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Reply to the reviewers

Manuscript number: RC-2025-02879 Corresponding author(s): Matteo Allegretti; Alia dos Santos

1. General Statements

In this study, we investigated the effects of paclitaxel on both healthy and cancerous cells, focusing on alterations in nuclear architecture. Our novel findings show that:

• Paclitaxel-induced microtubule reorganisation during interphase alters the perinuclear distribution of actin and vimentin. The formation of extensive microtubule bundles, in paclitaxel or following GFP-Tau overexpression, coincides with nuclear shape deformation, loss of regulation of nuclear envelope spacing, and alteration of the nuclear lamina.

• Paclitaxel treatment reduces Lamin A/C protein levels via a SUN2-dependent mechanism. SUN2, which links the lamina to the cytoskeleton, undergoes ubiquitination and consequent degradation following paclitaxel exposure.

• Lamin A/C expression, frequently dysregulated in cancer cells, is a key determinant of cellular sensitivity to, and recovery from, paclitaxel treatment.

Collectively, our data support a model in which paclitaxel disrupts nuclear architecture through two mechanisms: (i) aberrant nuclear-cytoskeletal coupling during interphase, and (ii) multimicronucleation following defective mitotic exit. This represents an additional mode of action for paclitaxel beyond its well-established mechanism of mitotic arrest.

We thank the reviewers for their time and constructive feedback. We have carefully considered all comments and have carried out a full revision. The updated manuscript now includes additional data showing:

• Overexpression of microtubule-associated protein Tau causes similar nuclear aberration phenotypes to paclitaxel. This supports our hypothesis that increased microtubule bundling directly leads to nuclear disruption in paclitaxel during interphase.

• Paclitaxel's effects on nuclear shape and Lamin A/C and SUN2 expression levels occur independently of cell division.

• Reduced levels of Lamin A/C and SUN2 upon paclitaxel treatment occur at the protein level via ubiquitination of SUN2.

• The effects of paclitaxel on the nucleus are conserved in breast cancer cells.

Full Revision

We have also edited our text and added further detail to clarify points raised by the reviewers. We believe that our revised manuscript is overall more complete, solid and compelling thanks to the reviewers' comments.

1. Point-by-point description of the revisions

Reviewer #1 Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown.

We thank the reviewer for this valuable comment. To elucidate the mechanism behind the decrease in Lamin A/C and SUN2 levels, we have now performed several additional experiments. First, we performed RT-qPCR to quantify mRNA levels of these genes, relative to the housekeeping gene GAPDH (Supplementary Figure 3B and O). The levels of SUN2 and LMNA mRNA remained the same between control and paclitaxel-treated cells, indicating that this effect instead occurs at the protein level. We have also tested post-translational modifications as a potential regulatory mechanism for Lamin A/C and SUN2. In addition to the phosphorylation of Ser404 which we had already tested (Supplementary Figure 3C), we have now included additional Phos-tag gel and Western blotting data showing that the overall phosphorylation status of Lamin A/C is not affected by paclitaxel (Supplementary Figure 3E and F). We also pulled-down Lamin A/C from cell lysates and then Western blotted for polyubiquitin and acetyl-lysine, which showed that the ubiquitination and acetylation states of Lamin A/C are also not affected by paclitaxel (Supplementary Figure 3G-I). However, Western blots for polyubiquitin of SUN2 pulled down from cell lysates showed that paclitaxel treatment results in significant SUN2 ubiquitination (Figure 3M and N). Therefore, we propose that the downregulation of SUN2 following paclitaxel treatment occurs by ubiquitin-mediated proteolysis.

The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

We addressed this important point by using an alternative method to stabilise/bundle microtubules in interphase, namely by overexpressing GFP-Tau, as suggested by reviewer 2. Following GFP- Tau overexpression, large microtubule bundles were observed throughout the cytoplasm (Figure 4A), and this resulted in a significant decrease in nuclear solidity (Figure 4B). Furthermore, in cells where microtubule bundles extensively contacted the nucleus, the nuclear lamina became unevenly distributed and appeared patchy (Figure 4C). This supports our hypothesis that the aberrations to nuclear shape and Lamin A/C localisation in paclitaxel-treated cells are due to the presence of microtubules bundles surrounding the nucleus.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

We thank the reviewer for raising this important point. We have addressed this by conducting additional repeats for the cell confluency measurements to increase the statistical power of our experiments (Figure 5A). Our data now show that GFP-lamin A/C had a statistically significant effect on rescuing cell growth at both 1 nM and 5 nM paclitaxel, while Lamin A/C knockdown exacerbated the inhibition of cell growth at 5 nM paclitaxel but not 1 nM paclitaxel (Figure 5A). In addition, we note that the duration of paclitaxel treatment before assessing viability was specified in the figure legend: "Bar graph comparing cell viability between wild-type (red), GFP-Lamin A/C overexpression (green), and Lamin A/C knockdown (blue) cells following 20 h incubation in 0, 1, 5, or 10 nM paclitaxel." We also repeated cell viability analysis after 48 h incubation in paclitaxel instead of 20 h to allow for a longer time for differences to take effect (Figure 5B).

We also added figures showing the cytoskeletal reorganisation at both 1 and 10 nM in addition to 0 and 5 nM (Supplementary Figure 1A) showing that microtubule bundling and condensation of actin into puncta correlated with increased paclitaxel concentration. Vimentin colocalised well with microtubules at all concentrations.

We have also included in our results section further clarification for the use of 5nM paclitaxel in this study. The new section reads as follows: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

We thank the reviewer for pointing out the need for clarification in the wording of our manuscript. We have reworded the title and relevant sections of our abstract, introduction, and discussion to make it clearer that the effects of paclitaxel on the nucleus are due to a combination of aberrant nuclear cytoskeletal coupling during interphase and multimicronucleation following mitotic slippage. We have also added additional data in support of the effect of paclitaxel on nuclear architecture during interphase. For this, we used serum-starved cells (which divide only very slowly such that the majority of cells do not pass through mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data confirmed that paclitaxel's effects on nuclear solidity, and Lamin A/C and SUN2 proteins levels can occur independently of cell division (Figure 2C; Figure 3H-J). Finally, when we overexpressed GFP-Tau (as discussed above) we observed similar aberrations to nuclear solidity and Lamin A/C localisation. This indicates that these effects occur due to microtubule bundling in interphase, especially as in our study GFP-Tau did not lead to multimicronucleation or appear to affect mitosis (Figure 4).

Below are the main changes to the text regarding the interphase effect of paclitaxel:

• Title: "Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling"

• Abstract: "Overall, our data supports nuclear architecture disruption, caused by both aberrant nuclear-cytoskeletal coupling during interphase and exit from defective mitosis, as an additional mechanism for paclitaxel beyond mitotic arrest."

• Introduction: "Here we propose that cancer cells have increased vulnerability to paclitaxel both during interphase and following aberrant mitosis due to pre-existing defects in their NE and nuclear lamina."

• Discussion: "Overall, our work builds on previous studies investigating loss of nuclear integrity as an anti-cancer mechanism of paclitaxel separate from mitotic arrest14,20,21. We propose that cancer cells show increased sensitivity to nuclear deformation induced by aberrant nuclear-cytoskeletal coupling and multimicronucleation following mitotic slippage. Therefore, we conclude that paclitaxel functions in interphase as well as mitosis, elucidating how slowly growing tumours are targeted."

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

As mentioned above, we have now included additional information in our Results section to clarify our paclitaxel dose range: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

We appreciate the reviewer's summary and thank them for their time. We believe our comprehensive revisions have addressed all comments, strengthening the manuscript and making it more robust and compelling.

Reviewer #2 Evidence, reproducibility and clarity This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state- of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

We thank the reviewer for the summary and for highlighting our efforts in using the latest imaging technical advances.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

We thank the reviewer for highlighting the lack of clarity in our wording. We have revised the title, abstract and relevant sections of the introduction and discussion to clarify our message that the effects of paclitaxel on the nucleus arise from a combination of aberrant nuclear-cytoskeletal coupling during interphase and multimicronucleation following exit from defective mitosis. We have also included additional data where we used slow-dividing, serum-starved cells (under these conditions, the majority of cells do not undergo mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data show that even in these cells there is a clear effect of paclitaxel on nuclear solidity, and Lamin A/C and SUN2 protein levels, further supporting our hypothesis that these phenotypes can occur independently of cell division (Figure 2C; Figure 3H-J). Furthermore, we performed additional experiments where we used overexpression of GFP-Tau as an alternative method of stabilising microtubules in interphase and observed similar aberrations to nuclear solidity and Lamin A/C localisation. As GFP-Tau overexpression did not lead to micronucleation or appear to affect mitosis, these data support the hypothesis that nuclear aberrations occur due to microtubule bundling in interphase (Figure 4). We discuss these experiments in more detail below. Finally, we have reworded the introduction to better introduce alternative hypotheses and mechanisms for paclitaxel's activity.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

We would like to note that the paper from Xiang-Xi Xu's lab (Smith et al, 2021) was cited in our original manuscript (reference 14 in both the original and revised manuscripts). We have now also included additional review articles from the Xiang-Xi Xu lab (PMID:36368286 20 and PMID: 35048083 21). Furthermore, we have clarified the wording in both the introduction and discussion to better reflect the current understanding of paclitaxel's mechanism and alternative hypotheses.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

We thank the reviewer for suggesting this control experiment using the microtubule stabiliser Tau. We have now included these experiments in the revised version of the manuscript (Figure 4). The overexpression of GFP-Tau supports our hypothesis that cytoskeletal reorganisation in paclitaxel exerts mechanical stress on the nucleus during interphase, resulting in nuclear deformation and aberrations to the nuclear lamina. In particular, GFP-Tau overexpression resulted in large microtubule bundles throughout the cytoplasm (Figure 4A). Notably, in cells where these bundles extensively contacted the nucleus, we observed a significant decrease in nuclear solidity (Figure 4B) accompanied by changes in nuclear lamina organisation, including a patchy lamina phenotype, similar to that induced by paclitaxel (Figure 4C).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

We thank the reviewer for raising important points regarding the specificity of paclitaxel's effects. We agree that microtubule stabilisation can induce myriad cellular changes, including alterations to focal adhesions and other cytoskeletal components. Our focus on Lamin A/C and nuclear morphology is grounded both in the established clinical relevance of nuclear mechanics in cancer and builds on mechanistic work from other groups.

Lamin A/C expression is commonly altered in cancer, and nuclear morphology is frequently used in cancer diagnosis35. Lamin A/C also plays a crucial role in regulating nuclear mechanics32 and, importantly, determines cell sensitivity to paclitaxel14. However, the mechanism by which Lamin A/C determines sensitivity of cancer cells to paclitaxel is unclear.

Our data are consistent with Lamin A/C being a determinant of paclitaxel survival sensitivity. We also provide evidence that paclitaxel itself reduces Lamin A/C protein levels and disrupts its organisation at the nuclear envelope. We directly link these effects to microtubule bundling around the nucleus and degradation of force-sensing LINC component SUN2, highlighting the importance of nuclear architecture and mechanics to overall cellular function. Furthermore, we show that recovery from paclitaxel treatment depends on Lamin A/C expression levels. This has clinical relevance, as unlike cancer cells, healthy tissue with non-aberrant lamina would be able to selectively recover from paclitaxel treatment.

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

We agree with the reviewer that Supplementary Figure 6 does not add significant value to the manuscript and thank the reviewer for pointing this out. We have removed it from the manuscript accordingly.

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

We thank the reviewer for pointing out the missing information. For our cryo-ET experiments, we collected data from 9 lamellae from paclitaxel-treated cells and 6 lamellae from control cells, with each lamella derived from a single cell. This information has now been added to the figure legend (Figure 2F).

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

We appreciate the reviewer's concerns and have performed major revisions to strengthen the significance of our study. Specifically, we conducted two key sets of experiments to validate our original conclusions: serum starvation to control for the effects of cell division, and overexpression of the microtubule stabiliser Tau to demonstrate that paclitaxel can affect the nucleus via its microtubule bundling activity in interphase.

By elucidating the mechanistic link between microtubule stabilisation and nuclear-cytoskeletal coupling, our findings contribute to our understanding of paclitaxel's multifaceted actions in cancer cells.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

Reviewer #3 Evidence, reproducibility and clarity The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

We thank the reviewer for the positive feedback.

Although similar ideas are published, which may be suitable to be cited? • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review. • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

We thank the reviewer for bringing to our attention these important review articles. In our initial manuscript, we only cited the original paper (14, also reference 14 in the original manuscript). We have now included citations to the suggested publications (20,21).

We would also like to emphasise how our manuscript distinguishes itself from the work of Smith et al.14,20,21:

• Cell-type focus: In their study 14, Smith et al. examined the effect of paclitaxel on malignant ovarian cancer cells and proposed that paclitaxel's effects on the nucleus are limited to cancer cells. However, our data extends these findings by demonstrating paclitaxel's effects in both cancerous and non-cancerous backgrounds.

• Cytoskeletal reorganisation: Smith et al. show reorganisation of microtubules in paclitaxel-treated cells14. Our data show re-organisation of other cytoskeletal components, including F-actin and vimentin.

• Multimicronucleation: Smith et al. propose that paclitaxel-induced multimicronucleation occurs independently of cell division14. Although we observe progressive nuclear abnormalities during interphase over the course of paclitaxel treatment, our data do not support this conclusion; we find that multimicronucleation occurs only following mitosis.

• Direct link between microtubule bundling and nuclear aberrations: We show that nuclear aberrations caused by paclitaxel during interphase (distinct from multimicronucleation) are directly linked to microtubule bundling around the nucleus, suggesting they result from mechanical disruption and altered force propagation.

• Lamin A/C regulation: Consistent with Smith et al.14, we show that Lamin A/C depletion leads to increased sensitivity to paclitaxel treatment. However, we further demonstrate that paclitaxel itself leads to reduced levels of Lamin A/C and that this effect occurs independently of mitosis and is mediated via force-sensing LINC component SUN2. Upon SUN2 knockdown, Lamin A/C levels are no longer affected by paclitaxel treatment.

• Recovery: Finally, our work reveals that cells expressing low levels of Lamin A/C recover less efficiently after paclitaxel removal. This might help explain how cancer cells could be more susceptible to paclitaxel.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

We thank the reviewer for this comment. Our initial study aimed to understand the effects of paclitaxel on nuclear architecture in non-aberrant backgrounds. To show that the observed effects of paclitaxel are also applicable to cancer cells, we have now repeated our main experiments using MDA-MB-231 human breast cancer cells (Supplementary Figure 1B; Supplementary Figure 3P-T). Similar to our findings in human fibroblasts, paclitaxel treatment of MDA-MB-231 led to cytoskeletal reorganisation (Supplementary Figure 1B), a decrease in nuclear solidity (Supplementary Figure 3P), aberrant (patchy) localisation of Lamin A/C (Supplementary Figure 3Q), and a reduction in Lamin A/C and SUN2 levels (Supplementary Figure 3R-T).

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

We have added further detail to the figure legend for clarification: "Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Different colours distinguish individual α-tubulin clusters, representing individual microtubule filaments or filament bundles."

Generally, the figures need additional description to be clear.

We have added further clarification and detail to our figure legends.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

We changed this sentence to: "Figure 3 - Paclitaxel treatment results in aberrant organisation of the nuclear lamina and decreased Lamin A/C levels via SUN2."

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?.

We thank the reviewer for pointing this out and we agree that the ratio of Lamin A to Lamin C can vary with culture conditions. To confirm that paclitaxel treatment reduces total Lamin A/C levels regardless of this ratio, we repeated the Western blot analysis in three additional biological replicates using cells in which Lamin C levels exceeded Lamin A levels. These experiments confirmed a comparable decrease in total Lamin A/C levels. Figure 3B and 3C have been updated accordingly.

Also, the effect on Lamin A/C and SUN2 levels are not significant of robust.

Decreased Lamin A/C and SUN2 levels following paclitaxel treatment were consistently seen across three or more biological repeats (Figure 3B-C), and this could be replicated in a different cell type (MDA-MB-231) (Supplementary Figure 3R-T). Furthermore, Western blotting results are consistent with the patchy Lamin A/C distribution observed using confocal and STORM following paclitaxel treatment (Figure 3A; Supplementary Figure 3A), where Lamin A/C appears to be absent from discrete areas of the lamina.

Any mechanisms are speculated for the reason for the reduction?

We have now included additional data which aims to shed light on the mechanism behind the decrease in Lamin A/C and SUN2 levels following paclitaxel treatment. We found that SUN2 is selectively degraded during paclitaxel treatment. Immunoprecipitation of SUN2 followed by Western blotting against Polyubiquitin C showed increased SUN2 ubiquitination in paclitaxel (Figure 3M and N). Furthermore, in our original manuscript, we showed that Lamina A/C levels remained unaltered during paclitaxel treatment in cells where SUN2 had been knocked down. We propose that changes in microtubule organisation affect force propagation to Lamin A/C specifically via SUN2 and that this leads to Lamina A/C removal and depletion. Future work will be needed to fully understand this mechanism.

In addition to the findings described above, we report no significant changes in mRNA levels for LMNA or SUN2 in paclitaxel (Supplementary Figure 3B and O). Phos-tag gels followed by Western blotting analysis for Lamin A/C also did not detect changes to the overall phosphorylation status of Lamin A/C due to paclitaxel treatment. This is in agreement with our initial data showing no changes to Lamin A/C Ser 404 phosphorylation levels (Supplementary Figure 3E and F). Finally, Lamin A/C immunoprecipitation experiments followed by Western blotting for Polyubiquitin C and acetyl-lysine showed no significant changes in the ubiquitination and acetylation state of Lamin A/C in paclitaxel-treated cells (Supplementary Figure 3G-I).

Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

The nuclear lamina and LINC complex proteins play a critical role in regulating nuclear integrity, stiffness and mechanical responsiveness to external forces28,31-33,54,75, as well as in maintaining the nuclear intermembrane distance69,74. In particular, SUN-domain proteins physically bridge the nuclear lamina to the cytoskeleton through interactions with Nesprins, thereby preserving the perinuclear space distance30,69,74. Mutations in Lamins have been shown to disrupt chromatin organization, alter gene expression, and compromise nuclear structural integrity, and experiments with LMNA knockout cells reveal that nuclear mechanical fragility is closely coupled to nuclear deformation47. Furthermore, nuclear-cytoskeletal coupling is essential during processes such as cell migration, where cells undergo stretching and compression of the nucleus; weakening or loss of the lamina in such cases compromises cell movement47,73. In our work, we show that alterations to nuclear Lamin A/C and SUN2 by paclitaxel treatment coincide with nuclear deformations (Figure 2A-D, F, G; Figure 3A-D, F, G; Supplementary Figure 3A, P-T) and that these deformations are reversible following paclitaxel removal (Supplementary Figure 4B-D). Our experiments also demonstrate that Lamin A/C expression levels significantly influence cell growth, cell viability, and cell recovery in paclitaxel (Figure 5). Therefore, drawing on current literature and our results, we propose that, during interphase, paclitaxel induces severe nuclear aberrations through the combined effects of: i) increased cytoskeletal forces on the NE caused by microtubule bundling; ii) loss of ~50% Lamin A/C and SUN2; iii) reorganisation of nucleo-cytoskeletal components.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

We thank the reviewer for the feedback and valuable suggestions. In response, we have included experiments using human breast cancer cell line MDA-MB-231 to further corroborate our findings and interpretations. We believe these additions have improved the clarity, robustness and impact of our manuscript, and we are grateful for the reviewer's contributions to its improvement.

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 presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years. Although similar ideas are published, which may be suitable to be cited?

• Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review.
• Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

Generally, the figures need additional description to be clear.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?. Also, the effect on Lamin A/C and SUN2 levels are not significant of robust. Any mechanisms are speculated for the reason for the reduction? Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

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 investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state-of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

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Referee #1

Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown. The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

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Reply to the reviewers

Manuscript number: RC-2025-02946

Corresponding author(s): Margaret, Frame

Roza, Masalmeh

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Reviewer #1

Evidence, reproducibility and clarity

Review of Masalmeh et al. Title: "FAK modulates glioblastoma stem cell energetics..."

Previous studies have implicated FAK and the related tyrosine kinase PYK2 in glioblastoma growth, cell migration, and invasion. Herein, using a murine stem cell model of glioblastoma, the authors used CRISPR to inactivate FAK, FAK-null cells selected and cloned, and lentiviral re-expression of murine FAK in the FAK-null cells (termed FAK Rx) was accomplished. FAK-/- cells were shown to possess epithelial characteristics whereas FAK Rx cells expressed mesenchymal markers and increased cell migration/invasion in vitro. Comparisons between FAK-/- and FAK Rx cells showed that FAK re-expressed increased mitochondrial respiration and amino acid uptake. This was associated with FAK Rx cells exhibiting filamentous mitochondrial morphology (potentially an OXPHOS phenotype) and decreased levels of MTFR1L S235 phosphorylation (implicated in mito morphology fragmentation). Mito and epithelial cell morphology of FAK-/- cells was reversed by treatment with Rho-kinase inhibitors that also increased mito metabolism and cell viability. Last, FAK-dependent glioblastoma tumor growth was shown by comparisons of FAK-/- and FAK Rx implantation studies.

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

Some questions that would enhance potential impact. 1. Cell generation. Please describe the analysis of FAK-/- clones in more detail. The "low viability" phenotype needs further explanation with regard to clonal expansion and growth characteristics?

Response:

• We included a better description and a supplementary figure in our revised manuscript to indicate that we have examined several FAK -/- clones and confirmed that our observations were not due to clonal variation; multiple clones displayed similar morphological changes (Figure S1D). We also show that the elongated mesenchymal-like morphology was observed at 48 h after nucleofecting the cells with the FAK‑expressing vector, before beginning G418 selection to enrich for cells expressing FAK (Figure S1C). We also included experiments to acutely modulate FAK signalling (detaching and seeding cells on fibronectin) (Figure S2D, E, F and Figure S3) to exclude the possibility that the profound effects are due to protocols/selection we used for generating FAK-deleted cells.
• Regarding the term "low viability", we have clarified in the text that there is no significant difference in cell number (Figure S1A) or 'cell viability' when it is assessed by trypan blue exclusion (a non-mitochondria-dependent read-out) (Figure S1B) between FAK-expressing FAK Rx and FAK-/- cells cultured for three days under normal conditions. Therefore, we agree the term 'cell viability' in this context could be confusing and have replace "cell viability" with "metabolic activity as measured by Alamar Blue." in Figure 1D and Figure 5B, and the corresponding text in the original manuscript. This wording more accurately reflects the data.

Figure 1F: need further support of MET change upon FAK KO and EMT reversion.

Response: We have added a heatmap (Figure S1E) illustrating the changes in protein expression of core-enriched EMT/MET genes products (by proteomics) after FAK gene deletion (EMT genes as defined in Howe et al., 2018) ; this strengthens the conclusion that the MET reversion morphological phenotype is accompanied by recognised MET protein changes.

Fig. 2: Need further support if FAK effects impact glycolysis or oxidative phosphorylation in particular as implicated by the stem cell model.

Response: We show that FAK impacts both glycolysis (Figure 2A, 2E, and 2F) and mitochondrial oxidative phosphorylation on the basis of the oxygen consumption rate (OCR) (Figure 2B, and 2D), showing both are contributing pathways to FAK-dependent energy production. We have clarified this in the text.

Is there a combinatorial potential between FAKi and chemotherapies used for glioblastoma. Need to build upon past studies.

Response: Yes, previous studies suggest that inhibiting FAK can sensitize GBM cells to chemotherapy (Golubovskaya et al., 2012; Ortiz-Rivera et al., 2023). We have included a paragraph in the discussion section to make sure this is clearer. Although it is not the subject of this study, we appreciate it is useful context.

The notation of changes in glucose transporter expression should be followed up with regard to the potential that FAK-expressing cells may have different uptake of carbon sources and other amino acids. Altered uptake could be one potential explanation for increase glycolysis and glutamine flux.

Response: We agree with the reviewer that glucose uptake could be contributing and we include data that 2 glucose transporters are indeed FAK-regulated namely Glucose transporter 1 (GLUT1, encoded by Slc2a1 gene) and Glucose transporter 3 (GLUT 3, encoded by Slc2a3 gene) (shown in Figure S2B and C).

It would be helpful to support the confocal microscopy of mitos with EM.

Response:

We are concerned (and in our experience) that Electron microscopy (EM) may introduce artefacts during sample preparation. In contrast, immunofluorescence sample preparation is less susceptible to artefacts. The SORA system we used is not a conventional point-scanning confocal microscope, but is a super-resolution module based on a spinning disk confocal platform (CSU-W1; Yokogawa) using optical pixel reassignment with confocal detection. This method enhances resolution in all dimensions with resolution in our samples measured at 120nm. This has been instructive in defining a new level of changes in mitochondrial morphology upon FAK gene deletion.

Lack of FAK expression with increased MTFR1 phosphorylation is difficult to interpret.

Response: We do not directly show that this phosphorylation event is causal in our experiments; however, we think it important to document this change since it has been published that phosphorylation of MTFR1 has been causally linked to the mitochondrial morphology we observed in other systems (Tilokani et al., 2022).

Need to have better support between loss of FAK and the increase in Rho signaling. Use of Rho kinase inhibitors is very limited and the context to FAK (and or Pyk2) remains unclear. Past studies have linked integrin adhesion to ECM as a linkage between FAK activation and the transient inhibition of RhoA GTP binding. Is integrin signaling and FAK involved in the cell and metabolism phenotypes in this new model?

Response: To better support the antagonistic effect of FAK on Rho-kinase (ROCK) signalling, we included a new experiment in which the integrin-FAK signalling pathway has been disrupted by treating FAK WT cells with an agent that causes detachment from the substratum, Accutase, and growing the cells in suspension in laminin-free medium. We present ROCK activity data, as judged by phosphorylated MLC2 at serine 19 (pMLC2 S19), relating this to induced FAK phosphorylation at Y397 (a surrogate for FAK activity) that is supressed after integrin disengagement. These measurements have been compared with conditions whereby integrin-FAK signalling is activated by growing the cells on laminin coated surfaces. We observed a time-dependent decrease in pFAK(Y397) levels (normalised to total FAK) in suspended cells compared to those spread on laminin, while pMLC2(S19) levels increased in a reciprocal manner over time in detached cells relative to spread cells (S4A and B). There is therefore an inverse relationship between integrin-FAK signalling and ROCK-MLC2 activity, consistent with findings from FAK gene deletion experiments. In the former case, we do not rely on gene deletion cell clones.

Significance

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

__Response: __

Deleting the gene encoding FAK in mouse embryonic fibroblasts leads to elevated Pyk2 expression (Sieg, 2000). However, in the GBM stem cell model we used here, Pyk2 was not expressed (determined by both transcriptomics and proteomics). We have included Figure S1E to show that PYK2 expression was undetectable in FAK -/- and FAK Rx cells at the RNA level (Figure S1F). We conclude that there is no compensatory increase in Pyk2 upon FAK loss in these cells. In the transformed neural stem cell model of GBM, we do not consistently or robustly detect nuclear FAK.

Review #2

Masalmeh and colleagues employ a neural stem/progenitor cell-based glioma model (NPE cells) to investigate the role of Focal Adhesion Kinase (FAK) in GBM, with a focus on potential links between the regulation of morphological/adhesive and metabolic GBM cell properties. For this, the authors employ wt cells alongside newly generated FAK-KO and -reexpressing cells, as well as pharmacological interventions to probe the relevance of specific signaling pathways. The authors´ main claims are that FAK crucially modulates glioma cell morphology, cell-cell and cell-substrate interactions and motility, as well as their metabolism, and that these effects translate to changes to relevant in vivo properties such as invasion and tumor growth.

My main issues are with the model chosen by the authors.

As per the methods section, generation of FAK-KO and -"Rx" NPE cells entailed protracted selection/expansion processes, which may have resulted in inadvertent selection for cellular/molecular properties unrelated to the desired one (loss or gain of FAK expression) and which may have had cascading effects on NPE cells. The authors nonetheless repeatedly claim the parameters they quantify, such as mitochondrial or cytoskeletal properties or metabolic features, to have directly resulted from FAK loss or reintroduction. Examples of such causal inferences are to be found in lines 123, 134/135, 165, 181. Such causal claims are, in my view, unsupported.

Acute perturbation of FAK expression/activity, genetically or pharmacologically, followed by a rapid assessment of the processes under investigation, would be needed to begin to assess causality, even if acute genetic perturbations may be technically challenging as sufficient gene expression reduction or restoration to physiologically relevant levels may be hard to achieve.

Response:

We would like to first comment on the model we used here, which we think will clarify the validity of our approach. The model is a transformed stem cell model of GBM that was published in (Gangoso et al., Cell, 2021) and is now used regularly in the GBM field. As mentioned in the response to Reviewer 1, we have added text (page 4 and 5 in the revised manuscript) and a new supplementary figure (Figure S1D) clarifying that the morphological changes we observed were consistent across multiple FAK -/- clones, showing this was not due to any inter-clonal variability. We also added images showing that the morphological changes were apparent at 48 h after nucleofecting FAK -/- cells with the FAK‑expressing vector specifically (not the empty vector), prior to starting G418 selection to enrich for FAK‑expressing cells (Figure S1C), addressing the worry that clonal variation and selection was the cause of the FAK-dependent phenotypes we observed. We believe that our model provides a type of well controlled, clean genetic cancer cell system of a type that is commonly used in cancer cell biology, allowing us to attribute phenotypes to individual proteins.

We have also carried out a more acute treatment by using the FAK inhibitor VS4718 to perturb FAK kinase activity and assessed the effects on glycolysis and glutamine oxidation after 48h treatment (Figure S2D, E and F). We found that treating the transformed neural stem cells (parental population) with FAK inhibitor (300nM VS4718) decreases glucose incorporation into glycolysis intermediates and glutamine incorporation into TCA cycle intermediates, consistent with a role for FAK's kinase activity in maintaining glycolysis and glutamine oxidation.

The employed pharmacological modulation of ROCK activity is the only approach that, given the presumably acute nature of the treatment, may have allowed the authors to probe the proposed functional links. The methods section of the manuscript does not however comprise details as to the duration of these treatments, which leaves open the possibility of long-term treatment having been carried out (data shown in Figure 5B refers to 72hr treatment).

__Response: __

We have added the duration of the treatment to the Methods section and Figure Legends, to clarify that cells were treated with ROCK inhibitors for 24h, before assessing the effects on mictochondria (Figure 4C, D, S4C and D) and glutamine oxidation (Figure 5A, and S5). For metabolic activity by AlamarBlue assay, cells were treated with ROCK inhibitors for 72h (Figure 5B).

Even in the case of ROCK inhibitor experiments, it is however unclear if and how the effects on cell morphology and adhesion, mitochondrial organization and metabolic activity may be connected to each other and, if at all, to FAK expression.

Given the above uncertainties due to the nature of the model and experimental approaches, it is hard to assess the reliability and thus the relevance of the findings.

Response:

FAK suppresses ROCK activity (as judged by pMLC2 S19, Figure 4A and B). Treating FAK -/- cells with two different ROCK inhibitors restored mesenchymal-like cell morphology, mitochondrial morphology and glutamine oxidation. As mentioned above, to strengthen our evidence for the antagonistic role of FAK in ROCK-MLC2 signalling, we have now introduced an experiment whereby integrin-FAK signalling was disrupted through treatment with a detachment agent (Accutase), and subsequently maintaining the cells in suspension in laminin-free medium. We assessed pMLC2 S19 levels (a measure of ROCK activity) relating this to FAK phosphorylation that is supressed after integrin disengagement. These results were evaluated relative to spread wild type cells growing on laminin where Integrin-FAK signalling was active (Figure S4A and B). We observed an inverse relationship between Integrin-FAK signalling and ROCK-MLC2 activity in keeping with our conclusions (Figure 4A and B).

Experimental support for the ability of cell-substrate interaction modulation to concomitantly impact cellular metabolism and motility/invasion would be significant both in terms of advancing our understanding of glioma cell biology and of its translational potential, but the evidence being provided is at best compatible with the proposed model.

Response: We carried out a new experiment to support the ability of cell-substrate interaction modulation to impact metabolism; specifically, we inhibited cell-substrate interactions by plating the cells on Poly-2-hydroxyethyl methacrylate (Poly 2-HEMA)-coated dishes. This suppressed FAK phosphorylation at Y397, as expected, with concomitant reduction in glutamine utilisation in the TCA cycle (Figure S3A, B and C).

My background/expertise is in developmental and adult neurogenesis, in vivo modelling of gliomagenesis and cell fate control/reprogramming, with a focus on molecular mechanisms of differentiation and quantitative aspects of lineage dynamics; molecular details of the control of cellular metabolism, cell-cell adhesion and cytoskeletal dynamics are not core expertise of mine.

We appreciate this reviewer's expertise are not necessarily in the cancer cell biology and genetic intervention aspects of our study. We hope that the explanations we have provided satisfy the reviewer that our conclusions are valid.

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Referee #2

Evidence, reproducibility and clarity

Masalmeh and colleagues employ a neural stem/progenitor cell-based glioma model (NPE cells) to investigate the role of Focal Adhesion Kinase (FAK) in GBM, with a focus on potential links between the regulation of morphological/adhesive and metabolic GBM cell properties. For this, the authors employ wt cells alongside newly generated FAK-KO and -reexpressing cells, as well as pharmacological interventions to probe the relevance of specific signaling pathways. The authors´ main claims are that FAK crucially modulates glioma cell morphology, cell-cell and cell-substrate interactions and motility, as well as their metabolism, and that these effects translate to changes to relevant in vivo properties such as invasion and tumor growth. My main issues are with the model chosen by the authors.

As per the methods section, generation of FAK-KO and -"Rx" NPE cells entailed protracted selection/expansion processes, which may have resulted in inadvertent selection for cellular/molecular properties unrelated to the desired one (loss or gain of FAK expression) and which may have had cascading effects on NPE cells. The authors nonetheless repeatedly claim the parameters they quantify, such as mitochondrial or cytoskeletal properties or metabolic features, to have directly resulted from FAK loss or reintroduction. Examples of such causal inferences are to be found in lines 123, 134/135, 165, 181. Such causal claims are, in my view, unsupported. Acute perturbation of FAK expression/activity, genetically or pharmacologically, followed by a rapid assessment of the processes under investigation, would be needed to begin to assess causality, even if acute genetic perturbations may be technically challenging as sufficient gene expression reduction or restoration to physiologically relevant levels may be hard to achieve.

The employed pharmacological modulation of ROCK activity is the only approach that, given the presumably acute nature of the treatment, may have allowed the authors to probe the proposed functional links. The methods section of the manuscript does not however comprise details as to the duration of these treatments, which leaves open the possibility of long-term treatment having been carried out (data shown in Figure 5B refers to 72hr treatment). Even in the case of ROCK inhibitor experiments, it is however unclear if and how the effects on cell morphology and adhesion, mitochondrial organization and metabolic activity may be connected to each other and, if at all, to FAK expression.

Significance

Given the above uncertainties due to the nature of the model and experimental approaches, it is hard to assess the reliability and thus the relevance of the findings.

Experimental support for the ability of cell-substrate interaction modulation to concomitantly impact cellular metabolism and motility/invasion would be significant both in terms of advancing our understanding of glioma cell biology and of its translational potential, but the evidence being provided is at best compatible with the proposed model.

My background/expertise is in developmental and adult neurogenesis, in vivo modelling of gliomagenesis and cell fate control/reprogramming, with a focus on molecular mechanisms of differentiation and quantitative aspects of lineage dynamics; molecular details of the control of cellular metabolism, cell-cell adhesion and cytoskeletal dynamics are not core expertise of mine.

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

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Referee #1

Evidence, reproducibility and clarity

Review of Masalmeh et al.

Title: "FAK modulates glioblastoma stem cell energetics..."

Previous studies have implicated FAK and the related tyrosine kinase PYK2 in glioblastoma growth, cell migration, and invasion. Herein, using a murine stem cell model of glioblastoma, the authors used CRISPR to inactivate FAK, FAK-null cells selected and cloned, and lentiviral re-expression of murine FAK in the FAK-null cells (termed FAK Rx) was accomplished. FAK-/- cells were shown to possess epithelial characteristics whereas FAK Rx cells expressed mesenchymal markers and increased cell migration/invasion in vitro. Comparisons between FAK-/- and FAK Rx cells showed that FAK re-expressed increased mitochondrial respiration and amino acid uptake. This was associated with FAK Rx cells exhibiting filamentous mitochondrial morphology (potentially an OXPHOS phenotype) and decreased levels of MTFR1L S235 phosphorylation (implicated in mito morphology fragmentation). Mito and epithelial cell morphology of FAK-/- cells was reversed by treatment with Rho-kinase inhibitors that also increased mito metabolism and cell viability. Last, FAK-dependent glioblastoma tumor growth was shown by comparisons of FAK-/- and FAK Rx implantation studies.

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

Some questions that would enhance potential impact.

1. Cell generation. Please describe the analysis of FAK-/- clones in more detail. The "low viability" phenotype needs further explanation with regard to clonal expansion and growth characteristics?
2. Figure 1F: need further support of MET change upon FAK KO and EMT reversion.
3. Fig. 2: Need further support if FAK effects impact glycolysis or oxidative phosphorylation in particular as implicated by the stem cell model.
4. Is there a combinatorial potential between FAKi and chemotherapies used for glioblastoma. Need to build upon past studies.
5. The notation of changes in glucose transporter expression should be followed up with regard to the potential that FAK-expressing cells may have different uptake of carbon sources and other amino acids. Altered uptake could be one potential explanation for increase glycolysis and glutamine flux.
6. It would be helpful to support the confocal microscopy of mitos with EM.
7. Lack of FAK expression with increased MTFR1 phosphorylation is difficult to interpret.
8. Need to have better support between loss of FAK and the increase in Rho signaling. Use of Rho kinase inhibitors is very limited and the context to FAK (and or Pyk2) remains unclear. Past studies have linked integrin adhesion to ECM as a linkage between FAK activation and the transient inhibition of RhoA GTP binding. Is integrin signaling and FAK involved in the cell and metabolism phenotypes in this new model?

Significance

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

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Reply to the reviewers

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

This manuscript addresses the question of whether inhibitors of the phosphatases Eya1-4 and of the kinase PLK1 provide an effective therapeutic approach to a range of cancers. Both Eyas and PLK1 have well documented roles in development, and have been implicated in a subset of tumors. Moreover, the authors have previously shown that PLK1 is a substrate of Eya phosphatase activity. Building on these previous findings, the authors assess the possibility of combining an Eya inhibitor, benzarone, with a PLK1 inhibitor, BI2536.

There are several concerns with the study: 1. The authors suggest that these two drugs are synergistic. Synergy is usually taken as indicative of a greater than additive effect of the two drugs. The ZIP synergy score tested here indicates that the combination of the two drugs has a synergy score between 0 and 10 (figure1, and figure 5). According to "Synergy Finder", "A ZIP synergy score of greater than 10 often indicates a strong synergistic effect, while a score less than -10 suggests a strong antagonistic effect. Scores between -10 and 10 are typically considered additive or near-additive." The data in figure 2 on mitotic cell fraction and on cell death also seems to be more of an additive effect of the two drugs than synergy. The data in figure 3 are also additive effects on RAD51. Therefore a conclusion that "These data indicate that the drug combination was broadly synergistic" seems unwarranted.

There is a general lack of nomenclature standardisation for defining synergy. Furthermore, multiple synergy models exist, with discrepancies between them. However, as the reviewer states, the prevailing view is that synergy is a combination effect that is stronger than the additive effect of the two drugs. Synergy scores derived from dose-response matrices using different synergy scoring models with scores that fall above 5 are considered truly synergistic (Malyutina A et al., 2019). To strengthen our conclusion of synergy between PLK1 and EYA inhibitors, we have calculated synergy scores using additional synergy models for both benzarone + BI2536 and benzarone + volasertib in H4 and T98G cell lines. Specifically, we find robust synergy (>5) using ZIP, HSA and Bliss calculations with the Benzarone + BI2536 drug combination in H4 cells and with Benzarone + Volasertib in H4 and T98G cells. Synergy scores for Benzarone + BI2536 fell just below 5 in T98G cells. These data are now included in Supplemental Fig S1G of the revised manuscript.

The discovery of synergistic drug combinations can be further strengthened by evaluating synergy across multiple cellular models. In this study, we have tested a total of 27 different cancer models that universally support synergy.

Regarding the phenotypic outcomes (mitotic cell fraction, cell death, RAD51 foci), we agree that the observed effects are additive. This is consistent with overall synergistic effects on viability being caused by a combination of additive mechanistic effects. We have amended the text in the revised manuscript to clarify this point.

There was no statistical difference in the synergy scores of the "high expressing" versus "low expressing cells". So the conclusion that the drug combination "t was effective at lower doses in cell lines with high levels of EYA1 and/or EYA4" seems unwarranted based on the data. Moreover, since there was no statistical difference in synergy between high and low expressing cells, stating that "the potential utility of the combination treatment depends on the specific overexpression of EYA1 and/or EYA4 in cancer cells," seems unwarranted by the data.

Synergy scores quantify the interaction between drugs, but do not capture absolute treatment effectiveness or dose sensitivity, both of which are crucial for therapeutic considerations. We have included the following sentence in the revised manuscript to clarify this distinction: “While synergy scores did not significantly differ between high and low EYA expressors, high EYA1/4 expression was associated with increased sensitivity to the combination treatment at lower doses, as evidenced by decreased cell viability.” We have also amended the conclusions in the Abstract and Discussion to reflect that the potential utility of the combination therapy in EYA1/4-high cancers is supported by potency rather than synergy scores alone.

Benzarone and benzbromarone and their derivatives have been shown to bind and inhibit Eya phosphatases, albeit at fairly high doses. However, these two compounds also have a number of other, unrelated targets. The only demonstration that Eyas are a target of benzarone in this study are the CETSA data in supplemental figure 1. The data here seem to represent an n of 1, with no error bars shown. Even more importantly, there is no control. Looking at the blot of actin, it seems as if there may be a benzarone- temperature effect on this protein as well. It would be very helpful to show some evidence that knockdown of Eya similarly synergizes with the PLK1 inhibitor, show data that benzarone is in fact inhibiting Eya activity in these cells by looking at known targets (ie the carboxyterminal tyrosine of H2AX), and other evidence of specificity.

The specificity of benzarone to the EYA proteins has been demonstrated previously using both in vitro phosphatase assays and the assessment of EYA-mediated pathways (Tadjuidje et al., 2012; Wang et al., 2021; Nelson et al., 2024). These publications have been cited in the manuscript. In addition, benzarone produces phenotypes consistent with the known functions of the EYAs (ie, reduction of PLK1 activity, reduction in RAD51 foci, G2/M arrest, and apoptosis). To further validate EYA target specificity, we have performed viability assays on control and EYA4-depleted HeLa, H4 and T98G cells in response to BI2536 treatment, demonstrating EYA4 depletion-mediated sensitization to BI2536. These data are now included in Fig 1H of the revised manuscript.

To strengthen our CETSA data, we have now included: (i) densitometry of actin, demonstrating a lack of benzarone-temperature effect, (ii) CETSA analysis for an additional cell line (T98G), demonstrating enhanced thermal stability of the EYAs in the presence of benzarone, and (iii) CETSA analysis of an additional protein (BUB1) to demonstrate target specificity. These data are now included in Supplemental Fig S1E and F of the revised manuscript.

The proteomic and transcriptomic data of cell lines that were vulnerable to the combination of BI2536 and benzarone implicate overall changes in chromatin with sensitivity. These findings call into question the idea that these two compounds are acting selectively on PLK1 and Eyas. The authors don't really provide any model for explaining this correlation of Nurd complex components with targeting Eyas and PLK1.

The proteomic and transcriptomic data demonstrate that sensitivity to the combination treatment is associated with higher expression of NuRD complex members and other chromatin regulators. This suggests that cell lines with certain chromatin configurations might be more susceptible to the combined inhibition of PLK1 and EYA. This does not undermine the demonstrated on-target effects of the two compounds, but rather suggests a potential contextual dependence of drug efficacy on chromatin state. Our data thereby implicate NuRD complex expression as a predictive biomarker for tumours that are likely to respond to EYA and PLK1 combination therapy. This has now been clarified in the discussion section of the revised manuscript.

Specificity of antibodies: I would like to see validation of the Eya antibodies, given the difficulty with such reagents in the field.

All EYA antibodies have now been validated by western blot analysis following siRNA-mediated depletion. These data are presented in Supplemental Fig S1A of the revised manuscript.

Reviewer #1 (Significance (Required)):

New therapies targeting glioblastoma would be welcome. It is not clear that the combination tested here is an effective approach to therapy. It would be necessary to know the targets of the combination and understand the mechanism so that the approach could be pursued further,

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

This study explores the sensitivity of cancer cell lines, particularly GBM cells, to dual inhibition of EYA and PLK1, aiming to uncover the connection between these pathways and the cancer stem cell state. Additionally, it investigates whether the NuRD complex modulates GBM cell responses to EYA and PLK1 inhibition. While the findings are interesting, further clarification is needed to establish the mechanistic links between EYA, PLK1, and NuRD, as well as a stronger rationale for their targeted inhibition in GBM therapy- this can be better clarified.

Some key comments and recommendations: The findings demonstrate that the combination of Benzarone (EYAi) and volasertib (PLKi) significantly reduced cell proliferation in H4 and T98G GBM cell lines, both of which show high expression of EYA. In contrast, the low EYA-expressing A172 cells exhibited limited response. A possible explanation is the inherently slower proliferation rate of A172 cells, which may reduce their dependence on G2/M arrest, thereby diminishing the impact of PLK1i. Does A172 line show a similar growth or cell division rate to H4 and T98G lines.

A172 cells have a slower proliferation rate than H4 or T98G cells, which may diminish their response to EYA/PLK1 inhibitors. However, in this study we have tested a total of 15 cancer cell lines and 12 GBM stem cell line models. No clear correlation between cell growth rate and sensitivity was observed. As a specific example, the low EYA expressing SJSA-1 cell line has a high proliferation rate but is a low responder to EYA1/PLK1 inhibitors.

Additionally, although protein expression levels of EYA were assessed across these cell lines, the activity and expression levels of PLK1 were not fully characterized. Since PLK1 is a crucial regulator of mitotic entry and DNA damage repair, its activity across cell lines may contribute to the observed variations in drug sensitivity. Could the authors investigate levels of PLK in these cell lines?

To address this point, we compared PLK1 expression levels across the panel of cancer cell lines used in our study. These data are now included in Supplemental Fig S1D of the revised manuscript, and show that PLK1 levels are comparable across the cell lines, indicating that baseline PLK1 abundance does not fully explain the observed differential sensitivity.

The study describes the combination treatment as synergistic in H4 and T98G cells, however this synergy is unclear in Fig 2A and Supplemental Fig S2A. The data suggest that H4 and T98G cells exhibit sensitivity to either EYA or PLK1 inhibition alone, with combined treatment showing enhanced effects rather than synergy. This distinction is evident as BI2536 alone induces robust G2/M arrest with decreased G1 and S phase cells. To validate these findings, combination treatment should be tested in additional GBM cell lines. Additionally, repeating FUCCI cell cycle assays in A172 and H4 cells, particularly in H4, where increased γH2AX and phospho-H3 were detected in response to individual inhibitors, would provide more definitive insights into treatment-induced cell cycle dynamics.

We agree that several of the phenotypic outcomes, for example G2/M arrest (Fig 2A) and micronuclei formation (Supplemental Fig S2A), produce additive rather than synergistic effects in the combination treated cells. The major claim of the study is that the combination treatment results in potent loss of cell viability in EYA1/EYA4 overexpressing cancer cell models. This is consistent with a combination of additive mechanistic effects causing overall synergistic effects on cancer cell viability. We have clarified this point in the revised manuscript.

We have previously struggled to get adequate FUCCI sensor expression in H4 cells. However, to address this point, we have quantified cell cycle phase distribution in H4 cells treated with benzarone, BI2536, and the drug combination, using our quantitative image-based cytometry data (Fig 3A, B). These data demonstrate an accumulation of H4 cells in G2/M following combination treatment, consistent with the FUCCI data from T98G cells. Cell cycle dynamics of H4 cells are now included in Supplemental Fig S2A of the revised manuscript.

A notable inconsistency: Figure 1 utilizes volasertib, whereas Figure 2 employs BI2536. Given that both inhibitors target PLK1 why these specific inhibitors were chosen for each experiment.

This is not the case. To clarify, BI2536 is used in both Fig 1 and 2. Volasertib is used in Supplemental Fig S1 to reproduce the synergy matrix, thereby demonstrating consistent results with a second PLK1 inhibitor.

The observation of increased Rad52 foci and sister chromatid exchange (SCE) upon EYA and PLK1 inhibition (Figure 3) is interesting. These findings suggest that dual inhibition impairs homologous recombination (HR), reinforcing the role of EYA and PLK1 in maintaining genomic stability.

We agree.

Figure 4 suggests that SJH1 cells, with low EYA expression, exhibit increased sensitivity to EYA inhibition - does this cell line show high expression of PLK or NuRD?

To clarify, Fig 4 shows that SJH1 cells, which display moderate levels of EYA expression, are highly sensitive to EYA/PLK1 inhibition. Consistent with the observed positive correlation between NuRD protein expression and EYA/PLK1 inhibitor sensitivity, SJH1 cells exhibit the highest levels of NuRD components relative to the other GBM stem cell lines. Expression levels of NuRD components across the slightly sensitive, moderately sensitive, and highly sensitive GBM stem cell lines from publicly available proteomic data and western blot analysis have now been included in Supplemental Fig S5A and B of the revised manuscript, further demonstrating this positive correlation.

It seems like EYA1 (HW1) and EY4 (SB2B and PB1) expression levels are better predictors of sensitivity to treatment, but not EYA2 and 3 (which is high in H4)- can the authors comment on this?

Overall, EYA1 and EYA4 expression levels are the major predictors of EYA/PLK1 inhibitor sensitivity in both the cancer cell lines (Fig 1) and the GBM stem cell models (Fig 4). EYA3 levels are also positively associated with sensitivity in the GBM stem cell models, but not in the cancer cell lines. Despite being consistently high, EYA2 expression levels were not associated with sensitivity in either model. These intricacies are likely to reflect functional differences between the proteins, and their ability to form different sub-complexes with each other. We have now clarified these points in the discussion of the revised manuscript.

Reviewer #2 (Significance (Required)):

It remains unclear whether NuRD complex involvement is independent of EYA expression levels. Since EYA and PLK1 regulate cell cycle progression and DNA repair, further investigation is needed to delineate their connection to NuRD-mediated chromatin remodeling and differentiation programs. Overall, this study provides some interesting evidence for targeting transcriptional and mitotic vulnerabilities in GBM but requires further validation of synergistic mechanisms, differential inhibitor effects, and NuRD complex involvement in regulating the EYA-PLK1 axis.

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

This manuscript extends the findings of the interactions between EYA family members and PLK1. The idea to combine EYA inhibitors and PLK1 inhibitors is a thoughtful approach. The effects on proliferation and DNA damage are useful. This effort is a combination of preclinical efforts and some mechanistic efforts and will require additional efforts to support the conclusions drawn.

Major concerns: 1. The preclinical studies will absolutely require in vivo studies. All brain tumor treatments are limited by delivery across the blood-brain barrier. It is critical to have intracranial survival studies to support the significance of the findings.

In this study, we have focused on in vitro models including cancer cell lines, GBM stem cell models and 3D tumor spheroids, to establish proof-of-principle as well as mechanistic insight for combined EYA/PLK1 inhibition. We recognize that blood-brain-barrier penetration and therapeutic efficacy in vivo are key translational steps; however, we feel that benzarone is a suboptimal drug candidate for in vivo evaluation. Future development of second-generation EYA inhibitors with higher potency, improved selectivity, and better blood-brain-barrier permeability, is currently underway by ourselves and other groups. These compounds are likely to be more suitable for future in vivo studies, including pharmacokinetic profiling, blood-brain-barrier penetration assays, and orthotopic intracranial tumour models to assess their therapeutic potential more rigorously.

Likewise, cancer stem cell studies require in vivo studies.

As outlined above, we feel that in vivo studies fall beyond the scope of this study.

The proper studies of sphere formation would include in vitro limiting dilution assays. I would suggest greater depth in stem cell and differentiation marker studies to understand what the connection to stemness is.

The limiting dilution assay is used to measure the self-renewal potential of cancer stem cells, and would be used in this context to determine whether the treatments impact cellular differentiation. This is not the focus of this study. Rather, we are interested in comparing drug sensitivity in cancer stem cells versus differentiated cancer cells. Nevertheless, this is a great suggestion for future investigation as part of a more detailed evaluation of stemness and how these drugs and drug combinations impact self-renewal.

DNA damage responses differ between cancer stem cells and differentiated tumor cells. I would suggest comparison of effects between matched cells with different cell states.

We agree that cancer stem cells and their differentiated counterparts often display distinct DNA damage responses. We have tried to mimimise the impact of these differences on the overall conclusions by using multiple cancer cell lines and GBM stem models. To address this comment, we performed western blot analysis of DNA damage response proteins in matched PB1 stem cells and differentiated cells, demonstrating comparable expression of DNA damage response proteins. These data have now been included in Supplemental Fig S5C of the revised manuscript.

While the inhibitors used may have general specificity for the molecular targets, I would suggest that the authors use genetic loss-of-function and gain-of-function studies to validate the findings. It is particularly important because the primary targets do not predict treatment responses. I would suggest that rescues with PLK1 phosphorylation mutants would be helpful.

Our data demonstrate that EYA expression levels are predictive of treatment response in both cancer cell lines and GBM stem cell models. To further validate EYA target specificity, we have used a genetic loss-of-function approach. Specifically, we performed viability assays on control and EYA4-depleted HeLa, H4 and T98G cells in response to BI2536 treatment, demonstrating EYA4 depletion-mediated sensitization to BI2536. These data are now included in Fig 1H of the revised manuscript.

We have previously performed comprehensive rescue experiments with PLK1 phosphorylation mutants (Fig 5C–K; Nelson et al., Nat. Commun. 2024). These experiments demonstrated that cell death in response to EYA depletion or inhibition is attributable to the phosphorylation status of pY445 on PLK1, with an accumulation of Y445 phosphorylation reducing PLK1 activity and functionality, culminating in the potent induction of mitotic cell death.

Figure 5 should be performed with several lines across different response groups.

Our study currently includes cell viability and proliferation data from multiple models including 15 cancer cell lines and 12 GBM stem cell line models, spanning different EYA expression levels, and displaying varying sensitivities to both single agents and the EYA/PLK1 combination treatment. We then narrowed the number of models significantly for follow-up analysis. In Fig 5, we selected the highly sensitive PB1 GBM stem cell line based on its ability to form and grow as spheroids. While we appreciate the suggestion to expand these analyses to additional lines, we would like to respectfully decline growing additional spheroids at this time due to limitations inherent in the expansion of these models. We believe that the current dataset adequately demonstrates the reproducibility and relevance of our findings across different response groups.

The molecular associations are currently just associations. I would suggest greater analysis using genetic manipulation to test causation.

To address this concern, we have performed additional experiments using siRNA-mediated knockdown of EYA4 in HeLa, H4 and T98G cells. These experiments demonstrate that depletion of EYA4 sensitizes cells to PLK1 inhibition, mimicking the effects observed with pharmacological EYA inhibition. These data have been included in Fig 1H of the revised manuscript, and provide additional functional evidence supporting a causal relationship between EYA activity and sensitivity to PLK1 inhibition.

Figure 6 should be better developed to include protein testing and validation.

To address this point, expression levels of NuRD components have been compared using publicly available proteomic datasets and western blot analysis across the slightly sensitive, moderately sensitive and highly sensitive GBM stem cell lines, supporting a positive correlation with sensitivity. These data have been included in Supplemental Fig S5A and B of the revised manuscript.

Reviewer #3 (Significance (Required)):

This is a modest advance in understanding how EYA family members may function with PLK1.

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Referee #3

Evidence, reproducibility and clarity

This manuscript extends the findings of the interactions between EYA family members and PLK1. The idea to combine EYA inhibitors and PLK1 inhibitors is a thoughtful approach. The effects on proliferation and DNA damage are useful. This effort is a combination of preclinical efforts and some mechanistic efforts and will require additional efforts to support the conclusions drawn.

Major concerns:

1. The preclinical studies will absolutely require in vivo studies. All brain tumor treatments are limited by delivery across the blood-brain barrier. It is critical to have intracranial survival studies to support the significance of the findings.

2. Likewise, cancer stem cell studies require in vivo studies.

3. The proper studies of sphere formation would include in vitro limiting dilution assays. I would suggest greater depth in stem cell and differentiation marker studies to understand what the connection to stemness is.

4. DNA damage responses differ between cancer stem cells and differentiated tumor cells. I would suggest comparison of effects between matched cells with different cell states.

5. While the inhibitors used may have general specificity for the molecular targets, I would suggest that the authors use genetic loss-of-function and gain-of-function studies to validate the findings. It is particularly important because the primary targets do not predict treatment responses. I would suggest that rescues with PLK1 phosphorylation mutants would be helpful.

6. Figure 5 should be performed with several lines across different response groups.

7. The molecular associations are currently just associations. I would suggest greater analysis using genetic manipulation to test causation.

8. Figure 6 should be better developed to include protein testing and validation.

Significance

This is a modest advance in understanding how EYA family members may function with PLK1.

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Referee #2

Evidence, reproducibility and clarity

This study explores the sensitivity of cancer cell lines, particularly GBM cells, to dual inhibition of EYA and PLK1, aiming to uncover the connection between these pathways and the cancer stem cell state. Additionally, it investigates whether the NuRD complex modulates GBM cell responses to EYA and PLK1 inhibition. While the findings are interesting, further clarification is needed to establish the mechanistic links between EYA, PLK1, and NuRD, as well as a stronger rationale for their targeted inhibition in GBM therapy- this can be better clarified.

