density of ER-PM MCSs and mislocalization of the MCS-resident lipid transfer protein RDGB. Genetic interaction studies with lace and RNAi-mediated knockdown of CPES support the conclusion that both ER ceramide accumulation and PM PE-Cer depletion contribute to the observed defects in dcert mutants. In addition, detergent-resistant membrane fractionation indicates altered plasma membrane organization in the absence of dcert. The study also reports upregulation of unfolded protein response transcripts, including IRE1 and PERK, suggesting increased ER stress. Finally, expression of human CERT rescues the reduced electrical response, demonstrating functional conservation across species.Overall the manuscript is well written that builds on established work and experiments are technically rigorous. The results are clearly presented and provide valuable insights into the physiological role of CERT.

      Major comments:

      1.The reduced ERG amplitude appears to be the central phenotype associated with the loss of dcert, and most of the experiments in this manuscript effectively build a mechanistic framework to explain this observation. However, the experiments addressing detergent-resistant membrane domains (DRMs) and the unfolded protein response (UPR) seem somewhat disconnected from the main focus of the study. The DRM and UPR data feel peripheral and could benefit from few experiments for functional linkage to the ERG defect or should be moved to supplementary. 2.The changes in ceramide species and reduction in PE-Cer are key findings of the study. These results should be further validated by performing a genetic rescue using the BAC or hCERT fly line to confirm that the lipidomic changes are specifically due to loss of CERT function. 3.Figure 2B-C and 2E-F: Representative images corresponding to the quantified data should be included to illustrate the changes in PIP2 and PI4P reporters. Given that the fluorescence intensity of the PIP2 reporter at the PM is reduced in the dcert mutant relative to control, the authors should also verify that the reporter is expressed at comparable levels across genotypes. 4.Figure 2J-K: The partial mislocalization of RDGB represents an important observation that could mechanistically explain the reduced resynthesis of PI4P and PIP2 and consequently, the decreased ERG amplitude in dcert mutants. However, this result requires further validation. First, the authors should confirm whether this mislocalization is specific to RDGB by performing co-staining with another ER-PM MCS marker, such as VAP-A, to assess whether overall MCS organization is disrupted. Second, the quantification of RDGB enrichment at ER-PM MCSs should be refined. From the representative images, RDGB appears redistributed toward the photoreceptor cell body, but the presented quantification does not clearly reflect this shift. The authors should therefore include an analysis comparing RDGB levels in the cell body versus the submicrovillar region across genotypes. This analysis should be repeated for similar experiments across the study. Additionally, the total RDGB protein level should be quantified and reported. Finally, since RDGB mislocalization could directly contribute to the decreased ERG amplitude, it would be valuable to test whether overexpression of RDGB in dcert mutants can rescue the ERG phenotype. 5.Figure 3F and I-J: Inclusion of appropriate WT and laceK05205/+ controls is necessary to allow proper interpretation of the results. These controls would strengthen the conclusions regarding the functional relationship between dcert and lace. 6.Figure 5C: The representative images shown here appear to contradict the findings described in Figure 2A. In Figure 5C, Rhodopsin 1 levels seem markedly reduced in the dcert mutants, whereas the text states that Rh1 levels are comparable between control and mutant photoreceptors. The authors should replace or reverify the representative images to ensure that they accurately reflect the conclusions presented in the text. 7.Figure 6D: The reported localization of hCERT to ER-PM MCSs is a key and potentially insightful observation, as it suggests the subcellular site of dcert activity in photoreceptors. However, the representative images provided are not sufficiently conclusive to support this claim. The authors should validate hCERT localization by co-staining with established markers like RDGB for ER-PM CNX99A for the ER and a Golgi marker since mammalian CERT is classically localized to ER-Golgi interfaces. Optionally, the authors could also quantify the relative distribution of hCERT among these compartments to provide a clearer assessment of its subcellular localization.

      Minor comments:

      1.In the first paragraph of introduction, authors should consider citing few of the key MCS literature. 2.Line 132: data not shown is not acceptable. Authors should consider presenting the findings in the supplemental figure. 3.The authors should include a comprehensive table or Excel sheet summarizing all statistical analyses. This should include the sample size, type of statistical test used and exact p-values. Providing this information will improve the transparency, reproducibility and overall rigor of the study. 4.The materials and methods section can be reorganized to include citation for flystocks which do not have stock number or RRIDs if the stocks were previously described but are not available from public repositories. They should expand on the details of various quantification methods used in the study. Finally including a section of Statistical analyses would further enhance transparency and reproducibility

      Referee cross-commenting

      1.I concur with Reviewer 1 regarding the need for more detailed reporting of statistical analyses. 2.I also agree with Reviewer 3 that the discussion should be expanded to address the age-dependent deterioration of ERG amplitude observed in the dcert mutants. This progressive decline could provide valuable insight into the long-term requirement of CERT function and signaling capacity at the photoreceptor membrane.

      Significance

      This study explores the physiological function of CERT, a LTP localized at MCSs in Drosophila photoreceptors and uncovers a novel role in regulating plasma membrane PE-Cer levels and GPCR-mediated signaling. These findings significantly advances our understanding of how CERT-mediated lipid transport regulates G-protein coupled phospholipase C signaling in vivo. This work also highlights Drosophila photoreceptors as a powerful system to analyze the physiological significance of lipid-dependent signaling processes. This work will be of interest to researchers in neuronal cell biology, membrane dynamics and lipid signaling community. This review is based on my expertise in neuronal cell biology.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Non-vesicular lipid transfer by lipid transfer proteins regulates organelle lipid compositions and functions. CERT transfers ceramide from the ER to Golgi to produce sphingomyelin, although CERT function in animal development and physiology is less clear. Using dcert1 (a protein-null allele), this paper shows a disruption of the sole Drosophila CERT gene causes reduced ERG amplitude in photoreceptors. While the level and localization of phototransduction machinery appears unaffected, the level of PIP2 and the localization of RDGB are perturbed. Collectively, these observations establish a novel link between CERT and phospholipase signaling in phototransduction. To understand the molecular mechanism further, the authors performed lipid chromatography and mass spec to characterize ceramide species in dcert1. This analysis reveals that whereas the total ceramide remains unaffected, most PE-ceramide species are reduced. The authors use lace mutant (serine palmitoyl transferase) and CPES (ceramide phosphoethanolamine synthase) RNAi to distinguish whether it is the accumulation of ceramide in the ER or the reduction of sphingolipid derivates in the Golgi that is the cause for the reduced ERG amplitude. Mutating one copy of lace reduces ceramide level by 50% and partially rescues the ERG defect, suggesting that the accumulation of ceramide in the ER is a cause. CPES RNAi phenocopies the reduced ERG amplitude, suggesting the production of certain sphingolipid is also relevant.

      Major comments:

      1. By showing the reduced PIP2 level, the decreased SMC sites at the base of rhabdomeres, and the diffused RDGB localization in dcert1, the authors favor the model, in which the disruption of ceramide metabolism affects PIP transport. However, it is unclear if the reduced PIP2 level (i.e., reduced PH-PLC::GFP staining) is specific to the rhabdomeres. It should be possible to compare PH-PLC::GFP signals in different plasma membranes between wildtype and dcert1. If PH-PLC::GFP signal is specifically reduced at the rhabdomeres, this conclusion will be greatly strengthened. In addition, the photoreceptor apical plasma membrane includes rhabdomere and stalk membrane. Is the PH-PLC::GFP signal at the stalk membrane also affected?
      2. The analysis of RDGB localization should be done in mosaic dcert1 retinas, which will be more convincing with internal control for each comparison. In addition, the phalloidin staining in Figure 2J shows distinct patterns of adherens junctions, indicating that the wildtype and dcert1 were imaged at different focal planes.
      3. The significance of ceramide species levels in dcert1 and GMR>CPESRNAi needs to be explained better. Do certain alterations represent accumulation of ceramides in the ER?
      4. The suppression by lace is interpreted as evidence that the reduced ERG amplitude in dcert1 is caused by ceramide accumulation in the ER. This interpretation seems preliminary as lace may interact with dcert genetically by other mechanisms.
      5. The authors show that ERG amplitude is reduced in GMR>CPESRNAi. While this phenocopying is consistent with the reduced ERG amplitude in dcert1 being caused by reduced production of PE-ceramide, GMR>CPESRNAi also shows an increase in total ceramide level. Could this support the hypothesis that reduced ERG amplitude is caused by an accumulation of ceramide elsewhere? In addition, is the ERG amplitude reduction in GMR>CPESRNAi sensitive to lace?
      6. Along the same line, while the total ceramide level is significantly reduced in lace heterozygotes, is the PE-ceramide level also reduced? If yes, wouldn't this be contradictory to PE-ceramide production being important for ERG amplitude?
      7. What is the explanation and significance for the age-dependent deterioration of ERG amplitude in dcert1? Likewise, the significance of no retinal degeneration is not clearly presented.
      8. The rescue of dcert1 phenotype by the expression of human CERT is a nice result. In addition to demonstrating a functional conservation, it allows a determination of CERT protein localization. However, the quality of images in Figure 6D should be improved. The phalloidin staining was rather poor, and the CNX99A in the lower panel was over-exposed, generating bleed-through signals at the rhabdomeres. In addition, the localization of hCERT should be explored further. For instance, does hCERT colocalize with RDGB? Is the hCERT localization altered in lace or GMR>CPESRNAi background?

      Minor comments:

      1. In Line 128, Df(732) should be Df(3L)BSC732.
      2. GMR-SMSrRNAi shows an increase in ERG peak amplitude. Is there an explanation for this?

      Significance

      As CERT mutations are implicated in human learning disability, a better understanding of CERT function in neuronal cells is certainly of interest. While the link between ceramide transport and phospholipase signaling is novel and interesting, this paper does not clearly explain the mechanism. In addition, as the ERG were measured long after the retinal cells were deficient in CERT or CPES, it is difficult to assess whether the observed phenotype is a primary defect. Furthermore, the quality of some images needs to be improved. Thus, I feel the manuscript in its current form is too preliminary.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This manuscript dissects the physiological function of ceramide transfer protein (CERT) by studying the phenotype of CERT null Drosophila.

      dCERT null animals have a reduced electrical response to light in their photoreceptors, reduced baseline PIP2 accumulation in the cells and delayed re-synthesis of PIP2 and its precursor, PI4P after light stimulation. There are also reduced ER:PM contact sites at the rhabdomere and a corresponding reduction in the localization of PI/PA exchange protein, RDGB at this site. Therefore, the animals seem to have an impaired ability for sustaining phototransduction, which is nonetheless milder than that seen after loss of RDGB, for example. In terms of biochemical function, there is no overall change in ceramides, with some minor increases in specific short chain pools. There is however a large decrease in PE-ceramide species, again selective for a few molecular species. Curiously, decreasing ceramides with a mutant in ceramide synthesis is able to partially rescue both the electrical response and RDGB localization in dCERT flies, implying the increased ceramide species contribute to the phenotype. In addition, a mutation in PE-ceramide synthase largely phenocopies the dCERT null, exhiniting both increases ceramides and decreased PE-ceramide.

      In addition, dCERT flies were shown to have reduced localization of some plasma membrane proteins to detergent-resistant membrane fractions, as well as up regulation of the IRE1 and PERK stress-response pathways. Finally, dCERT nulls could be rescued with the human CERT protein, demonstrating conservation of core physiological function between these animals. Surprisingly, CERT is reported to localize to the ER:PM junctions at rhabdomeres, as opposed to the expected ER:Golgi contact sites.

      Specific areas where the manuscript could be strengthened include:

      Figure 2 studies the phototransduction system. Although clear changes in PI4P and PIP2 are seen, it would be interesting to see if changed PA accumulation occur in the dCERT animals, since RDGB localization is disrupted: this is expected to cause PM PA accumulation along with reduced PIP2 synthesis.

      Lines 228-230 state: "These findings suggest an important contribution for reduced PE - Cer levels in the eye phenotypes of dcert". Does it not also suggest a contribution of the elevated ceramide species, since these are also observed in the CPES animals?

      Figure 6D is a key finding that human CERT localized to the rhabdomere at ER:PM contact sites, though the reviewer was not convinced by these images. Is the protein truly localized to the contact sites, or simply have a pool of over-expressed protein localized to the surrounding cytoplasm? It also does not rule out localization (and therefore function) at ER:PM contact sites.

      Statistics: There are a large number of t-tests employed that do not correct for multiple comparisons, for example in figures 3B, 3D, 3H, 4C, 6C, S2A, S2B, S3B and S3C.

      There are two Western blotting sections in the methods.

      Significance

      Overall, the manuscript is clearly and succinctly written, with the data well presented and mostly convincing. The paper demonstrates clear phenotypes associated with loss of dCERT function, with surprising consequences for the function of a signaling system localized to ER:PM contact sites. To this reviewer, there seem to be three cogent observations of the paper: (i) loss of dCERT leads to accumulation of ceramides and loss of PE-ceramide, which together drive the phenotype. (ii) this ceramide alteration disrupts ER:PM contact sites and thus impairs phototransduction and (iii) rescue by human CERT and its apparent localization to ER:PM contact sites implies a potential novel site of action. Although surprising and novel, the significance of these observations are a little unclear: there is no obvious mechanism by which the elevated ceramide species and decreased PE-ceramide causes the specific failure in phototrasnduction, and the evidence for a novel site of action of CERT at the ER:PM contact sites is not compelling. Therefore, although an interesting and novel set of observations, the manuscript does not reveal a clear mechanistic basis for CERT physiological function.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      This paper addresses a very interesting problem of non-centrosomal microtubule organization in developing Drosophila oocytes. Using genetics and imaging experiments, the authors reveal an interplay between the activity of kinesin-1, together with its essential cofactor Ensconsin, and microtubule organization at the cell cortex by the spectraplakin Shot, minus-end binding protein Patronin and Ninein, a protein implicated in microtubule minus end anchoring. The authors demonstrate that the loss of Ensconsin affects the cortical accumulation non-centrosomal microtubule organizing center (ncMTOC) proteins, microtubule length and vesicle motility in the oocyte, and show that this phenotype can be rescued by constitutively active kinesin-1 mutant, but not by Ensconsin mutants deficient in microtubule or kinesin binding. The functional connection between Ensconsin, kinesin-1 and ncMTOCs is further supported by a rescue experiment with Shot overexpression. Genetics and imaging experiments further implicate Ninein in the same pathway. These data are a clear strength of the paper; they represent a very interesting and useful addition to the field.

      The weaknesses of the study are two-fold. First, the paper seems to lack a clear molecular model, uniting the observed phenomenology with the molecular functions of the studied proteins. Most importantly, it is not clear how kinesin-based plus-end directed transport contributes to cortical localization of ncMTOCs and regulation of microtubule length.

      Second, not all conclusions and interpretations in the paper are supported by the presented data.

      We thank the reviewer for recognizing the impact of this work. In response to the insightful suggestions, we performed extensive new experiments that establish a well-supported cellular and molecular model (Figure 7). The discussion has been restructured to directly link each conclusion to its corresponding experimental evidence, significantly strengthening the manuscript.

      Below is a list of specific comments, outlining the concerns, in the order of appearance in the paper/figures.

      Figure 1. The statement: "Ens loading on MTs in NCs and their subsequent transport by Dynein toward ring canals promotes the spatial enrichment of the Khc activator Ens in the oocyte" is not supported by data. The authors do not demonstrate that Ens is actually transported from the nurse cells to the oocyte while being attached to microtubules. They do show that the intensity of Ensconsin correlates with the intensity of microtubules, that the distribution of Ensconsin depends on its affinity to microtubules and that an Ensconsin pool locally photoactivated in a nurse cell can redistribute to the oocyte (and throughout the nurse cell) by what seems to be diffusion. The provided images suggest that Ensconsin passively diffuses into the oocyte and accumulates there because of higher microtubule density, which depends on dynein. To prove that Ensconsin is indeed transported by dynein in the microtubule-bound form, one would need to measure the residence time of Ensconsin on microtubules and demonstrate that it is longer than the time needed to transport microtubules by dynein into the oocyte; ideally, one would like to see movement of individual microtubules labelled with photoconverted Ensconsin from a nurse cell into the oocyte. Since microtubules are not enriched in the oocyte of the dynein mutant, analysis of Ensconsin intensity in this mutant is not informative and does not reveal the mechanism of Ensconsin accumulation.

      As noted by Reviewer 3, the directional movement of microtubules traveling at ~140 nm/s from nurse cells toward the oocyte through Ring Canals was previously reported using a tagged Ens-MT binding domain reporter line by Lu et al. (2022). We have therefore added the citation of this crucial work in the novel version of the manuscript (lane 155-157) and removed the photo-conversion panel.

      Critically, however, our study provides mechanistic insight that was missing from this earlier work: this mechanism is also crucial to enrich MAPs in the oocyte. The fact that Dynein mutants fail to enrich Ensconsin is a crucial piece of evidence: it supports a model of Ensconsin-loaded MT transport (Figure 1D-1F).

      Figure 2. According to the abstract, this figure shows that Ensconsin is "maintained at the oocyte cortex by Ninein". However, the figure doesn't seem to prove it - it shows that oocyte enrichment of Ensonsin is partially dependent on Ninein, but this applies to the whole cell and not just to the cell cortex. Furthermore, it is not clear whether Ninein mutation affects microtubule density, which in turn would affect Ensconsin enrichment, and therefore, it is not clear whether the effect of Ninein loss on Ensconsin distribution is direct or indirect.

      Ninein plays a critical role in Ensconsin enrichment and microtubule organization in the oocyte (new Figure 2, Figure 3, Figure S3). Quantification of total Tubulin signal shows no difference between control and Nin mutant oocytes (new Figure S3 panels A, B). We found decreased Ens enrichment in the oocyte, and Ens localization on MTs and to the cell cortex (Figure 2E, 2F, and Figure S3C and S3D).

      Novel quantitative analyses of microtubule orientation at the anterior cortex, where MTs are normally preferentially oriented toward the posterior pole (Parton et al. 2011), demonstrate that Nin mutants exhibit randomized MT orientation compared to wild-type oocytes (new Figure 3C-3E).These findings establish that Ninein (although not essential) favors Ensconsin localization on MTs, Ens enrichment in the oocyte, ncMTOC cortical localization, and more robust MT orientation toward the posterior cortex. It also suggests that Ens levels in the oocyte acts as a rheostat to control Khc activation.

      The observation that the aggregates formed by overexpressed Ninein accumulate other proteins, including Ensconsin, supports, though does not prove their interactions. Furthermore, there is absolutely no proof that Ninein aggregates are "ncMTOCs". Unless the authors demonstrate that these aggregates nucleate or anchor microtubules (for example, by detailed imaging of microtubules and EB1 comets), the text and labels in the figure would need to be altered.

      We have modified the manuscript, we now refer to an accumulation of these components in large puncta, rather than aggregates, consistent with previous observations (Rosen et al., 2000). We acknowledge in the revised version that these puncta recruit Shot, Patronin and Ens without mentioning direct interaction (lane 218).

      Importantly, we conducted a more detailed characterization of these Ninein/Shot/Patronin/Ens-containing puncta in a novel Figure S4. To rigorously assess their nucleation capacity, we analyzed Eb1-GFP-labeled MT comets, a robust readout of MT nucleation (Parton et al., 2011, Nashchekin et al., 2016). While few Eb1-positive comets occasionally emanate from these structures, confirming their identity as putative ncMTOCs, these puncta function as surprisingly weak nucleation centers (new Figure S4 E, Video S1) and, their presence does not alter overall MT architecture (new Figure S4 F). Moreover, these puncta disappear over time, are barely visible at stage 10B, they do not impair oocyte development or fertility (Figure S4 G and Table 1).

      Minor comment: Note that a "ratio" (Figure 2C) is just a ratio, and should not be expressed in arbitrary units.

      We have amended this point in all the figures.

      Figure 3B: immunoprecipitation results cannot be interpreted because the immunoprecipitated proteins (GFP, Ens-GFP, Shot-YFP) are not shown. It is also not clear that this biochemical experiment is useful. If the authors would like to suggest that Ensconsin directly binds to Patronin, the interaction would need to be properly mapped at the protein domain level.

      This is a good point: the GFP and Ens-GFP immunoprecipitated proteins are now much clearly identified on the blots and in the figure legend (new Figure 4G). Shot-YFP IP, was used as a positive control but is difficult to be detected by Western blot due to its large size (>106 Da) using conventional acrylamide gels (Nashchekin et al., 2016).

      We now explicitly state that immunoprecipitations were performed at 4°C, where microtubules are fully depolymerized, thereby excluding undirect microtubule-mediated interactions. We agree with this reviewer: we cannot formally rule out interactions through bridging by other protein components. This is stated in the revised manuscript (lane 238-239).

      One of the major phenotypes observed by the authors in Ens mutant is the loss of long microtubules. The authors make strong conclusions about the independence of this phenotype from the parameters of microtubule plus-end growth, but in fact, the quality of their data does not allow to make such a conclusion, because they only measured the number of EB1 comets and their growth rate but not the catastrophe, rescue or pausing frequency."Note that kinesin-1 has been implicated in promoting microtubule damage and rescue (doi: 10.1016/j.devcel.2021).In the absence of such measurements, one cannot conclude whether short microtubules arise through defects in the minus-end, plus-end or microtubule shaft regulation pathways.

      We thank the reviewer for raising this important point. Our data demonstrate that microtubule (MT) nucleation and polymerization rates remain unaffected under Khc RNAi and ens mutant conditions, indicating that MT dynamics alterations must arise through alternative mechanisms.

      As the reviewer suggested, recent studies on Kinesin activity and MT network regulation are indeed highly relevant. Two key studies from the Verhey and Aumeier laboratories examined Kinesin-1 gain-of-function conditions and revealed that constitutively active Kinesin-1 induces MT lattice damage (Budaitis et al., 2022). While damaged MTs can undergo self-repair, Aumeier and colleagues demonstrated that GTP-tubulin incorporation generates "rescue shafts" that promote MT rescue events (Andreu-Carbo et al., 2022). Extrapolating from these findings, loss of Kinesin-1 activity could plausibly reduce rescue shaft formation, thereby decreasing MT rescue frequency and stability. Although this hypothesis is challenging to test directly in our system, it provides a mechanistic framework for the observed reduction in MT number and stability.

      Additionally, the reviewer highlighted the role of Khc in transporting the dynactin complex, an anti-catastrophe factor, to MT plus ends (Nieuwburg et al., 2017), which could further contribute to MT stabilization. This crucial reference is now incorporated into the revised Discussion.

      Importantly, our work also demonstrates the contribution of Ens/Khc to ncMTOC targeting to the cell cortex. Our new quantitative analyses of MT organization (new Figure 5 B) reveal a defective anteroposterior orientation of cortical MTs in mutant conditions, pointing to a critical role for cortical ncMTOCs in organizing the MT network.

      Taken together, we propose that the observed MT reduction and disorganization result from multiple interconnected mechanisms: (1) reduced rescue shaft formation affecting MT stability; (2) impaired transport of anti-catastrophe factors to MT plus ends; and (3) loss of cortical ncMTOCs, which are essential for minus-end MT stabilization and network organization. The Discussion has been revised to reflect this integrated model in a dedicated paragraph (“A possible regulation of MT dynamics in the oocyte at both plus end minus MT ends by Ens and Khc” lane 415-432).

      It is important to note in that a spectraplakin, like Shot, can potentially affect different pathways, particularly when overexpressed.

      We agree that Shot harbors multiple functional domains and acts as a key organizer of both actin and microtubule cytoskeletons. Overexpression of such a cytoskeletal cross-linker could indeed perturb both networks, making interpretation of Ens phenotype rescue challenging due to potential indirect effects.

      To address this concern, we selected an appropriate Shot isoform for our rescue experiments that displayed similar localization to “endogenous” Shot-YFP (a genomic construct harboring shot regulatory sequences) and importantly that was not overexpressed.

      Elevated expression of the Shot.L(A) isoform (see Western Blot Figure S8 A), considered as the wild-type form with two CH1 and CH2 actin-binding motifs (Lee and Kolodziej, 2002), showed abnormal localization such as strong binding to the microtubules in nurse cells and oocyte confirming the risk of gain-of-function artifacts and inappropriate conclusions (Figure S8 B, arrows).

      By contrast, our rescue experiments using the Shot.L(C) isoform (that only harbors the CH2 motif) provide strong evidence against such artifacts for three reasons. First, Shot-L(C) is expressed at slightly lower levels than a Shot-YFP genomic construct (not overexpressed), and at much lower levels than Shot-L(A), despite using the same driver (Figure S8 A). Second, Shot-L(C) localization in the oocyte is similar to that of endogenous Shot-YFP, concentrating at the cell cortex (Figure S8 B, compare lower and top panels). Taken together, these controls rather suggest our rescue with the Shot-L(C) is specific.

      Note that this Shot-L(C) isoform is sufficient to complement the absence of the shot gene in other cell contexts (Lee and Kolodziej, 2002).

      Unjustified conclusions should be removed: the authors do not provide sufficient data to conclude that "ens and Khc oocytes MT organizational defects are caused by decreased ncMTOC cortical anchoring", because the actual cortical microtubule anchoring was not measured.

      This is a valid point. We acknowledge that we did not directly measure microtubule anchoring in this study. In response, we have revised the discussion to more accurately reflect our observations. Throughout the manuscript, we now refer to "cortical microtubule organization" rather than "cortical microtubule anchoring," which better aligns with the data presented.

      Minor comment: Microtubule growth velocity must be expressed in units of length per time, to enable evaluating the quality of the data, and not as a normalized value.

      This is now amended in the revised version (modified Figure S7).

      A significant part of the Discussion is dedicated to the potential role of Ensconsin in cortical microtubule anchoring and potential transport of ncMTOCs by kinesin. It is obviously fine that the authors discuss different theories, but it would be very helpful if the authors would first state what has been directly measured and established by their data, and what are the putative, currently speculative explanations of these data.

      We have carefully considered the reviewer's constructive comments and are confident that this revised version fully addresses their concerns.

      First, we have substantially strengthened the connection between the Results and Discussion sections, ensuring that our interpretations are more directly anchored in the experimental data. This restructuring significantly improves the overall clarity and logical flow of the manuscript.

      Second, we have added a new comprehensive figure presenting a molecular-scale model of Kinesin-1 activation upon release of autoinhibition by Ensconsin (new Figure 7D). Critically, this figure also illustrates our proposed positive feedback loop mechanism: Khc-dependent cytoplasmic advection promotes cortical recruitment of additional ncMTOCs, which generates new cortical microtubules and further accelerates cytoplasmic transport (Figure 7 A-C). This self-amplifying cycle provides a mechanistic framework consistent with emerging evidence that cytoplasmic flows are essential for efficient intracellular transport in both insect and mammalian oocytes.

      Minor comment: The writing and particularly the grammar need to be significantly improved throughout, which should be very easy with current language tools. Examples: "ncMTOCs recruitment" should be "ncMTOC recruitment"; "Vesicles speed" should be "Vesicle speed", "Nin oocytes harbored a WT growth,"- unclear what this means, etc. Many paragraphs are very long and difficult to read. Making shorter paragraphs would make the authors' line of thought more accessible to the reader.

      We have amended and shortened the manuscript according to this reviewer feed-back. We have specifically built more focused paragraphs to facilitates the reading.

      Significance

      This paper represents significant advance in understanding non-centrosomal microtubule organization in general and in developing Drosophila oocytes in particular by connecting the microtubule minus-end regulation pathway to the Kinesin-1 and Ensconsin/MAP7-dependent transport. The genetics and imaging data are of good quality, are appropriately presented and quantified. These are clear strengths of the study which will make it interesting to researchers studying the cytoskeleton, microtubule-associated proteins and motors, and fly development.

      The weaknesses of this study are due to the lack of clarity of the overall molecular model, which would limit the impact of the study on the field. Some interpretations are not sufficiently supported by data, but this can be solved by more precise and careful writing, without extensive additional experimentation.

      We thank the reviewer for raising these important concerns regarding clarity and data interpretation. We have thoroughly revised the manuscript to address these issues on multiple fronts. First, we have substantially rewritten key sections to ensure that our conclusions are clearly articulated and directly supported by the data. Second, we have performed several new experiments that now allow us to propose a robust mechanistic model, presented in new figures. These additions significantly strengthen the manuscript and directly address the reviewer's concerns.

      My expertise is cell biology and biochemistry of the microtubule cytoskeleton, including both microtubule-associated proteins and microtubule motors.

      Reviewer #2

      Evidence, reproducibility and clarity

      In this manuscript, Berisha et al. investigate how microtubule (MT) organization is spatially regulated during Drosophila oogenesis. The authors identify a mechanism in which the Kinesin-1 activator Ensconsin/MAP7 is transported by dynein and anchored at the oocyte cortex via Ninein, enabling localized activation of Kinesin-1. Disruption of this pathway impairs ncMTOC recruitment and MT anchoring at the cortex. The authors combine genetic manipulation with high-resolution microscopy and use three key readouts to assess MT organization during mid-to-late oogenesis: cortical MT formation, localization of posterior determinants, and ooplasmic streaming. Notably, Kinesin-1, in concert with its activator Ens/MAP7, contributes to organizing the microtubule network it travels along. Overall, the study presents interesting findings, though we have several concerns we would like the authors to address. Ensconsin enrichment in the oocyte 1. Enrichment in the oocyte • Ensconsin is a MAP that binds MTs. Given that microtubule density in the oocyte significantly exceeds that in the nurse cells, its enrichment may passively reflect this difference. To assess whether the enrichment is specific, could the authors express a non-Drosophila MAP (e.g., mammalian MAP1B) to determine whether it also preferentially localizes to the oocyte?

      To address this point, we performed a new series of experiments analyzing the enrichment of other Drosophila and non-Drosophila MAPs, including Jupiter-GFP, Eb1-GFP, and bovine Tau-GFP, all widely used markers of the microtubule cytoskeleton in flies (see new Figure S2). Our results reveal that Jupiter-GFP, Eb1-GFP, and bovine Tau-GFP all exhibit significantly weaker enrichment in the oocyte compared to Ens-GFP. Khc-GFP also shows lower enrichment. These findings indicate that MAP enrichment in the oocyte is MAP-dependent, rather than solely reflecting microtubule density or organization. Of note, we cannot exclude that microtubule post-translational modifications contribute to differential MAP binding between nurse cells and the oocyte, but this remains a question for future investigation.

      The ability of ens-wt and ens-LowMT to induce tubulin polymerization according to the light scattering data (Fig. S1J) is minimal and does not reflect dramatic differences in localization. The authors should verify that, in all cases, the polymerization product in their in vitro assays is microtubules rather than other light-scattering aggregates. What is the control in these experiments? If it is just purified tubulin, it should not form polymers at physiological concentrations.

      The critical concentration Cr for microtubule self-assembly in classical BRB80 buffer found by us and others is around 20 µM (see Fig. 2c in Weiss et al., 2010). Here, microtubules were assembled at 40 µM tubulin concentration, i.e., largely above the Cr. As stated in the materials and methods section, we systematically induced cooling at 4°C after assembly to assess the presence of aggregates, since those do not fall apart upon cooling. The decrease in optical density upon cooling is a direct control that the initial increase in DO is due to the formation of microtubules. Finally, aggregation and polymerization curves are widely different, the former displaying an exponential shape and the latter a sigmoid assembly phase (see Fig. 3A and 3B in Weiss et al., 2010).

      Photoconversion caveatsMAPs are known to dynamically associate and dissociate from microtubules. Therefore, interpretation of the Ens photoconversion data should be made with caution. The expanding red signal from the nurse cells to the oocyte may reflect a any combination of dynein-mediated MT transport and passive diffusion of unbound Ensconsin. Notably, photoconversion of a soluble protein in the nurse cells would also result in a gradual increase in red signal in the oocyte, independent of active transport. We encourage the authors to more thoroughly discuss these caveats. It may also help to present the green and red channels side by side rather than as merged images, to allow readers to assess signal movement and spatial patterns better.

      This is a valid point that mirrors the comment of Reviewers 1 and 3. The directional movement of microtubules traveling at ~140 nm/s from nurse cells toward the oocyte via the ring canals was previously reported by Lu et al. (2022) with excellent spatial resolution. Notably, this MT transport was measured using a fusion protein containing the Ens MT-binding domain. We now cite this relevant study in our revised manuscript and have removed this redundant panel in Figure 1.

      Reduction of Shot at the anterior cortex• Shot is known to bind strongly to F-actin, and in the Drosophila ovary, its localization typically correlates more closely with F-actin structures than with microtubules, despite being an MT-actin crosslinker. Therefore, the observed reduction of cortical Shot in ens, nin mutants, and Khc-RNAi oocytes is unexpected. It would be important to determine whether cortical F-actin is also disrupted in these conditions, which should be straightforward to assess via phalloidin staining.

      As requested by the reviewer, we performed actin staining experiments, which are now presented in a new Figure S5. These data demonstrate that the cortical actin network remains intact in all mutant backgrounds analyzed, ruling out any indirect effect of actin cytoskeleton disruption on the observed phenotypes.

      MTs are barely visible in Fig. 3A, which is meant to demonstrate Ens-GFP colocalization with tubulin. Higher-quality images are needed.

      The revised version now provides significantly improved images to show the different components examined. Our data show that Ens and Ninein localize at the cell cortex where they co-localize with Shot and Patronin (Figure 2 A-C). In addition, novel images show that Ens extends along microtubules (new Figure 4 A).

      MT gradient in stage 9 oocytesIn ens-/-, nin-/-, and Khc-RNAi oocytes, is there any global defect in the stage 9 microtubule gradient? This information would help clarify the extent to which cortical localization defects reflect broader disruptions in microtubule polarity.

      We now provide quantitative analysis of microtubule (MT) array organization in novel figures (Figure 3D and Figure 5B). Our data reveal that both Khc RNAi and ens mutant oocytes exhibit severe disruption of MT orientation toward the posterior (new Figure 5B). Importantly, this defect is significantly less pronounced in Nin-/- oocytes, which retain residual ncMTOCs at the cortex (new Figure 3D). This differential phenotype supports our model that cortical ncMTOCs are critical for maintaining proper MT orientation toward the posterior side of the oocyte.

      Role of Ninein in cortical anchoringThe requirement for Ninein in cortical anchorage is the least convincing aspect of the manuscript and somewhat disrupts the narrative flow. First, it is unclear whether Ninein exhibits the same oocyte-enriched localization pattern as Ensconsin. Is Ninein detectable in nurse cells? Second, the Ninein antibody signal appears concentrated in a small area of the anterior-lateral oocyte cortex (Fig. 2A), yet Ninein loss leads to reduced Shot signal along a much larger portion of the anterior cortex (Fig. 2F)-a spatial mismatch that weakens the proposed functional relationship. Third, Ninein overexpression results in cortical aggregates that co-localize with Shot, Patronin, and Ensconsin. Are these aggregates functional ncMTOCs? Do microtubules emanate from these foci?

      We now provide a more comprehensive analysis of Ninein localization. Similar to Ensconsin (Ens), endogenous Ninein is enriched in the oocyte during the early stages of oocyte development but is also detected in NCs (see modified Figure 2 A and Lasko et al., 2016). Improved imaging of Ninein further shows that the protein partially co-localizes with Ens, and ncMTOCs at the anterior cortex and with Ens-bound MTs (Figure 2B, 2C).

      Importantly, loss of Ninein (Nin) only partially reduces the enrichment of Ens in the oocyte (Figure 2E). Both Ens and Kinesin heavy chain (Khc) remain partially functional and continue to target non-centrosomal microtubule-organizing centers (ncMTOCs) to the cortex (Figure 3A). In Nin-/- mutants, a subset of long cortical microtubules (MTs) is present, thereby generating cytoplasmic streaming, although less efficiently than under wild-type (WT) conditions (Figure 3F and 3G). As a non-essential gene, we envisage Ninein as a facilitator of MT organization during oocyte development.

      Finally, our new analyses demonstrate that large puncta containing Ninein, Shot, Patronin, and despite their size, appear to be relatively weak nucleation centers (revised Figure S4 E and Video 1). In addition, their presence does not bias overall MT architecture (Figure S4 F) nor impair oocyte development and fertility (Figure S4 G and Table 1).

      Inconsistency of Khc^MutEns rescueThe Khc^MutEns variant partially rescues cortical MT formation and restores a slow but measurable cytoplasmic flow yet it fails to rescue Staufen localization (Fig. 5). This raises questions about the consistency and completeness of the rescue. Could the authors clarify this discrepancy or propose a mechanistic rationale?

      This is a good point. The cytoplasmic flows (the consequence of cargo transport by Khc on MTs) generated by a constitutively active KhcMutEns in an ens mutant condition, are less efficient than those driven by Khc activated by Ens in a control condition (Figure 6C). The rescued flow is probably not efficient enough to completely rescue the Staufen localization at stage 10.

      Additionally, this KhcMutEns variant rescues the viability of embryos from Khc27 mutant germline clones oocytes but not from ens mutants (Table1). One hypothesis is that Ens harbors additional functions beyond Khc activation.

      This incomplete rescue of Ens by an active Khc variant could also be the consequence of the “paradox of co-dependence”: Kinesin-1 also transport the antagonizing motor Dynein that promotes cargo transport in opposite directions (Hancock et al., 2016). The phenotype of a gain of function variant is therefore complex to interpret. Consistent with this, both KhcMutEns-GFP and KhcDhinge2 two active Khc only rescues partially centrosome transport in ens mutant Neural Stem Cells (Figure S10).

      Minor points: 1. The pUbi-attB-Khc-GFP vector was used to generate the Khc^MutEns transgenic line, presumably under control of the ubiquitous ubi promoter. Could the authors specify which attP landing site was used? Additionally, are the transgenic flies viable and fertile, given that Kinesin-1 is hyperactive in this construct?

      All transgenic constructs were integrated at defined genomic landing sites to ensure controlled expression levels. Specifically, both GFP-tagged KhcWT and KhcMutEns were inserted at the VK05 (attP9A) site using PhiC31-mediated integration. Full details of the landing sites are provided in the Materials and Methods section. Both transgenic flies are homozygous lethal and the transgenes are maintained over TM6B balancers.

      On page 11 (Discussion, section titled "A dual Ensconsin oocyte enrichment mechanism achieves spatial relief of Khc inhibition"), the statement "many mutations in Kif5A are causal of human diseases" would benefit from a brief clarification. Since not all readers may be familiar with kinesin gene nomenclature, please indicate that KIF5A is one of the three human homologs of Kinesin heavy chain.

      We clarified this point in the revised version (lane 465-466).

      On page 16 (Materials and Methods, "Immunofluorescence in fly ovaries"), the sentence "Ovaries were mounted on a slide with ProlonGold medium with DAPI (Invitrogen)" should be corrected to "ProLong Gold."

      This is corrected.

      Significance

      This study shows that enrichment of MAP7/ensconsin in the oocyte is the mechanism of kinesin-1 activation there and is important for cytoplasmic streaming and localization non-centrosomal microtubule-organizing centers to the oocyte cortex

      We thank the reviewers for the accurate review of our manuscript and their positive feed-back.

      Reviewer #3

      Evidence, reproducibility and clarity

      The manuscript of Berisha et al., investigates the role of Ensconsin (Ens), Kinesin-1 and Ninein in organisation of microtubules (MT) in Drosophila oocyte. At stage 9 oocytes Kinesin-1 transports oskar mRNA, a posterior determinant, along MT that are organised by ncMTOCs. At stage 10b, Kinesin-1 induces cytoplasmic advection to mix the contents of the oocyte. Ensconsin/Map7 is a MT associated protein (MAP) that uses its MT-binding domain (MBD) and kinesin binding domain (KBD) to recruit Kinesin-1 to the microtubules and to stimulate the motility of MT-bound Kinesin-1. Using various new Ens transgenes, the authors demonstrate the requirement of Ens MBD and Ninein in Ens localisation to the oocyte where Ens activates Kinesin-1 using its KBD. The authors also claim that Ens, Kinesin-1 and Ninein are required for the accumulation of ncMTOCs at the oocyte cortex and argue that the detachment of the ncMTOCs from the cortex accounts for the reduced localisation of oskar mRNA at stage 9 and the lack of cytoplasmic streaming at stage 10b. Although the manuscript contains several interesting observations, the authors' conclusions are not sufficiently supported by their data. The structure function analysis of Ensconsin (Ens) is potentially publishable, but the conclusions on ncMTOC anchoring and cytoplasmic streaming not convincing.

      We are grateful that the regulation of Khc activity by MAP7 was well received by all reviewers. While our study focuses on Drosophila oogenesis, we believe this mechanism may have broader implications for understanding kinesin regulation across biological systems.

      For the novel function of the MAP7/Khc complex in organizing its own microtubule networks through ncMTOC recruitment, we have carefully considered the reviewers' constructive recommendations. We now provide additional experimental evidence supporting a model of flux self-amplification in which ncMTOC recruitment plays a key role. It is well established that cytoplasmic flows are essential for posterior localization of cell fate determinants at stage 10B. Slow flows have also been described at earlier oogenesis stages by the groups of Saxton and St Johnston. Building on these early publications and our new experiments, we propose that these flows are essential to promote a positive feedback loop that reinforces ncMTOC recruitment and MT organization (Figure 7).

      1) The main conclusion of the manuscript is that "MT advection failure in Khc and ens in late oogenesis stems from defective cortical ncMTOCs recruitment". This completely overlooks the abundant evidence that Kinesin-1 directly drives cytoplasmic streaming by transporting vesicles and microtubules along microtubules, which then move the cytoplasm by advection (Palacios et al., 2002; Serbus et al, 2005; Lu et al, 2016). Since Kinesin-1 generates the flows, one cannot conclude that the effect of khc and ens mutants on cortical ncMTOC positioning has any direct effect on these flows, which do not occur in these mutants.

      We regret the lack of clarity of the first version of the manuscript and some missing references. We propose a model in which the Kinesin-1- dependent slow flows (described by Serbus/Saxton and Palacios/StJohnston) play a central role in amplifying ncMTOC anchoring and cortical MT network formation (see model in the new Figure 7).

      2) The authors claim that streaming phenotypes of ens and khs mutants are due to a decrease in microtubule length caused by the defective localisation of ncMTOCs. In addition to the problem raised above, However, I am not convinced that they can make accurate measurements of microtubule length from confocal images like those shown in Figure 4. Firstly, they are measuring the length of bundles of microtubules and cannot resolve individual microtubules. This problem is compounded by the fact that the microtubules do not align into parallel bundles in the mutants. This will make the "microtubules" appear shorter in the mutants. In addition, the alignment of the microtubules in wild-type allows one to choose images in which the microtubule lie in the imaging plane, whereas the more disorganized arrangement of the microtubules in the mutants means that most microtubules will cross the imaging plane, which precludes accurate measurements of their length.

      As mentioned by Reviewer 4, we have been transparent with the methodology, and the limitations that were fully described in the material and methods section.

      Cortical microtubules in oocytes are highly dynamic and move rapidly, making it technically impossible to capture their entire length using standard Z-stack acquisitions. We therefore adopted a compromise approach: measuring microtubules within a single focal plane positioned just below the oocyte cortex. This strategy is consistent with established methods in the field, such as those used by Parton et al. (2011) to track microtubule plus-end directionality. To avoid overinterpretation, we explicitly refer to these measurements as "minimum detectable MT length," acknowledging that microtubules may extend beyond the focal plane, particularly at stage 10, where long, tortuous bundles frequently exit the plane of focus. These methodological considerations and potential biases are clearly described in the Materials and Methods section and the text now mentions the possible disorganization of the MT network in the mutant conditions (lane 272-273).

      In this revised version, we now provide complementary analyses of MT network organization.Beyond length measurements (and the mentioned limitations), we also quantified microtubule network orientation at stage 9, assessing whether cortical microtubules are preferentially oriented toward the posterior axis as observed in controls (revised Figure 3D and Figure 5B). While this analysis is also subject to the same technical limitations, it reveals a clear biological difference: microtubules exhibit posterior-biased orientation in control oocytes similar to a previous study (Parton et al., 2011) but adopt a randomized orientation in Nin-/-, ens, and Khc RNAi-depleted oocytes (revised Figure 3D and Figure 5B).

      Taken together, these complementary approaches, despite their technical constraints, provide convergent evidence for the role of the Khc/Ens complex in organizing cortical microtubule networks during oogenesis.

      3) "To investigate whether the presence of these short microtubules in ens and Khc RNAi oocytes is due to defects in microtubule anchoring or is also associated with a decrease in microtubule polymerization at their plus ends, we quantified the velocity and number of EB1comets, which label growing microtubule plus ends (Figure S3)." I do not understand how the anchoring or not of microtubule minus ends to the cortex determines how far their plus ends grow, and these measurements fall short of showing that plus end growth is unaffected. It has already been shown that the Kinesin-1-dependent transport of Dynactin to growing microtubule plus ends increases the length of microtubules in the oocyte because Dynactin acts as an anti-catastrophe factor at the plus ends. Thus, khc mutants should have shorter microtubules independently of any effects on ncMTOC anchoring. The measurements of EB1 comet speed and frequency in FigS2 will not detect this change and are not relevant for their claims about microtubule length. Furthermore, the authors measured EB1 comets at stage 9 (where they did not observe short MT) rather than at stage 10b. The authors' argument would be better supported if they performed the measurements at stage 10b.

      We thank the reviewer for raising this important point. The short microtubule (MT) length observed at stage 10B could indeed result from limited plus-end growth. Unfortunately, we were unable to test this hypothesis directly: strong endogenous yolk autofluorescence at this stage prevented reliable detection of Eb1-GFP comets, precluding velocity measurements.

      At least during stage 9, our data demonstrate that MT nucleation and polymerization rates are not reduced in both KhcRNAi and ens mutant conditions, indicating that the observed MT alterations must arise through alternative mechanisms.

      In the discussion, we propose the following interconnected explanations, supported by recent literature and the reviewers’ suggestions:

      1- Reduced MT rescue events. Two seminal studies from the Verhey and Aumeier laboratories have shown that constitutively active Kinesin-1 induces MT lattice damage (Budaitis et al., 2022), which can be repaired through GTP-tubulin incorporation into "rescue shafts" that promote MT rescue (Andreu-Carbo et al., 2022). Extrapolating from these findings, loss of Kinesin-1 activity could plausibly reduce rescue shaft formation, thereby decreasing MT stability. While challenging to test directly in our system, this mechanism provides a plausible framework for the observed phenotype.

      2- Impaired transport of stabilizing factors. As that reviewer astutely points out, Khc transports the dynactin complex, an anti-catastrophe factor, to MT plus ends (Nieuwburg et al., 2017). Loss of this transport could further compromise MT plus end stability. We now discuss this important mechanism in the revised manuscript.

      3- Loss of cortical ncMTOCs. Critically, our new quantitative analyses (revised Figure 3 and Figure 5) also reveal defective anteroposterior orientation of cortical MTs in mutant conditions. These experiments suggest that Ens/Khc-mediated localization of ncMTOCs to the cortex is essential for proper MT network organization, and possibly minus-end stabilization as suggested in several studies (Feng et al., 2019, Goodwin and Vale, 2011, Nashchekin et al., 2016).

      Altogether, we now propose an integrated model in which MT reduction and disorganization may result from multiple complementary mechanisms operating downstream of Kinesin-1/Ensconsin loss. While some aspects remain difficult to test directly in our in vivo system, the convergence of our data with recent mechanistic studies provides an interesting conceptual framework. The Discussion has been revised to reflect this comprehensive view in a dedicated paragraph (“A possible regulation of MT dynamics in the oocyte at both plus end minus MT ends by Ens and Khc” lane 415-432).

      4) The Shot overexpression experiments presented in Fig.3 E-F, Fig.4D and TableS1 are very confusing. Originally , the authors used Shot-GFP overexpression at stage 9 to show that there is a decrease of ncMTOCs at the cortex in ens mutants (Fig.3 E-F) and speculated that this caused the defects in MT length and cytoplasmic advection at stage 10B. However the authors later state on page 8 that : "Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14), resembling the patterns observed in controls (Figures 4B right panel and 4D). Moreover, while ens females were fully sterile, overexpression of Shot was sufficient to restore that loss of fertility (Table S1)". Is this the same UAS Shot-GFP and VP16 Gal4 used in both experiments? If so, this contradictions puts the authors conclusions in question.

      This is an important point that requires clarification regarding our experimental design.

      The Shot-YFP construct is a genomic insertion on chromosome 3. The ens mutation is also located on chromosome 3 and we were unable to recombine this transgene with the ens mutant for live quantification of cortical Shot. To circumvent this technical limitation, we used a UAS-Shot.L(C)-GFP transgenic construct driven by a maternal driver, expressed in both wild-type (control) and ens mutant oocytes. We validated that the expression level and subcellular localization of UAS-Shot.L(C)-GFP were comparable to those of the genomic Shot-YFP (new Figure S8 A and B).

      From these experiments, we drew two key conclusions. First, cortical Shot.L(C)-GFP is less abundant in ens mutant oocytes compared to wild-type (the quantification has been removed from this version). Second, despite this reduced cortical accumulation, Shot.L(C)-GFP expression partially rescues ooplasmic flows and microtubule streaming in stage 10B ens mutant oocytes, and restores fertility to ens mutant females.

      5) The authors based they conclusions about the involvement of Ens, Kinesin-1 and Ninein in ncMTOC anchoring on the decrease in cortical fluorescence intensity of Shot-YFP and Patronin-YFP in the corresponding mutant backgrounds. However, there is a large variation in average Shot-YFP intensity between control oocytes in different experiments. In Fig. 2F-G the average level of Shot-YFP in the control sis 130 AU while in Fig.3 G-H it is only 55 AU. This makes me worry about reliability of such measurements and the conclusions drawn from them.

      To clarify this point, we have harmonized the method used to quantify the Shot-YFP signals in Figure 4E with the methodology used in Figure 3B, based on the original images. The levels are not strictly identical (Control Figure 2 B: 132.7+/-36.2 versus Control Figure 4 E: 164.0+/- 37.7). These differences are usual when experiments are performed at several-month intervals and by different users.

      6) The decrease in the intensity of Shot-YFP and Patronin-YFP cortical fluorescence in ens mutant oocytes could be because of problems with ncMTOC anchoring or with ncMTOCs formation. The authors should find a way to distinguish between these two possibilities. The authors could express Ens-Mut (described in Sung et al 2008), which localises at the oocyte posterior and test whether it recruits Shot/Patronin ncMTOCs to the posterior.

      We tried to obtain the fly stocks described in the 2008 paper by contacting former members of Pernille Rørth's laboratory. Unfortunately, we learned that the lab no longer exists and that all reagents, including the requested stocks, were either discarded or lost over time. To our knowledge, these materials are no longer available from any source. We regret that this limitation prevented us from performing the straightforward experiments suggested by the reviewer using these specific tools.

      7) According to the Materials and Methods, the Shot-GFP used in Fig.3 E-F and Fig.4 was the BDSC line 29042. This is Shot L(C), a full-length version of Shot missing the CH1 actin-binding domain that is crucial for Shot anchoring to the cortex. If the authors indeed used this version of Shot-GFP, the interpretation of the above experiments is very difficult.

      The Shot.L(C) isoform lacks the CH1 domain but retains the CH2 actin-binding motif. Truncated proteins with this domain and fused to GST retains a weak ability to bind actin in vitro. Importantly, the function of this isoform is context-dependent: it cannot rescue shot loss-of-function in neuron morphogenesis but fully restores Shot-dependent tracheal cell remodeling (Lee and Kolodziej, 2002).

      In our experiments, when the Shot.L(C) isoform was expressed under the control of a maternal driver, its localization to the oocyte cortex was comparable to that of the genomic Shot-YFP construct (new Figure S8). This demonstrates unambiguously that the CH1 domain is dispensable for Shot cortical localization in oocytes, and that CH2-mediated actin binding is sufficient for this localization. Of note, a recent study showed that actin network are not equivalent highlighting the need for specific Shot isoforms harboring specialized actin-binding domain (Nashchekin et al., 2024).

      We note that the expression level of Shot.L(C)-GFP in the oocyte appeared slightly lower than that of Shot-YFP (expressed under endogenous Shot regulatory sequences), as assessed by Western blot (Figure S8 A).

      Critically, Shot.L(C)-GFP expression was substantially lower than that of Shot.L(A)-GFP (that harbored both the CH1 and CH2 domain). Shot.L(A)-GFP was overexpressed (Figure 8 A) and ectopically localized on MTs in both nurse cells and the ooplasm (Figure S8 B middle panel and arrow). These observations are in agreement that the Shot.L(C)-GFP rescue experiment was performed at near-physiological expression levels, strengthening the validity of our conclusions.

      8) Page 6 "converted in NCs, in a region adjacent to the ring canals, Dendra-Ens-labeled MTs were found in the oocyte compartment indicating they are able to travel from NC toward the oocyte through ring canals". I have difficulty seeing the translocation of MT through the ring canals. Perhaps it would be more obvious with a movie/picture showing only one channel. Considering that f Dendra-Ens appears in the oocyte much faster than MT transport through ring canals (140nm/s, Lu et al 2022), the authors are most probably observing the translocation of free Ens rather than Ens bound to MT. The authors should also mention that Ens movement from the NC to the oocyte has been shown before with Ens MBD in Lu et al 2022 with better resolution.

      We fully agree on the caveat mentioned by this reviewer: we may observe the translocation of free Dendra-Ensconsin. The experiment, was removed and replaced by referring to the work of the Gelfand lab. The movement of MTs that travel at ~140 nm/s between nurse cells toward the oocyte through the Ring Canals was reported before by Lu et al. (2022) with a very good resolution. Notably, this directional directed movement of MTs was measured using a fusion protein encompassing Ens MT-binding domain. We decided to remove this inclusive experiment and rather refer to this relevant study.

      9) Page 6: The co-localization of Ninein with Ens and Shot at the oocyte cortex (Figure 2A). I have difficulty seeing this co-localisation. Perhaps it would be more obvious in merged images of only two channels and with higher resolution images

      10) "a pool of the Ens-GFP co-localized with Ch-Patronin at cortical ncMTOCs at the anterior cortex (Figure 3A)". I also have difficulty seeing this.

      We have performed new high-resolution acquisitions that provide clearer and more convincing evidence for the localization cortical distribution of these proteins (revised Figure 2A-2C and Figure 4A). These improved images demonstrate that Ens, Ninein, Shot, and Patronin partially colocalize at cortical ncMTOCs, as initially proposed. Importantly, the new data also reveal a spatial distinction: while Ens localizes along microtubules extending from these cortical sites, Ninein appears confined to small cytoplasmic puncta adjacent but also present on cortical microtubules.

      11) "Ninein co-localizes with Ens at the oocyte cortex and partially along cortical microtubules, contributing to the maintenance of high Ens protein levels in the oocyte and its proper cortical targeting". I could not find any data showing the involvement of Ninein in the cortical targeting of Ens.

      We found decreased Ens localization to MTs and to the cell cortex region (new Figure S3 A-B).

      12) "our MT network analyses reveal the presence of numerous short MTs cytoplasmic clustered in an anterior pattern." "This low cortical recruitment of ncMTOCs is consistent with poor MT anchoring and their cytoplasmic accumulation." I could not find any data showing that short cortical MT observed at stage 10b in ens mutant and Khc RNAi were cytoplasmic and poorly anchored.

      The sentence was removed from the revised manuscript.

      13) "The egg chamber consists of interconnected cells where Dynein and Khc activities are spatially separated. Dynein facilitates transport from NCs to the oocyte, while Khc mediates both transport and advection within the oocyte." Dynein is involved in various activities in the oocyte. It anchors the oocyte nucleus and transports bcd and grk mRNA to mention a few.

      The text was amended to reflect Dynein involvement in transport activities in the oocyte, with the appropriate references (lane 105-107).

      14) The cartoons in Fig.2H and 3I exaggerate the effect of Ninein and Ens on cortical ncMTOCs. According to the corresponding graphs, there is a 20 and 50% decrease in each case.

      New cartoons (now revised Figure 3E and 4F), are amended to reflect the ncMTOC values but also MT orientation (Figure 3E).

      Significance

      Given the important concerns raised, the significance of the findings is difficult to assess at this stage.

      We sincerely thank the reviewer for their thorough evaluation of our manuscript. We have carefully addressed their concerns through substantial new experiments and analyses. We hope that the revised manuscript, in its current form, now provides the clarifications and additional evidence requested, and that our responses demonstrate the significance of our findings.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      Summary: This manuscript presents an investigation into the molecular mechanisms governing spatial activation of Kinesin-1 motor protein during Drosophila oogenesis, revealing a regulatory network that controls microtubule organization and cytoplasmic transport. The authors demonstrate that Ensconsin, a MAP7 family protein and Kinesin-1 activator, is spatially enriched in the oocyte through a dual mechanism involving Dynein-mediated transport from nurse cells and cortical maintenance by Ninein. This spatial enrichment of Ens is crucial for locally relieving Kinesin-1 auto-inhibition. The Ens/Khc complex promotes cortical recruitment of non-centrosomal microtubule organizing centers (ncMTOCs), which are essential for anchoring microtubules at the cortex, enabling the formation of long, parallel microtubule streams or "twisters" that drive cytoplasmic advection during late oogenesis. This work establishes a paradigm where motor protein activation is spatially controlled through targeted localization of regulatory cofactors, with the activated motor then participating in building its own transport infrastructure through ncMTOC recruitment and microtubule network organization.

      There's a lot to like about this paper! The data are generally lovely and nicely presented. The authors also use a combination of experimental approaches, combining genetics, live and fixed imaging, and protein biochemistry.

      We thank the reviewer for this enthusiastic and supportive review, which helped us further strengthen the manuscript.

      Concerns: Page 6: "to assay if elevation of Ninein levels was able to mis-regulate Ens localization, we overexpressed a tagged Ninein-RFP protein in the oocyte. At stage 9 the overexpressed Ninein accumulated at the anterior cortex of the oocyte and also generated large cortical aggregates able to recruit high levels of Ens (Figures 2D and 2H)... The examination of Ninein/Ens cortical aggregates obtained after Ninein overexpression showed that these aggregates were also able to recruit high levels of Patronin and Shot (Figures 2E and 2H)." Firstly, I'm not crazy about the use of "overexpressed" here, since there isn't normally any Ninein-RFP in the oocyte. In these experiments it has been therefore expressed, not overexpressed. Secondly, I don't understand what the reader is supposed to make of these data. Expression of a protein carrying a large fluorescent tag leads to large aggregates (they don't look cortical to me) that include multiple proteins - in fact, all the proteins examined. I don't understand this to be evidence of anything in particular, except that Ninein-RFP causes the accumulation of big multi-protein aggregates. While I can understand what the authors were trying to do here, I think that these data are inconclusive and should be de-emphasized.

      We have revised the manuscript by replacing overexpressed with expressed (lanes 211 and 212). In addition, we now provide new localization data in both cortical (new Figure S4 A, top) and medial focal planes (new Figure S4 A, bottom), demonstrating that Ninein puncta (the word used in Rosen et al, 2019), rather than aggregates are located cortically. We also show that live IRP-labelled MTs do not colocalize with Ninein-RFP puncta. In light of the new experiments and the comments from the other reviewers, the corresponding text has been revised and de-emphasized accordingly.

      Page 7: "Co-immunoprecipitations experiments revealed that Patronin was associated with Shot-YFP, as shown previously (Nashchekin et al., 2016), but also with EnsWT-GFP, indicating that Ens, Shot and Patronin are present in the same complex (Figure 3B)." I do not agree that association between Ens-GFP and Patronin indicates that Ens is in the same complex as Shot and Patronin. It is also very possible that there are two (or more) distinct protein complexes. This conclusion could therefore be softened. Instead of "indicating" I suggest "suggesting the possibility."

      We have toned down this conclusion and indicated “suggesting the possibility” (lane 238-239).

      Page 7: "During stage 9, the average subcortical MT length, taken at one focal plane in live oocytes (see methods)..." I appreciate that the authors have been careful to describe how they measured MT length, as this is a major point for interpretation. I think the reader would benefit from an explanation of why they decided to measure in only one focal plane and how that decision could impact the results.

      We appreciate this helpful suggestion. Cortical microtubules are indeed highly dynamic and extend in multiple directions, including along the Z-axis. Moreover, their diameter is extremely small (approximately 25 nm), making it technically challenging to accurately measure their full length with high resolution using our Zeiss Airyscan confocal microscope (over several, microns): the acquisition of Z-stacks is relatively slow and therefore not well suited to capturing the rapid dynamics of these microtubules. Consequently, our length measurements represent a compromise and most likely underestimate the actual lengths of microtubules growing outside the focal plane. We note that other groups have encountered similar technical limitations (Parton et al., 2011).

      Page 7: "... the MTs exhibited an orthogonal orientation relative to the anterior cortex (Figures 4A left panels, 4C and 4E)." This phenotype might not be obvious to readers. Can it be quantified?

      We have now analyzed the orientation of microtubules (MTs) along the dorso-ventral axis. Our analysis shows that ens, Khc RNAi oocytes (new Figure 5B), and, to a lesser extent, Nin mutant oocytes (new Figure 3D), display a more random MT orientation compared to wild-type (WT) oocytes. In WT oocytes, MTs are predominantly oriented toward the posterior pole, consistent with previous findings (Parton et al., 2011).

      Page 8: "Altogether, the analyses of Ens and Khc defective oocytes suggested that MT organization defects during late oogenesis (stage 10B) were caused by an initial failure of ncMTOCs to reach the cell cortex. Therefore, we hypothesized that overexpression of the ncMTOC component Shot could restore certain aspects of microtubule cortical organization in ens-deficient oocytes. Indeed, Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14)..." The data are clear, but the explanation is not. Can the authors please explain why adding in more of an ncMTOC component (Shot) rescues a defect of ncMTOC cortical localization?

      We propose that cytoplasmic ncMTOCs can bind the cell cortex via the Shot subunit that is so far the only component that harbors actin-binding motifs. Therefore, we propose that elevating cytoplasmic Shot increase the possibility of Shot to encounter the cortex by diffusion when flows are absent. This is now explained lane 282-285.

      I'm grateful to the authors for their inclusion of helpful diagrams, as in Figures 1G and 2H. I think the manuscript might benefit from one more of these at the end, illustrating the ultimate model.

      We have carefully considered and followed the reviewer’s suggestions. In response, we have included a new figure illustrating our proposed model: the recruitment of ncMTOCs to the cell cortex through low Khc-mediated flows at stage 9 enhances cortical microtubule density, which in turn promotes self-amplifying flows (new Figure 7, panels A to C). Note that this Figure also depicts activation of Khc by loss of auto-inhibition (Figure 7, panel D).

      I'm sorry to say that the language could use quite a bit of polishing. There are missing and extraneous commas. There is also regular confusion between the use of plural and singular nouns. Some early instances include:

      1. Page 3: thought instead of "thoughted."
      2. Page 5: "A previous studies have revealed"
      3. Page 5: "A significantly loss"
      4. Page 6: "troughs ring canals" should be "through ring canals"
      5. Page 7: lives stage 9 oocytes
      6. Page 7: As ens and Khc RNAi oocytes exhibits
      7. Page 7: we examined in details
      8. Page 7: This average MT length was similar in Khc RNAi and ens mutant oocyte..

      We apologize for errors. We made the appropriate corrections of the manuscript.

      Reviewer #4 (Significance (Required)):

      This work makes a nice conceptual advance by showing that motor activation controls its own transport infrastructure, a paradigm that could extend to other systems requiring spatially regulated transport.

      We thank the reviewers for their evaluation of the manuscript and helpful comments.

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

      Learn more at Review Commons


      Referee #4

      Evidence, reproducibility and clarity

      Summary: This manuscript presents an investigation into the molecular mechanisms governing spatial activation of Kinesin-1 motor protein during Drosophila oogenesis, revealing a regulatory network that controls microtubule organization and cytoplasmic transport. The authors demonstrate that Ensconsin, a MAP7 family protein and Kinesin-1 activator, is spatially enriched in the oocyte through a dual mechanism involving Dynein-mediated transport from nurse cells and cortical maintenance by Ninein. This spatial enrichment of Ens is crucial for locally relieving Kinesin-1 auto-inhibition. The Ens/Khc complex promotes cortical recruitment of non-centrosomal microtubule organizing centers (ncMTOCs), which are essential for anchoring microtubules at the cortex, enabling the formation of long, parallel microtubule streams or "twisters" that drive cytoplasmic advection during late oogenesis. This work establishes a paradigm where motor protein activation is spatially controlled through targeted localization of regulatory cofactors, with the activated motor then participating in building its own transport infrastructure through ncMTOC recruitment and microtubule network organization.

      There's a lot to like about this paper! The data are generally lovely and nicely presented. The authors also use a combination of experimental approaches, combining genetics, live and fixed imaging, and protein biochemistry.

      Concerns:

      Page 6: "to assay if elevation of Ninein levels was able to mis-regulate Ens localization, we overexpressed a tagged Ninein-RFP protein in the oocyte. At stage 9 the overexpressed Ninein accumulated at the anterior cortex of the oocyte and also generated large cortical aggregates able to recruit high levels of Ens (Figures 2D and 2H)... The examination of Ninein/Ens cortical aggregates obtained after Ninein overexpression showed that these aggregates were also able to recruit high levels of Patronin and Shot (Figures 2E and 2H)." Firstly, I'm not crazy about the use of "overexpressed" here, since there isn't normally any Ninein-RFP in the oocyte. In these experiments it has been therefore expressed, not overexpressed. Secondly, I don't understand what the reader is supposed to make of these data. Expression of a protein carrying a large fluorescent tag leads to large aggregates (they don't look cortical to me) that include multiple proteins - in fact, all the proteins examined. I don't understand this to be evidence of anything in particular, except that Ninein-RFP causes the accumulation of big multi-protein aggregates. While I can understand what the authors were trying to do here, I think that these data are inconclusive and should be de-emphasized.

      Page 7: "Co-immunoprecipitations experiments revealed that Patronin was associated with Shot-YFP, as shown previously (Nashchekin et al., 2016), but also with EnsWT-GFP, indicating that Ens, Shot and Patronin are present in the same complex (Figure 3B)." I do not agree that association between Ens-GFP and Patronin indicates that Ens is in the same complex as Shot and Patronin. It is also very possible that there are two (or more) distinct protein complexes. This conclusion could therefore be softened. Instead of "indicating" I suggest "suggesting the possibility."

      Page 7: "During stage 9, the average subcortical MT length, taken at one focal plane in live oocytes (see methods)..." I appreciate that the authors have been careful to describe how they measured MT length, as this is a major point for interpretation. I think the reader would benefit from an explanation of why they decided to measure in only one focal plane and how that decision could impact the results.

      Page 7: "... the MTs exhibited an orthogonal orientation relative to the anterior cortex (Figures 4A left panels, 4C and 4E)." This phenotype might not be obvious to readers. Can it be quantified?

      Page 8: "Altogether, the analyses of Ens and Khc defective oocytes suggested that MT organization defects during late oogenesis (stage 10B) were caused by an initial failure of ncMTOCs to reach the cell cortex. Therefore, we hypothesized that overexpression of the ncMTOC component Shot could restore certain aspects of microtubule cortical organization in ens-deficient oocytes. Indeed, Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14)..." The data are clear, but the explanation is not. Can the authors please explain why adding in more of an ncMTOC component (Shot) rescues a defect of ncMTOC cortical localization?

      I'm grateful to the authors for their inclusion of helpful diagrams, as in Figures 1G and 2H. I think the manuscript might benefit from one more of these at the end, illustrating the ultimate model.

      I'm sorry to say that the language could use quite a bit of polishing. There are missing and extraneous commas. There is also regular confusion between the use of plural and singular nouns. Some early instances include:

      1. Page 3: thought instead of "thoughted."
      2. Page 5: "A previous studies have revealed"
      3. Page 5: "A significantly loss"
      4. Page 6: "troughs ring canals" should be "through ring canals"
      5. Page 7: lives stage 9 oocytes
      6. Page 7: As ens and Khc RNAi oocytes exhibits
      7. Page 7: we examined in details
      8. Page 7: This average MT length was similar in Khc RNAi and ens mutant oocyte..

      Significance

      This work makes a nice conceptual advance by showing that motor activation controls its own transport infrastructure, a paradigm that could extend to other systems requiring spatially regulated transport.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      The manuscript of Berisha et al., investigates the role of Esconsin (Ens), Kinesin-1 and Ninein in organisation of microtubules (MT) in Drosophila oocyte. At stage 9 oocytes Kinesin-1 transports oskar mRNA, a posterior determinant, along MT that are organised by ncMTOCs. At stage 10b, Kinesin-1 induces cytoplasmic advection to mix the contents of the oocyte. Ensconsin/Map7 is a MT associated protein (MAP) that uses its MT-binding domain (MBD) and kinesin binding domain (KBD) to recruit Kinesin-1 to the microtubules and to stimulate the motility of MT-bound Kinesin-1. Using various new Ens transgenes, the authors demonstrate the requirement of Ens MBD and Ninein in Ens localisation to the oocyte where Ens activates Kinesin-1 using its KBD. The authors also claim that Ens, Kinesin-1 and Ninein are required for the accumulation of ncMTOCs at the oocyte cortex and argue that the detachment of the ncMTOCs from the cortex accounts for the reduced localisation of oskar mRNA at stage 9 and the lack of cytoplasmic streaming at stage 10b.

      Although the manuscript contains several interesting observations, the authors' conclusions are not sufficiently supported by their data. The structure function analysis of Ensconsin (Ens) is potentially publishable, but the conclusions on ncMTOC anchoring and cytoplasmic streaming not convincing

      1. The main conclusion of the manuscript is that "MT advection failure in Khc and ens in late oogenesis stems from defective cortical ncMTOCs recruitment". This completely overlooks the abundant evidence that Kinesin-1 directly drives cytoplasmic streaming by transporting vesicles and microtubules along microtubules, which then move the cytoplasm by advection (Palacios et al., 2002; Serbus et al, 2005; Lu et al, 2016). Since Kinesin-1 generates the flows, one cannot conclude that the effect of khc and ens mutants on cortical ncMTOC positioning has any direct effect on these flows, which do not occur in these mutants.
      2. The authors claim that streaming phenotypes of ens and khs mutants are due to a decrease in microtubule length caused by the defective localisation of ncMTOCs. In addition to the problem raised above, However, I am not convinced that they can make accurate measurements of microtubule length from confocal images like those shown in Figure 4. Firstly, they are measuring the length of bundles of microtubules and cannot resolve individual microtubules. This problem is compounded by the fact that the microtubules do not align into parallel bundles in the mutants. This will make the "microtubules" appear shorter in the mutants. In addition, the alignment of the microtubules in wild-type allows one to choose images in which the microtubule lie in the imaging plane, whereas the more disorganised arrangement of the microtubules in the mutants means that most microtubules will cross the imaging plane, which precludes accurate measurements of their length.
      3. "To investigate whether the presence of these short microtubules in ens and Khc RNAi oocytes is due to defects in microtubule anchoring or is also associated with a decrease in microtubule polymerization at their plus ends, we quantified the velocity and number of EB1comets, which label growing microtubule plus ends (Figure S3)." I do not understand how the anchoring or not of microtubule minus ends to the cortex determines how far their plus ends grow, and these measurements fall short of showing that plus end growth is unaffected. It has already been shown that the Kinesin-1-dependent transport of Dynactin to growing microtubule plus ends increases the length of microtubules in the oocyte because Dynactin acts as an anti-catastrophe factor at the plus ends. Thus, khc mutants should have shorter microtubules independently of any effects on ncMTOC anchoring. The measurements of EB1 comet speed and frequency in FigS2 will not detect this change and are not relevant for their claims about microtubule length. Furthermore, the authors measured EB1 comets at stage 9 (where they did not observe short MT) rather than at stage 10b. The authors' argument would be better supported if they performed the measurements at stage 10b.
      4. The Shot overexpression experiments presented in Fig.3 E-F, Fig.4D and TableS1 are very confusing. Originally , the authors used Shot-GFP overexpression at stage 9 to show that there is a decrease of ncMTOCs at the cortex in ens mutants (Fig.3 E-F) and speculated that this caused the defects in MT length and cytoplasmic advection at stage 10B. However the authors later state on page 8 that : "Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14), resembling the patterns observed in controls (Figures 4B right panel and 4D). Moreover, while ens females were fully sterile, overexpression of Shot was sufficient to restore that loss of fertility (Table S1)". Is this the same UAS Shot-GFP and VP16 Gal4 used in both experiments? If so, this contradictions puts the authors conclusions in question.
      5. The authors based they conclusions about the involvement of Ens, Kinesin-1 and Ninein in ncMTOC anchoring on the decrease in cortical fluorescence intensity of Shot-YFP and Patronin-YFP in the corresponding mutant backgrounds. However, there is a large variation in average Shot-YFP intensity between control oocytes in different experiments. In Fig. 2F-G the average level of Shot-YFP in the control sis 130 AU while in Fig.3 G-H it is only 55 AU. This makes me worry about reliability of such measurements and the conclusions drawn from them.
      6. The decrease in the intensity of Shot-YFP and Patronin-YFP cortical fluorescence in ens mutant oocytes could be because of problems with ncMTOC anchoring or with ncMTOCsformation. The authors should find a way to distinguish between these two possibilities. The authors could express Ens-Mut (described in Sung et al 2008), which localises at the oocyte posterior and test whether it recruits Shot/Patronin ncMTOCs to the posterior.
      7. According to the Materials and Methods, the Shot-GFP used in Fig.3 E-F and Fig.4 was the BDSC line 29042. This is Shot L(C), a full-length version of Shot missing the CH1 actin-binding domain that is crucial for Shot anchoring to the cortex. If the authors indeed used this version of Shot-GFP, the interpretation of the above experiments is very difficult.
      8. Page 6 "converted in NCs, in a region adjacent to the ring canals, Dendra-Ens-labeled MTs were found in the oocyte compartment indicating they are able to travel from NC toward the oocyte trough ring canals". I have difficulty seeing the translocation of MT through the ring canals. Perhaps it would be more obvious with a movie/picture showing only one channel. Considering that f Dendra-Ens appears in the oocyte much faster than MT transport through ring canals (140nm/s, Lu et al 2022) , the authors are most probably observing the translocation of free Ens rather than Ens bound to MT. The authors should also mention that Ens movement from the NC to the oocyte has been shown before with Ens MBD in Lu et al 2022 with better resolution.
      9. Page 6: The co-localization of Ninein with Ens and Shot at the oocyte cortex (Figure 2A). I have difficulty seeing this co-localisation. Perhaps it would be more obvious in merged images of only two channels and with higher resolution images
      10. "a pool of the Ens-GFP co-localized with Ch-Patronin at cortical ncMTOCs at the anterior cortex (Figure 3A)". I also have difficulty seeing this.
      11. "Ninein co-localizes with Ens at the oocyte cortex and partially along cortical microtubules, contributing to the maintenance of high Ens protein levels in the oocyte and its proper cortical targeting". I could not find any data showing the involvement of Ninein in the cortical targeting of Ens.
      12. "our MT network analyses reveal the presence of numerous short MTs cytoplasmic clustered in an anterior pattern." "This low cortical recruitment of ncMTOCs is consistent with poor MT anchoring and their cytoplasmic accumulation." I could not find any data showing that short cortical MT observed at stage 10b in ens mutant and Khc RNAi were cytoplasmic and poorly anchored.
      13. "The egg chamber consists of interconnected cells where Dynein and Khc activities are spatially separated. Dynein facilitates transport from NCs to the oocyte, while Khc mediates both transport and advection within the oocyte." Dynein is involved in various activities in the oocyte. It anchors the oocyte nucleus and transports bcd and grk mRNA to mention a few.
      14. The cartoons in Fig.2H and 3I exaggerate the effect of Ninein and Ens on cortical ncMTOCs. According to the corresponding graphs, there is a 20 and 50% decrease in each case.

      Significance

      Given the important concerns raised, the significance of the findings is difficult to assess at this stage.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Berisha et al. investigate how microtubule (MT) organization is spatially regulated during Drosophila oogenesis. The authors identify a mechanism in which the Kinesin-1 activator Ensconsin/MAP7 is transported by dynein and anchored at the oocyte cortex via Ninein, enabling localized activation of Kinesin-1. Disruption of this pathway impairs ncMTOC recruitment and MT anchoring at the cortex. The authors combine genetic manipulation with high-resolution microscopy and use three key readouts to assess MT organization during mid-to-late oogenesis: cortical MT formation, localization of posterior determinants, and ooplasmic streaming. Notably, Kinesin-1, in concert with its activator Ens/MAP7, contributes to organizing the microtubule network it travels along. Overall, the study presents interesting findings, though we have several concerns we would like the authors to address.

      Ensconsin enrichment in the oocyte

      1. Enrichment in the oocyte
        • Ensconsin is a MAP that binds MTs. Given that microtubule density in the oocyte significantly exceeds that in the nurse cells, its enrichment may passively reflect this difference. To assess whether the enrichment is specific, could the authors express a non-Drosophila MAP (e.g., mammalian MAP1B) to determine whether it also preferentially localizes to the oocyte?
        • The ability of ens-wt and ens-LowMT to induce tubulin polymerization according to the light scattering data (Fig. S1J) is minimal and does not reflect dramatic differences in localization. The authors should verify that, in all cases, the polymerization product in their in vitro assays is microtubules rather than other light-scattering aggregates. What is the control in these experiments? If it is just purified tubulin, it should not form polymers at physiological concentrations.
      2. Photoconversion caveats MAPs are known to dynamically associate and dissociate from microtubules. Therefore, interpretation of the Ens photoconversion data should be made with caution. The expanding red signal from the nurse cells to the oocyte may reflect a any combination of dynein-mediated MT transport and passive diffusion of unbound Ensconsin. Notably, photoconversion of a soluble protein in the nurse cells would also result in a gradual increase in red signal in the oocyte, independent of active transport. We encourage the authors to more thoroughly discuss these caveats. It may also help to present the green and red channels side by side rather than as merged images, to allow readers to assess signal movement and spatial patterns better.
      3. Reduction of Shot at the anterior cortex
        • Shot is known to bind strongly to F-actin, and in the Drosophila ovary, its localization typically correlates more closely with F-actin structures than with microtubules, despite being an MT-actin crosslinker. Therefore, the observed reduction of cortical Shot in ens, nin mutants, and Khc-RNAi oocytes is unexpected. It would be important to determine whether cortical F-actin is also disrupted in these conditions, which should be straightforward to assess via phalloidin staining.
        • MTs are barely visible in Fig. 3A, which is meant to demonstrate Ens-GFP colocalization with tubulin. Higher-quality images are needed.
      4. MT gradient in stage 9 oocytes In ens-/-, nin-/-, and Khc-RNAi oocytes, is there any global defect in the stage 9 microtubule gradient? This information would help clarify the extent to which cortical localization defects reflect broader disruptions in microtubule polarity.
      5. Role of Ninein in cortical anchoring The requirement for Ninein in cortical anchorage is the least convincing aspect of the manuscript and somewhat disrupts the narrative flow. First, it is unclear whether Ninein exhibits the same oocyte-enriched localization pattern as Ensconsin. Is Ninein detectable in nurse cells? Second, the Ninein antibody signal appears concentrated in a small area of the anterior-lateral oocyte cortex (Fig. 2A), yet Ninein loss leads to reduced Shot signal along a much larger portion of the anterior cortex (Fig. 2F)-a spatial mismatch that weakens the proposed functional relationship. Third, Ninein overexpression results in cortical aggregates that co-localize with Shot, Patronin, and Ensconsin. Are these aggregates functional ncMTOCs? Do microtubules emanate from these foci?
      6. Inconsistency of Khc^MutEns rescue The Khc^MutEns variant partially rescues cortical MT formation and restores a slow but measurable cytoplasmic flow yet it fails to rescue Staufen localization (Fig. 5). This raises questions about the consistency and completeness of the rescue. Could the authors clarify this discrepancy or propose a mechanistic rationale?

      Minor points:

      1. The pUbi-attB-Khc-GFP vector was used to generate the Khc^MutEns transgenic line, presumably under control of the ubiquitous ubi promoter. Could the authors specify which attP landing site was used? Additionally, are the transgenic flies viable and fertile, given that Kinesin-1 is hyperactive in this construct?
      2. On page 11 (Discussion, section titled "A dual Ensconsin oocyte enrichment mechanism achieves spatial relief of Khc inhibition"), the statement "many mutations in Kif5A are causal of human diseases" would benefit from a brief clarification. Since not all readers may be familiar with kinesin gene nomenclature, please indicate that KIF5A is one of the three human homologs of Kinesin heavy chain.
      3. On page 16 (Materials and Methods, "Immunofluorescence in fly ovaries"), the sentence "Ovaries were mounted on a slide with ProlonGold medium with DAPI (Invitrogen)" should be corrected to "ProLong Gold."