Some key comments and recommendations:

• The findings demonstrate that the combination of Benzarone (EYAi) and volasertib (PLKi) significantly reduced cell proliferation in H4 and T98G GBM cell lines, both of which show high expression of EYA. In contrast, the low EYA-expressing A172 cells exhibited limited response. A possible explanation is the inherently slower proliferation rate of A172 cells, which may reduce their dependence on G2/M arrest, thereby diminishing the impact of PLK1i. Does A172 line show a similar growth or cell division rate to H4 and T98G lines.

• Additionally, although protein expression levels of EYA were assessed across these cell lines, the activity and expression levels of PLK1 were not fully characterized. Since PLK1 is a crucial regulator of mitotic entry and DNA damage repair, its activity across cell lines may contribute to the observed variations in drug sensitivity. Could the authors investigate levels of PLK in these cell lines?

• The study describes the combination treatment as synergistic in H4 and T98G cells, however this synergy is unclear in Figure 2A and EV 2A. The data suggest that H4 and T98G cells exhibit sensitivity to either EYA or PLK1 inhibition alone, with combined treatment showing enhanced effects rather than synergy. This distinction is evident as BI2536 alone induces robust G2/M arrest with decreased G1 and S phase cells. To validate these findings, combination treatment should be tested in additional GBM cell lines. Additionally, repeating FUCCI cell cycle assays in A172 and H4 cells, particularly in H4, where increased γH2AX and phospho-H3 were detected in response to individual inhibitors, would provide more definitive insights into treatment-induced cell cycle dynamics.

• A notable inconsistency: Figure 1 utilizes volasertib, whereas Figure 2 employs BI2536. Given that both inhibitors target PLK1 why these specific inhibitors were chosen for each experiment.

• The observation of increased Rad52 foci and sister chromatid exchange (SCE) upon EYA and PLK1 inhibition (Figure 3) is interesting. These findings suggest that dual inhibition impairs homologous recombination (HR), reinforcing the role of EYA and PLK1 in maintaining genomic stability.

• Figure 4 suggests that SJH1 cells, with low EYA expression, exhibit increased sensitivity to EYA inhibition - does this cell line show high expression of PLK or NuRD?

• It seems like EYA1 (HW1) and EY4 (SB2B and PB1) expression levels are better predictors of sensitivity to treatment, but not EYA2 and 3 (which is high in H4)- can the authors comment on this?

Significance

It remains unclear whether NuRD complex involvement is independent of EYA expression levels. Since EYA and PLK1 regulate cell cycle progression and DNA repair, further investigation is needed to delineate their connection to NuRD-mediated chromatin remodeling and differentiation programs.

Overall, this study provides some interesting evidence for targeting transcriptional and mitotic vulnerabilities in GBM but requires further validation of synergistic mechanisms, differential inhibitor effects, and NuRD complex involvement in regulating the EYA-PLK1 axis.

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Referee #1

Evidence, reproducibility and clarity

This manuscript addresses the question of whether inhibitors of the phosphatases Eya1-4 and of the kinase PLK1 provide an effective therapeutic approach to a range of cancers. Both Eyas and PLK1 have well documented roles in development, and have been implicated in a subset of tumors. Moreover, the authors have previously shown that PLK1 is a substrate of Eya phosphatase activity. Building on these previous findings, the authors assess the possibility of combining an Eya inhibitor,benzarone, with a PLK1 inhibitor, BI2536.

There are several concerns with the study:

1. The authors suggest that these two drugs are synergistic. Synergy is usually taken as indicative of a greater than additive effect of the two drugs. The ZIP synergy score tested here indicates that the combination of the two drugs has a synergy score between 0 and 10 (figure1, and figure 5) . According to "Synergy Finder" , "A ZIP synergy score of greater than 10 often indicates a strong synergistic effect, while a score less than -10 suggests a strong antagonistic effect. Scores between -10 and 10 are typically considered additive or near-additive." The data in figure 2 on mitotic cell fraction and on cell death also seems to be more of an additive effect of the two drugs than synergy. The data in figure 3 are also additive effects on RAD51. Therefore a conclusion that "These data indicate that the drug combination was broadly synergistic" seems unwarranted. Indeed, the data form

2. There was no statistical difference in the synergy scores of the "high expressing" versus "low expressing cells". So the conclusion that the drug combination "t was effective at lower doses in cell lines with high levels of EYA1 and/or EYA4" seems unwarranted based on the data. Moreover, since there was no statistical difference in synergy between high and low expressing cells, stating that "the potential utility of the combination treatment depends on the specific overexpression of EYA1 and/or EYA4 in cancer cells," seems unwarranted by the data.

3. Benzarone and benzbromarone and their derivatives have been shown to bind and inhibit Eya phosphatases, albeit at fairly high doses. However, these two compounds also have a number of other, unrelated targets. The only demonstration that Eyas are a target of benzarone in this study are the CETSA data in supplemental figure 1. The data here seem to represent an n of 1, with no error bars shown. Even more importantly, there is no control. Looking at the blot of actin, it seems as if there may be a benzarone- temperature effect on this protein as well. It would be very helpful to show some evidence that knockdown of Eya similarly synergizes with the PLK1 inhibitor, show data that benzarone is in fact inhibiting Eya activity in these cells by looking at known targets (ie the carboxyterminal tyrosine of H2AX), and other evidence of specificity.

4. The proteomic and transcriptomic data of cell lines that were vulnerable to the combination of BI2536 and benzarone implicate overall changes in chromatin with sensitivity. These findings call into question the idea that these two compounds are acting selectively on PLK1 and Eyas. The authors don't really provide any model for explaining this correlation of Nurd complex components with targeting Eyas and PLK1.

5. Specificity of antibodies: I would like to see validation of the Eya antibodies, given the difficulty with such reagents in the field.

Significance

New therapies targeting glioblastoma would be welcome. It is not clear that the combination tested here is an effective approach to therapy. It would be necessary to know the targets of the combination and understand the mechanism so that the approach could be pursued further,

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Reply to the reviewers

We would like to thank the reviewers for taking the time to review our manuscript and for providing valuable comments on how to improve it. We are pleased to see that both reviewers recognize the novelty and importance of our study, its conceptual advance and potential clinical significance. They also noted the novelty and value of our functional mechanistic approach using epigenetic editing. Below, we provide a point-by-point response to their questions and points raised. The changes introduced in response to their feedback are highlighted in yellow in the revised manuscript file.

Point-by-point description of the revisions

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

Summary This study by Prada et al. aimed to explore DNA methylation and gene expression in primary EpCAMhigh/PDPNlow cells, consisting of for (probably) the largest part of AT2 cells, to understand the molecular mechanisms behind the impaired regeneration of alveolar epithelial progenitor cells in COPD. They found that higher or lower promoter methylation in COPD-associated cells was inversely correlated with changes in gene expression, with interferon signaling emerging as one of the most upregulated pathways in COPD. IRF9 was identified as the master regulator of interferon signaling in COPD. Targeted DNA demethylation of IRF9 in an A549 cell line resulted in a robust activation of its downstream target genes, including OAS1, OAS3, PSMB8, PSMB9, MX2 and IRF7, demonstrating that demethylation of IRF9 is sufficient to activate the IFN signaling pathway, validating IRF9 as a master regulator of IFN signaling in (alveolar) epithelial cells.

Major comments:

• To remove airways (and blood vessels) completely from the lung tissue is difficult, if not impossible. This means that the assumption that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells remains valid only if a quantitative analysis is conducted on the proportion of HT2-280pos cells in all samples in cytospins to exclude any significant contamination from bronchial epithelial cells. If authors cannot demonstrate >95% pure HT-280-positive cells, then the key conclusions suggesting that the epigenetic regulation of the IFN pathway might be crucial in AT2 progenitor cell regeneration could also potentially apply to bronchial progenitor cells. In addition, if >95% purity cannot be demonstrated, the data should be adjusted to account for differences in cell type composition.

__Response: __

We thank the reviewer for raising this important point. Although, as pointed out by the reviewer, we cannot guarantee that our sorted cells do not contain a minor contamination from respiratory / terminal bronchial cells, we carefully selected donors, tissue regions, and sorting strategy to ensure the highest possible enrichment of AT2 cells, as we explain below. We have now expanded the methods and results section and covered this point in the manuscript discussion.

• The lung tissue pieces we received were distal, as evidenced by the presence of pleura. We collected representative tissue pieces for histology to validate sample quality. Our protocol includes a dissection of all visible airways and vessels using a dissecting microscope, which were cryopreserved separately from distal parenchyma. Hence, the starting material for tissue dissociation was depleted from airways and vessels. The importance of vessel/airway removal for enrichment of distal alveolar cells was established by Tata's group (PMID: 35712012).
• We selected the AT2 sorting protocol (EpCAMpos/PDPNlow) based on previous publications that used tissue from both healthy and COPD lungs to separate AT2 cells from AT1 and airway basal cells, as AT1 and basal cells are both PDPNhigh (PMID: 22033268, PMID: 23117565; PMID: 35078977). This protocol was favoured due to the lack of information about HT2-280 expression and distribution in COPD lungs.
• The sort quality for each sample was assessed by the FACS analysis (back sorting) of the sorted cells, where we observed 95-97% purity (EpCAMpos/PDPNlow, __ 1G __shown below). In addition, we validated the sorting protocol and high AT2 enrichment from both no COPD and COPD tissues by immunostaining the FACS-sorted cells with HT2-280, an AT2 marker widely used in the field (strategy suggested by the reviewer) and observed that close to 100% of cells were positive for this marker (__Fig. 1H __shown below). However, we could not do it retrospectively for those patients, where we didn't have enough material. Sorting primary AT2 from small tissue pieces is challenging, and we need at least 20.000 cells to obtain high-quality methylation & RNA-seq data.

• AT2 marker genes (ABCA3, LPCAT1, LAMP3 and the surfactant genes SFTPA2, SFTPB and SFTPC) were among the top highly expressed genes in our RNA-seq data and were not significantly changed in COPD (please see expression data in __ S2A__ in the manuscript, and below for convenience), as well as Table 6, providing further evidence that the sorted cells carry a strong AT2 transcriptional signature. Fig. 1G* FACS plot examples showing the analysis of sorted AT2 cells (back sorting) from control (blue) and COPD (green) donors displayed over total cell lung suspensions (grey) H Representative IF staining of HT2-280 expression in sorted AT2 cells from no COPD (top) and COPD (bottom) donors. Nuclei (blue) were stained with DAPI, scale bars=20µm __Fig. S2A __Normalized read counts from RNA-seq data for AT2-specific genes in sorted AT2 cells from each donor (dots). Data points represent normalised counts from no COPD (blue), COPD I (light green) and COPD II-IV (dark green). Group median is shown as a black bar. *

• In agreement with a previous study which profiled bulk AT2 using expression arrays (PMID: 23117565), we also observed upregulation of IFN signaling pathway in COPD AT2s. The enrichment of IFNα/β signature was also observed in COPD in the inflammatory AT2 cluster (iAT2) in a recent scRNA-seq study (PMID: 36108172). As part of the revision, we compared the IFN gene signature identified in our bulk AT2 RNA-seq with a recent scRNA-seq study (published after the submission of our manuscript, PMID: 39147413) that profiled EpCAMpos cells from COPD and non-smoker donor lungs. We observed an upregulation of our IFN signature genes in AT2 in COPD (mostly in AT2c and rbAT2 subsets), suggesting that similar signatures were observed in COPD AT2s in this dataset as well (please see __ S4E-F__ below). ____Figure S4E Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (Hu et al, 2024). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from Hu et al, 2024. The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells.

• We have also carefully examined DNA methylation profiles across all samples. The density plots of our T-WGBS DNA methylation data are very similar among the individual samples in all 3 groups, indicating that the sorted cells consist mostly of a single cell type, as there are no obvious intermediate (25-75%) methylation peaks, as observed in cell mixtures ( 2A and the panel below). No reference DNA methylation profiles are available for respiratory or terminal bronchial cells; hence, we cannot compare how epigenetically different these cells would be from AT2 nor perform a deconvolution for potential minor contamination with distal airway cells. *Figure: DNA methylation density plots of sorted EpCAMpos/PDPNneg cells from no COPD (blue, n=3), COPD I (light green, n=3) and COPD II-IV (dark green, n=5) showing a homogeneous methylation pattern and low abundance at intermediate (25%-75%) methylation values across all profiled samples, indicating that the sorted cells were mostly of a single cell type. *

• We have now added a sentence to the limitations section of the discussion to cover that point specifically. CHANGES IN THE MANUSCRIPT:

AT2 cells were isolated by fluorescence-activated cell sorting (FACS) from cryopreserved distal lung parenchyma, depleted of visible airways and vessels of three no COPD controls, three COPD I and five COPD II-IV patients as previously described (24, 52, 53)

The isolated cells were positive for HT2-280, a known AT2 marker (54)*, as confirmed by immunofluorescence (Fig. 1H), validating the identity and high enrichment of the isolated AT2 populations. ** *

*Known AT2-specific genes, including ABCA3, LAMP3 and surfactant genes (SFTPA2, SFTPB and SFTPC) were among the top highly expressed genes and were not significantly changed in COPD AT2s (Fig. S2A, Table 6), further confirming the AT2-characteristic transcriptional signature of our isolated cells. *

However, 5-AZA is a global demethylating agent, and the observed effects may not be direct. To validate the epigenetic regulation of central AT2 pathways further, we took advantage of locus-specific epigenetic editing technology *(73). We focused on the IFN pathway because it was the most significantly enriched Gene Ontology (GO) term in our integrative analysis of TWGBS and RNA-seq data. Several IFN pathway members had associated hypomethylated DMRs within promoter-proximal regions and concomitant increased gene expression (Fig. 4C and S2C). Additionally, we confirmed the elevated expression of IFN-related genes with associated DMRs identified in our study in AT2 cells and AT2 cell subclusters from a recently published scRNA-seq cohort (74) (Fig. S4E-F). *

We observed upregulation of multiple IFN genes in AT2 in COPD, consistent with a previous expression array study (24). IFNα/β signaling was also enriched in COPD patients in the inflammatory AT2 cluster (iAT2) in a recent scRNA-seq study (84) and our INF signature genes were also upregulated in AT2c and AT2rb subsets in COPD, identified by another scRNA-seq study recently (74)*. ** *

Finally, despite careful removal of airways from distal lung tissue using a dissecting microscope, we cannot exclude the presence of some terminal/respiratory bronchiole cells in our FACS-isolated EpCAMpos/PDPNlow population. Recent scRNA-seq studies provided an unprecedented resolution and identified several epithelial subpopulations and transitional cells residing in the terminal/respiratory bronchioles and alveoli, including respiratory airway secretory cells (93), terminal airway-enriched secretory cells (28), terminal bronchiole-specific alveolar type-0 (AT0) (70), and emphysema-specific AT2 cells (74). These cells may contribute to alveolar repair in healthy and COPD lungs; however, with our bulk DNA methylation and RNA-seq study, we are unable to resolve all these subpopulations. Future development of single-cell methylation and non-reference-based algorithms for DNA methylation deconvolution will enable deeper epigenetic phenotyping of specific AT2 and bronchiolar cell subsets.

(Methods) Validation of IFN gene upregulation in a published scRNA-seq dataset

scRNA-seq data from (74), generously provided by M. Köningshoff, were processed using the default Seurat workflow (117). Expression of IFN-related genes was extracted and plotted as log-normalised gene expression levels in AT2 cells from control and COPD donors. Seurat's AddModuleScore() function was used to compute a gene set score for a custom IFN program using the genes listed in __Fig. S4E __and to analyse the IFN gene set scores in AT2 cell subclusters identified in (74). Briefly, average gene expression scores were computed for the gene set of interest, and the expression of control features (randomly selected) was subtracted as described in (118).

Fig. S4E and F: E. Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74). The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells.

• The overrepresentation of several keratins (KRT5, KRT14, KRT16, KRT17), mucins (MUC12, MUC13, MUC16, MUC20) and the transcription factor FoxJ1 is now attributed by the authors to a possible dysregulation of AT2 identity and differentiation in COPD (lines 282 - 284) where they cite refs 28, 69, 70. Authors try to support this with IF double stains for KRT5 and HT-280 to identify co-expression of KRT5 and HT2-280 in lung tissue (Figure S2H). However, the evidence for the co-expression of both markers could be presented more convincingly.

__Response: __

We found the potential co-expression of airway and alveolar markers in COPD lungs interesting and hence included it in the original manuscript. The initial discovery came from our bulk RNA-seq data, where we observed upregulation of several genes typically found in more proximal airways in COPD (mentioned above by the reviewer). Of note, some of them (e.g., FoxJ1) are expressed at very low levels. Following reviewer's comments, to validate possible colocalization of AT2 and airway markers on protein level, we performed further IF analysis. We took Z-stack images to demonstrate the co-localization of HT2-280 and Krt5 more convincingly and co-stained the same tissue regions with SCGB3A2 (a TASC/distal airway cell marker, PMID 36796082). Even though these are rare events, we were able to reproduce the existence of HT2-280/Krt5 positive, SCGB3A2 negative cells in the alveoli of COPD patients on the protein level (__Fig. S2H __and panels below). Although interesting, we decided to keep this finding in the supplement and did not include it in the discussion to focus the story on the epigenetic regulation of the IFN pathway, which is the main discovery of our study. We will investigate this observation in future studies.

Figure S2H and here: Examples of HT2-280/Krt5 double positive cells. Top, immunofluorescence staining of the alveolar region of a COPD II donor showing the existence of AT2 cells (HT2-280 positive (red), which are SCGB3A2 negative (green, left) but KRT5 positive (green, right). In conclusion, double-positive HT2-280/KRT5 cells are rare but present in the alveoli of COPD patients. Magnification: 20x. Scale bar: 50 µm. Bottom, Z-stack images highlighting HT2-280 (red) and KRT5 (green) double-positive cells at 63x magnification. Scale bar: 5 µm.

CHANGES IN THE MANUSCRIPT:

In addition, we observed an upregulation of several keratins (KRT5, KRT14, KRT16, KRT17) and mucins (MUC12, MUC13, MUC16, MUC20), suggesting a potential dysregulation of alveolar epithelial cell differentiation programs in COPD (Table 6, Fig. S2F). Immunofluorescence staining confirmed the presence of KRT5-positive cells in the distal lung in COPD and identified cells positive for both KRT5 and HT2-280 (Fig. S2H). Collectively, these results indicate a dysregulation of stemness and identity in the alveolar epithelial cells in COPD.

Fig. S2H legend: The zoomed-in panel (right corner, bottom) demonstrates the presence of rare HT2-280/KRT5 double-positive cells in the alveoli of COPD patients.* Slides were counterstained with DAPI, scale bars = 50µm, 20µm or 5µm, as displayed in images. *

• Double staining for KRT5 and HT2-280 did highlight the proximity of both cell types in lung tissue, underscoring the challenge of removing airways (including the smaller and terminal bronchi) from the tissue. In addition, HT-280/KRT5 co-expression is not consistent with recent studies from refs 28, 69, 70 where other markers for distal airway cell transition, such as SCGB3A2 and BPIFB1, have been demonstrated, which were not investigated in this study.

Response:

We provided a general overview of the different signatures observed in our data, but we could not validate every deregulated pathway or gene. We include the relevant tables detailing all differentially expressed genes and differentially methylated regions to enable and encourage the community to follow up on the data in subsequent studies.

As demonstrated above, we detect the co-occurrence of HT2-280/KRT5 staining on the protein level in the same cells in the alveoli of COPD patients. We would like to emphasize that alveolar epithelial cell identity in CODP lungs has not been investigated in detail on the protein or RNA level, and HT2-280/KRT5 co-expression/co-localization has not been directly tested in the studies mentioned by the reviewer since, among other reasons, the gene encoding HT2-280 has not been identified. Notably, a recent study (published after the submission of our manuscript) focusing on enriched epithelial cells from the distal lungs of COPD patients (PMID 35078977), identified an emphysema-specific AT2 subtype co-expressing the AT2 marker SFTPC and distal airway cell transition marker SCGB3A2, indicating that disease-specific AT2 populations with possible co-occurrence of AT2 and airway markers exist. In our dataset, SCGB3A2 was not deregulated (log2 fold change=0.22, adj p-value= 0.47), as shown in Table 6, and the HT2-280/Krt5 positive cells were negative for SCGB3A2 in our IF staining (see above).

BPIFB1 is one of the antimicrobial peptides genes with an associated DMR and is significantly upregulated in COPD cells in our study (log2 fold change=1.17, adj p-value=0.0016), as shown in the supplementary figure Fig S4C and here below for convenience.

Figure S4C Fold-change in gene expression of BPIFB1 in AT2 cells in COPD (RNA-seq) and A549 cells treated with 0.5µM AZA (RT-qPCR) compared to control samples. Left, RNA-seq data from AT2 cells (no COPD, blue, n=3; COPD II-IV, green, n=5). Right, A549 treated with AZA (orange, n=3) compared to control DMSO-treated cells (grey, n=3). The group median is shown as a black bar.

• The small (and not evenly divided) sample size of both COPD and non-COPD specimens may lead to a higher risk for false positive results as adjustments for multiple testing typically rely on the number of comparisons, and small sample sizes may not provide enough data points to adequately control for this.

__Response: __

We acknowledge the problem of testing for multiple traits with relatively small numbers of samples. The availability of donor tissue, especially from non-COPD and COPD-I donors, was limited, and we applied very strict donor matching and quality control criteria for sample inclusion to avoid additional variability and confounding factors. The importance of strict quality control in selecting appropriate control samples was highlighted in our previous study (PMID: 33630765), where we demonstrated that approximately 50% of distal lung tissue from cancer patients with normal spirometry has pathological changes. Hence, we believe that the quality of the tissue was paramount to the reliability of the data. Strict quality control and sample matching for multiple parameters, including age, BMI, smoking status and smoking history (critical for DNA methylation studies), and cancer type (for background tissue), is a key strength of our approach, but it inevitably limited our sample size.

First, all samples were cryopreserved and then processed in parallel in groups of 1 non-COPD and 2-3 COPD samples. This process included tissue dissociation, FACS sorting, back sorting (always), and immunofluorescence staining (when enough material was available). Cell pellets were stored at -80{degree sign}C until the entire cohort was ready for sequencing. This was done to limit the potential variation introduced by processing and sorting. RNA and DNA isolations were performed in parallel for all the sorted cell pellets, which were then sequenced as a single batch.

During data analysis, we applied stringent cutoffs for DMR detection to reduce the risk of false positives due to multiple comparisons and a small sample size. Specifically, we filtered for regions with at least 10% methylation difference and containing at least 3 CpGs. Additionally, we applied a non-parametric Wilcoxon test using average DMR methylation levels to remove potentially false-positive regions, as the t-statistic is not well suited for non-normally distributed values, as expected at very low/high (close to 0% / 100%) methylation levels. A significance level of 0.1 has been used. Therefore, we are confident that the rigorous analysis and strict criteria applied in this study allowed us to detect trustworthy DMRs that we could further functionally validate using epigenetic editing. All the details of the DMR analysis are provided in the methods section. To address this point and limitation, we have added the following paragraphs in the discussion section of the manuscript:

CHANGE IN THE MANUSCRIPT:

*The strengths of our study include the use of purified human alveolar type 2 epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD. *

However, we acknowledge several limitations of our study that warrant further investigation. First, the sample size was small. The use of strict quality criteria for donor selection limited the available samples, particularly for the ex-smoker control group. This resulted in an unequal distribution of COPD and control samples. This impacts the power of statistical analysis, particularly in the WGBS analysis, where millions of regions genome-wide are tested. Nevertheless, the clear negative correlation between promoter methylation and corresponding gene expression highlights the robustness of the DMR selection. Additionally, we were able to experimentally validate interferon-associated DMRs using epigenetic editing, highlighting the power of integrated epigenetic profiling in identifying disease-relevant regulators.

__Minor suggestions for improvement __

__Introduction __ • In general, refer to the actual experimental studies rather than review papers where appropriate.

Response:

We have now carefully checked all the references and amended them to refer to experimental studies when required.

• Clearly specify whether a study was conducted in mice or humans, as this distinction is crucial for understanding the relevance of the findings to COPD.

__Response: __

All our experiments were performed with human lung cells and tissues. No mouse samples were used. As suggested, we have now clearly stated that our study was performed using human tissue samples and cells in different parts of the manuscript, including the discussion, where we now explicitly highlight the strengths and limitations of our study.

CHANGES IN THE MANUSCRIPT:

...we generated whole-genome DNA methylation and transcriptome maps of sorted human primary alveolar type 2 cells (AT2) at different disease stages.

However, the regulatory circuits that drive aberrant gene expression programs in human AT2 cells in COPD are poorly understood

Therefore, we set out to profile DNA methylation of human AT2 cells at single CpG-resolution across COPD stages.

...*suggesting that aberrant epigenetic changes may drive COPD phenotypes in human AT2. *

To identify genome-wide DNA methylation changes associated with COPD in purified human AT2 cells...

The similarity of the methylation and gene expression profiles in the PCAs suggested that epigenetic and transcriptomic changes in human AT2 cells during COPD might be interrelated ...

*In this work, we demonstrate that genome-wide DNA methylation changes occurring in human AT2 cells may drive COPD pathology by dysregulating key pathways that control inflammation, viral immunity and AT2 regeneration. *

*Using high-resolution epigenetic profiling, we uncovered widespread alterations of the DNA methylation landscape in human AT2 cells in COPD that were associated with global gene expression changes. *

*Currently, it is unclear how cigarette smoking leads to changes in DNA methylation patterns in human AT2 *

The strengths of our study include the use of purified human alveolar epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD.

__Methods __ • Line 473, here is meant 3 ex-smoker controls instead of smoker controls?

__Response: __

All donors (no COPD and COPD) used in our study are ex-smokers. Matching the samples with regard to smoking status and history is critical for epigenetic studies, as cigarette smoke profoundly affects DNA methylation genome-wide (PMID: 38199042, PMID: 27651444). This has now been clarified in the revised manuscript.

CHANGE IN THE MANUSCRIPT____:

Of note, we included only ex-smokers in our profiling to avoid acute smoking-induced inflammation as a confounding factor (50)*. *

Importantly, we matched the smoking status and smoking history of all donors, which is key in epigenetic studies, as cigarette smoking profoundly impacts the DNA methylation landscape of tissues (96).

In total, 3 ex-smoker controls (no COPD), 3 mild COPD donors ex-smokers (GOLD I, COPD I) and 5 moderate-to-severe COPD donors ex-smokers (GOLD II-IV, COPD II-IV) were profiled (Fig. 1A-C, Table 1)

__Discussion __ • A list of limitation should be added to the discussion. One is the use of the alveolar cell line A549, which produces mucus, a characteristic more commonly associated with bronchial epithelial cells. (ref 43)l530:

__Response: __

The profiling was performed using purified primary human alveolar epithelial progenitor cells. For technical reasons, A549 cells were only used for validation of the results using epigenetic editing. The A549 phenotype depends on the growth medium used, in our case, Ham's F-12 medium, which is recommended for long-term A549 culture and promotes multilamellar body formation and differentiation toward an AT2-like phenotype (PMID: 27792742)__. __We are developing epigenetic editing technology for use in primary lung cells; however, the approach currently relies on the high efficiency of transient transfections, which cannot yet be achieved with primary adult AT2 cells. We were positively surprised by how well the methylation data obtained from patient AT2s translated into mechanistic insights when using A549 cells, despite being a cancer cell line. This suggests that the fundamental mechanisms of epigenetic regulation of IRF9 and the IFN signaling pathway are conserved between A549 and primary AT2 cells.

• Another limitation to consider is that cells were isolated primarily from individuals with lung cancer, except for patients with COPD stage IV. In particular as COPD stage II and IV samples were taken together. And discuss the small and unevenly divided sample size

__Response: __

We thank the reviewer for bringing up this important point, which we carefully considered when designing our study. To match our samples across the cohort, all the no-COPD, COPD I, and two of the COPD II-IV samples were obtained from cancer resections. In addition to other characteristics, like age, BMI and smoking status, we also matched the donors by cancer type (all profiled donors had squamous cell carcinoma). We collected lung tissue as far away from the carcinoma as possible and sent representative pieces for histological analysis by an experienced lung pathologist to confirm the absence of visible tumours. In addition, to ensure that our data represents COPD-relevant signatures, we intentionally included samples from three COPD donors undergoing lung resections (without a cancer background) in the profiling.

Following the reviewer's suggestion, to investigate the potential impact of non-cancer samples on driving the observed differences, we carefully checked the PCAs for both DNA methylation and RNA-seq. We could not identify a clear separation of no-cancer COPD samples from the cancer COPD samples (or other cancer samples) in any examined PCs, indicating no cofounding effect of cancer background in the samples. We observed that one sample contributing to PC2 is a non-cancer sample, but this was a rather sample-specific effect, as the other two non-cancer samples clustered together with the other severe COPD samples with a cancer background. Notably, in our DNA methylation data, we do not observe typical features of cancer methylomes, like global loss of DNA methylation or aberrant methylation of CpG islands (e.g., in tumour suppressor genes) (see Fig 2A), further suggesting that we do not "pick up" confounding cancer signatures in our data.

Following the comments from both reviewers, to clarify that point, we added the information about cancer and non-cancer samples to the PCA figures for DNA methylation (new Fig. 2B) and RNA-seq (new Fig. 3A) data in the revised manuscript, as shown below

CHANGE IN THE MANUSCRIPT____:

COPD samples from donors with a cancer background clustered together with the COPD samples from lung resections, confirming that we detected COPD-relevant signatures (Fig. 2B).

Fig.2B* Principal component analysis (PCA) of methylation levels at CpG sites with > 4-fold coverage in all samples. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

Unsupervised principal component analysis (PCA) on the top 500 variable genes revealed a clear influence of the COPD phenotype in separating no COPD and COPD II-IV samples, as previously observed with the DNA methylation analysis, irrespective of the cancer background of COPD samples (Fig.3A, Fig. S2B).

*Principal component analysis (PCA) of 500 most variable genes in RNA-seq analysis. PCA 1 and 2 are shown in Fig.3A, PCA 1 and 4 in Fig.S2B. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

__Response: __

We thank the reviewer for suggestions on how to improve the discussion of our manuscript. We have now added a strength/limitation section to our discussion and included the points suggested by both reviewers.

CHANGE IN THE MANUSCRIPT____:

The strengths of our study include the use of purified human alveolar epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD. Importantly, we matched the smoking status and smoking history of all donors, which is key in epigenetic studies, as cigarette smoking profoundly impacts the DNA methylation landscape of tissues (96). With the first genome-wide high-resolution methylation profiles of isolated cells across COPD stages, we offer novel insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD, expanding our understanding of how alterations in regulatory regions and specific genes could contribute to disease development. We identified IRF9 as a key IFN transcription factor regulated by DNA methylation. Notably, by targeting IRF9 through epigenetic modifications, we modulated the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. Epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting the regeneration of epithelial progenitor cells in the lungs. Further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment (33)*. *

*However, we acknowledge several limitations to our study that warrant further investigation. First is the small sample size and replication difficulty due to the lack of available data, common challenges for studies working with sparse human material and hard-to-purify cell populations. The use of strict quality criteria in donor selection limited the available samples, especially for the ex-smoker control group, leading to an unequal distribution of COPD and control samples. Overall, this impacts the power of statistical analysis, especially in the WGBS analysis, where millions of regions genome-wide are tested. Nevertheless, the clear negative correlation of promoter methylation to the corresponding gene expression highlights the robustness of the DMR selection. Furthermore, we could experimentally validate interferon-associated DMRs using epigenetic editing, highlighting the power of integrated epigenetic profiling for the discovery of disease-relevant regulators. *

Overall, we detected a higher number of correlated DMR-DEG associations using our simple promoter-proximal linkage compared to the GeneHancer approach. Assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the genes they regulate and can interact with their target genes through three-dimensional chromatin loops. Furthermore, enhancers can operate in a highly context-dependent manner, with the same enhancer regulating different genes depending on the cell type, developmental stage, or environmental signals. Determining which enhancer is active under specific conditions remains a hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

In addition, while WGBS provides unprecedented resolution and high coverage of the DNA methylation sites across the genome, it does not allow distinguishing 5-methylcytosine from 5-hydroxymethylcytosine. Therefore, we cannot exclude that some methylated sites we detected are 5-hydroxymethylated. However, as 5-hydroxymethylcytosine is present at very low levels in the lung tissue (97)*, its effect is likely marginal. *

Finally, despite careful removal of airways from distal lung tissue using a dissecting microscope, we cannot exclude the presence of some terminal/respiratory bronchiole cells in our FACS-isolated EpCAMpos/PDPNlow population. Recent scRNA-seq studies provided an unprecedented resolution and identified several epithelial subpopulations and transitional cells residing in the terminal/respiratory bronchioles and alveoli, including respiratory airway secretory cells (93), terminal airway-enriched secretory cells (28), terminal bronchiole-specific alveolar type-0 (AT0) (70), and emphysema-specific AT2 cells (74). These cells may contribute to alveolar repair in healthy and COPD lungs; however, with our bulk DNA methylation and RNA-seq study, we are unable to resolve all these subpopulations. Future development of single-cell methylation and non-reference-based algorithms for DNA methylation deconvolution will enable deeper epigenetic phenotyping of specific AT2 and bronchiolar cell subsets.

__References __ • Check references. For instance, there is no reference in the text to ref 43.

• Align format of references

__Response: __

We thank the reviewer for spotting this inconsistency. We have carefully checked and aligned the format of all references. The (old) reference 43 is now mentioned in the discussion part.

__Reviewer #1 (Significance (Required)): __

The strength of this study lies in its focus on the molecular mechanisms underlying the impaired regeneration of epithelial progenitor cells in COPD. The discovery of IRF9, which regulates IFN signaling and is prominently upregulated in COPD, together with the convincing validation of the epigenetic control of the IFN pathway by targeted DNA demethylation of the IRF9 gene, adds significant value to the COPD research field.

Main limitations of the study are the relatively small sample size of both COPD and non-COPD specimens and the claim that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells.

__- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. __

The nature and significance of the advance in epigenetic editing of IRF9 in COPD can be described as both conceptual and potentially clinical:

Conceptual Advance: The epigenetic editing of IRF9 enhances our understanding of the molecular mechanisms underlying COPD pathogenesis. By targeting IRF9 through epigenetic modifications, researchers were able to modulate the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. This approach offers insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD and expands our understanding of how alterations in specific gene methylation could contribute to disease progression.

Clinical Significance: The potential clinical significance of epigenetic editing of IRF9 lies in its implications for COPD therapy. If successful, epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting regeneration of epithelial progenitor cells in the lungs. Obviously, further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment.

__Response: __We thank the reviewer for recognising the importance of our study, its conceptual advance and potential clinical significance. We are pleased to see that the reviewer highlights the promise of epigenetic editing in both furthering our basic understanding of molecular mechanisms of chronic diseases and its future potential as a therapeutic strategy.

__- Place the work in the context of the existing literature (provide references, where appropriate). __ Few experimental papers have been published on epigenetic editing in lung diseases, with limited research available beyond the study referenced in citation 43. Song J, Cano-Rodriquez D, Winkle M, Gjaltema RA, Goubert D, Jurkowski TP, Heijink IH, Rots MG, Hylkema MN. Targeted epigenetic editing of SPDEF reduces mucus production in lung epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2017 Mar 1;312(3):L334-L347. doi: 10.1152/ajplung.00059.2016. Epub 2016 Dec 23. PMID: 28011616.

Response:

We thank the reviewer for recognising the uniqueness and novelty of our study and the lack of research on the functional understanding of DNA methylation in the context of lung and lung diseases.

- State what audience might be interested in and influenced by the reported findings.

This study is of broad interest to researchers investigating the pathogenesis and treatment of COPD.

__- Define your field of expertise with a few keywords to help the authors contextualize your point of view. __

Expertise in: Lung pathology, Immunology, COPD, Epigenetics

- Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Less expertise in: Epigenetic Editing

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

__Summary: __

This study aim to understand the molecular mechanisms underlying dysfunction in AT2 cells in COPD, by profiling bulk genome wide DNA methylation using Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) and RNA sequencing in selectively sorted primary AT2 cells. The study stands out in it's sequencing breadth and use of an incredibly difficult cell population, and has the potential to add substantially to our mechanistic understanding of epigenetic contributions to COPD. A further highlight is the concluding aspect of the study where the authors undertook targeted modification of specific CpG methylation, provided direct, site-specific evidence for transcriptional regulation by CpG methylation.

Response:

We thank the reviewer for recognizing the conceptual and methodological advance of our study and for noting the value of our functional mechanistic approach.

__Major comments: __

The authors clearly show that there is DNA methylation alteration in AT2 cells from COPD individuals that links functional to gene expression at some level. However, I think the statement "to identify genome-wide changes associated with COPD development and progression..." and similar other references to disease development understanding is not accurate given the DNA methylation primary comparison is between control and moderate to severe COPD, with no temporal detail or evidence that they drive progression rather than are a result of COPD development. The paragraph starting on line 186 where this is a addressed to some extent is quite vague and doesn't really provide confidence that DNAm dysregulation occurs at an early stage in this context. This can be addressed by changing the focus/style of the text.

__Response: __

Thank you for raising this point. We agree with the reviewer that our cross-sectional study describes the association of methylation changes with either COPD I or more established disease (COPD II-IV) and that the observed changes may be either the driver or a result of COPD development. This has been clarified in the revised manuscript, and we removed the statements about disease initiation and progression. This is an important point; hence, we added an extra line to the discussion to make that clear.

__CHANGE IN THE MANUSCRIPT____: __

Therefore, we set out to profile DNA methylation of human AT2 cells at single CpG-resolution across COPD stages to identify epigenetic changes associated with disease and combine this with RNA-seq expression profiles.

To identify epigenetic changes associated with COPD, we collected lung tissue from patients with different stages of COPD,

....to identify methylation changes associated with mild disease, we included TWGBS data from AT2 isolated from COPD I patients (n=3) in the analysis.

Currently, we do not know whether the identified DNA methylation changes are the cause or the consequence of the disease process and not much is known about the correlation of DNA methylation with disease severity.

*However, our study is cross-sectional, our cohort included only 3 COPD I donors, and we did not have any follow-up data on the patients, so future large-scale profiling of mild disease (or even pre-COPD cohorts) in an extended patient cohort will be crucial for a better understanding of early disease and its progression trajectories. *

__Results comments and suggestions: __

For the integrated analysis, there is a focus on DMRs in promoters with very little analysis on other regions. The paragraph starting on line 317 describes some analysis on enhancers but is very brief, doesn't include information on how many/which DMRs were included, making it hard to interpret the impact of the 147 DMRs and 93 genes identified - is this nearly all DMRs and genes analysed or very few? A comparison to the promoter analysis would be of interest. Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

__Response: __

We thank the reviewer for pointing out the importance of changes in enhancers. We agree that extending the enhancer analysis is very interesting. However, assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the gene they regulate, sometimes spanning hundreds of kilobases. They can interact with their target genes through three-dimensional chromatin loops, potentially bypassing nearby genes to activate more distant ones, making it difficult to confidently link specific enhancers to their target genes. Furthermore, enhancers can operate in a highly context-dependent manner. The same enhancer can regulate different genes depending on the cell type, developmental stage, or environmental signals. Another challenge is that enhancers often work in clusters or "enhancer landscapes," where multiple enhancers contribute to the regulation of a single gene. Disentangling the contribution of individual enhancers within such clusters and determining which enhancer is active under specific conditions remains an ongoing hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

One approach we tried to account for more distal regulatory regions was to assign DMRs to the nearest gene with a maximum distance of up to 100 kb using GREAT (Genomic Regions Enrichment of Annotations Tool) and simultaneously perform gene enrichment analysis of the associated genes. The old Figure S1C (now S1D) shows the top 10 enriched terms of either hyper- or hypomethylated DMRs, and Table 4 shows the full list of enriched terms. However, in this analysis, we did not integrate the results of the RNA-seq analysis. To demonstrate that we can correlate methylation with gene expression associations in this analysis, we then took a closer look at the WNT/b-catenin pathway, which contains 147 DMRs associated with 93 genes from the respective pathway (old Figure S3D, now S3G). Here, we showed that distal DMRs up to 100 kb away from the TSS show a high correlation with gene expression. We are including the two figures below for convenience:

*Left panels, functional annotation of genes located next to hypermethylated (top) and hypomethylated (bottom) DMRs using GREAT. Hits were sorted according to the binominal adjusted p-value and the top 10 hits are shown. The adjusted p-value is indicated by the color code and the number of DMR associated genes is indicated by the node size. Right panel, scatter plot showing distal DMR-DEG pairs associated with Wnt-signaling. Pairs were extracted from GREAT analysis (hypermethylated, DMR-DEG distance Following the reviewer's suggestion, we have now extended the enhancer analysis using the GeneHancer database, the most comprehensive, integrated resource of enhancer/promoter-gene associations. We used the GeneHancer version 5.14, which annotates 392,372 regulatory genomic elements (GeneHancer element) on the hg19 reference genome. Of the 25,028 DMRs, 18,289 DMRs (73% of all DMRs) coincided with at least one GeneHancer element, resulting in 19,661 DMR-GeneHancer associations. Next, we extracted the GeneHancer elements associated with protein-coding or long-non-coding RNAs genes, which left us with 2,144 DMR-GeneHancer associations. Next, we used only high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer associations. Of those, we selected the GeneHancer elements, which are linked to genes differentially expressed in our RNA-seq analysis resulting in a final table of 376 DMR-GeneHancer associations (Table 9 DMR_DEG_GeneHancer, Tab 2). Similar to the promoter-proximal analysis, we analysed the correlation of expression and methylation changes of the DMR-GeneHancer associations, demonstrating a high number of negatively and positively correlated events (Fig.S3D). Finally, we performed the gene enrichment analysis for positively and negatively correlating genes. We detected significant GO term enrichments only for negatively correlating genes (Fig.S3E and Table 10_Enrichment_results, Tab2).

CHANGE IN THE MANUSCRIPT

To harness the full resolution of our whole-genome DNA methylation data, we extended the analysis beyond promoter-proximal regions and assessed how epigenetic changes in distal regulatory regions (enhancers) may relate to transcriptional differences in COPD. As the assignment of enhancer elements to the corresponding genes is challenging, we tried two different approaches. First, we used the GeneHancer database (72) to link DMRs to regulatory genomic elements (GeneHancer element). Of the 25,028 DMRs, 18,289 DMRs (73%) coincided with at least one GeneHancer element. Of those 2,144 DMR-GeneHancer associations were linked either to protein-coding or lncRNA genes. Next, we filtered for high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer Elite associations. Of those, we selected the GeneHancer elements, which are linked to genes differentially expressed in our RNA-seq analysis, resulting in 376 DMR-GeneHancer associations (Table 9). Similar to the promoter-proximal analysis, we assessed the correlation of expression and methylation changes of the DMR-GeneHancer associations, demonstrating a high proportion of negatively and positively correlated events (Fig. S3E). Finally, we performed gene enrichment analysis for positively and negatively correlated genes. We detected significant GO term enrichments for negatively correlating genes only (Fig. S3F and Table 10), with the most pronounced term "regulation of tumor necrosis factor". In an alternative approach, we linked proximal and distal (within 100 kb from TSS) DMRs to the next gene using GREAT (57) (Fig S1C, Table 4) *and calculated Spearman correlation between DMRs and associated DEGs__. 147 DMRs were associated with high correlation rates with 93 genes from the WNT/β-catenin pathway (Fig. S3G)__, suggesting that DNA methylation may also drive the expression of genes of the WNT/β-catenin family. *

Figure S3E and F: E. Spearman correlation between gene expression and DMR methylation of DMRs assigned to gene regulatory elements using the GeneHancer database. F. GO-Term over-representation analysis of DEGs negatively correlated to DMRs in gene regulatory elements. The adjusted p-value is indicated by the color code and the percentage number of associated DEGs is indicated by the node size.

(Methods) For enhancer analysis, the GeneHancer database version 5.14, which annotates 392,372 regulatory genomic elements (GeneHancer element) on the hg19 reference genome, was used (72). Of the 25,028 DMRs 18,289 DMRs coincided with at least one GeneHancer element, resulting in 19,661 DMR-GeneHancer associations. Next, the GeneHancer elements were filtered for association with protein-coding or long-non-coding RNAs genes and high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer associations. Of those, the GeneHancer elements were selected, which are linked to differentially expressed genes in COPD resulting in a final table of 376 DMR-GeneHancer associations. Similar to the promoter-proximal analysis, the Spearman correlation of expression and methylation changes of the DMR-GeneHancer associations was assessed. GO gene enrichment analysis for positively and negatively correlating genes was done using Metascape (111).

A comparison to the promoter analysis would be of interest.

Response:

We detected more highly correlated (|correlation coefficient| > 0.5) DMR-DEG associations using our simple promoter proximal linkage (n=643) in comparison with the GeneHancer approach comprising annotated enhancer elements (n=327/2,144). Gene enrichment results pointed to the interferon pathway, which we could confirm using epigenetic editing. This pathway was not present in the GeneHancer analysis, indicating that regulation of the IFN pathway may be controlled by proximal elements.

CHANGE IN THE MANUSCRIPT____:

Overall, we detected a higher number of correlated DMR-DEG associations using our simple promoter-proximal linkage compared to the GeneHancer approach. Assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the genes they regulate and can interact with their target genes through three-dimensional chromatin loops. Furthermore, enhancers can operate in a highly context-dependent manner, with the same enhancer regulating different genes depending on the cell type, developmental stage, or environmental signals. Determining which enhancer is active under specific conditions remains a hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

Response:

We thank the reviewer for bringing up that point. To clarify, we defined the promoter regions for the analysis as regions located {plus minus} 6 kb (upstream and downstream) from the transcriptional start site (TSS). Since the term "promoter" often refers to the region upstream of the transcriptional start site, its use may have been misleading. For clarity, we changed the text correspondingly to __promoter proximal methylation __and explained in the methods how the regions for analysis were defined.

__CHANGE IN THE MANUSCRIPT____: __

"DMR association per gene promoter" was changed to "Gene promoter proximal DMRs"

Fig. S3B: "DMR in promoter" was changed to "promoter proximal DMR(s)"

"by DNA methylation changes in promoters" was changed to "by DNA methylation changes in promoter proximity"

"regulated by promoter methylation" was changed to "regulated by promoter-proximal methylation"

"analysis of the promoter DMRs" was changed to "analysis of the promoter-proximal DMRs"

"between promoter methylation" was changed to "between promoter proximal methylation"

Cytoscape was used to analyse negatively or positively correlated DMR DEG pairs. ClueGO (v2.5.6) analysis was conducted using all DEG associated with a promoter proximal DMR (+/- 6 kb from TSS) and the Spearman correlation coefficient 0.5 (112).

• Lines 299-301 - I'm not sure the graph in Fig S3A support the conclusion that there was a preferential negative relationship between DNAm and gene expression. Looks like there are a substantial number of cases where a positive relationship is observed and this needs to be acknowledged.

Response:

In this part, we refer to Fig S3C. In the left panel, downregulated genes clearly show higher counts for the hypermethylated DMRs, whereas the hypomethylated DMRs are enriched at upregulated genes (right panel), indicating a preference for negative correlation: lower methylation, higher gene expression. If there were no preference, we would expect a 50:50 ratio of hypo- and hypermethylated DMRs, and we observed a 77:23 ratio. Nevertheless, we agree that there is a substantial number of cases (n=151) with a high positive correlation, which we now highlight in the text. For clarity, we also modified the figure legend to indicate that a stacked histogram is represented in the panel.

__CHANGE IN THE MANUSCRIPT____: __

L303: Interestingly, 23.5% of the identified DMR DEG pairs (n=151) showed a positive correlation between gene expression and DNA methylation.

*Figure legend in Fig. S3C was changed to: C Stacked histogram showing location of hyper- and hypomethylated DMRs relative to the TSS of DEGs in downregulated (left) and upregulated (right) genes. *

• Line 307 - what are the "analysed DEGs"? Are they the methylation associated genes?

Response:

Those are the DEGs we identified in RNA-seq analysis. To clarify, we changed the text to "identified DEGs".

__CHANGE IN THE MANUSCRIPT____: __

• "analysed DEGs" was changed to "identified DEGs"*

• Line 307-309 - "Among the analyzed DEGs, 76.5% (492) displayed a negative correlation (16.8% of the total DEGs), indicating a possible direct regulation by DNA methylation, while 23.5% (151) showed a positive correlation between gene expression and DNA methylation" - are the authors suggesting the positive correlation doesn't indicate direct regulation?

__Response: __

Thank you for highlighting this point. We did not intend to suggest that negative correlation indicates direct regulation, while positive correlation suggests a lack thereof. To clarify that point, we have reformulated this sentence.

__CHANGE IN THE MANUSCRIPT____: __

Among the identified DEGs, 76.5% (n=492) displayed a negative correlation (16.8% of the total DEGs), consistent with a repressive role of promoter DNA methylation. Interestingly, 23.5% of the identified DEG (n=151) showed a positive correlation between gene expression and DNA methylation.

• Line 313 - why did the authors focus on only negatively correlated genes to identify their top dysregulated pathway of IFN signalling? Why not do pathway analysis on the DNAm associated genes separately to identify DNAm associated pathways?

Response:

We have also performed a pathway enrichment analysis using the positively correlated genes but did not identify any significantly enriched pathways/process/terms. When we examined the top hit of the gene set enrichment analysis, the interferon signaling pathway, we observed only negatively correlated DMR gene associations (Fig. 5B). Therefore, we decided to use only the negatively correlated DMRs, as using all correlated genes would give a higher background and dilute our results.

CHANGE IN THE MANUSCRIPT____:

Cytoscape was used to analyse negatively or positively correlated DMR DEG pairs. ClueGO (v2.5.6) analysis was conducted using all DEG associated with a promoter proximal DMR (+/- 6 kb from TSS) and the Spearman correlation coefficient 0.5 (113).

• A comparison of the gene expression data with previous data in AT2 cell/single cell data would strengthen the gene expression section.

__Response: __

We compared our gene expression signatures with the study of Fujino et al., who profiled sorted AT2 cells (EpCAMhighPDPNlow) from COPD/controls using expression arrays (PMID: 23117565). Consistent with our study, the authors also observed the upregulation of interferon signalling (among other pathways) in COPD AT2s. However, no raw data was available in the published manuscript for a more in-depth analysis.

Several recent scRNA-seq studies identified transcriptional signatures of COPD and control cells (e.g., PMIDs: 36108172, 35078977, 36796082, 39147413__). However, most studies did not match the smoking status of the control and COPD donors and looked at the whole lung tissue, with limited power to detect gene expression changes in distal alveolar cells. It is difficult to directly compare our data to the gene expression data from non-smokers vs COPD patients, as cigarette smoking profoundly remodels the epigenome and transcriptional signatures of cells. In addition, differences in technologies and depth of sequencing make such comparisons challenging. However, one study (PMID: 36108172) performed scRNA-seq analysis on 3 non-smokers, 4 ex-smokers and 7 COPD ex-smoker lungs. Despite relatively limited coverage of epithelial cells in the dataset (We also compared the main AT2 IFN signature identified in the integration of our DNA methylation in promoter-proximal regions and RNA-seq with a recent study (published after the submission of our manuscript, PMID: 39147413) that profiled EpCAMpos cells from COPD and control lungs (non-smokers) using scRNA-seq. We observed an upregulation of our IFN signature genes in AT2 in COPD (specifically in AT2-c and rbAT2 subsets), suggesting that similar signatures were observed in this dataset as well. However, ex-smokers were not included in this study, making direct comparisons difficult. We have now included the panels shown below as __Figure S4E and S4F:

Figure S4E and F: Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74)*. The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells. *

CHANGES IN THE MANUSCRIPT:

However, 5-AZA is a global demethylating agent, and the observed effects may not be direct. To validate the epigenetic regulation of central AT2 pathways further, we took advantage of locus-specific epigenetic editing technology (73). We focused on the IFN pathway because it was the most significantly enriched Gene Ontology (GO) term in our integrative analysis of TWGBS and RNA-seq data. Several IFN pathway members had associated hypomethylated DMRs within promoter-proximal regions and concomitant increased gene expression (Fig. 4C and Fig.S2C). Additionally, we confirmed the elevated expression of IFN-related genes with associated DMRs identified in our study in AT2 cells and AT2 cell subclusters from a recently published scRNA-seq cohort (74)* (Fig. S4E-F). *

(Methods) Validation of IFN gene upregulation in a published scRNA-seq dataset

scRNA-seq data from (74), generously provided by M. Köningshoff, were processed using the default Seurat workflow (117). Expression of IFN-related genes was extracted and plotted as log-normalised gene expression levels in AT2 cells from control and COPD donors. Seurat's AddModuleScore() function was used to compute a gene set score for a custom IFN program using the genes listed in __Fig. S4E __and to analyse the IFN gene set scores in AT2 cell subclusters identified in (74). Briefly, average gene expression scores were computed for the gene set of interest, and the expression of control features (randomly selected) was subtracted as described in (118).

Fig. S4 E and F. E. Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74). The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells. __ __

• The paragraph starting on line 173 feels a little redundant when we know there is RNA available to test if the differential DNAm links to altered gene expression - this selected of example regions/genes would be better placed after the gene expression has been reported, at which point you could say whether the linked genes displayed altered transcription.

Response:

The current structure (with DNA methylation, followed by RNA-seq and integration) is intentional and serves several important purposes. As this is the first genome-wide high-resolution COPD DNA methylation study of AT2, we aimed to describe the methylation landscape independently of gene expression (noting the limitation of current understanding of how DNA methylation regulates expression). This early focus on DMRs lays clear groundwork by highlighting potential regulatory elements and pathways that could be disrupted, independent of or even before corroborative transcriptional data. Additionally, positioning these examples early in the narrative helps to frame subsequent gene expression analyses. Once RNA data are introduced later, the reader can directly compare the methylation patterns with transcriptional outcomes, thereby enhancing the overall story. In other words, by first showcasing disease-relevant methylation changes, we underscore a hypothesis that these epigenetic modifications are functionally meaningful. The later integration of gene expression data then serves as a confirmatory or complementary layer, rather than the sole basis for inferring biological significance. This is important as we still do not fully understand the function of DNA methylation outside promoters, and its role is also important for splicing, 3D genome organisation, non-coding RNA regulation, enhancer regulation, etc.

• Similarly, the TF enrichment analysis is great but maybe would have added value to be done on DNA regions later shown to be linked to differential expression - was there different enrichment at DNA regions that are vs are not associated with altered expression? And could you test in vitro whether changing methylation of DNA (maybe a blunt too like 5-aza would be ok) alters TF binding (cut+run/ChIP?). Furthermore, it would be interesting to understand the TF sensitivity analysis within the context of positive versus negative DNA methylation:gene expression correlations.

Response:

As suggested by the reviewer, we now performed the TF enrichment analysis using the DMRs with a high correlation (|correlation coefficient|>0.5) between methylation and expression (Figure S3D) and expanded the method section to include TF analysis. We observed ETS domain motifs enriched at hypomethylated regions. They prefer unmethylated DNA (MethylMinus) and are therefore expected to bind with higher affinity to the respective DMRs in COPD. We agree with the reviewer that further verifying altered TF binding using cut&run or ChIP assays would be very interesting, but it is out of the scope of this manuscript. Such analysis is technically very challenging to perform with low numbers of primary AT2 cells and will be the focus of our follow-up mechanistic studies.

CHANGE IN THE MANUSCRIPT____:

Additionally, motif analysis of DMRs that were highly correlated (|Spearman correlation coefficient| > 0.5) with DEGs revealed a prominent enrichment of the cognate motif for ETS family transcription factors, such as ELF5, SPIB, ELF1 and ELF2 at hypomethylated DMRs (Fig. S3D). Interestingly, SPIB was shown to facilitate the recruitment of IRF7, activating interferon signaling (71)*, and our WGBS data uncovers SPIB motifs at hypomethylated DMRs, which aligns with its binding preferences at unmethylated DNA (methyl minus, Fig. S3D). *

Figure S3D: Enrichment of methylation-sensitive binding motifs at hypo- (right) and hypermethylated (left) DMRs, using DMRs with a high correlation (|Spearman correlation coefficient| > 0.5) between methylation and gene expression. Methylation-sensitive motifs were derived from Yin et al (64). Transcription factors, whose binding affinity is impaired upon methylation of their DNA binding motif, are shown in red (Methyl Minus), and transcription factors, whose binding affinity upon CpG methylation is increased, are shown in blue (Methyl Plus).

(Methods) To obtain information about methylation-dependent binding for transcription factor motifs which are enriched at DMRs, the results of a recent SELEX study (64)* were integrated into the analysis. They categorised transcription factors based on the binding affinity of their corresponding DNA motif to methylated or unmethylated motifs. Those whose affinity was impaired by methylation were categorised as MethylMinus, while those whose affinity increased were categorised as MethylPlus. A motif database of 1,787 binding motifs with associated methylation dependency was constructed. The log odds detection threshold was calculated for the HOMER motif search as follows. Bases with a probability > 0.7 got a score of log(base probability/0.25); otherwise, the score was set to 0. The final threshold was calculated as the sum of the scores of all bases in the motif. Motif enrichment analysis was carried out against a sampled background of 50,000 random regions with matching GC content using the findMotifsGenome.pl script of the HOMER software suite, omitting CG correction and setting the generated SELEX motifs as the motif database. *

__Methods: __ • The authors should include more detail of the TWGBS rather than directing the reader to a previous publication. Also DNA concentration post bisulfite conversion would be a useful metric to provide.

__Response: __

Following the suggestion, we have now expanded the details of TWGBS in the methods part of the manuscript. Due to limited space, we did not include a detailed protocol but instead referred to a published step-by-step protocol (55). Of note, we do not measure DNA concentration post-bisulfite conversion but consistently use the starting input of 30 ng of genomic DNA across all samples.

__CHANGE IN THE MANUSCRIPT____: __

(Methods): 15 pg of unmethylated DNA phage lambda was spiked in as a control for bisulfite conversion. Tagmentation was performed in TAPS buffer using an in-house purified Tn5 assembled with load adapter oligos (55) at 55 {degree sign}C for 8 min. Tagmentation was followed by purification using AMPure beads, oligo replacement and gap repair as described (55). Bisulfite treatment was performed using EZ DNA Methylation kit (Zymo) following the manufacturer's protocol.

*The T-WGBS library preparations were performed for all donors in parallel and sequenced in a single batch to minimize batch effects and technical variability. *

• Differential DNA methylation analysis: It is stated that DNA regions had to contain 3 CpG sites but was this within a defined DNA size range?

Response:

The maximum distance between individual CpGs within DMR was set to 300 bp. To clarify, we added that information to the methods part.

__CHANGE IN THE MANUSCRIPT____: __

*"regions with at least 10% methylation difference and containing at least 3 CpGs with a maximum distance of 300 bp between them. *

• Refence genome only provided for RNAseq not TWGBS?

__Response: __We used hg19 as the reference genome. The information on the reference genome for DNA methylation analysis was provided in the methods L574 (original manuscript_: "The reads were aligned to the transformed strands of the hg19 reference genome using BWA MEM")

• The tables do not appear in the PDF and I struggled to tally to the "Dataset" files provided if that is what they were referring to?

Response:

Full tables (uploaded as Datasets in the manuscript central due to their size) were uploaded together with the manuscript files. They are quite large and will not convert to pdf, so they may not have been included in the merged pdf file. We assume that they should be available to the reviewers with the other files and will clarify that with the editorial staff in the resubmission cover letter.

• For the gene expression analysis, can it be made clearer that a full analysis was done on COPD I samples. It is a little confusing to the reader as this was not done for DNAm so might be assumed the same targeted analysis on only genes found to be differentially expressed between control and COPD II-IV, but that cannot be the case as an overlap of COPD1 vs COPD II-IV genes if provided. For this overlap, do genes show the same effect direction?

__Response: __

To clarify, for the RNA-seq analysis, we performed DEG analysis for no-COPD versus COPD II-IV, as well as no-COPD versus COPD I. We then took all differentially expressed genes (presented in the Venn diagram) and plotted them for all samples as a heatmap. To split the genes into groups displaying similar effect directions, we applied a clustering approach and identified 3 main signatures. Cluster 3 primarily comprises genes unique to COPD I samples, which are associated with the adaptive immune system and hemostasis (Fig. 4E). In the other two clusters, we mainly observe a transitioning pattern from control to severe COPD samples, correlating with the FEV1 values of the patients. This has now been clarified in the manuscript.

• Replication is difficult on these studies as the samples are so difficult to come by. Also limited by sample size for the same reason. It doesn't mean the study is not worth doing and the data are still valuable. However, it may be pertinent to include technical validation of a few regions of interest, acknowledge the limitation (along side strengths) in the discussion, and perhaps provide actual p value rather than blanket Response:

We thank the reviewer for acknowledging the replication challenges for studies working with sparse human material and hard-to-purify cell populations. Following the reviewer's suggestion, we have now included a strengths and limitations section in the discussion where we summarised the points highlighted by both reviewers.

Regarding technical validation, we would like to note that the whole genome bisulfite sequencing (WGBS) technology, as well as the tagmentation-based WGBS (T-WGBS), have been validated in the past few years in several publications (e.g., PMID: 24071908) and shown to yield reliable DNA methylation quantification in comparison to other technologies (PMID: 27347756). For us, technical validation using alternative methods (e.g. bisulfite sequencing or pyrosequencing) is difficult as it requires significantly more input DNA than the low-input T-WGBS we have performed and obtaining sufficient amounts of material from primary human AT2 cells (especially from severe COPD) is not possible with the size of tissue we can access. However, while establishing the T-WGBS for this project, we initially validated our approach using Mass Array, a sequencing-independent method. For this, we performed T-WGBS on the commercially available smoker and COPD lung fibroblasts and selected 9 regions with different methylation levels for validation using a Mass Array. We obtained an excellent correlation between both methods, providing technical validation of T-WGBS and our analysis workflow. This validation was published in our earlier manuscript (PMID: 37143403), but we provided the data below for convenience.

Scatter plots showing correlation of average methylation obtained with T-WGBS and Mass Array from COPD and smoker fibroblasts. Each dot represents one region with varying methylation levels. The blue diagonal represents the linear regression. Shaded areas are confidence intervals of the correlation coefficient at 95%. Correlation coefficients and P values were calculated by the Pearson correlation method.

To enable further validation and follow-up by the community, we included the full list of DMRs, associated p-values and additional information for DNA methylation analysis (DMR width, n.CpGs, MethylDiff, etc) in Table 3 (Table_3_wgbs_dmr_info.xlsx) and the information about DEGs from RNA-seq in Table 6 (Table_6_RNAseq_DEG_info.xlsx).

• It isn't clear to me if DNA and RNA are from the same cells? The results say "cells matching those used for T-WGBS" but the methods suggest separate extractions so not the same cells? If they are not the same cells a comment on the implications of this should be included in the discussion for example, potentially some differences in cell type composition, storage time etc.

Response:

Lung tissue samples were freshly cryopreserved, and H&E slides derived from exemplary pieces of the tissue analyzed. Once we had a group of at least 3 samples comprising one non-COPD and 2 COPD samples, we processed them in parallel to limit sorting variation between control and disease samples. The sorted cells were counted, aliquoted and pelleted at 4{degree sign}C before flash freezing and storing at -80{degree sign}C. The storage time of the cell pellets varied between the donors. RNA and DNA were isolated from cell pellets collected from the same FACS sorting experiment; therefore, we do not expect differences in cell type composition. In addition, RNA and DNA isolation were performed for all sorted pellets in parallel. All library preparations for TWGBS and RNA-seq were performed for all donors in parallel and sequenced in a single batch to minimise batch effects and technical variability. This has now been clarified in the methods part of the manuscript.

__CHANGE IN THE MANUSCRIPT____: __

To minimize potential technical bias, samples from no COPD and COPD donors were processed in parallel in groups of 3 (one no COPD and 2 COPD samples).

RNA and genomic DNA for RNA-seq and TWGBS were isolated from identical aliquots of sorted cell pellets.

Genomic DNA was extracted from 1-2x104 sorted alveolar epithelial cells isolated from cryopreserved lung parenchyma from 11 different donors in parallel using QIAamp Micro Kit

The TWGBS library preparations were performed for all donors in parallel and sequenced in a single batch to minimize batch effects and technical variability.* *

RNA was isolated from flash-frozen pellets of 2x104 sorted AT2 cells from 11 different donors in parallel.

The RNA-seq library preparation for all donors was performed in parallel and all samples were sequenced in a single batch to minimize batch effects and technical variability.

• Line 193 the authors say "Since DMRs were overrepresented at cis-regulatory sites...." - "cis" needs to be defined. If you link DNAm regions to gene via "closest gene" does this not automatically mean you're outputs will be cis? Just needs better definition/explanation.

Response:

The term "cis‐regulatory sites" in our manuscript is intended to denote regulatory elements-such as enhancers, promoters, and other nearby control regions-that reside on the same chromosome and close to the genes they regulate. While it's true that linking a DMR to its closest gene captures a cis association, our phrasing emphasises that the DMRs are enriched specifically at these functional regulatory elements (Fig. 2E) rather than being randomly distributed. This usage aligns with established conventions in the field. To avoid any misunderstandings, we have now changed the term to gene regulatory sites.

__CHANGE IN THE MANUSCRIPT____: __

*We changed the "cis-regulatory sites" to "gene regulatory sites" *

__Minor comments: __

Line 157: "we identified site-specific differences....". Change to region specific?

Response:

This has now been corrected as suggested.

Line 102-103: needs a reference for the statement "Alterations in DNA methylation patterns have been implicated......"

Response:

Following the reviewer's suggestion, we added the relevant references (34-36) to this statement.

Line 266 - what does "strong dysregulation" mean? Large fold change, very significant?

Response:

We removed the word "strong" from this sentence.

Lines 423-425 - statement needs a reference

Response:

Following the reviewer's suggestion, we added the relevant reference to this statement.

Line 428 - word missing between "epigenetic , we"?

Response:

This has now been corrected. The text reads: "Through treatment with a demethylating drug and targeted epigenetic editing, we demonstrated the ability to modulate..."

Prior studies are well references, text and figures are clear and accurate.

__Reviewer #2 (Significance (Required)): __

This study has several strengths:

1) Sample collection and characterisation. AT2 cells are incredibly hard to come by and the authors should be commended to generating the samples. However, proximity to cancer is always a potential issue, especially in epigenetic studies. Is it feasible to include any analysis to show the samples derived from those with cancer don't drive the changes observed? Even a high level PCA or an edit of fig 2A with non-cancer in a different colour in supplemental - looks like there is one outlier, is that a non-cancer? Or a correlation of change in beta between control and cancer/COPD and control and non-cancer:COPD (for want a better phrase!). just an indicator that the non-cancer COPD samples are not driving differences.

Response:

We thank the reviewer for highlighting the value of generating data from hard-to-work-with AT2 populations and bringing up the important point of cancer proximity, which we considered very carefully when designing our study. To match our samples across the cohort, all the no-COPD, COPD I, and two of the COPD II-IV distal lung samples were obtained from cancer resections. In addition to other characteristics, like age, BMI and smoking status, we also matched the donors by cancer type (all profiled donors had squamous cell carcinoma). We collected lung tissue as far away from the carcinoma as possible and sent representative pieces for histological analysis by an experienced lung pathologist to confirm the absence of visible tumours. In addition, to ensure that our data represents COPD-relevant signatures, we intentionally included samples from three COPD donors undergoing lung resections (without a cancer background) in the profiling.

Following the reviewer's suggestion, to investigate the potential impact of non-cancer samples on driving the observed differences, we carefully checked the PCAs for both DNA methylation and RNA-seq. We could not identify a clear separation of no-cancer COPD samples from the cancer COPD samples (or other cancer samples) in any examined PCs, indicating no cofounding effect of cancer samples. We observed that one sample contributing to PC2 is a non-cancer sample, but this was a rather sample-specific effect, as the other two non-cancer samples clustered together with the other severe COPD samples with a cancer background. Notably, in our DNA methylation data, we do not observe typical features of cancer methylomes, like global loss of DNA methylation or aberrant methylation of CpG islands (e.g., in tumour suppressor genes) (see Fig. 2A), further suggesting that we do not "pick up" confounding cancer signatures in our data.

Following the comments from both reviewers, to clarify that point, we added the information about cancer and non-cancer samples to the PCA figures for DNA methylation (new Fig. 2B) and RNA-seq (new Fig. 3A) data in the revised manuscript, as shown below

CHANGE IN THE MANUSCRIPT____:

COPD samples from donors with a cancer background clustered together with the COPD samples from lung resections, confirming that we detected COPD-relevant signatures (Fig. 2B).

Fig. 2B.* Principal component analysis (PCA) of methylation levels at CpG sites with > 4-fold coverage in all samples. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

Unsupervised principal component analysis (PCA) on the top 500 variable genes revealed a clear influence of the COPD phenotype in separating no COPD and COPD II-IV samples, as previously observed with the DNA methylation analysis, irrespective of the cancer background of COPD samples (Fig.3A, Fig. S2B).

*Principal component analysis (PCA) of 500 most variable genes in RNA-seq analysis. PCA 1 and 2 are shown in Fig.3A, PCA 1 and 4 in Fig.S2B. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

2) This is the first time DNAm has been profiled in AT2 cells. It is incredibly difficult, valuable and novel data that will increase the fields capability technically, their understanding of functional mechanisms and potential translation considerably. It's audience will be primarily translational respiratory however the fundamental science aspect of gene expression regulation by DNA methylation with have wider reach across developmental and disease science.

Response:

We thank the reviewer for recognising the uniqueness and novelty of our study and highlighting the value and potential impact of our datasets for the lung field.

3) the functional analysis using targeted CRISPR-Cas9 is very well done and adds impact.

Response:

We thank the reviewer for recognising the strengths and added value of the functional analysis using epigenetic editing.

__Potential weaknesses/areas for development __

I feel the main weakness is the in the section integrating DNA methylation and gene expression. The rationale for a focus on various aspects, for example inversely related DNAm/gene expression pairs, the IFN pathway and IRF9, are not clear. Also further understanding of the differences between DNAm associated genes and non-DNAm associated genes could be expanded, at the pathway level, TF regulation level, effect size level (are DNAm associated changes to gene expression larger, enriched for earlier differential expression)

Response:

Our rationale for focusing on the inversely related DNAm/gene expression pairs in promoter proximal is purely data-driven, as they represent the biggest group in our data (Fig. 4A-B). Among those negatively correlated genes, we observed the strongest enrichment for the IFN pathway (Fig. C), making it an obvious, data-driven target for further studies. The negative correlation of expression and methylation for IFN pathway genes could be validated in 5-AZA assays in A549 cells (Fig. 5A). Next, we made an interaction network analysis showing IRF9 and STAT2 as master regulators (Fig. 5B) of the negatively correlated IFN genes. As IRF9 itself displayed a negative correlation between DNA methylation and expression (Fig. 5C), we used the associated DMR for further epigenetic editing (Fig. 5D-E). We performed the additional requested analyses of the enhancer-associated changes and genes, as described above. We fully agree with the reviewer that our data sets are a great resource and can be further used to elaborate on other relationships of DNA methylation and RNA expression or other pathways, but this is out of the scope of this study. To enable further studies by the research community, we provide all necessary information about DMRs and DEGs in the associated supplementary tables and the raw data through the EGA, as well as the CRISPRa editing assay.

The authors could comment on potential masking of differences between 5hmC and mC and the implications it may have

Response:

We thank the reviewer for bringing up this important point. Indeed, bisulfite sequencing cannot differentiate between methylated and hydroxymethylated cytosines; hence, some of the methylated sites may be hydroxymethylated. However, the overall levels of hydromethylation in differentiated adult tissues are very low (except for the brain), orders of magnitude lower compared to DNA methylation. Following the reviewer's suggestion, we have added a sentence in the limitation section of the discussion to clarify that point.

__CHANGE IN THE MANUSCRIPT: __

In addition, while WGBS provides unprecedented resolution and high coverage of the DNA methylation sites across the genome, it does not allow distinguishing 5-methylcytosine from 5-hydroxymethylcytosine. Therefore, we cannot exclude that some methylated sites we detected are 5-hydroxymethylated. However, the 5-hydroxymethylcytosine is present at very low levels in the lung tissue (97)*. ** *

Furthermore, while the rationale for looking at DMRs is clear, especially given the sample number, I am interested to understand what proportion of the assayed CpGs "fit" within the cut off stipulations of the DMR analysis - that is, is their potentially COPD effects at sparse CpG regions/individual CpG sites that are not being identified. A comment on this would be useful and seems the strength of profiling genome wide. I'm happy genome wide is beneficial it just feels a little circular that the authors have chosen whole genome to avoid the bias of the Illumina array and a focus on promotors, but have primarily reported promoter DNAm. This caught my attention again in the discussion where the authors state that cis-regulatory regions were also identified in their fibroblast data .....is this finding a factor of the analysis performed? (also a comparison of regions Identified in AT2 cells versus fibroblasts would be really interesting for a future paper)

Response:

We decided to focus our analysis on regions rather than individual CpG sites when looking at differential methylation, as DNA methylation is spatially correlated, and methylation changes in larger regions are more likely to have a biological function. Extending the analysis to single CpG sites would require a higher number of samples for a reliable analysis compared to the DMR analysis (as mentioned by the reviewer).

Of note, we addressed the platform comparison between Illumina array technology and WGBS in our previous fibroblast study (PMID: 37143403), where we compared our WGBS data with the published 450k array data of COPD parenchymal fibroblasts (Clifford et al., 2018). We observed only a marginal overlap between the CpGs from our DMRs and the CpGs probes available on the array (which was due to the differences in technologies used and the limited coverage of the 450K array in comparison to our genome-wide approach, in which we covered 18 million CpGs). Out of the 6279 DMRs identified in our fibroblast study, only 1509 DMRs overlapped with at least one CpG probe on the 450K array, and after removing low-quality CpGs from the array data, only 1419 DMRs were left. This comparison highlighted the increased resolution of the WGBS compared to Illumina arrays.

The reason why we focused on promoter proximal DMRs are the following: 1) the assignment of the enhancer elements in AT2 to the corresponding gene is still too inaccurate in the absence of AT2 specific enhancer chromatin maps 2) regulation at enhancers by DNA methylation might be more complex and might change (increase or attenuate) binding affinities of certain transcription factors (Fig.2H), which might lead to gene expression changes or 3) methylation changes might be an indirect effect of differential TF binding PMID: 22170606). However, we agree with the reviewer that despite these limitations, expanding the analysis beyond promoters adds value to the manuscript; hence, as described above, we expanded the analysis of non-promoter regions, including enhancers, in the revised manuscript.

We thank the reviewer for the suggestion to compare the regions identified in AT2 cells and fibroblasts in a future paper.

My expertise:Respiratory, cell biology, epigenetics.

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Referee #2

Evidence, reproducibility and clarity

Summary:

This study aim to understand the molecular mechanisms underlying dysfunction in AT2 cells in COPD, by profiling bulk genome wide DNA methylation using Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) and RNA sequencing in selectively sorted primary AT2 cells. The study stands out in it's sequencing breadth and use of an incredibly difficult cell population, and has the potential to add substantially to our mechanistic understanding of epigenetic contributions to COPD. A further highlight is the concluding aspect of the study where the authors undertook targeted modification of specific CpG methylation, provided direct, site-specific evidence for transcriptional regulation by CpG methylation.

Major comments:

The authors clearly show that there is DNA methylation alteration in AT2 cells from COPD individuals that links functional to gene expression at some level. However, I think the statement "to identify genome-wide changes associated with COPD development and progression..." and similar other references to disease development understanding is not accurate given the DNA methylation primary comparison is between control and moderate to severe COPD, with no temporal detail or evidence that they drive progression rather than are a result of COPD development. The paragraph starting on line 186 where this is a addressed to some extent is quite vague and doesn't really provide confidence that DNAm dysregulation occurs at an early stage in this context. This can be addressed by changing the focus/style of the text.

Results comments and suggestions:

For the integrated analysis, there is a focus on DMRs in promoters with very little analysis on other regions. The paragraph starting on line 317 describes some analysis on enhancers but is very brief, doesn't include information on how many/which DMRs were included, making it hard to interpret the impact of the 147 DMRs and 93 genes identified - is this nearly all DMRs and genes analysed or very few? A comparison to the promoter analysis would be of interest. Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

• Lines 299-301 - I'm not sure the graph in Fig S3A support the conclusion that there was a preferential negative relationship between DNAm and gene expression. Looks like there are a substantial number of cases where a positive relationship is observed and this needs to be acknowledged.

• Line 307 - what are the "analysed DEGs"? Are they the methylation associated genes?

• Line 307-309 - "Among the analyzed DEGs, 76.5% (492) displayed a negative correlation (16.8% of the total DEGs), indicating a possible direct regulation by DNA methylation, while 23.5% (151) showed a positive correlation between gene expression and DNA methylation" - are the authors suggesting the positive correlation doesn't indicate direct regulation?

• Line 313 - why did the authors focus on only negatively correlated genes to identify their top dysregulated pathway of IFN signalling? Why not do pathway analysis on the DNAm associated genes separately to identify DNAm associated pathways?

• A comparison of the gene expression data with previous data in AT2 cell/single cell data would strengthen the gene expression section.

• The paragraph starting on line 173 feels a little redundant when we know there is RNA available to test if the differential DNAm links to altered gene expression - this selected of example regions/genes would be better placed after the gene expression has been reported, at which point you could say whether the linked genes displayed altered transcription.

• Similarly, the TF enrichment analysis is great but maybe would have added value to be done on DNA regions later shown to be linked to differential expression - was there different enrichment at DNA regions that are vs are not associated with altered expression? And could you test in vitro whether changing methylation of DNA (maybe a blunt too like 5-aza would be ok) alters TF binding (cut+run/ChIP?). Furthermore it would be interesting to understand the TF sensitivity analysis within the context of positive versus negative DNA methylation:gene expression correlations.

Methods:

• The authors should include more detail of the TWGBS rather than directing the reader to a previous publication. Also DNA concentration post bisuphite conversion would be a useful metric to provide.

• Differential DNA methylation analysis: It is stated that DNA regions had to contain 3 CpG sites but was this within a defined DNA size range?

• Refence genome only provided for RNAseq not TWGBS?

• The tables do not appear in the PDF and I struggled to tally to the "Dataset" files provided if that is what they were referring to?

• For the gene expression analysis, can it be made clearer that a full analysis was done on COPD I samples. It is a little confusing to the reader as this was not done for DNAm so might be assumed the same targeted analysis on only genes found to be differentially expressed between control and COPD II-IV, but that cannot be the case as an overlap of COPD1 vs COPD II-IV genes if provided. For this overlap, do genes show the same effect direction?

• Replication is difficult on these studies as the samples are so difficult to come by. Also limited by sample size for the same reason. It doesn't mean the study is not worth doing and the data are still valuable. However, it may be pertinent to include technical validation of a few regions of interest, acknowledge the limitation (along side strengths) in the discussion, and perhaps provide actual p value rather than blanket < p 0.1, seems very lenient but may all be super significant (this may already be in the tables I wasn't able to find).

• It isn't clear to me if DNA and RNA are from the same cells? The results say "cells matching those used for T-WGBS" but the methods suggest separate extractions so not the same cells? If they are not the same cells a comment on the implications of this should be included in the discussion for example, potentially some differences in cell type composition, storage time etc.

• Line 193 the authors say "Since DMRs were overrepresented at cis-regulatory sites...." - "cis" needs to be defined. If you link DNAm regions to gene via "closest gene" does this not automatically mean you're outputs will be cis? Just needs better definition/explanation.

Minor comments:

• Line 157: "we identified site-specific differences....". Change to region specific?

• Line 102-103: needs a reference for the statement "Alterations in DNA methylation patterns have been implicated......"

• Line 266 - what does "strong dysregulation" mean? Large fold change, very significant?

• Lines 423-425 - statement needs a reference

• Line 428 - word missing between "epigenetic , we"?

• Prior studies are well references, text and figures are clear and accurate.

Significance

This study has several strengths:

1) Sample collection and characterisation. AT2 cells are incredibly hard to come by and the authors should be commended to generating the samples. However, proximity to cancer is always a potential issue, especially in epigenetic studies. Is it feasible to include any analysis to show the samples derived from those with cancer don't drive the changes observed? Even a high level PCA or an edit of fig 2A with non-cancer in a different colour in supplemental - looks like there is one outlier, is that a non-cancer? Or a correlation of change in beta between control and cancer/COPD and control and non-cancer:COPD (for want a better phrase!). just an indicator that the non-cancer COPD samples are not driving differences.

2) This is the first time DNAm has been profiled in AT2 cells. It is incredibly difficult, valuable and novel data that will increase the fields capability technically, their understanding of functional mechanisms and potential translation considerably. It's audience will be primarily translational respiratory however the fundamental science aspect of gene expression regulation by DNA methylation with have wider reach across developmental and disease science.

3) the functional analysis using targeted CRISPR-Cas9 is very well done and adds impact.

Potential weaknesses/areas for development:

I feel the main weakness is the in the section integrating DNA methylation and gene expression. The rationale for a focus on various aspects, for example inversely related DNAm/gene expression pairs, the IFN pathway and IRF9, are not clear. Also further understanding of the differences between DNAm associated genes and non-DNAm associated genes could be expanded, at the pathway level, TF regulation level, effect size level (are DNAm associated changes to gene expression larger, enriched for earlier differential expression) The authors could comment on potential masking of differences between 5hmC and mC and the implications it may have

Furthermore, while the rationale for looking at DMRs is clear, especially given the sample number, I am interested to understand what proportion of the assayed CpGs "fit" within the cut off stipulations of the DMR analysis - that is, is their potentially COPD effects at sparse CpG regions/individual CpG sites that are not being identified. A comment on this would be useful and seems the strength of profiling genome wide. I'm happy genomewide is beneficial it just feels a little circular that the authors have chosen whole genome to avoid the bias of the Illumina array and a focus on promotors, but have primarily reported promoter DNAm. This caught my attention again in the discussion where the authors state that cis-regulatory regions were also identified in their fibroblast data ..... is this finding a factor of the analysis performed? (also a comparison of regions Id'ed in AT2 cells versus fibroblasts would be really interesting for a future paper)

My expertise: Respiratory, cell biology, epigenetics.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This study by Prada et al. aimed to explore DNA methylation and gene expression in primary EpCAMhigh/PDPNlow cells, consisting for (probably) the largest part of AT2 cells, to understand the molecular mechanisms behind the impaired regeneration of alveolar epithelial progenitor cells in COPD. They found that higher or lower promoter methylation in COPD-associated cells was inversely correlated with changes in gene expression, with interferon signaling emerging as one of the most upregulated pathways in COPD. IRF9 was identified as the master regulator of interferon signaling in COPD. Targeted DNA demethylation of IRF9 in an A549 cell line resulted in a robust activation of its downstream target genes, including OAS1, OAS3, PSMB8, PSMB9, MX2 and IRF7, demonstrating that demethylation of IRF9 is sufficient to activate the IFN signaling pathway, validating IRF9 as a master regulator of IFN signaling in (alveolar) epithelial cells.

Major comments:

• To remove airways (and blood vessels) completely from the lung tissue is difficult, if not impossible. This means that the assumption that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells remains valid only if a quantitative analysis is conducted on the proportion of HT2-280pos cells in all samples in cytospins to exclude any significant contamination from bronchial epithelial cells. If authors cannot demonstrate >95% pure HT-280-positive cells, then the key conclusions suggesting that the epigenetic regulation of the IFN pathway might be crucial in AT2 progenitor cell regeneration could also potentially apply to bronchial progenitor cells. In addition, if >95% purity can not be demonstrated, the data should be adjusted to account for differences in cell type composition.

• The overrepresentation of several keratins (KRT5, KRT14, KRT16, KRT17), mucins (MUC12, MUC13, MUC16, MUC20) and the transcription factor FoxJ1 is now attributed by the authors to a possible dysregulation of AT2 identity and differentiation in COPD (lines 282 - 284) where they cite refs 28, 69, 70. Authors try to support this with IF double stains for KRT5 and HT-280 to identify co-expression of KRT5 and HT2-280 in lung tissue (Figure S2H). However, the evidence for the co-expression of both markers could be presented more convincingly.

• Double staining for KRT5 and HT2-280 did highlight the proximity of both cell types in lung tissue, underscoring the challenge of removing airways (including the smaller and terminal bronchi) from the tissue. In addition, HT-280/KRT5 co-expression in not consistent with recent studies from refs 28, 69, 70 where other markers for distal airway cell transition, such as SCGB3A2 and BPIFB1, have been demonstrated, which were not investigated in this study.

• The small (and not evenly divided) sample size of both COPD and non-COPD specimens may lead to a higher risk for false positive results as adjustments for multiple testing typically rely on the number of comparisons, and small sample sizes may not provide enough data points to adequately control for this.

Minor comments:

Introduction:

• In general, refer to the actual experimental studies rather than review papers where appropriate.

• Clearly specify whether a study was conducted in mice or humans, as this distinction is crucial for understanding the relevance of the findings to COPD.

Methods:

• Line 473, here is meant 3 ex-smoker controls instead of smoker controls?

Discussion:

• A list of limitation should be added to the discussion. One is the use of the alveolar cell line A549, which produces mucus, a characteristic more commonly associated with bronchial epithelial cells. (ref 43)

• Another limitation to consider is that cells were isolated primarily from individuals with lung cancer, except for patients with COPD stage IV. In particular as COPD stage II and IV samples were taken together.

• And discuss the small and unevenly divided sample size

References:

• Check references. For instance, there is no reference in the text to ref 43.

• Align format of references

Significance

The strength of this study lies in its focus on the molecular mechanisms underlying the impaired regeneration of epithelial progenitor cells in COPD. The discovery of IRF9, which regulates IFN signaling and is prominently upregulated in COPD, together with the convincing validation of the epigenetic control of the IFN pathway by targeted DNA demethylation of the IRF9 gene, adds significant value to the COPD research field.

Main limitations of the study are the relatively small sample size of both COPD and non-COPD specimens and the claim that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells.

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

The nature and significance of the advance in epigenetic editing of IRF9 in COPD can be described as both conceptual and potentially clinical: Conceptual Advance: The epigenetic editing of IRF9 enhances our understanding of the molecular mechanisms underlying COPD pathogenesis. By targeting IRF9 through epigenetic modifications, researchers were able to modulate the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. This approach offers insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD and expands our understanding of how alterations in specific gene methylation could contribute to disease progression. Clinical Significance: The potential clinical significance of epigenetic editing of IRF9 lies in its implications for COPD therapy. If successful, epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting regeneration of epithelial progenitor cells in the lungs. Obviously, further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment.

• Place the work in the context of the existing literature (provide references, where appropriate).

Few experimental papers have been published on epigenetic editing in lung diseases, with limited research available beyond the study referenced in citation 43. Song J, Cano-Rodriquez D, Winkle M, Gjaltema RA, Goubert D, Jurkowski TP, Heijink IH, Rots MG, Hylkema MN. Targeted epigenetic editing of SPDEF reduces mucus production in lung epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2017 Mar 1;312(3):L334-L347. doi: 10.1152/ajplung.00059.2016. Epub 2016 Dec 23. PMID: 28011616.

• State what audience might be interested in and influenced by the reported findings.

This study is of broad interest to researchers investigating the pathogenesis and treatment of COPD.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view.

Expertise in: Lung pathology, Immunology, COPD, Epigenetics

• Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Less expertise in: Epigenetic Editing

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Reply to the reviewers

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

Summary: findings and key conclusions Epithelial cell competition in larval imaginal discs involves signaling with the Sas ligand and Ptd10D receptor. In wild type cells both are typically found at the apical surface, but relocalize to the lateral cortex at the winner-loser interface. Ptd10D activation leads to reduced Ras signaling, increased pro-apoptotic Jnk signaling and consequently the elimination of loser cells. In the manuscript the authors address the role of the actin cytoskeleton in the context of the signaling controlling cell elimination in Drosophila larval eye imaginal discs. They interfere by clonal overexpression of the guanyl nucleotide exchange factor RhoGEF2 (RG2), which has previously been shown to induce dominant gain-of-function phenotypes by activation of Rho signaling. In this context the requirement of and genetic interactions with the other pathways implicated in cell elimination is tested. They find that RG2 induced cell elimination depends on PtD10D, Hippo signaling and Crumbs.

Major comments: claims and conclusions The experimental setting, using clonal analysis in imaginal discs, is straight-forward and well-established, including quantification of clone size and comparison of phenotypes. The presented data are of high quality and thus the direct conclusions are fully supported by the data as long as they refer to the actual experimental interference. What is not supported by the data is the generalization of the conclusions, i. e. that RG2 overexpression would be equivalent to Actin cytoskeletal deregulation. This equivalence is expressed in the title "Actin cytoskeletal deregulation, caused by RhoGEF2 overexpression.." and the summary " that actin cytoskeleton deregulated cells (as induced by RhoGEF2 overexpression (RhoGEF2OE))...". In my view such an equivalence is not justified. There is no doubt that RG2 overactivation affects the actin cytoskeleton in multiple ways, such as contractility via MyoII or polymerization via Dia, among others. There is also no doubt that other pathways are also directly or indirectly affected beside the actin cytoskeleton. The authors do not present data showing the specificity of RG2 overexpression. For example, the authors could investigate the phenotype and genetic interaction with an alternative way of interference, independent of RG2, of the actin cytoskeleton to support their conclusion. There is a second assumption, which may not be justified, that the function of the cytoskeleton would be generally downstream of cell polarity, see abstract l24 "triggering cytoskeletal deregulation (which occurs downstream of cell polarity disruptions)..". There are certainly cytoskeletal activities such as cell shape changes that mediate the execution of cell elimination. However interfering with the cortical cytoskeleton also affect the distribution of cortical polarity proteins. The authors do not present data to demonstrate the specificity of RG2 overexpression concerning a function downstream of cell polarity.

Response: We apologise for our phrasing of the title and the sentence in the summary that suggests that it is the actin cytoskeleton disruption caused by RhoGEF2 overexpression that is responsible for the effects on cell competition. We have rephrased the title and edited the text to avoid such an inference.

With regard to the reviewer’s second concern regarding the link between cell polarity disruption and actin cytoskeletal deregulation, there is indeed evidence that this occurs.

There are numerous examples of how cell polarity regulators affect the actin cytoskeleton in both Drosophila and mammalian cells (reviewed by Humbert et al., 2015, DOI 10.1007/978-3-319-14463-4_4). Indeed, in our previous paper (Brumby et al., 2011. PMID: 21368274), we found genetic evidence that the knockdown of the polarity regulator, dlg, cooperates with activated Ras (RasACT) to produce a hyperplastic eye phenotype, and that this phenotype is rescued by knockdown of actin cytoskeletal regulators like RhoGEF2 or Rho. This data suggests that these actin cytoskeleton regulators act downstream of cell polarity disruption to cooperate with RasACT. Furthermore, another study has shown that the activation of Myosin II is increased in scrib mutants and impairs Hippo pathway signaling, and is also required for the cooperation of scrib mutants with RasACT (Külshammer, et al., 2013. PMID: 23239028). Consistent with this finding, we have previously shown that RhoGEF2 acts via Rho, Rok, and Myosin II activation in cooperation with RasACT (Khoo et al., 2013. PMID: 23324326). Furthermore, another cell polarity regulator, Lgl, binds to and negatively regulates Myosin II function in Drosophila (Strand et al., 1994. PMID: 7962095; Betschinger et al., 2005. PMID: 15694314). Moreover, Drosophila Scrib and Dlg bind to GUK-holder/NHS1 (Nance–Horan syndrome-like 1), which is a regulator of the WAVE/SCAR-ARP2/3-branched F-actin pathway, and this interaction is required for epithelial tissue development (Caria et al., 2018. PMID: 29378849). Thus, although cell polarity gene loss can affect the actin cytoskeleton by different means, and RhoGEF2 can activate Rho to regulate various actin cytoskeletal effectors (Limk, Dia, PKN, Rok), what they have in common is the activation of Myosin II. To make this clearer, we have now added brief sections to the introduction and Discussion highlighting and contextualising evidence for the effect of cell polarity disruption on the actin cytoskeleton.

Reviewer #1 (Significance (Required)):

The study establishes genetic interactions and dependencies concerning cell elimination following a very specific experimental interference of RG2 overexpression. It remains unclear, however, to which degree these genetic interactions contribute to controlling cell competition in situations that are physiologically relevant. The generalization of RG2 overexpression as a specific test the function of the actin cytoskeleton is an interpretation not supported by the presented data and the experimental set up.

Response: Although RhoGEF2 overexpression does lead to actin cytoskeletal disruption via Rho effectors, the reviewer is correct that we do not know whether it is the actin cytoskeleton disruption per se that is involved in triggering cell competition. We have edited the text accordingly.

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

Summary: In the manuscript "Actin cytoskeletal deregulation, caused by RhoGEF2 overexpression, induces cell competition dependent on Ptp10D, Crumbs, and the Hippo signaling pathway", Natasha et al. investigate how actin cytoskeletal deregulation drives cell competition in the Drosophila eye disc. By overexpressing RhoGEF2 to induce cytoskeletal disruption and utilizing genetic knockdowns of various candidate genes, the authors examine the spatial distribution and interaction between normal and deregulated cell populations. Their findings demonstrate that cell competition and clone elimination in this context are dependent on sas-Ptp10D, scrib, and components of the Hippo signaling pathway. The study is well executed and provides a potentially impactful contribution to the field. The experimental design is solid, and the conclusions are generally well supported by the data. Only minor revisions are needed to strengthen clarity and presentation. Specific suggestions and comments regarding significance are listed below.

Major comments: There is a discrepancy between the representative images (Fig. 3A-C′) and the quantification in Fig. 3J. The statistical analysis may be limited by small sample size or suboptimal test choice (e.g., Kruskal-Wallis vs. ANOVA). Increasing the sample size and reassessing the statistical approach could strengthen this otherwise well-executed section.

Response: The data is normally distributed, so we have repeated the analysis using a one-way ANOVA (instead of the Kruskal-Wallis test – Initially we used this one because of the small sample number, but the data is normally distributed, and so a one-way ANOVA is appropriate). From examining all the images again, we can ascertain that there is indisputably less active caspase-3 staining in RhoGEF2-OE Ptp10D-KD compared to RhoGEF2-OE Dicer2. We have selected a more suitable image that better represents this snapshot of active caspase-3 staining in RhoGEF2-OE Ptp10D-KD. Also, a more representative control image is now shown, where some baseline active caspase-3 staining is present.

A minor concern relates to the interpretation and consistency of the statistical analyses used. For example, in Figure 5I, both the Kruskal-Wallis test and an unpaired t-test were used, with the authors stating that the t-test was applied specifically to compare wild-type and crb-/- clones (p = 0.0147). However, in the adjacent panel (Figure 5J), only a one-way ANOVA was used. This inconsistency may give the impression that the choice of statistical test in Figure 5I was influenced by a lack of significance with the Kruskal-Wallis test, rather than by experimental design. Unifying the statistical approach within related panels would improve clarity and minimize potential reader misinterpretation. Additionally, some of the statistical tests applied may not fully align with the underlying data distributions. Statistical methods used in parts of the manuscript may need to be reevaluated, and the rationale for their selection should be clarified in the text.

Response: We have checked the data carefully, plotted all the individual data sets in R, and the data is not normally distributed. Therefore, conducting a Kruskal-Wallis test is the best approach. This analysis shows that there is no significant difference between crb-/- and WT in our experimental setting. However, there is a slight trend towards increased crb-/- clone size. We have added a more detailed description of the statistical methods used in different situations in the Materials and Methods section.

In the section on how crb-/- affects actin distribution and accumulation within the tissue (Figure 6H′ and Supplementary Figure 5), it appears that F-actin may accumulate more prominently in cytoplasmic regions rather than at cell-cell junctions under crb-/- conditions. However, due to the current level of magnification, it is difficult to determine the precise subcellular localization. Although this question is somewhat tangential to the main focus of the manuscript and not essential for publication, it could be valuable, if the authors included a few higher-magnification images showing F-actin distribution in RhoGEF2OE Dicer2, RhoGEF2OE Ptp10D KD, and RhoGEF2OE crb-/- conditions. Including these in the supplementary figures could help clarify how actin cytoskeletal regulation is affected.

Response: We have added zoomed-in images to Figs 6G and 6H to show the effect on F-actin more clearly. It is possible that F-actin may be more prominent in the cytoplasm in crb-/- clones, however further experiments would be needed to provide more evidence for this, which are unfortunately beyond the scope of our capabilities at this time.

In Figure 6H′ the Diap1 signal in the RhoGEF2OE condition appears non-uniform, with noticeably weaker intensity on the left side of the image and stronger signal on the right. This asymmetry is not observed in the RhoGEF2OE crb-/- condition shown in Figure 6K′. It is unclear whether this pattern reflects a biological phenomenon consistently observed in RhoGEF2OE tissues or if it might result from technical factors such as uneven mounting or imaging. To prevent potential misinterpretation, we recommend clarifying this point, providing additional representative images if available, or replacing the current image with one that more clearly reflects the typical expression pattern.

Response: We assume the reviewer means Fig 6J, and we have replaced the image with a more representative one.

In Fig. 3B′, cleaved Caspase-3 appears localized to specific regions at the WT/RhoGEF2OE interface, suggesting spatial bias in Ptp10D-dependent elimination. This raises important questions about what determines regional susceptibility-are certain tissue conditions or cell states more prone to apoptosis in this context? Figure 3 raises the question of whether RhoGEF2OE-induced, actin-deregulated clones undergo dynamic changes, such as expanding or regressing, over the course of the larval stage. Such temporal variability could influence GFP⁺ clone size and the expression of apoptotic markers like cleaved Caspase-3 and Diap1. The stated use of the L3 stage, which spans ~48 hours (Tennessen & Thummel, 2011), lacks sufficient temporal resolution. Clarifying the timing of dissection and fixation relative to clone induction would improve interpretation of clone behavior and marker dynamics.

Response: While the reviewer raises an interesting question about spatial and temporal sensitivities to apoptosis upon genetic perturbations, we have conducted all of our experiments on samples obtained from the wandering L3 stage. We have added the following text to the Materials and Methods to make it clearer: “Wandering third-instar larvae (L3) were picked for all experiments, and for each experiment all larvae were of equivalent size.”.

Minor comments: GFP signal appears weaker in the wild-type group compared to experimental conditions, raising the question of whether image processing (e.g., contrast and color balance) was applied uniformly and if this difference reflects true variation in expression.

Response: Yes, images were always identically processed. We have stated in the Materials and Methods imaging section: “Laser intensity and gain was unchanged within each experimental group”.

For Figures 2, 3, and 5, including representative images for each eye phenotype category would clarify the scoring criteria. In Figure 5, the use of a "2.5" category in the main figure should be explained-does it correspond to category 3 or indicate an intermediate phenotype?

Response: Apologies for this error, and thanks to the reviewer for highlighting this. The “2.5” rating was a mistake based on a previous classification scale we used, and we have changed 2.5 to 3 in the graph. We have also included a new supplementary figure explaining our rankings (Supp Fig 10).

In Figure 5I, the y-axis range (0-150%) is broader than needed; adjusting it to 0-100% would better reflect the data and improve clarity.

Response: We have edited the Fig 5I graph accordingly.

The sentence from line 343- 348 is long and challenging to follow.

Response: We have reworded the sentence.

Missing the Figure number on Line 286.

Response: We have added the Figure number.

Reviewer #2 (Significance (Required)):

Significance: This study is well executed and rigorously addresses previously reported variations in phenotypic outcomes across laboratories. Beyond clarifying the role of Ptp10D in cell competition, the authors establish RhoGEF2 overexpression as a reliable method to induce cell competition and identify key molecular players involved in this process. This work represents a meaningful advance by introducing novel approaches and deepening understanding of known factors in clone elimination. The mosaic RhoGEF2 overexpression technique developed in this study provides a valuable tool for investigating cell-cell interactions at the tissue level, with broad applicability in basic research. This approach holds particular promise for probing.

Response: We thank the reviewer for their support of the significance and quality of our manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In the manuscript "Actin cytoskeletal deregulation, caused by RhoGEF2 overexpression, induces cell competition dependent on Ptp10D, Crumbs, and the Hippo signaling pathway", Natasha et al. investigate how actin cytoskeletal deregulation drives cell competition in the Drosophila eye disc. By overexpressing RhoGEF2 to induce cytoskeletal disruption and utilizing genetic knockdowns of various candidate genes, the authors examine the spatial distribution and interaction between normal and deregulated cell populations. Their findings demonstrate that cell competition and clone elimination in this context are dependent on sas-Ptp10D, scrib, and components of the Hippo signaling pathway. The study is well executed and provides a potentially impactful contribution to the field. The experimental design is solid, and the conclusions are generally well supported by the data. Only minor revisions are needed to strengthen clarity and presentation. Specific suggestions and comments regarding significance are listed below.

Major comments:

There is a discrepancy between the representative images (Fig. 3A-C′) and the quantification in Fig. 3J. The statistical analysis may be limited by small sample size or suboptimal test choice (e.g., Kruskal-Wallis vs. ANOVA). Increasing the sample size and reassessing the statistical approach could strengthen this otherwise well-executed section. A minor concern relates to the interpretation and consistency of the statistical analyses used. For example, in Figure 5I, both the Kruskal-Wallis test and an unpaired t-test were used, with the authors stating that the t-test was applied specifically to compare wild-type and crb-/- clones (p = 0.0147). However, in the adjacent panel (Figure 5J), only a one-way ANOVA was used. This inconsistency may give the impression that the choice of statistical test in Figure 5I was influenced by a lack of significance with the Kruskal-Wallis test, rather than by experimental design. Unifying the statistical approach within related panels would improve clarity and minimize potential reader misinterpretation. Additionally, some of the statistical tests applied may not fully align with the underlying data distributions. Statistical methods used in parts of the manuscript may need to be reevaluated, and the rationale for their selection should be clarified in the text. In the section on how crb-/- affects actin distribution and accumulation within the tissue (Figure 6H′ and Supplementary Figure 5), it appears that F-actin may accumulate more prominently in cytoplasmic regions rather than at cell-cell junctions under crb-/- conditions. However, due to the current level of magnification, it is difficult to determine the precise subcellular localization. Although this question is somewhat tangential to the main focus of the manuscript and not essential for publication, it could be valuable, if the authors included a few higher-magnification images showing F-actin distribution in RhoGEF2OE Dicer2, RhoGEF2OE Ptp10D KD, and RhoGEF2OE crb-/- conditions. Including these in the supplementary figures could help clarify how actin cytoskeletal regulation is affected. In Figure 6H′, the Diap1 signal in the RhoGEF2OE condition appears non-uniform, with noticeably weaker intensity on the left side of the image and stronger signal on the right. This asymmetry is not observed in the RhoGEF2OE crb-/- condition shown in Figure 6K′. It is unclear whether this pattern reflects a biological phenomenon consistently observed in RhoGEF2OE tissues or if it might result from technical factors such as uneven mounting or imaging. To prevent potential misinterpretation, we recommend clarifying this point, providing additional representative images if available, or replacing the current image with one that more clearly reflects the typical expression pattern. In Fig. 3B′, cleaved Caspase-3 appears localized to specific regions at the WT/RhoGEF2OE interface, suggesting spatial bias in Ptp10D-dependent elimination. This raises important questions about what determines regional susceptibility-are certain tissue conditions or cell states more prone to apoptosis in this context? Figure 3 raises the question of whether RhoGEF2OE-induced, actin-deregulated clones undergo dynamic changes, such as expanding or regressing, over the course of the larval stage. Such temporal variability could influence GFP⁺ clone size and the expression of apoptotic markers like cleaved Caspase-3 and Diap1. The stated use of the L3 stage, which spans ~48 hours (Tennessen & Thummel, 2011), lacks sufficient temporal resolution. Clarifying the timing of dissection and fixation relative to clone induction would improve interpretation of clone behavior and marker dynamics.

Minor comments:

GFP signal appears weaker in the wild-type group compared to experimental conditions, raising the question of whether image processing (e.g., contrast and color balance) was applied uniformly and if this difference reflects true variation in expression. For Figures 2, 3, and 5, including representative images for each eye phenotype category would clarify the scoring criteria. In Figure 5, the use of a "2.5" category in the main figure should be explained-does it correspond to category 3 or indicate an intermediate phenotype? In Figure 5I, the y-axis range (0-150%) is broader than needed; adjusting it to 0-100% would better reflect the data and improve clarity. The sentence from line 343- 348 is long and challenging to follow. Missing the Figure number on Line 286.

Significance

This study is well executed and rigorously addresses previously reported variations in phenotypic outcomes across laboratories. Beyond clarifying the role of Ptp10D in cell competition, the authors establish RhoGEF2 overexpression as a reliable method to induce cell competition and identify key molecular players involved in this process. This work represents a meaningful advance by introducing novel approaches and deepening understanding of known factors in clone elimination. The mosaic RhoGEF2 overexpression technique developed in this study provides a valuable tool for investigating cell-cell interactions at the tissue level, with broad applicability in basic research. This approach holds particular promise for probing

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Referee #1

Evidence, reproducibility and clarity

Summary: findings and key conclusions

Epithelial cell competition in larval imaginal discs involves signaling with the Sas ligand and Ptd10D receptor. In wild type cells both are typically found at the apical surface, but relocalize to the lateral cortex at the winner-loser interface. Ptd10D activation leads to reduced Ras signaling, increased pro-apoptotic Jnk signaling and consequently the elimination of loser cells. In the manuscript the authors address the role of the actin cytoskeleton in the context of the signaling controlling cell elimination in Drosophila larval eye imaginal discs. They interfere by clonal overexpression of the guanyl nucleotide exchange factor RhoGEF2 (RG2), which has previously been shown to induce dominant gain-of-function phenotypes by activation of Rho signaling. In this context the requirement of and genetic interactions with the other pathways implicated in cell elimination is tested. They find that RG2 induced cell elimination depends on PtD10D, Hippo signaling and Crumbs.

Major comments: claims and conclusions

The experimental setting, using clonal analysis in imaginal discs, is straight-forward and well-established, including quantification of clone size and comparison of phenotypes. The presented data are of high quality and thus the direct conclusions are fully supported by the data as long as they refer to the actual experimental interference. What is not supported by the data is the generalization of the conclusions, i. e. that RG2 overexpression would be equivalent to Actin cytoskeletal deregulation. This equivalence is expressed in the title "Actin cytoskeletal deregulation, caused by RhoGEF2 overexpression.." and the summary " that actin cytoskeleton deregulated cells (as induced by RhoGEF2 overexpression (RhoGEF2OE))...". In my view such an equivalence is not justified. There is no doubt that RG2 overactivation affects the actin cytoskeleton in multiple ways, such as contractility via MyoII or polymerization via Dia, among others. There is also no doubt that other pathways are also directly or indirectly affected beside the actin cytoskeleton. The authors do not present data showing the specificity of RG2 overexpression. For example, the authors could investigate the phenotype and genetic interaction with an alternative way of interference, independent of RG2, of the actin cytoskeleton to support their conclusion.<br /> There is a second assumption, which may not be justified, that the function of the cytoskeleton would be generally downstream of cell polarity, see abstract l24 "triggering cytoskeletal deregulation (which occurs downstream of cell polarity disruptions)..". There are certainly cytoskeletal activities such as cell shape changes that mediate the execution of cell elimination. However interfering with the cortical cytoskeleton also affect the distribution of cortical polarity proteins. The authors do not present data to demonstrate the specificity of RG2 overexpression concerning a function downstream of cell polarity.

Significance

The study establishes genetic interactions and dependencies concerning cell elimination following a very specific experimental interference of RG2 overexpression. It remains unclear, however, to which degree these genetic interactions contribute to controlling cell competition in situations that are physiologically relevant. The generalization of RG2 overexpression as a specific test the function of the actin cytoskeleton is an interpretation not supported by the presented data and the experimental set up.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

Cells need to adjust their gene expression pattern, including nutrient transporters and enzymes to process the available nutrient. How cells maintain the coordination between these processes is one of the most critical questions in biology. In this work authors elegantly combined a range of relevant experimental techniques, ranging from time-lapse microscopy, microfluidics, and mathematical modelling to address this question. Combining these methods, authors proposed a push-pull like mechanism, involving two pairs of repressors (Mth1, Std1 and Migs) in the glucose sensing network. In budding yeast there are multiple hexose transporter genes with varying affinity and transport rate. Authors postulated that on sensing glucose, cells switch between expressing high affinity glucose transporters (when extracellular glucose is low), and low affinity glucose transporters (in high extracellular glucose), and these processes are mediated by the pairs of repressors as mentioned earlier. Following the expressing patterns of fluorescently tagged hexose transporters and varying the extracellular glucose concentrations in media, authors proposed that pairs of repressors switch their activity depending on extracellular glucose level, and which is matched by the promoters of the hexose transporter genes to achieve optimality of glucose transport.

This study is elegantly designed and addressed an interesting question. The mechanism (push-pull involving two pairs of repressors) is plausible and justified by the data. Authors also presented a mathematical model and made predictions, which are also verified. We will recommend the publication of this work with minor modifications.

Major comments:

This study is well designed and experiments performed accordingly. We have only minor comments for revision.

Minor comments:

1. Although authors covered a wide array of literature, but while discussing tradeoffs and nutrient sensing, it will be good to include bacterial growth law and related literature, and physiological level tradeoffs should be discussed. Moreover, authors vouched that the push-pull mechanism helps to circumvent the rate-affinity tradeoff of the transporter, whereas expressing genes to more precisely corelate with the extracellular glucose level brings out physiological optimality. This rate-affinity tradeoff and its physiological role should be discussed clearly.
2. Authors described the ALCATRAS device in their previous publication, but for better clarity, a supplementary figure with schematic diagram and experimental plan should be included.
3. Microscopic images of transporter expression pattern should be shown as kymographs in the supplementary, in this version of the manuscript plots from processed microscopy images are shown only.
4. GFP was used to tag HXT1-7 as mentioned by the authors and expression of these genes are evaluated in separate experiments. We suggest including a schematic diagram describing the experimental design while using the microfluidic device and the experimental plan should be written in more detail in general. We found this part confusing. Did authors considered tagging two separate transporters with different fluorescent tag from either end of the affinity spectrum and showing the expression pattern in one experiment? Authors mentioned co expression of receptors at a particular glucose concentration over time, is this inferred from separate timelapse experiments? This need to be more clearly stated.
5. Please mark the second phase of media glucose concentration in panel 1C, 1% glucose phase is marked, please mark the other phases for clarity.
6. For the repressors to sense glucose and to initiate the push pull mechanism, there should be baseline glucose flux, which is not clearly mentioned in the manuscript. Authors mentioned that minimal intracellular glucose in absence of extracellular glucose and deployed a logistic function to increase intracellular glucose. The baseline glucose level is crucial, and authors should comment on this. Also, glucose mediated protection of HXT4 should be discussed in this context.
7. Figure 3B and 3C, details of the error bars should be mentioned in the figure legend.

Referee cross-commenting

All other reviewers also identified this study insightful and interesting, similar to our comments. We also agree with the suggestions made by other reviewers. Suggested changes and modifications can be addressed within a month as mentioned by most of the reviewers. Excellent point raised by other reviewers on technicalities and addressing those points will improve the readability of this work even more.

Significance

General assessment:

Use of innovative microfluidics platform to trap mother cells and following the gene expression pattern by fluorescence microscopy and combining the experimental approach with mathematical model are the strengths of this work. Whereas the proposed push-pull mechanism is not generalizable to other carbons. Model is merely used to fit the data, rather than making interesting predictions. Also how does the mechanism holds when cells are switched from other nutrient sources is also not clear in this work, which are the limitations of this work.

Advance

This work involves experimental technique and mathematical model to test the hypothesis. Use of custom-built microfluidics set up and live cell imaging to track gene expression levels in varying nutrient condition. This study links single cell level gene expression pattern to model and predict system level behavior. Nutrient sensing and subsequent rearrangement of gene regulatory network is an important question to address, and the proposed push-pull mechanism in this study adds up to the existing body of literature.

Audience:

This work is interdisciplinary and researchers across multiple fields will be interested in this work, including researchers interested in microbial nutrient sensing, systems biology, topology of gene regulatory network, metabolism, and general microbiology.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The yeast Saccharomyces cerevisiae possesses a large family of hexose transporters, the HXT genes. Some of these transporters play known roles in transport to feed metabolism while others seem to respond to glucose levels but have differing cellular functions, acting more as sensors than as drivers of carbon and energy production. The authors use single cell fluidics to monitor steady state expression of specific transporters under controlled glucose levels. The authors then used published information on the regulatory network of HXT4 gene expression to predict expression levels and confirm the role of the prior identified regulators. Thus, this work confirms prior work as to the levels of substrate leading to optimized expression of transporters and confirms the role of the identified regulatory network. The fact that the main single cell fluidics findings confirm the prior culture analyses affirms the utility of the prior work.

Major Comments:

1. The analysis uses protein expression levels (HXT4-GFP) as a proxy for transcriptional regulation. This study assumes no regulation of protein expression beyond transcription under steady state conditions. This seems like a reasonable assumption. However, for the dynamic change analysis (Figure 1 C, lines 70-78) loss of GFP-tagged protein from a single cell would be due not just to absence of transcription but also to differential rates of endocytosis and degradation, which could vary across the different HXTs. In cell populations the plasma membrane composition of the bud can be dynamically different from that of the mother cell and will reflect changes in transcription patterns. Meaning that cells with buds might have reduced expression due to the presence of the bud versus non-budding cells. And if buds are washed away during the time course of the experiment this could impact assessment of GFP signal - I am assuming controls were done to address this and should be included in the presentation. Did the authors consider this in their experimental design and interpretation?
2. The modeling was based upon the assumption of the validity of prior work and observations and authors show that models based upon that prior knowledge work to explain the single cell data. One wonders what perturbing prior modes of action would do to fit the data. That is, if the role of one regulator was downplayed or modified in concert with another would data still fit in a reasonable way? My concern again is that loss of signal (protein) is equated exclusively with transcription and not post-transcriptional regulation. This timeline in 1C and in fig 2 of 20 hours certainly would accommodate post-transcriptional regulation of protein levels.
3. Lines 142-150: two models are proposed: Std1 activating Snf1 with std1 deletion therefore hyperactivating Mig1. The second model is for Std1 to repress Mig2 with deletion of std1 then leading to hyperactivation of Mig2. It seems this could be directly tested using multiple deletant strains, or modified repressor proteins. For example, is the effect lost in a std1 mig1 double mutant?
4. Lines 121-122 the comment that comparing expressing GFP from the HXT4 promoter to GFP tagged HXT4 protein allows glucose to protect HXT4 from degradation needs to be explained.
5. Line 180-186: this is an important analysis - I assume binding sites for repressors/inducers of the HXT genes have been mapped -then the comparison to known promoter structure (lines 214-246) is a great test of the model. It seems the finding are consistent with previously published data on differential regulation of these promoters in full-culture studies.
6. Lines 293-299: one thing the authors should highlight is the contrast between these single cell studies and prior population studies that are influenced strongly by the heterogeneity between bud and mother cell plasma membrane composition. The mother cell can of course benefit from the differential expression in the daughter cell and the daughter cell benefits from the differential composition of the mother cell. This study shows that mother cells adapt membrane composition as well, but perhaps the potential role of cell membrane protein turnover should also be included.

no Minor Comments

Significance

It has been known for quite some time that glucose transport in the yeast Saccharomyces cerevisiae is dynamically regulated to optimize sugar depletion to sugar metabolism. This intricate system involves a family of hexose transporters of differing affinities for substrate, the timing and level of expression of which is regulated by both eternal hexose levels and internal ability to metabolize keeping cytoplasmic sugar levels low. Since facilitated diffusion systems can transport in both directions, the consumption of substrate assures the direction of uptake will be dominant. The authors demonstrate in this paper that differential expression of the known major regulators of HXT gene expression work in concert to adjust the expression patterns of transporters of differing affinities leading to optimization of hexose uptake. The study monitored changes in single cells and findings confirm prior work conducted in cell populations. One assumption has always been that the mother cell might "sacrifice" itself by not being able to dynamically clear the membrane of environmentally unmatched hexose transporters relying on the altered membrane composition of the bud. This work's focus on "mother cells" demonstrates that regulation still occurs if cells are allowed to reach a steady state. The timeline may be slower than bud adaptation, but these authors confirm that mother cells respond dynamically to glucose levels.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This is a very insightful work showing how to disentangle one of the most complex transcriptional networks in yeast (S. cerevisiae) by combining single-cell dynamics, dynamical-systems modeling, Bayesian-style inference, and genetic perturbations. The authors tackle a problem that has eluded quantitative resolution for over two decades-how yeast regulates its seven primary glucose importer genes (HXT1-HXT7) in response to both steady and temporally changing extracellular [glucose]. Their integrated experimental-theoretical approach delivers the most satisfying mechanistic and quantitative explanation to date, and I enthusiastically recommend this manuscript for publication.