      Significance

      This study shows that enrichment of MAP7/ensconsin in the oocyte is the mechanism of kinesin-1 activation there and is important for cytoplasmic streaming and localization non-centrosomal microtubule-organizing centers to the oocyte cortex

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This paper addresses a very interesting problem of non-centrosomal microtubule organization in developing Drosophila oocytes. Using genetics and imaging experiments, the authors reveal an interplay between the activity of kinesin-1, together with its essential cofactor Ensconsin, and microtubule organization at the cell cortex by the spectraplakin Shot, minus-end binding protein Patronin and Ninein, a protein implicated in microtubule minus end anchoring. The authors demonstrate that the loss of Ensconsin affects the cortical accumulation non-centrosomal microtubule organizing center (ncMTOC) proteins, microtubule length and vesicle motility in the oocyte, and show that this phenotype can be rescued by constitutively active kinesin-1 mutant, but not by Ensconsin mutants deficient in microtubule or kinesin binding. The functional connection between Ensconsin, kinesin-1 and ncMTOCs is further supported by a rescue experiment with Shot overexpression. Genetics and imaging experiments further implicate Ninein in the same pathway. These data are a clear strength of the paper; they represent a very interesting and useful addition to the field.

      The weaknesses of the study are two-fold. First, the paper seems to lack a clear molecular model, uniting the observed phenomenology with the molecular functions of the studied proteins. Most importantly, it is not clear how kinesin-based plus-end directed transport contributes to cortical localization of ncMTOCs and regulation of microtubule length.

      Second, not all conclusions and interpretations in the paper are supported by the presented data. Below is a list of specific comments, outlining the concerns, in the order of appearance in the paper/figures.

      1. Figure 1. The statement: "Ens loading on MTs in NCs and their subsequent transport by Dynein toward ring canals promotes the spatial enrichment of the Khc activator Ens in the oocyte" is not supported by data. The authors do not demonstrate that Ens is actually transported from the nurse cells to the oocyte while being attached to microtubules. They do show that the intensity of Ensconsin correlates with the intensity of microtubules, that the distribution of Ensconsin depends on its affinity to microtubules and that an Ensconsin pool locally photoactivated in a nurse cell can redistribute to the oocyte (and throughout the nurse cell) by what seems to be diffusion. The provided images suggest that Ensconsin passively diffuses into the oocyte and accumulates there because of higher microtubule density, which depends on dynein. To prove that Ensconsin is indeed transported by dynein in the microtubule-bound form, one would need to measure the residence time of Ensconsin on microtubules and demonstrate that it is longer than the time needed to transport microtubules by dynein into the oocyte; ideally, one would like to see movement of individual microtubules labelled with photoconverted Ensconsin from a nurse cell into the oocyte. Since microtubules are not enriched in the oocyte of the dynein mutant, analysis of Ensconsin intensity in this mutant is not informative and does not reveal the mechanism of Ensconsin accumulation.
      2. Figure 2. According to the abstract, this figure shows that Ensconsin is "maintained at the oocyte cortex by Ninein". However, the figure doesn't seem to prove it - it shows that oocyte enrichment of Ensonsin is partially dependent on Ninein, but this applies to the whole cell and not just to the cell cortex. Furthermore, it is not clear whether Ninein mutation affects microtubule density, which in turn would affect Ensconsin enrichment, and therefore, it is not clear whether the effect of Ninein loss on Ensconsin distribution is direct or indirect. The observation that the aggregates formed by overexpressed Ninein accumulate other proteins, including Ensconsin, supports, though does not prove their interactions. Furthermore, there is absolutely no proof that Ninein aggregates are "ncMTOCs". Unless the authors demonstrate that these aggregates nucleate or anchor microtubules (for example, by detailed imaging of microtubules and EB1 comets), the text and labels in the figure would need to be altered.

      Minor comment: Note that a "ratio" (Figure 2C) is just a ratio, and should not be expressed in arbitrary units. 3. Figure 3B: immunoprecipitation results cannot be interpreted because the immunoprecipitated proteins (GFP, Ens-GFP, Shot-YFP) are not shown. It is also not clear that this biochemical experiment is useful. If the authors would like to suggest that Ensconsin directly binds to Patronin, the interaction would need to be properly mapped at the protein domain level. 4. One of the major phenotypes observed by the authors in Ens mutant is the loss of long microtubules. The authors make strong conclusions about the independence of this phenotype from the parameters of microtubule plus-end growth, but in fact, the quality of their data does not allow to make such a conclusion, because they only measured the number of EB1 comets and their growth rate but not the catastrophe, rescue or pausing frequency. Note that kinesin-1 has been implicated in promoting microtubule damage and rescue (doi: 10.1016/j.devcel.2021). In the absence of such measurements, one cannot conclude whether short microtubules arise through defects in the minus-end, plus-end or microtubule shaft regulation pathways. It is important to note in that a spectraplakin, like Shot, can potentially affect different pathways, particularly when overexpressed. Unjustified conclusions should be removed: the authors do not provide sufficient data to conclude that "ens and Khc oocytes MT organizational defects are caused by decreased ncMTOC cortical anchoring", because the actual cortical microtubule anchoring was not measured.

      Minor comment: Microtubule growth velocity must be expressed in units of length per time, to enable evaluating the quality of the data, and not as a normalized value. 5. A significant part of the Discussion is dedicated to the potential role of Ensconsin in cortical microtubule anchoring and potential transport of ncMTOCs by kinesin. It is obviously fine that the authors discuss different theories, but it would be very helpful if the authors would first state what has been directly measured and established by their data, and what are the putative, currently speculative explanations of these data.

      Minor comment: The writing and particularly the grammar need to be significantly improved throughout, which should be very easy with current language tools. Examples: "ncMTOCs recruitment" should be "ncMTOC recruitment"; "Vesicles speed" should be "Vesicle speed", "Nin oocytes harbored a WT growth,"- unclear what this means, etc. Many paragraphs are very long and difficult to read. Making shorter paragraphs would make the authors' line of thought more accessible to the reader.

      Significance

      This paper represents significant advance in understanding non-centrosomal microtubule organization in general and in developing Drosophila oocytes in particular by connecting the microtubule minus-end regulation pathway to the Kinesin-1 and Ensconsin/MAP7-dependent transport. The genetics and imaging data are of good quality, are appropriately presented and quantified. These are clear strengths of the study which will make it interesting to researchers studying the cytoskeleton, microtubule-associated proteins and motors, and fly development.

      The weaknesses of this study are due to the lack of clarity of the overall molecular model, which would limit the impact of the study on the field. Some interpretations are not sufficiently supported by data, but this can be solved by more precise and careful writing, without extensive additional experimentation.

      My expertise is cell biology and biochemistry of the microtubule cytoskeleton, including both microtubule-associated proteins and microtubule motors.

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

      Learn more at Review Commons


      Reply to the reviewers

      Revision Plan

      Manuscript number: RC-2025-03208

      Corresponding author(s): Jared Nordman

      [The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

      • *

      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

      • *

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

      1. General Statements [optional]

      All three reviewers of our manuscript were very positive about our work. The reviewers noted that our work represents a necessary advance that is timely, addresses important issues in the chromatin field, and will of broad interest to this community. Given the nature of our work and the positive reviews, we feel that this manuscript would best be suited for the Journal of Cell Biology.

      2. Description of the planned revisions

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

      Summary:

      The authors investigate the function of the H3 chaperone NASP, which is known to bind directly to H3 and prevent degradation of soluble H3. What is unclear is where NASP functions in the cell (nucleus or cytoplasm), how NASP protects H3 from degradation (direct or indirect), and if NASP affects H3 dynamics (nuclear import or export). They use the powerful model system of Drosophila embryos because the soluble H3 pool is high due to maternal deposition and they make use of photoconvertable Dendra-tagged proteins, since these are maternally deposited and can be used to measure nuclear import/export rates.

      Using these systems and tools, they conclude that NASP affects nuclear import, but only indirectly, because embryos from NASP mutant mothers start out with 50% of the maternally deposited H3. Because of the depleted H3 and reduced import rates, NASP deficient embryos also have reduced nucleoplasmic and chromatin-associated H3. Using a new Dendra-tagged NASP allele, the authors show that NASP and H3 have different nuclear import rates, indicating that NASP is not a chaperone that shuttles H3 into the nucleus. They test H3 levels in embryos that have no nuclei and conclude that NASP functions in the cytoplasm, and through protein aggregation assays they conclude that NASP prevents H3 aggregation.

      Major comments:

      The text was easy to read and logical. The data are well presented, methods are complete, and statistics are robust. The conclusions are largely reasonable. However, I am having trouble connecting the conclusions in text to the data presented in Figure 4.

      First, I'm confused why the conclusion from Figure 4A is that NASP functions in the cytoplasm of the egg. Couldn't NASP be required in the ovary (in, say, nurse cell nuclei) to stimulate H3 expression and deposition into the egg? The results in 4A would look the same if the mothers deposit 50% of the normal H3 into the egg. Why is NASP functioning specifically in the cytoplasm when it is also so clearly imported into the nucleus? Maybe NASP functions wherever it is, and by preventing nuclear import, you force it to function in the cytoplasm. I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

      The concern raised by the reviewer regarding NASP function during oogenesis has been addressed in a previous work published from our lab. Unfortunately, we did not do a good job conveying this work in the original version of this manuscript. We demonstrated that total H3 levels are unaffected when comparing WT and NASP mutant stage 14 egg chambers. This means that the amount of H3 deposited into the eggs does not change in the absence of NASP. To address the reviewer's comment, we will change the text to make the link to our previous work clear.

      Second, an alternate conclusion from Figure 4D/E is that mothers are depositing less H3 protein into the egg, but the same total amount is being aggregated. This amount of aggregated protein remains constant in activated eggs, but additional H3 translation leads to more total H3? The authors mention that additional translation can compensate for reduced histone pools (line 416).

      Similar to our response above, the total amount of H3 in wild type and NASP mutant stage 14 egg chambers is the same. Therefore, mothers are depositing equal amounts of H3 into the egg. We will make the necessary changes in the text to make this point clear.

      As the function of NASP in the cytoplasm (when it clearly imports into the nucleus) and role in H3 aggregation are major conclusions of the work, the authors need to present alternative conclusions in the text or complete additional experiments to support the claims. Again, I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

      A common issue raised by all three reviewers was to more convincingly demonstrate that assay that we have used to isolate protein aggregates does, in fact, isolate protein aggregates. To verify this, we will be performing the aggregate isolation assay using controls that are known to induce more protein aggregation. We will perform the aggregation assay with egg chambers or extracts that are exposed to heat shock or the aggregation-inducing chemicals Canavanine and Azetidine-2-carboxylic acid. The chemical treatment was a welcome suggestion from reviewer #3. These experiments will significantly strengthen any claims based on the outcome of the aggregation assay.

      We will also make changes to the text and include other interpretations of our work as the reviewer has suggested.

      Data presentation:

      Overall, I suggest moving some of the supplemental figures to the main text, adding representative movie stills to show where the quantitative data originated, and moving the H3.3 data to the supplement. Not because it's not interesting, but because H3.3 and H3.2 are behaving the same.

      Where possible, we will make changes to the figure display to improve the logic and flow of the manuscript

      Fig 1:

      It would strengthen the figure to include representative still images that led to the quantitative data, mostly so readers understand how the data were collected.

      We will add representative stills to Figure 1 to help readers understand how the data is collected. We will also a representative H3-Dendra movie similar to the NASP supplemental movie.

      The inclusion of a "simulated 50% H3" in panel C is confusing. Why?

      We used a 50% reduction in H3 levels because that is reduction in H3 we measure in embryos laid by NASP-mutant mothers in our previous work. A reduction in H3 levels alone would be predicted to change the nuclear import rate of H3. Thus, having a quantitative model of H3 import kinetics was key in our understanding of NASP function in vivo. We will revise the text to make this clear.

      I would also consider normalizing the data between A and B (and C and D) by dividing NASP/WT. This could be included in the supplement (OPTIONAL)

      We can normalize the values and include the data in a supplemental figure.

      Fig S1:

      The data simulation S1G should be moved to the main text, since it is the primary reason the authors reject the hypothesis that NASP influences H3 import rates.

      This is a good point. We will move S1G into the Figure 1.

      Fig 2:

      Once again, I think it would help to include a few representative images of the photoconverted Dendra2 in the main text.

      We will add representative images of the photoconversion in Figure 2.

      I struggled with A/B, I think due to not knowing how the data were normalized. When I realized that the WT and NASP data are not normalized to each other, but that the NASP values are likely starting less than the WT values, it made way more sense. I suggest switching the order of data presentation so that C-F are presented first to establish that there is less chromatin-bound H3 in the first place, and then present A/B to show no change in nuclear export of the H3 that is present, allowing the conclusion of both less soluble AND chromatin-bound H3.

      The order of the presentation of the data was to test if NASP was acting as a nuclear receptor. Since Figure 1 compares the nuclear import, we wanted to address the nuclear export and provide a comprehensive analysis of the role of NASP in H3 nuclear dynamics before advancing on to other consequences of NASP depletion. We can add the graphs with the un-normalized values in the Supplemental Figure to show the actual difference in total intensity values.

      Fig S2:

      If M1-M3 indicate males, why are the ovaries also derived from males? I think this is just confusing labeling.

      We will change the labelling.

      Supplemental Movie S1:

      Beautiful. Would help to add a time stamp (OPTIONAL).

      Thank you! We will add the time stamp to the movie

      Fig 3:

      Panel C is the same as Fig S1A (not Fig 1A, as is said in the legend), though I appreciate the authors pointing it out in the legend. Also see line 276.

      We appreciate the reviewer for pointing this out. We will make the change in the text to correct this.

      Panel D is a little confusing, because presumably the "% decrease in import rate" cannot be positive (Y axis). This could be displayed as a scatter (not bar) as in Panels B/C (right) where the top of the Y axis is set to 0.

      We understand the reviewer's concern that the decrease value cannot be positive. We can adjust the y-axis so that it caps off at 0.

      Fig S3:

      A: What do the different panels represent? I originally thought developmental time, but now I think just different representative images? Are these age-matched from time at egg lay?

      The different panels show representative images. We can clarify that in the figure legend.

      C: What does "embryos" mean? Same question for Fig 4A.

      In this figure, embryos mean the exact number of embryos used to form the lysate for the western blot. We will clarify this in the figure legend.

      Fig 4:

      A: What does "embryos" mean? Number of embryos? Age in hours?

      In this figure, embryos mean the exact number of embryos used to form the lysate for the western blot. We will clarify this in the figure legend.

      C: Not sure the workflow figure panel is necessary, as I can't tell what each step does. This is better explained in methods. However I appreciated the short explanation in the text (lines 314-5).

      The workflow panel helps to identify the samples labelled as input and aggregate for the western blot analysis. Since our input in the western blots does not refer to the total protein lysate, we feel it is helpful to point out exactly what stage at the protocol we are utilizing the sample for our analysis.

      Minor comments:

      The authors should describe the nature of the NASP alleles in the main text and present evidence of robust NASP depletion, potentially both in ovaries and in embryos. The antibody works well for westerns (Fig S2B). This is sort of demonstrated later in Figure 4A, but only in NAAP x twine activated eggs.

      We appreciate the reviewer's comments about the NASP mutant allele. In our previous publication, we characterized the NASP mutant fly line and its effect on both stage 14 egg chambers and the embryos. We will emphasize the reference to our previous work in the text.

      Lines 163, 251, 339: minor typos

      Line 184: It would help to clarify- I'm assuming cytoplasmic concentration (or overall) rather than nuclear concentration. If nuclear, I'd expect the opposite relationship. This occurs again when discussing NASP (line 267). I suspect it's also not absolute concentration, but relative concentration difference between cytoplasm and nucleus. It would help clarify if the authors were more precise.

      We appreciate the reviewer's point and will add the clarification in the text.

      Line 189: Given that the "established integrative model" helps to reject the hypothesis that NASP is involved in H3 import, I think it's important to describe the model a little more, even though it's previously published.

      We will add few sentences giving a brief description of the model to the text.

      Line 203: "The measured rate of H3.2 export from the nucleus is negligible" clarify this is in WT situations and not a conclusion from this study.

      We will add the clarification of this statement in the text.

      Line 211: How can the authors be so sure that the decrease in WT is due to "the loss of non-chromatin bound nucleoplasmic H3.2-Dendra2?"

      From the live imaging experiments, the H3.2-Dendra2 intensity in the nucleus reduces dramatically upon nuclear envelope breakdown with the only H3.2-Dendra2 intensity remaining being the chromatin bound H3.2. Excess H3.2 is imported into the nucleus and not all of it is incorporated into the chromatin. This is a unique feature of the embryo system that has been observed previously. We mention that the intensity reduction is due to the loss of non-chromatin bound nucleoplasmic H3.2.

      Line 217: In the conclusion, the authors indicate that NASP indirectly affects soluble supply of H3 in the nucleoplasm. I do believe they've shown that the import rate effect is indirect, but I don't know why they conclude that the effect of NASP on the soluble nucleoplasmic H3 supply is indirect. Similarly, the conclusion is indirect on line 239. Yet, the authors have not shown it's not direct, just assumed since NASP results in 50% decrease to deposited maternal histones.

      We appreciate the feedback on the conclusions of Figure 2 from the reviewer. Our conclusions are primarily based on the effect of H3 levels in the absence of NASP in the early embryos. To establish direct causal effects, it would be important to recover the phenotypes by complementation experiments and providing molecular interactions to cause the effects. In this study we have not established those specific details to make conclusions of direct effects. We will change the text to make this more clear.

      Line 292: What is the nature of the NASP "mutant?" Is it a null? Similarly, what kind of "mutant" is the twine allele? Line 295.

      We will include descriptions of the NASP and twine mutants in the text.

      Line 316: Why did the authors use stage 14 egg chambers here when they previously used embryos? This becomes more clear later shortly, when the authors examine activated eggs, but it's confusing in text.

      The reason to use stage 14 egg chambers was to establish NASP function during oogenesis. We will modify the text to emphasize the reason behind using stage 14 egg chambers.

      Lines 343-348: It's unclear if the authors are drawing extended conclusions here or if they are drawing from prior literature (if so, citations would be required). For example, why during oogenesis/embryogenesis are aggregation and degradation developmentally separated?

      This conclusion is based primarily based on the findings from this study (Figure 4) and out previous published work. We will modify the text for more clarity.

      Lines 386-7: I do not understand why the authors conclude that H3 aggregation and degradation are "developmentally uncoupled" and why, in the absence of NASP, "H3 aggregation precedes degradation."

      This is based data in Figure 4 combined with our previous working showing that the total level of H3 in not changed in NASP-mutant stage 14 egg chambers. Aggregates seem to be more persistent in the stage 14 egg chambers (oogenesis) and they get cleared out upon egg activation (entry into embryogenesis). This provides evidence for aggregation occurring prior to degradation and these two events occurring in different developmental stages. We will change the text to make this more clear.

      Line 395: Why suddenly propose that NASP also functions in the nucleus to prevent aggregation, when earlier the authors suggest it functions only in the cytoplasm?

      We will make the necessary edits to ensure that the results don't suggest a role of NASP exclusive to the cytoplasm. Our findings highlight a cytoplasmic function of NASP, however, we do not want to rule out that this same function couldn't occur in the nucleus.

      Lines 409-413: The authors claim that histone deficiency likely does not cause the embryonic arrest seen in embryos from NASP mutant mothers. This is because H3 is reduced by 50% yet some embryos arrest long before they've depleted this supply. However, the authors also showed that H3 import rates are affected in these embryos due to lower H3 concentration. Since the early embryo cycles are so rapid, reduced H3 import rates could lead to early arrest, even though available H3 remains in the cytoplasm.

      We thank the reviewer for their suggestion. This conclusion is based on the findings from the previous study from our lab which showed that the majority of the embryos laid by NASP mutant females get arrested in the very early nuclear cycles (Reviewer #1 (Significance (Required)):

      The significance of the work is conceptual, as NASP is known to function in H3 availability but the precise mechanism is elusive. This work represents a necessary advance, especially to show that NASP does not affect H3 import rates, nor does it chaperone H3 into the nucleus. However, the authors acknowledge that many questions remain. Foremost, why is NASP imported into the nucleus and what is its role there?

      I believe this work will be of interest to those who focus on early animal development, but NASP may also represent a tool, as the authors conclude in their discussion, to reduce histone levels during development and examine nucleosome positioning. This may be of interest to those who work on chromatin accessibility and zygotic genome activation.

      I am a genetics expert who works in Drosophila embryogenesis. I do not have the expertise to evaluate the aggregate methods presented in Figure 4.

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

      Summary:

      This manuscript focuses on the role of the histone chaperone NASP in Drosophila. NASP is a chaperone specific to histone H3 that is conserved in mammals. Many aspects of the molecular mechanisms by which NASP selectively binds histone H3 have been revealed through biochemical studies. However, key aspects of NASP's in vivo roles remain unclear, including where in the cell NASP functions, and how it prevents H3 degradation. Through live imaging in the early Drosophila embryo, which possesses large amounts of soluble H3 protein, Das et al determine that NASP does not control nuclear import or export of H3.2 or H3.3. Instead, they find through differential centrifugation analysis that NASP functions in the cytoplasm to prevent H3 aggregation and hence its subsequent degradation.

      Major Comments:

      The protein aggregation assays raise several questions. From a technical standpoint, it would be helpful to have a positive control to demonstrate that the assay is effective at detecting protein aggregates. Ie. a genotype that exhibits increased protein aggregation; this could be for a protein besides H3. A common issue raised by all three reviewers was to more convincingly demonstrate that assay that we have used to isolate protein aggregates does, in fact, isolate protein aggregates. To verify this, we will be performing the aggregate isolation assay using controls that are known to induce more protein aggregation. We will perform the aggregation assay with egg chambers or extracts that are exposed to heat shock or the aggregation-inducing chemicals Canavanine and Azetidine-2-carboxylic acid. The chemical treatment was a welcome suggestion from reviewer #3. These experiments will significantly strengthen any claims based on the outcome of the aggregation assay.

      If NASP is not required to prevent H3 degradation in egg chambers, then why are H3 levels much lower in NASP input lanes relative to wild-type egg chambers in Fig 4D? We appreciate the reviewer's inputs regarding the reduced H3 levels in the NASP mutant egg chambers. We observe this reduction in H3 levels in the input because of the altered solubility of H3 which leads to the loss of H3 protein at different steps of the aggregate isolation assay. We will add a supplement figure showing H3 levels at different steps of the aggregate isolation assay. We do want to stress, however, that the total levels of H3 in stage 14 egg chambers does not change between WT and the NASP mutant.

      A corollary to this is that the increased fraction of H3 in aggregates in NASP mutants seems to be entirely due to the reduction in total H3 levels rather than an increase in aggregated H3. If NASP's role is to prevent aggregation in the cytoplasm, and degradation has not yet begun in egg chambers, then why are aggregated H3 levels not increased in NASP mutants relative to wild-type egg chambers? If the same number of egg chambers were used, shouldn't the total amount of histone be the same in the absence of degradation?

      In previously published work, we demonstrated that total H3 levels are unaffected when comparing WT and NASPmutant stage 14 egg chambers. This means that the amount of H3 deposited into the eggs does not change in the absence of NASP. To address the reviewer's comment, we will change the text to make the link to our previous work clear. As stated above, we will add a supplement figure showing H3 levels at different steps of the aggregate isolation assay.

      The live imaging studies are well designed, executed, and quantified. They use an established genotype (H3.2-Dendra2) in wild-type and NASP maternal mutants to demonstrate that NASP is not directly involved in nuclear import of H3.2. Decreased import is likely due to reduced H3.2 levels in NASP mutants rather than reduced import rates per se. The same methodology was used to determine that loss of NASP did not affect H3.2 nuclear export. These findings eliminate H3.2 nuclear import/export regulation as possible roles for NASP, which had been previously proposed.

      Thank you.

      Live imaging also conclusively demonstrates that the levels of H3.2 in the nucleoplasm and in mitotic chromatin are significantly lower in NASP mutants than wild-type nuclei. Despite these lower histone levels, the nuclear cycle duration is only modestly lengthened. The live imagining of NASP-Dendra2 nuclear import conclusively demonstrate that NASP and H3.2 are unlikely to be imported into the nucleus as one complex.

      Thank you.

      Minor Comments:

      Additional details on how the NASP-Dendra2 CRISPR allele was generated should be provided. In addition, additional details on how it was determined that this allele is functional should be provided (e.g. quantitative assays for fertility/embryo viability of NASP-Dendra2 females) We will make these additions to the text.

      If statistical tests are used to determine significance, the type of test used should be reported in the figure legends throughout.

      We will make the addition of the statistical tests to the figure legends.

      The western blot shown in Figure 4A looks more like a 4-fold reduction in H3 levels in NASP mutants relative to wild-type embryos, rather than the quantified 2-fold reduction. Perhaps a more representative blot can be shown.

      We have additional blots in the supplemental figure S3C. The quantification was performed after normalization to the total protein levels and we can highlight that in the figure legend.

      Reviewer #2 (Significance (Required)):

      As a fly chromatin biologist with colleagues that utilize mammalian experimental systems, I feel this manuscript will be of broad interest to the chromatin research community. Packaging of the genome into chromatin affects nearly every DNA-templated process, making the mechanisms by which histone proteins are expressed, chaperoned, and deposited into chromatin of high importance to the field. The study has multiple strengths, including high-quality quantitative imaging, use of a terrific experimental system (storage and deposition of soluble histones in early fly embryos). The study also answers outstanding questions in the field, specifically that NASP does not control nuclear import/export of histone H3. Instead, the authors propose that NASP functions to prevent protein aggregation. If this could be conclusively demonstrated, it would be valuable to the field. However, the protein aggregation studies need improvement. Technical demonstration that their differential centrifugation assay accurately detects aggregated proteins is needed. Further, NASP mutants do not exhibit increased H3 protein aggregation in the data presented. Instead, the increased fraction of aggregated H3 in NASP mutants seems to be due to a reduction in the overall levels of H3 protein, which is contrary to the model presented in this paper.

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

      This manuscript by Das et al. entitled "NASP functions in the cytoplasm to prevent histone H3 aggregation during early embryogenesis", explores the role of the histone chaperone NASP in regulating histone H3 dynamics during early Drosophila embryogenesis. Using primarily live imaging approaches, the authors found that NASP is not directly involved in the import or export of H3. Moreover, the authors claimed that NASP prevents H3 aggregation rather than protects against degradation.

      Major Comments:

      Figure 1A-B: The plotted data appear to have substantial dispersion. Could the authors include individual data points or provide representative images to help the reader assess variability?

      We chose to show unnormalized data in Figure 1 so readers could better compare the actual import values of H3 in the presence and absence of NASP. We felt it was a better representation of the true biological difference although raw data is more dispersive. We did also include normalized data in the supplement. Regardless, we will add representative stills to Figure 1 and include a H3-Dendra2 movie in the supplement to show the representative data.

      Given that the authors conclude that the reduced nuclear import is due to lowered H3 levels in NASP-deficient embryos, would overexpression of H3 rescue this phenotype? This would directly test whether H3 levels, rather than import machinery per se, drive the effect.

      We thank the reviewer for their valuable suggestion. We and others have tried to overexpress histones in the Drosophila early embryo without success. There must be an undefined feedback mechanism preventing histone overexpression in the germline. In fact, a recent paper has been deposited on bioRxiv (https://doi.org/10.1101/2024.12.23.630206) that suggest H4 protein could provide a feedback mechanism to prevent histone overexpression. While we would love to do this experiment, it is not technically feasible at this time.

      Figure 2A-B: The authors present the Relative Intensity of H3-Dendra2, but this metric obscures absolute differences between Control and NASP knockout embryos. Please include Total Intensity plots to show the actual reduction in H3 levels.

      We will add the total H3-Dendra2 intensity plots to the supplemental figure for the export curves.

      Additionally, Western blot analysis of nucleoplasmic H3 from wild-type vs. NASP-deficient embryos would provide essential biochemical confirmation of H3 level reductions.

      We will measure nuclear H3 levels by western from 0-2 hr embryos laid by WT and NASP mutant flies.

      Figure 4: To support the conclusion that NASP prevents H3 aggregation, I recommend performing aggregation assays by adding compounds that induce unfolding (amino acid analogues that induce unfolding, like canavanine or Azetidine-2-carboxylic acid) or using aggregation-prone H3 mutants.

      This is a very helpful suggestion! It is difficult to get chemicals into Drosophila eggs, but we will treat extracts directly with these chemicals. Additionally, we will use heat shocked eggs and extracts as an additional control.

      Inclusion of CMA and proteasome inhibition experiments could also clarify whether degradation pathways are secondarily involved or compensatory in the absence of NASP.

      The degradation pathway for H3 in the absence of NASP is unknown and a major focus of our future work is to define this pathway. Drosophila does not have a CMA pathway and therefore, we don't know how H3 aggregates are being sensed.

      Minor Comments:

      (1) The Introduction would benefit from mentioning the two NASP isoforms that exist in mammals (sNASP and tNASP), as this evolutionary context may inform interpretation of the Drosophila results.

      We will make the edits in the text to include that Drosophila NASP is the sole homolog of sNASP and that tNASP ortholog is not found in Drosophila.

      (2) Could the authors comment on the status of histone H4 in their experimental system? Given the observed cytoplasmic pool of H3, is it likely to exist as a monomer? If this H3 pool is monomeric, does that suggest an early failure in H3-H4 dimerization, and could this contribute to its aggregation propensity?

      In our previous work we noted that NASP binds more preferentially to H3 and the levels of H3 we much more reduced upon NASP depletion than H4. We pointed out in this publication that our data was consistent with H3 stores being monomeric in the Drosophila embryo. We don't' have a H4-Dendra2 line to test. In the future, however, this is something we are very keen to look at.

      Reviewer #3 (Significance (Required)):

      This work addresses a timely and important question in the field of chromatin biology and developmental epigenetics. The focus on histone homeostasis during embryogenesis and the cytoplasmic role of NASP adds a novel perspective. The live imaging experiments are a clear strength, providing valuable spatiotemporal insights. However, I believe that the manuscript would benefit significantly from additional biochemical validation to support and clarify some of the mechanistic claims.

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

      • *

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

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

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Das et al. entitled "NASP functions in the cytoplasm to prevent histone H3 aggregation during early embryogenesis", explores the role of the histone chaperone NASP in regulating histone H3 dynamics during early Drosophila embryogenesis. Using primarily live imaging approaches, the authors found that NASP is not directly involved in the import or export of H3. Moreover, the authors claimed that NASP prevents H3 aggregation rather than protects against degradation.

      Major Comments:

      Figure 1A-B: The plotted data appear to have substantial dispersion. Could the authors include individual data points or provide representative images to help the reader assess variability? Given that the authors conclude that the reduced nuclear import is due to lowered H3 levels in NASP-deficient embryos, would overexpression of H3 rescue this phenotype? This would directly test whether H3 levels, rather than import machinery per se, drive the effect.

      Figure 2A-B: The authors present the Relative Intensity of H3-Dendra2, but this metric obscures absolute differences between Control and NASP knockout embryos. Please include Total Intensity plots to show the actual reduction in H3 levels. Additionally, Western blot analysis of nucleoplasmic H3 from wild-type vs. NASP-deficient embryos would provide essential biochemical confirmation of H3 level reductions.

      Figure 4: To support the conclusion that NASP prevents H3 aggregation, I recommend performing aggregation assays by adding compounds that induce unfolding (amino acid analogues that induce unfolding, like canavanine or Azetidine-2-carboxylic acid) or using aggregation-prone H3 mutants. Inclusion of CMA and proteasome inhibition experiments could also clarify whether degradation pathways are secondarily involved or compensatory in the absence of NASP.

      Minor Comments:

      (1) The Introduction would benefit from mentioning the two NASP isoforms that exist in mammals (sNASP and tNASP), as this evolutionary context may inform interpretation of the Drosophila results.

      (2) Could the authors comment on the status of histone H4 in their experimental system? Given the observed cytoplasmic pool of H3, is it likely to exist as a monomer? If this H3 pool is monomeric, does that suggest an early failure in H3-H4 dimerization, and could this contribute to its aggregation propensity?

      Significance

      This work addresses a timely and important question in the field of chromatin biology and developmental epigenetics. The focus on histone homeostasis during embryogenesis and the cytoplasmic role of NASP adds a novel perspective. The live imaging experiments are a clear strength, providing valuable spatiotemporal insights. However, I believe that the manuscript would benefit significantly from additional biochemical validation to support and clarify some of the mechanistic claims.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      This manuscript focuses on the role of the histone chaperone NASP in Drosophila. NASP is a chaperone specific to histone H3 that is conserved in mammals. Many aspects of the molecular mechanisms by which NASP selectively binds histone H3 have been revealed through biochemical studies. However, key aspects of NASP's in vivo roles remain unclear, including where in the cell NASP functions, and how it prevents H3 degradation. Through live imaging in the early Drosophila embryo, which possesses large amounts of soluble H3 protein, Das et al determine that NASP does not control nuclear import or export of H3.2 or H3.3. Instead, they find through differential centrifugation analysis that NASP functions in the cytoplasm to prevent H3 aggregation and hence its subsequent degradation.

      Major Comments:

      1. The protein aggregation assays raise several questions.

      a. From a technical standpoint, it would be helpful to have a positive control to demonstrate that the assay is effective at detecting protein aggregates. Ie. a genotype that exhibits increased protein aggregation; this could be for a protein besides H3.

      b. If NASP is not required to prevent H3 degradation in egg chambers, then why are H3 levels much lower in NASP input lanes relative to wild-type egg chambers in Fig 4D?

      c. A corollary to this is that the increased fraction of H3 in aggregates in NASP mutants seems to be entirely due to the reduction in total H3 levels rather than an increase in aggregated H3. If NASP's role is to prevent aggregation in the cytoplasm, and degradation has not yet begun in egg chambers, then why are aggregated H3 levels not increased in NASP mutants relative to wild-type egg chambers? If the same number of egg chambers were used, shouldn't the total amount of histone be the same in the absence of degradation? 2. The live imaging studies are well designed, executed, and quantified. They use an established genotype (H3.2-Dendra2) in wild-type and NASP maternal mutants to demonstrate that NASP is not directly involved in nuclear import of H3.2. Decreased import is likely due to reduced H3.2 levels in NASP mutants rather than reduced import rates per se. The same methodology was used to determine that loss of NASP did not affect H3.2 nuclear export. These findings eliminate H3.2 nuclear import/export regulation as possible roles for NASP, which had been previously proposed. 3. Live imaging also conclusively demonstrates that the levels of H3.2 in the nucleoplasm and in mitotic chromatin are significantly lower in NASP mutants than wild-type nuclei. Despite these lower histone levels, the nuclear cycle duration is only modestly lengthened. 4. The live imagining of NASP-Dendra2 nuclear import conclusively demonstrate that NASP and H3.2 are unlikely to be imported into the nucleus as one complex.

      Minor Comments:

      1. Additional details on how the NASP-Dendra2 CRISPR allele was generated should be provided. In addition, additional details on how it was determined that this allele is functional should be provided (e.g. quantitative assays for fertility/embryo viability of NASP-Dendra2 females)
      2. If statistical tests are used to determine significance, the type of test used should be reported in the figure legends throughout.
      3. The western blot shown in Figure 4A looks more like a 4-fold reduction in H3 levels in NASP mutants relative to wild-type embryos, rather than the quantified 2-fold reduction. Perhaps a more representative blot can be shown.

      Significance

      As a fly chromatin biologist with colleagues that utilize mammalian experimental systems, I feel this manuscript will be of broad interest to the chromatin research community. Packaging of the genome into chromatin affects nearly every DNA-templated process, making the mechanisms by which histone proteins are expressed, chaperoned, and deposited into chromatin of high importance to the field. The study has multiple strengths, including high-quality quantitative imaging, use of a terrific experimental system (storage and deposition of soluble histones in early fly embryos). The study also answers outstanding questions in the field, specifically that NASP does not control nuclear import/export of histone H3. Instead, the authors propose that NASP functions to prevent protein aggregation. If this could be conclusively demonstrated, it would be valuable to the field. However, the protein aggregation studies need improvement. Technical demonstration that their differential centrifugation assay accurately detects aggregated proteins is needed. Further, NASP mutants do not exhibit increased H3 protein aggregation in the data presented. Instead, the increased fraction of aggregated H3 in NASP mutants seems to be due to a reduction in the overall levels of H3 protein, which is contrary to the model presented in this paper.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The authors investigate the function of the H3 chaperone NASP, which is known to bind directly to H3 and prevent degradation of soluble H3. What is unclear is where NASP functions in the cell (nucleus or cytoplasm), how NASP protects H3 from degradation (direct or indirect), and if NASP affects H3 dynamics (nuclear import or export). They use the powerful model system of Drosophila embryos because the soluble H3 pool is high due to maternal deposition and they make use of photoconvertable Dendra-tagged proteins, since these are maternally deposited and can be used to measure nuclear import/export rates.

      Using these systems and tools, they conclude that NASP affects nuclear import, but only indirectly, because embryos from NASP mutant mothers start out with 50% of the maternally deposited H3. Because of the depleted H3 and reduced import rates, NASP deficient embryos also have reduced nucleoplasmic and chromatin-associated H3. Using a new Dendra-tagged NASP allele, the authors show that NASP and H3 have different nuclear import rates, indicating that NASP is not a chaperone that shuttles H3 into the nucleus. They test H3 levels in embryos that have no nuclei and conclude that NASP functions in the cytoplasm, and through protein aggregation assays they conclude that NASP prevents H3 aggregation.

      Major comments:

      The text was easy to read and logical. The data are well presented, methods are complete, and statistics are robust. The conclusions are largely reasonable. However, I am having trouble connecting the conclusions in text to the data presented in Figure 4.

      First, I'm confused why the conclusion from Figure 4A is that NASP functions in the cytoplasm of the egg. Couldn't NASP be required in the ovary (in, say, nurse cell nuclei) to stimulate H3 expression and deposition into the egg? The results in 4A would look the same if the mothers deposit 50% of the normal H3 into the egg. Why is NASP functioning specifically in the cytoplasm when it is also so clearly imported into the nucleus? Maybe NASP functions wherever it is, and by preventing nuclear import, you force it to function in the cytoplasm. I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

      Second, an alternate conclusion from Figure 4D/E is that mothers are depositing less H3 protein into the egg, but the same total amount is being aggregated. This amount of aggregated protein remains constant in activated eggs, but additional H3 translation leads to more total H3? The authors mention that additional translation can compensate for reduced histone pools (line 416).

      As the function of NASP in the cytoplasm (when it clearly imports into the nucleus) and role in H3 aggregation are major conclusions of the work, the authors need to present alternative conclusions in the text or complete additional experiments to support the claims. Again, I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

      Data presentation:

      Overall, I suggest moving some of the supplemental figures to the main text, adding representative movie stills to show where the quantitative data originated, and moving the H3.3 data to the supplement. Not because it's not interesting, but because H3.3 and H3.2 are behaving the same.

      Fig 1:

      It would strengthen the figure to include representative still images that led to the quantitative data, mostly so readers understand how the data were collected. The inclusion of a "simulated 50% H3" in panel C is confusing. Why? I would also consider normalizing the data between A and B (and C and D) by dividing NASP/WT. This could be included in the supplement (OPTIONAL)

      Fig S1:

      The data simulation S1G should be moved to the main text, since it is the primary reason the authors reject the hypothesis that NASP influences H3 import rates.

      Fig 2:

      Once again, I think it would help to include a few representative images of the photoconverted Dendra2 in the main text. I struggled with A/B, I think due to not knowing how the data were normalized. When I realized that the WT and NASP data are not normalized to each other, but that the NASP values are likely starting less than the WT values, it made way more sense. I suggest switching the order of data presentation so that C-F are presented first to establish that there is less chromatin-bound H3 in the first place, and then present A/B to show no change in nuclear export of the H3 that is present, allowing the conclusion of both less soluble AND chromatin-bound H3.

      Fig S2:

      If M1-M3 indicate males, why are the ovaries also derived from males? I think this is just confusing labeling. Supplemental Movie S1: Beautiful. Would help to add a time stamp (OPTIONAL).

      Fig 3:

      Panel C is the same as Fig S1A (not Fig 1A, as is said in the legend), though I appreciate the authors pointing it out in the legend. Also see line 276. Panel D is a little confusing, because presumably the "% decrease in import rate" cannot be positive (Y axis). This could be displayed as a scatter (not bar) as in Panels B/C (right) where the top of the Y axis is set to 0.

      Fig S3:

      A: What do the different panels represent? I originally thought developmental time, but now I think just different representative images? Are these age-matched from time at egg lay? C: What does "embryos" mean? Same question for Fig 4A. Fig 4: A: What does "embryos" mean? Number of embryos? Age in hours? C: Not sure the workflow figure panel is necessary, as I can't tell what each step does. This is better explained in methods. However I appreciated the short explanation in the text (lines 314-5).