Yeast relies on seven passive hexose transporters (Hxt1-Hxt7) to import glucose, its preferred sugar; deleting all seven abolishes growth on glucose. The underlying regulatory network is exceptionally intricate, reflecting yeast's evolutionary priority for glucose. Two membrane sensors-Snf3 (high affinity) and Rgt2 (low affinity)-detect extracellular glucose and thereby inactivate two co-repressors, Mth1 and Std1, which modulate the DNA-binding factor Rgt1. Concurrently, intracellular glucose activates the SNF1 kinase, phosphorylating and exporting the repressor Mig1, while Mth1/Std1 also govern the transcription and stability of Mig2, another DNA-binding repressor. Together, Rgt1, Mig1, and Mig2 integrate these inputs to control HXT promoter activity (Fig. 2A). Importantly, Mth1 and Std1 do not directly bind to DNA and this complication - the protein-protein interaction that one cannot get from DNA sequence - is just one source of difficulty that the authors overcame.

To map the network's behavior, the authors used microfluidic "cages" housing single cells expressing GFP-tagged HXTs, monitoring fluorescence under three constant glucose levels-low (0.01%), medium (0.1%), and high (1%) (Fig. 1B-C). The authors confirm that steady-state Hxt abundances rank by transporter affinity. But the more important and surprising discovery is that when the cells were subjected to gradual glucose up-shifts and down-shifts, they discovered that some transporters transiently spike only when [glucose] rises and others only when [glucose] falls (Fig. 1C and Fig. S1F). This discovery establishes that the HXT network not only "senses" the absolute external [glucose] concentration but also the direction of the temporal change in external [glucose].

To understand how the regulatory network yields such intricate temporal changes in HXT expression, the authors first focused on the medium-affinity transporter, Hxt4. Targeted knockouts of Mig1/Mig2 versus Mth1/Std1 confirmed that Hxt4 dynamics arise from differential repressor kinetics. To formalize these findings, the authors built an ODE model grounded in literature-based constraints (pg. 13 of the Supplement) with explicit separation of repressor timescales. They rigorously fit the model to wild-type and knockout time series-exploring parameter sensitivity in depth (Fig. S5).

The authors discovered that their model and experiments converged on a push-pull mechanism: fast-acting Mig1/Mig2 dominate during glucose up-shifts, while slower Mth1/Std1 govern down-shifts, determining whether each HXT gene is repressed or de-repressed (i.e., "who gets there first"). Extending this analysis across all seven HXTs via approximate Bayesian computation revealed the most likely repressor-promoter interactions for each transporter, reducing a vast parameter space to unique or small sets of plausible regulatory schemes. The authors thus revealed what could be happening and which regulations are improbable - a more nuanced and comprehensive view than giving just one outcome for each HXT.

Overall, this work represents a role model - textbook-worthy - for quantitative systems biology. Beyond the rigor and novelty of its findings, the authors explain complex mathematical concepts with clarity, and the narrative flows logically from experiment to model to inference. This study provides a definitive mechanistic resolution of the HXT network and establishes a broadly applicable framework for dissecting dynamic and complex gene circuits.

Major points:

I don't recommend any new experiments or modeling; the major claims are already well supported by the data and models. Below are comments and questions intended to improve clarity and facilitate the reader's understanding. Please feel free to disregard any that you find not sensible or beyond the scope of the current work.

1. Preconditioning (Fig. 1B-C): What medium were cells in immediately before t = 0? Were they in log-phase or stationary-phase growth just prior to the glucose addition?
2. Transporter ranking in medium glucose: In the medium [glucose] regime, why is a low-affinity Hxt the second-most highly expressed, rather than the next-highest-affinity transporter? Could co-expression of multiple affinities (e.g., as a bet-hedging strategy) be advantageous? The Discussion section already mentions bet-hedging but I think you could further discuss ideas such as evolutionarily trained "Pavlovian" response or what the 2nd-ranking says about what the yeast anticipates as an upcoming change in the environment.
3. Defining low/medium/high regimes: Low = 0.01%, Medium = 0.1%, and High = 1%. This is indeed in line with the standard classification of [glucose] in the literature regarding HXTs. But how might your results change at intermediate concentrations - those between these three levels. Using the model, could you comment on whether HXT expression dynamics "sharply" change as a function of either the [glucose]/time or the final concentration of [glucose] after the ramping-up phase?
4. Rate-affinity trade-off (Lines 18-20): Give a brief explanation of the rate-affinity trade-off. Why does higher affinity necessarily entail a lower maximal transport rate (Vₘₐₓ) for passive transporters? Perhaps you can give an intuitive explanation backed by mass-action kinetics (e.g., to attain a higher affinity, the glucose-binding pocket on Hxt cannot be flipping rapidly back-and-forth between facing cytoplasm and extracellular space -- the binding pocket must allow sufficient time for molecule to find and bind it).
5. Single-transporter expression (Lines 39-40): It's unclear to me why cells would express only the "optimal" Hxt and suppress all others. For instance, a bet-hedging strategy might favor simultaneous expression of multiple affinities. Consider revising these lines or adding a brief explanation. Related to above is a subtle point I think that was glossed over: there must be a fitness cost associated with making too many copies of Hxtn. After all, why not make as many transporters as possible? Is the cell operating near the upper limit of Hxt abundance, beyond which there's a fitness cost? Is there a pareto-optimal-type front in the space of expression level and another axis? I think this could go into the Discussion section.
6. Hxt5 exception (Fig. 1B): Although Hxt5 follows a distinct regulatory scheme, it is most highly expressed at medium [glucose] (0.1%), consistent with its affinity like the other Hxts. I think you could mention this in lines 51-58.
7. Glucose-ramp details (Fig. 1C; Lines 66-67): You state that [glucose] rises from 0 to 1 % over 15 min and reaches 1 % at t = 3 h. However, the actual ramp slope ([glucose]/time) and when the [glucose] starts to increase from zero aren't specified. The Hxt5-GFP behavior and differing Hxt6/7 levels at t = 0 vs. t = 20 h suggest the ramp may begin later than t = 0. Please clarify these details in the caption and main text, and consider adding a [glucose] vs. time schematic above the panel in Fig. 1C (like in Fig. 1B).
8. Pre-t < 0 incubation (Fig. 1C): Related to point 1, how long were the cells incubated in pyruvate (or other medium) before t < 0? The Hxt6-GFP level at t = 20 h does not match that at t = 0; what is the timescale for Hxt6-GFP and Hxt7-GFP decay to steady state after glucose removal?
9. Hxt-GFP localization: Does the reported Hxt#-GFP level include fluorescence from both the plasma membrane and internal compartments (e.g., vacuole)? Clarifying which pools of fluorescence are quantified would help interpretation, even if they don't change the main conclusions are unchanged.
10. Predominantly transcriptional" wording (Lines 90-92): The phrase "...the regulation is predominantly transcriptional" should specify that it refers to the induction of HXT4 transcription during glucose down-ramping, rather than the subsequent decrease in Hxt4-GFP. The experiments do not rule out post-translational regulation (e.g., endocytosis) once glucose levels fall below a threshold.
11. Glucose "protection" of Hxt4 (Lines 121-122): The statement "we allowed glucose to protect Hxt4 from degradation" is unclear. First, Hxt4-GFP likely degrades at a different rate than free GFP-you could estimate its half-life from Fig. S3. Second, please explain precisely what "protection" means in the model or experiment.
12. Quantifying repressor kinetics (Lines 158-162): The push-pull mechanism is compelling, but it would be helpful to report the quantitative separation of timescales-e.g., how much faster do Mig1/Mig2 respond compared to Mth1/Std1? Including fold-difference would strengthen this explanation.
13. Mechanism of repressor regulation (Lines 197-213): Be clearer about whether and how changes in extracellular glucose alter the expression levels of Mth1, Std1, Mig1, and Mig2, as opposed to modulating say, how Mth1 and Std1 bind to Rgt2 protein. I think you could be clearer here about which regulatory steps (transcriptional, post-translational, or binding-affinity changes) are assumed in the model and supported by the data.

Minor points:

1. Abstract: Original: "...how an HXT for a medium-affinity transporter can be made to respond like the HXTs for the other transporters." Suggestion: "...how the gene-expression regulation of a medium-affinity HXT can be rewired to respond like that of any other HXT." (You might also generalize beyond "medium-affinity" if the converse holds.)
2. Lines 64-66: Please emphasize that the "synthetic complete medium" used for pre-conditioning contains no glucose.
3. Line 143: The phrase "low expression of the std1\Delta strain in glucose" is ambiguous-low expression of which gene or reporter? Please specify.
4. Line 240: Change "should weakened" to "should weaken."
5. Fig. S9 caption (typo) Change "Rtg1 sites are..." to "Rgt1 sites are...."

Hyun Youk.

Referee cross-commenting

I agree with the other reviewers' comments. The other reviewers noticed important points I have missed. But like them, I'm still supportive of the work being published with < 1 month spent on revision. I still don't recommend any further experiments or modeling.

Significance

This is a very insightful work showing how to disentangle one of the most complex transcriptional networks in yeast (S. cerevisiae) by combining single-cell dynamics, dynamical-systems modeling, Bayesian-style inference, and genetic perturbations. The authors tackle a problem that has eluded quantitative resolution for over two decades-how yeast regulates its seven primary glucose importer genes (HXT1-HXT7) in response to both steady and temporally changing extracellular [glucose]. Their integrated experimental-theoretical approach delivers the most satisfying mechanistic and quantitative explanation to date, and I enthusiastically recommend this manuscript for publication via Review Commons.

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Reply to the reviewers

Manuscript number: RC-2025-03083 Corresponding author(s): David Fay General Statements [optional] This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We greatly appreciate the input of the four reviewers, all of whom carried out a careful reading of our manuscript, provided useful suggestions for improvements, and were enthusiastic about the study including its thoroughness and utility to the field. Because the reviewers required no additional experiments, we were able to address their comments in writing.

However, in response to a comment from reviewer #4 we decided to add an additional new biological finding to our study given that our functional validation of proximity labeling targets was not extensive. Namely, we now show that a missense mutation affecting BCC-1, one of the top NEKL-MLT interactors identified by our proximity labeling screen, is a causative mutation (together with catp-1) in a strain isolated through a forward genetic screen for suppressors of nekl molting defects (new Fig 9C). This finding, combined with our genetic enhancer tests, further strengthens the functional relevance of proteins identified though our proximity labeling approach and highlights the synergy of proteomics combined with classical genetics.

Positive statements from reviewers include: Reviewer #1: Overall, this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner.

Reviewer #2: The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate... In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs.

Reviewer #3: Overall, the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. ...This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system.

Reviewer #4: Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data)... Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

Based on these reviews and comments, we believe that our manuscript is suitable for publication in a high-impact journal. 1. Point-by-point description of the revisions This section is mandatory. Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript.

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

*Proximity labeling has become a powerful tool for defining protein interaction networks and has been utilized in a growing number of multicellular model systems. However, while such an approach can efficiently generate a list of potential interactors, knowledge of the most appropriate controls and standardized metrics to judge the quality of the data are lacking. The study by Fay systematically investigates these questions using the C. elegans NIMA kinase family members NEKL-2 and NEKL-2 and their known binding partners MLT-2, MLT-3 and MLT-4. The authors perform eight TurboID experiments each with multiple NEKL and MLT proteins and explore general metrics for assessing experimental outcomes as well as how each of the individual metrics correlates with one another. They also compare technical and biological replicates, explore strategies for identifying false positives and investigate a number of variations in the experimental approach, such as the use of N- versus C-terminal tags, depletion of endogenous biotinylated proteins, combining auxin-inducible degradation, and the use of gene ontology analysis to identify physiological interactors. Finally, the authors validate their findings by demonstrating that a number of the candidate identified functionally interact with NEKL-2 or components of the WASH complex. *

Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. Below I list major and minor concerns.

Major concerns * 1. My biggest issue with the manuscript is that a lot is made of the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). The authors perform experiments using DIA and DDA approaches but do not directly compare the outcomes. As a result there is really no way to know if one approach is better than the other. I would suggest the authors either perform the necessary analysis to compare the two approaches or tone down their promotion of DIA.* We agree and have scaled back any statements comparing DDA to DIA as our manuscript did not address this directly. We also now point out this caveat in our closing thoughts section, while referencing other studies that compared the two (lines 926-929). Our main point was to convey that DIA worked well for our proximity labeling studies but has seen little use by the model organism field. Surprising (to us), DIA was also considerably less expensive than DDA options.

2. Line 75, The authors promote the use of data-independent acquisition (DIA) without defining what this approach is and how it differs from the more conventional data-dependent acquisition. As a non-mass spectroscopist, I found myself with lots of question concerning DIA, what it is and how it differs from DDA. I think it would really be helpful to expand the description of DIA and its comparison with DDA in the introduction. As non-mass-spectroscopists ourselves, we understand the reviewer's point. Because the paper is quite long, we were trying to avoid non-essential information. We have now added some information to explain some of the key differences between DDA and DIA. We have also included references for readers who may want to learn more. (lines 77-80)

Minor concerns: * Line 92 typo. I believe the authors meant to say NEKL-2-MLT-2-MLT-4. * Corrected. (line 95)

Line169. Is exogenous the correct word to use here? It suggests that you are talking about non-worm proteins, but I know you are not. Corrected. Changed to "Moreover, the detection of biotinylated proteins may be difficult if the bait-TurboID fusion is expressed at low levels..." (line 181).

Line 177 typo (D) should be (C). Corrected. (line 1122)

Figure 1C: Lucky Charms may sue you for infringement of their trademarked marshmallow treats. Thank you for picking up on this. The authors accept full responsibility for any resulting lawsuits.

Figure 1D. The NEKL-2::TurboID band is indicated with a green triangle in the figure but the figure legend states that green triangles indicate mNG::TurboID control. I know this triangle is a shade off the triangle that indicates mNG::TurboID but it's really hard to see the difference. All of the differently colored triangles in panel F are unnecessary. I would either just pick one color for all non-control bait proteins or better yet, only use a triangle to point to bands that are not obvious. For instance I don't need the triangles that point to NEKL-2 -3 and -4 fusion proteins. These are just distracting. We understand the reviewer's point. We colored the triangles to match the colors used for the proteins in the figures. We have now added "bright green triangles with white outlines" (Fig 1 legend) to indicate the Pdpy-7::mNG::TurboID control" and changed triangles in the corresponding figures. Although we would be fine with removing or changing the triangles, we think that they may aid somewhat with clarity.

Line: 316: Conceivably, another factor that could contribute to the counterintuitive upregulation of some proteins in the N2 samples is related to the fusion proteins that are being expressed in the TurboID lines. A partially functional bait protein (one with a level of activity similar to nekl-2(fd81) that may not result in an obvious phenotype) could directly or indirectly affect gene expression leading to lower levels of a subset of proteins in the TurboID samples. The same could be said for fusion proteins with a gain-of-function effect. This is an interesting idea, and we tested this possibility by looking for consistent overlap between N2-up proteins between biological replates of individual bait proteins. We now include a representative Venn diagram in S3C Fig to highlight this comparison. In summary, although we cannot rule out this possibility, our analysis did not support the widespread occurrence of this effect in our study. We also made certain that our statement regarding N2 up proteins was not too definitive. (lines 285-288)

*Fig 3 B-E. I am a little confused how the data in these graphs is normalized. For instance, I would have expected that for NEKL-3 in panel B, that the normalized (log2) intensity value in N2 be set at 0 as it is for NEKL-2. Maybe I just don't have enough information on how these plots were generated. * The difference is that in the N2 sample, NEKL-3 was detected but NEKL-2 was not. The numbers themselves are assigned by the Spectronaut software used to quantify the DIA results but are not meaningful beyond indicating relative amounts (intensity values) of a given protein within an individual biological experiment. We've added some lines to the figure legend to make this clearer. (lines 1165-1169)

*Figure 6C legend is not correct. * Corrected. (line 1214)

Line 575: Figure reference should be Fig. S5G. The authors should check to make sure all references to supplemental figures include correct panel information. Corrected. (line 464) In addition, we have now gone through the manuscript and added panel numbers references where applicable. Note that the addition of a new supplemental file has shifted the numbering.

Line 576. The authors reference a study by Artan and colleagues and report a weak correlation between their study and that of Artan. They reference figure S4 but it should be Fig S5H. Apologies and many thanks to the reviewer for catching these errors. (line 464)

Line 652. The authors note that numerous proteins were present at substantially reduced levels in the mNG::TurboID samples and suggest that sticky proteins may have been outcompeted or otherwise excluded from beads incubated with the mNG::TurboID lysates. Why would sticky proteins only be a problem in these samples? The reasoning is not clear to me. The idea was that in the sample with very high levels of biotinylated proteins (mNG::TurboID), the surface of the beads might become saturated with high-affinity biotinylated proteins. This could prevent or out complete the binding of random proteins that are not biotinylated but nevertheless have some affinity to the beads ("sticky" proteins). We have reworded this section to make this clearer. (lines 546-550)

Line 745: The term "bait overlaps" is a bit vague. Ultimately, I figured out what it meant but it was not immediately obvious. We have changed this to "overlap between baits" and made this section clearer. (line 624-628)

*S7B Fig. Why is actin missing from the eluate? * In S7B we refer to the purified eluate as the "eluate", which may have caused some confusion. In other sections of the manuscript, we refer to the bead-bound proteins as the "purified eluate" (Figs 1 and 5). For the purified eluate a portion of the streptavidin beads are boiled in sample buffer to elute the bound proteins before running a western. Actin would not be expected in these samples because it's (presumably) not biotinylated in our samples and doesn't detectably bind the beads. This result was seen in all relevant westerns in S1 Data. For consistency, however, we've gone through all our files to make sure we consistently use the term "purified eluate" versus "eluate", which is less specific.

L*ine 873: The authors state the extent of overlap in GO terms between the various experiments and provide percentages. I tried to extract this information from Figure 8C and came up with different values. For instance, in the case of Molecular Function, they state that they observed a 54% overlap between NEKL-2 and NEKL-3 but in the Venn diagram in Figure 8C I see that the NEKL-2 and NEKL-3 experiments had 71 (25+46) GO terms in common. Out of 98 GO terms for NEKL-2 or 104 for NEKL-3 the percentage I got is closer to 72. Am I analyzing this correctly? * Thanks for checking this. We believe our method for calculating the percent overlap is correct. In the case of NEKL-2/NEKL-3 overlap for Molecular Function, there are 131 total unique terms, of which 71 overlap, giving a 54% overlap. In the case of NEKL-2/NEKL-3 overlap for Biological Process, however, we made an error in arithmetic (415 unique, 239 overlap), such that the correct percentage is 58%, which we have corrected in the text.

*Reviewer #1 (Significance (Required)): *

*Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. *

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

*This study expanded the use of data-independent acquisition-mass spectrometry (DIA-MS) in TurboID proximity-labeling proteomics to identify novel interactors of NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4 complexes in C. elegans. The authors described several useful metrics to evaluate the quality of TurboID experiments, such as using the percentage of upregulated genes, the percentage of proteins present only in bait-TurboID experiments as compared to N2 controls, and the percentage of endogenously biotinylated carboxylases as internal controls. Further, the authors introduced methodological variability across 23 TurboID experiments and evaluated any improvement to the resulting data, such as N-terminally tagging bait proteins with TurboID, depleting endogenous carboxylases, and auxin-inducible degradation of known complex members. Finally, this study identified the kinase folding chaperone CDC-37 and the WASH complex component DDL-2 as novel interactors with the NEKL-MLT complexes through an RNAi-based enhancer approach following their identification by TurboID. *

Major comments: * The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate. We only have minor comments.*

Minor comments: * •In the western blot in Fig 1 why does the mNG::Turbo have two bands? * Thank you for point this out. To our knowledge this is a breakdown product that was especially prevalent in replicate 3 (also see S1 Data), which we chose to shown because all the NEKL-MLTs were clearly visible in this western. The expected size of the mNeonGreen::TurboID (including linker and tags) is ~68 kDa and our blots are roughly consistent those of Artan et al., (2001). This lower band was not evident in Exp 8. We have now included a statement in the figure legend to indicate that the upper band is the full-length protein whereas the lower band is likely to be a breakdown product (lines 1141-1142).

•Fig 2B is difficult to parse as a reader. Columns labeled "Upreg," "Downreg," "TurboID only," "N2 only," "Filter-1," "Filter-2," and "Epi %" could be moved to Supplemental. Fold change vs N2 could be represented as a bar chart, allowing for trends between fold change and the metrics Upreg %, Turbo %, and Carboxylase % to be seen more clearly. Further, rows headed "Carboxylase depletion," "DDA," and "Auxin treated" could be presented as separate panels to better match the distinct points made in the text. After serious consideration we have made several changes including the addition of S2 Fig, which may provide readers with a better visual representation of the bait and prey fold changes observed in all our experiments. However, we feel that the detailed data embedded in Fig 2 is the most concise and accurate means by which to convey our full results and is key to our methodological conclusions. As such we did not want to relegate this information to a supplemental table. We note that this figure was not found to be problematic by other reviewers, although we do understand the points made by this reviewer.

•Line 179: in vivo should be italicized Because journals differ in their stylistic practices, we are currently waiting before doing our final formatting. We did keep our use of Latin phrases consistently non-italicized in the draft.

•Lines 215-217: The comparison between Western blot expression levels and prior fluorescent reporter levels is unclear. Could be reformatted to make it clearer that relative expression of the different NEKL-MLTs in this study is consistent with prior data. We reformatted this sentence to improve clarity. (lines 205-207)

*•Lines 267-268: The final line of the passage is unclear and can be removed. * This sentence has been removed.

•Lines 311-313: This study is able to use the recovery of bait and known interactor proteins as internal controls to determine the quality of each experiment, but this may not always be the case for other users' experiments. The authors should comment on how Upreg %, a value influenced by many factors, can actually be used as a quality check when a bait protein has no known interactors. We have added language to highlight this point. (lines 344-348)

*•Line 702: There is a [new REF] that should be removed * As described above, we have now included this finding on bcc-1 as part of this manuscript (Fig 9C).

•The approach used mixed stage animals, but some genes oscillate or are transiently expressed. Please discuss cost-benefit of mixed stage vs syncing. This is an important point. We have added a discussion on the benefits and drawbacks of using mixed stages to the discussion. (lines 901-911)

*•Authors were working on hypodermally expressed proteins. It would be valuable to discuss what tissues are amenable to TurboID. Ie are the cases where there are few cells (anchor cell, glial sockets, etc) that it will be extremely challenging to perform this technique * We agree that certain tissues/proteins will not be amenable to proximity labeling. We believe that we have addressed this point together with the above comment throughout the manuscript and now on lines 936-940.

•Authors mention approaches such as nanobodies, split Turbo. Based on their experiences it would be valuable to add Discussion on strengths and weaknesses of these approaches to guide folks considering TurboID and DIA-MS experiments in C. elegans Because we have not tested these methods, we feel that we cannot provide a great deal of insight into these alternate approaches. We mention and reference these methods in the introduction so that readers are aware of them.

*Reviewer #2 (Significance (Required)): *

•Advance in technique: This study expands the use cases of data-independent acquisition MS method (DIA-MS) in C. elegans, which fragments all ions independent of the initial MS1 data. The benefits of this approach include better reproducibility across technical replicates and better recovery of low abundance peptides, which are critical for advancing our ability to capture weak and transient interactions.

•The use of DIA-MS in this study has improved our understanding of the partners of these NEKL-MLTs in membrane trafficking, molting, and cell adhesion within the epidermis.

•In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs.

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

*Summary: *

Fay and colleagues perform a series of proximity labeling experiments in C. elegans followed by thorough and rational analysis of the resulting biotinylated proteins identified by LC-MS/MS. The overall goals of the study are to evaluate different techniques and provide practical guidance on how to achieve success. The major takeaways are that integration of data-independent acquisition (DIA) along with comparison of endogenously tagged TurboID alleles to soluble TurboID expressed in the same tissue results in improved detection of bona-fide interactors and reduced numbers of false-positives.

*Major comments: *

Overall the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. I have no major concerns with the manuscript and focus primarily on improving the accessibility of this important contribution to the scientific community. As such, I suggest that the authors:

1) Provide more explanation of and rationale for using DIA. This is not yet a standard technique and most basic biomedical scientists will be unaware of the jargon. As I expect many labs in the C. elegans community and beyond will be interested in the guidance provided in this manuscript, the introduction offers a great opportunity to bring the reader up to speed, as opposed to sending them to the complicated proteomics analysis literature. We have added some additional context (lines 77-80) as well as new references. We note that getting into the technical differences between DIA and DDA, beyond what we briefly mention, would take a substantial amount of space, may not be of interest to many readers, and can be found through standard internet and (sigh) AI-based searches.

*2) Provide a better overview of the various protocols tested (Experiments 1-8). Maybe at the beginning of the results, and maybe with an accompanying schematic. As currently written, it is difficult to figure out details regarding how the experiments vary and why. * We have now added a short paragraph to better inform the reader at the front end regarding the major experiments. (lines 139-146).

3) As to be expected, expression of TurboID tags at endogenous levels via low abundance proteins in a complex multicellular system results in somewhat weak signals that flirt with the limit of detection. Perhaps by combining tagged alleles within the same complex (NEKL-3/MLT-3 or NEKL-2/MLT-2/MLT-4) the signals could be boosted? Tandem tags, either on one end or multiple ends of proteins might help as well. As the authors point out, a benefit of tagging the two NEKL-MLT complexes is that there are strong loss-of-function phenotypes (lethal molting defects) to help evaluate whether a tagging strategy results in a non-functional complex. THESE EXPERIMENTS ARE OPTIONAL and might simply be discussed at the authors discretion. These are interesting ideas that we have now incorporated into our discussion. (lines 936-940)

*Minor Comments: *

*1) Figure 3A is cropped on the right. * Thank you for catching this. Corrected.

*2) Better define [new REF] on line 702. * We have added new results (Fig 9C), obviating the need for this reference.

***Referee cross-comments** *

Overall, I am in agreement with, and supportive of, the other reviewers' comments.

*Reviewer #3 (Significance (Required)): *

*Significance: *

Proximity labeling is often proposed as a technique to determine interaction networks of proteins in vivo, but in practice it remains challenging for most labs to execute a successful experiment, especially within the context of multicellular model organisms. Fay and colleagues provide a much needed roadmap for how to best approach proximity labeling experiments in C. elegans that will likely apply to other model systems.

They establish a rigorous approach by choosing to endogenously tag components of two essential NEKL-MLT complexes required for C. elegans molting. These complexes are relatively low abundance as they are only expressed in a single cell type, the hyp7 epidermal syncytium. In addition, as inactivation of any member of the complexes results in molting defects, they have a powerful selection for functional tags. Thus, they have set a high bar for themselves in order to discern whether a given variation on the experimental approach results in improved detection of interactors and fewer false positives.

*Potential areas for improvement include lowering the expression level of the skin-specific soluble TurboID used to determine non-specific biotinylation events. This control results in much higher levels of biotinylation compared to the TurboID-tagged NEKL-MLT alleles and likely affects their analysis, which they openly admit. In addition, to reduce the high level of background biotinylation signals generated by endogenous carboxylases, they adopt a depletion strategy pioneered by other researchers but this does not offer major improvements in detection of specific signals. The source of these conflicting results remains to be determined. It is also curious that auxin-inducible degradation of components of the NEKL-MLT complexes did not robustly alter the resulting biotinylating capacity of other members. This approach should be evaluated in subsequent studies. Finally, as mentioned in Major Comment #3 (above), it would be interesting to see if combining TurboID tags within the same complex might improve signal-to-background ratios. *

This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system. I am a cell biologist that uses a variety of genetic, molecular and biochemical approaches, mostly centered around C. elegans. I have used LC/MS-MS in our studies but have relatively little expertise in evaluating all aspects of proteomic pipelines.

*Reviewer #4 (Evidence, reproducibility and clarity (Required)): *

*Fay et al. describe an extensive proximity labeling BioID study in C. elegans with TurboID and DIA-LCMS analysis. They chose the NEKL-2/3 kinases and their known interactors MLT-2/3/4 as TurboID-fused bait proteins (C- and partially N-terminal fusions encoded from CRISPR-mediated genome edited genes). With eight biological replicates (and three to four technical replicates each) and with the unmodified wildtype or mNeonGreen-TurboID expressing worms as controls, a comprehensive dataset was generated. Although starting from quite different abundances of the bait-fusions within the cell lysates all bait proteins and known complex-binding partners were convincingly enriched with capturing streptavidin beads after only one hour of incubation with the lysate. This confirms the general applicability of TurboID-BioID approach in C. elegans. The BioID method typically gives rise to large proteomics datasets (up to more than thousand proteins identified after biotin capture) with several tens to hundreds enriched proteins (against negative control strains) as potential proteins that localize proximal to the bait-TurboID protein. However, substantial variations of candidates between biological replicates are frequently observed in BioID experiments. The authors scrutinized their dataset towards indicative metrics, filters and cutoffs in order to separate high-confidence from low-confidence candidates. With the workflow applied the authors melt down the number of candidates to 15 proteins that were grouped in four functional groups reasonably associated to NEKL-MLT function. *

Successful BioID experiments depend on reliable enrichment quantification with mass spectrometry using control cell lines that require a carefully bait-tailored design. Those must adequately express TurboID controls matching the abundance of the bait-TurboID fusion protein and its biotinylation activity. After affinity capture, sample preparation and LCMS data acquisition there is no silver bullet towards the identification true bait neighbors. Fay et al. elaborately describe their considerations and workflow towards high-confidence candidates. The workflow considered (i) data analysis with Volcano plots to account for statistical reproducibility of biological replicates against negative controls, (ii) fraction of proteins only detected in the positive or negative controls thus evading the fold-enrichment quantification approach, (iii) evaluation of variations in carboxylase enrichment as a measure for variations in the general biotin capture quality between experiments, (iv) an assessment of technical reproducibility with scatter plots and Venn diagrams, (v) exclusion of potentially false positives, e.g. promiscuously biotinylated non-proximal proteins, through comparisons with control worms expressing a non-localized mNeonGreen-TurboID fusion protein, (vi) batch effects, (vii) the impact of endogenous biotinylated carboxylases through depletion, (viii) gene ontology analysis of enriched proteins, (ix) weighing data according to the quality of individual experiments according to the afore mentioned metrics, and finally (x) genetic interaction studies to functionally associate high-confidence candidates with the bait.

*Major comments: *

Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data). However, although the authors claim to seek for substrates of the kinase complex they drew no further attention to the phosphorylation status of the captured proteins. Haven't the MS data been analyzed in this respect? Information regarding this issue would enhance the manuscript. Data generation and method description appear reproducible for readers. Also, the statistical analyses appear adequate. The authors should also consider to deposit their MS raw and analysis data in a public repository (e.g. PRIDE) for future reviewing processes and as reference data for readers and followers. Our raw MS data have been deposited by the Arkansas Proteomics Facility. I have followed up to ensure that they are publicly available.

*Minor comments: *

The authors should combine supplementary data files to reduce the number of single files readers have to deal with. We have combined these files as suggested.

The authors should avoid the term "upregulation" or "increased biotinylation" when capture enrichment is meant. We agree with reviewer's point. We now use the terms enriched versus reduced or up versus down, depending on the context, and clearly define these terms. These changes have been incorporated throughout the manuscript.

*Reviewer #4 (Significance (Required)): *

The manuscript presents a robust BioID proteomics screening for co-localizing proteins of NEKL-2/3 kinases and their known interactors MLT-2/3/4. The ongoing validation of their functional interactions and whether the protein candidates reflect phosphorylation substrates or else remains elusive and is announced for upcoming manuscripts. The knowledge gain in terms of molecular mechanisms with NEKL-2/3 MLT-2/3/4 involvement in C. elegans is therefore limited to a table of - promising - interacting candidates that have to be studied further. Information about the phosphorylation status of the captured proteins from the MS data are not given. However, knowing the protein candidates will be of interest for groups working with these complexes (or the identified potentially interacting proteins) either in C. elegans or any other organism. Also, in-depth proteomics screenings with novel approaches such as BioID have to be established for individual organisms. For C. elegans there is only one prior BioID publication (Holzer et al. 2022). Many of the aspects discussed here have also been addressed earlier for BioIDs in other organisms and are not principally new. However, the presented study can be of conceptual interest for labs delving into or entangled with the BioID method in C. elegans or other organisms. The study addresses especially proteomics groups working on protein-protein interactions using proximity labeling/MS approaches. Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

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

Fay et al. describe an extensive proximity labeling BioID study in C. elegans with TurboID and DIA-LCMS analysis. They chose the NEKL-2/3 kinases and their known interactors MLT-2/3/4 as TurboID-fused bait proteins (C- and partially N-terminal fusions encoded from CRISPR-mediated genome edited genes). With eight biological replicates (and three to four technical replicates each) and with the unmodified wildtype or mNeonGreen-TurboID expressing worms as controls, a comprehensive dataset was generated. Although starting from quite different abundances of the bait-fusions within the cell lysates all bait proteins and known complex-binding partners were convincingly enriched with capturing streptavidin beads after only one hour of incubation with the lysate. This confirms the general applicability of TurboID-BioID approach in C. elegans. The BioID method typically gives rise to large proteomics datasets (up to more than thousand proteins identified after biotin capture) with several tens to hundreds enriched proteins (against negative control strains) as potential proteins that localize proximal to the bait-TurboID protein. However, substantial variations of candidates between biological replicates are frequently observed in BioID experiments. The authors scrutinized their dataset towards indicative metrics, filters and cutoffs in order to separate high-confidence from low-confidence candidates. With the workflow applied the authors melt down the number of candidates to 15 proteins that were grouped in four functional groups reasonably associated to NEKL-MLT function.

Successful BioID experiments depend on reliable enrichment quantification with mass spectrometry using control cell lines that require a carefully bait-tailored design. Those must adequately express TurboID controls matching the abundance of the bait-TurboID fusion protein and its biotinylation activity. After affinity capture, sample preparation and LCMS data acquisition there is no silver bullet towards the identification true bait neighbors. Fay et al. elaborately describe their considerations and workflow towards high-confidence candidates. The workflow considered (i) data analysis with Volcano plots to account for statistical reproducibility of biological replicates against negative controls, (ii) fraction of proteins only detected in the positive or negative controls thus evading the fold-enrichment quantification approach, (iii) evaluation of variations in carboxylase enrichment as a measure for variations in the general biotin capture quality between experiments, (iv) an assessment of technical reproducibility with scatter plots and Venn diagrams, (v) exclusion of potentially false positives, e.g. promiscuously biotinylated non-proximal proteins, through comparisons with control worms expressing a non-localized mNeonGreen-TurboID fusion protein, (vi) batch effects, (vii) the impact of endogenous biotinylated carboxylases through depletion, (viii) gene ontology analysis of enriched proteins, (ix) weighing data according to the quality of individual experiments according to the afore mentioned metrics, and finally (x) genetic interaction studies to functionally associate high-confidence candidates with the bait.

Major comments:

Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data). However, although the authors claim to seek for substrates of the kinase complex they drew no further attention to the phosphorylation status of the captured proteins. Haven't the MS data been analyzed in this respect? Information regarding this issue would enhance the manuscript. Data generation and method description appear reproducible for readers. Also, the statistical analyses appear adequate. The authors should also consider to deposit their MS raw and analysis data in a public repository (e.g. PRIDE) for future reviewing processes and as reference data for readers and followers.

Minor comments:

The authors should combine supplementary data files to reduce the number of single files readers have to deal with. The authors should avoid the term "upregulation" or "increased biotinylation" when capture enrichment is meant.

Significance

The manuscript presents a robust BioID proteomics screening for co-localizing proteins of NEKL-2/3 kinases and their known interactors MLT-2/3/4. The ongoing validation of their functional interactions and whether the protein candidates reflect phosphorylation substrates or else remains elusive and is announced for upcoming manuscripts. The knowledge gain in terms of molecular mechanisms with NEKL-2/3 MLT-2/3/4 involvement in C. elegans is therefore limited to a table of - promising - interacting candidates that have to be studied further. Information about the phosphorylation status of the captured proteins from the MS data are not given. However, knowing the protein candidates will be of interest for groups working with these complexes (or the identified potentially interacting proteins) either in C. elegans or any other organism. Also, in-depth proteomics screenings with novel approaches such as BioID have to be established for individual organisms. For C. elegans there is only one prior BioID publication (Holzer et al. 2022). Many of the aspects discussed here have also been addressed earlier for BioIDs in other organisms and are not principally new. However, the presented study can be of conceptual interest for labs delving into or entangled with the BioID method in C. elegans or other organisms. The study addresses especially proteomics groups working on protein-protein interactions using proximity labeling/MS approaches. Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

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:

Fay and colleagues perform a series of proximity labeling experiments in C. elegans followed by thorough and rational analysis of the resulting biotinylated proteins identified by LC-MS/MS. The overall goals of the study are to evaluate different techniques and provide practical guidance on how to achieve success. The major takeaways are that integration of data-independent acquisition (DIA) along with comparison of endogenously tagged TurboID alleles to soluble TurboID expressed in the same tissue results in improved detection of bona-fide interactors and reduced numbers of false-positives.

Major comments:

Overall the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. I have no major concerns with the manuscript and focus primarily on improving the accessibility of this important contribution to the scientific community. As such, I suggest that the authors:

1. Provide more explanation of and rationale for using DIA. This is not yet a standard technique and most basic biomedical scientists will be unaware of the jargon. As I expect many labs in the C. elegans community and beyond will be interested in the guidance provided in this manuscript, the introduction offers a great opportunity to bring the reader up to speed, as opposed to sending them to the complicated proteomics analysis literature.
2. Provide a better overview of the various protocols tested (Experiments 1-8). Maybe at the beginning of the results, and maybe with an accompanying schematic. As currently written, it is difficult to figure out details regarding how the experiments vary and why.
3. As to be expected, expression of TurboID tags at endogenous levels via low abundance proteins in a complex multicellular system results in somewhat weak signals that flirt with the limit of detection. Perhaps by combining tagged alleles within the same complex (NEKL-3/MLT-3 or NEKL-2/MLT-2/MLT-4) the signals could be boosted? Tandem tags, either on one end or multiple ends of proteins might help as well. As the authors point out, a benefit of tagging the two NEKL-MLT complexes is that there are strong loss-of-function phenotypes (lethal molting defects) to help evaluate whether a tagging strategy results in a non-functional complex. THESE EXPERIMENTS ARE OPTIONAL and might simply be discussed at the authors discretion.

Minor Comments:

1. Figure 3A is cropped on the right.
2. Better define [new REF] on line 702.

Referee cross-comments

Overall, I am in agreement with, and supportive of, the other reviewers' comments.

Significance

Proximity labeling is often proposed as a technique to determine interaction networks of proteins in vivo, but in practice it remains challenging for most labs to execute a successful experiment, especially within the context of multicellular model organisms. Fay and colleagues provide a much needed roadmap for how to best approach proximity labeling experiments in C. elegans that will likely apply to other model systems.

They establish a rigorous approach by choosing to endogenously tag components of two essential NEKL-MLT complexes required for C. elegans molting. These complexes are relatively low abundance as they are only expressed in a single cell type, the hyp7 epidermal syncytium. In addition, as inactivation of any member of the complexes results in molting defects, they have a powerful selection for functional tags. Thus, they have set a high bar for themselves in order to discern whether a given variation on the experimental approach results in improved detection of interactors and fewer false positives.

Potential areas for improvement include lowering the expression level of the skin-specific soluble TurboID used to determine non-specific biotinylation events. This control results in much higher levels of biotinylation compared to the TurboID-tagged NEKL-MLT alleles and likely affects their analysis, which they openly admit. In addition, to reduce the high level of background biotinylation signals generated by endogenous carboxylases, they adopt a depletion strategy pioneered by other researchers but this does not offer major improvements in detection of specific signals. The source of these conflicting results remains to be determined. It is also curious that auxin-inducible degradation of components of the NEKL-MLT complexes did not robustly alter the resulting biotinylating capacity of other members. This approach should be evaluated in subsequent studies. Finally, as mentioned in Major Comment #3 (above), it would be interesting to see if combining TurboID tags within the same complex might improve signal-to-background ratios.

This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system. I am a cell biologist that uses a variety of genetic, molecular and biochemical approaches, mostly centered around C. elegans. I have used LC/MS-MS in our studies but have relatively little expertise in evaluating all aspects of proteomic pipelines.

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Referee #2

Evidence, reproducibility and clarity

This study expanded the use of data-independent acquisition-mass spectrometry (DIA-MS) in TurboID proximity-labeling proteomics to identify novel interactors of NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4 complexes in C. elegans. The authors described several useful metrics to evaluate the quality of TurboID experiments, such as using the percentage of upregulated genes, the percentage of proteins present only in bait-TurboID experiments as compared to N2 controls, and the percentage of endogenously biotinylated carboxylases as internal controls. Further, the authors introduced methodological variability across 23 TurboID experiments and evaluated any improvement to the resulting data, such as N-terminally tagging bait proteins with TurboID, depleting endogenous carboxylases, and auxin-inducible degradation of known complex members. Finally, this study identified the kinase folding chaperone CDC-37 and the WASH complex component DDL-2 as novel interactors with the NEKL-MLT complexes through an RNAi-based enhancer approach following their identification by TurboID.

Major comments:

The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate. We only have minor comments.

Minor comments:

• In the western blot in Fig 1 why does the mNG::Turbo have two bands?
• Fig 2B is difficult to parse as a reader. Columns labeled "Upreg," "Downreg," "TurboID only," "N2 only," "Filter-1," "Filter-2," and "Epi %" could be moved to Supplemental. Fold change vs N2 could be represented as a bar chart, allowing for trends between fold change and the metrics Upreg %, Turbo %, and Carboxylase % to be seen more clearly. Further, rows headed "Carboxylase depletion," "DDA," and "Auxin treated" could be presented as separate panels to better match the distinct points made in the text.
• Line 179: in vivo should be italicized
• Lines 215-217: The comparison between Western blot expression levels and prior fluorescent reporter levels is unclear. Could be reformatted to make it clearer that relative expression of the different NEKL-MLTs in this study is consistent with prior data.
• Lines 267-268: The final line of the passage is unclear and can be removed.
• Lines 311-313: This study is able to use the recovery of bait and known interactor proteins as internal controls to determine the quality of each experiment, but this may not always be the case for other users' experiments. The authors should comment on how Upreg %, a value influenced by many factors, can actually be used as a quality check when a bait protein has no known interactors.
• Line 702: There is a [new REF] that should be removed
• The approach used mixed stage animals, but some genes oscillate or are transiently expressed. Please discuss cost-benefit of mixed stage vs syncing.
• Authors were working on hypodermally expressed proteins. It would be valuable to discuss what tissues are amenable to TurboID. Ie are the cases where there are few cells (anchor cell, glial sockets, etc) that it will be extremely challenging to perform this technique
• Authors mention approaches such as nanobodies, split Turbo. Based on their experiences it would be valuable to add Discussion on strengths and weaknesses of these approaches to guide folks considering TurboID and DIA-MS experiments in C. elegans

Significance

• Advance in technique: This study expands the use cases of data-independent acquisition MS method (DIA-MS) in C. elegans, which fragments all ions independent of the initial MS1 data. The benefits of this approach include better reproducibility across technical replicates and better recovery of low abundance peptides, which are critical for advancing our ability to capture weak and transient interactions.
• The use of DIA-MS in this study has improved our understanding of the partners of these NEKL-MLTs in membrane trafficking, molting, and cell adhesion within the epidermis.
• In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs

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Referee #1

Evidence, reproducibility and clarity

Proximity labeling has become a powerful tool for defining protein interaction networks and has been utilized in a growing number of multicellular model systems. However, while such an approach can efficiently generate a list of potential interactors, knowledge of the most appropriate controls and standardized metrics to judge the quality of the data are lacking. The study by Fay systematically investigates these questions using the C. elegans NIMA kinase family members NEKL-2 and NEKL-2 and their known binding partners MLT-2, MLT-3 and MLT-4. The authors perform eight TurboID experiments each with multiple NEKL and MLT proteins and explore general metrics for assessing experimental outcomes as well as how each of the individual metrics correlates with one another. They also compare technical and biological replicates, explore strategies for identifying false positives and investigate a number of variations in the experimental approach, such as the use of N- versus C-terminal tags, depletion of endogenous biotinylated proteins, combining auxin-inducible degradation, and the use of gene ontology analysis to identify physiological interactors. Finally, the authors validate their findings by demonstrating that a number of the candidate identified functionally interact with NEKL-2 or components of the WASH complex.

Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. Below I list major and minor concerns.

Major concerns

1. My biggest issue with the manuscript is that a lot is made of the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). The authors perform experiments using DIA and DDA approaches but do not directly compare the outcomes. As a result there is really no way to know if one approach is better than the other. I would suggest the authors either perform the necessary analysis to compare the two approaches or tone down their promotion of DIA.
2. Line 75, The authors promote the use of data-independent acquisition (DIA) without defining what this approach is and how it differs from the more conventional data-dependent acquisition. As a non-mass spectroscopist, I found myself with lots of question concerning DIA, what it is and how it differs from DDA. I think it would really be helpful to expand the description of DIA and its comparison with DDA in the introduction.

Minor concerns:

Line 92 typo. I believe the authors meant to say NEKL-2-MLT-2-MLT-4.

Line169. Is exogenous the correct word to use here? It suggests that you are talking about non-worm proteins, but I know you are not.

Line 177 typo (D) should be (C).

Figure 1C: Lucky Charms may sue you for infringement of their trademarked marshmallow treats.

Figure 1D The NEKL-2::TurboID band is indicated with a green triangle in the figure but the figure legend states that green triangles indicate mNG::TurboID control. I know this triangle is a shade off the triangle that indicates mNG::TurboID but it's really hard to see the difference. All of the differently colored triangles in panel F are unnecessary. I would either just pick one color for all non-control bait proteins or better yet, only use a triangle to point to bands that are not obvious. For instance I don't need the triangles that point to NEKL-2 -3 and -4 fusion proteins. These are just distracting.

Line: 316: Conceivably, another factor that could contribute to the counterintuitive upregulation of some proteins in the N2 samples is related to the fusion proteins that are being expressed in the TurboID lines. A partially functional bait protein (one with a level of activity similar to nekl-2(fd81) that may not result in an obvious phenotype) could directly or indirectly affect gene expression leading to lower levels of a subset of proteins in the TurboID samples. The same could be said for fusion proteins with a gain-of-function effect.

Fig 3 B-E. I am a little confused how the data in these graphs is normalized. For instance, I would have expected that for NEKL-3 in panel B, that the normalized (log2) intensity value in N2 be set at 0 as it is for NEKL-2. Maybe I just don't have enough information on how these plots were generated.

Figure 6C legend is not correct.

Line 575: Figure reference should be Fig. S5G. The authors should check to make sure all references to supplemental figures include correct panel information.

Line 576. The authors reference a study by Artan and colleagues and report a weak correlation between their study and that of Artan. They reference figure S4 but it should be Fig S5H.

Line 652. The authors note that numerous proteins were present at substantially reduced levels in the mNG::TurboID samples and suggest that sticky proteins may have been outcompeted or otherwise excluded from beads incubated with the mNG::TurboID lysates. Why would sticky proteins only be a problem in these samples? The reasoning is not clear to me.

Line 745: The term "bait overlaps" is a bit vague. Ultimately, I figured out what it meant but it was not immediately obvious.

S7B Fig. Why is actin missing from the eluate?

Line 873: The authors state the extent of overlap in GO terms between the various experiments and provide percentages. I tried to extract this information from Figure 8C and came up with different values. For instance, in the case of Molecular Function, they state that they observed a 54% overlap between NEKL-2 and NEKL-3 but in the Venn diagram in Figure 8C I see that the NEKL-2 and NEKL-3 experiments had 71 (25+46) GO terms in common. Out of 98 GO terms for NEKL-2 or 104 for NEKL-3 the percentage I got is closer to 72. Am I analyzing this correctly?

Significance

Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

Summary: The authors present ASPEN - a tool for allelic imbalance estimation in haplotype-resolved single-cell RNA-seq data. Besides the mean of the allelic ratio, ASPEN manages to assess its under- and overdispersion as well as perform group-level comparisons. Dr. Wong with colleagues applied ASPEN to the simulated and publicly available single-cell data from mouse brain organoids and T cells. They showed a general applicability of the tool to this type of data, compared it with scDALI in terms of statistical power, and made numerous conclusions regarding the allele-specific regulation of housekeeping and cell-specific gene expression in general and during cell differentiation, as well as identified examples of X inactivation, imprinting and random monoallelic expression.

Major comments:

1. Considering biological insights, the authors focus on genes with the allelic imbalance variance being lower than expected based on the gene expression level, and find them being enriched by the processes essential for cell integrity. I am curious if the variation depends on the number of available cells as well, i.e. housekeeping genes may be more stably expressed from cell to cell. In this context, the authors can compare their results with the stably expressed genes from Lin et al. [https://doi.org/10.1093/gigascience/giz106].
2. Continuing with the concerns regarding gene expression level changes, authors do not provide information about the differential expression of their findings. Even where they mention "F1 hybrids revealed 33 genes with significant changes in mean allelic expression and 193 with dynamic variance, independent of total expression changes (Supp. Fig. 3B; Supp. Table 4)" in "Allelic variance reveals transcriptional plasticity across cell states" I could not find the relevant info in the corresponding Figure and Supplementary table. Furthermore, it was shown that low number of cells and gene expression level can affect allelic imbalance estimates as well as lead to false positive random monoallelic expression [https://doi.org/10.1371/journal.pcbi.1008772]. The authors admit it but do not properly discuss how it is related to their RME examples. Are they lowly expressed and/or detected in a limited number of cells?
3. The histogram provided in Figure 5C suggests the general RME preference towards maternal (C57BL/6J) haplotype. Can it be caused by the reference mapping bias? The authors suggest the total shifts of a null allelic mean, 0.52 for T cells and 0.54 for brain organoid, being the result of a reference mapping bias. However, using parental genomes should have eliminated this problem unless a substantial part of individual variants were missed due to the strict quality filters.
4. Among the genes demonstrating a dynamic allelic imbalance variance during early neurogenesis, the authors found several examples involved in autism spectrum disorders and neuroanatomical phenotypes in mice. They suggest the temporal modulation of variance as a possible regulatory mechanism which may be perturbed in disease states. However, it is hard to estimate the significance of this finding without any enrichment tests. How many disease relevant genes among those with dynamic variance can be expected by chance?

Minor comments:

1. Methods would definitely benefit from proofreading, e.g. there are mistakes in the beta-binomial distribution formula, log-transformed gene-level dispersion distribution (it does not follow N(0,1) with zero mean) and gamma likelihood function. Is rho a shape parameter instead of a rate? Specifically, I suggest describing the equitations from the "Bayesian shrinkage implementation" section in more detail. Why does the formula for corrected theta provided in the article deviate from the one presented on github https://github.com/ewonglab/ASPEN/blob/main/R/allelic_imbalance.R, i.e. "thetaCorrected = N/(N-K) * (theta + theta_smoothed(delta/(N-K)))/(1 + (delta/(N-K)))" where K = 1, instead of "thetaCorrected = (N-1)/N * (theta + theta_smootheddelta)/(1 + delta)"? Both gamma and rho also deviate from the script as far as I understood. Moreover, a few steps from the Methods remained unclear to me. First, does ASPEN apply a fixed theta threshold (i.e. of 0.001 from the manual or 0.005 from the article) or performs a more sophisticated MAD-based procedure? Does ASPEN obtain the stabilized thetas using N = 20 and theta = 10, followed by ML to correct both parameters and recalculate the posterior dispersion? Why do tests for static and dynamic allelic variance use different gene-level thetas, stabilized and non-stabilized ones? Does it affect the sensitivity and specificity of group-level analysis?
2. Besides formulas, there are minor mistakes throughout the text as well. As such, I assume the sentence "In the dyn-mean test, the dispersion parameter (set to the stabilized group-level value)" from the "Detecting dynamic changes" section should include global dispersion, not the one estimated on the group-level. In the section "Allelic variance reveals transcriptional plasticity across cell states" FDR threshold of 0.5 is mentioned instead of 0.05. Figure captions also contain minor mistakes such as "Genes below the dashed line were excluded from the trend modelling" from Figure 4 which corresponds to B instead of C.
3. Why does Figure 5B contain missing allelic ratio estimations? If it is due to the expression filters, please mention it in the caption.
4. Given the principles of the dynamic tests, I would suggest calling them "differential", "ANOVA-like" or "group-level" instead of dynamic, since there is no actual possibility to account for the continuous changes over time.
5. The example of differential variance from Figure 6D is not very clear to me and Supplementary Figure 5C does not help. I suggest adding histograms to emphasize changes in the allelic imbalance variation.
6. The authors managed to uniquely map and unambiguously assign 20-38% of total reads. The weighted allocation procedure from Choi et al. [https://doi.org/10.1038/s41467-019-13099-0] might help to increase the total coverage.
7. The discrete low dispersion values in Figures 2, 3A, 4B, 5C and 6A possibly stem from rounding to 4 decimal places. I suggest increasing the accuracy to improve the visual clarity.
8. The sentence "Of these, 27 were X-linked, consistent with random X-inactivation dynamics in female cells, and five (Bex2, Ndufb11, Pcsk1n, Sh3bgrl, Uba1) displayed signatures of incomplete X inactivation, by demonstrating largely monoallelic expression in each cell" in the "Monoallelic expression reveals regulatory complexity" section should be rephrased to reflect the proportion of cells demonstrating both alleles expressed.

Significance

Nowadays the allele-specific gene expression analysis using single-cell RNA-seq data is widely used to study allele-specific bursting [https://doi.org/10.1186/s13059-017-1200-8], imprinting, X chromosome inactivation [https://doi.org/10.1038/s42003-022-03087-4] and other processes [https://doi.org/10.1016/j.tig.2024.07.003].

1. My field of expertise mostly includes bioinformatic analysis of allele-specific expression and gene regulation using bulk sequencing data. However, to the best of my knowledge, there are three publicly available modern solutions allowing to assess the allelic imbalance using single-cell gene expression data: scDALI published in January 2022 [https://doi.org/10.1186/s13059-021-02593-8], Airpart published in May 2022 [https://doi.org/10.1093/bioinformatics/btac212] and DAESC published in 2023 [https://doi.org/10.1038/s41467-023-42016-9], with the latter not being mentioned by the authors.
2. While the authors used simulations to compare ASPEN to scDALI-Hom in terms of sensitivity, I could not find any specificity estimates. The reasons for the statement "ASPEN demonstrated high sensitivity (98%) and specificity (92%) with a low false positive rate (<12%), confirming its capacity to distinguish distinct modes of regulatory variation during lineage differentiation (Fig. 4G)" are also unclear to me since Figure 4G only demonstrates a true positive rate in test and control simulations. Should not FPR be equal to 1 - specificity?
3. Moreover, I suggest authors compare ASPEN to Airpart and DAESC along with scDALI as it can underline the scenarios where ASPEN is the best or the only option. Moreover, all these tools can estimate either heterogenous (scDALI-Het) or dynamic (Airpart, DAESC) allelic imbalance which can be compared to the allelic variance and group-level tests, respectively.

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Referee #2

Evidence, reproducibility and clarity

The authors introduce ASPEN (Allele-Specific Parameter Estimation in scRNA-seq), a statistical framework designed to model cis-regulatory variation in single-cell RNA-sequencing data, and demonstrate that ASPEN effectively detects cell state-specific allelic imbalances. Using simulated datasets, the authors show that ASPEN outperforms existing methods (e.g., scDALI) in both sensitivity and specificity. Furthermore, they demonstrate that ASPEN can be used to further dissect allelic imbalance, enabling the identification of random monoallelic expression (RME), gene expression pulsing, and dynamic regulatory shifts.

My main concerns are:

• Framework similarity with scDALI: The ASPEN framework shares many conceptual similarities with scDALI. It is not clear why ASPEN significantly outperforms scDALI. The authors should elaborate more clearly on the differences between the two approaches and provide a detailed explanation for the observed improvements.
• Scalability and runtime: The manuscript does not report computational performance metrics (e.g., runtime, memory usage), which would be important for users planning to apply ASPEN to large-scale datasets.
• Comparison to additional tools: While the comparison to scDALI is appropriate, including benchmarking against other recent allele-specific methods (e.g., SCALE, AirPart) would strengthen the evaluation and broaden its relevance.
• User guidance: A figure or supplementary table summarizing required inputs, preprocessing steps, recommended parameters, and filtering strategies would be highly beneficial for potential users.
• Time-series smoothing: The manuscript would benefit from a clearer explanation of how time-series smoothing is implemented within ASPEN, particularly in dynamic cell state contexts.

Significance

The ASPEN framework is useful for identifying single cell ASE and related analysis, which currently is under developed. It is timely and the framework is rigorous and flexible and driving by the data.

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Referee #1

Evidence, reproducibility and clarity

This is an interesting paper, which introduces a new approach and software ASPEN for analysis of allele-specific gene expression, which is applied to transcriptomes of F1 hybrids of mouse lines. The manuscript introduces an interesting statistical technique, which up to my knowledge is correct and brings about new biological results, identifying genes with systematically decreased or increased expression variance and statle allelic expression ratio, which seems to be controlled by the regulatory machinery.

The manuscript has some shortcomings in presentation, it is written very concisely, especially in its methods part, and is somewhat difficult to follow.

I'm not sure that the authors make the correct claim in the manuscript. The title and the abstract says that the manuscript discusses the cis-regulatory heterogeneity, but in fact there is very little in the manuscript about gene regulation per ce. The study demonstrates that allele specific expression is controlled by some yet unknown mechanisms, rather than a product of technical noise and then presents a number of examples of different pathways which the increased and decreased allele specific variance. Also the manuscript presents several examples of shifts in the variance of particular genes in temporal development.

Yet, the manuscript tells virtually nothing about regulation, thus the conclusion that 'ASPEN enables the interrogation of cis regulatory effects on gene expression' is not justified in its literal terms; what ASPEN does it quantifies the allele-specific transcription activity effects in a single cell transcriptomics experiment. Mechanistically the observed effects can be explained by any regulatory effect like DNA methylation, chromatin structure or whatever. To prove that cis-regulatory effects are important here the authors need to show the allele specific nature of transcription factor binding (for instance by showing the TF binding motifs destroyed/created by variants). It is more difficult to take into account the chromatin effects without ATAC experiments but it might be that ATAC-seq experiments are available for parental line and there is a differential DNA accessibility in the locality of genes of interest. I think only with such mechanistic illustrations one can conclude that cis-regulatory interactions play a major role here.

As an other option, the authors may publish the study per se but with a changed title, the abstract and the discussion, formulating it in a more phenomenological way.

Minor note

In Figures 2-5 the low variance genes are shown with dots occupying lines parallele to x axis. This can be related to some wrong digitising of variance or to a low numbers of reads contributing to the variance. Please double check.

Significance

The paper introduces a new interesting statistical approach for quantifying allele specific transcription from the single cell data, using Bayesian shrinkage technique similar to that used in edgeR. The paper has clear biological meaning demonstrating that there are genes with a decreased variability in gene expression. I believe, the paper draws attention to the interesting area of facts and as such may be published.

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Reply to the reviewers

Manuscript number: RC-2025-03031

Corresponding author(s): Lara-Pezzi, Enrique and Gómez-Gaviro, María Victoria

1. General Statements [optional]

Dear Editors,

Following the review of our article entitled "Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation", we propose below a number of experiments to be performed in order to address the issues raised by the reviewers.

While we acknowledge the limitations of the full CnAβ1 knockout mouse and we unfortunately lack a tissue-specific knockout mouse, we believe that the proposed new experiments together with the (abundant) existing information in the paper will help clarify the concerns raised by the reviewers.

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)):

*The current study examines the metabolic phenotype of mice lack the calcineurin variant CnAb1 (CnAb1KO). On a high fat diet, CnAb1KO mice gain less weight compared to WT controls, which is accompanied by improvements in obesity-related metabolic dysfunction, such as glucose/insulin intolerance and hyperlipidemia. The authors attribute most of the observed phenotypes to enhanced brown fat function, notably fatty acid catabolism and the thermogenic capacity. Mechanistically, the authors propose that CnAb1KO increases FoxO1 transcriptional activity, as a result of reduced mTOR/Akt signaling, which in turn mediates the hyper-catabolism of BAT in CnAb1KO mice. *

* Major comments: *

*Q1. The main issue of the study is it's not hypothesis driven. Based on high fat diet-induced metabolic phenotype of the whole body CnAb1KO mice, the authors put together a mechanism focusing on potential roles of CnAb1 in BAT functions that affect systemic metabolic homeostasis. However, the rationales to establish this link were based largely on correlative results and at times incorrect data interpretation (for instance, using the expression of Myf5 and Pax7 as markers for brown adipocyte differentiation). The sequential event from CnAb1 loss of function to reduced mTOR signaling and increased FoxO1 activity (or conversely, how CnAb1 increases mTOR signaling to reduce FoxO1 activity) has not been mechanistically characterized. There are also no studies to explain how FoxO1 is involved in brown fat differentiation and hyper-catabolism of BAT downstream of the CnAb1-mTOR pathway. In addition, the UCP-1 FoxO1KO experiment in Fig. 6 fails to provide strong evidence to support the claim. Thus, there are many gaps between the observed phenotype and the proposed mechanism. *

A1. We thank the reviewer for the insightful comments. We agree with the reviewer that, historically, this project did not originally focus on the BAT. Instead, we arrived at the BAT after ruling out other possibilities to explain the reduced body weight observed in these animals, together with the reduced body temperature after starvation, which was our first observation. While the BAT involvement was not our first hypothesis a priori, we do not agree that this would invalidate or reduce the interest of our work. While our initial evidence may have been correlative at first, the FoxO1 BAT-specific knockout experiments and the AAV/Ucp1-Cre CnAβ1 expression restoration experiments prove that the BAT is indeed involved in the phenotype observed in CnAβ1Δi12 (KO) mice. It is likely that other organs may be also involved (since the phenotype is not fully prevented by the BAT-specific approaches) but the BAT is definitely involved.

To further substantiate the involvement of the BAT in the improved metabolic phenotype observed in CnAβ1Δi12 mice, we propose to perform BAT transplantation, monitoring body weight over 8 weeks following transplantation. If successful, BAT transplantation from CnAβ1Δi12 mice into WT mice should improve their metabolic response to high-fat diet (HFD), thereby reinforcing the role of the BAT in these mice.

        In addition, we propose to measure the __*levels of so-called batokines*__ FGF21, VEGFA, IL6, and also of 12,13-diHOME in BAT and serum from 12-week-old chow and HFD mice.

        With regards to Pax7 and Myf5, while we agree that these are common precursors to other lineages (skeletal muscle), we show in Fig. S1E additional differentiation markers such as Cox2 and Cpt1b. __*The 5 markers assessed showed an increase in *____*CnAβ1Δi12 mice, pointing towards a cell-autonomous effect of the absence of CnAβ1 on the BAT*__. Nevertheless, to further substantiate the accelerated differentiation of brown preadipocytes in the absence of CnAβ1, we propose to __*measure the expression of additional BAT markers*__ (although they are not exclusive of BAT), such as Ucp1, Prdm16, PPARγ, and AdipoQ in brown preadipocytes isolated from 6–8-week-old mice.

        With regards to the activation of mTOR (specifically mTORC2) by CnAβ1, we published this in previous papers from our group: Gómez-Salinero et al (Cell Chem Biol, 2016), Felkin et al (Circulation, 2011), Lara-Pezzi et al (J Cell Biol 2007), Padrón-Barthe et al (J Am Coll Cardiol 2018). The mechanism involves the interaction between CnAβ1 and mTORC2 in cellular membranes. Knockdown of CnAβ1 results in mTORC2 mislocalisation and Akt inhibition. In addition, we show in Fig. 6C in this paper that PTEN inhibition reduces the improved differentiation of BAT adipocytes from CnAβ1Δi12 mice, further involving the Akt pathway in the observed phenotype. Furthermore, Fig. 6 shows a significant increase in body weight and BAT weight in BAT-specific FoxO1 knockout CnAβ1Δi12 mice, together with a significant decrease in different Pnpla1, Irf4, and Bcat2 expression. While we agree that the reversal of the phenotype is only partial, the effect of knocking out FoxO1 in the BAT of CnAβ1Δi12 mice is both statistically significant and biologically relevant. We would be happy to provide additional information at the Editors’ request. In addition, we propose to carry out __BAT preadipocyte differentiation experiments comparing cells isolated from CnAβ1Δi12 mice to those isolated from CnAβ1Δi12 mice with BAT-specific FoxO1 knockout__.

Q2. A second issue is that most of the phenotypes can be explained by the difference in weight gain. With the available data, it's difficult to pinpoint the tissue origin(s) mediating the weight gain/loss phenotype. The authors would first need to generate a BAT-CnAb1KO mouse line to convincingly show a main role for BAT CnAb1 in systemic metabolic homeostasis. There are also many problems with data presentations/interpretations of the metabolic phenotyping studies. For example, Fig. 1A shows that CnAb1KO mice are about 5 g lighter than controls. However, Fig. 1G indicates a 10 g difference in fat mass. The EM images in Fig. 3B are of poor quality, which seems to suggest that HFD fed CnAb1KO mice have the highest mitochondrial density. Lastly, in Fig. 4C/D, the authors interpret the reduced FFA and glycerol levels in CnAb1KO after b3-agonist injection as increased fatty acid burning by BAT, which is incorrect. If anything, the reduced glycerol release in the KO mice would suggest a reduction in lipolysis. However, the most likely explanation is that WT mice have more fat mass and as such, more fat hydrolysis.

A2. While we agree with the reviewer that some of the features may be explained by reduced body weight gain (reduced WAT weight, for instance), many other changes showed by CnAβ1Δi12 mice cannot be explained by reduced body weight gain alone, including higher expression of differentiation markers in BAT, higher number of mitochondria in BAT, or improved cold-tolerance, among others. Therefore, we respectfully disagree with the reviewer’s opinion.

Unfortunately, we do not have a tissue-specific CnAβ1 knockout mouse and we cannot commit to having one in the short term. While we acknowledge the limitations of using a full knockout mouse, we provided several pieces of evidence that the BAT is involved in the observed phenotype, as pointed out in the discussion: 1) Placing CnAβ1Δi12 mice in thermoneutral conditions mitigated the weight loss. 2) Reintroducing CnAβ1 in BAT with a CnAβ1-overexpressing virus partially prevented the weight loss. 3) Minimal changes in mitochondrial gene expression were observed in skeletal muscle and liver, suggesting that the phenotype is primarily driven by alterations in BAT. 4) BAT adipocytes from CnAβ1Δi12 mice differentiated more effectively than those from wild type mice, suggesting a cell-autonomous effect. While a direct effect of CnAβ1 on WAT cannot be entirely ruled out, our results strongly suggest that loss of CnAβ1 in BAT is a major contributor to the observed metabolic changes.

        With regards to Fig. 1E, this is an estimation of fat weight from __MRI__ images. We agree with the reviewer that this is obviously wrong and we will __revise this quantification__. We propose to __add measurements of subcutaneous WAT__, which we also have, to further support the difference observed in eWAT.

        With regards to Fig. 3B, we agree that some of the individual figures may have been poorly chosen, but the graph in Fig. 3C (which quantifies the electron microscopy pictures) clearly shows that the reduction in mitochondria in WT mice as a result of HFD feeding is prevented in CnAβ1Δi12 mice. Fig. 3C does not show an increase in mitochondria with HFD, as implied by the reviewer based on Fig. 3B. We propose to __provide adequate panels for Fig. 3B that better reflect the averages shown in Fig. 3C__.

        Regarding Fig. 4C and D, we thank the reviewer for this correction, which we agree with. We still believe that the BAT of CnAβ1Δi12 mice is burning fat more effectively than that of WT mice, but we agree that these experiments are not the proof of this claim. We will__ move or remove panels C and D from Fig. 4__ and focus this figure on thermogenic capacity.

        To assess systemic lipolysis, we will __measure in vivo serum levels of NEFA__ (non-esterified fatty acids) __and glycerol__ in 12-week-old mice fed a HFD. Additionally, to evaluate BAT lipolytic activation, we will perform __BAT explant and *ex vivo* experiments__ to determine the lipolysis rate. This should provide valuable information supporting the role of the BAT in the observed phenotype in CnAβ1Δi12 mice.

*Q3. The authors should take a fresh, unbiased look at existing data, form a testable hypothesis and design a series of new experiments (including new tissue-specific KO mice) to assess the function of CnAb1 in BAT or other tissues responsible for the metabolic phenotype. If BAT is indeed involved, the authors need to mechanistically determine the role of CnAb1 in brown adipocyte differentiation vs BAT function and explain why the ratio of CnAb1/CnAb2 ratio matters in this context, as this is the basis for the entire study. A revision addressing main issues of the manuscript will not likely to be completed in a typical revision time (e.g. 3 months). *

A3. As explained above, unfortunately we do not have tissue-specific CnAβ1 knockout mice. If the Editors consider that this is essential for resubmission of a revised article, we are afraid that we cannot comply. This said, we believe that our manuscript contains relevant data about metabolic regulation by the CnAβ1 calcineurin isoform that are new and relevant to the field.

        Our data provide clear evidence that the BAT is indeed involved in the phenotype observed in CnAβ1Δi12 mice, as explained in our previous answers above. It may not be the *only* tissue involved, but it is most definitely involved. The BAT transplant experiments will add further evidence of this.

        We already show evidence of the role of CnAβ1 (or rather, its absence) in the differentiation of BAT pre-adipocytes (Fig. S1E and Fig. 6C) and we will __provide additional evidence through the proposed new experiments__. Similarly, we provide evidence of the role of CnAβ1 in BAT weight, transcriptional profile, lipid content, and number of mitochondria. Also here, we believe that __the proposed experiments will reinforce this aspect of the paper__.

Reviewer #1 (Significance (Required)):

*Q4. The thermogenic capacity of brown and beige adipocytes has shown promise as a means to reduce fat burden to treat obesity and related metabolic diseases. Identification of brown/beige adipocyte promoting mechanisms may provide druggable targets for therapeutic development. As such, the topic and findings of the current study would be of interest to researchers in the metabolism and drug development fields. The weakness of the study is that it's descriptive and the authors jump to conclusions without strong supporting evidence. Most of the metabolic phenotypes associated with CnAb1KO mice are likely secondary to the weight difference. The rationale to focus on BAT is not well justified. A well-thought-out approach would be needed to identify the tissue origins mediating the metabolic phenotypes of CnAb1KO mice and to dissect the underlying mechanisms. *

*Reviewer's field of expertise: adipose tissue biology, systemic metabolic regulation, immunometabolism *

A4. We agree with the reviewer about the potential relevance of our findings. The shortcomings pointed out in this comment have been addressed above. Overall, we thank the reviewer for their thorough review of our ms.

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

*The manuscript entitled « Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation » by Dr Lara-Pezzi and colleagues describes the role of the calcium/calmodulin dependent serine/threonine phosphatase catalytic subunit calcineurin variant CnAß1 in brown adipose tissue physiology and function. Through the use of global CnAß1 KO mice, the authors show that these mice are resistant to diet-induced obesity, have increased thermogenesis due to increased mitochondrial activity, decreased body weight, improved glucose homeostasis, increased fatty acid oxidation. The authors also demonstrate that these effect are mostly mediated through improved brown adipose tissue (BAT) function, through increased Foxo1 activation in BAT. Genetic deletion of Foxo1 in BAT resulted in increased body weight and impaired mitochondrial gene expression. In addition, the authors also correlate their findings to potential CNAß1 polymorphism from the UK biobank associated to improved metabolic traits in humans (blood glucose mainly). *

Although interesting, the conclusion are not always supported by the data. The manuscript requires additional experiments to further consolidate their claims.

*Q1. It should be mentioned that all experiments are performed in global CnAβ1 KO mice. Thus, it is difficult to assess the cell-autonomous role if this protein in BAT function (even if an AAV9 driving CnAβ1 expression is used; or if other tissues have been studied). This should be discussed at least as a limitation of the study, except if floxed mice are available. *

A1. We thank the reviewer for the positive comments about our work.

Unfortunately, we do not have a tissue-specific CnAβ1 knockout mouse. However, we believe we provide abundant evidence of the involvement of the BAT in the phenotype observed in CnAβ1Δi12 mice, including the following: 1) Placing CnAβ1Δi12 mice in thermoneutral conditions mitigated the weight loss. 2) Reintroducing CnAβ1 in BAT with a CnAβ1-overexpressing virus partially prevented the weight loss. 3) Minimal changes in mitochondrial gene expression were observed in skeletal muscle and liver, suggesting that the phenotype is primarily driven by alterations in BAT. 4) BAT adipocytes from CnAβ1Δi12 mice differentiated more effectively than those from wild type mice, suggesting a cell-autonomous effect. While a direct effect of CnAβ1 on WAT cannot be entirely ruled out, our results strongly suggest that loss of CnAβ1 in BAT is a major contributor to the observed metabolic changes.

This said, we fully agree with the reviewer to acknowledge in the discussion the limitation of using a full knockout mouse for this study.

Q2. Is there good antibodies for CnAβ1? The protein levels of the protein should be shown in, at least, adipose tissues of WT and KO mice under chow and HFD.

A2. There is no good antibody against CnAβ1. The main reason is that the C-ter domain of this isoform is not very immunogenic. We did try to generate an antibody, but we got no immune response against the unique C-ter domain. We do have an old antibody generated against CnAβ1 years ago. We propose to try to perform WB and immunohistochemistry in WT and ____CnAβ1Δi12 mice. However, we need to be clear that we cannot make any commitments towards these results, since the antibody may not work. In any case, we believe that the RT-PCR results, which clearly discriminate both isoforms, are very clear.

*Q3. A general comment is that most of the conclusions are drawn from qRT-PCR data. It lacks functional experiments that may reinforce the conclusion. For example, did the authors measure mitochondrial function in BAT of WT and KO mice using different substrate (fatty acids, glucose, ...)? *

A3. We thank the reviewer for this suggestion and we therefore propose to include in the revised paper measurements of mitochondrial activity with different substrates in WT and ____CnAβ1Δi12 mice.

*Q4. Lack of validation of the mouse model used (CnAβ1 expression in BAT upon AAV9 over expression confirmed? What about the other tissues?). *

A4. We showed in Fig. 5E the increase in CnAβ1 expression in the BAT of Ucp1-Cre mice infected with the floxed AAV-CnAβ1 virus. We propose to include similar expression analyses in other tissues.

Reviewer #2 (Significance (Required)):

Q5. This is a novel study addressing the role of CnAβ1 in energy homeostasis, more specifically in BAT function. This study reports for the first time the role of CnAβ1 in energy homeostasis, with new mechanistic insights related to the crosstalk between CnAβ1 and Foxo1.

The authors have previously described the role of this protein in cardiac function. There are not a lot of publications describing the function of this protein, thus this study may be interested for the community working on diabetes/obesity/cardio-metabolic field.

*Limitations : see below (lack of functional data, ...). *

A5. We thank the reviewer for these comments, with which we agree.

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

As much as we would like to have a tissue-specific CnAβ1 knockout mouse, the reality is that we do not have it. In any case, we believe that our paper provides a considerable amount of data that is relevant to the field.

We remain open to incorporating the suggested experiments, or others, should they be considered necessary to further strengthen the manuscript.

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Referee #2

Evidence, reproducibility and clarity

The manuscript entitled « Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation » by Dr Lara-Pezzi and colleagues describes the role of the calcium/calmodulin dependent serine/threonine phosphatase catalytic subunit calcineurin variant CnAß1 in brown adipose tissue physiology and function. Through the use of global CnAß1 KO mice, the authors show that these mice are resistant to diet-induced obesity, have increased thermogenesis due to increased mitochondrial activity, decreased body weight, improved glucose homeostasis, increased fatty acid oxidation. The authors also demonstrate that these effect are mostly mediated through improved brown adipose tissue (BAT) function, through increased Foxo1 activation in BAT. Genetic deletion of Foxo1 in BAT resulted in increased body weight and impaired mitochondrial gene expression. In addition, the authors also correlate their findings to potential CNAß1 polymorphism from the UK biobank associated to improved metabolic traits in humans (blood glucose mainly).

Although interesting, the conclusion are not always supported by the data. The manuscript requires additional experiments to further consolidate their claims.

It should be mentioned that all experiments are performed in global CnAβ1 KO mice. Thus, it is difficult to assess the cell-autonomous role if this protein in BAT function (even if an AAV9 driving CnAβ1 expression is used; or if other tissues have been studied). This should be discussed at least as a limitation of the study, except if floxed mice are available. Is there good antibodies for CnAβ1? The protein levels of the protein should be shown in, at least, adipose tissues of WT and KO mice under chow and HFD.

A general comment is that most of the conclusions are drawn from qRT-PCR data. It lacks functional experiments that may reinforce the conclusion. For example, did the authors measure mitochondrial function in BAT of WT and KO mice using different substrate (fatty acids, glucose, ...) ? Lack of validation of the mouse model used (CnAβ1 expression in BAT upon AAV9 over expression confirmed? What about the other tissues?).

Significance

This is a novel study addressing the role of CnAβ1 in energy homeostasis, more specifically in BAT function. This study reports for the first time the role of CnAβ1 in energy homeostasis, with new mechanistic insights related to the crosstalk between CnAβ1 and Foxo1.

The authors have previously described the role of this protein in cardiac function. There are not a lot of publications describing the function of this protein, thus this study may be interested for the community working on diabetes/obesity/cardio-metabolic field.

Limitations: see below (lack of functional data, ...).

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Referee #1

Evidence, reproducibility and clarity

The current study examines the metabolic phenotype of mice lack the calcineurin variant CnAb1 (CnAb1KO). On a high fat diet, CnAb1KO mice gain less weight compared to WT controls, which is accompanied by improvements in obesity-related metabolic dysfunction, such as glucose/insulin intolerance and hyperlipidemia. The authors attribute most of the observed phenotypes to enhanced brown fat function, notably fatty acid catabolism and the thermogenic capacity. Mechanistically, the authors propose that CnAb1KO increases FoxO1 transcriptional activity, as a result of reduced mTOR/Akt signaling, which in turn mediates the hyper-catabolism of BAT in CnAb1KO mice.

Major comments:

1. The main issue of the study is it's not hypothesis driven. Based on high fat diet-induced metabolic phenotype of the whole body CnAb1KO mice, the authors put together a mechanism focusing on potential roles of CnAb1 in BAT functions that affect systemic metabolic homeostasis. However, the rationales to establish this link were based largely on correlative results and at times incorrect data interpretation (for instance, using the expression of Myf5 and Pax7 as markers for brown adipocyte differentiation). The sequential event from CnAb1 loss of function to reduced mTOR signaling and increased FoxO1 activity (or conversely, how CnAb1 increases mTOR signaling to reduce FoxO1 activity) has not been mechanistically characterized. There are also no studies to explain how FoxO1 is involved in brown fat differentiation and hyper-catabolism of BAT downstream of the CnAb1-mTOR pathway. In addition, the UCP-1 FoxO1KO experiment in Fig. 6 fails to provide strong evidence to support the claim. Thus, there are many gaps between the observed phenotype and the proposed mechanism.
2. A second issue is that most of the phenotypes can be explained by the difference in weight gain. With the available data, it's difficult to pinpoint the tissue origin(s) mediating the weight gain/loss phenotype. The authors would first need to generate a BAT-CnAb1KO mouse line to convincingly show a main role for BAT CnAb1 in systemic metabolic homeostasis. There are also many problems with data presentations/interpretations of the metabolic phenotyping studies. For example, Fig. 1A shows that CnAb1KO mice are about 5 g lighter than controls. However, Fig. 1G indicates a 10 g difference in fat mass. The EM images in Fig. 3B are of poor quality, which seems to suggest that HFD fed CnAb1KO mice have the highest mitochondrial density. Lastly, in Fig. 4C/D, the authors interpret the reduced FFA and glycerol levels in CnAb1KO after b3-agonist injection as increased fatty acid burning by BAT, which is incorrect. If anything, the reduced glycerol release in the KO mice would suggest a reduction in lipolysis. However, the most likely explanation is that WT mice have more fat mass and as such, more fat hydrolysis.
3. The authors should take a fresh, unbiased look at existing data, form a testable hypothesis and design a series of new experiments (including new tissue-specific KO mice) to assess the function of CnAb1 in BAT or other tissues responsible for the metabolic phenotype. If BAT is indeed involved, the authors need to mechanistically determine the role of CnAb1 in brown adipocyte differentiation vs BAT function and explain why the ratio of CnAb1/CnAb2 ratio matters in this context, as this is the basis for the entire study. A revision addressing main issues of the manuscript will not likely to be completed in a typical revision time (e.g. 3 months).

Significance

The thermogenic capacity of brown and beige adipocytes has shown promise as a means to reduce fat burden to treat obesity and related metabolic diseases. Identification of brown/beige adipocyte promoting mechanisms may provide druggable targets for therapeutic development. As such, the topic and findings of the current study would be of interest to researchers in the metabolism and drug development fields. The weakness of the study is that it's descriptive and the authors jump to conclusions without strong supporting evidence. Most of the metabolic phenotypes associated with CnAb1KO mice are likely secondary to the weight difference. The rationale to focus on BAT is not well justified. A well-thought-out approach would be needed to identify the tissue origins mediating the metabolic phenotypes of CnAb1KO mice and to dissect the underlying mechanisms.

Reviewer's field of expertise: adipose tissue biology, systemic metabolic regulation, immunometabolism

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Reply to the reviewers

REVIEWER 1

This is an important and solid study that identified sequences that can improve circRNA translation and that as or more importantly are very short and hence are suitable for generating of efficient protein expressing circRNAs. This manuscript fills an important gap in the field, and it is highly significant. The study is well controlled, the rationale clear and the results conclusive with no major flaws.

• While this is a minor concern as the vector has been used before, it will greatly improve the quality of the paper if the authors could just verify that the vector only generates circRNA molecules and not linear concatenamers. To do so the authors can focus only in their control and the most optimal transcripts and perform northern blot or well controlled RNAseR experiments to show that all RNA molecules containing the back splicing junction are circular We thank the reviewer for raising this point. As suggested, we performed RNaseR resistance assays on our three most efficient candidates driving cGFP translation (VCIP, T3-glo, and T3-U3) to confirm that all derived RNA molecules containing the back-splicing junction are circular. As proof of this, cGFP proved strongly resistant to RNase R (new Fig. S1N), confirming its circular structure. We further ruled out the possibility that molecules other than the circRNA encoding GFP serve as templates for translation from our vectors. Specifically, ad hoc PCR amplifications performed for this purpose (new Fig. S1M) showed no bands that would indicate the presence of concatemers. Indeed, ad hoc PCR amplifications (new Fig. S1M) revealed no bands indicative of concatemer formation. The primers used and the expected sizes of the amplicons are schematically represented in new Fig. S1M. In brief, we used a divergent primers set spanning the BSJ (3-4) to specifically detect the mature circRNA and a set of convergent primers (1-2) pairing on the GFP ORF, thus detecting both the circRNA and its linear precursor as well as the putative concatemer expected. Although a ~1 kb band was expected if a trans-splicing by-product was present, no such band was observed (new Fig. S1M). Moreover, RT-PCR amplification of the cGFP back-splice junction was markedly more efficient when reverse transcription was primed with random hexamers than with oligo(dT), priming total RNA or preferentially polyA+ RNA, respectively. These results are expected for a circRNA, as also indicated by the fact that the circZNF609 positive control behaves in a similar manner. Collectively, these results confirm the circular nature of our transcript and exclude translation originating from possible concatemers.

• These results are shown in new Fig. S1M and S1N and described in the text as follows: “Importantly, we ruled out the possibility that templates other than the GFP-encoding circRNA drive translation from our best performing constructs (V-cGFP, T3-glo-cGFP and T3-U3-cGFP). Ad hoc PCRs amplifications (Fig. S1M) revealed no bands indicative of concatemer formation. The left panel of Fig. S1M schematically illustrates the primer sets and expected amplicons sizes. In particular, we used a divergent primers set spanning the BSJ (3-4) to specifically detect the mature circRNA and a set of convergent primers (1-2) pairing on the GFP ORF detecting both the circRNA and its linear precursor as well as the putative concatemer expected. Although a ~1 kb band was expected if a trans-splicing by-product was present, no such band was observed. Moreover, RT-PCR amplification of the cGFP back-splice junction was markedly more efficient when reverse transcription was primed with random hexamers than with oligo(dT), priming total RNA or preferentially polyA+ RNA, respectively (Fig. S1M). These results are consistent with the circularity of the transcripts tested and coherent with the results obtained for circZNF609, used as control (Fig. S1M). Finally, cGFP resulted resistant to RNAseR treatment (Fig. S1N), further supporting its circular nature.”*

• There is a repetition of the world "a" in the abstract. We thank the reviewer for the attention paid to our text, we removed the extra “a” from the abstract.

• All circRNA translation studies should be cited when describing translation of circRNAs. We thank the reviewer for the suggestions, we corrected the mistake present in the text and included extra referenced about circRNA translation.

*Specifically, we included: *

• Fan, X., Yang, Y., Chen, C. et al. Pervasive translation of circular RNAs driven by short IRES-like elements. Nat Commun 13, 3751 (2022). https://doi.org/10.1038/s41467-022-31327-y
• Chen CK et al. Structured elements drive extensive circular RNA translation. Mol Cell. 2021 Oct 21; 81(20):4300-4318.e13.doi: 10.1016/j.molcel.2021.07.042. Epub 2021 Aug 25. PMID: 34437836; PMCID: PMC8567535.
• Obi P, Chen YG. The design and synthesis of circular RNAs. Methods. 2021 Dec;196:85-103. doi: 10.1016/j.ymeth.2021.02.020. Epub 2021 Mar 2. PMID: 33662562; PMCID: PMC8670866.
• Fukuchi, K., Nakashima, Y., Abe, N. et al. Internal cap-initiated translation for efficient protein production from circular mRNA. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02561-8
• Du, Y., Zuber, P.K., Xiao, H. et al. Efficient circular RNA synthesis for potent rolling circle translation. Nat. Biomed. Eng 9, 1062–1074 (2025). https://doi.org/10.1038/s41551-024-01306-3
• Wang F, Cai G, Wang Y, Zhuang Q, Cai Z, Li Y, Gao S, Li F, Zhang C, Zhao B, Liu X. Circular RNA-based neoantigen vaccine for hepatocellular carcinoma immunotherapy. MedComm (2020). 2024 Jul 29;5(8):e667. doi: 10.1002/mco2.667. PMID: 39081513; PMCID: PMC11286538.
• Andries O, Mc Cafferty S, De Smedt SC, Weiss R, Sanders NN, Kitada T. N(1)-methylpseudouridine-incorporated mRNA outperforms pseudouridine-incorporated mRNA by providing enhanced protein expression and reduced immunogenicity in mammalian cell lines and mice. J Control Release. 2015 Nov 10;217:337-44. doi: 10.1016/j.jconrel.2015.08.051. Epub 2015 Sep 3. PMID: 26342664.
• Yang Y, Fan X, Mao M, Song X, Wu P, Zhang Y, Jin Y, Yang Y, Chen LL, Wang Y, Wong CC, Xiao X, Wang Z. Extensive translation of circular RNAs driven by N6-methyladenosine. Cell Res. 2017 May;27(5):626-641. doi: 10.1038/cr.2017.31. Epub 2017 Mar 10. PMID: 28281539; PMCID: PMC5520850. REVIEWER 2

Circular RNAs (circRNAs) have attracted significant interest due to their unique properties, which make them promising tools for expressing exogenous proteins of therapeutic value. However, several limitations must be addressed before circRNAscan become a biologically and economically viable platform for the biotech industry.One of the main challenges is the reliance on large, highly structured sequences withinternal ribosome entry site (IRES) activity to initiate translation of the downstream open reading frame. In this study, the authors propose an alternative strategy that combines the 5′ untranslated region (5′UTR) of a previously characterized natural circRNA(circZNF609) with a short 13-nt nucleotide sequence shown to act as a translational enhancer. By evaluating the activity of various constructs containing a reporter geneacross multiple cell lines, they identify the most efficient and compact sequence, 63-nt long, capable of boosting translation within a circular RNA context.

Major Comments:

• This study is well-executed and relies on standard in vitro molecular biology techniques, which are adequate to support the conclusions drawn. *We thank the reviewer for the very positive opinion on the execution of our study. *

• The experimental procedures are clearly described, and the statistical analyses have been performed according to accepted standards. *We thank the reviewer for the very positive comment about the analyses we performed. *

Minor Comments:

• The manuscript would greatly benefit from a comprehensive revision to improve clarity and language. Involving a native English speaker during the editing process could significantly enhance the manuscript's readability and overall quality. The Results section would benefi t from closer attention, as certain parts of the description are attimes confusing and could be clarifi ed for better reader comprehension. We thank the reviewer for the input. We performed a huge revision of the text to improve language quality and enhance readability. We extended the descriptions in the results sections in order to explicit and clarify our data.

• The references should be carefully reviewed for accuracy and consistency-forinstance, references 9 and 10 appear to require correction or clarifi cation. We thank the reviewer for the careful reading of our paper. We amended the reference section, and we expanded it.

Reviewer #2 (Significance (Required)):

This study addresses a critical bottleneck in RNA therapeutics. The use of the proposed short sequences could significantly enhance the in vivo activity of protein-encoding circular RNAs. A highly efficient, compact translational enhancer has thepotential to substantially improve the therapeutic applicability of circRNAs and broaden their range of applications. Given the potential utility of these findings, we would anticipate pursuing intellectual property (IP) protection. To further strengthen the study, future work should include additional data on polysome association and a detailed analysis of the secondary structure of the 66-nt enhancer sequence. This work should be of broad interest to molecular biologists working on RNA biology, translation, and RNA-based therapeutics. I expect the identified sequence will betested by multiple laboratories to evaluate its strength and versatility, further underscoring the potential impact of this study. For context, I am actively engaged in research on non-coding RNAs.

• *

REVIEWER 3

In this brief report, the authors take advantage of circular RNA expression plasmids to define elements that can be used to enable efficient translation. They test a handful of known IRES elements as well as short translation enhancing elements (TEEs) for their ability to promote translation of circular GFP and c-ZNF609 reporters. They focus on one particular element that is of a short length and seems to work as well as longer IRES elements. My major concern relates to possible alternative sources of the translated proteins, which the authors have not ruled out (see below). I find themanuscript to be too preliminary in its current state.

• Work from the Meister group (Ho-Xuan et al 2020 Nucleic Acids Res 48:10368) has shown that apparent translation from circRNA over-expression plasmids is not from circular RNAs, but instead from trans-splicing linear by-products. The authors have not ruled out such alternative explanations here, e.g. by using deletion constructs that prevent backsplicing. We thank the reviewer for raising this point. *We ruled out the possibility that molecules other than the circRNA encoding GFP serve as templates for translation from our vectors. Specifically, ad hoc PCR amplifications performed for this purpose (new Fig. S1M) showed no bands that would indicate the presence of concatemers. Indeed, ad hoc PCR amplifications (new Fig. S1M) revealed no bands indicative of concatemer formation. The primers used and the expected sizes of the amplicons are schematically represented in new Fig. S1M. In particular, we used a divergent primers set spanning the BSJ (3-4) to specifically detect the mature circRNA and a set of convergent primers (1-2) pairing on the GFP ORF detecting both the circRNA and its linear precursor as well as the putative concatemer expected. Although a ~1 kb band was expected if a trans-splicing by-product was present, no such band was observed. Moreover, RT-PCR amplification of the cGFP back-splice junction was markedly more efficient when reverse transcription was primed with random hexamers than with oligo(dT), priming total RNA or preferentially polyA+ RNA, respectively. These results are expected for a circRNA, as also indicated by the fact that the circZNF609 positive control behaves in a similar manner. Collectively, these results confirmed the circular nature of our transcript and excluded translation originating from possible concatemers. *

These results are shown in new Fig. S1M and S1N and described in the text as follows: “Importantly, we ruled out the possibility that templates other than the GFP-encoding circRNA drive translation from our top constructs (V-cGFP, T3-glo-cGFP and T3-U3-cGFP). Ad hoc PCRs amplifications (Fig. S1M) revealed no bands indicative of concatemer formation. The left panel of Fig. S1M schematically illustrates the primer sets and expected amplicons sizes. In brief, we used a divergent primers set spanning the BSJ (3-4) to specifically detect the mature circRNA and a set of convergent primers (1-2) pairing on the GFP ORF detecting both the circRNA and its linear precursor as well as the putative concatemer expected. Although a ~1 kb band was expected if a trans-splicing by-product was present, no such band was observed (new Fig. S1M). Moreover, RT-PCR amplification of the cGFP back-splice junction was markedly more efficient when reverse transcription was primed with random hexamers than with oligo(dT), priming total RNA or preferentially polyA+ RNA, respectively (Fig. S1M). These results are consistent with the circularity of the transcripts tested (Fig. S1M). Importantly, cGFP PCR amplifications showed similar results as a validated endogenous circRNA, namely circZNF609, used as control (Fig. S1M, right panel), confirming the circular nature of cGFP. Finally, cGFP resulted resistant to RNAseR treatment (Fig. S1N), further supporting its circular nature.”* *

• Echoing the point above, the overall results would be stronger if the authors couldconfirm IRES activity using highly pure, in vitro transcribed RNAs that are transfected into cells * We thank the reviewer for this suggestion. Unfortunately, we are currently unable to produce synthetic circular molecules in-house, and the cost and time for purchasing synthetic ones are prohibitive. Nevertheless, we have performed the experiments described above to ensure the circularity of the transcripts tested.*

• The authors should also confirm their IRES activity using standard dual luciferase reporter (linear) constructs which have long been a standard approach in the field. We thank the reviewer for raising this point. As recommended, we cloned our three best candidates (VCIP, T3-glo, and T3-U3) into the pRL-TK/pGL3 dual-luciferase vector to assess their IRES activity (producing the vectors VCIP-Luc, T3-glo-Luc, and T3-U3-Luc), transfected them into RD cells, and, after 24 h of incubation, measured luciferase activity to assess the IRES performance of each candidate. From our analyses, VCIP and T3-U3 confirmed their IRES activity, although showing different relative efficiency, whereas T3-glo was inactive in the linear luciferase context. This finding is consistent with previous observations (Legnini et al., 2017) showing that the performance of IRES sequences in a linear luciferase reporter may differ from their activity when driving translation from a circRNA template. Overall, these results highlight the need for further investigation into the sequences and contexts specifically governing circRNA translation, rather than relying solely on knowledge derived from linear RNAs. *The results are shown below. We did not include them in the text to not overcomplicate the readability. However, we are happy to add and discuss them if required. *

***

***

Bar plot representing the relative luciferase activity deriving from VCIP-Luc (“V”), T3-glo-Luc (“T3-glo”), and T3-U3-Luc (“T3-U3”)*. Dual luciferase assay was performed and Renilla luciferase activity from each candidate was normalized against the Firefly luciferase. An empty ptKRL-pgl3 vector was used as reference. The ratio of each sample versus its experimental control was tested by two-tailed Student’s t test. * indicates a Student’s t test-derived p-value * *

• Methods, Plasmids Construction Section: Rather than including long lists of oligos and forcing a reader to figure out the final product that was cloned, it would be more intuitive if the authors provided the full sequences of the ORF and IRES sequencesthat were tested. We thank the reviewer for the comment, we added the sequences to the methods (Supplementary Table 1).

• The manuscript needs extensive English editing. Parts of it are also formatted in anunusual style, especially the introduction where it seems like each paragraph is a single sentence. As requested by the reviewer, we edited the text to make the language and content more accessible to readers.

• References included by the authors are selective and surprisingly do not include Chen et al (2021) Mol Cell 20:4300-4318 which already defined IRES elements for circRNAs that are fairly small. *Thank you for pointing this out. We have now cited the elegant work of Chen et al. (2021, Mol Cell 20:4300–4318) in the revised manuscript. While Chen and colleagues screened IRES-like elements of roughly 200 nt, our study was designed to uncover an even more minimal motif. The elements we report are therefore markedly shorter, highlighting a complementary, rather than overlapping, aspect of IRES available for driving circRNA translation. However, we now refer to Chen et al. in our text. *

• Error bars in Fig 2, especially Fig 2B, are huge. It seems impossible to make any conclusion given the large variety across these experiments. Thank you for your input. Although the error bars appear relatively large, the overall conclusions remain robust, as also noted by the other reviewers: both T3-glo and T3-U3 are intrinsically compact elements, yet they drive translation as efficiently as larger canonical IRESs. The error bars largely reflect the inherent variability of transient transfection assays, which naturally increases with the number of constructs examined. To strengthen our dataset without discarding existing replicates, we chose not to repeat experiments in the previously tested lines. Instead, we assessed our vectors in an additional model, the D283 medulloblastoma cell line. In this setting, we unexpectedly observed that the EMCV IRES surpasses the VCIP IRES, opposite to what we saw in the other lines, yet even here the short elements we identified remain strong competitors (new Fig. 2C, S2G, S2H). The evaluation of multiple CDSs across several cell lines, make our findings to be solid and well supported.

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Referee #3

Evidence, reproducibility and clarity

In this brief report, the authors take advantage of circular RNA expression plasmids to define elements that can be used to enable efficient translation. They test a handful of known IRES elements as well as short translation enhancing elements (TEEs) for their ability to promote translation of circular GFP and c-ZNF609 reporters. They focus on one particular element that is of a short length and seems to work as well as longer IRES elements. My major concern relates to possible alternative sources of the translated proteins, which the authors have not ruled out (see below). I find the manuscript to be too preliminary in its current state.

Major comments:

• Work from the Meister group (Ho-Xuan et al 2020 Nucleic Acids Res 48:10368) has shown that apparent translation from circRNA over-expression plasmids is not from circular RNAs, but instead from trans-splicing linear by-products. The authors have not ruled out such alternative explanations here, e.g. by using deletion constructs that prevent backsplicing.
• Echoing the point above, the overall results would be stronger if the authors could confirm IRES activity using highly pure, in vitro transcribed RNAs that are transfected into cells.
• The authors should also confirm their IRES activity using standard dual luciferase reporter (linear) constructs which have long been a standard approach in the field.
• Methods, Plasmids Construction Section: Rather than including long lists of oligos and forcing a reader to figure out the final product that was cloned, it would be more intuitive if the authors provided the full sequences of the ORF and IRES sequences that were tested.
• The manuscript needs extensive English editing. Parts of it are also formatted in an unusual style, especially the introduction where it seems like each paragraph is a single sentence.
• References included by the authors are selective and surprisingly do not include Chen et al (2021) Mol Cell 20:4300-4318 which already defined IRES elements for circRNAs that are fairly small.
• Error bars in Fig 2, especially Fig 2B, are huge. It seems impossible to make any conclusion given the large variety across these experiments.

Minor comments:

• Provide a reference for the claim in the introduction that "the smaller the RNA to be circularized, the greater the circularization efficiency".
• Supplemental Table: Please clarify what each qPCR primer was used for. E.g. what does "49 hung rev" refer to?
• Fig 1C should be explained better. What do the numbers in white refer to? In the main text, it is written that "Furthermore, we also added TEEs elements upstream the VCIP IRES" but Fig 1C suggests they were inserted downstream.

Referees cross-commenting

I stand by my comments regarding the need for the authors to perform additional controls and validation.

Significance

This work is most relevant for researchers aiming to use circular RNAs as therapeutic modalities to express proteins. Defining optimal methods, including IRES elements, that enable maximal translational output would be helpful. Note, however, that this is far from the first study to look for IRES elements in circular RNAs (e.g. Chen et al (2021) Mol Cell 20:4300-4318) which did it in a much more extensive manner.

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Referee #2

Evidence, reproducibility and clarity

Circular RNAs (circRNAs) have attracted significant interest due to their unique properties, which make them promising tools for expressing exogenous proteins of therapeutic value. However, several limitations must be addressed before circRNAs can become a biologically and economically viable platform for the biotech industry. One of the main challenges is the reliance on large, highly structured sequences with internal ribosome entry site (IRES) activity to initiate translation of the downstream open reading frame. In this study, the authors propose an alternative strategy that combines the 5′ untranslated region (5′UTR) of a previously characterized natural circRNA (circZNF609) with a short 13-nt nucleotide sequence shown to act as a translational enhancer. By evaluating the activity of various constructs containing a reporter gene across multiple cell lines, they identify the most efficient and compact sequence, 63-nt long, capable of boosting translation within a circular RNA context.

Major Comments:

• This study is well-executed and relies on standard in vitro molecular biology techniques, which are adequate to support the conclusions drawn.
• The experimental procedures are clearly described, and the statistical analyses have been performed according to accepted standards.

Minor Comments:

• The manuscript would greatly benefit from a comprehensive revision to improve clarity and language. Involving a native English speaker during the editing process could significantly enhance the manuscript's readability and overall quality. The Results section would benefit from closer attention, as certain parts of the description are at times confusing and could be clarified for better reader comprehension.
• The references should be carefully reviewed for accuracy and consistency-for instance, references 9 and 10 appear to require correction or clarification.

Significance

This study addresses a critical bottleneck in RNA therapeutics. The use of the proposed short sequences could significantly enhance the in vivo activity of protein-encoding circular RNAs. A highly efficient, compact translational enhancer has the potential to substantially improve the therapeutic applicability of circRNAs and broaden their range of applications.

Given the potential utility of these findings, we would anticipate pursuing intellectual property (IP) protection.

To further strengthen the study, future work should include additional data on polysome association and a detailed analysis of the secondary structure of the 66-nt enhancer sequence.

This work should be of broad interest to molecular biologists working on RNA biology, translation, and RNA-based therapeutics. I expect the identified sequence will be tested by multiple laboratories to evaluate its strength and versatility, further underscoring the potential impact of this study.

For context, I am actively engaged in research on non-coding RNAs.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript entitled "A Short 63-Nucleotide Element Promotes Efficient circRNA Translation", Biagi et al. aim to identify sequences and layouts that would allow high expression of proteins from an engineered circular RNA (circRNA). Briefly, the authors utilize a circRNA-producing plasmid that produces a GFP protein encoded across the splice junction when translated and test different IRESs in combination with Translation Enhancing Element (TEEs). While performing these experiments they found that a short sequence containing the TEE (13-glo) is enough to promote significant levels of translation while keeping the size of the circRNA small. The authors then tested whether the presence of a spacer could help improving translation and identified a 50base sequence that in combination with the TEE can promote very efficient translation. The authors then went on and showed that this element can promote the translation from a circRNA expressing another protein (in this case was a circRNA-encoded peptide), demonstrating the versatility of this approach. Moreover, the authors showed that their approach can promote translation in other cell lines.

This is an important and solid study that identified sequences that can improve circRNA translation and that as or more importantly are very short and hence are suitable for generating of efficient protein expressing circRNAs. This manuscript fills an important gap in the field, and it is highly significant. The study is well controlled, the rationale clear and the results conclusive with no major flaws. While this is a minor concern as the vector has been used before, it will greatly improve the quality of the paper if the authors could just verify that the vector only generates circRNA molecules and not linear concatamers. To do so the authors can focus only in their control and the most optimal transcripts and perform northern blot or well controlled RNAseR experiments to show that all RNA molecules containing the back splicing junction are circular.

Minor comments:

• There is a repetition of the world "a" in the abstract.
• All circRNA translation studies should be cited when describing translation of circRNAs.

Significance

While other studies have identified sequences that can drive circRNA translation, this study has done a great job identifying a very short sequence and additional requirements for optimal translation. This is an important study that will be of high interest for the molecular, cell biology and general biology communities.

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A detailed response to the reviewer comments has been uploaded as a separate file. It contains several embedded figures that cannot be shown through this posting option

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Referee #3

Evidence, reproducibility and clarity

Hamadou, Alunno et al. have found evidence for the notion that although translational regulation plays a key role in determining cell behavior, few studies have explored how single nucleotide polymorphisms (SNPs) affect mRNA translation. They developed a method to analyze allele-specific expression in both total and polysome-associated mRNA using RNA-seq data from HCT116 cells. This approach revealed 40 potential "tranSNPs"-SNPs linked to differences in translation between alleles. One SNP, rs1053639 (T/A) in the 3' untranslated region of the DDIT4 gene, was found to influence translation: the T allele was more often associated with polysomes. Cells engineered to carry the TT genotype produced more DDIT4 protein than those with the AA genotype, especially when exposed to stressors like Thapsigargin or Nutlin that boost DDIT4 transcription. The authors found that the RNA-binding protein RBMX mediates this allele-specific protein expression. Knocking down RBMX in TT cells lowered DDIT4 protein levels to those seen in AA cells. Functionally, TT cells suppressed mTORC1 activity more effectively under ER stress, whereas AA cells had a growth advantage in cell culture and in zebrafish models. In human cancer data from TCGA, individuals with the AA genotype had poorer outcomes under a recessive genetic model.

The manuscript needs major revision due to additional data interpretation, lack of statistical analysis, and lack of mechanistic and causal insights. The paper is overall correlative and descriptive and has not enough data to claim a translation regulation aspect of DDIT4 and the protein product to cause the observed genotypic differences stemming from a SNP in the 3' UTR. The paper reads as a collection of individual findings that do not seem to be very cohesive and ranges from polysome-seq, RBP binding, ER stress, mTOR activity, cellular co-culture tumor models and zebrafish tumor models. I wish the authors would have focused on one aspect and described one finding well. Without addressing these fundamental concerns, the study's core claims regarding p53-dependent responses in cancer remain unsubstantiated. Overall, this reviewer supports the publication in a Review Commons journal dependent on that the points of criticism are adequately addressed in the course of a major revision.

Major comments:

1. Fig.1: The presentation of the location of the tranSNPs in the target mRNAs from polysome data should be presented in a schematic in Fig.1. It should be emphasized; what fold change was considered relevant to select mRNA targets. Do SNPs overlap other regulatory element in the 3' UTRs of the mRNA targets?
2. Fig.2: If mRNA steady-state levels and protein levels are not affected by the SNP, what mechanism can be assumed for translation? Can you perform luciferase reporter mRNA experiments with the different SNPS under ER/thapsigargin stress conditions? Can you isolate the region that has the SNP and show that the effect on translation is local?
3. Fig.3: Given the subtle differences in polysome association of mRNA distributions in the mutants, the polysomes need quantifications of the area under the curve in 3 categories: sub-polysomal, light and heavy polysomes. The overall decreased translation of all 3 mRNAs in tg-stress cells of the AA SNPs needs to be explained. This effect is not specific to DDIT4.
4. Fig.4: The cherry-picking based on CLIP data of RBMX needs to be addressed more. A pulldown of all 3 identified RBPs needs to be done to determine if RBMX is the strongest regulator of DDIT4 via the 3' UTR. The EMSA in (A) needs to be quantified to determine the Kd. In (C) the RBMX is mainly nuclear which does not align with the translation effect on DDIT4 mRNA. Please explain. The effect on localization upon RBMX on DDIT4 protein seems subtle. Are there more dominant mechanisms at play for translation regulation other than via RBMX?
5. Fig.5: How do you interpret the TT-specific effect on mTOR activity? Is there a link between RBMX binding, DDIT4 protein levels/activity and mTOR? The stats in (F) are missing.
6. Fig.6: The rationale for these sets of experiments is not clear. Is it expected that the DDIT4 protein alone and its regulation through the AA phenotype is affecting global translation? Thapsigargin is a global ER stress but the expectation is not that DDIT4 itself is such a strong global regulator. This figure can move to the supplement.
7. Fig.7: The data in (A) is very clear, can you expand a bit on that how translation regulation of the genotypes in co-culture can have such a strong effect? The data in (C) needs to be reevaluated with stats as there does not seem to be a strong difference.
8. Fig.8: How much is the AA-induced tumor growth in zebrafish comparable to a co-culture tumour model? Again, how are the DDIT4 proteins levels derived from AA related and responsible for this?

Minor comment:

1. The manuscript is littered with non-intuitive abbreviations that make the figures less accessible without reading all main text. Please simplify and reduce abbreviations.

Significance

The manuscript needs major revision due to additional data interpretation, lack of statistical analysis, and lack of mechanistic and causal insights. The paper is overall correlative and descriptive and has not enough data to claim a translation regulation aspect of DDIT4 and the protein product to cause the observed genotypic differences stemming from a SNP in the 3' UTR. The paper reads as a collection of individual findings that do not seem to be very cohesive and ranges from polysome-seq, RBP binding, ER stress, mTOR activity, cellular co-culture tumor models and zebrafish tumor models. I wish the authors would have focused on one aspect and described one finding well. Without addressing these fundamental concerns, the study's core claims regarding p53-dependent responses in cancer remain unsubstantiated. Overall, this reviewer supports the publication in a Review Commons journal dependent on that the points of criticism are adequately addressed in the course of a major revision.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, Hamadou et al. describe the functional characterization of a 3'UTR SNP (rs1053639) in the DDIT4 gene that influences mRNA localization and translation. The authors use polysome profiling, isogenic HCT116 clones, and molecular assays to link the SNP to allele-specific protein expression, proposing a mechanistic role for RBMX and potentially m6A. The manuscript is clearly written and presents compelling evidence to support the authors conclusion.

Major Comments:

1. The comparison between TT and AA clones relies on a very limited number of HCT116-derived edited lines. The possibility that the observed differences in DDIT4 translation are due to clonal artifacts cannot be excluded. The authors could partially address this by transfecting the luciferase reporters carrying the A or T allele into both AA and TT clones to assess whether genotype-specific effects persist independently of clone background.
2. All functional assays are restricted to HCT116 cells. It is essential that key findings, such as especially allele-specific effects on protein levels and mRNA localization, are validated in at least one additional cell line to generalize the findings.
3. While TT and AA clones show differences in DDIT4 protein levels, the downstream biological effects (e.g., in co-culture or zebrafish xenografts) are modest and not clearly attributable to DDIT4 expression. The authors should strengthen this connection by manipulating DDIT4 expression (e.g., knockdown or overexpression) in both genotypic backgrounds to determine whether the observed growth or localization phenotypes are DDIT4-dependent.

Minor Comments:

1. Fig4B: IgG controls for the RIP-qPCR are missing.
2. Figure 7C is not properly aligned and the total proportion of cells is not 100%.
3. The discussion section, while informative, is overly long and could be more concise and focused to improve readability and impact.

Significance

The authors present a novel and sound pipeline to identify SNPs that regulate mRNA translation using allelic differences in polysome association. Using this approach, they focus on rs1053639 in the 3'UTR of DDIT4 and provide convincing evidence of its impact on mRNA localization and protein expression in HCT116 cells. While the molecular findings are robust, the biological consequences appear relatively modest, and the proposed clinical relevance remains speculative at this stage.

Overall, the study will be of primary interest to a specialized audience of researchers in the fields of post-transcriptional regulation, RNA biology, and functional genomics. The proof-of-concept framework may also attract broader interest for its potential applications in understanding non-coding genetic variation in cancer biology.

Reviewer expertise: p53 biology, molecular cancer biology

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Referee #1

Evidence, reproducibility and clarity

This study investigates the role of a 3'UTR SNP variant in DDIT4 mRNA on allele specific expression at post transcriptional level. The authors have previously developed an experimental approach to identify differences in allele specific transcript distribution in polysomes vs. This was done using polysome profiling combined with RNA-seq analysis of polysome associated and total RNA fractions. This systematic approach identified 40 candidate transcripts exhibiting differential polysome association between reference and variant alleles, indicating post transcriptional effects. Focusing on DDIT4, the study demonstrated that the SNP variant alters subcellular mRNA localization patterns between cytoplasm and nucleus through an impaired interaction with a specific RNA binding protein. Since DDIT4 functions as a negative regulator of mTORC1 signalling, the study examined the mTOR pathway status in homozygous reference and variant genotypes. Using genome-edited cell lines revealed enhanced proliferative capacity of the homozygous AA variant in both co-culture assays and zebrafish xenograft models. I agree with the authors that we don't know much about allele specific effects on mRNA translation mechanisms. However this study doesn't provide much evidence for translational effects either because the differences appear to be mostly due to the impaired export of the variant RNA from the nucleus. Irrespective, the findings are very important as they show how genetic variants in non-coding regions can result in changes of expression at posttranscriptional level.<br /> A comprehensive suite of experimental approaches was utilized to systematically assess both the SNP's impact on mRNA translation and the gene specific functional consequences for DDIT4. The manuscript is well written and presents the work with great clarity.

Major comments

"HCT116, about 11% of genes with analyzable heterozygous SNPs show a difference in AF between paired total and polysome-bound mRNAs, suggesting allele-specific post-transcriptional and translational control." For the remaining candidate transcripts that did not undergo targeted experimental validation like DDIT4, it remains possible that the observed allele specific translational effects could be attributed to other SNPs located elsewhere within these transcripts or to combinatorial effects involving multiple variants. Have the authors considered this possibility? The authors employed RNA probes designed to mimic the secondary structures of the T and A alleles of endogenous DDIT4 mRNA. Could you clarify the exact composition of these probes, do they contain a partial DDIT4 3'UTR sequence? Is it possible that the probes lack critical sequences required for complete protein recognition? Figure 3A - the authors suggest that "in the mock condition, AA cells showed a slight reduction in translation efficiency for the DDIT4 mRNA, as revealed by higher relative abundance in lighter polysomes (fraction 9)" I am not convinced that this is the case, first because the number of ribosomes per mRNA doesn't necessarily reflect translation efficiency and also the TT seems to have increased monosome fraction, and overall to me the profile suggests of slightly reduced translation for TT. Was the nucleotide sequence of the binding site of RBMX determined and if so is this sequence present within the DDIT4 3'UTR?

Minor

Could the authors maybe define what is meant by "analyzable" SNPs or genes? What was the rationale for the selection of HCT116 cells, from a quick search it appears that DDIT4 effects on mTORC1 inhibition could be cell type specific ("mediates mTORC1 inhibition in fibroblasts and thymocytes, but not in hepatocytes"), have the authors considered other cell types Results section 2: Editing of HCT116 cell... I appreciate the clear methodological explanations provided in this section; however, the manuscript might benefit from more concise organization with substantial portions of this descriptive content relocated to the Methods section. Regarding statistical presentation, I recommend reporting exact significance values rather than using threshold indicators (ns, , *, etc.). This approach provides more informative and transparent statistical reporting as differences between "non-significant" and "significant" designations can be minimal neighbouring p-values that fall on opposite sides of arbitrary thresholds and may be misleadingly interpreted. For instance in Figure 2D, the comparison between TT and AA genotypes may approach statistical significance, and displaying the actual p-values would allow readers to better assess the strength of evidence. Fig 3 What is the significance of the control mRNAs? According to the plots it seems as if these also have variants TT/AA? Figure 5A why does AA clone 6 look so different on the gel? "rs1053639 genotype, a relatively common SNP" - what is the estimated frequency of the SNP?