      Minor comments:

      The authors should describe the nature of the NASP alleles in the main text and present evidence of robust NASP depletion, potentially both in ovaries and in embryos. The antibody works well for westerns (Fig S2B). This is sort of demonstrated later in Figure 4A, but only in NAAP x twine activated eggs.

      Lines 163, 251, 339: minor typos Line 184: It would help to clarify- I'm assuming cytoplasmic concentration (or overall) rather than nuclear concentration. If nuclear, I'd expect the opposite relationship. This occurs again when discussing NASP (line 267). I suspect it's also not absolute concentration, but relative concentration difference between cytoplasm and nucleus. It would help clarify if the authors were more precise. Line 189: Given that the "established integrative model" helps to reject the hypothesis that NASP is involved in H3 import, I think it's important to describe the model a little more, even though it's previously published. Line 203: "The measured rate of H3.2 export from the nucleus is negligible" clarify this is in WT situations and not a conclusion from this study. Line 201: How can the authors be so sure that the decrease in WT is due to "the loss of non-chromatin bound nucleoplasmid H3.2-Dendra2?" Line 217: In the conclusion, the authors indicate that NASP indirectly affects soluble supply of H3 in the nucleoplasm. I do believe they've shown that the import rate effect is indirect, but I don't know why they conclude that the effect of NASP on the soluble nucleoplasmic H3 supply is indirect. Similarly, the conclusion is indirect on line 239. Yet, the authors have not shown it's not direct, just assumed since NASP results in 50% decrease to deposited maternal histones. Line 292: What is the nature of the NASP "mutant?" Is it a null? Similarly, what kind of "mutant" is the twine allele? Line 295. Line 316: Why did the authors use stage 14 egg chambers here when they previously used embryos? This becomes more clear later shortly, when the authors examine activated eggs, but it's confusing in text. Lines 343-348: It's unclear if the authors are drawing extended conclusions here or if they are drawing from prior literature (if so, citations would be required). For example, why during oogenesis/embryogenesis are aggregation and degradation developmentally separated? Lines 386-7: I do not understand why the authors conclude that H3 aggregation and degradation are "developmentally uncoupled" and why, in the absence of NASP, "H3 aggregation precedes degradation." Line 395: Why suddenly propose that NASP also functions in the nucleus to prevent aggregation, when earlier the authors suggest it functions only in the cytoplasm? Lines 409-413: The authors claim that histone deficiency likely does not cause the embryonic arrest seen in embryos from NASP mutant mothers. This is because H3 is reduced by 50% yet some embryos arrest long before they've depleted this supply. However, the authors also showed that H3 import rates are affected in these embryos due to lower H3 concentration. Since the early embryo cycles are so rapid, reduced H3 import rates could lead to early arrest, even though available H3 remains in the cytoplasm.

      Significance

      The significance of the work is conceptual, as NASP is known to function in H3 availability but the precise mechanism is elusive. This work represents a necessary advance, especially to show that NASP does not affect H3 import rates, nor does it chaperone H3 into the nucleus. However, the authors acknowledge that many questions remain. Foremost, why is NASP imported into the nucleus and what is its role there?

      I believe this work will be of interest to those who focus on early animal development, but NASP may also represent a tool, as the authors conclude in their discussion, to reduce histone levels during development and examine nucleosome positioning. This may be of interest to those who work on chromatin accessibility and zygotic genome activation.

      I am a genetics expert who works in Drosophila embryogenesis. I do not have the expertise to evaluate the aggregate methods presented in Figure 4.

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

      Learn more at Review Commons


      Reply to the reviewers

      Review report for 'Sterols regulate ciliary membrane dynamics and hedgehog signaling in health and disease', Lamazière et al.

      Reviewer #1

      In this manuscript, Lamazière et al. address an important understudied aspect of primary cilium biology, namely the sterol composition in the ciliary membrane. It is known that sterols especially play an important role in signal transduction between PTCH1 and SMO, two upstream components of the Hedgehog pathway, at the primary cilium. Moreover, several syndromes linked to cholesterol biosynthesis defects present clinical phenotypes indicative of altered Hh signal transduction. To understand the link between ciliary membrane sterol composition and Hh signal transduction in health and disease, the authors developed a method to isolate primary cilia from MDCK cells and coupled this to quantitative metabolomics. The results were validated using biophysical methods and cellular Hh signaling assays. While this is an interesting study, it is not clear from the presented data how general the findings are: can cilia be isolated from different mammalian cell types using this protocol? Is the sterol composition of MDCK cells expected to the be the same in fibroblasts or other cell types? Without this information, it is difficult to judge whether the conclusions reached in fibroblasts are indeed directly related to the sterol composition detected in MDCK cells. Below is a detailed breakdown of suggested textual changes and experimental validations to strengthen the conclusions of the manuscript.

      We would like to thank the reviewer for their helpful comments

      Major comments:

      • It appears that the comparison has been made between ciliary membranes and the rest of the cell's membranes, which includes many other membranes besides the plasma membrane. This significantly weakens the conclusions on the sterol content specific to the cilium, as it may in fact be highly similar to the rest of the plasma membrane. It is for example known that lathosterol is biosynthesized in the ER, and therefore the non-presence in the cilium may reflect a high abundance in the ER but not necessarily in the plasma membrane.

      The reviewer is correct that we compared the sterol composition of the primary ciliary membrane to the average of the remaining cellular membranes. We agree that this broader reference fraction contains multiple intracellular membranes, including ER- and Golgi-derived compartments, and therefore does not isolate the plasma membrane specifically. We would like to emphasize that our study did not aim to compare the cilium directly to the plasma membrane, nor did we claim that the comparison was in any way related to the plasma membrane. It is also worth noting that previous studies in other ciliated organisms have reported a higher cholesterol content in cilia compared to the plasma membrane, suggesting that the two membranes may not be compositionally identical despite their continuity. However, we concur that determining the sterol composition of the MDCK plasma membrane would provide valuable context and enable a comparison with the membrane continuous with the ciliary membrane. Hence, we are willing to try isolating plasma membrane in the same cellular contexts.

      • While the protocol to isolate primary cilium from MDCK cells is a valuable addition to the methods available, it would be good to at least include a discussion on its general applicability. Have the authors tried to use this protocol on fibroblasts for example?

      Thank you for the reviewer's positive comment on the value of the ciliary isolation protocol. Indeed, we have attempted to apply the same approach to other ciliated cell types, namely IMCD3 and MEF cells. In the case of IMCD3 cells, we were able to isolate primary cilia using the same general strategy; however, we are still refining the preparation, as the overall yield is lower than in MDCK cells and the amount of material obtained is currently insufficient for comprehensive biochemical analyses. With MEF (fibroblast) cells, the procedure proved even more challenging, as the yield of isolated cilia was extremely low. This difficulty is likely due to the shorter length of fibroblast cilia and to their positioning beneath the cell body, which probably makes them more resistant to detachment. Overall, these observations suggest that while the protocol can be adapted to other cell types, its efficiency depends on cellular architecture. We have added a discussion of these aspects in the revised manuscript to clarify the method's current scope and limitations (lines 492-502).

      • Some of the conclusions in the introduction (lines 75-80) seem to be incorrectly phrased based on the data: in basal conditions, ciliary membranes are already enriched in cholesterol and desmosterol, and the treatment lowers this in all membranes.

      We agree, this was modified in the revised manuscript (lines 75-80).

      • There seems to be little effect of simvastatin on overall cholesterol levels. Can the authors comment on this result? How would the membrane fluidity be altered when mimicking simvastatin-induced composition? Since the effect on Hh signaling appears to be the biggest (Figure 5B) under simvastatin treatment, it would be interesting to compare this against that found for AY9944 treatment. Also, the authors conclude that the effects of simvastatin treatment on ciliary membrane sterol composition are the mildest, however, one could argue that they are the strongest as there is a complete lack of desmosterol.

      We thank the reviewer for these insightful comments. Regarding the modest overall effect of simvastatin on cholesterol levels, we would like to note that MDCK cells are an immortalized epithelial cell line with high metabolic plasticity. Such cancer-like cell types are known to exhibit enhanced de novo lipogenesis, particularly under culture conditions with ample glucose availability. This compensatory lipid biosynthesis can partially counterbalance pharmacological inhibition of the cholesterol biosynthetic pathway. Because simvastatin acts upstream in the pathway (at HMG-CoA reductase), its inhibition primarily reduces early intermediates rather than fully depleting end-product cholesterol, explaining the relatively mild changes observed in total cholesterol content.

      Concerning desmosterol, we agree with the reviewer that its complete loss under simvastatin treatment is a striking finding that deserves further discussion. Interestingly, our data show that simvastatin treatment produces the strongest inhibition of pathway activation (as measured by SMO activation), but the weakest effect on signal transduction downstream of constitutively active SMOM2. This dichotomy suggests that the absence of desmosterol may preferentially affect the activation step of Hedgehog signaling at the ciliary membrane, without equally impacting downstream propagation. We have expanded the Result section to highlight this potential role of desmosterol in the activation phase of Hedgehog signaling and to contrast it with the effects observed under AY9944 treatment (lines 463-469).

      It is not clear to me why the authors have chosen to use SAG to activate the Hh pathway, as this is a downstream mode of activation and bypasses PTCH1 (and therefore a potentially sterol-mediated interaction between the two proteins). It would be very informative to compare the effect of sterol modulation on the ability of ShhN vs SAG to activate the pathway.

      Our study aims to demonstrate that the sterol composition of the ciliary membrane plays an essential role in the proper functioning of the Hedgehog (Hh) signaling pathway, comparable in importance to that of oxysterols and free cholesterol. Because ShhN itself is covalently modified by cholesterol, and Smoothened (SMO) can be directly activated by both oxysterols and cholesterol, we reasoned that using a non-native SMO agonist such as SAG would allow us to specifically assess defects arising from alterations in membrane-bound sterols. In this way, pathway activation by SAG provides a more direct readout of the functional contribution of ciliary membrane sterols to SMO activity, independent of potential confounding effects related to ShhN processing, secretion, or PTCH1-mediated regulation.

      • The conclusions about the effect of tamoxifen on SMO trafficking in MEFs should be validated in human patient cells before being able to conclude that there is a potential off-target effect (line 438). Also, if that is the case, the experiment of tamoxifen treatment of EBP KO cells should give an additional effect on SMO trafficking. Also, could the CDPX2 phenotypes in patients be the result of different cell types being affected than the fibroblast used in this study?

      We agree that carrying the proposed experiment would be a good way to assess a potential off-target effect. However, such validation is beyond the scope of the present study, as this comment on off-target effect was aimed primarily to propose a mechanistic hypothesis to explain the differences observed in Hedgehog pathway activation between patient-derived fibroblasts and tamoxifen-treated MEFs. We leaned towards this hypothesis because drug treatments are known for their overall variable specificity, but we agree other hypotheses are possible, and among them the difference in cell type, as both are fibroblasts but from different origin. We rephrased this passage in the revised manuscript (lines 447-448 ).

      Regarding the reviewer's third point, we fully agree that the CDPX2 phenotype in patients is unlikely to arise solely from fibroblast dysfunction. Nevertheless, fibroblasts are the only patient-derived cells currently available to us, and they provide a useful model for assessing ciliary signaling. It is reasonable to expect that similar defects could occur in other, more physiologically relevant cell types.

      • For the experiments with the SMO-M2 mutant, it would be useful to show the extent of pathway activation by the mutant compared to SAG or ShhN treatment of non-transfected cells. Moreover, it will be necessary to exclude any direct effects of the compound treatment on the ability of this mutant to traffic to the primary cilium, which can easily be done using fluorescence microscopy as the mutant is tagged with mCherry.

      The SmoM2 mutant is indeed a well-characterized constitutively active form of Smoothened that has been extensively studied by us and others. It is well established that this mutant correctly localizes to the primary cilium and robustly activates the Hedgehog pathway in MEFs (see Eguether et al., Dev. Cell, 2014 or Eguether et al, mol.biol.cell, 2018). In our study, we have already included supporting evidence for pathway activation in Supplementary Figure S1b, showing Gli1 expression levels in untreated MEFs transfected with SmoM2, which illustrates the extent of its activation compared to ligand-induced conditions.

      In line with the reviewer's recommendation, we will additionally include microscopy data showing SmoM2 localization in MEFs treated with the different sterol modulators. These data should confirm that the observed effects are not due to altered ciliary trafficking of the mutant protein but instead reflect changes in downstream signaling or membrane composition.

      Minor comments:

      Line 74: 'in patients', should be rephrased to 'patient-derived cells'

      This was modified in the revised manuscript

      Figure 2A: What do the '+/-' indicate? They seem to be erroneously placed.

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Figure 2B: no label present for which bar represents cilia/other membranes

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Figure 2C: this representation is slightly deceptive, since the difference between cells and cilia for lanosterol is not significantly different as shown in figure 2A.

      This representation has been removed in the revised figures.

      Figure 3A: it would be useful to also show where 8-DHC is in the biosynthetic pathway.

      This has been modified in the revised figures.

      Line 373: the title should be rephrased as it infers that DHCR7 was blocked in model membranes, which is not the case.

      This has been modified in the revised manuscript.

      Lines 377-384: this paragraph seems to be a mix of methods and some explanation, but should be rephrased for clarity.

      We believe the technical information within this paragraph are useful for the understanding of the reader. We would rather leave as is unless recommended by other reviewers or editorial staff.

      Line 403: 'which could explain the resulting defects in Hedgehog signaling': how and what defects? At this point in the study no defects in Hh signaling have been shown.

      This has been modified in the revised manuscript.

      Figure 4D: 'd' is missing

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Line 408: SAG treatment resulted in slightly shorter cilia: this is not the case for just SAG treated cilia, but only for the combination of SAG + AY9944. However, in that condition there appears to be a subpopulation of very short cilia, are those real?

      This is correct, this is not the case for untreated cilia, but the short population is real, not only in AY9944 but also in Tamoxifen and Simvastatin. Again, the relevance and significance of minor cilia length change is unclear and we are not trying to draw any other conclusion from this than saying that the ciliary compartment is modified.

      Figure 5b: it would be good to add that all conditions contained SAG.

      This has been modified in the revised figures.

      Figure 5D: Since it is shown in Fig 5C that there are no positive cilia -SAG, there is no point to have empty graphs in Fig 5D on the left side, nor can any statistics be done. Similarly for 5K.

      We think this is still worth having in the figure. As the reviewer noted in one of his next comment, there are cases where Smoothened or Patched can be abnormally distributed (see also Eguether et al, mol biol cell, 2018). This shows that we checked all conditions for presence or absence of Smo and that there is no signal to be found. We would rather leave it as is unless asked otherwise by editorial staff.

      Figure 5E: it is not clearly indicated what is visualized in the inserts, sometimes it's a box, sometimes a line and they seem randomly integrated into the images.

      We apologize for the oversight - the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Figure 5H: is this the intensity in just SMO positive cilia? If yes, this should be indicated, and the line at '0' for WT-SAG should be removed. I am also surprised there is then ns found for WT vs SLO, since in WT there are no positive cilia, but in SLO there are a few, so it appears to be more of a black-white situation. Perhaps it would be useful to split the data from different experiments to see if it consistently the case that there is a low percentage of SMO positive cilia in SLO cells.

      Yes, as in the rest of figure 5, the fluorescence intensity of Smo is only taken into account in SMO positive cells. This is now indicated in figure legend (lines 890, 898, 903 ). As for Smo positive, this is a good suggestion. We checked and for cilia in non-activated SLO patients, there are 8 positive cilia over a total of 240 counted cilia, mainly from one of the experiments. We could remove the data or leave as is given that the result is not significant.

      Fig S1: panels are inverted compared to mentioning in the text.

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Methods-pharmacological treatments: there appear to be large differences in concentrations chosen to treat MDCK versus MEF cells - can the authors comment on these choices and show that the enzymes are indeed inhibited at the indicated concentrations?

      We thank the reviewer for this important comment. The concentrations of the pharmacological treatments were optimized separately for MDCK and MEF cells based on cell-type-specific tolerance. For each compound, we used the highest concentration that produced no detectable cytotoxicity or morphological changes. These conditions ensured that the treatments were effective (as seen by changes in sterol composition in MDCK cilia and Hh pathway phenotypes in treated MEFs) and compatible with cell viability and ciliation. Although we did not directly assay enzymatic inhibition in each case, the selected concentrations are consistent with those previously reported to inhibit the targeted enzymes in similar cellular contexts.

      Compound

      Typical Concentration Range in Mammalian Cell Culture

      Typical Exposure Duration

      Example Cell Types

      Representative Peer-Reviewed References

      AY9944 (DHCR7 inhibitor)

      1-10 µM widely used; 1 µM for minimal on-target effects; 2.5-10 µM for robust sterol shifts

      24-72 h; some sterol studies up to several days

      HEK293, fibroblasts, neuronal cells, macrophages

      Kim et al., J Biol Chem, 2001 - used 1 µM in dose-response experiments.; Haas et al., Hum Mol Genet, 2007 - 1 µM in cell-based assays.; Recent macrophage sterol study - 2.5-10 µM to induce 7-DHC accumulation.

      Simvastatin (HMG-CoA reductase inhibitor)

      0.1-10 µM common; 1-10 µM most widely used for robust pathway inhibition

      24-72 h

      Diverse mammalian lines, including liver, fibroblasts, epithelial cells

      Bytautaite et al., Cells (2020) - discusses common in-vitro ranges (1-10 µM).; Mullen et al., 2011 - used 10 µM simvastatin, noting it is a standard in-vitro concentration.

      Tamoxifen (modulator of sterol metabolism)

      1-20 µM; 1-5 µM for mild/longer treatments; 10-20 µM in cancer/cilia signaling studies

      24-72 h (longer treatments often at 1-5 µM)

      MDCK, MEFs, MCF-7, diverse epithelial lines

      Schlottmann et al., Cells (2022) - used 5-25 µM in sterol-related cell studies.; MCF-7 literature - 0.1-1 µM for estrogenic signaling, higher (5-10 µM) for metabolic/sterol pathway effects.; Additional cancer cell work indicating similar ranges.

      This information has been clarified in the revised Methods section (lines 222-224).

      (optional): it would be interesting to include a gamma-tubulin staining on the cilium prep to see if there is indeed a presence of the basal body as suggested by the proteomics data.

      Thank you, we will try this.

      There are many spelling mistakes and inconsistencies throughout the manuscript and its figures (mix of French and English for example) so careful proofreading would be warranted. Moreover, there are many mentionings of 'Hedgehog defects' or 'Hedgehog-linked', where in fact it is a defect in or link to the Hedgehog pathway, not the protein itself. This should be corrected.

      We thank the reviewer for noting these issues. We apologize for the inconsistencies observed in the initial submission, as mentioned previously, some of the figures inadvertently included earlier versions, which may have contributed to the errors identified. All figures have now been carefully revised and updated in the resubmitted manuscript.

      Regarding the text, we are surprised to hear about the spelling inconsistencies, as the manuscript was professionally proofread prior to submission (documentation can be provided upon request). Nevertheless, we have conducted an additional round of thorough proofreading to ensure consistency throughout the text and figures.

      Finally, we have corrected all instances of "Hedgehog defects" or "Hedgehog-linked" to the more accurate phrasing "Hedgehog pathway defect" or "Hedgehog pathway-linked," as suggested by the reviewer throughout the manuscript.

      Reviewer #1 (Significance (Required)):

      The study of ciliary membrane composition is highly relevant to understand signal transduction in health and disease. As such, the topic of this manuscript is significant and timely. However, as indicated above, there are limitations to this study, most notably the comparison of ciliary membrane versus all cellular membranes (rather than the plasma membrane), which weakens the conclusions that can be drawn. Moreover, cell-type dependency should be more thoroughly addressed. There certainly is a methodological advance in the form of cilia isolation from MDCK cells, however, it is unclear how broadly applicable this is to other mammalian cell types.

      We would like to thank the reviewer for their helpful comments and we appreciate the reviewer's recognition of the relevance and timeliness of studying ciliary membrane composition in the context of signaling regulation. We fully acknowledge that our comparison was made between the primary ciliary membrane and the total cellular membrane fraction, which encompasses multiple intracellular membranes. Our intent, however, was to obtain a global overview of how the ciliary membrane differs from the average membrane environment within the cell, thereby highlighting features that are unique to the cilium as a signaling organelle. This approach provides valuable baseline information that complements, rather than replaces, future targeted comparisons with the plasma membrane. As mentioned in this reply, we aim at carrying out these experiments before publication. Regarding cell-type dependency, we concur that ciliary lipid composition may vary between cell types, reflecting differences in their functional specialization. Our method was intentionally established in MDCK cells, which are epithelial and highly ciliated, to ensure sufficient yield and reproducibility. We have initiated trials with other mammalian cell types, including IMCD3 and MEF cells, and while yields remain limited, preliminary results indicate that the approach is adaptable with further optimization. Thus, our current work establishes a robust and reproducible proof of concept in a mammalian model, providing the first detailed sterol fingerprint of a mammalian primary cilium.

      We believe this constitutes a significant methodological and conceptual advance, as it opens the way for systematic exploration of ciliary lipid composition across diverse mammalian systems and pathological contexts.

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

      Overview Accumulating evidence suggests that sterols play critical roles in signal transduction within the primary cilium, perhaps most notably in the Hedgehog cascade. However, the precise sterol composition of the primary cilium, and how it may change under distinct biological conditions, remains unknown, in part because of the lack of reproducible, widely accepted procedures to purify primary cilia from mammalian cultured cells. In the present study, the authors have designed a method to isolate the cilium from the MDCK cells efficiently and then utilized this procedure in conjunction with mass spectrometry to systematically analyze the sterol composition of the ciliary membrane, which they then compare to the sterol composition of the cell body. By analyzing this sterol profiling. the authors claim that the cilium has a distinct sterol composition from the cell body, including higher levels of cholesterol and desmosterol but lower levels of 8-DHC and & Lathosterol. This manuscript further demonstrates that alteration of sterol composition within cilia modulates Hedgehog signaling. These results strengthen the link between dysregulated Hedgehog signaling and defects in cholesterol biosynthesis pathways, as observed in SLOS and CDPX2.

      While the ability to isolate primary cilia from cultured MDCK cells represents an important technical achievement, the central claim of the manuscript - that cilia have a different sterol composition from the cell body - is not adequately supported by the data, and more rigorous comparisons between the ciliary membrane and key organellar membranes (such as plasma membrane) are required to make this claim. Moreover, although the authors have repeatedly mention that the ciliary sterol composition is "tightly regulated" there is no evidence provided to support such claim. At best, the data suggest that the cilium and cell body may differ in sterol composition (though even that remains uncertain), but no underlying regulatory mechanisms are demonstrated. In addition, much of the 2nd half of the paper represents a rehash of experiments with sterol biosynthesis inhibitors that have already been published in the literature, making the conceptual advance modest at best. Lastly, the link between CDPX2 and defective Hedgehog signaling is tenuous.

      We would like to thank the reviewer for their helpful comments

      Major comments

      Figure 1. C) Although the isolation of cilium from the MDCK cells using dibucaine treatment seems to be very efficient, the quality control of their fractionation procedure to monitor the isolation is limited to a single western blot of the purified cilia vs. cell body samples, with no representative data shown from the sucrose gradient fractionation steps. Given that prior studies (including those from the Marshall lab cited in this manuscript) found that 1) sucrose gradient fractionation was essential to obtain relatively pure ciliary fractions, and 2) the ciliary fractions appear to spread over many sucrose concentrations in those prior studies , the authors should have included the comparison of the fractionation profile from the sucrose gradient while isolating the primary cilium. This additional information would have further clarified and supported the efficiency of their proposed method.

      We thank the reviewer for their insightful comments regarding the quality control of our ciliary fractionation. We would like to clarify several important methodological aspects that distinguish our approach from those used in the studies cited (including those from the Marshall lab). In the cited work, the authors used a continuous sucrose gradient ranging from 30 % to 45 %, which allowed visualization of the distribution of ciliary proteins across the gradient. In contrast, we employed a discontinuous sucrose gradient (25 % / 50 %) optimized for higher recovery and reproducibility in our hands. In our preparation, the primary cilia consistently localize at the interface between the 25 % and 50 % layers. We systematically collect five 1 mL fractions from this interface and use fractions 1-3 for downstream analyses, as fractions 4-5 are typically already depleted of ciliary material. This targeted collection ensures good enrichment and low contamination, while avoiding unnecessary dilution of the limited ciliary sample. We also note that the prior studies the reviewer refers to were optimized for proteomic analyses, and therefore used actin as a marker of contamination from the cell body. In our case, the downstream application is lipidomic profiling, for which such protein-based contamination markers are not directly informative, since no reliable lipid marker exists to differentiate between organelle membranes. For this reason, we limited the protein-level validation to a semi-quantitative assessment of ciliary enrichment using ARL13B Western blotting, which robustly reports the presence and enrichment of ciliary membranes. Finally, to complement this targeted validation, we performed proteomic analysis followed by Gene Ontology (GO) Enrichment Analysis using the PANTHER database. This analysis evaluates the overrepresentation of proteins associated with ciliary structures and functions relative to the background frequency in the Canis lupus familiaris proteome. The resulting enrichment profile confirms that the isolated material is highly enriched in ciliary components and somewhat depleted of non-ciliary contaminants, thereby serving as an unbiased and global assessment of sample specificity and purity. We believe that, together, these methodological choices provide a rigorous and quantitative validation of our fractionation efficiency and support the robustness of the cilia isolation protocol used in this study.

      1. D) The authors presented proteomic data for the peptides analyzed from the isolated cilia in the form of GO term analysis; however, they did not provide examples of different proteins enriched within their fractionation procedure, aside from Arl13b shown in the blot. Including a summary table with representative proteins identified in the isolated ciliary fraction, along with the relative abundance or percentage distribution of these proteins, would make the data more informative.

      We thank the reviewer for this valuable suggestion. As mentioned in the manuscript, our proteomic dataset includes numerous hallmark components of the cilium, such as 18 IFT proteins, 4 BBS proteins, and several Hedgehog pathway components (including SuFu and Arl13b), as well as axonemal (Tubulin, Kinesin, Dynein) and centrosomal proteins (Centrin, CEPs, γ-Tubulin, and associated factors). This composition demonstrates that the isolated fraction is highly enriched in bona fide ciliary components while retaining a small proportion of basal body proteins, which is expected given their physical continuity. Importantly, our dataset shows a 70% overlap with the ciliary proteome published by Ishikawa et al. and a 41% overlap with the CysCilia consortium's list of potential ciliary proteins, which supports both the specificity and reliability of our isolation procedure. Regarding the suggestion to present relative protein abundances, we would like to clarify that defining "relative to what" is challenging in this context. The stoichiometry of ciliary proteins is largely unknown, and relative abundance normalized to total protein content can be misleading, as ciliary structural and signaling components differ greatly in copy number and membrane association. For this reason, we chose to highlight in the text proteins such as BBS and IFTs, which are known to be of low abundance within the cilium; their detection supports the depth and specificity of our proteomic coverage. In addition, we performed an unbiased Gene Ontology (GO) Enrichment Analysis using the PANTHER database, which provides a systematic and quantitative overview of the biological processes and cellular components overrepresented in our dataset relative to the canine proteome. This analysis with regard to purity wa already discussed in the submitted manuscript discussion. To further address the reviewer's comment, we will include as a supplemental table in the revised manuscript, a summary table listing representative ciliary proteins identified in our fraction, including those overlapping with the CysCilia (Gold ans potential lists), CiliaCarta and Ishikawa/Marshall proteomes. This addition should make the dataset more transparent and informative while preserving scientific rigor.

      Figure 2.

      The authors represented the comparison of sterol content within the cilia versus whole cell (as cell membranes). Since different organelles have a very diverse degree of cholesterol contents within them, for instance plasma membrane itself is around 50 mol% cholesterol levels while organelles like ER have barely any cholesterol. Thus, comparing these two samples and claiming a 2.5-fold increase in cholesterol levels is misleading. A more appropriate comparison would be between isolated primary cilia and isolated plasma membranes (procedures to isolate plasma membranes have been described previously, e.g., Naito et al., eLife 2019; Das et al, PNAS 2013. The absence of such controls makes it difficult to fully validate the reported magnitude of sterols enrichment in cilia relative to the cell surface.

      As already discussed above for reviewer 1, we would like to emphasize that our study did not aim to compare the cilium directly to the plasma membrane, nor did we claim that the comparison was in any way related to the plasma membrane. Our intent, was to obtain a global overview of how the ciliary membrane differs from the average membrane environment within the cell, thereby highlighting features that are unique to the cilium as a signaling organelle. This approach provides valuable baseline information that complements, rather than replaces, future targeted comparisons with the plasma membrane. However, we concur that determining the sterol composition of the MDCK plasma membrane would provide valuable context and enable a comparison with the membrane continuous with the ciliary membrane. Hence, we are willing to try isolating plasma membrane in the same cellular contexts, and we thank the reviewer for the proposed literature.

      Also, because dibucaine was used here to isolate MDCK cilia, a control experiment to exclude possible effects of the dibucaine treatment on sterol biosynthesis would be helpful.

      Thank you for this comment, we will verify this point by quantifying by GC-MS the sterol content of whole MDCK cells with and without 15 minutes-dibucaine treatments.

      Figure 3.

      Tamoxifen is a potent drug for nuclear hormone receptor activity and thus can independently influence various cellular processes. As several experiments in the later sections of the manuscript rely on tamoxifen treatment of cells, it is important that the authors include appropriate controls for tamoxifen treatment, to confirm that the observed effects do not stem from effects on nuclear hormone receptor activity. This would ensure that the observed effects can be confidently attributed to the experimental manipulation rather than to the intrinsic effects of tamoxifen.

      The reviewer is right, tamoxifen, like many drugs, has pleiotropic effects in different cell processes. Aware of this possible issue, we turned to a genetic model creating a CRISPR-CAS9 mediated knock down of EBP, the enzyme targeted by tamoxifen. We showed in figure 5 that the results between tamoxifen treated cells and CRIPSR EBP cells were in accordance with one another, showing that, for hedgehog signaling, the effect of tamoxifen recapitulates the effect of the enzyme KO.

      Figure4. The authors present the results of spectroscopy studies to analyze generalized polarization (GP) of liposomes in vitro , but only processed data are shown, and the raw spectra are not provided. The authors need to present representative spectra to enable the readers to interact the raw data from the experiments.

      This has been added to new supplemental figure 1 and corresponding figure legend (lines 898-904)

      Figure5. B) The experiment shown Gli1 mRNA levels following treatment with inhibitors of cholesterol biosynthesis, but similar findings have already been reported previously (e.g., Cooper et al, Nature Genetics 2003; Blassberg et al, Hum Mol Genet 2016), and the present results do not provide a significant conceptual advance over those earlier studies.

      We thank the reviewer for this comment and for highlighting the importance of earlier studies on Hedgehog (Hh) signaling and cholesterol metabolism. While we fully agree that confirming and extending established findings has intrinsic scientific value, we respectfully disagree with the assertion that our work does not provide conceptual novelty.

      The seminal work by Cooper et al. (Nature Genetics, 2003) indeed laid the foundation for linking sterol metabolism to Hedgehog signaling, and we cite it as such. However, that study was conducted in chick embryos, a model that is relatively distant from mammalian systems and human pathophysiology. Moreover, their approach relied heavily on cyclodextrin-mediated cholesterol depletion, which is non-specific and extracts multiple sterols from membranes (discussed in this article lines 512-516). In contrast, our study employs pharmacological inhibitors targeting specific enzymes in the sterol biosynthetic pathway, thereby allowing us to modulate distinct steps and intermediates in a controlled and mechanistically informative manner. We also extend these analyses to patient-derived fibroblasts and CRISPR-engineered cells, providing direct human and genetic validation of the observed effects. Importantly, we complement these cellular studies with biochemical characterization of isolated ciliary membranes from MDCK cells, enabling a direct assessment of how specific sterol alterations affect ciliary composition and Hh pathway function - an angle not addressed in prior work.

      Regarding Blassberg et al. (Hum. Mol. Genet., 2016), we agree that part of our findings recapitulates their observations on SMO-related signaling defects, which we view as an important confirmation of reproducibility. However, their study primarily sought to distinguish whether Hh pathway impairment in SLOS results from 7-DHC accumulation or cholesterol depletion, concluding that cholesterol deficiency was the main cause. Our results expand on this by demonstrating that perturbations extend beyond these two sterols, and that additional intermediates in the biosynthetic pathway also impact ciliary membrane composition and signaling competence. Furthermore, our experiments using the constitutively active SmoM2 mutant show that Hh signaling defects are not restricted to SMO activation per se, revealing a broader disruption of the signaling machinery within the cilium.

      Finally, neither of the above studies examined CDPX2 patient-derived cells or the consequences of EBP enzyme deficiency on Hh signaling. Our finding that this pathway is altered in this genetic context represents, to our knowledge, a novel link between CDPX2 and Hedgehog pathway dysfunction.

      Taken together, our work builds upon and extends previous findings by integrating cell-type-specific, biochemical, and patient-based analyses to provide a more comprehensive and mechanistically detailed view of how sterol composition of the ciliary membrane regulates Hedgehog signaling.

      In addition, the authors analyze the effect of these inhibitors on SAG stimulation, but the experiment lacks the control for Gli mRNA levels in the absence of SAG treatment. Without this control, it is impossible to know where the baseline in the experiment is and how large the effects in question really are.

      Below, we provide the data expressed using the ΔΔCt method (NT + SAG normalized to NT - SAG), which more clearly illustrates the magnitude of the effect in question. As similar qPCR-based Hedgehog pathway activation assays in MEFs have been published previously (see Eguether et al., Dev. Cell 2014; Eguether et al., Mol. Biol. Cell 2018), our goal here was not to re-establish the assay itself but to highlight the comparative effects across experimental conditions. In addition, one of the datasets was obtained using a new batch of SAG, which exhibited stronger pathway activation across all conditions (visible as higher overall expression levels). To ensure valid statistical comparisons across experiments and to focus on relative rather than absolute activation, we therefore chose to present the data as fold change values, which provides a more robust and statistically consistent measure for cross-condition analysis.

      J-K) The data represented in these panels for SAG treatment as fraction of Smo and its fluorescence intensity for the same sample appears to be inconsistent between the two graphs. Under SAG treatment for EBP mutants shows higher Smo fluorescence intensity while Smo positive cilia seems to be less than the wild type control cells. If the number of Smo+ cilia (quantified by eye) differs between conditions, shouldn't the quantification of Smo intensity within cilia show a similar difference?

      We thank the reviewer for this careful observation. The apparent discrepancy arises because the two panels quantify different parameters. In panel (j), we counted the percentage of cilia positive for SMO (i.e., cilia in which SMO was detected above background). In contrast, panel (k) reports the fluorescence intensity of SMO, but this measurement was performed only within the SMO-positive cilia identified in panel (j). This distinction has now been explicitly clarified in the figure legend, as also suggested by Reviewer 1.

      Taken together, these two analyses indicate that although fewer cilia display detectable SMO accumulation in the EBP mutant cells, the amount of SMO present within those cilia that do recruit it is comparable to wild-type levels (as reflected by the non-significant difference in fluorescence intensity). This interpretation helps explain the partial functional preservation of Hedgehog signaling in this condition and contrasts with cases such as AY9944 treatment, where both the number of SMO-positive cilia and the SMO intensity are reduced.

      1. I) The rationale for using SmoM2 in the analysis of cholesterol metabolism-related diseases such as SLOS and CDPX2 is unclear. The SmoM2 variant is primarily associated with cancer rather than cholesterol biosynthesis defects and its relevance either of these disorders is not immediately apparent.

      We thank the reviewer for this pertinent observation. We fully agree that SmoM2 was originally identified as an oncogenic mutation and is not directly associated with cholesterol biosynthesis disorders. However, our rationale for using this mutant was mechanistic rather than pathological. SmoM2 is a constitutively active form of SMO that triggers pathway activation independently of upstream components such as PTCH1 or ligand-mediated regulation.

      By using SmoM2, we aimed to determine whether the signaling defects observed under conditions that alter sterol metabolism (e.g., treatment with AY9944 or tamoxifen) occur upstream or downstream of SMO activation. The results demonstrate that, even when SMO is constitutively active, the Hedgehog pathway remains impaired under AY9944 treatment-and to a lesser extent with tamoxifen-indicating that these sterol perturbations disrupt the pathway beyond the level of SMO activation itself. In contrast, cells treated with simvastatin maintain normal pathway responsiveness, reinforcing the specificity of this effect.

      This experiment is therefore central to our study, as it reveals that sterol imbalance can hinder Hedgehog signaling even in the presence of an active SMO, providing new insight into how membrane composition influences downstream signaling competence.

      Minor corrections

      1. Line 385 seems to be a bit confusing which mentions cilia were treated with AY9944 - do the authors mean that cells were been treated with the drugs before isolation of cilia, or were the purified cilia actually treated with the drugs?

      Thank you, this has been modified in the revised manuscript

      The authors should add proper label in Figure 2 panel b for the bars representing the cilia and cell membranes.

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Panels in Figure S1 should be re-arranged according to the figure legend and figure reference in line 450.

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Legend for the Figure S1b should be corrected as data sets in graph represents 7 points while technical replicates in legend shows 6 experimental values.

      Thank you, this has been modified in the revised manuscript

      The labels for drug in Figure 3 and 5 should be corrected from tamoxifene to tamoxifen and simvastatine to simvastatin.

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      Reviewer #2 (Significance (Required)):

      In the present study, the authors have designed a method to isolate the cilium from the MDCK cells efficiently and then utilized this procedure in conjunction with mass spectrometry to systematically analyze the sterol composition of the ciliary membrane, which they then compare to the sterol composition of the cell body. By analyzing this sterol profiling. the authors claim that the cilium has a distinct sterol composition from the cell body, including higher levels of cholesterol and desmosterol but lower levels of 8-DHC and & Lathosterol. This manuscript further demonstrates that alteration of sterol composition within cilia modulates Hedgehog signaling. These results strengthen the link between dysregulated Hedgehog signaling and defects in cholesterol biosynthesis pathways, as observed in SLOS and CDPX2.

      While the ability to isolate primary cilia from cultured MDCK cells represents an important technical achievement, the central claim of the manuscript - that cilia have a different sterol composition from the cell body - is not adequately supported by the data, and more rigorous comparisons between the ciliary membrane and key organellar membranes (such as plasma membrane) are required to make this claim. Moreover, although the authors have repeatedly mention that the ciliary sterol composition is "tightly regulated" there is no evidence provided to support such claim. At best, the data suggest that the cilium and cell body may differ in sterol composition (though even that remains uncertain), but no underlying regulatory mechanisms are demonstrated. In addition, much of the 2nd half of the paper represents a rehash of experiments with sterol biosynthesis inhibitors that have already been published in the literature, making the conceptual advance modest at best. Lastly, the link between CDPX2 and defective Hedgehog signaling is tenuous.

      We thank the reviewer for this detailed summary and for acknowledging the technical advance represented by our method for isolating primary cilia from MDCK cells. However, we respectfully disagree with several aspects of the reviewer's assessment of our work.

      As we elaborated in our responses to earlier comments, particularly regarding Figure 5, we disagree with the characterization of part of our study as a "rehash", a somewhat derogatory word, of previously published experiments. Our approach differs from earlier studies by relying on specific pharmacological modulation of defined enzymes in the sterol biosynthesis pathway, rather than using non-specific agents such as cyclodextrins, and by linking these manipulations to direct biochemical measurements of ciliary sterol composition. This strategy allows, for the first time, a targeted and physiologically relevant examination of how specific sterol perturbations affect Hedgehog signaling.

      Regarding our statement that ciliary sterol composition is "tightly regulated," we acknowledge that we have not yet explored the underlying molecular mechanisms of this regulation. Nevertheless, the experimental evidence supporting this statement lies in the variation of ciliary sterol composition across multiple treatments that strongly perturb cellular sterols. Despite broad cellular changes, the ciliary sterol profile remains very resilient for some parameters, an observation that, in our view, strongly supports the idea of a selective or regulated process maintaining ciliary sterol identity. This conclusion does not depend on comparison with other membrane compartments.

      We also respectfully disagree that the observed differences between cilia and the cell body (which doesn't equal to plasma membrane) are "uncertain." The consistent enrichment in cholesterol and desmosterol, combined with the relative depletion in 8-DHC and lathosterol, were detected across independent replicates using robust lipidomic profiling and are statistically supported. These findings are, to our knowledge, the first quantitative demonstration of a sterol fingerprint specific to a mammalian cilium.