Significance

It is a substantial study and a very interesting story. The findings will be of interest for a broad audience, because it combines elements of basic research and clinical significance. The work allows for interpretation of an allele specific genomic variant outside of the coding region and it reveals the importance of similar characterisation of other SNPs.

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Response to the Reviews

We thank the reviewers for their input and detailed feedback, which has helped us improve both the manuscript and the Microscopy Nodes software. Based on the comments, we have implemented new features, currently available as version 2.2.1 of Microscopy Nodes. We have edited the text and figures of the manuscript to reflect these changes and add clarification where needed.

Reviewer #1

Evidence, reproducibility and clarity

*The work by Gros et al. presents a paper introducing Microscopy Nodes, a new plugin for Blender 3D visualization software designed to import and visualize multi-dimensional (up to 5D) light and electron microscopy datasets. Given that Blender is not directly suited for such tasks, this plugin significantly simplifies the process, making its visualization engine accessible to a wide range of researchers without prior knowledge of Blender. The plugin supports importing volumes and labels from generic TIF or modern OME-Zarr image formats and includes supplementary video tutorials on YouTube to facilitate basic understanding of the visualization workflows.

Major comments: - The manuscript suggests that Microscopy Nodes can easily handle large datasets, as evidenced by the showcases. However, in my personal tests, I was unable to import a moderate TIF stack of about 5GB, which is considerably smaller than the showcased datasets. Post-import, a data cube was displayed, but the Blender interface became unresponsive. The manuscript should include a section stating limitations and addressing issues and providing suggestions for visualization of large datasets.*

We want to thank the reviewer for this valuable comment, which led us to find a core issue in Blender’s large data handling. Specifically, Blender’s rasterized pipeline causes issues with > 4 GiB of data loaded. This issue does not occur in the raytraced (Cycles) renderer, which is why we had not previously encountered it.

To address this, we have extended the reloading workflow of Microscopy Nodes to provide a workaround for this. If the data is larger than 4 Gibibytes (GiB) (per timepoint, or per timepoint per channel), Microscopy Nodes now automatically downsamples these data during import. While using these downsampled options is recommended for adjusting the visualization settings, the user can then still make their animation and reload their data to the largest scale for the final render by using the raytraced (Cycles) renderer. Additionally, we have raised this bug with the core Blender developers, and hope to work this out in the long term (blender/blender#136263).

We reflect these changes in the manuscript in the segment:

“Blender currently has a notable limitation that its default ‘quick’ rasterized rendering engines (such as ‘EEVEE’, but also the viewport ‘Surface’ and ‘Wireframe’ modes) do not support more than 4 Gibibytes (GiB) of volumetric data. The raytracing render mode ‘Cycles’, however, can handle large volumetric data. To allow users with large data to flexibly use Microscopy Nodes, we implemented a reloading scheme, where one first loads a smaller version of the data (under 4 GiB per timeframe for all loaded channels combined) - and only upon final render in Cycles, exchange it for the full/larger scale copy (Fig 3A). This downscaling of data offers additional benefits as it allows for fast adjustment of the render settings on e.g. a personal computer which can eventually be transferred to a larger workstation or HPC cluster for the final render at full resolution. This feature is critical as working in Cycles with larger files requires sufficient RAM to fit the (temporary) VDB files comfortably. For example, multiple figures in this manuscript were made on a 32GB RAM M1 Macbook Pro (Fig 1A, Video SV1, Fig 1D, Figure 2A-D, Fig S2A-B), but for larger data or long movies the movies were made on workstations or prepared on a laptop and then transferred to an HPC cluster for final rendering.”

* - The feature of importing Zarr-datasets over HTTP is great, but the import process was very slow in my tests, even on a robust network. For reference, loading 1.8 GB of the PRPE1_4x dataset at s1 level took 52 minutes. This raises concerns about potential code issues and general usability of the suggested workflow.*

We believe that this loading time may have been caused by the same issue that plagued all of our datasets of >4GB outside of the raytraced mode, as we have not seen loading issues like that. Moreover, Microscopy Nodes now supports Zarr version to Zarr 3/OME-Zarr 0.5, which allows ‘sharded’ Zarr datasets, which should be even faster at loading large blocks of data at the same time, as Microscopy Nodes does.

- The onsite documentation is a bit outdated and fails to fully describe the plugin settings.

We have updated our documentation to offer new written tutorials, which include full start-up tutorials, but also for some key extra instructions.

- The YouTube tutorials feature an outdated version of the plugin, which could confuse the general microscopy audience. These should be updated to better align with the current plugin functionality. Additionally, using smaller, easily accessible datasets for these tutorials would improve user testing experiences. Hosting complete (downsampled) demo project folder on platforms like zenodo.org could also enhance usability of such tutorials.

We have made a new series of YouTube tutorials that align with the current interface of Microscopy Nodes. These tutorials include public datasets, allowing users to follow along easily. We have chosen to also retain the older tutorials for users running legacy versions of the plugin, as they cover different workflows.

- The manuscript describes a novel dataset used in Fig. 2, but no reference is provided. Additionally, practical implementation of the coloring description for Fig. 2D can be unclear for inexperienced users, necessitating either step-by-step instructions or the provision of downsampled Blender files to aid understanding.

We have now shared the OME-Zarr address in the text (https://uk1s3.embassy.ebi.ac.uk/idr/share/microscopynodes/FIBSEM_dino_masks.zarr), and included this both in the manuscript and the tutorials. Additionally, to guide the implementation and explain the logic behind the coloring we introduced additional panels in Fig S1 and Fig S2 to showcase the shader setups used for this image.

[OPTIONAL] When importing labels, they can be assigned to individual materials only if initially split into multiple color channels. It would be great if the same logic is implemented when those materials are provided as indices within a single color channel. There can be a switch to define the logic used during the import process: e.g. the current one, when the objects are just colored based on a color map, or when they are arranged as individual materials as done when labels are imported from multiple color channels.

We agree with the reviewer and to address this concern with the update to version 2.2, we have implemented a new colorpicking system (See Fig 3B, inset 3, Fig 3C), this allows users to choose between a single color, various continuous, or categorical color maps.

Minor comments: - The manuscript shows nice visualizations of time series, light, and electron microscopy datasets, but in its current state, it is targeted more for light microscopy, where the signal is white. On the other hand, many EM datasets are rendered in inverted contrast (TEM-like), where the signal is black. To render such volume properly, it is needed to go into the Shading tab and flip the color ramp. Would it be possible to perhaps define the data type during import to accommodate various data types or perhaps select the flipped color ramp when the emission mode is switched off? It could make it easier for inexperienced EM users to use the plugin.

To address this, we include new default settings, with ‘invert colormaps on load’ option in the preferences, and default colors per channel (See Fig S4). We have also implemented a new color picking system in version 2.2 (See Fig 3B, inset 3, Fig 3C) that hopefully makes it easier before and after load to change colors.

- It was not completely clear to me whether it is possible to render a single/multiple EM slices using the inverted (TEM-like) contrast. For example, XY, XZ, YZ ortho slices across the volume. The manuscript contains: "This visualization is also supported in Blender, allowing for arbitrary selections of viewing angles (Fig 2B).", but it is not clear how to achieve that.

We introduced an additional explanation in Fig S1A and added a separate density window in the default shader to make this opaque view easier. To get a single slicing plane, users can reduce the scale of the slicing cube in one axis, at it is now also explained in Fig S2B.

- In 3D microscopy, it is quite common to have data with anisotropic voxels. As a result, the surfaces may require smoothing. I was not able to quickly find a way to smooth the surfaces (at least smooth modifiers for surfaces did not work for me). Is it possible to apply smoothing during the import of labels, or alternatively, smoothing of the generated surfaces can be a topic for an additional YouTube video.

The smoothness of the loaded masks can be indirectly affected in the preferences by changing the mesh resolution (changing the relative amount of vertices per pixel), but can be further affected by operations such as the Blender “Smooth” or e.g. the “Smooth by Laplacian” modifiers. To guide the users in doing so, we have included instructions for smoothing in the written tutorials on the website https://aafkegros.github.io/MicroscopyNodes/tutorials/surface_smoothing/ .

- It is also typical to have somewhat custom color maps for materials. It would be great if the plugin remembers the previously used color map for labels.

We have implemented new Preference settings, which include default colors and colormaps per channel, improving customization and reproducibility. This new option is described in Figure S4.

* - The pixel size edit box rounds up the values to 2 digits after the dot. Could it be changed to accommodate 3 or 4 digits as the units are um.*

Blender’s interface truncates the display, but stores higher-precision values internally, and become visible when users click or edit the values. We have added support for alternative pixel units to reduce the impact of the truncation.

- Import is not working when: - Start Blender - Select Data storage: with project - Overwrite files: on, set env: on, chunked: on - Select a file to import - Save Blender file - Pressing the Load button gives an error: "Empty data directory - please save the project first before using With Project saving."

We thank the reviewer for finding this bug which is now fixed in version 2.2.

- I was not able to play the downloaded supplementary video 3 using my VLC media player, while it was working fine in a browser. The video can be opened but looks distorted and heavily zoomed in. It may need to be re-saved from a video editor.

We have recompiled this video.

- References 12 and 16 are URL links instead of proper references to articles.

Thanks for catching this mistake in our bibliography. We have corrected this.

Significance

*This work effectively bridges a gap in the availability of tools for 3D microscopy dataset visualization. While many visualization programs exist, the high-quality ones are often expensive and thus not accessible to all researchers. The integration of Blender with Microscopy Nodes democratizes access to high-quality 3D visualization, enabling researchers to explore datasets and models from multiple perspectives, potentially leading to new discoveries and enhancing the understanding of key study findings. Despite its limitations, my experience with the plugin was engaging and useful. I would like to thank the authors for such useful work!

Limitations: - There remains a steep learning curve associated with using Microscopy Nodes, primarily due to Blender's complexity. More comprehensive tutorials could help mitigate this. - The conversion of imported images to Blender's internal 32-bit format results in a 4x increase in data size for 8-bit datasets. - Managing moderate-sized volumes (5-10 GB) can be challenging without clear strategies for effective handling. - The import of Zarr-datasets over the net is notably slow.

Audience: The plugin is suitable for a broad audience with a basic understanding of 3D visualization concepts, providing a solid foundation for exploring Blender's extensive features and options for optimal visualizations.

Reviewer expertise: Light microscopy, electron microscopy, image segmentation and analysis, software development, no experience with Blender*

Reviewer #2

*Evidence, reproducibility and clarity *

*Summary:

The article introduces Microscopy Nodes, a Blender add-on designed to simplify the loading and visualization of 3D microscopy data. It supports TIF and OME-Zarr images, handling datasets with up to five dimensions. The authors present different visualization modes, including volumetric rendering, isosurfaces, and label masks, demonstrating the application in light and electron microscopy. They provide examples using expansion microscopy, electron microscopy, and real-time imaging, highlighting how the tool enhances scientific communication and interactive visualization.

Comments:

However, some key aspects could be improved to enhance usability and reproducibility:

Example datasets: The images used in the YouTube tutorials were not accessible, making it difficult to reproduce the workflows shown in the figures and tutorials. It would be helpful if the authors provided direct links to the datasets or ensured that the same examples used in the tutorials were readily available for replication.*

We created new and updated tutorials and for all new tutorials, the data is now easily available from an S3 server.

Input file specifications: The article does not clearly detail how input files should be formatted. Many users will pre-visualize images in Fiji to convert their original images to a compatible format. It would be beneficial to specify which formats are supported for hyperstack creation, including details on bit depth, dimension ordering, label formats, and metadata compatibility, if applicable.

We have added new documentation on this on the website and in the manuscript. The addon can take 8, 16, and 32 bit data, and any dimension order (with the letters tzcyx) and pixel size. Dimension order and pixel size can be edited in the GUI. This is reflected in the manuscript in the rewritten section in Design and Implementation:

“It can handle 8bit to 32bit integer and floating point data, although all data types will be resaved into 32bit floating point VDB files, which can cause temporary files to take up more space than the original. Microscopy Nodes loads 2D to 5D files of containing data across time, z, y, x and channels, in arbitrary order (can be remapped in the user interface as well, Fig 3B, inset 2). To focus on relevant data, users can clip the time axis, which can be useful for long videos.”

* Hardware requirements: The article does not discuss RAM or hardware constraints in detail. In testing, attempting to load two images into the same project caused the program to freeze (tested on Mac M1). Specifying hardware requirements and limitations would help users manage expectations when working with large datasets.*

We have since found a limitation in the Blender engine that indeed limits the amount of data loaded (see also comment by Reviewer 1). Currently, rasterized engines are capped at 4 GiB, and only the raytraced engine can handle larger data. As such, the Microscopy Nodes pipeline, where one works with small images until it is time to render a final version, and the data is only exchanged for the final render, is still viable. To make this easier, we now also included optional downscaling for Tif images. This is described in the rewritten section on Design and Implementation:

“Blender currently has a notable limitation that its default ‘quick’ rasterized rendering engines (such as ‘EEVEE’, but also the viewport ‘Surface’ and ‘Wireframe’ modes) do not support more than 4 Gibibytes (GiB) of volumetric data. The raytracing render mode ‘Cycles’, however, can handle large volumetric data. To allow users with large data to flexibly use Microscopy Nodes, we implemented a reloading scheme, where one first loads a smaller version of the data (under 4 GiB per timeframe for all loaded channels combined) - and only upon final render in Cycles, exchange it for the full/larger scale copy (Fig 3A). This downscaling of data offers additional benefits as it allows for fast adjustment of the render settings on e.g. a personal computer which can eventually be transferred to a larger workstation or HPC cluster for the final render at full resolution. This feature is critical as working in Cycles with larger files requires sufficient RAM to fit the (temporary) VDB files comfortably. For example, multiple figures in this manuscript were made on a 32GB RAM M1 Macbook Pro (Fig 1A, Video SV1, Fig 1D, Figure 2A-D, Fig S2A-B), but for larger data or long movies the movies were made on workstations or prepared on a laptop and then transferred to an HPC cluster for final rendering.”

Significance

*General Assessment:

One of the major strengths of this work is its seamless compatibility with Blender, a powerful and widely used animation and 3D rendering tool. Integrating advanced visualization techniques from the animation and graphics industry into scientific imaging opens new possibilities for presenting complex microscopy data in an intuitive and accessible way. Additionally, the support for OME-Zarr is particularly valuable, as this format represents a major shift in bioimaging towards scalable, cloud-compatible, and standardized data storage solutions. The adoption of OME-Zarr facilitates large-scale data handling and improves interoperability across imaging platforms, making this integration a significant step forward for the field. Overall, the greatest strength of the tool lies in its flexibility for rendering microscopy data, but its accessibility for users without Blender experience might be a challenge.

Advance in the Field This work introduces a novel solution to the visualization challenges in microscopy by leveraging Blender's advanced rendering capabilities.

Audience This paper will be of interest to: Bioimage researchers seeking to enhance their microscopy data visualization. Image analysis tool developers interested in integrating advanced visualization into their workflows.

Field of Expertise This review is based on expertise in image analysis, segmentation, and 3D biological data visualization.*

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

The paper "Microscopy Nodes: Versatile 3D Microscopy Visualization with Blender" presents an easy and accessible approach for microscopists and microscopy users to visualize their data in a different and more controlled way. The authors have developed a plug-in script that enables the integration of complex 3D datasets into Blender, a widely used software for 3D visualization and illustration. By leveraging Blender's advanced rendering engine, the plug-in provides greater control over the scene, enviromint and presentation of the 3D data.

I believe that this development, especially when combined with additional analysis tools can be of a great value for microscopist and advanced users to presenting their 3D data sets.

However, at this stage, the paper does not seem to fully demonstrate the benefits of using Microscopy Nodes. To enhance the paper impact, it would be helpful for the authors to further emphasize and provide examples of how Blender's rendering specifically improves data presentation and, in turn, enhances the understanding of the data compared to existing solutions. Specifically, the authors claim at the end of the introduction that their development provides powerful tools for high-quality, visually compelling presentations, enabling "more effective communication of 3D biological data." I believe this statement should be supported by a figure comparing currently available visualization methods and demonstrating how using Blender enhances data presentation and by which enhances the communication of the results. *

*Additionally, at the end of the first paragraph of the results, the authors say: "These options allow us to combine the data and its analyzed interpretation in the same representation with Microscopy Nodes." However, this capability already exists in currently available software. Aside from now being able to achieve this in Blender, what additional benefits does it offer? *

We now include a new Table 1, to showcases which requirements for visualizing complex biological data are available in different visualization software, and discuss this in the text:

“Although several tools for 3D visualization of bioimages already exist and offer essential features for microscopy data (Table 1), many are proprietary, and open-source alternatives often struggle to deliver a comprehensive user experience, such as advanced animation and annotation controls. Proprietary solutions may offer some of these capabilities, but they are frequently limited by licensing costs, platform restrictions, and a lack of customizability. In contrast, Blender is a mature, well-supported open-source platform with a large community of developers that excels in both animation and visualization. By integrating microscopy-specific functionality through Microscopy Nodes, Blender becomes a uniquely powerful solution that bridges the gap between high-end graphics capabilities and the specialized needs of bioimage visualization.”

Additionally, we attempted to remake Figure 2C and 2D in the EM-field standard software Amira, but were not able to. This is because without an advanced light scattering algorithm, it is very hard to see the depth in the nucleus, and the semi-transparent masks do show each other behind them, but cannot interact with the volume.

We chose not to include this in the actual manuscript, as we are not experts at the Amira software, and will, by the nature of this manuscript, present a challenge that Blender is especially good at, such as here the combination of scattering light and semitransparent masks.

* In the last sentence of the second paragraph of the results, it is stated: "Blender powered by Microscopy Nodes: the ability to combine microscopy data with any 3D illustration in the same 3D environment." Could you please elaborate on the accuracy of the models that can be built and provide guidelines for achieving this using the data coordinates imported by Microscopy Nodes? If the illustrations are purely freehand and do not require specific accuracy, it would be helpful to clarify the advantages of creating them within the same environment rather than separately, as many scientists currently do. Additionally, if the inclusion of 3D model illustrations is one of the key advantages of using Blender, I believe it would be beneficial to present this in a figure rather than only in the supplementary video. *

We thank the reviewer for this comment and agree that in the previously submitted version of Microscopy Nodes, it was very difficult to align objects accurately, as the coordinate space was not transparent. A hurdle in this was the fact that Blender only works well with the unit ‘meters’. To address this issue, we now provide a choice of mapping the physical size to meters, as shown in the new interface (See Fig 3B, inset 5). Here the user can choose from the default ‘px -> cm’ (this will always look fine for a quick look) to options such as ‘nm -> m’ or ‘µm -> m’, which, combined with the new choice for adjusting the object origin upon load, allow users to treat the Blender coordinate space as based on the actual physical scales. Additionally, other Blender addons, such as Molecular Nodes (Reference 25 of the manuscript), also allow for accurate localization for cryo-EM datasets.

We appreciate the note that we should more clearly display the ability to show our illustrations and the data together in the figure and have added a visualization to show this in Figure 1C.

* Reviewer #3 (Significance (Required)):

The significance of the paper at this stage is primarily technical and mainly relevant to the field of microscopy

My field of expertise is microscopy and 3D visualization of models using mainly Maya3D and AMIRA.*

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 paper "Microscopy Nodes: Versatile 3D Microscopy Visualization with Blender" presents an easy and accessible approach for microscopists and microscopy users to visualize their data in a different and more controlled way. The authors have developed a plug-in script that enables the integration of complex 3D datasets into Blender, a widely used software for 3D visualization and illustration. By leveraging Blender's advanced rendering engine, the plug-in provides greater control over the scene, enviromint and presentation of the 3D data.

I believe that this development, especially when combined with additional analysis tools can be of a great value for microscopist and advanced users to presenting their 3D data sets.

However, at this stage, the paper does not seem to fully demonstrate the benefits of using Microscopy Nodes. To enhance the paper impact, it would be helpful for the authors to further emphasize and provide examples of how Blender's rendering specifically improves data presentation and, in turn, enhances the understanding of the data compared to existing solutions.

Specifically, the authors claim at the end of the introduction that their development provides powerful tools for high-quality, visually compelling presentations, enabling "more effective communication of 3D biological data." I believe this statement should be supported by a figure comparing currently available visualization methods and demonstrating how using Blender enhances data presentation and by which enhances the communication of the results.

Additionally, at the end of the first paragraph of the results, the authors say: "These options allow us to combine the data and its analyzed interpretation in the same representation with Microscopy Nodes." However, this capability already exists in currently available software. Aside from now being able to achieve this in Blender, what additional benefits does it offer?

In the last sentence of the second paragraph of the results, it is stated: "Blender powered by Microscopy Nodes: the ability to combine microscopy data with any 3D illustration in the same 3D environment." Could you please elaborate on the accuracy of the models that can be built and provide guidelines for achieving this using the data coordinates imported by Microscopy Nodes? If the illustrations are purely freehand and do not require specific accuracy, it would be helpful to clarify the advantages of creating them within the same environment rather than separately, as many scientists currently do. Additionally, if the inclusion of 3D model illustrations is one of the key advantages of using Blender, I believe it would be beneficial to present this in a figure rather than only in the supplementary video.

Significance

The significance of the paper at this stage is primarily technical and mainly relevant to the field of microscopy

My field of expertise is microscopy and 3D visualization of models using mainly Maya3D and AMIRA.

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 article introduces Microscopy Nodes, a Blender add-on designed to simplify the loading and visualization of 3D microscopy data. It supports TIF and OME-Zarr images, handling datasets with up to five dimensions. The authors present different visualization modes, including volumetric rendering, isosurfaces, and label masks, demonstrating the application in light and electron microscopy. They provide examples using expansion microscopy, electron microscopy, and real-time imaging, highlighting how the tool enhances scientific communication and interactive visualization.

Comments:

However, some key aspects could be improved to enhance usability and reproducibility:

Example datasets: The images used in the YouTube tutorials were not accessible, making it difficult to reproduce the workflows shown in the figures and tutorials. It would be helpful if the authors provided direct links to the datasets or ensured that the same examples used in the tutorials were readily available for replication.

Input file specifications: The article does not clearly detail how input files should be formatted. Many users will pre-visualize images in Fiji to convert their original images to a compatible format. It would be beneficial to specify which formats are supported for hyperstack creation, including details on bit depth, dimension ordering, label formats, and metadata compatibility, if applicable.

Hardware requirements: The article does not discuss RAM or hardware constraints in detail. In testing, attempting to load two images into the same project caused the program to freeze (tested on Mac M1). Specifying hardware requirements and limitations would help users manage expectations when working with large datasets.

Significance

General Assessment:

One of the major strengths of this work is its seamless compatibility with Blender, a powerful and widely used animation and 3D rendering tool. Integrating advanced visualization techniques from the animation and graphics industry into scientific imaging opens new possibilities for presenting complex microscopy data in an intuitive and accessible way. Additionally, the support for OME-Zarr is particularly valuable, as this format represents a major shift in bioimaging towards scalable, cloud-compatible, and standardized data storage solutions. The adoption of OME-Zarr facilitates large-scale data handling and improves interoperability across imaging platforms, making this integration a a significant step forward for the field. Overall, the greatest strength of the tool lies in its flexibility for rendering microscopy data, but its accessibility for users without Blender experience might be a challenge.

Advance in the Field

This work introduces a novel solution to the visualization challenges in microscopy by leveraging Blender's advanced rendering capabilities.

Audience

This paper will be of interest to: Bioimage researchers seeking to enhance their microscopy data visualization. Image analysis tool developers interested in integrating advanced visualization into their workflows.

Field of Expertise

This review is based on expertise in image analysis, segmentation, and 3D biological data visualization.

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 by Gros et al. presents a paper introducing Microscopy Nodes, a new plugin for Blender 3D visualization software designed to import and visualize multi-dimensional (up to 5D) light and electron microscopy datasets. Given that Blender is not directly suited for such tasks, this plugin significantly simplifies the process, making its visualization engine accessible to a wide range of researchers without prior knowledge of Blender. The plugin supports importing volumes and labels from generic TIF or modern OME-Zarr image formats and includes supplementary video tutorials on YouTube to facilitate basic understanding of the visualization workflows.

Major comments:

• The manuscript suggests that Microscopy Nodes can easily handle large datasets, as evidenced by the showcases. However, in my personal tests, I was unable to import a moderate TIF stack of about 5GB, which is considerably smaller than the showcased datasets. Post-import, a data cube was displayed, but the Blender interface became unresponsive. The manuscript should include a section stating limitations and addressing issues and providing suggestions for visualization of large datasets.
• The feature of importing Zarr-datasets over HTTP is great, but the import process was very slow in my tests, even on a robust network. For reference, loading 1.8 GB of the PRPE1_4x dataset at s1 level took 52 minutes. This raises concerns about potential code issues and general usability of the suggested workflow.
• The onsite documentation is a bit outdated and fails to fully describe the plugin settings.
• The YouTube tutorials feature an outdated version of the plugin, which could confuse the general microscopy audience. These should be updated to better align with the current plugin functionality. Additionally, using smaller, easily accessible datasets for these tutorials would improve user testing experiences. Hosting complete (downsampled) demo project folder on platforms like zenodo.org could also enhance usability of such tutorials.
• The manuscript describes a novel dataset used in Fig. 2, but no reference is provided. Additionally, practical implementation of the coloring description for Fig. 2D can be unclear for inexperienced users, necessitating either step-by-step instructions or the provision of downsampled Blender files to aid understanding.

[OPTIONAL] When importing labels, they can be assigned to individual materials only if initially split into multiple color channels. It would be great if the same logic is implemented when those materials are provided as indices within a single color channel. There can be a switch to define the logic used during the import process: e.g. the current one, when the objects are just colored based on a color map, or when they are arranged as individual materials as done when labels are imported from multiple color channels.

Minor comments:

• The manuscript shows nice visualizations of time series, light, and electron microscopy datasets, but in its current state, it is targeted more for light microscopy, where the signal is white. On the other hand, many EM datasets are rendered in inverted contrast (TEM-like), where the signal is black. To render such volume properly, it is needed to go into the Shading tab and flip the color ramp. Would it be possible to perhaps define the data type during import to accommodate various data types or perhaps select the flipped color ramp when the emission mode is switched off? It could make it easier for inexperienced EM users to use the plugin.
• It was not completely clear to me whether it is possible to render a single/multiple EM slices using the inverted (TEM-like) contrast. For example, XY, XZ, YZ ortho slices across the volume. The manuscript contains: "This visualization is also supported in Blender, allowing for arbitrary selections of viewing angles (Fig 2B).", but it is not clear how to achieve that.
• In 3D microscopy, it is quite common to have data with anisotropic voxels. As a result, the surfaces may require smoothing. I was not able to quickly find a way to smooth the surfaces (at least smooth modifiers for surfaces did not work for me). Is it possible to apply smoothing during the import of labels, or alternatively, smoothing of the generated surfaces can be a topic for an additional YouTube video.
• It is also typical to have somewhat custom color maps for materials. It would be great if the plugin remembers the previously used color map for labels.

• The pixel size edit box rounds up the values to 2 digits after the dot. Could it be changed to accommodate 3 or 4 digits as the units are um.

• Import is not working when:

• Start Blender
• Select Data storage: with project
• Overwrite files: on, set env: on, chunked: on
• Select a file to import
• Save Blender file
• Pressing the Load button gives an error: "Empty data directory - please save the project first before using With Project saving."
• I was not able to play the downloaded supplementary video 3 using my VLC media player, while it was working fine in a browser. The video can be opened but looks distorted and heavily zoomed in. It may need to be re-saved from a video editor.
• References 12 and 16 are URL links instead of proper references to articles.

Significance

This work effectively bridges a gap in the availability of tools for 3D microscopy dataset visualization. While many visualization programs exist, the high-quality ones are often expensive and thus not accessible to all researchers. The integration of Blender with Microscopy Nodes democratizes access to high-quality 3D visualization, enabling researchers to explore datasets and models from multiple perspectives, potentially leading to new discoveries and enhancing the understanding of key study findings. Despite its limitations, my experience with the plugin was engaging and useful. I would like to thank the authors for such useful work!

Limitations:

• There remains a steep learning curve associated with using Microscopy Nodes, primarily due to Blender's complexity. More comprehensive tutorials could help mitigate this.
• The conversion of imported images to Blender's internal 32-bit format results in a 4x increase in data size for 8-bit datasets.
• Managing moderate-sized volumes (5-10 GB) can be challenging without clear strategies for effective handling.
• The import of Zarr-datasets over the net is notably slow.

Audience: The plugin is suitable for a broad audience with a basic understanding of 3D visualization concepts, providing a solid foundation for exploring Blender's extensive features and options for optimal visualizations.

Reviewer expertise: Light microscopy, electron microscopy, image segmentation and analysis, software development, no experience with Blender

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):

In Arabidopsis, DNA demethylation is catalyzed by a family of DNA glycosylases including DME, ROS1, DML2, and DML3. DME activity in the central cell leads to the hypomethylation of maternal alleles in endosperm. While ROS1, DML2, and DML3 function in vegetative tissues to prevent spreading DNA methylation from TE boundaries, their function in the endosperm was unclear.<br /> Using whole genome methylome analysis, the authors showed that ROS1 prevents hypermethylation of paternal alleles in the endosperm thus promotes epigenetic symmetry between maternal and paternal genomes.<br /> The approach and experimental desighs are appropriate, and the key conclusions are adequately supported by the results.<br /> However, there is not sufficient evidence to support the claim that DME demethylates the maternal allele at ROS1-dependent biallelically-demethylated regions. To clarify the issue, the authors could analyze if there is an overlap between DMRs identified in ros1 endosperm and those identified in dme endosperm using published data. If there is any, the authors could show a genome browser example of DMR including dme data.

Response: Thank you for your insight on our work. To address your concern and further test our model that DME prevents methylation of the maternal allele at regions where ROS1 is prevents methylation of the paternal allele, we turned to the allele-specific bisulfite-sequencing data published in Ibarra et al 2012. These data were from endosperm isolated at 7-8 DAP from aborting seeds of dme-2 +/- (Col-gl) plants pollinated by L_er_. Our analysis of these data is now included in Figures 6 and 7 and Supplemental Figures 13-17. We show that when the loss-of-function allele dme-2 is inherited maternally, average methylation of the maternal allele increases at ROS1-dependent regions (in the revised version of the paper now referred to as ROS1 paternal, DME maternal regions) from less than 10% CG methylation to approximately 40% CG methylation (Fig. 6D), consistent with our previous analysis using the non-allelic Hsieh et al 2009 data (now moved to Supplemental Figure 15). These results thus provide additional evidence that DME removes maternal allele methylation at regions where ROS1 removes paternal allele methylation (compare Fig. 6B and 6D). We included relevant genome browser examples in Figure 7E and Supplemental Figure 14. In the revised version, the relationship between ROS1 and DME is further expanded upon in the text.

Reviewer #1 (Significance):

Endosperm is a tissue unique to flowering plants. Though it is an ephemeral tissue, the endosperm plays essential roles for seed development and germination. The endosperm is also the site genomic imprinting occurs, and it has a distinct epigenomic landscape. This work provides a new insight that ROS1 may antagonize imprinted gene expression in the endosperm. However, it was not shown whether imprinted gene expression is indeed affected in ros1, or whether the ros1 mutation has phenotypic consequences. These results would be useful to discuss the evolution and significance of genomic imprinting.

Response: We agree that the biological significance of ROS1-mediated paternal allele demethylation is presently unknown. We performed RNA-seq on wild-type and ros1 3C and 6C endosperm nuclei, but these data were unfortunately not of high enough quality to include in the manuscript. In the Discussion we suggest that disrupting ROS1-mediated paternal allele demethylation might lead to a gain of imprinting over evolutionary time. In future work we are planning to address potential relationships to gene imprinting using a molecular, RNA-sequencing approach as well as an evolutionary comparative approach. As expected, given the expectation that imprinted genes are associated with a parent-of-origin specific epigenetic mark, we did not find any relationship between known imprinted genes and ROS1-dependent regions that are biallelically-demethylated regions in wild-type endosperm (see lines 362-372).

Reviewer #2 (Evidence, reproducibility and clarity):

SUMMARY

Hemenway and Gehring present evidence that the paternal genome in Arabidopsis endosperm is demethylated at several hundred loci by the DNA glycosylase/lyase ROS1. The evidence is primarily based on analysis of DNA methylation of ros1 mutants and of hybrid crosses where each parental genome can be differentiated by SNPs. I have some comments/questions/concerns, two of them potentially serious, but I think Hemenway and Gehring can address them through additional analyses of data that they already have available and a bit of clarification in writing.

Response: Thank you for your thoughtful review of this study. Your insight and suggestions have helped add clarity to the paper.

MAJOR COMMENTS:

1. Could the excess methylation in ros1-3 relative to ros1-7 shown in Figures 1A and 1C be explained by a second mutation in the ros1-3 background that elevates methylation at some loci? Any mutation that increased RdDM at these loci, for example could have this effect. This could confound the identification and interpretation of biallelicly demethylated loci.

Response: We propose a simpler explanation for the additional hypermethylation observed in ros1-3: ros1-3 is a loss-of-function (null) allele whereas ros1-7 is likely a hypomorphic allele. For clarity, we have added a diagram of all of the alleles used in this study as Supplemental Figure 1B. The ros1-3 allele was first described in Penterman et al, PNAS, 2007. It is a T-DNA insertion allele that was isolated in the Ws accession and then backcrossed 6 times to Col-0, greatly minimizing the risk of unlinked secondary mutations being present. There is no genetic evidence that there is another T-DNA insertion in this line. The ros1-7 allele was described in Williams et al, Plos Genet, 2015. It was isolated from the Arabidopsis Col-0 TILLING population and is missense mutation (E956K) in a residue in the glycosylase domain that is conserved among the four DNA glycosylases. It is known that ROS1 transcripts are produced from the ros1-7 allele (Williams et al 2015). We observe less hypermethylation in the ros1-7 background compared to the ros1-3 background, and thus propose that the ros1-7 allele is a hypomorphic allele of ROS1. The use of two independent ros1 mutant alleles for initial endosperm methylation profiling strengthens the findings of our study. Importantly, regions that are hypermethylated in ros1-3 are also hypermethylated in ros1-7, but to a lesser extent, and vice versa (Fig 1D, Supplemental Figs. 3 and 4).

We also use a third allele in this study, ros1-1, which is a nonsense allele in the C24 accession. Notably, we find that the regions are demethylated on both maternal and paternal alleles in wild-type C24 gain DNA methylation primarily on the paternal allele in ros1-1 endosperm (Figure 4C,D and Supplemental Figure 10). This is discussed further in response to your second point.

Given these lines of evidence, a gain-of-function mutation in a methylation pathway, like RdDM, in the ros1-3 background is an unlikely explanation for increased hypermethylation compared to ros1-7. The use of three independent ros1 alleles for methylation profiling, all of which lead to the same conclusions, is a major strength of our study.

1. It appears that the main focus of the manuscript, the existence of loci that are paternally demethylated by ROS1, is supported by a set of 274 DMRs. This is a small number relative to the size of the genome and raises suspicions of rare false positives. Even the most stringent p-values that DMR-finding tools report do not guarantee that the DMRs are actually reproducible in an independent experiment. Demonstrating overlap between these 274 DMRs and an independently defined set using a different WT control and different ros1 allele would suffice to remove this concern. It appears that authors already have the needed raw data with ros1-1 and ros1-7 alleles.

Response: First, we should clarify that paternal demethylation by ROS1 is supported by more than the 274 DMRs. All ros1 CG hyperDMRs show an increase in paternal allele methylation in ros1 (Fig. 4B,D). The 274 DMRs are a distinct subset defined as having less methylation on the maternal allele than the paternal allele in ros1 endosperm and where there is no maternal allele hypomethylation in wild-type endosperm (refer to Fig. 5B).

We agree with your sentiments about DMR-finders and we are cautious of relying exclusively on DMR calls when making conclusions. We verify the nature of identified DMRs using metaplots and weighted average comparisons throughout the paper, which we think increases confidence in the conclusions and goes beyond a simple DMR-calling approach.

We argue that we have replicated the major conclusion of the paper, that ROS1 prevents paternal allele hypermethylation at target regions in the endosperm, in the following ways:

1. In the dataset without allelic-specific methylation information (Figures 1-3), we found that both ros1-3 and ros1-7 CG hyperDMRs have a limited capacity for hypermethylation in the endosperm relative to leaf or sperm (Table 1, Fig 3, Supplemental Fig. 4). In the allele-specific dataset, ros1-3 CG hyperDMRs were revealed to have particularly low maternal mCG relative to paternal mCG in ros1 mutant endosperm (Fig 4A-B, Supplemental Fig. 10).
2. We found that ros1-3 and ros1-1 hyperDMRs, which we identified using non-allelic data, are biased for paternal allele hypermethylation in the endosperm of F1 hybrids (Fig 4B,D). The replicability of the paternal bias in hypermethylation in both ros1-3 in the Col-0 ecotype and ros1-1 in the C24 ecotype is a critical result, and we have moved the ros1-1 hyperDMR plots from the supplement to main figure 4C-D in the revised version of the manuscript as a result of your comment.
3. The 274 DMRs identified as “biallelically-demethylated, ROS1-dependent” are by definition replicated between reciprocal cross directions. (Note that we now refer to these regions as ROS1 paternal, DME maternal regions in the revision.) Regions in this category had to be called as maternally-hypomethylated in both ros1-1 x ros1-3 and ros1-3 x ros1-1 endosperm. These regions also had to not be identified as maternally-hypomethylated in both C24 x Col-0 and Col-0 x C24. We hope this is clarified for readers by Table 1, which we have included based on your suggestion in comment #3, as well as other clarifying edits we made in this section of the paper.comparisons between maternal and paternal methylation in endosperm, DMRs defined by comparison between mutants and wildtype, and more. These need clearer descriptions of which sets are being referred to throughout the main text and in figure legends. A table summarizing them might help (not in the supplement). Use of consistent and precisely defined terms would help. Stating the number of DMRs along with the name for each set would help a lot, even though this would make for some redundancy. (The number of DMRs in each set not only helps with interpretation but also act as a sort of ID). The reason I put this as a major concern is because the text and figures are difficult to understand, and it is currently hard to evaluate both the results and the authors' conclusions from those results.

Response: Thank you for your feedback and suggestions. We have edited the main text so that only one descriptive name is used for each DMR type throughout the paper. We have also renamed regions for greater clarity. The previous “ROS1-independent, maternally demethylated regions” are now referred to as “DME maternal regions”. The previous “ROS1-_independent, biallelically-demethylated regions” are now referred to as “_ROS1 paternal, DME maternal regions”. These changes provide greater clarity and also emphasize the role of DME at regions that are paternally hypermethylated in ros1. We have added Table 1 to summarize the DMR classes of interest.

MINOR COMMENTS

1. The sRNA results in Figure 2B are difficult to interpret because they do not reveal anything about the number of TEs that have siRNAs overlapping them or their flanks. While the magnitude of some of the highest endosperm sRNA peaks is higher than the embryo peaks, that could be explained by a small number of TEs with large numbers of sRNAs. To make this result more interpretable, we also need some information about how many TEs have a significant number of sRNAs associated with them in endosperm and embryo in each region (e.g., middle, 5', 3', and flanks of TEs). What a "significant number of sRNAs" is would be up to the authors to decide based on the distribution of sRNA counts they observe for TEs. Perhaps the top quartile of TEs? Combined with the same analysis done in parallel with non-ROS1 target TEs, this would reveal whether there is any evidence for ROS1 counteracting sRNA-driven methylation spread from TEs.

Response: Thank you for the suggestion. We now present these data and the data for individual TEs underlying the metaplots in Supplemental Figure 7. As suggested by the reviewer, ROS1 TEs do not have uniformly higher levels of sRNA in their flanks in the endosperm compared to the embryo. We have modified our interpretations accordingly.

1. The statement "we are likely underestimating the true degree of differential methylation among genotypes" should be validated and partially quantified using a methylation metaplot like Figure 2A, but substitute DMRs for TEs. Related to that, Figure 1B needs an indicator of scale in bp.

Response: We have now included a methylation metaplot over ros1-3 hyperDMRs and ros1-7 hyperDMRs as Supplemental Figure 3 These plots show that indeed there is additional hypermethylation in DMR-proximal regions. We have added a scale bar to Figure 1B and other browser examples in the paper.

1. The statement "Over half of ROS1 target regions identified in the ros1-3 mutant endosperm were within 1 kb or intersecting a TE (Fig. 1D)" is hard to interpret without some kind of ROS1 non-target regions or whole-genome control comparison. How different are the numbers in Fig. 1D from a random expectation?

Response: We have now included a control for random regions in Figure 1E. We define these as regions where there was sufficient methylation data coverage and a low enough methylation level in wild-type to detect hypermethylation if it existed.

1. The sentence at line 262 is confusing. Is the comparison between dme mutant and ros1 mutant or between different types of regions? And it appears that the comparison value is missing in the "3-5% CG methylation gain..." e.g., "3-5% CG methylation vs 10-20%" or something like that.

Response: This section has been re-written as we now focus on allele-specific dme endosperm methylation data for our comparisons.

1. The dme mutant data in Figure 5C appear to be key to the model in Figure 7. The relative impact of the dme mutant in the two types of regions should be quantified.

Response: Thank you for this comment. To further probe our model that DME prevents hypermethylation of the maternal allele at regions where ROS1 is preventing hypermethylation of the paternal allele, we turned to the allele-specific bisulfite-sequencing data published in Ibarra et al 2012 (see also response to reviewer #1). Using these data, we show that when the loss-of-function allele dme-2 is inherited maternally, ROS1 paternal, DME maternal regions (previous referred to as ROS1-_dependent, biallelically-demethylated regions) are CG hypermethylated on the maternal allele (Figure 6D). Thus, these results both replicate the observations made with the Hsieh et al 2009 data, and provide additional evidence that _DME prevents maternal allele hypermethylation at regions were ROS1 is preventing paternal allele hypermethylation. These results have replaced the Hsieh et al 2009 results in Figure 6, and we have moved the analysis of Hsieh et al 2009 data to Supplemental Figure 15.

1. Looks like sRNA methods are missing.

Response: Thank you for identifying this. We previously included the reference for the analyzed dataset we used and the method for plotting under an unclear section header. These methods are now in the section “Analysis of average methylation and 24-nt sRNA patterns for features of interest”, and we have added additional reference to the specific dataset we used.

1. Supplemental Figure 1 is hard to interpret since it only list gene IDs, not gene names.

Response: As suggested, we have added gene names to this figure.

The last comments are suggestions for increasing the impact of this study:

1. Figure 2A and 3B suggest that ROS1 target TEs show demethylation in their flanks but not in the TE themselves. This is an interesting result. If it is true, more DMRs would be expected in the ROS1 target flanks than in the ROS1 target TEs. Reporting how many ROS1 target TEs have DMRs in them and what proportion have DMRs in their flanking 1-Kb regions would answer this question. Given the significance of this result, it also deserves a bit more context: Is the magnitude of increased methylation flanking TEs in ros1 mutant endosperm different than in ros1 mutant leaves or other tissue? Does methylation in TE flanks behave the way in dme mutant endosperm?

Response: We define “ROS1 target TEs” (now referred to more simply as ROS1 TEs) as TEs within 1kb or intersecting a ros1-3 hyperDMR. Consistent with your interpretation, 80% of the TEs in this category do not have a DMR overlapping them, instead they have a TE within 1kb. We now mention this in the text on line 150.

The total level of DNA methylation at ROS1 TEs is lower in the endosperm than in leaf, as DNA methylation levels are overall lower in endosperm than in leaf. The magnitude of increased methylation flanking TEs in ros1 mutant endosperm is not different between the two tissues. This is observable in Supplemental Fig. 5 in the revised version of the paper, and we report this result in the revised text. In the revision we also present methylation profiles of DME TEs in WT and ros1 endosperm (Fig. 7B-D). DME TEs are hypomethylated in both the body and flanks in WT and ros1.

1. The idea of biallelic demethylation has been theoretically suggested in maize to explain weak overlap between endosperm DMRs and imprinting (Gent et al 2022). If that were true in Arabidopsis, then ROS1 target, biallelicly demethylated loci would be less likely to have imprinted expression than maternally demethylated loci. This prediction could be tested using available data in Arabidopsis.

Response: Indeed, as you hypothesize, there are no known imprinted genes (Pignatta et al 2014) associated with biallelically-demethylated, ROS1-dependent regions (now referred to as ROS1 paternal, DME maternal regions). Expectedly, there are imprinted genes associated with maternally-demethylated regions (now referred to as DME regions). 23 imprinted genes identified in the Pignatta et al 2014 study are within 1 kb or intersecting a DME region. This is discussed on lines 364-374.

1. There is currently no evidence for biological significance of biallelicly demethylated loci. Knowing where they are in the genome might give some hints. A figure like Fig. 1D but specifically showing the biallelicly demethylated DMRs would be valuable.

Response: This is now included in Figure 7A.

1. It is hard to make the comparisons between genotypes and parental genomes in Figure 6 and know what they mean. Maybe a different way of displaying the data would help. Or maybe even a different labeling system could make it a little more accessible.

Response: We have revised this figure (now Fig. 8) in the following ways, which we believe address your comments and clarify the main conclusions:

Figure 8C is now a boxplot comparing methylation of the paternal allele of ROS1 paternal, DME maternal regions (previously referred to as biallelically-demethylated, ROS1-dependent regions) across endosperm ROS1 genotypes. This plot shows increased methylation of paternal alleles when the paternal parent is a ros1 mutant, regardless of whether the resultant F1 endosperm is homozygous or heterozygous for ros1 (columns 3, 4, 6).

Figure 8B remains as a scatterplot, where we can observe significant correlation between individual ROS1 paternal, DME maternal regions in homozygous ros1 endosperm and heterozygous ros1/+ endosperm. Note that paternal allele methylation is higher in homozygous ros1 endosperm for most regions.

Reviewer #2 (Significance):

Demethylation of the maternal genome in endosperm has been the subject of much research because it can result in genomic imprinting of gene expression. The enzymes responsible, DNA glycosylases/lyases, also demethylate DNA in other cell types as well, where DNA methylation is not confined to one parental genome (biallelic or biparental as opposed to uniparental demethylation). To the best of my knowledge, the extent or even existence of biallelelic demethylation in endosperm has not been studied until now (except for a superficial look in a bioRxiv preprint, https://www.biorxiv.org/content/10.1101/2024.07.31.606038v1). Hemenway and Gehring have carried out a thoughtful and detailed analysis of the topic in Arabidopsis at least as far as it depends on the DNA glycosylase ROS1.

A limitation is that the study design would miss biallelic demethylation by any of the other three DNA glycosylases in Arabidopsis. A second limitation is that there is no clear biological significance, just some conjecture about evolution. Nonetheless, given the novelty of the topic, biological significance may follow.

The audience for biallelic DNA demethylation in Arabidopsis endosperm is certainly in the "specialized" category, but its relevance to the larger topic of gene regulation in endosperm will attract a larger audience.

Response: With regard to the other demethylases, note that we also profiled methylation in ros1 dml2 dml3 triple mutant endosperm. We did not find evidence for many DMRs that were present in the triple mutant that were not present in the ros1 single mutant. We do not rule out a function for DML2 or DML3 in the endosperm, but this is not observed at the level of bulk endosperm.

The reviewer is correct that we have shown a molecular phenotype (paternal allele hypermethylation) and not a developmental or morphological phenotype. A function that occurs in one parent but not the other is, to us, exciting. Our thoughts about how this finding might relate to imprinting are indeed speculative, but not wildly so.

Reviewer #3 (Evidence, reproducibility and clarity):

DNA demethylases play a key role in DNA methylation patterning during flowering plant reproduction. The demethylase DME, in particular, is critical for proper endosperm development. While the function of DME in endosperm development has been explored, the contributions of the other demethylases in the same family, ROS1, DML2 and DML3 in Arabidopsis, have not yet been investigated. In vegetative tissues, ROS1 prevents hypermethylation of some loci. In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses below.

Response: Thank you for your thoughtful review of our paper. Your questions and suggestions have been invaluable in revising the work.

I think making a few simple changes to streamline nomenclature would improve readability. For example, in the section starting on line 129, the same set of genomic features are called ROS1 target-proximal TEs, TEs that are near a ROS1 target region, and ROS1 target-associated TE regions. Also for example in line 254 "regions that are maternally-demethylated in wild-type endosperm, and are not dependent on ROS1 for proper demethylation" - are these the same as the "ROS1-independent, maternally-demethylated" regions in Fig. 5a? Given how complex these terms are, being consistent throughout the manuscript really helps the reader.

Response: We edited the text and figures so that only one descriptive name is used for each DMR class or region throughout the paper. Thank you for this feedback; these edits have made the paper much clearer.

Is there any notable effect of ros1 on gene expression in endosperm? Endosperm is a terminal tissue, so maintaining DNA methylation boundaries as ROS1 does in vegetative tissues seems less important. It begs the question of why ROS1 is doing this in endosperm, is it just because it's there, or is there an endosperm-specific function? Exploring effects on imprinting would be particularly interesting (does loss of ROS1 'create' imprinted loci at these newly asymmetrically methylated sites?) but probably beyond the scope of the present work.

Response: We agree, the question of the functional consequence of ROS1 activity in the endosperm is something we are keen to address in future work. We performed RNA-seq on wild-type and ros1 3C and 6C endosperm nuclei, but these data were unfortunately not of high enough quality to include in the manuscript. We are in particular interested in this question you have proposed – if loss of ROS1 can ‘create’ imprinted loci. We are planning to address this both using a molecular, RNA-sequencing approach as well as an evolutionary comparative approach. This is an important and exciting future direction.

Is DME expressed in sperm, or is expression of DME affected in ros1 sperm or endosperm? One other explanation for ros1 hypermethylation occurring primarily on the paternal allele is that, potentially, DME can substitute for ROS1 in the central cell where DME is already very active, but not in sperm cells. Related, how well expressed is ROS1 vs. DME in sperm cells?

Response: This is an important series of questions, and something we are very interested in as well. Studies of Arabidopsis pollen have shown that both ROS1 and DME, while they prevent some hypermethylation in sperm, are more active in the vegetative nucleus of pollen than in sperm. ROS1 is expressed at a low level in the microspore and bicellular pollen and DME is expressed at a low level throughout pollen development. We have included Supplemental Fig. 17 with available expression data to make this point in the paper. Likely, any effects of loss of ROS1 or DME on sperm DNA methylation are inherited from precursor cells (Ibarra et al 2012, Calarco et al 2012, Khouider et al 2021). Your proposal that perhaps DME can sub in for ROS1 in the central cell but not in sperm is intriguing. Unfortunately there’s not enough data in the central cell to convincingly address this at this time.

To investigate the relationship between DME and ROS1 in the male germline, we used the bisulfite-sequencing data generated in sperm cells in Khouider et al 2021. We calculated average DNA methylation levels in dme/+, ros1, dme/+;ros1, and wild-type Col-0 sperm cells at ROS1 paternal, DME maternal regions, shown in Supplemental Fig. 18A. We observed little increase in mCG methylation in dme/+ sperm relative to wild-type Col-0 sperm. This is consistent with your proposed model that DME is unable to demethylate these regions outside of the female germline. As expected, there is increased mCG in ROS1 paternal, DME maternal regions in ros1-3 mutant sperm relative to wild-type Col-0 sperm. DME maternal regions are highly methylated in wild-type Col-0 sperm.

Fig 2b shows that ROS1 target-associated TEs are enriched for sRNAs in endosperm relative to embryo, whereas the reverse is true for non-ROS1-assoc TEs. Since TEs are not always well annotated and some may be missing from this analysis, what about trying the reverse analysis - are regions enriched for 24nt sRNAs in endosperm significantly hypermethylated in ros1 endosperm? All regions or only some?

Response: We performed an analysis to address your inquiry and observed a low magnitude increase in DNA methylation in ros1 mutant endosperm at regions defined by Erdmann et al as more sRNA producing in the endosperm relative to the embryo (endosperm DSRs). Endosperm DSRs are generally lowly methylated in wild-type endosperm, as was observed originally in Erdmann et al 2017. Small increases in DNA methylation are observed at endosperm DSRs in all sequence contexts in ros1 endosperm. Overall, this is consistent with ROS1 targets being a subset of sRNA-producing regions in the endosperm. This analysis is now included in Supplemental Fig. 7C.

What is the relationship between previously-defined DME targets and ROS1 targets identified in this paper? DME tends to target small euchromatic TE bodies, whereas Fig. 3 suggests that ROS1 helps prevent methylation spreading on the outer edges of the TEs, rather than in the TE body. Do all DME targets tend to be adjacent to or flanked by ROS1 target sites? Or are the TEs affected by DME (in body) and by ROS1 (at edges) largely nonoverlapping? Fig. 5a suggests that the ROS1-dependent, biallelically-demethylated sites are both DME and ROS1 targets, but how often do these really appear to overlap? More than by chance?

Response: We have sought to address your comments through a series of analyses that we have included in Fig. 7 and Supplemental Fig. 16. We found that ROS1 paternal, DME maternal regions (formerly referred to as ROS1-dependent, biallelically-demethylated regions) and DME maternal regions (formerly referred to as ROS1-independent, maternally-demethylated regions) do not occupy the same genomic regions. However, we do observe some evidence for ROS1 activity in flanking regions of DME targets (Fig. 6A, Fig. 7B-D). To look at TEs specifically, as you suggest, we first identified TEs that were within 1kb or intersecting a DME maternal region. Based on our characterization of these regions, we assume these to be DME-targeted TEs. We then performed ends analysis to see if there was evidence of ROS1 activity at the ends of these TEs. Indeed, at a global level there is a slight hypermethylation of the paternal allele in a ros1 mutant at the end of these DME TEs (Fig. 7B). To better visualize how many DME TEs are showing ROS1 activity at their ends, we then plotted the difference between the median ros1-3 methylation and median Col-0 values in the non-allelic endosperm for each TE in a clustered heatmap (Fig. 7C). The parent-of-origin data does not have enough coverage for clustering in this way, so we used the non-allelic data. A small fraction of “DME TEs” gain methylation in the ros1 mutant endosperm relative to wild-type (Fig. 7C-D).

Are the TEs whose boundaries are demethylated by ROS1 more likely to be expressed in vegetative or endosperm tissues than TEs not affected by loss of ROS1? Expressed TEs likely produce more sRNAs, which would increase RdDM in a way that might need to be more actively countered by ROS1 than transcriptionally silent or evolutionarily older TEs.

Response: This is an interesting line of inquiry, although perhaps out of the scope of our present study. It has been shown that TEs demethylated by ROS1 are targeted by the RdDM pathway in Arabidopsis vegetative tissue (Tang et al 2016). Using data from Erdmann et al 2017, we looked at 24 nt sRNAs at ROS1-TEs in the endosperm and embryo (Supplemental Fig. 7). sRNA production at ROS1 TE-flanking regions is observed in both embryo and endosperm, but clearly not all ROS1 TEs produce 24 nt sRNA production in the seed. Future work comparing sRNA profiles in a ros1 mutant to those of wild-type could inform our understanding of TE spreading in a ros1 mutant, as would a comprehensive analysis of TE expression, again in both a ros1 mutant and in wild-type. It’s unclear to us if the endosperm would be the most informative or useful tissue to perform such analyses in.

Fig6 - as noted in the text, one way to test whether demethylation by ROS1 occurs before or after fertilization is to provide functional ROS1 through only one parent via reciprocal WT x ros-1 crosses, so that the endosperm always has ROS1 but either sperm or central cell does not, and see if this can rescue the paternal hypermethylation. If ROS1 acts prior to fertilization, then paternal ROS1 will rescue ros1 hypermethylation, but maternal ROS1 won't. If after fertilization, then either maternally or paternally supplied ROS1 will rescue the hypermethylation phenotype (assuming both are well expressed). Thus, to distinguish the two, it is sufficient to test whether maternally supplied ROS1 in an otherwise mutant background can rescue the hypermethylation phenotype, which is what is shown in Fig. 6. However, I think it's also important to show that paternally supplied ROS1 can also rescue the hypermethylation phenotype, which is not currently shown. The plots showing no effect on maternal mCG aren't as informative, since maternal methylation levels are mostly unaffected by ros1 anyway. Instead of comparing pairs of samples in a scatterplot, it might be clearer to show paternal mCG across all four comparisons (WT x WT, WT x ros1, ros1 x WT, and ros1 x ros1) side by side in a heatmap, using clustering to group similar behavior.

Response: We have revised this figure, now Fig. 8, in the following ways, which we believe addresses your comments and clarify the main conclusions (see same response to reviewer 2 for point 14):

Figure 8B remains as a scatterplot, where we observe significant correlation between individual ROS1 paternal, DME maternal regions in homozygous ros1 endosperm and heterozygous ros1/+ endosperm. Note that paternal allele methylation is higher in homozygous ros1 endosperm for most regions.

Figure 8C is now a boxplot comparing methylation of the paternal allele of ROS1 paternal, DME maternal regions (previously referred to as biallelically-demethylated, ROS1-dependent regions) across endosperm ROS1 genotypes. This plot shows increased methylation of paternal alleles when the paternal parent is a ros1 mutant, regardless of whether the resultant F1 endosperm is homozygous or heterozygous for ros1 (columns 3, 4, 6).

I would also suggest including a little more information in the main plots rather than only in the figure legends. For example, in Fig 2 including a label of 'ROS1-associated TE' for the two plots on the left, and 'TEs not associated with ROS1' on the right. Or for example in Fig. 3a indicating 'ros1-3 CG hyperDMRs' somewhere on the plot. This would just help make the figures easier to read at a glance. Please add common gene names to figures, instead just the ATG gene ID (Fig. S1a).

Response: Thank you for this feedback, we have made the suggested edits and additional edits of a similar nature.

Minor:<br /> - Fig. 1E is referenced in the text before Fig. 1D<br /> - Fig. S4 and S5 - there are more lines in the plot than the 6 genotypes listed in the legend, do these represent different replicates? If so that should be noted in the legend<br /> - Fig. 1B has no color legend for the different methylation sequence contexts (looks like same as 1A,C but should indicate either in plot or legend)<br /> - Line 42 should be "correspond to TE ends"<br /> - Line 93 "Based on previous studies..." should have references to those studies<br /> - When referring to the protein (rather than the genetic locus or mutant), ROS1 should not be italicized - for example line 130<br /> - Line 150 "we conclude that the loss"<br /> - Should add a y=x line to scatterplots, like those in Fig. 6<br /> - In fig. 1d, it's hard to evaluate the significance of the overlap of ROS1 targets with genes and TEs. Comparing these numbers to a control where the ROS1 targets have been randomly shuffled would help.

Response: We have made edits and additions where requested.

Reviewer #3 (Significance):

In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses.

Response: Thank you for your comments. We have worked on streamlining the text and analysis.

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Referee #3

Evidence, reproducibility and clarity

DNA demethylases play a key role in DNA methylation patterning during flowering plant reproduction. The demethylase DME, in particular, is critical for proper endosperm development. While the function of DME in endosperm development has been explored, the contributions of the other demethylases in the same family, ROS1, DML2 and DML3 in Arabidopsis, have not yet been investigated. In vegetative tissues, ROS1 prevents hypermethylation of some loci. In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses below.

I think making a few simple changes to streamline nomenclature would improve readability. For example, in the section starting on line 129, the same set of genomic features are called ROS1 target-proximal TEs, TEs that are near a ROS1 target region, and ROS1 target-associated TE regions. Also for example in line 254 "regions that are maternally-demethylated in wild-type endosperm, and are not dependent on ROS1 for proper demethylation" - are these the same as the "ROS1-independent, maternally-demethylated" regions in Fig. 5a? Given how complex these terms are, being consistent throughout the manuscript really helps the reader.

Is there any notable effect of ros1 on gene expression in endosperm? Endosperm is a terminal tissue, so maintaining DNA methylation boundaries as ROS1 does in vegetative tissues seems less important. It begs the question of why ROS1 is doing this in endosperm, is it just because it's there, or is there an endosperm-specific function? Exploring effects on imprinting would be particularly interesting (does loss of ROS1 'create' imprinted loci at these newly asymmetrically methylated sites?) but probably beyond the scope of the present work.

Is DME expressed in sperm, or is expression of DME affected in ros1 sperm or endosperm? One other explanation for ros1 hypermethylation occurring primarily on the paternal allele is that, potentially, DME can substitute for ROS1 in the central cell where DME is already very active, but not in sperm cells. Related, how well expressed is ROS1 vs. DME in sperm cells?

Fig 2b shows that ROS1 target-associated TEs are enriched for sRNAs in endosperm relative to embryo, whereas the reverse is true for non-ROS1-assoc TEs. Since TEs are not always well annotated and some may be missing from this analysis, what about trying the reverse analysis - are regions enriched for 24nt sRNAs in endosperm significantly hypermethylated in ros1 endosperm? All regions or only some?

What is the relationship between previously-defined DME targets and ROS1 targets identified in this paper? DME tends to target small euchromatic TE bodies, whereas Fig. 3 suggests that ROS1 helps prevent methylation spreading on the outer edges of the TEs, rather than in the TE body. Do all DME targets tend to be adjacent to or flanked by ROS1 target sites? Or are the TEs affected by DME (in body) and by ROS1 (at edges) largely nonoverlapping? Fig. 5a suggests that the ROS1-dependent, biallelically-demethylated sites are both DME and ROS1 targets, but how often do these really appear to overlap? More than by chance?

Are the TEs whose boundaries are demethylated by ROS1 more likely to be expressed in vegetative or endosperm tissues than TEs not affected by loss of ROS1? Expressed TEs likely produce more sRNAs, which would increase RdDM in a way that might need to be more actively countered by ROS1 than transcriptionally silent or evolutionarily older TEs.

Fig6 - as noted in the text, one way to test whether demethylation by ROS1 occurs before or after fertilization is to provide functional ROS1 through only one parent via reciprocal WT x ros-1 crosses, so that the endosperm always has ROS1 but either sperm or central cell does not, and see if this can rescue the paternal hypermethylation. If ROS1 acts prior to fertilization, then paternal ROS1 will rescue ros1 hypermethylation, but maternal ROS1 won't. If after fertilization, then either maternally or paternally supplied ROS1 will rescue the hypermethylation phenotype (assuming both are well expressed). Thus, to distinguish the two, it is sufficient to test whether maternally supplied ROS1 in an otherwise mutant background can rescue the hypermethylation phenotype, which is what is shown in Fig. 6. However, I think it's also important to show that paternally supplied ROS1 can also rescue the hypermethylation phenotype, which is not currently shown. The plots showing no effect on maternal mCG aren't as informative, since maternal methylation levels are mostly unaffected by ros1 anyway. Instead of comparing pairs of samples in a scatterplot, it might be clearer to show paternal mCG across all four comparisons (WT x WT, WT x ros1, ros1 x WT, and ros1 x ros1) side by side in a heatmap, using clustering to group similar behavior.

I would also suggest including a little more information in the main plots rather than only in the figure legends. For example, in Fig 2 including a label of 'ROS1-associated TE' for the two plots on the left, and 'TEs not associated with ROS1' on the right. Or for example in Fig. 3a indicating 'ros1-3 CG hyperDMRs' somewhere on the plot. This would just help make the figures easier to read at a glance. Please add common gene names to figures, instead just the ATG gene ID (Fig. S1a).

Minor:

• Fig. 1E is referenced in the text before Fig. 1D
• Fig. S4 and S5 - there are more lines in the plot than the 6 genotypes listed in the legend, do these represent different replicates? If so that should be noted in the legend
• Fig. 1B has no color legend for the different methylation sequence contexts (looks like same as 1A,C but should indicate either in plot or legend)
• Line 42 should be "correspond to TE ends"
• Line 93 "Based on previous studies..." should have references to those studies
• When referring to the protein (rather than the genetic locus or mutant), ROS1 should not be italicized - for example line 130
• Line 150 "we conclude that the loss"
• Should add a y=x line to scatterplots, like those in Fig. 6
• In fig. 1d, it's hard to evaluate the significance of the overlap of ROS1 targets with genes and TEs. Comparing these numbers to a control where the ROS1 targets have been randomly shuffled would help.

Significance

In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses

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Referee #2

Evidence, reproducibility and clarity

Summary

Hemenway and Gehring present evidence that the paternal genome in Arabidopsis endosperm is demethylated at several hundred loci by the DNA glycosylase/lyase ROS1. The evidence is primarily based on analysis of DNA methylation of ros1 mutants and of hybrid crosses where each parental genome can be differentiated by SNPs. I have some comments/questions/concerns, two of them potentially serious, but I think Hemenway and Gehring can address them through additional analyses of data that they already have available and a bit of clarification in writing.

Major comments:

1. Could the excess methylation in ros1-3 relative to ros1-7 shown in Figures 1A and 1C be explained by a second mutation in the ros1-3 background that elevates methylation at some loci? Any mutation that increased RdDM at these loci, for example could have this effect. This could confound the identification and interpretation of biallelicly demethylated loci.
2. It appears that the main focus of the manuscript, the existence of loci that are paternally demethylated by ROS1, is supported by a set of 274 DMRs. This is a small number relative to the size of the genome and raises suspicions of rare false positives. Even the most stringent p-values that DMR-finding tools report do not guarantee that the DMRs are actually reproducible in an independent experiment. Demonstrating overlap between these 274 DMRs and an independently defined set using a different WT control and different ros1 allele would suffice to remove this concern. It appears that authors already have the needed raw data with ros1-1 and ros1-7 alleles.
3. Because of the multiple sets of DMRs identified and used throughout the paper, it is hard to follow which one is which. There are DMRs defined solely by one sequence context, DMRs defined by all three contexts merged, DMRs defined by comparisons between maternal and paternal methylation in endosperm, DMRs defined by comparison between mutants and wildtype, and more. These need clearer descriptions of which sets are being referred to throughout the main text and in figure legends. A table summarizing them might help (not in the supplement). Use of consistent and precisely defined terms would help. Stating the number of DMRs along with the name for each set would help a lot, even though this would make for some redundancy. (The number of DMRs in each set not only helps with interpretation but also act as a sort of ID). The reason I put this as a major concern is because the text and figures are difficult to understand, and it is currently hard to evaluate both the results and the authors' conclusions from those results.

Minor comments

1. The sRNA results in Figure 2B are difficult to interpret because they do not reveal anything about the number of TEs that have siRNAs overlapping them or their flanks. While the magnitude of some of the highest endosperm sRNA peaks is higher than the embryo peaks, that could be explained by a small number of TEs with large numbers of sRNAs. To make this result more interpretable, we also need some information about how many TEs have a significant number of sRNAs associated with them in endosperm and embryo in each region (e.g., middle, 5', 3', and flanks of TEs). What a "significant number of sRNAs" is would be up to the authors to decide based on the distribution of sRNA counts they observe for TEs. Perhaps the top quartile of TEs? Combined with the same analysis done in parallel with non-ROS1 target TEs, this would reveal whether there is any evidence for ROS1 counteracting sRNA-driven methylation spread from TEs.
2. The statement "we are likely underestimating the true degree of differential methylation among genotypes" should be validated and partially quantified using a methylation metaplot like Figure 2A, but substitute DMRs for TEs. Related to that, Figure 1B needs an indicator of scale in bp.
3. The statement "Over half of ROS1 target regions identified in the ros1-3 mutant endosperm were within 1 kb or intersecting a TE (Fig. 1D)" is hard to interpret without some kind of ROS1 non-target regions or whole-genome control comparison. How different are the numbers in Fig. 1D from a random expectation?
4. The sentence at line 262 is confusing. Is the comparison between dme mutant and ros1 mutant or between different types of regions? And it appears that the comparison value is missing in the "3-5% CG methylation gain..." e.g., "3-5% CG methylation vs 10-20%" or something like that.
5. The dme mutant data in Figure 5C appear to be key to the model in Figure 7. The relative impact of the dme mutant in the two types of regions should be quantified.
6. Looks like sRNA methods are missing.
7. Supplemental Figure 1 is hard to interpret since it only list gene IDs, not gene names.

The last comments are suggestions for increasing the impact of this study:<br /> 11. Figure 2A and 3B suggest that ROS1 target TEs show demethylation in their flanks but not in the TE themselves. This is an interesting result. If it is true, more DMRs would be expected in the ROS1 target flanks than in the ROS1 target TEs. Reporting how many ROS1 target TEs have DMRs in them and what proportion have DMRs in their flanking 1-Kb regions would answer this question. Given the significance of this result, it also deserves a bit more context: Is the magnitude of increased methylation flanking TEs in ros1 mutant endosperm different than in ros1 mutant leaves or other tissue? Does methylation in TE flanks behave the way in dme mutant endosperm?<br /> 12. The idea of biallelic demethylation has been theoretically suggested in maize to explain weak overlap between endosperm DMRs and imprinting (Gent et al 2022). If that were true in Arabidopsis, then ROS1 target, biallelicly demethylated loci would be less likely to have imprinted expression than maternally demethylated loci. This prediction could be tested using available data in Arabidopsis.<br /> 13. There is currently no evidence for biological significance of biallelicly demethylated loci. Knowing where they are in the genome might give some hints. A figure like Fig. 1D but specifically showing the biallelicly demethylated DMRs would be valuable.<br /> 14. It is hard to make the comparisons between genotypes and parental genomes in Figure 6 and know what they mean. Maybe a different way of displaying the data would help. Or maybe even a different labeling system could make it a little more accessible.

Significance

Demethylation of the maternal genome in endosperm has been the subject of much research because it can result in genomic imprinting of gene expression. The enzymes responsible, DNA glycosylases/lyases, also demethylate DNA in other cell types as well, where DNA methylation is not confined to one parental genome (biallelic or biparental as opposed to uniparental demethylation). To the best of my knowledge, the extent or even existence of biallelelic demethylation in endosperm has not been studied until now (except for a superficial look in a bioRxiv preprint, https://www.biorxiv.org/content/10.1101/2024.07.31.606038v1). Hemenway and Gehring have carried out a thoughtful and detailed analysis of the topic in Arabidopsis at least as far as it depends on the DNA glycosylase ROS1.

A limitation is that the study design would miss biallelic demethylation by any of the other three DNA glycosylases in Arabidopsis. A second limitation is that there is no clear biological significance, just some conjecture about evolution. Nonetheless, given the novelty of the topic, biological significance may follow.

The audience for biallelic DNA demethylation in Arabidopsis endosperm is certainly in the "specialized" category, but its relevance to the larger topic of gene regulation in endosperm will attract a larger audience.

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Referee #1

Evidence, reproducibility and clarity

In Arabidopsis, DNA demethylation is catalyzed by a family of DNA glycosylases including DME, ROS1, DML2, and DML3. DME activity in the central cell leads to the hypomethylation of maternal alleles in endosperm. While ROS1, DML2, and DML3 function in vegetative tissues to prevent spreading DNA methylation from TE boundaries, their function in the endosperm was unclear.

Using whole genome methylome analysis, the authors showed that ROS1 prevents hypermethylation of paternal alleles in the endosperm thus promotes epigenetic symmetry between maternal and paternal genomes.<br /> The approach and experimental desighs are appropriate, and the key conclusions are adequately supported by the results.

However, there is not sufficient evidence to support the claim that DME demethylates the maternal allele at ROS1-dependent biallelically-demethylated regions. To clarify the issue, the authors could analyze if there is an overlap between DMRs identified in ros1 endosperm and those identified in dme endosperm using published data. If there is any, the authors could show a genome browser example of DMR including dme data.

Significance

Endosperm is a tissue unique to flowering plants. Though it is an ephemeral tissue, the endosperm plays essential roles for seed development and germination. The endosperm is also the site genomic imprinting occurs, and it has a distinct epigenomic landscape. This work provides a new insight that ROS1 may antagonize imprinted gene expression in the endosperm. However, it was not shown whether imprinted gene expression is indeed affected in ros1, or whether the ros1 mutation has phenotypic consequences. These results would be useful to discuss the evolution and significance of genomic imprinting.

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Reviewer comment: *“The authors did not clarify whether the observed protection to PTZ-induced convulsions after mild TBI is due to the reduced size of gap junctions and/or increased activity in hemichannels.” And “The super-resolution imaging only assesses Cx43 gap junction plaque size and density but not the non-junctional portion of Cx43.” *

Response and planned revision: To determine whether seizure protection in Cx43 S368A mice is due to reduced gap junction plaque density or reduced hemichannel function, we will conduct solubility assays to assess the ratio of insoluble (junctional) to soluble (cytoplasmic/hemichannel) Cx43 in Cx43S368A and C57BL/6 control mice after TBI/sham (as in Fig. 2A-D currently only in C57BL/6 control mice). In parallel, we will perform EtBr uptake assays in acute brain slices from Cx43S368A and C57BL/6 control animals to assess hemichannel function.

Additionally, we will include super-resolution images without background subtraction, which show diffuse staining indicative of soluble Cx43. Of note, even at super-resolution individual gap junctions or hemichannels cannot be resolved. They appear as diffuse signal (currently not visible in our super-resolution images due to image deconvolution and background substration performed to isolate Cx43 plaques). Super-resolution imaging was used to count Cx43 gap junction plaque densities and size. Cx43 gap junction plaques are dense accruals of Cx43 immunostaining reminiscent functional and closed gap junctions. Complimentary experiments measured soluble (cytoplasmic Cx43 and hemichannels) and insoluble Cx43 (gap junctions) using biochemistry (Fig. 2A-D).

Reviewer comment: “The immunofluorescent images for Fig. 2E and Fig. 5 were not counterstained for astrocytes or cell membrane. How can the authors be sure that these are expressed by astrocytes and not other cells in the brain?”

Response and planned revision: Cx43 is predominantly expressed in astrocytes, with expression levels 10–100 times higher than in brain endothelial cells (e.g., Zhang et al., 2014; Vanlandewijck et al., Nature, 2018). As shown in Supplementary Fig. 2, our immunohistochemistry data reveal no overlap between Cx43 and endothelial cell markers, confirming that our staining protocol does not detect Cx43 in endothelial cells. Instead, the apparent localization of Cx43 along blood vessels reflects expression in astrocytic endfeet, which closely ensheath the vasculature. To further support this conclusion, we will conduct quantitative co-localization analyses of Cx43 with markers for neurons, microglia, oligodendrocytes, and NG2 glia in both Cx43S368A and C57BL/6 control mice. Additionally, we will include plots generated from publicly available single-cell RNA sequencing datasets to show that Cx43 mRNA is highly enriched in astrocytes and present at much lower levels in endothelial cells of the brain vasculature.

• *

Reviewer comment about developmental contributions to the phenotype of Cx43 S368A animals.

Response: We cannot exclude a potential developmental component to the observed seizure protection in Cx43S368A mice. We included discussion of this possibility in the revised manuscript.

Reviewer comments indicative of a lack of clarity around rationale and intent of specific experiments.

Response: We thoroughly revised the Results section to explicitly state the rationale and purpose of each experiment. For example:

Reviewer comment: “The immunofluorescent images for Fig. 1D and E were taken at low resolution compared to the Cx43 puncta size. This does not allow accurate quantification of the Cx43 GJs or HCs.”

Response: The purpose of this experiment was to assess the heterogeneity of Cx43 expression (both junctional and non-junctional portions) with spatial resolution across a larger brain area. Complementary experiments here are quantification of protein amounts using western blot (Fig. 1B), quantification of junctional versus non-junctional Cx43 using the solubility assay and quantification of Cx43 plaques using super-resolution imaging (Fig. 2).

Reviewer comment: “TBI did not change Cx43 plaque size or density (Fig. 5). What was the rationale for examining the effects in the S368A mutant?”

Response: We found an increase in phosphorylated Cx43 at ____S____368 after TBI and Cx43__S368A mutants are protected from seizures after administration of PTZ suggesting an important role for this specific Cx43 phosphorylation site in pathology. __We discussed in the manuscript that “in cardiovascular infection/disease has demonstrated maintenance of gap junction coupling (Gy et al., 2011; Padget et al., 2024) while reduced hemichannel opening probability was reported (Hirschhäuser et al., 2021) in Cx43S368A mice”, suggesting that the protective phenotype is likely due to modification of either Cx43 gap junctions or hemichannels. However, functional consequences on Cx43 biology upon phosphorylation at S368 or lack thereof in the Cx43S368A mutant remain unexplored in the brain. Cx43 plaque size and density are reflective of Cx43 gap junctions and was therefore examined in Cx43S368A mice to reveal potential mechanism by which this mouse mutant is protected from seizures (even in the absence of TBI).

Reviewer comment: * “The IC50 for Tat-Gap19 for Cx43 HC is ~7 μM (Tocris). How can using it at 2 μM be effective?”*

Response: We reviewed our lab records and confirmed that 2 μM was a typographical error. The actual concentration used was 200 μM. This is consistent with the dose-response literature for astrocytes (e.g., Walrave et al., Glia 2018; Abudara et al., Front. Cell. Neurosci. 2014). We now included these references in the manuscript.

Reviewer comment: “Unclear whether mice in Fig. 4C received TBI.”

Response: We clarified that these mice were naïve, i.e. not subjected to TBI or sham procedures. This is now explicitly stated in both the Methods and the Results.

Reviewer comment: “CBX or Tat-Gap19 do not affect the phosphorylation state of Cx43.”

Response: We clarified that we used CBX and Tat-Gap19 as established gap junction and hemichannel blockers, irrespective of phosphorylation state. We now noted that Tat-GAP19 is a Cx43 mimetic peptide to specifically block Cx43 hemichannels.

Reviewer comment: “It is unclear whether the EtBr quantification in Fig. 3D is for S100β+ astrocytes.”

Response: We clarified that the quantification in Fig. 3D was performed exclusively in S100β+ astrocytes. Although neurons may take up EtBr under inflammatory conditions, they do not express Cx43 (as will be shown in Fig. 1 and Supplementary Data).

Reviewer comment: “I believe that the 'W.' in ref 'W. Chen et al., 2018' is unnecessary.”

Response: We will use the journal citation style implemented by a reference manager in the final version of the manuscript.

Reviewer request to include two references related to phosphorylation and hemichannel permeability and the role of gap junctional coupling in epilepsy.

Response: The PNAS reference was added to the manuscript.

That reduction in gap junctional communication is a relevant factor in epilepsy is discussed in the introduction where we also cite original literature of the authors of the proposed review article: “Many pathologies (Gajardo-Gómez et al., 2017; Masaki, 2015; Orellana et al., 2011; Sarrouilhe et al., 2017; Vis et al., 1998; Wang et al., 2018), including traumatic brain injury (TBI) (B. Chen et al., 2017; W. Chen et al., 2019; Wu et al., 2013; Xia et al., 2024) and acquired epilepsy (Bedner et al., 2015; Deshpande et al., 2017; Walrave et al., 2018) present with altered Cx43 regulation, and are often equated with GJ dysfunction.”

We feel that citing the original manuscripts more accurately reflect the current knowledge around the role of Cx43 in the context of epilepsy and other pathologies. Reader’s access to the original literature also highlights the gaps in knowledge more precisely that this manuscript seeks to close.

Reviewer comment: “I think the data of this manuscript is missing a control animal that would present all the compensation changes that occur during development that occur in mice carrying the mutated Cx43. Alternatively, a doable experiment would be the use of inducible KO/KI.”

Response: Previous studies investigating the role of Cx43 in neuronal excitability have primarily used full or conditional knockout models, as described in our introduction. Interestingly, these studies report that global deletion of Cx43 increases seizure susceptibility. However, such models eliminate all Cx43-dependent functions—both junctional and non-junctional—making it difficult to pinpoint the specific mechanisms underlying the observed effects. They do not distinguish whether increased excitability results from loss of gap junction coupling, disruption of hemichannel function, or depletion of cytoplasmic Cx43 signaling. In contrast, our current study does not aim to eliminate Cx43, but instead employs a targeted approach to interrogate the functional significance of a regulatory phosphorylation site, S368. This site is dynamically phosphorylated following TBI and has been previously associated—albeit only through correlative data—with seizure activity and other neuropathologies. By isolating the contribution of this post-translational modification while preserving overall Cx43 expression, our study provides novel mechanistic insight into how phosphorylation modulates Cx43 function and astrocyte-mediated regulation of brain excitability.

We appreciate the thoughtful suggestion to generate a conditional knock-in model to isolate developmental from acute effects of the Cx43 S368A mutation. However, the GJA1 gene locus is not amenable to this type of targeting (we explored this possibility with a . We also considered AAV-mediated CRISPR/dCas9 editing as an alternative, but current limitations in CNS transduction efficiency, promoter specificity, and guide RNA availability for precise point mutation insertion make this approach similarly unfeasible at this stage. Thus, while we acknowledge the developmental caveat (which we now discuss in the manuscript), the current manuscript provides novel and meaningful insight into the role of the Cx43S368 regulatory phosphorylation site in the context of astrocyte biology and seizure susceptibility and forms a strong foundation for future studies.

Thank you again for the opportunity to revise and strengthen our manuscript. We believe these planned experiments and clarifications address the reviewers' concerns in a thorough and scientifically rigorous manner.

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Referee #2

Evidence, reproducibility and clarity

This manuscript describes interesting findings on the effect of a Cx43 mutant that is not phosphorylated in Ser368. The authors did not clarify whether the observed protection to PTZ-induced convulsions after mild TBI is due to the reduced size of gap junctions and/or increased activity in hemichannels. A limitation of this work is that Cx43 S368A forms smaller gap junctions revealing an important phenotype change and therefore there is no appropriate control unless they generate a cell-specific inducible Cx43 KO.<br /> In previous studies, it has been proposed that reduction in gap junctional communication is a relevant factor in epilepsy, which is not discussed (Please see doi: 10.3390/cells12121669) in the present manuscript. Also, Bao and collaborators have demonstrated that Cx43 hemichannels phosphorlated by PKC present reduced permeability to molecules but continuos permeable to smaller molecules (doi.org/10.1073/pnas.060315410). This is an important finding that should be mentioned in the intruduuction and considered in the discussion sections.

Referee cross-commenting

Reviewer 1:

Dear Reviewer #2, The idea of performing control experiments in the point-mutant Cx43 or KO/KI mouse makes sense. If you think this is essential, then please enter it into your overall comments. However, performing this experiment will not be easily done within the one month revision time frame you proposed. Cheers.

Reviewer 2:

I think the data of this manuscript is missing a control animal that would present all the compensation changes that occur during development that occur in mice carrying the mutated Cx43. Alternatively, a doable experiment would be the use of inducible KO/KI. When comparing susceptibility to any drug it is very important to count with the best control possible. Otherwise, the results cannot be interpreted as cause-effect response.

Reviewer 1:

I agree with reviewer #2 that adding those two references will improve the ms. For the second ref mentioned, the doi link did not work; does reviewer #2 mean this ref: https://doi.org/10.1073/pnas.0603154104? "Change in permeant size selectivity by phosphorylation of connexin 43 gap-junctional hemichannels by PKC

Significance

If completed and/or interpreted carefully it could be relevant to enrich our knowledge on the importance of glial Cx43 in epilepsy.

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Referee #1

Evidence, reproducibility and clarity

Summary

Muñoz-Ballester et al. investigated the effects of TBI on Cx43 expression and function following TBI. They have examined the potential role of Cx43-containing gap junctions (GJs) and/or hemichannels (HCs), in their phosphorylated and unphosphorylated forms, in the mouse cortex. The experiments and hypotheses are simple and direct, but the results are not strong and generally correlative.

Major comments

• The immunofluorescent images for Fig. 1D and E were taken at low resolution compared to the Cx43 puncta size. This does not allow accurate quantification of the Cx43 GJs or HCs.
• The immunofluorescent images for Fig. 2E and Fig. 5 (super resolution images) were not countered stained for astrocytes (e.g. S100β) or cell membrane. How can the authors be sure that these are expressed by astrocytes and not other cells in the brain? Also, even if countered stained for astrocytes, the punctae will indicate the total but not cell surface pool of Cx43, making it difficult to interpret the impact of surface GJs and HCs in TBI.
• The IC50 for Tat-Gap19 for Cx43 HC is ~7 μM (Tocris). How can using it at 2 μM be effective?
• TBI did not change Cx43 plaque size or density (Fig. 5). What was the rationale for examining the effects in the S368A mutant?
• CBX or Tat-Gap19 do not affect the phosphorylation state of Cx43.

Minor comments

• Fig 3D: it is unclear whether the quantification is for S100β+ astrocytes or not. Is there uptake of EtBr in neurons due to inflammatory effects?
• Fig. 4C: It is unclear whether these mice have received TBI or not.
• I believe that the "W." in ref "W. Chen et al., 2018" (p.30) is unnecessary.

Referee cross-commenting

Reviewer 1:

Dear Reviewer #2, The idea of performing control experiments in the point-mutant Cx43 or KO/KI mouse makes sense. If you think this is essential, then please enter it into your overall comments. However, performing this experiment will not be easily done within the one month revision time frame you proposed. Cheers.

Reviewer 2:

I think the data of this manuscript is missing a control animal that would present all the compensation changes that occur during development that occur in mice carrying the mutated Cx43. Alternatively, a doable experiment would be the use of inducible KO/KI. When comparing susceptibility to any drug it is very important to count with the best control possible. Otherwise, the results cannot be interpreted as cause-effect response.

Reviewer 1:

I agree with reviewer #2 that adding those two references will improve the ms. For the second ref mentioned, the doi link did not work; does reviewer #2 mean this ref: https://doi.org/10.1073/pnas.0603154104? "Change in permeant size selectivity by phosphorylation of connexin 43 gap-junctional hemichannels by PKC

Significance

The strength of this study is using a single-point mutant mouse of the Cx43 to assess the role of Cx43 phosphorylation in TBI-induced seizure susceptibility, pinpointing the molecular target. One limitation is that, while the S368A mutant directly addresses the seizure susceptibility issue, pharmacological treatments like CBX and Tat-Gap19 do not test the effects of phosphorylation. Another weakness is that the key mechanism underlying the effects of TBI on Cx43 is still unclear. This is because TBI does not change Cx43 plaque size (Fig. 5), it alters EtBr dye uptake in cells that may or may not be astrocytes (Fig. 3), and it changes Cx43 solubility, but this is correlative for GJs vs HCs. The overall idea of Cx43 contributing to seizures and TBI is interesting for the general neuroscience community. However, this study can use more direct experimentation to support its hypothesis.

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Reply to the reviewers

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

Summary The authors focused on medaka retinal organoids to investigate the mechanism underlying the eye cup morphogenesis. The authors succeeded to induce lens formation in fish retinal organoids using 3D suspension culture with minimal growth factor-containing media containing the Hepes. At day 1, Rx3:H2B-GFP＋ cells appear in the surface region of organoids. At day 1.5, Prox1+cells appear in the interface area between the organoid surface and the core of central cell mass, which develops a spherical-shaped lens later. So, Prox1+ cells covers the surface of the internal lens cell core. At day 2, foxe3:GFP+ cells appear in the Prox1+ area, where early lens fiber marker, LFC, starts to be expressed. In addition, foxe3:GFP+ cells show EdU+ incorporation, indicating that foxe3:GFP+ cells have lens epithelial cell-characters. At day 4, cry:EGFP+ cells differentiate inside the spherical lens core, whose the surface area consists of LFC+ and Prox1+ cells. Furthermore, at day 4, the lens core moves towards the surface of retinal organoids to form an eye-cup like structure, although this morphogenesis "inside out" mechanism is different from in vivo cellular "outside -in" mechanism of eye cup formation. From these data, the authors conclude that optic cup formation, especially the positioning of the lens, is established in retinal organoids though the different mechanism of in vivo morphogenesis.

Overall, manuscript presentation is nice. However, there are still obscure points to understand background mechanism. My comments are shown below.

Major comments 1) At the initial stage of retinal organoid morphogenesis, a spherical lens is centrally positioned inside the retinal organoids, by covering a central lens core by the outer cell sheet of retinal precursor cells. I wonder if the formation of this structure may be understood by differential cell adhesive activity or mechanical tension between lens core cells and retinal cell sheet, just like the previous study done by Heisenberg lab on the spatial patterning of endoderm, mesoderm and ectoderm (Nat. Cell Biol. 10, 429 - 436 (2008)). Lens core cells may be integrated inside retinal cell mass by cell sorting through the direct interaction between retinal cells and lens cells, or between lens cells and the culture media. After day 1, it is also possible to understand that lens core moves towards the surface of retinal organoids, if adhesive/tensile force states of lens core cells may be change by secretion of extracellular matrix. I wonder if the authors measure physical property, adhesive activity and solidness, of retinal precursor cells and lens core cells. If retinal organoids at day 1 are dissociated and cultured again, do they show the same patterning of internal lens core covering by the outer retinal cell sheet? *Response: The question, whether different adhesive activity is involved in cell sorting and lens formation is indeed very intriguing. To address this point, we will include additional experiment (see Revision Plan, experiment 1). This experiment will be based on the dissociation and re-aggregation of lens-forming organoids as suggested by the reviewer. To monitor cell type specific sorting, we will employ a lens progenitor reporter line Foxe3::GFP and the retina-specific Rx2::H2B-RFP. If different adhesive activities of lens and retinal progenitor cells are involved and drive the process of cell sorting, dissociation and re-aggregation will result in cell sorting based on their identity. *

2) Optic cup is evaginated from the lateral wall of neuroepithelium of the diencephalon. In zebrafish, cell movement occurs from the pigment epithelium to the neural retina during eye morphogenesis in an FGF-dependent manner. How the medaka optic cup morphogenesis is coordinated? I also wonder if the authors conduct the tracking of cell migration during optic cup morphogenesis to reveal how cell migration and cell division are regulated in lens of the Medaka retinal organoids. It is also interesting to examine how retinal cell movement is coordinated during Medaka retinal organoids. Response: Looking into the detail of how optic cup-looking tissue arrangement of ocular organoids is achieved on cellular level is of course interesting. Our previous study showed that optic vesicles of medaka retinal organoids do not form optic cups (for details please see Zilova et al., 2021, eLIFE). We assume that the formation of cup-looking structure of the ocular organoids is mediated by the following processes: establishment of retina and lens domains at the specific region of the organoid – retina on the surface and lens in the center (see Figure S2 d and Figure 3e, and Figure 4). Further dislocation of the centrally formed lens towards the organoid periphery through the retina layer, places the lens to the periphery while retinal cells stay static. We assume that the “cup-like” shape is acquired by extrusion of the lens from the center of the organoid. To clarify this process with respect to tissue rearrangements and cell movements, we will include additional experiments (see Revision Plan, experiment 2) and follow lens- and retina-fated cells (by employing lens-specific Foxe3::GFP and retina-specific Rx2::H2B-RFP reporter lines) through the process of lens extrusion to dissect individual contribution of retinal/lens cells to this process (cross-reference with Reviewer #2).

3) The authors showed that blockade of FGF signaling affects lens fiber differentiation in day 1-2, whereas lens formation seems to be intact in the presence of FGF receptor inhibitor in day 0-1. I suggest the authors to examine which tissue is a target of FGF signaling in retinal organoids, using markers such as pea3, which is a downstream target of ERK branch of FGF signaling. Since FGF signaling promotes cell proliferation, is the lens core size normal in SU5402-treated organoids from day 0 to day 1?

Response: Assessing the activity of FGF signaling (cross-reference to Reviewer #3) in the organoids is indeed an important point. To address which tissue is the target of FGF signaling we will include additional experiments and assess the phosphorylation status of ERK (pERK) and expression of the ERK downstream target pea3, as suggested by the reviewer (see Revision Plan, experiment 3). That will allow to identify the tissue within the organoid responding to the Fgf signaling.

Lens core size of organoids treated with SU5402 from day 0 to day 1 is fully comparable to the control (please see Figure 6b).

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4) Fig. 3f and 3g indicate that there is some cell population located between foxe3:GFP+ cells and rx2:H2B-RFP+ cells. What kind of cell-type is occupied in the interface area between foxe3:GFP+ cells and rx2:H2B-RFP+ cells?

Response: That is for sure an interesting question. We are aware of this population of cells. We currently do not have data that would with certainty clarify the fate of those cells. We are currently following up on that question with the use of scRNA sequencing, however we will not be able to address this question in the current manuscript.* * 5) Fig. 5e indicates the depth of Rx3 expression at day 1. Is the depth the thickness of Rx3 expressing cell sheet, which covers the central lens core in the organoids? If so, I wonder if total cell number of Rx3 expressing cell sheet may be different in each seeded-cell number, because thickness is the same across each seeded-cell number, but the surface area size may be different depending on underneath the lens core size. Please clarify this point.

*Response: Yes. Figure 5e indicates the thickness of the cell sheet expressing Rx3 that lies on the surface of the organoid. Indeed, the number of Rx3-expressing cells (and lens cells) scales with the size of the organoid as stated in the submitted manuscript. *

• *

6) Noggin application inhibits lens formation at day 0-1. BMP signaling regulates formation of lens placode and olfactory placode at the early stage of development. It is interesting to examine whether Noggin-treated organoid expands olfactory placode area. Please check forebrain territory markers.

Response: What tissue differentiates at the expense of the lens in BMP inhibitor-treated organoids is of course an intriguing question. To address the identity of cells differentiated under this condition we will include an additional experiment (see Revision Plan, experiment 4 as suggested by the reviewer). We will check for the expression of Lhx2, Otx2 and Huc/D to address this point.

I have no minor comments

**Referees cross-commenting**

I agree that all reviewers have similar suggestions, which are reasonable and provided the same estimated time for revision.

Reviewer #1 (Significance (Required)):

Strength: This study is unique. The authors examined eye cup morphogenesis using fish retinal organoids. Eye cup normally consists of the lens, the neural retina, pigment epithelium and optic stalk. However, retinal organoids seem to be simple and consists of two cell types, lens and retina. Interestingly, a similar optic cup-like structure is achieved in both cases; however, underlying mechanism is different. It is interesting to investigate how eye morphogenesis is regulated in retinal organoids,under the unconstrained embryo-free environment.

Limitation: Description is OK, but analysis is not much profound. It is necessary to apply a bit more molecular and cellular level analysis, such as tracking of cell movement and visualization of FGF signnaling in organoid tissues.

Advancement: The current study is descriptive. Need some conceptual advance, which impact cell biology field or medical science.

Audience: The target audience of current study are still within ophthalmology and neuroscience community people, maybe translational/clinical rather than basic biology. To beyond specific fields, need to formulate a general principle for cell and developmental biology.

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

In this study from Stahl et al., the authors demonstrate that medaka pluripotent embryonic cells can self-organise into eye organoids containing both retina and lens tissues. While these organoids can self-organize into an eye structure that resembles the vertebrate eye, they are built from a fundamentally different morphogenetic process - an "inside-out" mechanism where the lens forms centrally and moves outward, rather than the normal "outside-in" embryonic process. This is a very interesting discovery, both for our understanding of developmental biology and the potential for tissue engineering applications. The study would benefit from some additional experiments and a few clarifications.

The authors suggest that the lens cells are the ones that move from the central to a more superficial position. Is this an active movement of lens cells or just the passive consequence of the retina cells acquiring a cup shape? Are the retina cells migrating behind the lens or the lens cells pushing outwards? High-resolution imaging of organoid cup formation, tracking retina cells in combination with membrane labeling of all cells would help elucidate the morphogenetic processes occurring in the organoids. Membrane labeling would also be useful as Prox1 positive lens cells appear elongated in embryos while in the organoids, cell shapes seem less organised, less compact and not elongated (for example as shown in Fig 3f,g).

Response: Looking into the detail of how optic cup-looking tissue arrangement of ocular organoids is achieved on cellular level is of course interesting. We assume that the formation of cup-looking structures of the ocular organoids is mediated by following processes: establishment of retina and lens domains at a specific region of the organoid – retina on the surface and lens in the center (see Figure S2 d and Figure 3e, and Figure 4). Further dislocation of centrally formed lenses towards the organoid periphery through the retina layer, place the lens to the periphery while retinal cells stay static. We assume that the “cup-like” shape is acquired by extrusion of the lens. To clarify this process with respect to tissue rearrangements and cell movements, we will include additional experiments (see Revision Plan, experiment 2). We will follow lens- and retina-fated cells (by employing lens-specific Foxe3::GFP and retina-specific Rx2::H2B-RFP reporter lines) through the process of lens extrusion to dissect the individual contribution of retinal/lens cells to this process (cross-reference with Reviewer #1).

The organoids could be a useful tool to address how cell fate is linked to cell shape acquisition. In the forming organoids, retinal tissue initially forms on the outside, while non-retinal tissue is located in the centre; this central tissue later expresses lens markers. Do the authors have any insights into why fate acquisition occurs in this pattern? Is there a difference in proliferation rates between the centrally located cells and the external ones? Could it be that highly proliferative cells give rise to neural retina (NR), while lower proliferating cells become lens? *Response: The question how is the retinal and lens domain established in this specific manner is indeed intriguing and very interesting. We dedicated a part of the discussion to this topic. We discuss the role of the diffusion limit and the potential contribution of BMB and FGF signaling to this arrangement. Additional experiments (see Revision Plan, experiment 3) addressing the source and target tissues of FGF and BMP signaling in the organoid will ultimately bring more clarity to our understanding of the tissue arrangements in the organoid. *

*Although analysis of the proliferation rate of the cells at the surface and in the central region of the organoid might possibly show some differences in the proliferation rates between lens and retinal cells, we do not have any indications, that the proliferation rate itself would be instructive or superior to the cell fate decisions. *

What happens in organoids that do not form lenses? Do these organoids still generate foxe3 positive cells that fail to develop into a proper lens structure? And in the absence of lens formation, does the retina still acquire a cup shape?

*Response: Lens formation is primarily dependent on acquisition/specification of Foxe3-expressing lens placode progenitors. If those are not present, a lens does not develop. Once Foxe3-expressing progenitors are established, a lens is formed in unperturbed conditions (measured by the presence of expression of crystallin proteins). In such conditions, organoids that do not have a lens, do not carry Foxe3-expressing cells. *

*In the absence of the lens, the organoid is composed of retinal neuroepithelium, that does not form an optic cup (for details of such phenotypes please see Zilova et al., 2021, eLIFE). *

The author suggest that lens formation occurs even in the absence of Matrigel. Is the process slower in these conditions? Are the resulting organoids smaller? While there are indeed some LFC expressing cells by day2, these cells are not very well organised and the pattern of expression seems dotty. Moreover, LFC staining seems to localise posterior to the LFC negative, lens-like structure (e.g. Fig.S1 3o'clock). How do these organoids develop beyond day 4? Do they maintain their structural integrity at later stages? The role of HEPES in promoting organoid formation is intriguing. Do the authors have any insights into why it is important in this context? Have the authors tried other culture conditions and does culture condition influence the morphogenetic pathways occurring within the organoids? *Response: We thank the reviewer for pointing this out. We were not clear in the wording and describing of our observation. Indeed, Matrigel is not required for acquisition of lens fate, which can be demonstrated with the expression of lens-specific markers. However, the presence of Matrigel has a profound impact on the structural aspects of organoid formation. Matrigel is essential for organization of retinal-committed cells into the retinal epithelium (Zilova et al., 2021, eLIFE). The absence of the structure of the retinal epithelium can indeed negatively impact on the cellular organization and the overall lens structure. To clarify the contribution of the Matrigel to the speed of organoid lens development and to the overall structure of the organoid lens we will perform additional experiments (see Revision Plan, experiment 5). With the use of Foxe3::GFP reporter line we will measure the onset of the lens-specific gene expression. In addition, we will use the immunohistochemistry to assess the gross morphology and size of the organoids grown without the Matrigel (cross-reference with Reviewer #3). *

*The role of the HEPES in lens formation is indeed very intriguing and currently under investigation. As HEPES is mainly used to regulate pH of the culture media and pH might have an impact on multiple cellular processes, it will require significant time investment to dissect molecular mechanism underlying the effect of HEPES on the process of lens formation (cross reference with Reviewer #3) and therefore cannot be addressed in the current manuscript. *

**Referees cross-commenting** Pleased to see that all the other reviewers are positive about the study and raise similar concerns and comments

Reviewer #2 (Significance (Required)):

This is a very interesting paper, and it will be important to determine whether this alternative morphogenetic process is specific to medaka or if similar developmental routes can be recapitulated in organoid cultures from other vertebrate species.

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

Summary: The manuscript by Stahl and colleagues reports an approach to generate ocular organoids composed of retinal and lens structures, derived from Medaka blastula cells. The authors present a comprehensive characterisation of the timeline followed by lens and retinal progenitors, showing these have distinct origins, and that they recapitulate the expression of differentiation markers found in vivo. Despite this molecular recapitulation, morphogenesis is strikingly different, with lens progenitors arising at the centre of the organoid, and subsequently translocating to the outside.

Comments:

-The manuscript presents a beautiful set of high quality images showing expression of lens differentiation markers over time in the organoids. The set of experiments is very robust, with high numbers of organoids analysed and reproducible data. The mechanism by which lens specification is promoted in these organoids is, however, poorly analysed, and the reader does not get a clear understanding of what is different in these experiments, as compared to previous attempts, to support lens differentiation. There is a mention to HEPES supplementation, but no further analysis is provided, and the fact that the process is independent of ECM contradicts, as the authors point out, previous reports. The manuscript would benefit from a more detailed analysis of the mechanisms that lead to lens differentiation in this setting.

*Response: The role of the HEPES in lens formation is indeed very intriguing and under current investigation. As HEPES is mainly used to regulate pH of the culture media and pH might have an impact on multiple cellular processes it will require a significant time investment to dissect molecular mechanism underlying the effect of HEPES on the process of lens formation (cross reference with Reviewer #2) and therefore unfortunately cannot be addressed in the current manuscript. *

*To clarify the contribution of the Matrigel to the organoid lens development we will perform additional experiments (see Revision Plan, experiment 5). With the use of Foxe3::GFP reporter line we will measure the onset of the lens-specific gene expression. In addition, we will use the immunohistochemistry to assess the gross morphology and size of the organoids grown without the Matrigel (cross-reference with Reviewer #2). * -The markers analysed to show onset of lens differentiation in the organoids seem to start being expressed, in vivo, when the lens placode starts invaginating. An analysis of earlier stages is not presented. This would be very informative, allowing to determine whether progenitors differentiate as placode and neuroepithelium first, to subsequently continue differentiating into lens and retina, respectively. Could early placodal and anterior neural plate markers be analysed in the organoids? This would provide a more complete sequence of lens vs retina differentiation in this model.

Response: Yes. The figures show the expression of lens and retinal markers in the embryo in later developmental stages and the timing of their expression can be documented with higher temporal resolution. In the revised version of the manuscript, we will provide the information about the onset of expression of Rx3::H2B-GFP (retina) and Foxe3::GFP (lens) (see attached figure). Rx3 represents one of the earlies markers labeling the presumptive eye field within the region of the anterior neural plate (S16, late gastrula). FoxE3::GFP expression can be detected within the head surface ectoderm before the lens placode is formed showing that Foxe3 is a suitable marker of placodal progenitors in medaka.

*We are convinced that the onset of Rx3 and Foxe3-driven reporters is early enough to make the claim about the separate origin of the lens (placodal) and retinal (anterior neuroectoderm) tissues within the ocular organoids. *

-The analysis of BMP and Fgf requirement for lens formation and differentiation is suggestive, but the source of these signals is not resolved or mentioned in the manuscript. Are BMP4 and Fgf8 expressed by the organoids? Where are they coming from?

Response: Indeed, addressing the source of BMP and FGF activation would bring more clarity in understanding the mechanism of retina/lens specification within the ocular organoids (cross reference with Reviewer #1). To address this point, we will include additional experiments (see Revision Plan, experiment 3). We will analyze the expression of respective ligands (Bmp4 and Fgf8) and activation of downstream effectors of BMP and FGF signaling pathways within the ocular organoids as suggested by Reviewer #1 and Reviewer #3.

• *

-The fact that the lens becomes specified in the centre of the organoid is striking, but it is for me difficult to visualise how it ends up being extruded from the organoid. Did the authors try to follow this process in movies? I understand that this may be technically challenging, but it would certainly help to understand the process that leads to the final organisation of retinal and lens tissues in the organoid. There is no discussion of why the morphogenetic mechanism is so different from the in vivo situation. The manuscript would benefit from explicitly discussing this. Response: Following the extruding lens in vivo is indeed very relevant suggestion. To clarify the process of ocular organoid formation in the respect of tissue rearrangements and cell movements, we will include additional experiment (see Revision Plan, experiment 2). We will follow lens- and retina-fated cells (by employing lens-specific Foxe3::GFP and retina-specific Rx2::H2B-RFP reporter lines) through the process of lens extrusion (cross-reference with Reviewer #1 and Reviewer #2).

**Referees cross-commenting**

We all seem to have similar comments and concerns. I think overall the suggestions are feasible and realistic for the timeframe provided.

Reviewer #3 (Significance (Required)):

This study describes a reproducible approach to differentiate ocular organoids composed of lens and retinal tissues. The characterisation of lens differentiation in this model is very detailed, and despite the morphogenetic differences, the molecular mechanisms show many similarities to the in vivo situation. The manuscript however does not highlight, in my opinion, why this model may be relevant. Clearly articulating this relevance, particularly in the discussion, will enhance the study and provide more clarity to the readers regarding the significance of the study for the field of organoid research, ocular research and regenerative studies.

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

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

Summary: The manuscript by Stahl and colleagues reports an approach to generate ocular organoids composed of retinal and lens structures, derived from Medaka blastula cells. The authors present a comprehensive characterisation of the timeline followed by lens and retinal progenitors, showing these have distinct origins, and that they recapitulate the expression of differentiation markers found in vivo. Despite this molecular recapitulation, morphogenesis is strikingly different, with lens progenitors arising at the centre of the organoid, and subsequently translocating to the outside.

Comments:

• The manuscript presents a beautiful set of high quality images showing expression of lens differentiation markers over time in the organoids. The set of experiments is very robust, with high numbers of organoids analysed and reproducible data. The mechanism by which lens specification is promoted in these organoids is, however, poorly analysed, and the reader does not get a clear understanding of what is different in these experiments, as compared to previous attempts, to support lens differentiation. There is a mention to HEPES supplementation, but no further analysis is provided, and the fact that the process is independent of ECM contradicts, as the authors point out, previous reports. The manuscript would benefit from a more detailed analysis of the mechanisms that lead to lens differentiation in this setting.
• The markers analysed to show onset of lens differentiation in the organoids seem to start being expressed, in vivo, when the lens placode starts invaginating. An analysis of earlier stages is not presented. This would be very informative, allowing to determine whether progenitors differentiate as placode and neuroepithelium first, to subsequently continue differentiating into lens and retina, respectively. Could early placodal and anterior neural plate markers be analysed in the organoids? This would provide a more complete sequence of lens vs retina differentiation in this model.
• The analysis of BMP and Fgf requirement for lens formation and differentiation is suggestive, but the source of these signals is not resolved or mentioned in the manuscript. Are BMP4 and Fgf8 expressed by the organoids? Where are they coming from?
• The fact that the lens becomes specified in the centre of the organoid is striking, but it is for me difficult to visualise how it ends up being extruded from the organoid. Did the authors try to follow this process in movies? I understand that this may be technically challenging, but it would certainly help to understand the process that leads to the final organisation of retinal and lens tissues in the organoid. There is no discussion of why the morphogenetic mechanism is so different from the in vivo situation. The manuscript would benefit from explicitly discussing this.

Referees cross-commenting

We all seem to have similar comments and concerns. I think overall the suggestions are feasible and realistic for the timeframe provided.

Significance

This study describes a reproducible approach to differentiate ocular organoids composed of lens and retinal tissues. The characterisation of lens differentiation in this model is very detailed, and despite the morphogenetic differences, the molecular mechanisms show many similarities to the in vivo situation. The manuscript however does not highlight, in my opinion, why this model may be relevant. Clearly articulating this relevance, particularly in the discussion, will enhance the study and provide more clarity to the readers regarding the significance of the study for the field of organoid research, ocular research and regenerative studies.

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 study from Stahl et al., the authors demonstrate that medaka pluripotent embryonic cells can self-organise into eye organoids containing both retina and lens tissues. While these organoids can self-organize into an eye structure that resembles the vertebrate eye, they are built from a fundamentally different morphogenetic process - an "inside-out" mechanism where the lens forms centrally and moves outward, rather than the normal "outside-in" embryonic process. This is a very interesting discovery, both for our understanding of developmental biology and the potential for tissue engineering applications. The study would benefit from some additional experiments and a few clarifications. The authors suggest that the lens cells are the ones that move from the central to a more superficial position. Is this an active movement of lens cells or just the passive consequence of the retina cells acquiring a cup shape? Are the retina cells migrating behind the lens or the lens cells pushing outwards? High-resolution imaging of organoid cup formation, tracking retina cells in combination with membrane labeling of all cells would help elucidate the morphogenetic processes occurring in the organoids. Membrane labeling would also be useful as Prox1 positive lens cells appear elongated in embryos while in the organoids, cell shapes seem less organised, less compact and not elongated (for example as shown in Fig 3f,g). The organoids could be a useful tool to address how cell fate is linked to cell shape acquisition. In the forming organoids, retinal tissue initially forms on the outside, while non-retinal tissue is located in the centre; this central tissue later expresses lens markers. Do the authors have any insights into why fate acquisition occurs in this pattern? Is there a difference in proliferation rates between the centrally located cells and the external ones? Could it be that highly proliferative cells give rise to neural retina (NR), while lower proliferating cells become lens?

What happens in organoids that do not form lenses? Do these organoids still generate foxe3 positive cells that fail to develop into a proper lens structure? And in the absence of lens formation, does the retina still acquire a cup shape?

The author suggest that lens formation occurs even in the absence of Matrigel. Is the process slower in these conditions? Are the resulting organoids smaller? While there are indeed some LFC expressing cells by day2, these cells are not very well organised and the pattern of expression seems dotty. Moreover, LFC staining seems to localise posterior to the LFC negative, lens-like structure (e.g. Fig.S1 3o'clock). How do these organoids develop beyond day 4? Do they maintain their structural integrity at later stages? The role of HEPES in promoting organoid formation is intriguing. Do the authors have any insights into why it is important in this context? Have the authors tried other culture conditions and does culture condition influence the morphogenetic pathways occurring within the organoids?

Referees cross-commenting

Pleased to see that all the other reviewers are positive about the study and raise similar concerns and comments

Significance

This is a very interesting paper, and it will be important to determine whether this alternative morphogenetic process is specific to medaka or if similar developmental routes can be recapitulated in organoid cultures from other vertebrate species.

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 focused on medaka retinal organoids to investigate the mechanism underlying the eye cup morphogenesis. The authors succeeded to induce lens formation in fish retinal organoids using 3D suspension culture with minimal growth factor-containing media containing the Hepes. At day 1, Rx3:H2B-GFP＋ cells appear in the surface region of organoids. At day 1.5, Prox1+cells appear in the interface area between the organoid surface and the core of central cell mass, which develops a spherical-shaped lens later. So, Prox1+ cells covers the surface of the internal lens cell core. At day 2, foxe3:GFP+ cells appear in the Prox1+ area, where early lens fiber marker, LFC, starts to be expressed. In addition, foxe3:GFP+ cells show EdU+ incorporation, indicating that foxe3:GFP+ cells have lens epithelial cell-characters. At day 4, cry:EGFP+ cells differentiate inside the spherical lens core, whose the surface area consists of LFC+ and Prox1+ cells. Furthermore, at day 4, the lens core moves towards the surface of retinal organoids to form an eye-cup like structure, although this morphogenesis "inside out" mechanism is different from in vivo cellular "outside -in" mechanism of eye cup formation. From these data, the authors conclude that optic cup formation, especially the positioning of the lens, is established in retinal organoids though the different mechanism of in vivo morphogenesis.

Overall, manuscript presentation is nice. However, there are still obscure points to understand background mechanism. My comments are shown below.

Major comments

1. At the initial stage of retinal organoid morphogenesis, a spherical lens is centrally positioned inside the retinal organoids, by covering a central lens core by the outer cell sheet of retinal precursor cells. I wonder if the formation of this structure may be understood by differential cell adhesive activity or mechanical tension between lens core cells and retinal cell sheet, just like the previous study done by Heisenberg lab on the spatial patterning of endoderm, mesoderm and ectoderm (Nat. Cell Biol. 10, 429 - 436 (2008)). Lens core cells may be integrated inside retinal cell mass by cell sorting through the direct interaction between retinal cells and lens cells, or between lens cells and the culture media. After day 1, it is also possible to understand that lens core moves towards the surface of retinal organoids, if adhesive/tensile force states of lens core cells may be change by secretion of extracellular matrix. I wonder if the authors measure physical property, adhesive activity and solidness, of retinal precursor cells and lens core cells. If retinal organoids at day 1 are dissociated and cultured again, do they show the same patterning of internal lens core covering by the outer retinal cell sheet?
2. Optic cup is evaginated from the lateral wall of neuroepithelium of the diencephalon. In zebrafish, cell movement occurs from the pigment epithelium to the neural retina during eye morphogenesis in an FGF-dependent manner. How the medaka optic cup morphogenesis is coordinated? I also wonder if the authors conduct the tracking of cell migration during optic cup morphogenesis to reveal how cell migration and cell division are regulated in lens of the Medaka retinal organoids. It is also interesting to examine how retinal cell movement is coordinated during Medaka retinal organoids.
3. The authors showed that blockade of FGF signaling affects lens fiber differentiation in day 1-2, whereas lens formation seems to be intact in the presence of FGF receptor inhibitor in day 0-1. I suggest the authors to examine which tissue is a target of FGF signaling in retinal organoids, using markers such as pea3, which is a downstream target of ERK branch of FGF signaling. Since FGF signaling promotes cell proliferation, is the lens core size normal in SU5402-treated organoids from day 0 to day 1?
4. Fig. 3f and 3g indicate that there is some cell population located between foxe3:GFP+ cells and rx2:H2B-RFP+ cells. What kind of cell-type is occupied in the interface area between foxe3:GFP+ cells and rx2:H2B-RFP+ cells?
5. Fig. 5e indicates the depth of Rx3 expression at day 1. Is the depth the thickness of Rx3 expressing cell sheet, which covers the central lens core in the organoids? If so, I wonder if total cell number of Rx3 expressing cell sheet may be different in each seeded-cell number, because thickness is the same across each seeded-cell number, but the surface area size may be different depending on underneath the lens core size. Please clarify this point.
6. Noggin application inhibits lens formation at day 0-1. BMP signaling regulates formation of lens placode and olfactory placode at the early stage of development. It is interesting to examine whether Noggin-treated organoid expands olfactory placode area. Please check forebrain territory markers.

I have no minor comments

Referees cross-commenting

I agree that all reviewers have similar suggestions, which are reasonable and provided the same estimated time for revision.

Significance

Strength: This study is unique. The authors examined eye cup morphogenesis using fish retinal organoids. Eye cup normally consists of the lens, the neural retina, pigment epithelium and optic stalk. However, retinal organoids seem to be simple and consists of two cell types, lens and retina. Interestingly, a similar optic cup-like structure is achieved in both cases; however, underlying mechanism is different. It is interesting to investigate how eye morphogenesis is regulated in retinal organoids,under the unconstrained embryo-free environment.

Limitation: Description is OK, but analysis is not much profound. It is necessary to apply a bit more molecular and cellular level analysis, such as tracking of cell movement and visualization of FGF signnaling in organoid tissues.