      Finally, while we agree that the mechanistic link between CDPX2 and defective Hedgehog signaling warrants further exploration, the data we present, combining pharmacological inhibition (tamoxifen), CRISPR-mediated EBP knockout, and SMOM2 activation assays, all consistently indicate a functional impairment of the Hedgehog pathway under EBP deficiency. This is further reinforced by clinical reports describing Hedgehog-related phenotypes in CDPX2 patients. We therefore believe that our work provides a solid experimental and conceptual basis for connecting EBP dysfunction to Hedgehog signaling defects.

      In summary, our study introduces a validated and reproducible method for mammalian cilia isolation, provides the first detailed sterol composition profile of primary cilia, and establishes a functional link between ciliary sterol imbalance and Hedgehog pathway modulation. We believe these findings represent a meaningful conceptual advance and a valuable resource for the field

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

      Lamaziere et al. describe an improved protocol for isolating primary cilia from MDCK cells for downstream lipidomics analysis. Using this protocol, they characterize sterol profile of MDCK cilia membrane under standard growth conditions and following pharmacological perturbations that are meant to mimic SLOS and CDPX2 disorders in humans. The authors then assess the impact of the same pharmacological manipulations on Shh pathway activity and validate their findings from these experiments using orthogonal genetic approaches. Major and minor concerns that require attention prior to publication are outlined below.

      We would like to thank the reviewer for their comments

      Major 1.Since the extent of contamination of the cilia preps with non-cilia membranes is unclear, and variability between replicates is not reported, it makes interpretation of changes in cilia membrane sterol composition in response to pharmacological manipulations somewhat difficult to interpret. Discussing reproducibility of cilia sterol composition between replicates (and including corresponding data) could alleviate these concerns to some extent.

      We thank the reviewer for this comment. We would like to clarify that variability between replicates is indeed reported throughout the manuscript. In Figures 2 and 3, all data are presented as mean {plus minus} SEM, as indicated in the figure legends. Specifically, the data in Figure 2 are derived from six independent experiments, reflecting the central dataset used for comparative analyses, while the data in Figure 3 are based on three independent experiments.

      We also note that the overall variability between replicates is low, further supporting the reproducibility of our ciliary sterol composition measurements. This consistency across independent biological replicates provides confidence that the differences observed between cilia and the cell body are robust and not due to stochastic contamination or technical variation.

      2.An abundant non-ciliary membrane protein (rather than GAPDH) may be a more appropriate loading control in Fig. 1C.

      This is a valuable comment and we will find a non-ciliary membrane protein to complement this experiment.

      3.Fig. 2b - which bar corresponds to cells and which one to cilia? What do numbers inside bars represent? Please label accordingly.

      We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

      4.Fig. 3b-d, right panels - please define what numbers inside bars represent

      Thank you, this was done in the revised manuscript. The numbers are reports of absolute quantification.

      5.The font in Figs 2, 3, and 4 is very small and difficult to read. Please make the font and/or panels bigger to improve readability.

      We did our best to enlarge font despite space limitations, but we are willing to work with editorial staff to improve readability as suggested.

      6.It would help to have a diagram of the key steps in the cholesterol synthesis pathway for reference early in the paper rather than in figure 3.

      We thank the reviewer for his comment, but we don't understand why this would be helpful as we only use sterol modulators involving the pathway's enzyme in fig3. We are open to discussion with editorial staff about moving it up to fig2. If they feel this is needed

      7.The authors need to discuss why/how global inhibition of enzymes (e.g. via AY9944 treatment) in a cell could cause reduction in cholesterol levels only in the cilium and not in other cell membranes (see also point 1). Yet, tamoxifen treatment lowers cholesterol across the board.

      We thank the reviewer for these insightful comments. Regarding the modest overall effect of simvastatin on cholesterol levels, we would like to note that MDCK cells are an immortalized epithelial cell line with high metabolic plasticity. Such cancer-like cell types are known to exhibit enhanced de novo lipogenesis, particularly under culture conditions with ample glucose availability. This compensatory lipid biosynthesis can partially counterbalance pharmacological inhibition of the cholesterol biosynthetic pathway. Because simvastatin acts upstream in the pathway (at HMG-CoA reductase), its inhibition primarily reduces early intermediates rather than fully depleting end-product cholesterol, explaining the relatively mild changes observed in total cholesterol content. . This has been added in a new paragraph in the revised manuscript (lines 371-378).

      8.Fig. 5c, g, and j - statistical analyses are missing and need to be added in support of conclusions drawn in the text of the manuscript.

      Thank you, this has been done in the revised manuscript

      9.The decrease in the fraction of Smo+ cilia observed in EBP KO cells is mild (panel j, no statistics), and there is possibly a clone-specific effect here as well (statistical analysis is needed to determine if EBP139 is indeed different from WT and whether EBP139 and 141 are different from each other). Similarly, Smo fluorescence intensity after SAG treatment (panel k) is the same in WT and EBP KO cells, while there is a marked difference in intraciliary Smo intensity after tamoxifen treatment. The author's conclusion "...we were able to show that results with human cells aligned with our tamoxifen experiments" (line 436) should be modified to more accurately reflect the presented data. Ditto conclusions on lines 440-442, 530-531. In fact, it is the lack of Hh phenotypes in CDPX2 patients that is consistent with the EBP KO data presented in the paper.

      We thank the reviewer for this detailed comment. We have now performed the requested statistical analyses and incorporated them into the revised manuscript.

      The new analyses confirm that both EBP139 and EBP141 CRISPR KO clones show a statistically significant reduction in the fraction of Smo⁺ cilia compared to WT cells. They also reveal that the two clones differ significantly from each other, consistent with the expected clonal variability inherent to independently derived CRISPR lines.

      Despite this variability, several lines of evidence support our conclusion that the EBP KO phenotypes align with the effects observed after tamoxifen treatment:

      1- Directionally consistent reduction in Smo⁺ cilia:

      Although the magnitude of the decrease differs between clones, both clones display a significant reduction compared to WT, paralleling the reduction observed in tamoxifen-treated cells. This directional consistency is the key point for comparing pharmacological and genetic perturbations.

      2-Converging evidence from SmoM2 experiments:

      Tamoxifen treatment also reduces pathway output in the context of SmoM2 overexpression. This supports the interpretation that both EBP inhibition (tamoxifen) and EBP loss (CRISPR KO) impair Hedgehog signaling at the level of ciliary function, albeit more mildly than AY9944/SLOS-like perturbations.

      3-Interpretation of Smo intensity (panel k):

      As clarified in the revised text, the fluorescence intensities in panel K correspond only to cilia that are Smo-positive. The absence of a difference in intensity therefore does not contradict the observed reduction in the number of Smo⁺ cilia. Rather, it explains why the phenotype is milder than that observed for SLOS/AY9944: when Smo is able to enter the cilium, its enrichment level is comparable to WT.

      4- Clinical relevance for CDPX2:

      While Hedgehog-related phenotypes in CDPX2 patients may be milder or under-reported, several documented features, such as polydactyly (10% of cases), as well as syndactyly and clubfoot, are classically associated with ciliary/Hedgehog signaling defects. This clinical pattern is consistent with the milder yet detectable defects we observe in EBP KO cells.

      Minor •Line 310: 'intraflagellar' rather than 'intraciliary' transport particle B is a more conventional term

      We agree that intraflagellar is more conventional than intraciliary, but in this case, this is how the GO term is labeled in the database. In our opinion, it should stay as is.

      • Fig. 2c - typos in the color key, is grey meant to be "cells" and blue "cilia"? Individual panels are not referenced in the text

      This panel has been removed thanks to comment from reviewer 1 and 3 finding it misleading.

      • Lines 357-358: "Notably, AY9944 treatment led to a greater reduction in cholesterol content as well as a greater increase in 7-DHC and 8-DHC in cilia than in the other cell membranes" - the authors need to support this statement with appropriate statistical analysis

      We respectfully believe there may be a misunderstanding in the reviewer's concern. In all cases, our comparisons are made between treated vs. untreated conditions within each compartment (cell bulk vs. ciliary membrane), and the statistical significance of these differences is already reported as determined by a Mann-Whitney test. In every case, the changes observed are greater in cilia than in the cell body. The statement in the manuscript simply summarizes this quantitative observation. However, if the reviewer feels that an additional statistical test directly comparing the magnitude of the two compartment-specific changes would strengthen the claim, we are willing to include this analysis. Alternatively, if preferred, we can remove the sentence entirely, as the comparison is already clearly visible in Figure 3b.

      • Line 473 - unclear what is meant by "olfactory cilia are mainly sensory and not primary". Primary cilia are sensory.

      We agree, primary cilia are sensory, but still different from cilia belonging to sensory epithelia like retina photoreceptors or olfactory cilia. Nevertheless, this statement was modified in revised manuscript

      • Line 551: 'data not shown'. Please include the data that you would like to discuss or remove discussion of these data from the manuscript.

      The data is not shown because there is nothing to show, as we discussed in that sentence, use of cholesterol probe resulted in the disappearance of primary cilia altogether. We are willing to work with editorial staff to find a better way of expressing this idea.

      Reviewer #3 (Significance (Required)):

      Overall, the manuscript expands our knowledge of cilia membrane composition and reports an interesting link between SLOS and Shh signaling defects, which could at least in part explain SLOS patients' symptoms. The findings reported in the manuscript could be of interest to a broad audience of cell biologists and geneticists.

      We would like to thank the reviewer for his recognition of the importance of this work

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Lamaziere et al. describe an improved protocol for isolating primary cilia from MDCK cells for downstream lipidomics analysis. Using this protocol, they characterize sterol profile of MDCK cilia membrane under standard growth conditions and following pharmacological perturbations that are meant to mimic SLOS and CDPX2 disorders in humans. The authors then assess the impact of the same pharmacological manipulations on Shh pathway activity and validate their findings from these experiments using orthogonal genetic approaches. Major and minor concerns that require attention prior to publication are outlined below.

      Major

      1. Since the extent of contamination of the cilia preps with non-cilia membranes is unclear, and variability between replicates is not reported, it makes interpretation of changes in cilia membrane sterol composition in response to pharmacological manipulations somewhat difficult to interpret. Discussing reproducibility of cilia sterol composition between replicates (and including corresponding data) could alleviate these concerns to some extent.
      2. An abundant non-ciliary membrane protein (rather than GAPDH) may be a more appropriate loading control in Fig. 1C.
      3. Fig. 2b - which bar corresponds to cells and which one to cilia? What do numbers inside bars represent? Please label accordingly.
      4. Fig. 3b-d, right panels - please define what numbers inside bars represent
      5. The font in Figs 2, 3, and 4 is very small and difficult to read. Please make the font and/or panels bigger to improve readability.
      6. It would help to have a diagram of the key steps in the cholesterol synthesis pathway for reference early in the paper rather than in figure 3.
      7. The authors need to discuss why/how global inhibition of enzymes (e.g. via AY9944 treatment) in a cell could cause reduction in cholesterol levels only in the cilium and not in other cell membranes (see also point 1). Yet, tamoxifen treatment lowers cholesterol across the board.
      8. Fig. 5c, g, and j - statistical analyses are missing and need to be added in support of conclusions drawn in the text of the manuscript.
      9. The decrease in the fraction of Smo+ cilia observed in EBP KO cells is mild (panel j, no statistics), and there is possibly a clone-specific effect here as well (statistical analysis is needed to determine if EBP139 is indeed different from WT and whether EBP139 and 141 are different from each other). Similarly, Smo fluorescence intensity after SAG treatment (panel k) is the same in WT and EBP KO cells, while there is a marked difference in intraciliary Smo intensity after tamoxifen treatment. The author's conclusion "...we were able to show that results with human cells aligned with our tamoxifen experiments" (line 436) should be modified to more accurately reflect the presented data. Ditto conclusions on lines 440-442, 530-531. In fact, it is the lack of Hh phenotypes in CDPX2 patients that is consistent with the EBP KO data presented in the paper.

      Minor

      • Line 310: 'intraflagellar' rather than 'intraciliary' transport particle B is a more conventional term
      • Fig. 2c - typos in the color key, is grey meant to be "cells" and blue "cilia"? Individual panels are not referenced in the text
      • Lines 357-358: "Notably, AY9944 treatment led to a greater reduction in cholesterol content as well as a greater increase in 7-DHC and 8-DHC in cilia than in the other cell membranes" - the authors need to support this statement with appropriate statistical analysis
      • Line 473 - unclear what is meant by "olfactory cilia are mainly sensory and not primary". Primary cilia are sensory.
      • Line 551: 'data not shown'. Please include the data that you would like to discuss or remove discussion of these data from the manuscript.

      Significance

      Overall, the manuscript expands our knowledge of cilia membrane composition and reports an interesting link between SLOS and Shh signaling defects, which could at least in part explain SLOS patients' symptoms. The findings reported in the manuscript could be of interest to a broad audience of cell biologists and geneticists.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Overview

      Accumulating evidence suggests that sterols play critical roles in signal transduction within the primary cilium, perhaps most notably in the Hedgehog cascade. However, the precise sterol composition of the primary cilium, and how it may change under distinct biological conditions, remains unknown, in part because of the lack of reproducible, widely accepted procedures to purify primary cilia from mammalian cultured cells. In the present study, the authors have designed a method to isolate the cilium from the MDCK cells efficiently and then utilized this procedure in conjunction with mass spectrometry to systematically analyze the sterol composition of the ciliary membrane, which they then compare to the sterol composition of the cell body. By analyzing this sterol profiling. the authors claim that the cilium has a distinct sterol composition from the cell body, including higher levels of cholesterol and desmosterol but lower levels of 8-DHC and & Lathosterol. This manuscript further demonstrates that alteration of sterol composition within cilia modulates Hedgehog signaling. These results strengthen the link between dysregulated Hedgehog signaling and defects in cholesterol biosynthesis pathways, as observed in SLOS and CDPX2.

      While the ability to isolate primary cilia from cultured MDCK cells represents an important technical achievement, the central claim of the manuscript - that cilia have a different sterol composition from the cell body - is not adequately supported by the data, and more rigorous comparisons between the ciliary membrane and key organellar membranes (such as plasma membrane) are required to make this claim. Moreover, although the authors have repeatedly mention that the ciliary sterol composition is "tightly regulated" there is no evidence provided to support such claim. At best, the data suggest that the cilium and cell body may differ in sterol composition (though even that remains uncertain), but no underlying regulatory mechanisms are demonstrated. In addition, much of the 2nd half of the paper represents a rehash of experiments with sterol biosynthesis inhibitors that have already been published in the literature, making the conceptual advance modest at best. Lastly, the link between CDPX2 and defective Hedgehog signaling is tenuous.

      Major comments

      Figure 1.

      C) Although the isolation of cilium from the MDCK cells using dibucaine treatment seems to be very efficient, the quality control of their fractionation procedure to monitor the isolation is limited to a single western blot of the purified cilia vs. cell body samples, with no representative data shown from the sucrose gradient fractionation steps. Given that prior studies (including those from the Marshall lab cited in this manuscript) found that 1) sucrose gradient fractionation was essential to obtain relatively pure ciliary fractions, and 2) the ciliary fractions appear to spread over many sucrose concentrations in those prior studies , the authors should have included the comparison of the fractionation profile from the sucrose gradient while isolating the primary cilium. This additional information would have further clarified and supported the efficiency of their proposed method. D) The authors presented proteomic data for the peptides analyzed from the isolated cilia in the form of GO term analysis; however, they did not provide examples of different proteins enriched within their fractionation procedure, aside from Arl13b shown in the blot. Including a summary table with representative proteins identified in the isolated ciliary fraction, along with the relative abundance or percentage distribution of these proteins, would make the data more informative.

      Figure 2.

      The authors represented the comparison of sterol content within the cilia versus whole cell (as cell membranes). Since different organelles have a very diverse degree of cholesterol contents within them, for instance plasma membrane itself is around 50 mol% cholesterol levels while organelles like ER have barely any cholesterol. Thus, comparing these two samples and claiming a 2.5-fold increase in cholesterol levels is misleading. A more appropriate comparison would be between isolated primary cilia and isolated plasma membranes (procedures to isolate plasma membranes have been described previously, e.g., Naito et al., eLife 2019; Das et al, PNAS 2013. The absence of such controls makes it difficult to fully validate the reported magnitude of sterols enrichment in cilia relative to the cell surface. Also, because dibucaine was used here to isolate MDCK cilia, a control experiment to exclude possible effects of the dibucaine treatment on sterol biosynthesis would be helpful.

      Figure 3.

      Tamoxifen is a potent drug for nuclear hormone receptor activity and thus can independently influence various cellular processes. As several experiments in the later sections of the manuscript rely on tamoxifen treatment of cells, it is important that the authors include appropriate controls for tamoxifen treatment, to confirm that the observed effects do not stem from effects on nuclear hormone receptor activity. This would ensure that the observed effects can be confidently attributed to the experimental manipulation rather than to the intrinsic effects of tamoxifen.

      Figure4.

      The authors present the results of spectroscopy studies to analyze generalized polarization (GP) of liposomes in vitro , but only processed data are shown, and the raw spectra are not provided. The authors need to present representative spectra to enable the readers to interact the raw data from the experiments.

      Figure5.

      B) The experiment shown Gli1 mRNA levels following treatment with inhibitors of cholesterol biosynthesis, but similar findings have already been reported previously (e.g., Cooper et al, Nature Genetics 2003; Blassberg et al, Hum Mol Genet 2016), and the present results do not provide a significant conceptual advance over those earlier studies. In addition, the authors analyze the effect of these inhibitors on SAG stimulation, but the experiment lacks the control for Gli mRNA levels in the absence of SAG treatment. Without this control, it is impossible to know where the baseline in the experiment is and how large the effects in question really are. J-K) The data represented in these panels for SAG treatment as fraction of Smo and its fluorescence intensity for the same sample appears to be inconsistent between the two graphs. Under SAG treatment for EBP mutants shows higher Smo fluorescence intensity while Smo positive cilia seems to be less than the wild type control cells. If the number of Smo+ cilia (quantified by eye) differs between conditions, shouldn't the quantification of Smo intensity within cilia show a similar difference? I) The rationale for using SmoM2 in the analysis of cholesterol metabolism-related diseases such as SLOS and CDPX2 is unclear. The SmoM2 variant is primarily associated with cancer rather than cholesterol biosynthesis defects and its relevance either of these disorders is not immediately apparent.

      Minor corrections

      1. Line 385 seems to be a bit confusing which mentions cilia were treated with AY9944 - do the authors mean that cells were been treated with the drugs before isolation of cilia, or were the purified cilia actually treated with the drugs?
      2. The authors should add proper label in Figure 2 panel b for the bars representing the cilia and cell membranes.
      3. Panels in Figure S1 should be re-arranged according to the figure legend and figure reference in line 450.
      4. Legend for the Figure S1b should be corrected as data sets in graph represents 7 points while technical replicates in legend shows 6 experimental values.
      5. The labels for drug in Figure 3 and 5 should be corrected from tamoxifene to tamoxifen and simvastatine to simvastatin.

      Significance

      In the present study, the authors have designed a method to isolate the cilium from the MDCK cells efficiently and then utilized this procedure in conjunction with mass spectrometry to systematically analyze the sterol composition of the ciliary membrane, which they then compare to the sterol composition of the cell body. By analyzing this sterol profiling. the authors claim that the cilium has a distinct sterol composition from the cell body, including higher levels of cholesterol and desmosterol but lower levels of 8-DHC and & Lathosterol. This manuscript further demonstrates that alteration of sterol composition within cilia modulates Hedgehog signaling. These results strengthen the link between dysregulated Hedgehog signaling and defects in cholesterol biosynthesis pathways, as observed in SLOS and CDPX2.

      While the ability to isolate primary cilia from cultured MDCK cells represents an important technical achievement, the central claim of the manuscript - that cilia have a different sterol composition from the cell body - is not adequately supported by the data, and more rigorous comparisons between the ciliary membrane and key organellar membranes (such as plasma membrane) are required to make this claim. Moreover, although the authors have repeatedly mention that the ciliary sterol composition is "tightly regulated" there is no evidence provided to support such claim. At best, the data suggest that the cilium and cell body may differ in sterol composition (though even that remains uncertain), but no underlying regulatory mechanisms are demonstrated. In addition, much of the 2nd half of the paper represents a rehash of experiments with sterol biosynthesis inhibitors that have already been published in the literature, making the conceptual advance modest at best. Lastly, the link between CDPX2 and defective Hedgehog signaling is tenuous.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Review report for 'Sterols regulate ciliary membrane dynamics and hedgehog signaling in health and disease', Lamazière et al.

      In this manuscript, Lamazière et al. address an important understudied aspect of primary cilium biology, namely the sterol composition in the ciliary membrane. It is known that sterols especially play an important role in signal transduction between PTCH1 and SMO, two upstream components of the Hedgehog pathway, at the primary cilium. Moreover, several syndromes linked to cholesterol biosynthesis defects present clinical phenotypes indicative of altered Hh signal transduction. To understand the link between ciliary membrane sterol composition and Hh signal transduction in health and disease, the authors developed a method to isolate primary cilia from MDCK cells and coupled this to quantitative metabolomics. The results were validated using biophysical methods and cellular Hh signaling assays. While this is an interesting study, it is not clear from the presented data how general the findings are: can cilia be isolated from different mammalian cell types using this protocol? Is the sterol composition of MDCK cells expected to the be the same in fibroblasts or other cell types? Without this information, it is difficult to judge whether the conclusions reached in fibroblasts are indeed directly related to the sterol composition detected in MDCK cells. Below is a detailed breakdown of suggested textual changes and experimental validations to strengthen the conclusions of the manuscript.

      Major comments:

      • It appears that the comparison has been made between ciliary membranes and the rest of the cell's membranes, which includes many other membranes besides the plasma membrane. This significantly weakens the conclusions on the sterol content specific to the cilium, as it may in fact be highly similar to the rest of the plasma membrane. It is for example known that lathosterol is biosynthesized in the ER, and therefore the non-presence in the cilium may reflect a high abundance in the ER but not necessarily in the plasma membrane.
      • While the protocol to isolate primary cilium from MDCK cells is a valuable addition to the methods available, it would be good to at least include a discussion on its general applicability. Have the authors tried to use this protocol on fibroblasts for example?
      • Some of the conclusions in the introduction (lines 75-80) seem to be incorrectly phrased based on the data: in basal conditions, ciliary membranes are already enriched in cholesterol and desmosterol, and the treatment lowers this in all membranes.
      • There seems to be little effect of simvastatin on overall cholesterol levels. Can the authors comment on this result? How would the membrane fluidity be altered when mimicking simvastatin-induced composition? Since the effect on Hh signaling appears to be the biggest (Figure 5B) under simvastatin treatment, it would be interesting to compare this against that found for AY9944 treatment. Also, the authors conclude that the effects of simvastatin treatment on ciliary membrane sterol composition are the mildest, however, one could argue that they are the strongest as there is a complete lack of desmosterol.
      • It is not clear to me why the authors have chosen to use SAG to activate the Hh pathway, as this is a downstream mode of activation and bypasses PTCH1 (and therefore a potentially sterol-mediated interaction between the two proteins). It would be very informative to compare the effect of sterol modulation on the ability of ShhN vs SAG to activate the pathway.
      • The conclusions about the effect of tamoxifen on SMO trafficking in MEFs should be validated in human patient cells before being able to conclude that there is a potential off-target effect (line 438). Also, if that is the case, the experiment of tamoxifen treatment of EBP KO cells should give an additional effect on SMO trafficking. Also, could the CDPX2 phenotypes in patients be the result of different cell types being affected than the fibroblast used in this study?
      • For the experiments with the SMO-M2 mutant, it would be useful to show the extent of pathway activation by the mutant compared to SAG or ShhN treatment of non-transfected cells. Moreover, it will be necessary to exclude any direct effects of the compound treatment on the ability of this mutant to traffic to the primary cilium, which can easily be done using fluorescence microscopy as the mutant is tagged with mCherry.

      Minor comments:

      Line 74: 'in patients', should be rephrased to 'patient-derived cells'

      Figure 2A: What do the '+/-' indicate? They seem to be erroneously placed.

      Figure 2B: no label present for which bar represents cilia/other membranes

      Figure 2C: this representation is slightly deceptive, since the difference between cells and cilia for lanosterol is not significantly different as shown in figure 2A.

      Figure 3A: it would be useful to also show where 8-DHC is in the biosynthetic pathway.

      Line 373: the title should be rephrased as it infers that DHCR7 was blocked in model membranes, which is not the case.

      Lines 377-384: this paragraph seems to be a mix of methods and some explanation, but should be rephrased for clarity.

      Line 403: 'which could explain the resulting defects in Hedgehog signaling': how and what defects? At this point in the study no defects in Hh signaling have been shown.

      Figure 4D: 'd' is missing

      Line 408: SAG treatment resulted in slightly shorter cilia: this is not the case for just SAG treated cilia, but only for the combination of SAG + AY9944. However, in that condition there appears to be a subpopulation of very short cilia, are those real?

      Figure 5b: it would be good to add that all conditions contained SAG.

      Figure 5D: Since it is shown in Fig 5C that there are no positive cilia -SAG, there is no point to have empty graphs in Fig 5D on the left side, nor can any statistics be done. Similarly for 5K.

      Figure 5E: it is not clearly indicated what is visualized in the inserts, sometimes it's a box, sometimes a line and they seem randomly integrated into the images.

      Figure 5H: is this the intensity in just SMO positive cilia? If yes, this should be indicated, and the line at '0' for WT-SAG should be removed. I am also surprised there is then ns found for WT vs SLO, since in WT there are no positive cilia, but in SLO there are a few, so it appears to be more of a black-white situation. Perhaps it would be useful to split the data from different experiments to see if it consistently the case that there is a low percentage of SMO positive cilia in SLO cells. Fig S1: panels are inverted compared to mentioning in the text.

      Methods-pharmacological treatments: there appear to be large differences in concentrations chosen to treat MDCK versus MEF cells - can the authors comment on these choices and show that the enzymes are indeed inhibited at the indicated concentrations?

      (optional): it would be interesting to include a gamma-tubulin staining on the cilium prep to see if there is indeed a presence of the basal body as suggested by the proteomics data.

      There are many spelling mistakes and inconsistencies throughout the manuscript and its figures (mix of French and English for example) so careful proofreading would be warranted. Moreover, there are many mentionings of 'Hedgehog defects' or 'Hedgehog-linked', where in fact it is a defect in or link to the Hedgehog pathway, not the protein itself. This should be corrected.

      Significance

      The study of ciliary membrane composition is highly relevant to understand signal transduction in health and disease. As such, the topic of this manuscript is significant and timely. However, as indicated above, there are limitations to this study, most notably the comparison of ciliary membrane versus all cellular membranes (rather than the plasma membrane), which weakens the conclusions that can be drawn. Moreover, cell-type dependency should be more thoroughly addressed. There certainly is a methodological advance in the form of cilia isolation from MDCK cells, however, it is unclear how broadly applicable this is to other mammalian cell types.

    1. 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

      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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Referee #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.

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

      Learn more at Review Commons


      Reply to the reviewers

      We would like to thank all the reviewers for their valuable comments and criticisms. We have thoroughly revised the manuscript and the resource to address all the points raised by the reviewers. Below, we provide a point-by-point response for the sake of clarity.

      Reviewer #1

      __Evidence, reproducibility and clarity __

      Summary: This manuscript, "MAVISp: A Modular Structure-Based Framework for Protein Variant Effects," presents a significant new resource for the scientific community, particularly in the interpretation and characterization of genomic variants. The authors have developed a comprehensive and modular computational framework that integrates various structural and biophysical analyses, alongside existing pathogenicity predictors, to provide crucial mechanistic insights into how variants affect protein structure and function. Importantly, MAVISp is open-source and designed to be extensible, facilitating reuse and adaptation by the broader community.

      Major comments: - While the manuscript is formally well-structured (with clear Introduction, Results, Conclusions, and Methods sections), I found it challenging to follow in some parts. In particular, the Introduction is relatively short and lacks a deeper discussion of the state-of-the-art in protein variant effect prediction. Several methods are cited but not sufficiently described, as if prior knowledge were assumed. OPTIONAL: Extend the Introduction to better contextualize existing approaches (e.g., AlphaMissense, EVE, ESM-based predictors) and clarify what MAVISp adds compared to each.

      We have expanded the introduction on the state-of-the-art of protein variant effects predictors, explaining how MAVISp departs from them.

      - The workflow is summarized in Figure 1(b), which is visually informative. However, the narrative description of the pipeline is somewhat fragmented. It would be helpful to describe in more detail the available modules in MAVISp, and which of them are used in the examples provided. Since different use cases highlight different aspects of the pipeline, it would be useful to emphasize what is done step-by-step in each.

      We have added a concise, narrative description of the data flow for MAVISp, as well as improved the description of modules in the main text. We will integrate the results section with a more comprehensive description of the available modules, and then clarify in the case studies which modules were applied to achieve specific results.

      OPTIONAL: Consider adding a table or a supplementary figure mapping each use case to the corresponding pipeline steps and modules used.

      We have added a supplementary table (Table S2) to guide the reader on the modules and workflows applied for each case study

      We also added Table S1 to map the toolkit used by MAVISp to collect the data that are imported and aggregated in the webserver for further guidance.

      - The text contains numerous acronyms, some of which are not defined upon first use or are only mentioned in passing. This affects readability. OPTIONAL: Define acronyms upon first appearance, and consider moving less critical technical details (e.g., database names or data formats) to the Methods or Supplementary Information. This would greatly enhance readability.

      We revised the usage of acronyms following the reviewer’s directions of defying them at first appearance.

      • The code and trained models are publicly available, which is excellent. The modular design and use of widely adopted frameworks (PyTorch and PyTorch Geometric) are also strong points. However, the Methods section could benefit from additional detail regarding feature extraction and preprocessing steps, especially the structural features derived from AlphaFold2 models. OPTIONAL: Include a schematic or a table summarizing all feature types, their dimensionality, and how they are computed.

      We thank the reviewer for noticing and praising the availability of the tools of MAVISp. Our MAVISp framework utilizes methods and scores that incorporate machine learning features (such as EVE or RaSP), but does not employ machine learning itself. Specifically, we do not use PyTorch and do not utilize features in a machine learning sense. We do extract some information from the AlphaFold2 models that we use (such as the pLDDT score and their secondary structure content, as calculated by DSSP), and those are available in the MAVISp aggregated csv files for each protein entry and detailed in the Documentation section of the MAVISp website.

      • The section on transcription factors is relatively underdeveloped compared to other use cases and lacks sufficient depth or demonstration of its practical utility. OPTIONAL: Consider either expanding this section with additional validation or removing/postponing it to a future manuscript, as it currently seems preliminary.

      We have removed this section and included a mention in the conclusions as part of the future directions.

      Minor comments: - Most relevant recent works are cited, including EVE, ESM-1v, and AlphaFold-based predictors. However, recent methods like AlphaMissense (Cheng et al., 2023) could be discussed more thoroughly in the comparison.

      We have revised the introduction to accommodate the proper space for this comparison.

      • Figures are generally clear, though some (e.g., performance barplots) are quite dense. Consider enlarging font sizes and annotating key results directly on the plots.

      We have revised Figure 2 and presented only one case study to simplify its readability. We have also changed Figure 3, whereas retained the other previous figures since they seemed less problematic.

      • Minor typographic errors are present. A careful proofreading is highly recommended. Below are some of the issues I identified: Page 3, line 46: "MAVISp perform" -> "MAVISp performs" Page 3, line 56: "automatically as embedded" -> "automatically embedded" Page 3, line 57: "along with to enhance" -> unclear; please revise Page 4, line 96: "web app interfaces with the database and present" -> "presents" Page 6, line 210: "to investigate wheatear" -> "whether" Page 6, lines 215-216: "We have in queue for processing with MAVISp proteins from datasets relevant to the benchmark of the PTM module." -> unclear sentence; please clarify Page 15, line 446: "Both the approaches" -> "Both approaches" Page 20, line 704: "advantage of multi-core system" -> "multi-core systems"

      We have done a proofreading of the entire article, including the points above

      Significance

      General assessment: the strongest aspects of the study are the modularity, open-source implementation, and the integration of structural information through graph neural networks. MAVISp appears to be one of the few publicly available frameworks that can easily incorporate AlphaFold2-based features in a flexible way, lowering the barrier for developing custom predictors. Its reproducibility and transparency make it a valuable resource. However, while the technical foundation is solid and the effort substantial, the scientific narrative and presentation could be significantly improved. The manuscript is dense and hard to follow in places, with a heavy use of acronyms and insufficient explanation of key design choices. Improving the descriptive clarity, especially in the early sections, would greatly enhance the impact of this work.

      Advance

      to the best of my knowledge, this is one of the first modular platforms for protein variant effect prediction that integrates structural data from AlphaFold2 with bioinformatic annotations and even clinical data in an extensible fashion. While similar efforts exist (e.g., ESMfold, AlphaMissense), MAVISp distinguishes itself through openness and design for reusability. The novelty is primarily technical and practical rather than conceptual.

      Audience

      this study will be of strong interest to researchers in computational biology, structural bioinformatics, and genomics, particularly those developing variant effect predictors or analyzing the impact of mutations in clinical or functional genomics contexts. The audience is primarily specialized, but the open-source nature of the tool may diffuse its use among more applied or translational users, including those working in precision medicine or protein engineering.

      Reviewer expertise: my expertise is in computational structural biology, molecular modeling, and (rather weak) machine learning applications in bioinformatics. I am familiar with graph-based representations of proteins, AlphaFold2, and variant effects based on Molecular Dynamics simulations. I do not have any direct expertise in clinical variant annotation pipelines.

      Reviewer #2

      __Evidence, reproducibility and clarity __

      Summary: The authors present a pipeline and platform, MAVISp, for aggregating, displaying and analysis of variant effects with a focus on reclassification of variants of uncertain clinical significance and uncovering the molecular mechanisms underlying the mutations.

      Major comments: - On testing the platform, I was unable to look-up a specific variant in ADCK1 (rs200211943, R115Q). I found that despite stating that the mapped refseq ID was NP_001136017 in the HGVSp column, it was actually mapped to the canonical UniProt sequence (Q86TW2-1). NP_001136017 actually maps to Q86TW2-3, which is missing residues 74-148 compared to the -1 isoform. The Uniprot canonical sequence has no exact RefSeq mapping, so the HGVSp column is incorrect in this instance. This mapping issue may also affect other proteins and result in incorrect HGVSp identifiers for variants.

      We would like to thank the reviewer for pointing out these inconsistencies. We have revised all the entries and corrected them. If needed, the history of the cases that have been corrected can be found in the closed issues of the GitHub repository that we use for communication between biocurators and data managers (https://github.com/ELELAB/mavisp_data_collection). We have also revised the protocol we follow in this regard and the MAVISp toolkit to include better support for isoform matching in our pipelines for future entries, as well as for the revision/monitoring of existing ones, as detailed in the Method Section. In particular, we introduced a tool, uniprot2refseq, which aids the biocurator in identifying the correct match in terms of sequence length and sequence identity between RefSeq and UniProt. More details are included in the Method Section of the paper. The two relevant scripts for this step are available at: https://github.com/ELELAB/mavisp_accessory_tools/

      - The paper lacks a section on how to properly interpret the results of the MAVISp platform (the case-studies are helpful, but don't lay down any global rules for interpreting the results). For example: How should a variant with conflicts between the variant impact predictors be interpreted? Are specific indicators considered more 'reliable' than others?

      We have added a section in Results to clarify how to interpret results from MAVISp in the most common use cases.

      • In the Methods section, GEMME is stated as being rank-normalised with 0.5 as a threshold for damaging variants. On checking the data downloaded from the site, GEMME was not rank-normalised but rather min-max normalised. Furthermore, Supplementary text S4 conflicts with the methods section over how GEMME scores are classified, S4 states that a raw-value threshold of -3 is used.

      We thank the reviewer for spotting this inconsistency. This part in the main text was left over from a previous and preliminary version of the pre-print, we have revised the main text. Supplementary Text S4 includes the correct reference for the value in light of the benchmarking therewithin.

      • Note. This is a major comment as one of the claims is that the associated web-tool is user-friendly. While functional, the web app is very awkward to use for analysis on any more than a few variants at once. The fixed window size of the protein table necessitates excessive scrolling to reach your protein-of-interest. This will also get worse as more proteins are added. Suggestion: add a search/filter bar. The same applies to the dataset window.

      We have changed the structure of the webserver in such a way that now the whole website opens as its own separate window, instead of being confined within the size permitted by the website at DTU. This solves the fixed window size issue. Hopefully, this will improve the user experience.

      We have refactored the web app by adding filtering functionality, both for the main protein table (that can now be filtered by UniProt AC, gene name or RefSeq ID) and the mutations table. Doing this required a general overhaul of the table infrastructure (we changed the underlying engine that renders the tables).

      • You are unable to copy anything out of the tables.
      • Hyperlinks in the tables only seem to work if you open them in a new tab or window.

      The table overhauls fixed both of these issues

      • All entries in the reference column point to the MAVISp preprint even when data from other sources is displayed (e.g. MAVE studies).

      We clarified the meaning of the reference column in the Documentation on the MAVISp website, as we realized it had confused the reviewer. The reference column is meant to cite the papers where the computationally-generated MAVISp data are used, not external sources. Since we also have the experimental data module in the most recent release, we have also refactored the MAVISp website by adding a “Datasets and metadata” page, which details metadata for key modules. These include references to data from external sources that we include in MAVISp on a case-by-case basis (for example the results of a MAVE experiment). Additionally, we have verified that the papers using MAVISp data are updated in https://elelab.gitbook.io/mavisp/overview/publications-that-used-mavisp-data and in the csv file of the interested proteins.

      Here below the current references that have been included in terms of publications using MAVISp data:

      SMPD1

      ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase

      Biochim Biophys Acta Mol Basis Dis

      38782304

      https://doi.org/10.1016/j.bbadis.2024.167260

      TRAP1

      Point mutations of the mitochondrial chaperone TRAP1 affect its functions and pro-neoplastic activity

      Cell Death & Disease

      40074754

      https://doi.org/10.1038/s41419-025-07467-6

      BRCA2

      Saturation genome editing-based clinical classification of BRCA2 variants

      Nature

      39779848

      0.1038/s41586-024-08349-1

      TP53, GRIN2A, CBFB, CALR, EGFR

      TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins

      Cell Death & Disease

      37085483

      10.1038/s41419-023-05780-6

      KIF5A, CFAP410, PILRA, CYP2R1

      Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

      Computational and Structural Biotechnology Journal

      38022694

      https://doi.org/10.1016/j.csbj.2023.10.031

      KRAS

      Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep

      Brief Bioinform

      39708841

      https://doi.org/10.1093/bib/bbae664

      OPTN

      Decoding phospho-regulation and flanking regions in autophagy-associated short linear motifs

      Communications Biology

      40835742

      10.1038/s42003-025-08399-9

      DLG4,GRB2,SMPD1

      Deciphering long-range effects of mutations: an integrated approach using elastic network models and protein structure networks

      JMB

      40738203

      doi: 10.1016/j.jmb.2025.169359

      Entering multiple mutants in the "mutations to be displayed" window is time-consuming for more than a handful of mutants. Suggestion: Add a box where multiple mutants can be pasted in at once from an external document.

      During the table overhaul, we have revised the user interface to add a text box that allows free copy-pasting of mutation lists. While we understand having a single input box would have been ideal, the former selection interface (which is also still available) doesn’t allow copy-paste. This is a known limitation in Streamlit.

      Minor comments

      • Grammar. I appreciate that this manuscript may have been compiled by a non-native English speaker, but I would be remiss not to point out that there are numerous grammar errors throughout, usually sentence order issues or non-pluralisation. The meaning of the authors is mostly clear, but I recommend very thoroughly proof-reading the final version.

      We have done proofreading on the final version of the manuscript

      • There are numerous proteins that I know have high-quality MAVE datasets that are absent in the database e.g. BRCA1, HRAS and PPARG.

      Yes, we are aware of this. It is far from trivial to properly import the datasets from multiplex assays. They often need to be treated on a case-by-case basis. We are in the process of carefully compiling locally all the MAVE data before releasing it within the public version of the database, so this is why they are missing. We are giving priorities to the ones that can be correlated with our predictions on changes in structural stability and then we will also cover the rest of the datasets handling them in batches. Having said this, we have checked the dataset for BRCA1, HRAS, and PPARG. We have imported the ones for PPARG and BRCA1 from ProtGym, referring to the studies published in 10.1038/ng.3700 and 10.1038/s41586-018-0461-z, respectively. Whereas for HRAS, checking in details both the available data and literature, while we did identify a suitable dataset (10.7554/eLife.27810), we struggled to understand what a sensible cut-off for discriminating between pathogenic and non-pathogenic variants would be, and so ended up not including it in the MAVISp dataset for now. We will contact the authors to clarify which thresholds to apply before importing the data.

      • Checking one of the existing MAVE datasets (KRAS), I found that the variants were annotated as damaging, neutral or given a positive score (these appear to stand-in for gain-of-function variants). For better correspondence with the other columns, those with positive scores could be labelled as 'ambiguous' or 'uncertain'.

      In the KRAS case study presented in MAVISP, we utilized the protein abundance dataset reported in (http://dx.doi.org/10.1038/s41586-023-06954-0) and made available in the ProteinGym repository (specifically referenced at https://github.com/OATML-Markslab/ProteinGym/blob/main/reference_files/DMS_substitutions.csv#L153). We adopted the precalculated thresholds as provided by the ProteinGym authors. In this regard, we are not really sure the reviewer is referring to this dataset or another one on KRAS.

      • Numerous thresholds are defined for stabilizing / destabilizing / neutral variants in both the STABILITY and the LOCAL_INTERACTION modules. How were these thresholds determined? I note that (PMC9795540) uses a ΔΔG threshold of 1/-1 for defining stabilizing and destabilizing variants, which is relatively standard (though they also say that 2-3 would likely be better for pinpointing pathogenic variants).

      We improved the description of our classification strategies for both modules in the Documentation page of our website. Also, we explained more clearly the possible sources of ‘uncertain’ annotations for the two modules in both the web app (Documentation page) and main text. Briefly, in the STABILITY module, we consider FoldX and either Rosetta or RaSP to achieve a final classification. We first classify one and the other independently, according to the following strategy:

      If DDG ≥ 3, the mutation is Destabilizing If DDG ≤ −3, the mutation is Stabilizing If −2 We then compare the classifications obtained by the two methods: if they agree, then that is the final classification, if they disagree, then the final classification is Uncertain. The thresholds were selected based on a previous study, in which variants with changes in stability below 3 kcal/mol were not featuring a markedly different abundance at cellular level [10.1371/journal.pgen.1006739, 10.7554/eLife.49138]

      Regarding the LOCAL_INTERACTION module, it works similarly as for the Stability module, in that Rosetta and FoldX are considered independently, and an implicit classification is performed for each, according to the rules (values in kcal/mol)

      If DDG > 1, the mutation is Destabilizing. If DDG Each mutation is therefore classified for both methods. If the methods agree (i.e., if they classify the mutation in the same way), their consensus is the final classification for the mutation; if they do not agree, the final classification will be Uncertain.

      If a mutation does not have an associated free energy value, the relative solvent accessible area is used to classify it: if SAS > 20%, the mutation is classified as Uncertain, otherwise it is not classified.

      Thresholds here were selected according to best practices followed by the tool authors and more in general in the literature, as the reviewer also noticed.

      • "Overall, with the examples in this section, we illustrate different applications of the MAVISp results, spanning from benchmarking purposes, using the experimental data to link predicted functional effects with structural mechanisms or using experimental data to validate the predictions from the MAVISp modules."

      The last of these points is not an application of MAVISp, but rather a way in which external data can help validate MAVISp results. Furthermore, none of the examples given demonstrate an application in benchmarking (what is being benchmarked?).

      We have revised the statements to avoid this confusion in the reader.

      • Transcription factors section. This section describes an intended future expansion to MAVISp, not a current feature, and presents no results. As such, it should be moved to the conclusions/future directions section.

      We have removed this section and included a mention in the conclusions as part of the future directions.

      • Figures. The dot-plots generated by the web app, and in Figures 4, 5 and 6 have 2 legends. After looking at a few, it is clear that the lower legend refers to the colour of the variant on the X-axis - most likely referencing the ClinVar effect category. This is not, however, made clear either on the figures or in the app.

      The reviewer’s interpretation on the second legend is correct - it does refer to the ClinVar classification. Nonetheless, we understand the positioning of the legend makes understanding what the legend refers to not obvious. We also revised the captions of the figures in the main text. On the web app, we have changed the location of the figure legend for the ClinVar effect category and added a label to make it clear what the classification refers to.

      • "We identified ten variants reported in ClinVar as VUS (E102K, H86D, T29I, V91I, P2R, L44P, L44F, D56G, R11L, and E25Q, Fig.5a)" E25Q is benign in ClinVar and has had that status since first submitted.

      We have corrected this in the text and the statements related to it.

      Significance

      Platforms that aggregate predictors of variant effect are not a new concept, for example dbNSFP is a database of SNV predictions from variant effect predictors and conservation predictors over the whole human proteome. Predictors such as CADD and PolyPhen-2 will often provide a summary of other predictions (their features) when using their platforms. MAVISp's unique angle on the problem is in the inclusion of diverse predictors from each of its different moules, giving a much wider perspective on variants and potentially allowing the user to identify the mechanistic cause of pathogenicity. The visualisation aspect of the web app is also a useful addition, although the user interface is somewhat awkward. Potentially the most valuable aspect of this study is the associated gitbook resource containing reports from biocurators for proteins that link relevant literature and analyse ClinVar variants. Unfortunately, these are only currently available for a small minority of the total proteins in the database with such reports. For improvement, I think that the paper should focus more on the precise utility of the web app / gitbook reports and how to interpret the results rather than going into detail about the underlying pipeline.

      We appreciate the interest in the gitbook resource that we also see as very valuable and one of the strengths of our work. We have now implemented a new strategy based on a Python script introduced in the mavisp toolkit to generate a template Markdown file of the report that can be further customized and imported into GitBook directly (​​https://github.com/ELELAB/mavisp_accessory_tools/). This should allow us to streamline the production of more reports. We are currently assigning proteins in batches for reporting to biocurator through the mavisp_data_collection GitHub to expand their coverage. Also, we revised the text and added a section on the interpretation of results from MAVISp. with a focus on the utility of the web-app and reports.

      In terms of audience, the fast look-up and visualisation aspects of the web-platform are likely to be of interest to clinicians in the interpretation of variants of unknown clinical significance. The ability to download the fully processed dataset on a per-protein database would be of more interest to researchers focusing on specific proteins or those taking a broader view over multiple proteins (although a facility to download the whole database would be more useful for this final group).

      While our website only displays the dataset per protein, the whole dataset, including all the MAVISp entries, is available at our OSF repository (https://osf.io/ufpzm/), which is cited in the paper and linked on the MAVISp website. We have further modified the MAVISp database to add a link to the repository in the modes page, so that it is more visible.

      My expertise. - I am a protein bioinformatician with a background in variant effect prediction and large-scale data analysis.

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

      Evidence, reproducibility and clarity:

      Summary:

      The authors present MAVISp, a tool for viewing protein variants heavily based on protein structure information. The authors have done a very impressive amount of curation on various protein targets, and should be commended for their efforts. The tool includes a diverse array of experimental, clinical, and computational data sources that provides value to potential users interested in a given target.

      Major comments:

      Unfortunately I was not able to get the website to work correctly. When selecting a protein target in simple mode, I was greeted with a completely blank page in the app window. In ensemble mode, there was no transition away from the list of targets at all. I'm using Firefox 140.0.2 (64-bit) on Ubuntu 22.04. I would like to explore the data myself and provide feedback on the user experience and utility.

      We have tried reproducing the issue mentioned by the reviewer, using the exact same Ubuntu and Firefox versions, but unfortunately failed to produce it. The website worked fine for us under such an environment. The issue experienced by the reviewer may have been due to either a temporary issue with the web server or a problem with the specific browser environment they were working in, which we are unable to reproduce. It would be useful to know the date that this happened to verify if it was a downtime on the DTU IT services side that made the webserver inaccessible.

      I have some serious concerns about the sustainability of the project and think that additional clarifications in the text could help. Currently is there a way to easily update a dataset to add, remove, or update a component (for example, if a new predictor is published, an error is found in a predictor dataset, or a predictor is updated)? If it requires a new round of manual curation for each protein to do this, I am worried that this will not scale and will leave the project with many out of date entries. The diversity of software tools (e.g., three different pipeline frameworks) also seems quite challenging to maintain.

      We appreciate the reviewer’s concerns about long-term sustainability. It is a fair point that we consider within our steering group, who oversee and plans the activities and meet monthly. Adding entries to MAVISp is moving more and more towards automation as we grow. We aim to minimize the manual work where applicable. Still, an expert-based intervention is really needed in some of the steps, and we do not want to renounce it. We intend to keep working on MAVISp to make the process of adding and updating entries as automated as possible, and to streamline the process when manual intervention is necessary. From the point of view of the biocurators, they have three core workflows to use for the default modules, which also automatically cover the source of annotations. We are currently working to streamline the procedures behind LOCAL_INTERACTION, which is the most challenging one. On the data manager and maintainers' side, we have workflows and protocols that help us in terms of automation, quality control, etc, and we keep working to improve them. Among these, we have workflows to use for the old entries updates. As an example, the update of erroneously attributed RefSeq data (pointed out by reviewer 2) took us only one week overall (from assigning revisions and importing to the database) because we have a reduced version of Snakemake for automation that can act on only the affected modules. Also, another point is that we have streamlined the generation of the templates for the gitbook reports (see also answer to reviewer 2).

      The update of old entries is planned and made regularly. We also deposit the old datasets on OSF for transparency, in case someone needs to navigate and explore the changes. We have activities planned between May and August every year to update the old entries in relation to changes of protocols in the modules, updates in the core databases that we interact with (COSMIC, Clinvar etc). In case of major changes, the activities for updates continue in the Fall. Other revisions can happen outside these time windows if an entry is needed or a specific research project and needs updates too.

      Furthermore, the community of people contributing to MAVISp as biocurators or developers is growing and we have scientists contributing from other groups in relation to their research interest. We envision that for this resource to scale up, our team cannot be the only one producing data and depositing it to the database. To facilitate this we launched a pilot for a training event online (see Event page on the website) and we will repeat it once per year. We also organize regular meetings with all the active curators and developers to plan the activities in a sustainable manner and address the challenges we encounter.

      As stated in the manuscript, currently with the team of people involved, automatization and resources that we have gathered around this initiative we can provide updates to the public database every third month and we have been regularly satisfied with them. Additionally, we are capable of processing from 20 to 40 proteins every month depending also on the needs of revision or expansion of analyses on existing proteins. We also depend on these data for our own research projects and we are fully committed to it.

      Additionally, we are planning future activities in these directions to improve scale up and sustainability:

      • Streamlining manual steps so that they are as convenient as fast as possible for our curators, e.g. by providing custom pages on the MAVISp website
      • Streamline and automatize the generation of useful output, for instance the reports, by using a combination of simple automation and large language models
      • Implement ways to share our software and scripts with third parties, for instance by providing ready made (or close to) containers or virtual machines
      • For a future version 2 if the database grows in a direction that is not compatible with Streamlit, the web data science framework we are currently using, we will rewrite the website using a framework that would allow better flexibility and performance, for instance using Django and a proper database backend. On the same theme, according to the GitHub repository, the program relies on Python 3.9, which reaches end of life in October 2025. It has been tested against Ubuntu 18.04, which left standard support in May 2023. The authors should update the software to more modern versions of Python to promote the long-term health and maintainability of the project.

      We thank the reviewer for this comment - we are aware of the upcoming EOL of Python 3.9. We tested MAVISp, both software package and web server, using Python 3.10 (which is the minimum supported version going forward) and Python 3.13 (which is the latest stable release at the time of writing) and updated the instructions in the README file on the MAVISp GitHub repository accordingly.

      We plan on keeping track of Python and library versions during our testing and updating them when necessary. In the future, we also plan to deploy Continuous Integration with automated testing for our repository, making this process easier and more standardized.

      I appreciate that the authors have made their code and data available. These artifacts should also be versioned and archived in a service like Zenodo, so that researchers who rely on or want to refer to specific versions can do so in their own future publications.

      Since 2024, we have been reporting all previous versions of the dataset on OSF, the repository linked to the MAVISp website, at https://osf.io/ufpzm/files/osfstorage (folder: previous_releases). We prefer to keep everything under OSF, as we also use it to deposit, for example, the MD trajectory data.

      Additionally, in this GitHub page that we use as a space to interact between biocurators, developers, and data managers within the MAVISp community, we also report all the changes in the NEWS space: https://github.com/ELELAB/mavisp_data_collection

      Finally, the individual tools are all available in our GitHub repository, where version control is in place (see Table S1, where we now mapped all the resources used in the framework)

      In the introduction of the paper, the authors conflate the clinical challenges of variant classification with evidence generation and it's quite muddled together. They should strongly consider splitting the first paragraph into two paragraphs - one about challenges in variant classification/clinical genetics/precision oncology and another about variant effect prediction and experimental methods. The authors should also note that they are many predictors other than AlphaMissense, and may want to cite the ClinGen recommendations (PMID: 36413997) in the intro instead.

      We revised the introduction in light of these suggestions. We have split the paragraph as recommended and added a longer second paragraph about VEPs and using structural data in the context of VEPs. We have also added the citation that the reviewer kindly recommended.

      Also in the introduction on lines 21-22 the authors assert that "a mechanistic understanding of variant effects is essential knowledge" for a variety of clinical outcomes. While this is nice, it is clearly not the case as we can classify variants according to the ACMG/AMP guidelines without any notion of specific mechanism (for example, by combining population frequency data, in silico predictor data, and functional assay data). The authors should revise the statement so that it's clear that mechanistic understanding is a worthy aspiration rather than a prerequisite.

      We revised the statement in light of this comment from the reviewer

      In the structural analysis section (page 5, lines 154-155 and elsewhere), the authors define cutoffs with convenient round numbers. Is there a citation for these values or were these arbitrarily chosen by the authors? I would have liked to see some justification that these assignments are reasonable. Also there seems to be an error in the text where values between -2 and -3 kcal/mol are not assigned to a bin (I assume they should also be uncertain). There are other similar seemingly-arbitrary cutoffs later in the section that should also be explained.

      We have revised the text making the two intervals explicit, for better clarity.

      On page 9, lines 294-298 the authors talk about using the PTEN data from ProteinGym, rather than the actual cutoffs from the paper. They get to the latter later on, but I'm not sure why this isn't first? The ProteinGym cutoffs are somewhat arbitrarily based on the median rather than expert evaluation of the dataset, and I'm not sure why it's even worth mentioning them when proper classifications are available. Regarding PTEN, it would be quite interesting to see a comparison of the VAMP-seq PTEN data and the Mighell phosphatase assay, which is cited on page 9 line 288 but is not actually a VAMP-seq dataset. I think this section could be interesting but it requires some additional attention.

      We have included the data from Mighell’s phosphatase assay as provided by MAVEdb in the MAVISp database, within the experimental_data module for PTEN, and we have revised the case study, including them and explaining better the decision of supporting both the ProteinGym and MAVEdb classification in MAVISp (when available). See revised Figure3, Table 1 and corresponding text.

      The authors mention "pathogenicity predictors" and otherwise use pathogenicity incorrectly throughout the manuscript. Pathogenicity is a classification for a variant after it has been curated according to a framework like the ACMG/AMP guidelines (Richards 2015 and amendments). A single tool cannot predict or assign pathogenicity - the AlphaMissense paper was wrong to use this nomenclature and these authors should not compound this mistake. These predictors should be referred to as "variant effect predictors" or similar, and they are able to produce evidence towards pathogenicity or benignity but not make pathogenicity calls themselves. For example, in Figure 4e, the terms "pathogenic" and "benign" should only be used here if these are the classifications the authors have derived from ClinVar or a similar source of clinically classified variants.

      The reviewer is correct, we have revised the terminology we used in the manuscript and refers to VEPs (Variant Effect Predictors)

      Minor comments:

      The target selection table on the website needs some kind of text filtering option. It's very tedious to have to find a protein by scrolling through the table rather than typing in the symbol. This will only get worse as more datasets are added.

      We have revised the website, adding a filtering option. In detail, we have refactored the web app by adding filtering functionality, both for the main protein table (that can now be filtered by UniProt AC, gene name, or RefSeq ID) and the mutations table. Doing this required a general overhaul of the table infrastructure (we changed the underlying engine that renders the tables).

      The data sources listed on the data usage section of the website are not concordant with what is in the paper. For example, MaveDB is not listed.

      We have revised and updated the data sources on the website, adding a metadata section with relevant information, including MaveDB references where applicable.

      Figure 2 is somewhat confusing, as it partially interleaves results from two different proteins. This would be nicer as two separate figures, one on each protein, or just of a single protein.

      As suggested by the reviewer, we have now revised the figure and corresponding legends and text, focusing only on one of the two proteins.

      Figure 3 panel b is distractingly large and I wonder if the authors could do a little bit more with this visualization.

      We have revised Figure 3 to solve these issues and integrating new data from the comparison with the phosphatase assay

      Capitalization is inconsistent throughout the manuscript. For example, page 9 line 288 refers to VampSEQ instead of VAMP-seq (although this is correct elsewhere). MaveDB is referred to as MAVEdb or MAVEDB in various places. AlphaMissense is referred to as Alphamissense in the Figure 5 legend. The authors should make a careful pass through the manuscript to address this kind of issues.

      We have carefully proofread the paper for these inconsistencies

      MaveDB has a more recent paper (PMID: 39838450) that should be cited instead of/in addition to Esposito et al.

      We have added the reference that the reviewer recommended

      On page 11, lines 338-339 the authors mention some interesting proteins including BLC2, which has base editor data available (PMID: 35288574). Are there plans to incorporate this type of functional assay data into MAVISp?

      The assay mentioned in the paper refers to an experimental setup designed to investigate mutations that may confer resistance to the drug venetoclax. We started the first steps to implement a MAVISp module aimed at evaluating the impact of mutations on drug binding using alchemical free energy perturbations (ensemble mode) but we are far from having it complete. We expect to import these data when the module will be finalized since they can be used to benchmark it and BCL2 is one of the proteins that we are using to develop and test the new module.

      Reviewer #3 (Significance (Required)):

      Significance:

      General assessment:

      This is a nice resource and the authors have clearly put a lot of effort in. They should be celebrated for their achievments in curating the diverse datasets, and the GitBooks are a nice approach. However, I wasn't able to get the website to work and I have raised several issues with the paper itself that I think should be addressed.

      Advance:

      New ways to explore and integrate complex data like protein structures and variant effects are always interesting and welcome. I appreciate the effort towards manual curation of datasets. This work is very similar in theme to existing tools like Genomics 2 Proteins portal (PMID: 38260256) and ProtVar (PMID: 38769064). Unfortunately as I wasn't able to use the site I can't comment further on MAVISp's position in the landscape.

      We have expanded the conclusions section to add a comparison and cite previously published work, and linked to a review we published last year that frames MAVISp in the context of computational frameworks for the prediction of variant effects. In brief, the Genomics 2 Proteins portal (G2P) includes data from several sources, including some overlapping with MAVISp such as Phosphosite or MAVEdb, as well as features calculated on the protein structure. ProtVar also aggregates mutations from different sources and includes both variant effect predictors and predictions of changes in stability upon mutation, as well as predictions of complex structures. These approaches are only partially overlapping with MAVISp. G2P is primarily focused on structural and other annotations of the effect of a mutation; it doesn’t include features about changes of stability, binding, or long-range effects, and doesn’t attempt to classify the impact of a mutation according to its measurements. It also doesn’t include information on protein dynamics. Similarly, ProtVar does include information on binding free energies, long effects, or dynamical information.

      Audience:

      MAVISp could appeal to a diverse group of researchers who are interested in the biology or biochemistry of proteins that are included, or are interested in protein variants in general either from a computational/machine learning perspective or from a genetics/genomics perspective.

      My expertise:

      I am an expert in high-throughput functional genomics experiments and am an experienced computational biologist with software engineering experience.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The authors present MAVISp, a tool for viewing protein variants heavily based on protein structure information. The authors have done a very impressive amount of curation on various protein targets, and should be commended for their efforts. The tool includes a diverse array of experimental, clinical, and computational data sources that provides value to potential users interested in a given target.

      Major comments:

      Unfortunately I was not able to get the website to work properly. When selecting a protein target in simple mode, I was greeted with a completely blank page in the app window, and in ensemble mode, there was no transition away from the list of targets at all. I'm using Firefox 140.0.2 (64-bit) on Ubuntu 22.04. I would have liked to be able to explore the data myself and provide feedback on the user experience and utility.

      I have some serious concerns about the sustainability of the project and think that additional clarifications in the text could help. Currently is there a way to easily update a dataset to add, remove, or update a component (for example, if a new predictor is published, an error is found in a predictor dataset, or a predictor is updated)? If it requires a new round of manual curation for each protein to do this, I am worried that this will not scale and will leave the project with many out of date entries. The diversity of software tools (e.g., three different pipeline frameworks) also seems quite challenging to maintain.

      On the same theme, according to the GitHub repository, the program relies on Python 3.9, which reaches end of life in October 2025. It has been tested against Ubuntu 18.04, which left standard support in May 2023. The authors should update the software to more modern versions of Python to promote the long-term health and maintainability of the project.

      I appreciate that the authors have made their code and data available. These artifacts should also be versioned and archived in a service like Zenodo, so that researchers who rely on or want to refer to specific versions can do so in their own future publications.

      In the introduction of the paper, the authors conflate the clinical challenges of variant classification with evidence generation and it's quite muddled together. The y should strongly consider splitting the first paragraph into two paragraphs - one about challenges in variant classification/clinical genetics/precision oncology and another about variant effect prediction and experimental methods. The authors should also note that they are many predictors other than AlphaMissense, and may want to cite the ClinGen recommendations (PMID: 36413997) in the intro instead.

      Also in the introduction on lines 21-22 the authors assert that "a mechanistic understanding of variant effects is essential knowledge" for a variety of clinical outcomes. While this is nice, it is clearly not the case as we are able to classify variants according to the ACMG/AMP guidelines without any notion of specific mechanism (for example, by combining population frequency data, in silico predictor data, and functional assay data). The authors should revise the statement so that it's clear that mechanistic understanding is a worthy aspiration rather than a prerequisite.

      In the structural analysis section (page 5, lines 154-155 and elsewhere), the authors define cutoffs with convenient round numbers. Is there a citation for these values or were these arbitrarily chosen by the authors? I would have liked to see some justification that these assignments are reasonable. Also there seems to be an error in the text where values between -2 and -3 kcal/mol are not assigned to a bin (I assume they should also be uncertain). There are other similar seemingly-arbitrary cutoffs later in the section that should also be explained.

      On page 9, lines 294-298 the authors talk about using the PTEN data from ProteinGym, rather than the actual cutoffs from the paper. They get to the latter later on, but I'm not sure why this isn't first? The ProteinGym cutoffs are somewhat arbitrarily based on the median rather than expert evaluation of the dataset and I'm not sure why it's even worth mentioning them when proper classifications are available. Regarding PTEN, it would be quite interesting to see a comparison of the VAMP-seq PTEN data and the Mighell phosphatase assay, which is cited on page 9 line 288 but is not actually a VAMP-seq dataset. I think this section could be interesting but it requires some additional attention.

      The authors mention "pathogenicity predictors" and otherwise use pathogenicity incorrectly throughout the manuscript. Pathogenicity is a classification for a variant after it has been curated according to a framework like the ACMG/AMP guidelines (Richards 2015 and amendments). A single tool cannot predict or assign pathogenicity - the AlphaMissense paper was wrong to use this nomenclature and these authors should not compound this mistake. These predictors should be referred to as "variant effect predictors" or similar, and they are able to produce evidence towards pathogenicity or benignity but not make pathogenicity calls themselves. For example, in Figure 4e, the terms "pathogenic" and "benign" should only be used here if these are the classifications the authors have derived from ClinVar or a similar source of clinically classified variants.

      Minor comments:

      The target selection table on the website needs some kind of text filtering option. It's very tedious to have to find a protein by scrolling through the table rather than typing in the symbol. This will only get worse as more datasets are added.

      The data sources listed on the data usage section of the website are not concordant with what is in the paper. For example, MaveDB is not listed.

      I found Figure 2 to be a bit confusing in that it partially interleaves results from two different proteins. I think this would be nicer as two separate figures, one on each protein, or just of a single protein.

      Figure 3 panel b is distractingly large and I wonder if the authors could do a little bit more with this visualization.

      Capitalization is inconsistent throughout the manuscript. For example, page 9 line 288 refers to VampSEQ instead of VAMP-seq (although this is correct elsewhere). MaveDB is referred to as MAVEdb or MAVEDB in various places. AlphaMissense is referred to as Alphamissense in the Figure 5 legend. The authors should make a careful pass through the manuscript to address this kind of issues.

      MaveDB has a more recent paper (PMID: 39838450) that should be cited instead of/in addition to Esposito et al.

      On page 11, lines 338-339 the authors mention some interesting proteins including BLC2, which has base editor data available (PMID: 35288574). Are there plans to incorporate this type of functional assay data into MAVISp?

      Significance

      General assessment:

      This is a nice resource and the authors have clearly put a lot of effort in. They should be celebrated for their achievments in curating the diverse datasets, and the GitBooks are a nice approach. However, I wasn't able to get the website to work and I have raised several issues with the paper itself that I think should be addressed.

      Advance:

      New ways to explore and integrate complex data like protein structures and variant effects are always interesting and welcome. I appreciate the effort towards manual curation of datasets. This work is very similar in theme to existing tools like Genomics 2 Proteins portal (PMID: 38260256) and ProtVar (PMID: 38769064). Unfortunately as I wasn't able to use the site I can't comment further on MAVISp's position in the landscape.

      Audience:

      MAVISp could appeal to a diverse group of researchers who are interested in the biology or biochemistry of proteins that are included, or are interested in protein variants in general either from a computational/machine learning perspective or from a genetics/genomics perspective.

      My expertise:

      I am an expert in high-throughput functional genomics experiments and am an experienced computational biologist with software engineering experience.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors present a pipeline and platform, MAVISp, for aggregating, displaying and analysis of variant effects with a focus on reclassification of variants of uncertain clinical significance and uncovering the molecular mechanisms underlying the mutations.

      Major comments:

      • On testing the platform, I was unable to look-up a specific variant in ADCK1 (rs200211943, R115Q). I found that despite stating that the mapped refseq ID was NP_001136017 in the HGVSp column, it was actually mapped to the canonical UniProt sequence (Q86TW2-1). NP_001136017 actually maps to Q86TW2-3, which is missing residues 74-148 compared to the -1 isoform. The Uniprot canonical sequence has no exact RefSeq mapping, so the HGVSp column is incorrect in this instance. This mapping issue may also affect other proteins and result in incorrect HGVSp identifiers for variants.
      • The paper lacks a section on how to properly interpret the results of the MAVISp platform (the case-studies are useful, but don't lay down any global rules for interpreting the results). For example: How should a variant with conflicts between the variant impact predictors be interpreted? Are certain indicators considered more 'reliable' than others?
      • In the Methods section, GEMME is stated as being rank-normalised with 0.5 as a threshold for damaging variants. On checking the data downloaded from the site, GEMME was not rank-normalised but rather min-max normalised. Furthermore, Supplementary text S4 conflicts with the methods section over how GEMME scores are classified, S4 states that a raw-value threshold of -3 is used.
      • Note. This is a major comment as one of the claims is that the associated web-tool is user-friendly. While functional, the web app is very awkward to use for analysis on any more than a few variants at once.
        • The fixed window size of the protein table necessitates excessive scrolling to reach your protein-of-interest. This will also get worse as more proteins are added. Suggestion: add a search/filter bar.
        • The same applies to the dataset window.
        • You are unable to copy anything out of the tables.
        • Hyperlinks in the tables only seem to work if you open them in a new tab or window.
        • All entries in the reference column point to the MAVISp preprint even when data from other sources is displayed (e.g. MAVE studies).
        • Entering multiple mutants in the "mutations to be displayed" window is time-consuming for more than a handful of mutants. Suggestion: Add a box where multiple mutants can be pasted in at once from an external document.

      Minor comments

      • Grammar. I appreciate that this manuscript may have been compiled by a non-native English speaker, but I would be remiss not to point out that there are numerous grammar errors throughout, usually sentence order issues or non-pluralisation. The meaning of the authors is mostly clear, but I recommend very thoroughly proof-reading the final version.
      • There are numerous proteins that I know have high-quality MAVE datasets that are absent in the database e.g. BRCA1, HRAS and PPARG.
      • Checking one of the existing MAVE datasets (KRAS), I found that the variants were annotated as damaging, neutral or given a positive score (these appear to stand-in for gain-of-function variants). For better correspondence with the other columns, those with positive scores could be labelled as 'ambiguous' or 'uncertain'.
      • Numerous thresholds are defined for stabilizing / destabilizing / neutral variants in both the STABILITY and the LOCAL_INTERACTION modules. How were these thresholds determined? I note that (PMC9795540) uses a ΔΔG threshold of 1/-1 for defining stabilizing and destabilizing variants, which is relatively standard (though they also say that 2-3 would likely be better for pinpointing pathogenic variants).
      • "Overall, with the examples in this section, we illustrate different applications of the MAVISp results, spanning from benchmarking purposes, using the experimental data to link predicted functional effects with structural mechanisms or using experimental data to validate the predictions from the MAVISp modules."

      The last of these points is not an application of MAVISp, but rather a way in which external data can help validate MAVISp results. Furthermore, none of the examples given demonstrate an application in benchmarking (what is being benchmarked?). - Transcription factors section. This section describes an intended future expansion to MAVISp, not a current feature, and presents no results. As such, it should probably be moved to the conclusions/future directions section. - Figures. The dot-plots generated by the web app, and in Figures 4, 5 and 6 have 2 legends. After looking at a few, it is clear that the lower legend refers to the colour of the variant on the X-axis - most likely referencing the ClinVar effect category. This is not, however, made clear either on the figures or in the app. - "We identified ten variants reported in ClinVar as VUS (E102K, H86D, T29I, V91I, P2R, L44P, L44F, D56G, R11L, and E25Q, Fig.5a)"

      E25Q is benign in ClinVar and has had that status since first submitted.

      Significance

      Platforms that aggregate predictors of variant effect are not a new concept, for example dbNSFP is a database of SNV predictions from variant effect predictors and conservation predictors over the whole human proteome. Predictors such as CADD and PolyPhen-2 will often provide a summary of other predictions (their features) when using their platforms. MAVISp's unique angle on the problem is in the inclusion of diverse predictors from each of its different moules, giving a much wider perspective on variants and potentially allowing the user to identify the mechanistic cause of pathogenicity. The visualisation aspect of the web app is also a useful addition, although the user interface is somewhat awkward. Potentially the most valuable aspect of this study is the associated gitbook resource containing reports from biocurators for proteins that link relevant literature and analyse ClinVar variants. Unfortunately, these are only currently available for a small minority of the total proteins in the database with such reports.

      For improvement, I think that the paper should focus more on the precise utility of the web app / gitbook reports and how to interpret the results rather than going into detail about the underlying pipeline.

      In terms of audience, the fast look-up and visualisation aspects of the web-platform are likely to be of interest to clinicians in the interpretation of variants of unknown clinical significance. The ability to download the fully processed dataset on a per-protein database would be of more interest to researchers focusing on specific proteins or those taking a broader view over multiple proteins (although a facility to download the whole database would be more useful for this final group).

      My expertise.

      • I am a protein bioinformatician with a background in variant effect prediction and large-scale data analysis.
    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary: This manuscript, "MAVISp: A Modular Structure-Based Framework for Protein Variant Effects," presents a significant new resource for the scientific community, particularly in the interpretation and characterization of genomic variants. The authors have developed a comprehensive and modular computational framework that integrates various structural and biophysical analyses, alongside existing pathogenicity predictors, to provide crucial mechanistic insights into how variants affect protein structure and function. Importantly, MAVISp is open-source and designed to be extensible, facilitating reuse and adaptation by the broader community.

      Major comments:

      • While the manuscript is formally well-structured (with clear Introduction, Results, Conclusions, and Methods sections), I found it challenging to follow in some parts. In particular, the Introduction is relatively short and lacks a deeper discussion of the state-of-the-art in protein variant effect prediction. Several methods are cited but not sufficiently described, as if prior knowledge were assumed. OPTIONAL: Extend the Introduction to better contextualize existing approaches (e.g., AlphaMissense, EVE, ESM-based predictors) and clarify what MAVISp adds compared to each.
      • The workflow is summarized in Figure 1(b), which is visually informative. However, the narrative description of the pipeline is somewhat fragmented. It would be helpful to describe in more detail the available modules in MAVISp, and which of them are used in the examples provided. Since different use cases highlight different aspects of the pipeline, it would be useful to emphasize what is done step-by-step in each. OPTIONAL: Consider adding a table or a supplementary figure mapping each use case to the corresponding pipeline steps and modules used.
      • The text contains numerous acronyms, some of which are not defined upon first use or are only mentioned in passing. This affects readability. OPTIONAL: Define acronyms upon first appearance, and consider moving less critical technical details (e.g., database names or data formats) to the Methods or Supplementary Information. This would greatly enhance readability.
      • The code and trained models are publicly available, which is excellent. The modular design and use of widely adopted frameworks (PyTorch and PyTorch Geometric) are also strong points. However, the Methods section could benefit from additional detail regarding feature extraction and preprocessing steps, especially the structural features derived from AlphaFold2 models. OPTIONAL: Include a schematic or a table summarizing all feature types, their dimensionality, and how they are computed.
      • The section on transcription factors is relatively underdeveloped compared to other use cases and lacks sufficient depth or demonstration of its practical utility. OPTIONAL: Consider either expanding this section with additional validation or removing/postponing it to a future manuscript, as it currently seems preliminary.

      Minor comments:

      • Most relevant recent works are cited, including EVE, ESM-1v, and AlphaFold-based predictors. However, recent methods like AlphaMissense (Cheng et al., 2023) could be discussed more thoroughly in the comparison.
      • Figures are generally clear, though some (e.g., performance barplots) are quite dense. Consider enlarging font sizes and annotating key results directly on the plots.
      • Minor typographic errors are present. A careful proofreading is highly recommended. Below are some of the issues I identified:

      Page 3, line 46: "MAVISp perform" -> "MAVISp performs"

      Page 3, line 56: "automatically as embedded" -> "automatically embedded"

      Page 3, line 57: "along with to enhance" -> unclear; please revise

      Page 4, line 96: "web app interfaces with the database and present" -> "presents"

      Page 6, line 210: "to investigate wheatear" -> "whether"

      Page 6, lines 215-216: "We have in queue for processing with MAVISp proteins from datasets relevant to the benchmark of the PTM module." -> unclear sentence; please clarify

      Page 15, line 446: "Both the approaches" -> "Both approaches"

      Page 20, line 704: "advantage of multi-core system" -> "multi-core systems"

      Significance

      General assessment: the strongest aspects of the study are the modularity, open-source implementation, and the integration of structural information through graph neural networks. MAVISp appears to be one of the few publicly available frameworks that can easily incorporate AlphaFold2-based features in a flexible way, lowering the barrier for developing custom predictors. Its reproducibility and transparency make it a valuable resource. However, while the technical foundation is solid and the effort substantial, the scientific narrative and presentation could be significantly improved. The manuscript is dense and hard to follow in places, with a heavy use of acronyms and insufficient explanation of key design choices. Improving the descriptive clarity, especially in the early sections, would greatly enhance the impact of this work.

      Advance: to the best of my knowledge, this is one of the first modular platforms for protein variant effect prediction that integrates structural data from AlphaFold2 with bioinformatic annotations and even clinical data in an extensible fashion. While similar efforts exist (e.g., ESMfold, AlphaMissense), MAVISp distinguishes itself through openness and design for reusability. The novelty is primarily technical and practical rather than conceptual.

      Audience: this study will be of strong interest to researchers in computational biology, structural bioinformatics, and genomics, particularly those developing variant effect predictors or analyzing the impact of mutations in clinical or functional genomics contexts. The audience is primarily specialized, but the open-source nature of the tool may diffuse its use among more applied or translational users, including those working in precision medicine or protein engineering.

      Reviewer expertise: my expertise is in computational structural biology, molecular modeling, and (rather weak) machine learning applications in bioinformatics. I am familiar with graph-based representations of proteins, AlphaFold2, and variant effects based on Molecular Dynamics simulations. I do not have any direct expertise in clinical variant annotation pipelines.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      Reply to the Reviewers

      I thank the Referees for their...

      Referee #1

      1. The authors should provide more information when...

      Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

      Response: We expanded the comparison

      Minor comments:

      1. The text contains several...

      Response: We added...

      Referee #2

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Meroni and colleagues present evidence that CIP2A is required to recruit the SMX complex to sites of replication stress in mitotic cells. Whilst the data generated when using U2OS cells seems to support a role for CIP2A in recruiting the SMX complex to sites of replication stress to facilitate MiDAS, as the authors point out, this pathway is not conserved in DLD1 cells. Although the authors suggest that this discrepancy in the data may relate to the fact that U2OS cells are ALT positive and the DLD1 cells are not, there is no experimental evidence to support this hypothesis. It would have been nice if the authors had backed up this hypothesis with data relating to how CIP2A regulates the SMX-MiDAS pathway in other ALT positive and negative cell lines. Taken together, after reading this manuscript, I am left wondering whether CIP2A is really important for SMX-dependent MiDAS or whether it is phenomenon that is found in some commonly used cancer cell lines and not others. Whilst it is important to publish conflicting results as they can explain why some research labs can reproduce published data and others can't, I think this manuscript would benefit from assessment of the role of CIP2A in mediating the recruitment of the SMX complex to carry out MiDAS in a variety of additional cancer cell lines and also non-cancer cell lines, such as RPE1-hTERT cells to obtain some sort of consensus about the importance of CIP2A in dealing with mitotic replication stress.

      Comments:

      1. Fig.2A-E: Can the authors comment on the difference in number of APH-induced FANCD2, SLX4, Mus81 and XPF foci in mitotic U2OS cells? Given that SLX4 should be recruiting both XPF and Mus81, there is a disparity between the numbers of mitotic foci given that there are approximately 30 FANCD3 foci per mitotic cell following APH treatment. Additionally, why do the XPF foci not increase after APH exposure?
      2. Fig.2G: I would say that the 'full rescue' of Mus81 foci in the CIP2A KO cells complemented with WT CIP2A is not hugely convincing since there is only a difference of 1-2 foci between the WT and CIP2A KO cells treated with APH.
      3. Fig.3A: I am not really sure how biologically meaningful a difference of 0.03-0.04 EdU foci per chromosome is when comparing BRCA2 KO DLD1 cells treated with control siRNA versus CIP2A siRNA. Would it not have been better to treat the BRCA2 KO DLD1 cells with APH?
      4. Fig.3H-I: Given that the reduction in MiDAS in the CIP2A KO cl.7 cell line is likely a clonal artifact not related to the loss of CIP2A, it is unclear how to interpret the data about the EdU foci pattern on chromosomes presented in Fig.3H-I and its relevance to CIP2A. Therefore, I am not sure this data really adds anything to the manuscript.
      5. Fig.4H: The difference in Mus81 foci per mitotic cell with/without the expression of B6L is only one focus per mitotic cell. Based on this, it is difficult to make any real conclusions about whether the TOPBP1-CIP2A interaction is really required for the recruitment of Mus81 to sites of mitotic replication stress.

      Significance

      As mentioned above, it is clear that the role of CIP2A in regulating the mitotic replication stress response by promoting recruitment of the SMX complex to sites of mitotic replication stress to promote MiDAS is complicated and may be specific to some cancer cell lines and not others. Whilst it is not clear what the underlying reason for this is, this manuscript would definitely benefit from additional analysis of this pathway in other cancer and non-cancer cell lines to obtain a consensus about the role of CIP2A.

      This manuscript would appeal to fundamental research scientists interested in understanding the mechanisms underlying DNA damage repair, the replication stress response and mitotic regulation.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In the manuscript entitled "CIP2A Mediates the Recruitment of the SLX4-MUS81-XPF Tri-Nuclease Complex in Mitosis and Protects Against Replication Stress" by Meroni et al the authors have characterized localization of the CIP2A-TopBP1 complex as well as some aspects of its function in U2OS and DLD1 cell lines exposed to different types of stress. They find that replication stress due to BRCA2 KO, APH or ATRi results in increased focus formation of the CIP2A-TopBP1 complex in mitotic cells. Moreover, the authors find significant decrease in EdU incorporartion in mitotic cells when disrupting CIP2A in (i) U2OS exposed to ATRi or Aph; (ii) in DLD1 BRCA2 KO; (iii) in one clone of DLD1 with Cip2A KO, and a non significant decrease the other DLD1 with Cip2A KO that they tested. Thus, under most of the tested conditions CIP2A is facilitating MiDAS. However, the authors find that expression of a previously characterised fragment of TopBP1 called B6L, which disrupts CIP2A-TopBP1 interaction, does not inhibit MiDAS in DLD1 cells.

      Major comments:

      It is convincing but not surprising that CIP2A-TopBP1 form more foci in mitotic cells after replication stress. The authors statement in the abstract: "We demonstrate that in the absence of CIP2A, cells fail to recruit the SLX4-MUS81-XPF (SMX) tri-nuclease complex to sites of under-replicated DNA in mitosis, resulting in a high incidence of lagging chromosomes during anaphase and subsequent micronuclei formation" is not supported by experiments. The authors indeed show that absence of CIP2A leads to lagging chromosomes during anaphase and subsequent micronuclei formation (which has previously been shown) but they have not shown that it is the failure to recruit the SMX complex that results in the phenotypes they mention. The authors should rephrase or remove this claim.

      There is a discrepancy between the B6L-mediated disruption of TopBP1-CIP2A interaction having no effect on MiDAS in DLD1 cells (fig. 4F) whereas knockout of CIP2A in DLD1 cells seem to have an effect (fig 3E). The most obvious explanation for this observation is that the B6L peptide does not fully abolish TopBP1-CIP2A interaction and can still allow for some SLX4-MUS81 recruitment that is not visible as foci but still sufficient to induce MiDAS. To understand whether MiDAS in DLD1 expressing B6L is dependent on the fraction of TopBP1 that can still form foci (according to Fig 4D) the authors must co-stain for TopBP1 together with EdU detection to address whether they observe any colocalization of TopBP1 with MiDAS.

      Many of the experiments are only performed with two independent replicates. The authors must perform 3 independent replicates. Also, it is not clear how many cells were analysed for each replicate. This should be clearly stated and the mean of each replicate should always be shown. Statistical analyses should be carried out using the means of the replicates. The authors must provide data showing the efficiency of CIP2A knockdown and CIP2A expression in the complementation assay (Fig. 2G)

      Minor comments:

      The authors should change "U-2 OS" in the figures to "U2OS" for consistency.

      In figure 4D - is the increase with APH and S1 significant compared to S1 alone?

      Figure 3 B and C. It is worrying that there is a huge difference in the EdU foci/mitotic cell in untreated condition from panel B to pabel C.

      Fig 3F - is the increase in EdU incorporation after complementation significant?

      For figure 3I representative images should be added

      Significance

      The data presented in the manuscript is of high quality but unfortunately does not present a big advance compared to current knowledge. Nevertheless, it is useful to have side-by-side comparison of different cell lines and conditions and IF localization studies. Given the therapeutic interest in the CIP2A-TopBP1 pathway it is important to get all the details right and researches with interest in DNA repair during mitosis will have interest in this work.

      Moreover, in this manuscript the authors demonstrate that the impact of CIP2A disruption on MiDAS is variable across different cell lines-and even between individual clones. The concept of MiDAS is still clouded by considerable ambiguity, possibly due to earlier studies overstating the consequences of knockdown or knockout. It is therefore great that this manuscript presents clear, unbiased observations, highlighting both inter-cell line differences and the partial nature of the effects. This kind of nuanced reporting is valuable for the field.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary and Significance

      This is a timely and exciting study that provides us with some new molecular insights into mitotic DNA repair. It builds on previous studies that identified the CIP2A-TOPBP1 complex as a molecular tether that connects broken DNA ends that get transmitted from interphase into mitosis (PMID: 30898438, 35842428, 35842428). The results are also largely complementary with those of Martin et al. (BioRxiv preprint at https://doi.org/10.1101/2024.11.12.621593) and de Haan et al. (BioRxiv preprint at https://www.biorxiv.org/content/10.1101/2025.04.03.647079v1).

      The authors report three main findings, as summarized below.

      1) The CIP2A oncoprotein is involved in the cellular response to replication stress in mitosis.

      2) CIP2A is required for the recruitment of SLX4, MUS81, and XPF into foci during mitosis. SLX4 is a well-established protein scaffold for multiple DNA repair factors, including three structure-selective endonucleases called SLX1, MUS81-EME1, and XPF-ERCC1 that together, form the SMX tri-nuclease that removes DNA repair intermediates and chromosome entanglements during mitosis. In some cell lines, the SMX complex is required for mitotic DNA synthesis at sites of under-replicated DNA, thus ensuring complete DNA replication prior to cell division.

      3) The role(s) of CIP2A in MiDAS are cell line-dependent/context-dependent.

      In general, this is a solid body of microscopy-based work that includes appropriate cell models and experimental controls. The manuscript is well-written, and the data is presented coherently. The main findings will have important implications for researchers interested in mitotic DNA damage, genome stability, and cancer biology. After addressing the points below, I believe this manuscript will be suitable for publication.

      Major comments

      1) Figure 1C: The CIP2A-TOPBP1 PLA experiments are lacking critical controls, namely cells lacking or depleted of CIP2A and TOPBP1. These controls are necessary to provide confidence for the results presented in Figure 1C. If these controls are too expensive or time-demanding for the manuscript, then I recommend removing the PLA data from Figure 1C.

      2) In Figure 2, the authors conclude that the loss of SLX4, XPF, and MUS81 foci in CIP2A depleted cells is synonymous with the loss of recruitment to DNA lesions. However, I can think of many other reasons that could explain the loss of foci. For example, do the authors know that the proteins are expressed to similar levels in cells with and without CIP2A (this should be tested by a simple western blot). Along the same vein, a biochemical fractionation and western blot of the soluble vs chromatin-bound fraction would complement and substantiate their microscopy-based assays in Figure 2. If the fractionation is not possible, then the text should be adjusted accordingly.

      3) The experimental set-up in Figure 2 probes whether CIP2A mediates the recruitment of SMX subunits - SLX4, XPF, MUS81 - but not the SMX complex per se, which would require the study of SLX4 point mutants that selectively ablate the interactions with XPF or MUS81 (but not CIP2A). As such, I suggest that they rephrase their wording appropriately.

      4) Western blots must be provided to substantiate the experiments performed with siRNA (Figure 1G-J, Figure 2A-E and 2H, Figure 3A-D, Figure 5B-D). Similarly, the authors should provide western blots to confirm the BRCA2 and CIP2A statuses in their KO cell lines, as well as the complementation cell lines. In the absence of this information, it is difficult for someone to make an independent and meaningful interpretation of their data.

      5) Most of the data presented in this manuscript is derived from n = 2 biological replicates. All of the experiments reported in the study should be repeated for n = 3 biological replicates.

      6) Since the authors report the median of their data, they should also report the interquartile range or confidence interval to display the uncertainty.

      Minor comments

      1) The references can be improved by acknowledging some of the foundational papers on SLX4 and the SMX tri-nuclease.

      1.a) Page 3: Neither Minocherhomji et al. 2015 nor Pedersen et al. 2015 were the first to describe SLX4 as a scaffold for structure-selective endonucleases. The founding papers were published in 2009 (Svendsen et al. 2009, Munoz et al. 2009, Fekairi et al. 2009, Andersen et al. 2009) with important mechanistic studies on nuclease activation reported in 2013 (Wyatt et al. 2013, Castor et al. 2013) and 2017 (Wyatt et al. 2017).

      1.b) Page 6: The authors should cite Wyatt et al. 2013, alongside Castor et al. 2013 and Garner et al. 2013 since these 3 articles were published at similar times. They may also want to acknowledge previous work from the Hickson and Rosselli labs showing that XPF-ERCC1 and MUS81-EME1 are recruited to fragile sites in mitosis.

      2) To improve broad readability, the authors should remove the following abbreviations: Aph and WT.

      3) In several figures, the authors show that a given treatment causes a very small change in the number of foci observed per mitotic cell. Although the values may be statistically different, it is important that they discuss the biological significance of these small effects - for example, I am not convinced that a difference of 2-3 foci per cell is sufficient to induce a robust cellular response.

      4) The methods could be expanded to ensure reproducibility, particularly with respect to the drug treatments (e.g., timing, washes, etc.).

      Significance

      This is a timely and exciting study that provides us with some new molecular insights into mitotic DNA repair. It builds on previous studies that identified the CIP2A-TOPBP1 complex as a molecular tether that connects broken DNA ends that get transmitted from interphase into mitosis (PMID: 30898438, 35842428, 35842428). The results are also largely complementary with those of Martin et al. (BioRxiv preprint at https://doi.org/10.1101/2024.11.12.621593) and de Haan et al. (BioRxiv preprint at https://www.biorxiv.org/content/10.1101/2025.04.03.647079v1).

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

      Learn more at Review Commons


      Reply to the reviewers

      We thank the reviewers for their detailed comments, which have already helped us improve our manuscript. The responses below detail changes we have already made as part of the Review Commons revision plan, and further changes we expect to make in a longer revision period.


      __Reviewer #1 __

      Major points __ It is mentioned throughout the manuscript that 3 plates were evaluated per line. I believe these are independently differentiated plates. This detail is critical concerning rigor and reproducibility. This should be clearly stated in the Methods section and in the first description of the experimental system in the Results section for Figure 1.__

      These experimental details have now been clarified. Unless otherwise stated, all findings were confirmed in three independently differentiated plates from the same line or at least one differentiation from each of three lines.

      For the patient-specific lines - how many lines were derived per patient?

      This has now been clarified in the methods. Microfluidic reprogramming of a small number of amniocytes produces one line per patient representing a pool of clones. Subcloning from individual cells would not be possible within the timeframe of a pregnancy.

      Methods: For patient-specific iPSC lines, one independent iPSC line was obtained per patient following microfluidic mmRNA reprogramming.

      Was the Vangl2 variant introduced by prime editing? Base editing? The details of the methods are sparse.

      We have now expanded these details:

      Methods: VANGL2 knock-in lines were generated using CRSIPR-Cas9 homology directed repair editing by Synthego (SO-9291367-1). The guide sequence was AUGAGCGAAGGGUGCGCAAG and the donor sequence was CAATGAGTACTACTATGAGGAGGCTGAGCATGAGCGA AGGGTGTGCAAGAGGAGGGCCAGGTGGGTCCCTGGGGGAGAAGAGGAGAG. Sequence modification was confirmed by Sanger sequencing before delivery of the modified clones, and Sanger sequencing was repeated after expansion of the lines (Supplementary Figure 5) as well as SNP arrays (Illumina iScan, not shown) confirming genomic stability.

      Some additional suggestions for improvement. __ The abstract could be more clearly written to effectively convey the study's importance. Here are some suggestions.__

      Line 26: Insert "apicobasal" before "elongation" - the way it is written, I initially interpreted it as anterior-posterior elongation.

      Line 29: Please specify that the lines refer to 3 different established parent iPSC lines with distinct origins and established using different reprogramming methods, plus 2 control patient-derived lines. - The reproducibility of the cell behaviors is impressive, but this is not captured in the abstract.

      Line 32: add that this mutation was introduced by CRISPR-Cas9 base/prime editing.

      The last sentence of the abstract states that the study only links apical constriction to human NTDs, but also reveals that neural differentiation and apical-basal elongation were found. __ The introduction could also use some editing. __ Line 71: insert "that pulls actin filaments together" after "power strokes" __ Line 73: "apically localized," do you mean "mediolaterally" or "radially"? __ Line 75: Can you specify that PCP components promote "mediolaterally orientated" apical constriction __ Lines 127: Specify that NE functions include apical basal elongation and neurodifferentiation are disrupted in patient-derived models__

      These text changes have all been made.

      Reviewer #2:____ __ __Major comments: __ 1. Figure 1. The authors use F-actin to segment cell areas. Perhaps this could be done more accurately with ZO-1, as F-actin cables can cross the surface of a single cell. In any case, the authors need to show a measure of segmentation precision: segmented image vs. raw image plus a nuclear marker (DAPI, H2B-GFP), so we can check that the number of segmented cells matches the number of nuclei.__

      We used ZO-1 to quantify apical areas of the VANGL2-konckin lines in Figure 3. Segmentation of neuroepithelial apical areas based on F-actin staining is commonplace in the field (e.g. Fig 9 of Bogart & Brooks 2025 as a recent example), and is generally robust because the cell junctions are much brighter than any apical fibres not associated with the apical cortex. However, we accept that at earlier stages of differentiation there may be more apical fibres when cells are cuboidal. We have therefore repeated our analysis of apical area using ZO-1 staining as suggested, shown in the new Supplementary Figure 1, analysing a more temporally-detailed time course in one iPSC line. This new analysis confirms our finding of lack of apical area change between days 2-4 of differentiation, then progressive reduction of apical area between days 4-8, further validating our system. Including nuclear images is not helpful because of the high nuclear index of pseudostratified epithelia (e.g. see Supplementary Figure 7) which means that nuclei overlap along the apicobasal axis. Individual nuclei cannot be related to their apical surface in projected images.

      __2.Lines 156-166. The authors claim that changes in gene expression precede morphological changes. I am not convinced this is supported by their data. Fig. 1g (epithelial thickness) and Fig. 1k (PAX6 expression) seem to have similar dynamics. The authors can perform a cross-correlation between the two plots to see which Δt gives maximum correlation. If Δt __We are happy to do this analysis fully in revision. __Our initial analysis performing cross-correlation between apical area and CDH2 protein in one line shows the highest cross-correlation at Δt = -1, suggesting neuroepithelial CDH2 increases before apical area decreases. In contrast, the same analysis comparing apical area versus PAX6 shows Δt = 0, suggesting concurrence. This analysis will be expanded to include the other markers we quantified and the manuscript text amended accordingly. We are keen to undertake additional experiments to test whether these cells swap their key cadherins - CDH1 and CDH2 - before they begin to undergo morphological changes (see the response to Reviewer 3's minor comment 1 immediately below).

      3. Figure 2d. The laser ablation experiment in the presence of ROCK inhibitor is clear, as I can easily see the cell outlines before and after the experiment. In the absence of ROCK inhibitor, the cell edges are blurry, and I am not convinced the outline that the authors drew is really the cell boundary. Perhaps the authors can try to ablate a larger cell patch so that the change in area is more defined.

      The outlines on these images are not intended to show cell boundaries, but rather link landmarks visible at both timepoints to calculate cluster (not cell) change in area. This is as previously shown in Galea et al Nat Commun 2021 and Butler et al J Cell Sci 2019. We have now amended the visualisation of retraction in Figure 2 to make representation of differences between conditions more intuitive.

      4. Figure 2d. Do the cells become thicker after recoil?

      This is unlikely because the ablated surface remains in the focal plane. Unfortunately, we are unable to image perpendicularly to the direction of ablation to test whether their apical surface moves in Z even by a very small amount. This has now been clarified in the results:

      Results: The ablated surface remained within the focal plane after ablation, indicating minimal movement along the apical-basal axis.

      5. Figure 3. The authors mention their previous study in which they show that Vangl2 is not cell-autonomously required for neural closure. It will be interesting to study whether this also the case in the present human model by using mosaic cultures.

      We agree with the reviewer that this is one of the exciting potential future applications of our model, which will first require us to generate stable fluorescently-tagged lines (to identify those cells which lack VANGL2). We will also need to extensively analyze controls to validate that mixing fluo-tagged and untagged lines does not alter the homogeneity of differentiation, or apical constriction, independently of VANGL2 deletion. As such, the reviewer is suggesting an altogether new project which carries considerable risk and will require us to secure dedicated funding to undertake.

      6. Lines 403-415. The authors report poor neural induction and neuronal differentiation in GOSB2. As far as I understand, this phenotype does not represent the in vivo situation. Thus, it is not clear to what extent the in vitro 2D model describes the human patient.

      The GOSB2 iPSC line we describe does represent the in vivo situation in Med24 knockout mouse embryos, but is clearly less severe because we are still able to detect MED24 protein expressed in this line. We do not have detailed clinical data of the patient from which this line was obtained to determine whether their neurological development is normal. However, it is well established that some individuals who have spina bifida also have abnormalities in supratentorial brain development. It is therefore likely that abnormalities in neuron differentiation/maturation are concomitant with spina bifida. Our findings in the GOSB2 line complement earlier studies which also identified deficiencies in the ability of patient-derived lines to form neurons, but were unable to functionally assess neuroepithelial cell behaviours we studied. This has now been clarified in the discussion:

      Discussion: *Neuroepithelial cells of the GOSB2 line described here, which has partial loss of MED24, similarly produces a thinner neuroepithelium with larger apical areas. Although apical areas were not analysed in mouse models of Med24 deletion, these embryos also have shorter and non-pseudostratified neuroepithelium. *

      Our GOSB2 line - which retains readily detectable MED24 protein - is clearly less severe than the mouse global knockout, and the clinical features of the patient from which this line was derived are milder than the phenotype of Med24 knockout embryos68. Mouse embryos lacking one of Med24's interaction partners in the mediator complex, Med1, also have thinner neuroepithelium and diminished neuronal differentiation but successfully close their neural tube85.

      7.The experimental feat to derive cell lines from amniotic fluid and to perform experiments before birth is, in my view, heroic. However, I do not feel I learned much from the in vitro assays. There are many genetic changes that may cause the in vivo phenotype in the patient. The authors focus on MED24, but there is not enough convincing evidence that this is the key gene. I would like to suggest overexpression of MED24 as a rescue experiment, but I am not sure this is a single-gene phenotype. In addition, the fact that one patient line does not differentiate properly leads me to think that the patient lines do not strengthen the manuscript, and that perhaps additional clean mutations might contribute more.

      We thank the reviewer for their praise of our personalised medicine approach and fully agree that neural tube defects are rarely monogenic. The patient lines we studied were not intended to provide mechanistic insight, but rather to demonstrate the future applicability of our approach to patient care. Our vision is that every patient referred for fetal surgery of spina bifida will have amniocytes (collected as part of routine cystocentesis required before surgery) reprogrammed and differentiated into neuroepithelial cells, then neural progenitors, to help stratify their post-natal care. One could also picture these cells becoming an autologous source for future cell-based therapies if they pass our reproducible analysis pipeline as functional quality control. This has now been clarified in the discussion:

      Discussion____: The multi-genic nature of neural tube defect susceptibility, compounded by uncontrolled environmental risk factors (including maternal age and parity102), mean that patient-derived iPSC models are unlikely to provide mechanistic insight. They do provide personalised disease models which we anticipate will enable functional validation of genetic diagnoses for patients and their parents' recurrence risk in future pregnancies, and may eventually stratify patients' postnatal care. We also envision this model will enable quality control of patient-derived cells intended for future autologous cell replacement therapies, as is being developed in post-natal spinal cord injury103.

      Minor comments: __ 1.Figure 1c. Text is cropped at the edge of the image.__

      This image has been corrected.

      Reviewer #2 (Significance (Required)): __ ...In addition, the model was unsuccessful in one of the two patient-derived lines, which limits generalizability and weakens claims of patient-specific predictive value.__

      We disagree with the reviewer that "the model was unsuccessful in one of the two patient-derived lines". The GOSB1 line demonstrated deficiency of neuron differentiation independently of neuroepithelial biomechanical function, whereas the GOSB2 line showed earlier failure of neuroepithelial function. We also do not, at this stage, make patient-specific predictive claims: this will require longer-term matching of cell model findings with patient phenotypes over the next 5-10 years.

      Reviewer #3: Major comments __ 1) One of my few concerns with this work is that the relative constriction of the apical surface with respect to the basal surface is not directly quantified for any of the experiments. This worry is slightly compounded by the 3D reconstructions Figure 1h, and the observation that overall cell volume is reduced and cell height increased simultaneously to area loss. Additionally, the net impact of apical constriction in tissues in vivo is to create local or global curvature change, but all the images in the paper suggest that the differentiated neural tissues are an uncurved monolayer even missing local buckles. I understand that these cells are grown on flat adherent surfaces limiting global curvature change, but is there evidence of localized buckling in the monolayer? While I believe-along with the authors-that their phenotypes are likely failures in apical constriction, I think they should work to strengthen this conclusion. I think the easiest way (and hopefully using data they already have) would be to directly compare apical area to basal area on a cell wise basis for some number of cells. Given the heterogeneity of cells, perhaps 30-50 cells per condition/line/mutant would be good? I am open to other approaches; this just seems like it may not require additional experiments.__

      As the reviewer observes, our cultures cannot bend because they are adhered on a rigid surface. The apical and basal lengths of the cultures will therefore necessarily be roughly equal in length. Some inwards bending of the epithelium is expected at the edges of the dish, but these cannot be imaged. The live imaging we show in Figure 2 illustrates that, just as happens in vivo, apical constriction is asynchronous. This means not all cells will have 'bottle' shapes in the same culture. We now illustrate the evolution of these shapes in more detail in Supplementary Figure 1 (shown in point 2.1 above).

      Additionally, the reviewer's comment motivated us to investigate local buckles in the apical surface of our cultures when their apical surfaces are dilated by ROCK inhibition. We hypothesised that the very straight apical surface in normal cultures is achieved by a balance of apical cell size and tension with pressure differences at the cell-liquid interface. Consistent with our expectation, the apical surface of ROCK-inhibited cultures becomes wrinkled (new Supplementary Figure 3). The VANGL2-KI lines do not develop this tortuous apical surface (as shown in Figure 3), which is to be expected given their modification is present throughout differentiation unlike the acute dilation caused by ROCK inhibition.

      This new data complements our visualisation of apical constriction in live imaging, apical accumulation of phospho-myosin, and quantification of ROCK-dependent apical tension as independent lines of evidence that our cultures undergo apical constriction.

      2) Another slight experimental concern I have regards the difference in laser ablation experiments detailed in Figure 3h-i from those of Figure 2d-e. It seems like WT recoil values in 3h-I are more variable and of a lower average than the earlier experiments and given that it appears significance is reached mainly by impact of the lower values, can the authors explain if this variability is expected to be due to heterogeneity in the tissue, i.e. some areas have higher local tension? If so, would that correspond with more local apical constriction?

      There is no significant difference in recoil between the control lines in Figures 2 and 3, albeit the data in Figure 3 is more variable (necessitating more replicates: none were excluded). We also showed laser ablation recoil data in Supplementary Figure 10, in which we did identify a graphing error (now corrected, also no significant difference in recoil from the other control groups).

      Minor comments __ 1) There seems to be a critical window at day 5 of the differentiation protocol, both in terms of cell morphology and the marker panel presented in Figure 1i. Do the authors have any data spanning the hours from day 5 to 6? If not, I don't think they need to generate any, but do I think this is a very interesting window worthy of further discussion for a couple of reasons. First, several studies of mouse neural tube closure have shown that various aspects of cell remodeling are temporally separable. For example, between Grego-Bessa et al 2016 and Brooks et al 2020 we can infer that apicobasal elongation rapidly increases starting at E8.5, whereas apical surface area reduction and constriction are apparent somewhat earlier at E8.0. I think it would be interesting to see if this separability is conserved in humans. Second, is there a sense of how the temporal correlation between the pluripotent and early neural fate marker data presented here corroborate or contradict the emerging set of temporally resolved RNA seq data sets of mouse development at equivalent early neural stages?__

      Cell shape analysis between days 5 and 6 has now been added (see the response to point 2.1 below). As the reviewer predicted, this is a transition point when apical area begins to decrease and apicobasal elongation begins to increase.

      We also thank the reviewer for this prompt to more closely compare our data to the previous mouse publications, which we have added to the discussion. The Grego-Bessa 2016 paper appears to show an increase in thickness between E7.75 and E8.5, but these are not statistically compared. Previous studies showed rapid apicobasal elongation during the period of neural fold elevation, when neuroepithelial cells apically constrict. This has now been added to the discussion:

      Discussion In mice, neuroepithelial apicobasal thickness is spatially-patterned, with shorter cells at the midline under the influence of SHH signalling14,77,78. Apicobasal thickness of the cranial neural folds increases from ~25 µm at E7.75 to ~50 µm at E8.579: closely paralleling the elongation between days 2 and 8 of differentiation in our protocol. The rate of thickening is non-uniform, with the greatest increase occurring during elevation of the neural folds80, paralleled in our model by the rapid increase in thickness between days 4-6 as apical areas decrease. Elevation requires neuroepithelial apical constriction and these cells' apical area also decreases between E7.75 and E8.5 in mice79, but we and others have recently shown that this reduction is both region and sex-specific14,81. Specifically, apical constriction occurs in the lateral (future dorsal) neuroepithelium: this corresponds with the identity of the cells generated by the dual SMAD inhibition model we use56. More recently, Brooks et al82 showed that the rapid reduction in apical area from E8-E8.5 is associated with cadherin switching from CDH1 (E-cadherin) to CDH2 (N-cadherin). This is also directly paralleled in our human system, which shows low-level co-expression of CDH1 and CDH2 at day 4 of differentiation, immediately before apical area shrinks and apicobasal thickness increases.

      Prompted by the in vivo data in Brooks et al (2025)82, we are keen to further explore the timing of CDH1/CDH2 switching versus apical constriction with new experimental data in revisions.

      2) Can the authors elaborate a bit more on what is known regarding apicobasal thickening and pseudo-stratification and how their work fits into the current understanding in the discussion? This is a very interesting and less well studied mechanism critical to closure, which their model is well suited to directly address. I am thinking mainly of the Grego-Bessa at al., 2016 work on PTEN, though interestingly the work of Ohmura et al., 2012 on the NUAK kinases also shows reduced tissue thickening (and apical constriction) and I am sure I have missed others. Given that the authors identify MED24 as a likely candidate for the lack of apicobasal thickening in one of their patient derived lines, is there any evidence that it interacts with any of the known players?

      We have now added further discussion on the mechanisms by which the neuroepithelium undergoes apicobasal elongation. Nuclear compaction is likely to be necessary to allow pseudostratification and apicobasal elongation. The reviewer's comment has led us to realise that diminished chromatin compaction is a potential outcome of MED24 down-regulation in our GOSB2 patient-derived line. Figure 4D suggests the nuclei of our MED24 deficient patient-derived line are less compacted than control equivalents and we propose to quantify nuclear volume in more detail to explore this possibility.

      Additionally, we have already expanded our discussion as suggested by the reviewer:

      Discussion: *Mechanistic separability of apical constriction and apicobasal elongation is consistent with biomechanical modelling of Xenopus neural tube closure showing that both are independently required for tissue bending61. Nonetheless, neuroepithelial apical constriction and apicobasal elongation are co-regulated in mouse models: for example, deletion of Nuak1/283, Cfl184, and Pten79 all produce shorter neuroepithelium with larger apical areas. Neuroepithelial cells of the GOSB2 line described here, which has partial loss of MED24, similarly produces a thinner neuroepithelium with larger apical areas. Although apical areas were not analysed in mouse models of Med24 deletion, these embryos also have shorter and non-pseudostratified neuroepithelium. *

      Our GOSB2 line - which retains readily detectable MED24 protein - is clearly less severe than the mouse global knockout, and the clinical features of the patient from which this line was derived are milder than the phenotype of Med24 knockout embryos68. Mouse embryos lacking one of Med24's interaction partners in the mediator complex, Med1, also have thinner neuroepithelium and diminished neuronal differentiation but successfully close their neural tube85. As general regulators of polymerase activity, MED proteins have the potential to alter the timing or level of expression of many other genes, including those already known to influence pseudostratification or apicobasal elongation. MED depletion also causes redistribution of cohesion complexes86 which may impact chromatin compaction, reducing nuclear volume during differentiation.

      3) Is there any indication that Vangl2 is weakly or locally planar polarized in this system? Figure 2F seems to suggest not, but Supplementary Figure 5 does show at least more supracellular cable like structures that may have some polarity. I ask because polarization seems to be one of the properties that differs along the anteroposterior axis of the neural plate, and I wonder if this offers some insight into the position along the axis that this system most closely models?

      VANGL2 does not appear to be planar polarised in this system. This is similar to the mouse spinal neuroepithelium, in which apical VANGL2 is homogenous but F-actin is planar polarised (Galea et al Disease Models and Mechanisms 2018). We do observe local supracellular cable-like enrichments of F-actin in the apical surface of iPSC-derived neuroepithelial cells. _We propose to compare the length of F-actin cables and coherency of their orientation at the start and end of neuroepithelial differentiation, and in wild-type versus VANGL2-mutant epithelia._

      4) I think some of the commentary on the strengths and limitations of the model found in the Results section should be collated and moved to the discussion in a single paragraph. For example ' This could also briefly touch on/compare to some of the other models utilizing hiPSCs (These are mentioned briefly in the intro, but this comparison could be elaborated on a bit after seeing all the great data in this work).

      These changes have now been made:

      __Discussion: __Some of these limitations, potentially including inclusion of environmental risk factors, can be addressed by using alternative iPSC-derived models93,94. For example, if patients have suspected causative mutations in genes specific to the surface (non-neural) ectoderm, such as GRHL2/3, 3D models described by Karzbrun et al49 or Huang et al95 may be informative. Characterisation of surface ectoderm behaviours in those models is currently lacking. These models are particularly useful for high-throughput screens of induced mutations95, but their reproducibility between cell lines, necessary to compare patient samples to non-congenic controls, remains to be validated. Spinal cell identities can be generated in human spinal cord organoids, although these have highly variable morphologies96,97. As such, each iPSC model presents limitations and opportunities, to which this study contributes a reductionist and highly reproducible system in which to quantitatively compare multiple neuroepithelial functions.

      5) While the authors are generally good about labeling figures by the day post smad inhibition, in some figures it is not clear either from the images or the legend text. I believe this includes supplemental figures 2,5,6,8, and 10 (apologies if I simply missed it in one or more of them)

      These have now been added.

      6) The legend for Figure 2 refers to a panel that is not present and the remaining panel descriptions are off by a letter. I'm guessing this is a versioning error as the text itself seems largely correct, but it may be good to check for any other similar errors that snuck in

      This has now been corrected.

      7) The cell outlines in Figure 3d are a bit hard to see both in print and on the screen, perhaps increase the displayed intensity?

      This has now been corrected.

      8) The authors show a fascinating piece of data in Supplementary Figure 1, demonstrating that nuclear volume is halved by day 8. Do they have any indication if the DNA content remains constant (e.g., integrated DAPI density)? I suppose it must, and this is a minor point in the grand scheme, but this represents a significant nuclear remodeling and may impact the overall DNA accessibility.

      We agree with the reviewer that the reduction in nuclear volume is important data both because it informs understanding of the reduction in total cell volume, and because it suggests active chromatin compaction during differentiation. Unfortunately, the thicker epithelium and superimposition of nuclei in the differentiated condition means the laser light path is substantially different, making direct comparisons of intensity uninterpretable. Additionally, the apical-most nuclei will mostly be in G2/M phase due to interkinetic nuclear migration. As such, the comparison of DAPI integrated density between epithelial morphologies would not be informative.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Ampartzidis et al., significantly extends the human induced pluripotent stem cell system originally characterized by the same group as a tool for examining cellular remodeling during differentiation stages consistent with those of human neural tube closure (Ampartzidis et al., 2023). Given that there are no direct ways to analyze cellular activity in human neural tube closure in vivo, this model represents an important platform for investigating neural tube defects which are a common and deleterious human developmental disease. Here, the authors carefully test whether this system is robust and reproducible when using hiPSC cells from different donors and pluripotency induction methods and find that despite all these variables the cellular remodeling programs that occur during early neural differentiation are statistically equivalent, suggesting that this system is a useful experimental substrate. Additionally, the carefully selected donor populations suggest these aspects of human neural tube closure are likely to be robust to sexual dimorphism and to reasonable levels of human genetic background variation, though more fully testing that proposition would require significant effort and be beyond the scope of the current work. Subsequent to this careful characterization, the authors next tested whether this system could be used to derive specific insights into cell remodeling during early neural differentiation. First, they used a reverse genetics approach to knock in a human point mutation in the critical regulator of planar cell polarity and apical constriction, Vangl2. Despite being identified in a patient, this R353C variant has not been directly functionally tested in a human system. The authors find that this variant, despite showing normal expression and phospho-regulation, leads to defects consistent with a failure in apical constriction, a key cell behavior required to drive curvature change during cranial closure. Finally, the authors test the utility of their hiPSC platform to understand human patient-specific defects by differentiating cells derived from two clinical spina bifida patients. The authors identify that one of these patients is likely to have a significant defect in fully establishing early proneural identity as well as defects in apicobasal thickening. While early remodeling occurs normally in the other patient, the authors observe significant defects in later neuronal induction and maturation. In addition, using whole exome sequencing the authors identify candidate variant loci that could underly these defects.

      Major comments

      1) One of my few concerns with this work is that the relative constriction of the apical surface with respect to the basal surface is not directly quantified for any of the experiments. This worry is slightly compounded by the 3D reconstructions Figure 1h, and the observation that overall cell volume is reduced and cell height increased simultaneously to area loss. Additionally, the net impact of apical constriction in tissues in vivo is to create local or global curvature change, but all the images in the paper suggest that the differentiated neural tissues are an uncurved monolayer even missing local buckles. I understand that these cells are grown on flat adherent surfaces limiting global curvature change, but is there evidence of localized buckling in the monolayer? While I believe-along with the authors-that their phenotypes are likely failures in apical constriction, I think they should work to strengthen this conclusion. I think the easiest way (and hopefully using data they already have) would be to directly compare apical area to basal area on a cell wise basis for some number of cells. Given the heterogeneity of cells, perhaps 30-50 cells per condition/line/mutant would be good? I am open to other approaches; this just seems like it may not require additional experiments.

      2) Another slight experimental concern I have regards the difference in laser ablation experiments detailed in Figure 3h-i from those of Figure 2d-e. It seems like WT recoil values in 3h-I are more variable and of a lower average than the earlier experiments and given that it appears significance is reached mainly by impact of the lower values, can the authors explain if this variability is expected to be due to heterogeneity in the tissue, i.e. some areas have higher local tension? If so, would that correspond with more local apical constriction?

      Minor comments

      1) There seems to be a critical window at day 5 of the differentiation protocol, both in terms of cell morphology and the marker panel presented in Figure 1i. Do the authors have any data spanning the hours from day 5 to 6? If not, I don't think they need to generate any, but do I think this is a very interesting window worthy of further discussion for a couple of reasons. First, several studies of mouse neural tube closure have shown that various aspects of cell remodeling are temporally separable. For example, between Grego-Bessa et al 2016 and Brooks et al 2020 we can infer that apicobasal elongation rapidly increases starting at E8.5, whereas apical surface area reduction and constriction are apparent somewhat earlier at E8.0. I think it would be interesting to see if this separability is conserved in humans. Second, is there a sense of how the temporal correlation between the pluripotent and early neural fate marker data presented here corroborate or contradict the emerging set of temporally resolved RNA seq data sets of mouse development at equivalent early neural stages?

      2) Can the authors elaborate a bit more on what is known regarding apicobasal thickening and pseudo-stratification and how their work fits into the current understanding in the discussion? This is a very interesting and less well studied mechanism critical to closure, which their model is well suited to directly address. I am thinking mainly of the Grego-Bessa at al., 2016 work on PTEN, though interestingly the work of Ohmura et al., 2012 on the NUAK kinases also shows reduced tissue thickening (and apical constriction) and I am sure I have missed others. Given that the authors identify MED24 as a likely candidate for the lack of apicobasal thickening in one of their patient derived lines, is there any evidence that it interacts with any of the known players?

      3) Is there any indication that Vangl2 is weakly or locally planar polarized in this system? Figure 2F seems to suggest not, but Supplementary Figure 5 does show at least more supracellular cable like structures that may have some polarity. I ask because polarization seems to be one of the properties that differs along the anteroposterior axis of the neural plate, and I wonder if this offers some insight into the position along the axis that this system most closely models?

      4) I think some of the commentary on the strengths and limitations of the model found in the Results section should be collated and moved to the discussion in a single paragraph. For example ' This could also briefly touch on/compare to some of the other models utilizing hiPSCs (These are mentioned briefly in the intro, but this comparison could be elaborated on a bit after seeing all the great data in this work).

      5) While the authors are generally good about labeling figures by the day post smad inhibition, in some figures it is not clear either from the images or the legend text. I believe this includes supplemental figures 2,5,6,8, and 10 (apologies if I simply missed it in one or more of them)

      6) The legend for Figure 2 refers to a panel that is not present and the remaining panel descriptions are off by a letter. I'm guessing this is a versioning error as the text itself seems largely correct, but it may be good to check for any other similar errors that snuck in

      7) The cell outlines in Figure 3d are a bit hard to see both in print and on the screen, perhaps increase the displayed intensity?

      8) The authors show a fascinating piece of data in Supplementary Figure 1, demonstrating that nuclear volume is halved by day 8. Do they have any indication if the DNA content remains constant (e.g., integrated DAPI density)? I suppose it must, and this is a minor point in the grand scheme, but this represents a significant nuclear remodeling and may impact the overall DNA accessibility.

      Significance

      Overall, I am enthusiastic about this work and believe it represents a significant step forward in the effort to establish precision medicine approaches for diagnoses of the patient-specific causative cellular defects underlying human neural tube closure defects. This work systematizes an important and novel tool to examine the cellular basis of neural tube defects. While other hiPSC models of neural tube closure capture some tissue level dynamics, which this model does not, they require complex microfluidic approaches and have limited accessibility to direct imaging of cell remodeling. Comparatively, the relative simplicity of the reported model and the work demonstrating its tractability as a patient-specific and reverse genetic platform make it unique and attractive. This work will be of interest to a broad cross section of basic scientists interested in the cellular basis of tissue remodeling and/or the early events of nervous system development as well as clinical scientists interested in modeling the consequences of patient specific human genetic deficits identified in neural tube defect pregnancies.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The authors' work focuses on studying cell morphological changes during differentiation of hPSCs into neural progenitors in a 2D monolayer setting. The authors use genetic mutations in VANGL2 and patient-derived iPSCs to show that (1) human phenotypes can be captured in the 2D differentiation assay, and (2) VANGL2 in humans is required for neural contraction, which is consistent with previous studies in animal models. The results are solid and convincing, the data are quantitative, and the manuscript is well written. The 2D model they present successfully addresses the questions posed in the manuscript. However, the broad impact of the model may be limited, as it does not contain NNE cells and does not exhibit tissue folding or tube closure, as seen in neural tube formation. Patient-derived lines are derived from amniotic fluid cells, and the experiments are performed before birth, which I find to be a remarkable achievement, showing the future of precision medicine.

      Major comments:

      1.Figure 1. The authors use F-actin to segment cell areas. Perhaps this could be done more accurately with ZO-1, as F-actin cables can cross the surface of a single cell. In any case, the authors need to show a measure of segmentation precision: segmented image vs. raw image plus a nuclear marker (DAPI, H2B-GFP), so we can check that the number of segmented cells matches the number of nuclei. 2.Lines 156-166. The authors claim that changes in gene expression precede morphological changes. I am not convinced this is supported by their data. Fig. 1g (epithelial thickness) and Fig. 1k (PAX6 expression) seem to have similar dynamics. The authors can perform a cross-correlation between the two plots to see which Δt gives maximum correlation. If Δt < 0, then it would suggest that gene expression precedes morphology, as they claim. Fig. 1j shows that NANOG drops before the morphological changes, but loss of NANOG is not specific to neural differentiation and therefore should not be related to the observed morphological changes. 3.Figure 2d. The laser ablation experiment in the presence of ROCK inhibitor is clear, as I can easily see the cell outlines before and after the experiment. In the absence of ROCK inhibitor, the cell edges are blurry, and I am not convinced the outline that the authors drew is really the cell boundary. Perhaps the authors can try to ablate a larger cell patch so that the change in area is more defined. 4.Figure 2d. Do the cells become thicker after recoil? 5.Figure 3. The authors mention their previous study in which they show that Vangl2 is not cell-autonomously required for neural closure. It will be interesting to study whether this also the case in the present human model by using mosaic cultures. 6.Lines 403-415. The authors report poor neural induction and neuronal differentiation in GOSB2. As far as I understand, this phenotype does not represent the in vivo situation. Thus, it is not clear to what extent the in vitro 2D model describes the human patient. 7.The experimental feat to derive cell lines from amniotic fluid and to perform experiments before birth is, in my view, heroic. However, I do not feel I learned much from the in vitro assays. There are many genetic changes that may cause the in vivo phenotype in the patient. The authors focus on MED24, but there is not enough convincing evidence that this is the key gene. I would like to suggest overexpression of MED24 as a rescue experiment, but I am not sure this is a single-gene phenotype. In addition, the fact that one patient line does not differentiate properly leads me to think that the patient lines do not strengthen the manuscript, and that perhaps additional clean mutations might contribute more.

      Minor comments:

      1.Figure 1c. Text is cropped at the edge of the image.

      Significance

      This study establishes a quantitative, reproducible 2D human iPSC-to-neural-progenitor platform for analyzing cell-shape dynamics during differentiation. Using VANGL2 mutations and patient-derived iPSCs, the work shows that (1) human phenotypes can be captured in a 2D differentiation assay and (2) VANGL2 is required for neural contraction (apical constriction), consistent with animal studies. The results are solid, the data are quantitative, and the manuscript is well written. Although the planar system lacks non-neural ectoderm and does not exhibit tissue folding or tube closure, it provides a tractable baseline for mechanistic dissection and genotype-phenotype mapping. The derivation of patient lines from amniotic fluid and execution of experiments before birth is a remarkable demonstration that points toward precision-medicine applications, while motivating rescue strategies and additional clean genetic models. However, overall I did not learn anything substantively new from this manuscript; the conclusions largely corroborate prior observations rather than extend them. In addition, the model was unsuccessful in one of the two patient-derived lines, which limits generalizability and weakens claims of patient-specific predictive value.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Ampartzidis et al. report the establishment of an iPSC-derived neuroepithelial model to examine how mutations from spina bifida patients disrupt fundamental cellular properties that underlie neural tube closure. The authors utilize an adherent neural induction protocol that relies on dual SMAD inhibition to differentiate three previously established iPSC lines with different origins and reprogramming methods. The analysis is comprehensive and outstanding, demonstrating reproducible differentiation, apical-basal elongation, and apical constriction over an 8-day period among the 3 lines. In inhibitor studies, it is shown that apical constriction is dependent on ROCK and generates tension, which can be measured using an annular laser ablation assay. Since this pathway is dependent on PCP signaling, which is also implicated in neural tube defects, the authors investigated whether VANGL2 is required by generating 2 lines with a pathogenic patient-derived sequence variant. Both lines showed reduced apical constriction and reduced tension in the laser ablation assays. The authors then established lines obtained from amniocentesis, including 2 control and 2 spina bifida patient-derived lines. These remarkably exhibited different defects. One line showed defects in apical-basal elongation, while the other showed defects in neural differentiation. Both lines were sequenced to identify candidate variants in genes implicated in NTDs. While no smoking gun was found in the line that disrupts neural differentiation (as is often the case with NTDs), compound heterozygous MED24 variants were found in the patient whose cells were defective in apical-basal elongation. Since MED24 has been linked to this phenotype, this finding is especially significant.

      Some details are missing regarding the method to evaluate the rigor and reproducibility of the study.

      Major points

      It is mentioned throughout the manuscript that 3 plates were evaluated per line. I believe these are independently differentiated plates. This detail is critical concerning rigor and reproducibility. This should be clearly stated in the Methods section and in the first description of the experimental system in the Results section for Figure 1. For the patient-specific lines - how many lines were derived per patient? Was the Vangl2 variant introduced by prime editing? Base editing? The details of the methods are sparse.<br /> Some additional suggestions for improvement.<br /> The abstract could be more clearly written to effectively convey the study's importance. Here are some suggestions Line 26: Insert "apicobasal" before "elongation" - the way it is written, I initially interpreted it as anterior-posterior elongation. Line 29: Please specify that the lines refer to 3 different established parent iPSC lines with distinct origins and established using different reprogramming methods, plus 2 control patient-derived lines. - The reproducibility of the cell behaviors is impressive, but this is not captured in the abstract. Line 32: add that this mutation was introduced by CRISPR-Cas9 base/prime editing The last sentence of the abstract states that the study only links apical constriction to human NTDs, but also reveals that neural differentiation and apical-basal elongation were found. The introduction could also use some editing. Line 71: insert "that pulls actin filaments together" after "power strokes" Line 73: "apically localized," do you mean "mediolaterally" or "radially"? Line 75: Can you specify that PCP components promote "mediolaterally orientated" apical constriction Lines 127: Specify that NE functions include apical basal elongation and neurodifferentiation are disrupted in patient-derived models

      Significance

      This paper is significant not only for verifying the cell behaviors necessary for neural tube closure in a human iPSC model, but also for establishing a robust assay for the functional testing of NTD-associated sequence variants. This will not only demonstrate that sequence variants result in loss of function but also determine which cellular behaviors are disrupted.

    1. Author Response

      Reviewer #1:

      Summary:

      In this paper, the authors utilize CRISPR-Cas9 to generate two different DMD cell lines. The first is a DMD human myoblast cell line that lacks exon 52 within the dystrophin gene. The second is a DMD patient cell line that is missing miRNA binding sites within the regulatory regions of the utrophin gene, resulting in increased utrophin expression. Then, the authors proceeded to test antisense oligonucleotides and utrophin up-regulators in these cell lines.

      Overall opinion (expanded in more detail below).

      The paper suffers from the following weaknesses:

      1) The protocol used to generate the myoblast cell lines is rather inefficient and is not new.

      2) Many of the data figures are of low quality and are missing proper controls (detailed in points 5,7,10, 12, 13,14)

      Detailed critiques:

      1) The title needs to be changed. The method used by the authors is inefficient. The title should instead focus on the two cell lines generated.

      We appreciate the reviewer’s comments: thanks to them, we have realized the focus of the manuscript should be in the new models we described and less in the methodology used to create them.

      Originally, we wanted to share the problems we faced when applying new CRISPR/Cas9 edition techniques to myoblasts: our conversations with other researchers in the field confirmed that many were having similar problems. However, the reviewer is right in the fact that there are many ways around this problem. We do describe ours and we are working in a new version of the manuscript with additional data to characterize our new models further and where the method used to create them, although included, is not the main focus of the manuscript. In this new version we will change the title accordingly.

      2) Line 104: The authors declare that the efficiency of CRISPR/Cas9 is currently too low to provide therapeutic benefit for DMD in vivo. There are lots of papers that show efficient recovery of dystrophin in small and large animals following CRISPR/Cas9 therapy. The authors should cite them properly.

      Thank you for your appreciation. We have reviewed the literature again to include new evidences of efficient dystrophin recovery as well as other studies with lower efficiency.

      3) Figures 1, 2,3, and 4 can be merged into one figure.

      4) Figure 2A and 2B can be moved to supplementary.

      5) Figure 2C and 2D are not clear. Are the duplicates the same? Please invert the black and white colors of the blots.

      Thank you for your comments. We have inverted the colors of the blots and changed the marks used in figure 2C and 2D to clarify that duplicates are indeed the same sample, assayed in duplicates. We have also merged figures 1 and 4 and moved figures 2 and 3 to supplementary in this new version.

      6) Figure 3: In order to optimize the efficiency of myoblast transfection, the plasmids containing the Cas9 and the sgRNA should have different fluorophores (GFP and mCherry). This approach would increase the percentage of positive edited clones among the clones sorted.

      We think the reviewer may have misunderstood our methodology: we are not using a plasmid with the Cas9 and another with the sgRNA, we are using two plasmids, both containing Cas9 and each a different sgRNA. We did try to use two different plasmids, one expressing GFP and one expressing puromycin resistance, but we found out that single GFP positive cell selection plus puromycin selection was too inefficient. We could have tried with two different fluorophores, but we tested the tools we had in our hands first and were successful at obtaining enough clones to continue with their characterization, so we did so instead of a further optimization to our editing protocol.

      7) Figure 4A: In the text, the authors state that only 1 clone had the correct genomic edit, but from the PCR genotyping in this figure shows at least 2 positive clones (number 4 and 7).

      Thank you for your appreciation. As you said, we got two positive clones (as we also indicate in figure 3B) but we completed the full characterization of one of them (clone number 7= DMD-UTRN-Model). In the new version of the manuscript we explain this further.

      8) Figure 4C: The authors should address whether one or both copies of the UTRN gene was edited in their clones.

      Thank you for your comment. Both copies of the UTRN gene were edited in our clones. We have included this information both in the text and in the figure 4 legend.

      9) Figure 4 B and D: The authors should report the sequence below the electropherograms.

      Thank you for this correction, we have included the sequence under the electropherograms.

      10) Figure 5B: This western blot is of poor quality. Also, the authors should specify that the samples are differentiated myoblasts. Lastly, a standard protein should be included as a loading control.

      Thank you for your comment. Poor quality of dystrophin and utrophin western blots was the main reason to validate a new method in our laboratory to measure these proteins directly in cell culture (1) like an alternative to western blotting. Since then, the myoblot method has been routinely used by us and in collaboration with other groups and companies. We included the western blot as it is sometimes easier for those used to this technique to be able to assess a blot in which there is no dystrophin expression. As you pointed out, our samples were all differentiated myotubes, not myoblasts, and we have modified this accordingly. Thank you very much for pointing out this mistake

      On the other hand, as described in the methods, Revert TM 700 Total Protein Stain (Li-Cor) and alpha-actinin were included as standards in dystrophin and utrophin western blots, respectively.

      11) Figure 5E: We would like to see triplicates for the level of Utrophin expression.

      We thank the reviewer for his/her recommendation, but we do not consider western blotting a good quantitative technique, we have included western blots to show the expression/absence of protein at the same level. We have included many more replicates than needed to show at the level of utrophin by myoblots. We acknowledge that western blotting is the preferred method for some reviewers, so in the new version of our manuscript we clearly indicate the value we give to each technique, being myoblots our choice for quantification.

      12) Figure 6: A dystrophin western blot should be included to demonstrate protein recovery following antisense oligonucleotide treatment. Also, the RT-PCR data could be biased as you can have preferential amplification of shorter fragments.

      Thank you for your recommendation but as we have explained before, myoblots have been validated in our laboratory to replace western blot for accurate dystrophin quantification in cell culture.

      13) Figure 6A: Invert the black and white colors. The authors should also report the control sequences and sequences of the clones under the electropherograms.

      Thank you for your suggestion, we have inverted the colors and added the sequences under the electropherograms.

      14) Figure 6B: Control myoblasts should be included in figure 5C.

      Thank you for this correction, we will include control myoblasts in the new manuscript version.

      15) Figure S2A: Invert the black and white colors.

      Thank you for your suggestion, we have inverted the colors.

      Reviewer #2:

      The work from Soblechero-Martín et al reports the generation of a human DMD line deleted for exon 52 using CRISPR technology. In addition, the authors introduced a second mutation that leads to upregulation of utrophin, a protein similar to dystrophin, which has been considered as a therapeutic surrogate. The authors provide a careful description of the methodology used to generate the new cell line and have conducted meticulous evaluations to test the validity of the reagents.

      However, if the main purpose of this cell line is to perform drug or small molecule compound screenings, a single line might not be sufficient to draw robust conclusions. The generation of additional DMD lines in different genetic backgrounds using the reagents developed in this study will strengthen the work and will be of interest to the DMD field.

      Thank you for your appreciation. We think that a well characterized immortalized culture, like the one we describe is sufficient for compound screening, as described in other recently published studies (2), (3). About the other suggestion, we have indeed used our method to generate other cultures for collaborators, but they will be reported in their own publications, as they are interested in them as tools in their own research projects.

      Further, the future use of the edited DMD line with upregulated utrophin is unclear. The utrophin upregulation adds a complexity to this line that might complicate the assessment of screened compounds. In contrast, this line could be used to test if overexpression of utrophin generates myotubes that produce increased force compared to the control DMD line.

      We think we may have not explained our screening platform well enough. Our suggestion is to offer our newly generated culture ALONGSIDE the original unedited culture: the original is treated with potential drug candidates, while the new one may or may not be treated, if these drug candidates are thought to act by activating the edited region (see an example in the figure below). In this case, the new culture will be a reliable positive control to the effects that may be reported in the unedited cultures by the drug candidates. We will make this clear in the new version of the manuscript.

      Created with BioRender.com

      In summary, while there is support and enthusiasm for the techniques and methodological approach of the study, the future use of this single line might be dubious and could be strengthened if additional lines are generated.

      We share the reviewer’s enthusiasm for this approach, and we have included in the new version of the manuscript further characterization of this new cell culture that we think would demonstrate its usefulness better.

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-03220

      Corresponding author(s): Ryusuke Niwa, Yuko Shimada-Niwa, and Wei Sun

      Dear Editors,

      We are pleased to submit our revised manuscript of RC-2025-03220R. The reviewers’ comments from Review Commons are presented in italic.

      For submission of our current revised manuscript, we provide two Word files, which are the “clean” and “Track-and-Change” files. Page and line numbers described below correspond to those of the “clean” file. The “Track-and-Change” file might be helpful for Reviewers to find what we have changed for the current revision.

      We hope that the revised version is now suitable for the next stage of evaluation.

      Sincerely,

      Ryusuke Niwa, Yuko Shimada-Niwa, and Wei Sun

      1. General Statements [optional]

      We sincerely thank the reviewers for their thoughtful feedback on our initial submission. Experiments that we will conduct and the revisions on the manuscript that have already been incorporated are detailed below in the point-by-point response. For this revised submission, two versions of the manuscript are provided: a clean copy and a tracked-changes file. Page and line numbers mentioned below refer to the clean version, while the tracked-changes file is intended to help reviewers easily identify the revisions made.

      In preparing the revision plan, we have included additional data, some of which were generated in collaboration with new contributors. Accordingly, we would like to propose adding Yuichi Shichino and Shintaro Iwasaki as co-authors to acknowledge their contributions.

      2. Description of the planned revisions__ __

      __

      - Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

      Author response:

      In response to the second part of the criticism, we will further validate the observed phenotypes by examining tissue and nuclear size, chromosomal structure, and the levels of Fibrillarin and RpS6 proteins in the prothoracic glands and salivary glands of NudC mutants.

      __

      - It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

      Author response:

      To address these structural concerns more clearly, we plan to apply established protocols to obtain higher-resolution images and gather more detailed information on chromosome morphology.

      __ - Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

      Author response:

      To further confirm DNA damage in NudC knockdown salivary gland cells, we plan to perform a TUNEL assay, which detects DNA fragmentation associated with damage.

      We would like to note that, in the current manuscript, we have shown that depletion of NudC, eIF5, RpLP0-like, or Nopp140 increased γH2Av levels, suggesting activation of the DNA damage response (Figures 6B and 6C).

      __

      *The authors claim that NudC has a dual role as a cell cycle/cytoskeleton regulator and as a ribosome biogenesis factor. However, because NudC knockdown reduces nuclear size and ploidy (Figures 1F and 2H-2I), the authors cannot exclude that decreased rDNA dosage and nucleolar volume contribute to reduced rRNA signals and that the effects seen are due to a NudC involvement in endoreplication, the rRNA reduction being a consequence of lower polyploidy. Different allelic combinations of NudC induce larval growth defects (Figure S5), consistent with a NudC role in endoreplication. To circumvent this, the authors could genetically modulate endocycle progression (e.g., E2F or Fzr overexpression) in the NudC RNAi background to test whether inducing endoreplication rescues rRNA production and nucleolar volume. This would establish causality between the endocycle state and rRNA output and clarify whether NudC's primary role is in RiBi or endocycle control. *

      Author response: In response to Reviewer #2’s suggestion, we plan to genetically modify the progression of the endocycle by inducing continuous expression of Cyclin E (CycE), E2F1, and Fzr in NudC RNAi salivary glands to test whether promoting endoreplication can restore rRNA production and nucleolar volume.

      In fact, we have attempted to rescue the developmental arrest in animals with NudC-deficient prothoracic glands (PGs) by inducing continuous expression of CycE. Two constructs, UAS-CycE-1 (BDSC#30725) and UAS-CycE-2 (BDSC#30924), were used. UAS-CycE-1 has previously been shown to rescue developmental arrest in PG-specific TOR loss-of-function animals (Ohhara, Kobayashi, and Yamanaka. PLoS Genetics 13 (1): e1006583, 2017). We introduced each construct into NudC knockdown PGs. However, continuous expression of CycE did not restore development (Figure A as shown below), suggesting that NudC functions in the polyploid cells extend beyond endocycle regulation. We do not currently plan to include the PG data shown in Figure A in the revised manuscript. We will evaluate whether it would be meaningful to present PG data alongside salivary gland results once we have obtained and analyzed data from the salivary gland rescue experiment.

      __Figure A. _Survival and developmental progression following continuous expression of CycE._ __Control (phtm>dicer2, +), NudC knockdown (phtm>dicer2, NudC RNAi), and NudC RNAi + CycE (phtm>dicer2, NudC RNAi, CycE) flies were analyzed at 10 days after hatching (10 dAH). Dead indicates dead larvae; L3 denotes third-instar larvae. Sample sizes (number of flies) are shown below each bar.

      __

      *The conclusion that NudC maintains rRNA levels is derived from salivary gland RNAi phenotypes with strong reductions in ITS1/ITS2 and 18S/28S signals (Figure 4B-4K) and reduced 28S by Northern (Figure 4L), plus corroboration in fat body cells (Figure S7). The authors verified knockdown using two independent RNAi lines for growth phenotypes and NudC::GFP reduction (Figure S2) and generated a UAS-FLAG::NudC transgene (Key Resources), but rRNA measurements were reported for only one RNAi line without rescue. Rescue of the rRNA phenotype by transgenic NudC re-expression, or replication of the rRNA decrease with a second, non-overlapping RNAi, would directly attribute the effect to NudC. In the absence of these standard validation controls, an off-target explanation remains plausible. *

      Author response:

      We plan to analyze rRNA FISH signals in salivary glands and fat bodies using a second, non-overlapping RNAi strain to confirm the reproducibility of the observed effects.

      __ - The authors report in Fig. 2 elevated γH2Av in SG cells upon NudC knockdown and interpret this as evidence of chromosome destabilization. They also state that apoptosis is not observed in Fig S10. However, the increase in γH2Av could reflect transient or early apoptotic events or other stress responses triggered by NudC depletion, rather than direct defects in endoreplication or genome stability. I suggest that the authors clarify this important point, for example, by co-expressing apoptotic inhibitors such as P35, or by using the TUNEL assay, which is more sensitive than anti-Caspase3 or Dcp1 antibodies.

      Author response:

      We plan to perform a TUNEL assay on salivary gland cells to evaluate apoptosis associated with NudC depletion.

      __ - Activation of the JNK pathway is often accompanied by apoptosis. It would strengthen the conclusions if the authors included a positive control to confirm that apoptosis is not induced under these experimental conditions, ensuring that the observed effects are specific to autophagy and not confounded by cell death.

      Author response:

      We will analyze pJNK and autophagy levels in animals expressing a constitutively-active form of hemipterous (hep) (hep[CA] ) under the control of fkh-GAL4 driver as a positive control. hep encodes the Drosophila JNK kinase, and it is well established that forced expression of hep[CA] induces JNK phosphorylation and activation.

      __ - In Figure S1, reduction of NudC in the fat body appears to induce a starvation-like phenotype, suggesting a potential impairment of metabolic or nutrient-sensing pathways. It would be important to determine whether modulation of nutrient-responsive signaling could rescue this phenotype. Specifically, have the authors examined whether activation of the TOR or PI3K pathways mitigates the effects of NudC knockdown? Assessing pathway activity (e.g., via phospho-S6K or phospho-Akt levels) or performing genetic rescue experiments with pathway activators could clarify whether the observed phenotypes are mediated through disrupted nutrient signaling rather than a secondary effect of general cellular stress. Such analyses could also provide a mechanistic explanation for the increased autophagy observed in these cells.

      Author response:

      1. We will analyze phospho-S6K levels in salivary glands and fat bodies by immunostaining.
      2. To activate the TOR pathway in NudC RNAi fat bodies, we will overexpress Rheb, an established upstream activator of the TOR pathway in Drosophila, which has been shown to robustly increase TOR signaling and S6K phosphorylation.

        __ - The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing. In addition, this data could be further strengthened by expanding the analysis of autophagy to other cell types. For example, examining autophagy in fat body cells, where autophagy plays a primary physiological role associated with rRNA accumulation (Fig. S7), rather than a reduction like in SG (Fig. 4), could provide a useful comparison for the function of NudC between polyploid cells.

      Author response:

      In response to the second part of the reviewer’s comment, we will conduct additional experiments using anti-Atg8a immunostaining and/or LysoTracker staining to analyze autophagy in NudC RNAi fat bodies and prothoracic glands. These experiments will help further characterize the cellular responses associated with NudC depletion.

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


      __

      -The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

      Author response:

      In response to the suggestion from Reviewer #1, we have revised the title from “NudC moonlights in ribosome biogenesis and homeostasis in Drosophila melanogaster polyploid cells” to “NudC moonlights in ribosome biogenesis and homeostasis in polyploid cells of Drosophila melanogaster” to place greater emphasis on “polyploid cells.”

      Regarding mitotic cells, we have added new data in the revised manuscript (Figure S7; lines 249–256 and 417–418) demonstrating that NudC regulates apoptosis and stress responses in mitotic imaginal wing disc cells. However, as the main focus of our study remains polyploid cells, we have chosen to retain the emphasis in the title.

      __

      - Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

      Author response:

      In response to the first half of criticism, the two RNAi lines used for NudC target distinct sequences. We have added the corresponding RNAi target sites to Figure S4A for clarity.

      __

      - Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

      Author response:

      In response to Reviewer #1’s suggestion, we have revised lines 261–262 to avoid using the word "confirm." The new sentence reads: “Immunostaining with the P-body marker Me31B reveals numerous cytoplasmic P-bodies in NudC-deficient SG cells,” which appears in lines 293–295.

      __

      - Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

      Author response:

      We have followed Reviewer #1’s suggestion and revised the sentence in lines 35–37 to: “In this study, we discovered a role for the gene NudC (nuclear distribution C, dynein complex regulator) in RiBi within polyploid cells of Drosophila melanogaster larvae.”

      __

      - Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

      Author response:

      To correct the misrepresentation in line 66, we have revised the sentence to: “RP mRNAs are synthesized by RNA polymerase II, and exported to the cytoplasm for translation. Then, RPs are imported into the nucleus, where they localize to the nucleolus.” in lines 70–73.

      __ - Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

      Author response:

      To improve the representation, we have revised the sentences in lines 73 – 78 as follows: “Within the nucleolus, rRNAs and RPs assemble into pre-40S and pre-60S subunits. immature versions of the small (40S) and large (60S) subunits, respectively, that undergo maturation with numerous ribosome biogenesis factors (RBFs) (Greber, 2016). The 40S and 60S subunits are then transported separately to the cytoplasm, where they combine to form functional 80S ribosomes, capable of sustaining protein synthesis (Pelletier et al., 2018).”

      __ - Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

      Author response:

      As Reviewer #1 suggested, we have cited two, Marygold et al. (2007) entitled “The ribosomal protein genes and Minute loci of Drosophila melanogaster” and Recasens-Alvarez et al. (2021) entitled “Ribosomopathy-associated mutations cause proteotoxic stress that is alleviated by TOR inhibition” along with He et al. (2015). The inappropriate citation to Brehme (1939) has been removed.

      __ - Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

      Author response:

      We would like to clarify that the phenotype observed with fkh-GAL4-driven NudC RNAi was specific to salivary glands, and no obvious phenotypes were detected in the surrounding fat body cells, which do not express fkh-GAL4. In this context, the adjacent fat body cells serve as an internal control.

      In the revised manuscript, the sentence has been rewritten as: “In contrast, the fat body cells surrounding NudC-deficient SGs did not show this reduction (Figure S9),” in lines 323–324.

      __ - Figure 6A. Hoechst is misspelled.

      __

      - Fig. 2 I - Hoeschest should be Hoescht.

      Author response:

      We have fixed the error.

      __ *- Given that prothoracic gland (PG) size influences ecdysone production, the finding that NudC knockdown alters PG cell size, morphology, and cytoskeletal organization raises the possibility that ecdysone synthesis or signaling may also be affected. This, in turn, could explain the delayed maturation phenotype observed in Figure 1. I recommend testing whether ectopic activation of ecdysone signaling, for instance through 20-hydroxyecdysone (20E) supplementation, can rescue the defects in PG size and developmental timing. Such an experiment would strengthen the link between NudC function, PG morphology, and ecdysone-dependent developmental progression. *

      Author response:

      We have conducted experiments showing that developmental defects in NudC RNAi animals can be partially rescued by administering 20E. Approximately 32% of NudC RNAi larvae fed with 20E completed pupariation. These new data have been added to Figure S1B and are described in the main text (lines 165-168).

      Regarding PG size, our experiments show that PG growth remains inhibited following 20E administration (Figure B as shown below). This observation indicates that treatment with exogenous 20E does not restore PG growth in NudC RNAi animals, suggesting that other factors may be required for normal PG development beyond ecdysone supplementation.

      Because this analysis is not the main focus of our manuscript, we currently plan not to include these data in the revised manuscript.

      Figure B. Prothoracic gland (PG) size ____after 20E administration.

      To assess whether 20E supplementation could restore PG size, control (phtm>dicer2, +) and NudC RNAi (phtm>dicer2, NudC RNAi) larvae were transferred at 60 hours after hatching (hAH) to standard medium containing 20E dissolved in 100% ethanol. Control groups were transferred to medium containing the same volume of 100% ethanol at the same time point. PG size was quantified at the wandering stage. Sample sizes (number of glands) are shown below each bar. Bars represent mean ± SD. **p * *

      __ - Additionally, qRT-PCR can be performed to assess the expression levels of ecdysone precursors or target genes in whole larvae, serving as a readout of ecdysone activity, including dilp8, which is usually upregulated when ecdysone levels are reduced.

      Author response: To investigate ecdysone biosynthesis, Halloween genes including nvd, spok, sro, phm, dib, and sad were measured by conducting qRT-PCR. In NudC RNAi animals, nvd, sro and phm were suppressed at late L3 stage, indicating that NudC in the PG is required for ecdysone biosynthesis. The new data are described in Figure S1A and in the main text (lines 159-164) in the revised manuscript.

      __ - The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing.

      Author response:

      Regarding the image quality issue, we have provided improved images of anti-Atg8a immunostaining in the salivary gland mosaic clones (Figure 8B) and included additional data from SG-specific knockdown cells (Supplemental Figures S13A-S13F) to provided quantitative results.

      __ - Furthermore, including experiments in other cell types, such as imaginal disc cells, where apoptosis is more readily induced, would help determine whether the effects of NudC knockdown are specific to polyploid cells or are more broadly applicable.

      Author response: We found that apoptosis was observed in NudC RNAi wing discs. In the revised manuscript, we have included this data in Figure S7 and referenced it in the main text (lines 249–256).

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

      __ - Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

      Author response:

      As Reviewer #1 indicated, we indeed observed that internal transcribed spacer (ITS) levels decrease in NudC knockdown salivary glands, but increase in knockdown fat bodies. Our hypothesis is that, as noted in the Discussion (lines 529–534), ribosome abundance is typically linked to protein synthesis. Salivary gland cells, which are highly active in protein production, may be particularly sensitive to disruptions in ribosome biogenesis. Therefore, NudC may maintain appropriate levels of rRNA with its impact varying according to the specific regulatory mechanisms of each cell type. We do not have a further explanation for this phenomenon, and therefore we have retained the original sentences without adding new ones.

      __ - The data presented in Fig 4 show that NudC knockdown reduces pre-rRNA (ITS1/ITS2) and mature 18S/28S rRNAs in a tissue-specific manner. However, it remains unclear whether these reductions have functional consequences for ribosome assembly and translation. I recommend that the authors perform polysome profiling or an equivalent assay to assess the impact of NudC loss on actively translating ribosomes. This approach would provide a quantitative readout of translation efficiency and clarify whether the observed rRNA defects lead to impaired protein synthesis. Additionally, polysome profiling could help explain the tissue-specific differences observed between salivary glands and fat body cells.

      Author response:

      We performed ribosome fractionation using wild-type salivary glands and repeated the experiment three times with 56–62 gland pairs per sample. As shown in Figure C, the polyribosome peaks (grey lines) are not prominent, indicating that a much larger number of glands would be required for robust polysome profiling. Given that NudC RNAi salivary glands are significantly smaller than wild-type glands, collecting enough tissue for equivalent profiling would be technically difficult. Therefore, we concluded that obtaining sufficient RNAi samples for polysome profiling is extremely challenging, and these data have not been included in the revised manuscript.

      On the other hand, we would like to emphasize that we observed a significant reduction in O-propargyl puromycin (OPP) labeling in NudC-deficient salivary gland cells (Figure 3B), which provides strong evidence for reduced translational activity.

      __Figure C. Ribosomal fraction profiles of wild-type salivary glands. __Salivary glands from the late L3 larvae were dissected for analysis. Polyribosome peaks are indicated in grey. The number of salivary gland pairs used for each sample is shown above each bar.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      NudC (Nuclear Distribution Protein C) is a conserved, dynein-associated protein that plays a critical role in nuclear positioning and neuronal development. It functions as a co-chaperone, stabilizing components of the dynein-motor complex, thereby facilitating proper microtubule-dependent nuclear migration and intracellular transport. In developing neurons, NudC is essential for correct dendritic morphogenesis, ensuring nuclei and dendritic processes attain their proper spatial organization. Loss or knockdown of nudC leads to defects in nuclear localization, aberrant dendritic architecture, and mitotic stress, which can predispose cells to apoptosis. Highlighting NudC as a pivotal regulator of intracellular dynamics, cytoskeletal organization, In this paper, the authors propose a role for the gene in regulating ribosomal biogenesis. However, the interpretation of these results remains somewhat unclear, as the observed effects on ribosome biogenesis could potentially result from nonspecific cellular stress or toxicity caused by gene knockdown in polyploid cells. At this stage, the link between NudC and the regulation of ribosomal biogenesis is not fully convincing. Additional experiments could help clarify whether this relationship is direct or secondary to other cellular effects. I suggest conducting additional experiments to strengthen this hypothesis; for example, by examining whether knocking down NudC would give similar effects as observed for other genes that regulate RiBi in other organs and tissues where ribosomal biogenesis and stress responses have been well-characterized, such as the imaginal discs. Comparing the results across these different tissues would help clarify whether the effects of gene knockdown are specific to polyploid cells or represent a more general cellular response.

      Suggested experiments to sustain the paper:

      1. Given that prothoracic gland (PG) size influences ecdysone production, the finding that NudC knockdown alters PG cell size, morphology, and cytoskeletal organization raises the possibility that ecdysone synthesis or signaling may also be affected. This, in turn, could explain the delayed maturation phenotype observed in Figure 1. I recommend testing whether ectopic activation of ecdysone signaling, for instance through 20-hydroxyecdysone (20E) supplementation, can rescue the defects in PG size and developmental timing. Such an experiment would strengthen the link between NudC function, PG morphology, and ecdysone-dependent developmental progression.
      2. Additionally, qRT-PCR can be performed to assess the expression levels of ecdysone precursors or target genes in whole larvae, serving as a readout of ecdysone activity, including dilp8, which is usually upregulated when ecdysone levels are reduced.
      3. The authors report in Fig. 2 elevated γH2Av in SG cells upon NudC knockdown and interpret this as evidence of chromosome destabilization. They also state that apoptosis is not observed in Fig S10. However, the increase in γH2Av could reflect transient or early apoptotic events or other stress responses triggered by NudC depletion, rather than direct defects in endoreplication or genome stability. I suggest that the authors clarify this important point, for example, by co-expressing apoptotic inhibitors such as P35, or by using the TUNEL assay, which is more sensitive than anti-Caspase3 or Dcp1 antibodies.
      4. The data presented in Fig 4 show that NudC knockdown reduces pre-rRNA (ITS1/ITS2) and mature 18S/28S rRNAs in a tissue-specific manner. However, it remains unclear whether these reductions have functional consequences for ribosome assembly and translation. I recommend that the authors perform polysome profiling or an equivalent assay to assess the impact of NudC loss on actively translating ribosomes. This approach would provide a quantitative readout of translation efficiency and clarify whether the observed rRNA defects lead to impaired protein synthesis. Additionally, polysome profiling could help explain the tissue-specific differences observed between salivary glands and fat body cells.
      5. Activation of the JNK pathway is often accompanied by apoptosis. It would strengthen the conclusions if the authors included a positive control to confirm that apoptosis is not induced under these experimental conditions, ensuring that the observed effects are specific to autophagy and not confounded by cell death.
      6. In Figure S1, reduction of NudC in the fat body appears to induce a starvation-like phenotype, suggesting a potential impairment of metabolic or nutrient-sensing pathways. It would be important to determine whether modulation of nutrient-responsive signaling could rescue this phenotype. Specifically, have the authors examined whether activation of the TOR or PI3K pathways mitigates the effects of NudC knockdown? Assessing pathway activity (e.g., via phospho-S6K or phospho-Akt levels) or performing genetic rescue experiments with pathway activators could clarify whether the observed phenotypes are mediated through disrupted nutrient signaling rather than a secondary effect of general cellular stress. Such analyses could also provide a mechanistic explanation for the increased autophagy observed in these cells.
      7. The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing. In addition, this data could be further strengthened by expanding the analysis of autophagy to other cell types. For example, examining autophagy in fat body cells, where autophagy plays a primary physiological role associated with rRNA accumulation (Fig. S7), rather than a reduction like in SG (Fig. 4), could provide a useful comparison for the function of NudC between polyploid cells.
      8. Furthermore, including experiments in other cell types, such as imaginal disc cells, where apoptosis is more readily induced, would help determine whether the effects of NudC knockdown are specific to polyploid cells or are more broadly applicable.

      Significance

      NudC is a conserved dynein-associated protein essential for nuclear positioning, dendritic morphogenesis, and intracellular transport. This study suggests a novel role for NudC in regulating ribosome biogenesis, potentially linking cytoskeletal organization with protein synthesis and cellular homeostasis. Validating this connection across different tissues could reveal whether NudC serves as a general coordinator of intracellular architecture and translational capacity, providing new insights into how cells integrate structural and biosynthetic functions.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In this manuscript, Duoduo Shi and colleagues, propose that NudC, previously known for its role in dynein regulation, has a second role as a critical regulator of ribosome biogenesis (RiBi) in Drosophila melanogaster polyploid cells, where its depletion reduces rRNA levels and ribosome abundance, triggering a compensatory homeostatic response that upregulates ribosomal proteins and biogenesis factors, similar to the response observed upon depletion of established ribosome biogenesis factors.

      Strengths

      The authors propose a novel role for NudC as a regulator of ribosome biogenesis (RiBi) which is dynein-independent and they provide a detailed homeostatic response to RiBi stress.

      Weaknesses

      NudC downregulation may be affecting the endocycle and an endoreplication defect may drive rRNA reduction.

      Major comments

      The authors claim that NudC has a dual role as a cell cycle/cytoskeleton regulator and as a ribosome biogenesis factor. However, because NudC knockdown reduces nuclear size and ploidy (Figures 1F and 2H-2I), the authors cannot exclude that decreased rDNA dosage and nucleolar volume contribute to reduced rRNA signals and that the effects seen are due to a NudC involvement in endoreplication, the rRNA reduction being a consequence of lower polyploidy. Different allelic combinations of NudC induce larval growth defects (Figure S5), consistent with a NudC role in endoreplication. To circumvent this, the authors could genetically modulate endocycle progression (e.g., E2F or Fzr overexpression) in the NudC RNAi background to test whether inducing endoreplication rescues rRNA production and nucleolar volume. This would establish causality between the endocycle state and rRNA output and clarify whether NudC's primary role is in RiBi or endocycle control.

      The conclusion that NudC maintains rRNA levels is derived from salivary gland RNAi phenotypes with strong reductions in ITS1/ITS2 and 18S/28S signals (Figure 4B-4K) and reduced 28S by Northern (Figure 4L), plus corroboration in fat body cells (Figure S7). The authors verified knockdown using two independent RNAi lines for growth phenotypes and NudC::GFP reduction (Figure S2) and generated a UAS-FLAG::NudC transgene (Key Resources), but rRNA measurements were reported for only one RNAi line without rescue. Rescue of the rRNA phenotype by transgenic NudC re-expression, or replication of the rRNA decrease with a second, non-overlapping RNAi, would directly attribute the effect to NudC. In the absence of these standard validation controls, an off-target explanation remains plausible.

      Minor comments

      Fig. 2 I - Hoeschest should be Hoescht

      Significance

      The findings shown in this manuscript introduce a new player in endoreplication/ribosome biogenesis, a protein previously know as a dynein regulator. The strengths of the work lie on its novelty and thorough analysis of the cellular phenotypes induced by NudC depletion. However, its weaknesses are related to some claims not completely backed by the data, with some uncertainties related with a possible function of NudC in endoreplication.

      This basic research work will be of interest to a broad cell and developmental biology community as they provide a novel cellular function of a known protein. It is of specific interest to the specialized field of polyploidy and ribosome biogenesis.

      Field of expertise:

      Drosophila, morphogenesis, tubulogenesis, cytoskeleton, DNA damage and repair.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary: This manuscript describes evidence for a role for the Nuclear distribution C dynein complex regulator (NudC) in ribosome biogenesis (RiBi) independent of its role in microtubule-associated dynein function.

      Evidence: NudC was picked up in a screen for genes affecting ecdysteroid biosynthesis, a process that occurs in the prothoracic gland (PG; an endocrine organ). In the absence of ecdysone, larvae fail to pupate. Consistent with this finding, the authors find that prothoracic RNAi knockdown of NudC results in a failure in pupation and a decrease in total PG size. They also show defects in polytene chromosome architecture and a mild decrease in overall DNA content. They then turn to the salivary gland (SG) to further characterize the phenotypes associated with NudC knockdown. First, they show that an endogenously tagged version of NudC is abundant in the cytosol and has very weak nuclear staining in the region of the nucleolus (marked by the very low levels of DAPI staining). Knockdown of NudC using RNAi results in reduced NudC-GFP staining, a reduction in SG size, and a reduction in nuclear size. They also find that the SG polytene chromosomes are abnormal and that the production of a SG glue protein as measured by Sgs3-GFP levels and electron dense secretory granules is significantly reduced with NudC knockdown. Interestingly, they also observe the presence of abundant virus-like particles in the nucleus (these structures are thought to originate from retrotransposons and are an indicator of stress). Consistent with increased cellular stress, the authors show activation of JNK signalling. Ultrastructural analysis reveals an abnormally organized ER with an apparent loss of ER-associated ribosomes. They do see other electron dense structures in the cytosol, which they provide evidence (see below) of being P-bodies (structures associated with mRNA). They show that, consistent with a decrease in ribosomes, protein translation is reduced. This is supported by FISH experiments where they show significant decreases in ribosomal RNA (rRNA) transcript levels and decreased translation. Seeing the significant decreases in rRNA levels prompted them to look at overall changes in gene expression, where they discovered that both ribosomal protein gene expression as well as expression of other genes involved in ribosome biogenesis (RiBi) are upregulated with knockdown of NudC. They confirm the changes in mRNA for two genes by showing that levels of the corresponding proteins are also upregulated based on immunostaining of SG cells in which NudC is knocked down. Linking NudC function to a response to defects in RiBi, they shown that SG knockdown of several ribosomal biogenesis factors (RBFs) have similar chromosome structural defects and result in an increase in expression of ribosomal protein genes and of NudC itself. Finally, they show that knock down of genes encoding proteins linked to NudC function in microtubule dynamics do not have any of the same phenotypes as knockdown of NudC and RBFs. Altogether, their data support a moonlighting function for NudC in ribosome biogenesis. Moreover, defects in RiBi wherein ribosomal RNAs are decreased seem to result in compensatory changes where both RBFs and ribosomal protein genes are upregulated.

      Major issues:

      The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

      Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

      Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

      It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

      Minor points:

      Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

      Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

      Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

      Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

      Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

      Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

      Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

      Figure 6A. Hoechst is misspelled.

      Referee cross-commenting

      I think the other reviewers have valid criticisms. I think among the most critical issues to sort out is (1) what is wrong with the chromosomes, (2) are diploid tissues also affected, (3) are the RIBI phenotypes a primary or secondary consequence of nudC loss. I'm not sure how easy it is to do ribosomal profiling on tissues dissected from larvae as the third reviewer is suggesting.

      Significance

      It is a novel discovery that a protein regulating microtubule dynamics is moonlighting, presumably in the nucleolus, to regulate rRNA synthesis or stabilization. A little information regarding mechanism of action would make this a much more exciting paper - how does it do it? Right now, it is unclear whether rRNA synthesis or maintenance is being regulated and there are no hypotheses regarding how this protein localizes to nucleoli and exactly what it is doing there. Is it regulating all RNA Pol I-dependent transcription? Is it involved in processing or stabilizing rRNAs? The description of the chromosomal defects also fall short of satisfying. As is, this paper probably of most interest to those who study ribosome biogenesis - an important topic, but without more mechanistic insight, not so interesting to a more general audience.

      My expertise

      I am an experienced Drosophila biologist who is familiar with the system and who fully understands all of the experiments presented in this manuscript and the relevance of the findings.