Advancement: The current study is descriptive. Need some conceptual advance, which impact cell biology field or medical science.

Audience: The target audience of current study are still within ophthalmology and neuroscience community people, maybe translational/clinical rather than basic biology. To beyond specific fields, need to formulate a general principle for cell and developmental biology.

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

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Reply to the reviewers

__We thank the reviewers for the supportive suggestions and comments. We have addressed all comments underneath the original text in red. As suggested, we added to line numbers to the text and use these numbers to refer to the changes made. __

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

The manuscript is well written and presents solid data, most of which is statistically analyzed and sound. Given that the author's previous comprehensive publications on seipin organization and interactions, it might be beneficial (particularly in the title and abstract) to emphasize that this manuscript focuses on the metabolic regulation of lipid droplet assembly by Ldb16, to distinguish it from previous work. Perhaps one consideration, potentially interesting, involves changes in lipid droplet formation under the growth conditions used for galactose-mediated gene induction.

We thank the reviewer for the supportive comments and suggestions.

Comments: (1) Fig. 3 and 4. The galactose induction of lipid droplet biogenesis in are1∆/2∆ dga1∆ lro1∆ cells though activation of a GAL1 promoter fusion to DGA1 is a sound approach for regulating lipid droplet formation. Although unlikely, carbon sources can impact lipid droplet proliferation and (potentially interesting) metabolic changes under growth in non-fermentable carbon sources may impact lipid droplet biogenesis; in fact, oleate has significant effects (e.g. PMID: 21422231; PMID: 21820081). The GAL1 promoter is a very strong promoter and the overexpression of DGA1 via this heterologous promoter might itself cause unforeseen changes. Affirmation of the results using another induction system might be beneficial.

We thank the reviewer for these suggestions. In this study we focused on the organisation of the yeast seipin complex during the process of LD formation. We chose to use galactose-based induction of Dga1 because this is a well-established and widely used assay in the field, extensively characterized by many groups over the years. The tight control it provides, enabling synchronous and rapid LD induction, makes it the method of choice for many researchers. Importantly, the LDs formed using this assay are morphologically normal and involve the same components as LDs formed under other conditions.

Regarding the role of metabolism in LD formation, it is worth noting that galactose is metabolized by yeast primarily through fermentation, following its conversion to UDP-glucose. Therefore, its use does not involve drastic metabolic changes. The impact of metabolism in LD biogenesis is an interesting question but it falls beyond the scope of the current study.

(2) Fig. 3B. Although only representative images are shown, the panel convincingly shows that lipid droplets do form upon galactose induction of DGA1 in are1∆/2∆ dga1∆ lro1∆ cells. However, it does not show to what extent. Are lipid droplets synthesized at WT levels? How many cells were counted? How many lipid droplets per cell? Is there a statistical difference with respect to WT cells?

We did not assess these parameters in this study. The aim of the study was to assess the relations between components of the seipin complex with and without lipid droplets. For this purpose, inducing lipid droplet formation over a 4-hour period was sufficient to address that specific question. As mentioned above, LDs formed using this assay are morphologically normal and involve the same components as LDs formed under other conditions. This being said, it is known that prolonged overexpression of Dga1 (> 12hours) can lead to enlarged LDs.

(3) Fig. 2D. It is not clear how standard deviation can be meaningfully applied to two data points, let alone providing a p-value. For some of these experiments, triplicate trials might provide a more robust statistical sampling.

We thank the reviewer for this suggestion. We have added 2 more repeats to the Co-IP in figure 2.

Reviewer #1 (Significance (Required)):

Klug and Carvalho report on the lipid droplet architecture of the yeast seipin complex. Specifically, the mechanism of yeast seipin Sei1 binding to Ldo16 and the subsequent recruitment of Ldb45 is analyzed. These results follow from a recent publication (PMID: 34625558) from the same authors and aims to define a more precise role for the components of the seipin complex. Using photo-crosslinking, Ldo45 and Ldo16 interactions are analyzed in the context of lipid droplet assembly.

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

Summary:

Klug and Carvalho apply a photo-crosslinking approach, which has been extensively used in the Carvalho group, to investigate the subunit interactions of the seipin complex in yeast. The authors apply this approach to further study possible changes within the seipin complex following induction of neutral lipid synthesis and lipid droplet (LD) formation. The authors propose that Ldo45 makes contact with Ldb16 and that the seipin complex subunits assemble even in the absence of LDs.

Major comments:

Overall, this is a focused and well-executed study on one of the fundamental structural components of LDs. The study addresses the subunit interactions of the seipin complex but does not look into their functional consequences, for example how the mutations on Ldb16 that affect its interaction with Ldo45, influence LD formation; similarly, the authors make the interesting observation that Ldo16 may be differentially affected by the lack of neutral lipids (Fig. 3A) but this observation is not explored.

We thank the reviewer for this comment. The Ldb16 mutations analyzed in this study have been previously characterized by us (see Klug et al., 2021 – Figure 3) and exhibit a mild defect in lipid droplet (LD) formation. This phenotype is unlikely to result from impaired Ldo16/45 recruitment, as deletion of Ldo proteins causes only a very mild effect on LD formation (as shown in Teixeira et al., 2018 and Eisenberg-Bord et al., 2018).

We agree that the differential effect on Ldo proteins by the absence of neutral lipids is particularly interesting. However, its exploration falls outside of the scope of the current study and should be thoroughly investigated in the future.

1. For the crosslinking pull-downs (Fig. 1), it seems that the authors significantly overexpress (ADH1 promoter) the Ldb16 subunit that carries the various photoreactive amino acid residues, while keeping the other (tagged) seipin complex members at endogenous levels. Would not this imbalance affect the assembly of the complex and therefore the association of the different subunits with each other?

We thank the reviewer for this comment. The in vivo site-specific crosslinking is highly sensitive methodology to detect protein-protein interactions in a position-dependent manner. However, one of the caveats of the approach is the low efficiency of amber stop codon suppression and BPA incorporation. To mitigate this limitation, we (and others) induce the expression of the amber-containing protein (in this case Ldb16) from a strong constitutive promoter such as ADH1. Therefore, despite using a strong promoter, the overall levels of LDB16 remain comparable to endogenous levels due to the inherently low efficiency of amber suppression. Moreover, it is known that when not bound to Sei1, Ldb16 is rapidly degraded in a proteasome dependent manner (Wang, C.W. 2014), further preventing its accumulation.

Although the authors do show delta4 cells with no LDs (Fig. 3B, 0h), galactose-inducible systems in yeast are known to be leaky. Given that the authors' conclusion that the complex is "pre-assembled" irrespective of the addition of galactose, I think it would be important to confirm biochemically that there is no neutral lipid at time point 0. Alternatively, it may be better to simply compare wt vs dga1 lro1 or are1are2 mutants - there is no need for GAL induction since the authors look at one time point only.

Among the various regulable promoters, GAL1 shows a superior level of control. For example, expression of essential genes from GAL1 promoter frequently leads to cell death in glucose containing media, a condition that represses GAL1 promoter. Having said this, we cannot exclude that minute amounts of DGA1 are expressed prior galactose induction. However, if this is the case, the resulting levels of TAG are insufficient to be detected by sensitive lipid dyes and to induce LDs, as noted by the reviewer. Therefore, we believe our conclusions remain valid. This is consistent that we use in the text, where we refer to LD formation rather than complete loss of neutral lipids. To make this absolutely clear we replaced the word “presence” to “abundance” in line 236.

Lastly, we do not agree with the reviewer that using double mutants (are1/2 or dga1/lro1 mutants) would be sufficient since these mutations are not sufficient to abolish LD formation – a key aspect of this study. The GAL1 system allows us to monitor 2 time points in the same cells –no LDs (time 0h) and with LDs (Time 4h). The system proposed by the reviewer would only allow a snap shot of steady state levels in different cells rather than within the same cell culture.

Some methodological issues could be better detailed. For example, which of the three delta4 strains was used to induce neutral lipid in Fig. 4B? How exactly were the quantifications in Fig. 4D performed (I assume they were done under non-saturating band intensity conditions, as for some residues it is difficult to conclude whether the blot aligns with the quantification results).

We thank the reviewer for these comments. We have clarified the strain number in the figure legend of figure 4B (strain yPC12630).

We have also added the following text in rows 437-441 in the methods section: “Reactive bands were detected by ECL (Western Lightning ECL Pro, Perkin Elmer #NEL121001EA), and visualized using an Amersham Imager 600 (GE Healthcare Life Sciences). Data quantification was performed using Image Studio software (Li-Cor) to measure line intensity under non saturating conditions.”

"our findings support the notion that Ldo45 is important for early steps of LD formation as previously proposed" I find this statement confusing given that the authors claim that Ldo45 is already bound to the complex before LD formation.

We thank the reviewer for raising this important point. We believe that our findings support previous hypotheses on the role of Ldo45. It has been suggested that Ldo45 is important for the early stages of lipid droplet (LD) formation (Teixeira et al., 2018; Eisenberg-Bord et al., 2018). As such, Ldo45 would need to be recruited to the seipin complex before or at the onset of LD formation. The observation that Ldo45 is present at the complex prior to LD formation provides strong support for its role in the initial steps of this process.

To clarify this idea in the manuscript, we have revised the sentence on line 310 as follows:

“Irrespective of the mechanism, our findings support the notion that Ldo45 plays a role in the early steps of LD formation, as previously proposed…”

The model in Fig. 5 is essentially the same as the one shown in Fig. 1G.

To aid the reader and avoid confusion, we intentionally used a similar color scheme throughout the manuscript. This may contribute to the perception that the figures are very similar. However, there are clear distinctions between them. In Figure 1G, we summarize our findings regarding the positioning of Ldo45 within the complex and note that we do not yet have data on Ldo16. Building upon these findings, in Figure 5 we speculate where Ldo16 might interact with Ldb16 and highlight that recruitment of both Ldo16 and Ldo45 increases with neutral lipid availability.

Therefore, we believe that both figures serve distinct and complementary purposes, and that each is useful for communicating our overall message.

Minor comments

In the pull-downs in Fig. 2C, it seems that full-length Ldb16 is not enriched after the FLAG IP. What is the reason of this?

We thank the reviewer for raising this interesting aspect. We do not know why this occurs, but it is clear that full length Ldb16 is not efficiently pulled down. We could speculate that this has to do with access to the FLAG moiety at the C terminus that may become inaccessible due to interactions or folding in the long unstructured C-terminus of Ldb16. This might explain why when we truncate the C terminus in the 1-133 mutant we achieve a more efficient IP.

At the blots at Fig. 2C and 3A, the anti-Dpm1 Ab seems to recognize in the IP fractions a band labelled as non-specific, however this band is absent from the input.

We thank the reviewer for raising this. This non-specific band is the light chain of the antibody used in the pull down that detaches from the matrix during elution – thus not found in the input. This is a common non-specific band that appears in Co-IP blots.

Reviewer #2 (Significance (Required)):

Regulation of seipin function is essential for proper LD biogenesis in eukaryotes, so this study addresses a fundamental question in the field. As stated above some functional analysis that goes beyond the biochemistry would be beneficial. There is some overlap with a recently published paper from the Wang group that also examines the assembly of seipin in yeast.

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

The manuscript by Klug and Carvalho investigates the interaction of the yeast seipin complex (Sei1 and Ldb16) with Ldo45 and Ldo16. Using a site-specific photocrosslinking approach, the authors map some residues of the seipin complex in contact Ldo45, demonstrating that Ldo45 likely binds to Ldb16 in the center of the Sei1-Ldb16 complex. They find that both Ldo45 and Ldo16 copurify with Ldb16. Complex assembly is demonstrated to occur independently of the presence of neutral lipids. An Ldb16 mutant, harbouring the transmembrane domain (1-133) but lacking the cytosolic region (previously shown to allow normal LD formation and still bind to Sei1) showed photocrosslinks with Ldo45, but not Ldo16. No crosslinks between Sei1 and either Ldo45 or Ldo16 were detected.

Major: 1. Figure 2 shows CoIPs using different Ldb16 mutants/truncations to test for binding of Ldo45 and Ldo16. Both Ldo16 and Ldo45 copurify with full length Ldb16. Loss of the cytosolic part of Ldb16 strongly reduced binding of both Ldo45 and Ldo16, indicating that the TM-Helix-TM domain of Ldb16 (1-133) alone is not sufficient for proper binding of Ldo45 or Ldo16. The quantifications (2D and 2E) presented for this CoIP represent a n=2 with mean, standard deviation and statistics. To be a meaningful statistical analysis, the authors need to increase their n to at least n=3. In addition, they refer to the statistics they use here as "two-sided Fischer's T-test" in the respective Figure legend. To my knowledge, there is no such test, either it is Student's T-test or Fischer's exact test? Can the authors please clarify?

We thank the reviewer for this comment and suggestions. We have now included 2 additional repeats for this experiment and the results essentially support our conclusion.

The two-sided Fischer’s T-test is the name of the test in Graphpad- Prism. We wanted to acknowledge the test name so that the reader can trace the exact test we used in the program.

1. Figure 2E shows the same data as 2D with different normalization to highlight the differences between binding to the domain 1-133 per se and binding to this domain when the linker helix is mutated. These mutations seem to cause a further decrease in binding of both Ldo45 and Ldo16. Still, effects are rather small, and the n=2 does not allow any meaningful statistical tests. To make this point, the authors should increase their sample number (at least n=3) to show that this difference is indeed meaningful and to allow statistical analysis.

We thank the reviewer for this comment and suggestions. We have now included 2 additional repeats for this experiment and the results essentially support our conclusion.

For Ldo16, no crosslinks were detected with Ldb16 TM-HelixTM domain (Figure 1). In line, CoIP demonstrated that the interaction between Ldo16 and Ldb16 was strongly reduced when the Ldb16 domain 1-133 was used for IP. Still, additional mutation of the linker helix in this 1-133 domain further reduced this interaction (to a similar extend as for Ldo45). Could the authors please clarify why the additional mutations in the linker helix region also decreased the binding of Ldo16, though the authors conclude from their crosslinking approach in Fig. 1 that Ldo16 does not interact with this region?

We thank the reviewer for raising this point. Our negative crosslinking results for Ldo16 do not exclude the possibility of binding to that region; rather, they indicate that we were unable to detect Ldo16 there. Additionally, mutations in the linker helix may influence how Ldb16 interacts with seipin, including its positioning within the seipin ring and the membrane bilayer. These structural changes could, in turn, affect Ldo16 recruitment in ways that we do not fully understand.

Similarly, also in 4D, a quantification with n=2 is presented, showing that some of the crosslinks are more prominently detectable when LD biogenesis is induced. The findings of this manuscript are completely based on results obtained with CoIP and photocrosslinking, and quantification of a sufficient n to allow statistical analysis will be essential.

While we agree that additional experiments are useful for the Co-IP because of variability between experiments, this is less of a concern for the photocrosslinking experiments. In the case of photocrosslinking, we typically see much less variability and normally, for a given position, the effects are much more “black and white”- either there is a crosslink or not.

Why is there nowhere a blot with crosslinked Ldb16 bands shown (but only non-crosslinked Ldb16, e.g. Fig. 1C)?

We thank the reviewer for this comment. In all cases the amount of crosslinked product is very minor. This is particularly obvious in the case of Ldb16, where the non-crosslinked species dominates in the blots (as can be observed in figure S1B).

Figure 3: The authors conclude that galactose-induced expression of either Dga1, Lro1 or Are1 in cells lacking all four enzymes for neutral lipid synthesis (quadruple deletion mutant) increases the levels of Ldb16. However, I do not see any difference on the FLAG-Ldb16 blot when comparing Ldb16 levels in the quadruple deletion mutant with or without Dga1, Lro1 or Are1, and no quantification is presented that might reveal very subtle differences not visible on the blot.

We agree with the reviewer and modified the text to more accurately describe our results.

OPTIONAL: Have the authors considered to assess which sites/domains of Ldo45 and Ldo16 are employed to bind to Ldb16?

This is a logical next step that will be undertaken in a future study.

Minor: 1. Page numbers would have been helpful to refer to specific text sections.

Page numbers have been added

1. Figure 3C: Unclear to me why the authors label a part of their immunoblot where they detected HA with OSW5?

This was a mistake and has been corrected

1. Figure 4D and corresponding figure legend could be improved in respect to labeling to clarify.

we have added an X axis label and made extra clarifications in the legend

1. Please correct his sentence: "These variants we expressed in cells where the other subunits of the Sei1 complex were epitope tagged to facilitate detection and expressed their endogenous loci."

This sentence has been corrected

Reviewer #3 (Significance (Required)):

This is a short and interesting study completely based on UV-induced site-specific photocrosslinking and CoIPs that provides some new insights into the interaction surface between the Seipin complex and Ldo45 and the interaction between Ldo16 and Ldb16. Though in parts still premature, these findings will likely be of interest to the large community interested in lipid metabolism, expanding the role of Ldb16 from neutral lipid binding to regulator recruitment.

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

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Klug and Carvalho investigates the interaction of the yeast seipin complex (Sei1 and Ldb16) with Ldo45 and Ldo16. Using a site-specific photocrosslinking approach, the authors map some residues of the seipin complex in contact Ldo45, demonstrating that Ldo45 likely binds to Ldb16 in the center of the Sei1-Ldb16 complex. They find that both Ldo45 and Ldo16 copurify with Ldb16. Complex assembly is demonstrated to occur independently of the presence of neutral lipids. An Ldb16 mutant, harbouring the transmembrane domain (1-133) but lacking the cytosolic region (previously shown to allow normal LD formation and still bind to Sei1) showed photocrosslinks with Ldo45, but not Ldo16. No crosslinks between Sei1 and either Ldo45 or Ldo16 were detected.

Major:

1. Figure 2 shows CoIPs using different Ldb16 mutants/truncations to test for binding of Ldo45 and Ldo16. Both Ldo16 and Ldo45 copurify with full length Ldb16. Loss of the cytosolic part of Ldb16 strongly reduced binding of both Ldo45 and Ldo16, indicating that the TM-Helix-TM domain of Ldb16 (1-133) alone is not sufficient for proper binding of Ldo45 or Ldo16. The quantifications (2D and 2E) presented for this CoIP represent a n=2 with mean, standard deviation and statistics. To be a meaningful statistical analysis, the authors need to increase their n to at least n=3. In addition, they refer to the statistics they use here as "two-sided Fischer's T-test" in the respective Figure legend. To my knowledge, there is no such test, either it is Student's T-test or Fischer's exact test? Can the authors please clarify?
2. Figure 2E shows the same data as 2D with different normalization to highlight the differences between binding to the domain 1-133 per se and binding to this domain when the linker helix is mutated. These mutations seem to cause a further decrease in binding of both Ldo45 and Ldo16. Still, effects are rather small, and the n=2 does not allow any meaningful statistical tests. To make this point, the authors should increase their sample number (at least n=3) to show that this difference is indeed meaningful and to allow statistical analysis.
3. For Ldo16, no crosslinks were detected with Ldb16 TM-HelixTM domain (Figure 1). In line, CoIP demonstrated that the interaction between Ldo16 and Ldb16 was strongly reduced when the Ldb16 domain 1-133 was used for IP. Still, additional mutation of the linker helix in this 1-133 domain further reduced this interaction (to a similar extend as for Ldo45). Could the authors please clarify why the additional mutations in the linker helix region also decreased the binding of Ldo16, though the authors conclude from their crosslinking approach in Fig. 1 that Ldo16 does not interact with this region?
4. Similarly, also in 4D, a quantification with n=2 is presented, showing that some of the crosslinks are more prominently detectable when LD biogenesis is induced. The findings of this manuscript are completely based on results obtained with CoIP and photocrosslinking, and quantification of a sufficient n to allow statistical analysis will be essential.
5. Why is there nowhere a blot with crosslinked Ldb16 bands shown (but only non-crosslinked Ldb16, e.g. Fig. 1C)?
6. Figure 3: The authors conclude that galactose-induced expression of either Dga1, Lro1 or Are1 in cells lacking all four enzymes for neutral lipid synthesis (quadruple deletion mutant) increases the levels of Ldb16. However, I do not see any difference on the FLAG-Ldb16 blot when comparing Ldb16 levels in the quadruple deletion mutant with or without Dga1, Lro1 or Are1, and no quantification is presented that might reveal very subtle differences not visible on the blot.

OPTIONAL: Have the authors considered to assess which sites/domains of Ldo45 and Ldo16 are employed to bind to Ldb16?

Minor:

1. Page numbers would have been helpful to refer to specific text sections.
2. Figure 3C: Unclear to me why the authors label a part of their immunoblot where they detected HA with OSW5?
3. Figure 4D and corresponding figure legend could be improved in respect to labeling to clarify.
4. Please correct his sentence: "These variants we expressed in cells where the other subunits of the Sei1 complex were epitope tagged to facilitate detection and expressed their endogenous loci."

Significance

This is a short and interesting study completely based on UV-induced site-specific photocrosslinking and CoIPs that provides some new insights into the interaction surface between the Seipin complex and Ldo45 and the interaction between Ldo16 and Ldb16. Though in parts still premature, these findings will likely be of interest to the large community interested in lipid metabolism, expanding the role of Ldb16 from neutral lipid binding to regulator recruitment.

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:

Klug and Carvalho apply a photo-crosslinking approach, which has been extensively used in the Carvalho group, to investigate the subunit interactions of the seipin complex in yeast. The authors apply this approach to further study possible changes within the seipin complex following induction of neutral lipid synthesis and lipid droplet (LD) formation. The authors propose that Ldo45 makes contact with Ldb16 and that the seipin complex subunits assemble even in the absence of LDs.

Major comments:

Overall, this is a focused and well-executed study on one of the fundamental structural components of LDs. The study addresses the subunit interactions of the seipin complex but does not look into their functional consequences, for example how the mutations on Ldb16 that affect its interaction with Ldo45, influence LD formation; similarly, the authors make the interesting observation that Ldo16 may be differentially affected by the lack of neutral lipids (Fig. 3A) but this observation is not explored.

1. For the crosslinking pull-downs (Fig. 1), it seems that the authors significantly overexpress (ADH1 promoter) the Ldb16 subunit that carries the various photoreactive amino acid residues, while keeping the other (tagged) seipin complex members at endogenous levels. Would not this imbalance affect the assembly of the complex and therefore the association of the different subunits with each other?
2. Although the authors do show delta4 cells with no LDs (Fig. 3B, 0h), galactose-inducible systems in yeast are known to be leaky. Given that the authors' conclusion that the complex is "pre-assembled" irrespective of the addition of galactose, I think it would be important to confirm biochemically that there is no neutral lipid at time point 0. Alternatively, it may be better to simply compare wt vs dga1 lro1 or are1are2 mutants - there is no need for GAL induction since the authors look at one time point only.
3. Some methodological issues could be better detailed. For example, which of the three delta4 strains was used to induce neutral lipid in Fig. 4B? How exactly were the quantifications in Fig. 4D performed (I assume they were done under non-saturating band intensity conditions, as for some residues it is difficult to conclude whether the blot aligns with the quantification results).
4. "our findings support the notion that Ldo45 is important for early steps of LD formation as previously proposed" I find this statement confusing given that the authors claim that Ldo45 is already bound to the complex before LD formation.
5. The model in Fig. 5 is essentially the same as the one shown in Fig. 1G.

Minor comments

In the pull-downs in Fig. 2C, it seems that full-length Ldb16 is not enriched after the FLAG IP. What is the reason of this?

At the blots at Fig. 2C and 3A, the anti-Dpm1 Ab seems to recognize in the IP fractions a band labelled as non-specific, however this band is absent from the input.

Significance

Regulation of seipin function is essential for proper LD biogenesis in eukaryotes, so this study addresses a fundamental question in the field. As stated above some functional analysis that goes beyond the biochemistry would be beneficial. There is some overlap with a recently published paper from the Wang group that also examines the assembly of seipin in yeast.

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

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Referee #1

Evidence, reproducibility and clarity

The manuscript is well written and presents solid data, most of which is statistically analyzed and sound. Given that the author's previous comprehensive publications on seipin organization and interactions, it might be beneficial (particularly in the title and abstract) to emphasize that this manuscript focuses on the metabolic regulation of lipid droplet assembly by Ldb16, to distinguish it from previous work. Perhaps one consideration, potentially interesting, involves changes in lipid droplet formation under the growth conditions used for galactose-mediated gene induction.

Comments:

1. Fig. 3 and 4. The galactose induction of lipid droplet biogenesis in are1∆/2∆ dga1∆ lro1∆ cells though activation of a GAL1 promoter fusion to DGA1 is a sound approach for regulating lipid droplet formation. Although unlikely, carbon sources can impact lipid droplet proliferation and (potentially interesting) metabolic changes under growth in non-fermentable carbon sources may impact lipid droplet biogenesis; in fact, oleate has significant effects (e.g. PMID: 21422231; PMID: 21820081). The GAL1 promoter is a very strong promoter and the overexpression of DGA1 via this heterologous promoter might itself cause unforeseen changes. Affirmation of the results using another induction system might be beneficial.
2. Fig. 3B. Although only representative images are shown, the panel convincingly shows that lipid droplets do form upon galactose induction of DGA1 in are1∆/2∆ dga1∆ lro1∆ cells. However, it does not show to what extent. Are lipid droplets synthesized at WT levels? How many cells were counted? How many lipid droplets per cell? Is there a statistical difference with respect to WT cells?
3. Fig. 2D. It is not clear how standard deviation can be meaningfully applied to two data points, let alone providing a p-value. For some of these experiments, triplicate trials might provide a more robust statistical sampling.

Significance

Klug and Carvalho report on the lipid droplet architecture of the yeast seipin complex. Specifically, the mechanism of yeast seipin Sei1 binding to Ldo16 and the subsequent recruitment of Ldb45 is analyzed. These results follow from a recent publication (PMID: 34625558) from the same authors and aims to define a more precise role for the components of the seipin complex. Using photo-crosslinking, Ldo45 and Ldo16 interactions are analyzed in the context of lipid droplet assembly.

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Reply to the reviewers

1. Response to reviewers

We would like to thank the reviewers for carefully reading our manuscript and for their valuable comments in support for the publication of our investigation of rapid promoter evolution of accessory gland genes between Drosophila species and hybrids. We are glad to read that the reviewers find our work interesting and that it provides valuable insights into the regulation and divergence of genes through their promoters. We are encouraged by their acknowledgement of the overall quality of the work and the importance of our analyses in advancing the understanding of cis-regulatory changes in species divergence.

2. Point-by-point description of the revisions

Reviewer #

Reviewer Comment

Author Response/Revision

Reviewer 1

The authors test the hypothesis that promoters of genes involved in insect accessory glands evolved more rapidly than other genes in the genome. They test this using a number of computational and experimental approaches, looking at different species within the Drosophila melanogaster complex. The authors find an increased amount of sequence divergence in promoters of accessory gland proteins. They show that the expression levels of these proteins are more variable among species than randomly selected proteins. Finally, they show that within interspecific hybrids, each copy of the gene maintains its species-specific expression level.

We thank Reviewer 1 for their detailed review and positive feedback on our manuscript, and for their helpful suggestions. We have now fully addressed the points raised by Reviewer 1 and have provided the suggested clarifications and revisions to improve the flow, readability, and presentation of the data, which we believe have improved the manuscript significantly.

The work is done with expected standards of controls and analyses. The claims are supported by the analysis. My main criticism of the manuscript has to do not with the experiments or conclusion themselves but with the presentation. The manuscript is just not very well written, and following the logic of the arguments and results is challenging.

The problem begins with the Abstract, which is representative of the general problems with the manuscript. The Abstract begins with general statements about the evolution of seminal fluid proteins, but then jumps to accessory glands and hybrids, without clarifying what taxon is being studied, and what hybrids they are talking about. Then, the acronym Acp is introduced without explanation. The last two sentences of the Abstract are very cumbersome and one has to reread them to understand how they link to the beginning of the Abstract.

More generally, if this reviewer is to be seen as an "average reader" of the paper, I really struggled through reading it, and did not understand many of the arguments or rationale until the second read-through, after I had already read the bottom line. The paragraph spanning lines 71-83 is another case in point. It is composed of a series of very strongly worded sentences, almost all starting with a modifier (unexpectedly, interestingly, moreover), and supported by citations, but the logical flow doesn't work. Again, reading the paragraph after I knew where the paper was going was clearer, but on a first read, it was just a list of disjointed statements.

Since most of the citations are from the authors' own work, I suspect they are assuming too much prior understanding on the part of the reader. I am sure that if the authors read through the manuscript again, trying to look through the eyes of an external reader, they will easily be able to improve the flow and readability of the text.

We thank the reviewer for their detailed feedback and are glad that they acknowledge our work fully supports the claims of our manuscript. We also appreciate their helpful suggestions for improving the readability of the manuscript and have done our best to re-write the abstract and main text where indicated. In particular, the paragraph between lines 71-83 have been rewritten and we have taken care to write to non-expert readers.

1) In the analysis of expression level differences, it is not clear what specific stage / tissue the levels taken from the literature refer to. Could it be that the source of the data is from a stage or tissue where seminar fluid proteins will be expressed with higher variability in general (not just inter-specifically) and this could be skewing the results? Please add more information on the original source of the data and provide support for their validity for this type of comparison.

These were taken from publicly available adult male Drosophila datasets, listed in the data availability statement and throughout the manuscript. We have provided more detail on the tissue used for analysis of Acp gene expression levels.

2) The sentence spanning lines 155-157 needs more context.

We have added more context to lines 155-157.

3) Line 203-204: What are multi-choice enhancers?

We replaced the sentence with "... such as rapidly evolving enhancers or nested epistasis enhancer networks"

4) Figure 1: The terminology the authors use, comparing the gene of interest to "Genome" is very confusing. They are not comparing to the entire genome but to all genes in the genome, which is not the same.

We have changed the word "genome" to "all genes in the genome" on the reviewer's suggestion.

5) Figure 2: Changes between X vs. Y is redundant (either changes between X and Y or changes in X vs. Y).

We assume that the reviewer is referring to Fig. 2B, which does not measure changes between X and Y, but changes in distribution between Acps and the control group. We have explained this in the figure legend.

The manuscript addresses a general question in evolutionary biology - do control regions diverge more quickly protein coding regions. The answer is that yes, they do, but this is actually not very surprising. The work is probably thus of more interest to people interested in the copulatory proteins or in the evolution of mating systems, than to people interested in broader evolutionary questions.

We appreciate this reviewer's recognition of the significance of our work and would like to point out that there are very few studies looking at promoter evolution as detailed in the introduction. Of particular relevance, our study using Acp genes allows us to directly test the impact of promoter mutations on the expression by comparing two alleles in male accessory glands of Drosophila hybrids. Male accessory glands consist of only two secretory cell types allowing us to study evolution of gene expression in a single cell type (Acps are either expressed in main cells or secondary cells). Amid this unique experimental set up we can conclude that promoter mutations can act dominant, in contrast to mutations in protein coding regions, which are generally recessive. Thus, our study is unique in pointing out a largely overseen aspect of gene evolution.

Reviewer 2

This manuscript explores promoter evolution of genes encoding seminal fluid proteins expressed in the male accessory gland of Drosophila and finds cis-regulatory changes underlie expression differences between species. Although these genes evolve rapidly it appears that the coding regions rarely show signs of positive selection inferring that changes in their expression and hence promoter sequences can underlie the evolution of their roles within and among species.

We thank Reviewer 2 for their thorough review, positive feedback on the importance of our work, and suggestions for improving the manuscript. We have addressed all points raised by the reviewer, including analysis of Acp coding region evolution, additional analyses of hybrid expression data, and improved the clarity of the text.

Figure 1 illustrates evidence that the promoter regions of these gene have accumulated more changes than other sampled genes from the Drosophila genome. While this convinces that the region upstream of the transcription start site has diverged considerably in sequence (grey line compared to black line), Figure 1A also suggests the "Genespan" region which includes the 5'UTR but presumably also part of the coding region is also highly diverged. It would be useful to see how the pattern extends into the coding region further to compare further to the promoter region (although Fig 1H does illustrate this more convincingly).

The reviewer raises an interesting point, and certainly all parts of genes evolve. Fig. 1A shows the evolutionary rates of Acps compared to the genome average from phyloP27way scores calculated from 27 insect species. Since these species are quite distant it is unsurprising that they show divergence in coding regions as well as promoter regions. In fact, we addressed whether promoter regions evolve fast in closely related Drosophila species in Fig. 1H compared to coding regions. We have included an additional analysis of coding region evolution in Figure 1B.

Figure 2 presents evidence for significant changes in (presumably levels of) expression of male accessory gland protein (AcP) genes and ribosomal proteins genes between pairs of species, which is reflected in the skew of expression compared to randomly selected genes.

Correct, we have rephrased the statement for clarity.

Figure 3 shows detailed analysis for 3 selected AcP genes with significantly diverged expression. The authors claim this shows 'substitution' hotspots in the promoter regions of all 3 genes but this could be better illustrated by extending the plots in B-D further upstream and downstream to compare to these regions.

We picked the 300-nucleotide promoter region for this analysis as it accumulated significant changes as shown in Fig. 1E-H, and extending the G plots (Fig. 3B-D) to regions with lower numbers of sequence changes would not substantially change the conclusion. Specifically, this analysis identifies sequence change hotspots within fast-evolving promoter regions, rather than comparing promoter regions to other genomic regions, as we previously addressed. The plot is based on a cumulative distribution function and the significant positive slope in the upstream region where promoters are located identifies a hotspot for accumulation of substitutions. There could be other hotspots, but the point being made is that significant hotspots consistently appear in the promoter region of these three genes.

Figure 4 shows the results of expression analysis in parental lines of each pair of species and F1 hybrids. However the results are very difficult to follow in the figure and in the relevant text. While the schemes in A, C. E and G are helpful, the gel images are not the best quality and interpretations confusing. An additional scheme is needed to illustrate hypothetical outcomes of trans change, cis change and transvection to help interpret the gels. On line 169 (presumably referring to panels D and F although C and D are cited on the next line) the authors claim that Obp56f and CG11598 'were more expressed in D. melanogaster compared to D. simulans' but in the gel image the D. sim band is stronger for both genes (like D. sechellia) compared to the D. mel band. The authors also claim that the patterns of expression seen in the F1s are dominant for one allele and that this must be because of transvection. I agree this experiment is evidence for cis-regulatory change. However the interpretation that it is caused by transvection needs more explanation/justification and how do the authors rule out that it is not a cis X trans interaction between the species promoter differences and differences in the transcription factors of each species in the F1? Also my understanding is that transvection is relatively rare and yet the authors claim this is the explanation for 2/4 genes tested.

We appreciate the reviewer's comments on Figure 4 and the opportunity to improve its clarity. To address these concerns, we have carefully checked the figure citations and corrected any inconsistencies.

The reviewer raises an important point about our interpretation of transvection. We have expanded our discussion of this result to consider why transvection is a plausible explanation for the observed dominance patterns and also consider cis x trans interactions between species-specific promoters and transcription factor binding. While rare, transvection likely has more relevance in hybrid regulatory contexts involving homologous chromosome pairing which we discuss this in the revised text.

Line 112 states that the melanogaster subgroup contains 5 species - this is incorrect - while this study looked at 5 species there are more species in this subgroup such as mauritiana and santomea.

We have corrected the statement about the number of species in the melanogaster subgroup.

Lines 131-134 could explain better what the conservation scores and their groupings mean and the rationale for this approach.

We have clarified what the conservation scores and their groupings mean and the rationale for this approach.

Line 162 - the meaning of the sentence starting on this line is unclear - it sounds very circular.

We have rephrased the statement for more clarity.

Line 168 should cite Fig 4 H instead of F.

We have amended citation of Fig 4F to H.

Reviewer 3

In this study, McQuarrie et al. investigate the evolution of promoters of genes encoding accessory gland proteins (Acps) in species within the D. melanogaster subgroup. Using computational analyses and available genomic and transcriptomic datasets, they demonstrate that promoter regions of Acp genes are highly diverse compared to the promoters of other genes in the genome. They further show that this diversification correlates with changes in gene expression levels between closely related species. Complementing these computational analyses, the authors conduct experiments to test whether differences in expression levels of four Acp genes with highly diverged promoter regions are maintained in hybrids of closely related species. They find that while two Acp genes maintain their expression level differences in hybrids, the other two exhibit dominance of one allele. The authors attribute these findings to transvection. Based on their data, they conclude that rapid evolution of Acp gene promoters, rather than changes in trans, drives changes in Acp gene expression that contribute to speciation.

We thank Reviewer 3 for their thorough review and suggestions. We further thank the reviewer for acknowledging the importance of our findings and for pointing out that it contributes to our understanding of speciation. We have thoroughly addressed all comments from the reviewer and significantly revised the manuscript. We believe that this has greatly improved the manuscript.

Unfortunately, the presented data are not sufficient to fully support the conclusions. While many of the concerns can be addressed by revising the text to moderate the claims and acknowledge the methodological limitations, some key experiments require repetition with more controls, biological replicates, and statistical analyses to validate the findings.

Specifically, some of the main conclusions heavily rely on the RT-PCR experiments presented in Figure 4, which analyze the expression of four Acp genes in hybrid flies. The authors use PCR and RFLP to distinguish species-specific alleles but draw quantitative conclusions from what is essentially a qualitative experiment. There are several issues with this approach. First, the experiment includes only two biological replicates per sample, which is inadequate for robust statistical analysis. Second, the authors did not measure the intensity of the gel fragments, making it impossible to quantify allele-specific expression accurately. Third, no control genes were used as standards to ensure the comparability of samples.

The gold standard for quantifying allele-specific expression is using real-time PCR methods such as TaqMan assays, which allow precise SNP genotyping. To address this major limitation, the authors should ideally repeat the experiments using allele-specific real-time PCR assays. This would provide a reliable and quantitative measurement of allele-specific expression.

If the authors cannot implement real-time PCR, an alternative (though less rigorous) approach would be to continue using their current method with the following adjustments:

• Include a housekeeping gene in the analysis as an internal control (this would require identifying a region distinguishable by RFLP in the control).

• Quantify the intensity of the PCR products on the gel relative to the internal standard, ensuring proper normalization.

• Increase the sample size to allow for robust statistical analysis.

These experiments could be conducted relatively quickly and would significantly enhance the validity of the study's conclusions.

We thank the reviewer for their detailed suggestions for improving the conclusions in Fig. 4. Indeed, incorporating a housekeeping gene as a control supports our results for qualitative analysis of gene expression in hybrids assessing each allele individually (Fig 4), and improves interpretation for non-experts. We have also quantified differential gene expression in hybrids between species alleles and the log2 fold change from D. melanogaster. In addition, we have included an additional analysis in the new Fig. 5 which analyses RNA-seq expression changes in D. melanogaster x D. simulans hybrid male accessory glands. We believe these additions have significantly improved the manuscript and its conclusions.

While the following comments are not necessarily minor, they can be addressed through revisions to the text without requiring additional experimental work. Some comments are more conceptual in nature, while others concern the interpretation and presentation of the experimental results. They are provided in no particular order.

1. A key limitation of this study is the use of RNA-seq datasets from whole adult flies for interspecies gene expression comparisons. Whole-body RNA-seq inherently averages gene expression across all tissues, potentially masking tissue-specific expression differences. While Acp genes are likely restricted to accessory glands, the non-Acp genes and the random gene sets used in the analysis may have broader expression profiles. As a result, their expression might be conserved in certain tissues while diverging in others- an aspect that whole-body RNA-seq cannot capture. The authors should acknowledge that tissue-specific RNA-seq analyses could provide a more precise understanding of expression divergence and potentially reveal reduced conservation when considering specific tissues independently.

We have added a section discussing the limitations in gene expression analysis in the discussion. In addition, we have included an additional Figure analysing gene expression in hybrid male accessory glands (Fig. 5).

1. The statement in line 128, "Consistent with this model," does not accurately reflect the findings presented in Figures 2A and B. Specifically, the data in Figure 2A show that Acp gene expression divergence is significantly different from the divergence of non-Acp genes or a random sample only in the comparison between D. melanogaster and D. simulans. However, when these species are compared to D. yakuba, Acp gene expression divergence aligns with the divergence patterns of non-Acp genes or random samples. In contrast, Figure 2B shows that the distribution of expression changes is skewed for Acp genes compared to random control samples when D. melanogaster or D. simulans are compared to D. yakuba. However, this skew is absent when the two D. melanogaster and D. simulans are compared. Therefore, the statement in line 128 should be revised to accurately reflect these nuanced results and the trends shown in Figure 2A and B.

We have updated the statement for clarity. Here, the percentage of Acps showing significant gene expression changes is greater between more closely related species, but the distribution of expression changes increases between more distantly related species.

1. The statement in lines 136-138, "Acps were enriched for significant expression changes in the faster evolving group across all species," while accurate, overlooks a key observation. This trend was also observed in other groups, including those with slower evolving promoters, in some of the species' comparisons. Therefore, the enrichment is not unique to Acps with rapidly evolving promoters, and this should be explicitly acknowledged in the text.

This is a valid point, and we have updated this statement as suggested.

1. It would be helpful for the authors to explain the meaning of the d score at the beginning of the paragraph starting in line 131, to ensure clarity for readers unfamiliar with this metric.

This scoring method is described in the methods sections, and we have now included reference to thorough explanation of how d was calculated at the indicated section.

1. In Figure 2C-E - the title of the Y-axis does not match the text. If it represents the percentage of genes with significant expression changes, as in Figure 2A, the discrepancies between the percentages in this figure and those in Figure 2A need to be addressed.

We have updated the method used to categorise significant changes in gene expression in the text and the figure legend for clarity.

1. The experiment in Figure 3 needs a better explanation in the text. What is the analysis presented in Figure 3B-D. How many species were compared?

We have added additional details in the results section and an explanation of how sequence change hotspots were calculated in the results section is available.

1. The concept of transvection should be omitted from this manuscript. First, the definition provided by the authors is inaccurate. Second, even if additional experiments were to convincingly show that one allele in hybrid animals is dominant over the other, there are alternative explanations for this phenomenon that do not involve transvection. The authors may propose transvection as a potential model in the discussion, but they should do so cautiously and explicitly acknowledge the possibility of other mechanisms.

We have updated the text to more conservatively discuss transvection, moving this to the discussion section with additional possibilities discussed.

1. The statement at the end of the introduction is overly strong and would benefit from more cautious phrasing. For instance, it could be reworded as: "These findings suggest that promoter changes, rather than genomic background, play a significant role in driving expression changes, indicating that promoter evolution may contribute to the rise of new species."

We have reworded this line following the reviewer's suggestion.

1. Line 32 of the abstract: The term "Acp" is introduced without explaining what it stands for. Please define it as "Accessory gland proteins (Acp)" when it first appears.

We have updated the manuscript to define Acp where it is first mentioned.

1. Line 61: The phrase "...through relaxed,..." is unclear. Specify what is relaxed (e.g., "relaxed selective pressures").

We have included description of relaxed selective pressures.

1. The sentence in lines 74-76, starting in "Interestingly,...." Needs revision for clarity.

We have removed the word interestingly.

1. Line 112: Revise "we focused on the melanogaster subgroup which is made up of five species" to: "we focused on the melanogaster subgroup, which includes five species."

We have made this change in the text.

1. In line 144 use the phrase "promoter conservation" instead of "promoter evolution"

We have updated the phrasing.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this study, McQuarrie et al. investigate the evolution of promoters of genes encoding accessory gland proteins (Acps) in species within the D. melanogaster subgroup. Using computational analyses and available genomic and transcriptomic datasets, they demonstrate that promoter regions of Acp genes are highly diverse compared to the promoters of other genes in the genome. They further show that this diversification correlates with changes in gene expression levels between closely related species. Complementing these computational analyses, the authors conduct experiments to test whether differences in expression levels of four Acp genes with highly diverged promoter regions are maintained in hybrids of closely related species. They find that while two Acp genes maintain their expression level differences in hybrids, the other two exhibit dominance of one allele. The authors attribute these findings to transvection. Based on their data, they conclude that rapid evolution of Acp gene promoters, rather than changes in trans, drives changes in Acp gene expression that contribute to speciation.

Major comments:

Unfortunately, the presented data are not sufficient to fully support the conclusions. While many of the concerns can be addressed by revising the text to moderate the claims and acknowledge the methodological limitations, some key experiments require repetition with more controls, biological replicates, and statistical analyses to validate the findings.

Specifically, some of the main conclusions heavily rely on the RT-PCR experiments presented in Figure 4, which analyze the expression of four Acp genes in hybrid flies. The authors use PCR and RFLP to distinguish species-specific alleles but draw quantitative conclusions from what is essentially a qualitative experiment. There are several issues with this approach. First, the experiment includes only two biological replicates per sample, which is inadequate for robust statistical analysis. Second, the authors did not measure the intensity of the gel fragments, making it impossible to quantify allele-specific expression accurately. Third, no control genes were used as standards to ensure the comparability of samples.

The gold standard for quantifying allele-specific expression is using real-time PCR methods such as TaqMan assays, which allow precise SNP genotyping. To address this major limitation, the authors should ideally repeat the experiments using allele-specific real-time PCR assays. This would provide a reliable and quantitative measurement of allele-specific expression.

If the authors cannot implement real-time PCR, an alternative (though less rigorous) approach would be to continue using their current method with the following adjustments:

• Include a housekeeping gene in the analysis as an internal control (this would require identifying a region distinguishable by RFLP in the control).
• Quantify the intensity of the PCR products on the gel relative to the internal standard, ensuring proper normalization.
• Increase the sample size to allow for robust statistical analysis. These experiments could be conducted relatively quickly and would significantly enhance the validity of the study's conclusions.

Minor comments

While the following comments are not necessarily minor, they can be addressed through revisions to the text without requiring additional experimental work. Some comments are more conceptual in nature, while others concern the interpretation and presentation of the experimental results. They are provided in no particular order. 1. A key limitation of this study is the use of RNA-seq datasets from whole adult flies for interspecies gene expression comparisons. Whole-body RNA-seq inherently averages gene expression across all tissues, potentially masking tissue-specific expression differences. While Acp genes are likely restricted to accessory glands, the non-Acp genes and the random gene sets used in the analysis may have broader expression profiles. As a result, their expression might be conserved in certain tissues while diverging in others- an aspect that whole-body RNA-seq cannot capture. The authors should acknowledge that tissue-specific RNA-seq analyses could provide a more precise understanding of expression divergence and potentially reveal reduced conservation when considering specific tissues independently. 2. The statement in line 128, "Consistent with this model," does not accurately reflect the findings presented in Figures 2A and B. Specifically, the data in Figure 2A show that Acp gene expression divergence is significantly different from the divergence of non-Acp genes or a random sample only in the comparison between D. melanogaster and D. simulans. However, when these species are compared to D. yakuba, Acp gene expression divergence aligns with the divergence patterns of non-Acp genes or random samples. In contrast, Figure 2B shows that the distribution of expression changes is skewed for Acp genes compared to random control samples when D. melanogaster or D. simulans are compared to D. yakuba. However, this skew is absent when the two D. melanogaster and D. simulans are compared. Therefore, the statement in line 128 should be revised to accurately reflect these nuanced results and the trends shown in Figure 2A and B. 3. The statement in lines 136-138, "Acps were enriched for significant expression changes in the faster evolving group across all species," while accurate, overlooks a key observation. This trend was also observed in other groups, including those with slower evolving promoters, in some of the species' comparisons. Therefore, the enrichment is not unique to Acps with rapidly evolving promoters, and this should be explicitly acknowledged in the text. 4. It would be helpful for the authors to explain the meaning of the d score at the beginning of the paragraph starting in line 131, to ensure clarity for readers unfamiliar with this metric. 5. In Figure 2C-E - the title of the Y-axis does not match the text. If it represents the percentage of genes with significant expression changes, as in Figure 2A, the discrepancies between the percentages in this figure and those in Figure 2A need to be addressed. 6. The experiment in Figure 3 needs a better explanation in the text. What is the analysis presented in Figure 3B-D. How many species were compared? 7. The concept of transvection should be omitted from this manuscript. First, the definition provided by the authors is inaccurate. Second, even if additional experiments were to convincingly show that one allele in hybrid animals is dominant over the other, there are alternative explanations for this phenomenon that do not involve transvection. The authors may propose transvection as a potential model in the discussion, but they should do so cautiously and explicitly acknowledge the possibility of other mechanisms. 8. The statement at the end of the introduction is overly strong and would benefit from more cautious phrasing. For instance, it could be reworded as: "These findings suggest that promoter changes, rather than genomic background, play a significant role in driving expression changes, indicating that promoter evolution may contribute to the rise of new species."

Text edits:

Throughout the manuscripts there are incomplete sentences and sentences that are not clear. Below is a list of corrections:

1. Line 32 of the abstract: The term "Acp" is introduced without explaining what it stands for. Please define it as "Accessory gland proteins (Acp)" when it first appears.
2. Line 61: The phrase "...through relaxed,..." is unclear. Specify what is relaxed (e.g., "relaxed selective pressures").
3. The sentence in lines 74-76, starting in "Interestingly,...." Needs revision for clarity.
4. Line 112: Revise "we focused on the melanogaster subgroup which is made up of five species" to: "we focused on the melanogaster subgroup, which includes five species."
5. In line 144 use the phrase "promoter conservation" instead of "promoter evolution"

Significance

This study addresses an important question in evolutionary biology: how seminal fluid proteins achieve rapid evolution despite showing limited adaptive changes in their coding regions. By focusing on accessory gland proteins (Acps) and examining their promoter regions, the authors suggest promoter-driven evolution as a potential mechanism for rapid seminal fluid protein diversification. While this hypothesis is intriguing and can contribute to our understanding of speciation, more rigorous analysis and experimental validation would be needed to support the conclusions. The revised manuscript can be of interest to fly geneticists and to scientists in the fields of gene regulation and evolution.

Keywords for my expertise: Enhancers, transcriptional regulation, development, evolution, Drosophila.

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Referee #2

Evidence, reproducibility and clarity

Summary

This manuscript explores promoter evolution of genes encoding seminal fluid proteins expressed in the male accessory gland of Drosophila and finds cis-regulatory changes underlie expression differences between species. Although these genes evolve rapidly it appears that the coding regions rarely show signs of positive selection inferring that changes in their expression and hence promoter sequences can underlie the evolution of their roles within and among species.

Major comments

Figure 1 illustrates evidence that the promoter regions of these gene have accumulated more changes than other sampled genes from the Drosophila genome. While this convinces that the region upstream of the transcription start site has diverged considerably in sequence (grey line compared to black line), Figure 1A also suggests the "Genespan" region which includes the 5'UTR but presumably also part of the coding region is also highly diverged. It would be useful to see how the pattern extends into the coding region further to compare further to the promoter region (although Fig 1H does illustrate this more convincingly).

Figure 2 presents evidence for significant changes in (presumably levels of) expression of male accessory gland protein (AcP) genes and ribosomal proteins genes between pairs of species, which is reflected in the skew of expression compared to randomly selected genes.

Figure 3 shows detailed analysis for 3 selected AcP genes with significantly diverged expression. The authors claim this shows 'substitution' hotspots in the promoter regions of all 3 genes but this could be better illustrated by extending the plots in B-D further upstream and downstream to compare to these regions.

Figure 4 shows the results of expression analysis in parental lines of each pair of species and F1 hybrids. However the results are very difficult to follow in the figure and in the relevant text. While the schemes in A, C. E and G are helpful, the gel images are not the best quality and interpretations confusing. An additional scheme is needed to illustrate hypothetical outcomes of trans change, cis change and transvection to help interpret the gels. On line 169 (presumably referring to panels D and F although C and D are cited on the next line) the authors claim that Obp56f and CG11598 'were more expressed in D. melanogaster compared to D. simulans' but in the gel image the D. sim band is stronger for both genes (like D. sechellia) compared to the D. mel band. The authors also claim that the patterns of expression seen in the F1s are dominant for one allele and that this must be because of transvection. I agree this experiment is evidence for cis-regulatory change. However the interpretation that it is caused by transvection needs more explanation/justification and how do the authors rule out that it is not a cis X trans interaction between the species promoter differences and differences in the transcription factors of each species in the F1? Also my understanding is that transvection is relatively rare and yet the authors claim this is the explanation for 2/4 genes tested.

Minor comments

Line 112 states that the melanogaster subgroup contains 5 species - this is incorrect - while this study looked at 5 species there are more species in this subgroup such as mauritiana and santomea.

Lines 131-134 could explain better what the conservation scores and their groupings mean and the rationale for this approach.

Line 162 - the meaning of the sentence starting on this line is unclear - it sounds very circular.

Line 168 should cite Fig 4 H instead of F.

Significance

This paper is generally well written although some sections would benefit from more explanation. The paper demonstrates cis-regulatory changes between the promoters of orthologs of male accessory gland genes underlie expression differences but that the species differences are not always reflected in hybrids, which the authors interpret as being caused by transvection although there could be other explanations. Overall this provides new insights into the regulation and divergence of these interesting genes. The paper does not explore the consequences of these changes in gene expression although this is discussed to some extent in the Discussion section.

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Referee #1

Evidence, reproducibility and clarity

The authors test the hypothesis that promoters of genes involved in insect accessory glands evolved more rapidly than other genes in the genome. They test this using a number of computational and experimental approaches, looking at different species within the Drosophila melanogaster complex. The authors find an increased amount of sequence divergence in promoters of accessory gland proteins. They show that the expression levels of these proteins are more variable among species than randomly selected proteins. Finally, they show that within interspecific hybrids, each copy of the gene maintains its species-specific expression level.

The work is done with expected standards of controls and analyses. The claims are supported by the analysis. My main criticism of the manuscript has to do not with the experiments or conclusion themselves but with the presentation. The manuscript is just not very well written, and following the logic of the arguments and results is challenging. The problem begins with the Abstract, which is representative of the general problems with the manuscript. The Abstract begins with general statements about the evolution of seminal fluid proteins, but then jumps to accessory glands and hybrids, without clarifying what taxon is being studied, and what hybrids they are talking about. Then, the acronym Acp is introduced without explanation. The last two sentences of the Abstract are very cumbersome and one has to reread them to understand how they link to the beginning of the Abstract.

More generally, if this reviewer is to be seen as an "average reader" of the paper, I really struggled through reading it, and did not understand many of the arguments or rationale until the second read-through, after I had already read the bottom line. The paragraph spanning lines 71-83 is another case in point. It is composed of a series of very strongly worded sentences, almost all starting with a modifier (unexpectedly, interestingly, moreover), and supported by citations, but the logical flow doesn't work. Again, reading the paragraph after I knew where the paper was going was clearer, but on a first read, it was just a list of disjointed statements.

Since most of the citations are from the authors' own work, I suspect they are assuming too much prior understanding on the part of the reader. I am sure that if the authors read through the manuscript again, trying to look through the eyes of an external reader, they will easily be able to improve the flow and readability of the text.

More specific comments:

1. In the analysis of expression level differences, it is not clear what specific stage / tissue the levels taken from the literature refer to. Could it be that the source of the data is from a stage or tissue where seminar fluid proteins will be expressed with higher variability in general (not just inter-specifically) and this could be skewing the results? Please add more information on the original source of the data and provide support for their validity for this type of comparison.
2. The sentence spanning lines 155-157 needs more context.
3. Line 203-204: What are multi-choice enhancers?
4. Figure 1: The terminology the authors use, comparing the gene of interest to "Genome" is very confusing. They are not comparing to the entire genome but to all genes in the genome, which is not the same.
5. Figure 2: Changes between X vs. Y is redundant (either changes between X and Y or changes in X vs. Y).

Significance

The manuscript addresses a general question in evolutionary biology - do control regions diverge more quickly protein coding regions. The answer is that yes, they do, but this is actually not very surprising. The work is probably thus of more interest to people interested in the copulatory proteins or in the evolution of mating systems, than to people interested in broader evolutionary questions.

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Reply to the reviewers

Below is a point-by-point response to reviewers concerns.

Main changes are colored in red in the revised manuscript.

Reviewer #1 (Significance (Required)):

General assessment:

This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

Audience: broad interests; including DNA replication, 3D genome structure, and basic research

Expertise: DNA replication and DNA damage repair within the chromatin environment.

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

By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks.

This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation.

We thank Reviewer 1 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

However, the following revisions could strengthen the manuscript:

Major:

Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.

We agree with the reviewer that testing the model in higher eukaryotes would be highly informative. The availability of Repli-HiC on one hand and higher resolution microscopy on the other could enable insightful quantitative analyses. With our formalism, it is in principle already possible to capture realistic 1D replication dynamics as the integrated mathematical formalism (by Arbona et al. ref. [63]) was already used to model human genome S-phase. In addition, the formalism developed for chain duplication is generic and can be contextualized to any species. However, when addressing the problem in 3D, we would likely require including other crucial structural features such as TADs or compartments. Such a model would require an extensive characterization worthy of its own publication. These considerations are now mentioned in the Discussion as exciting future perspectives (Page 17).

On the other hand, we would like to highlight that, while very minimal in many aspects, our model includes many layers of complexity (explicit replication, different forks interactions, stochastic 1D replication dynamics, physical constraints at the nuclear level). In addition, addressing this problem in budding yeast offers the great advantage of simultaneously capturing at the same time both the local and global spatio-temporal properties of DNA replication and to focus first only on those aspects and not on the interplay with other mechanisms like A/B compartmentalization (absent in yeast) that may add confusions in the data analysis and comparison with experimental data . Studying such an interplay is a very important and challenging question that, we believe, goes beyond the scope of the present work.

Validation with Repli-HiC or Time-Resolved Techniques

The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.

We thank the reviewer for the suggestion. Unfortunately, corroborating our HiC results using Repli-HiC or HiChIP would require developing and adapting the protocols to budding yeast which is well beyond the scope of this work mainly focused on computational modelling. In addition, we believe that the signature found in our Hi-C data is clear and significant enough to demonstrate the effect.

However, we included in the Discussion (Page 15) a more detailed description on how our work compares with the Repli-HiC study in mammals. In particular, we added a new supplementary figure (new Fig. S23) where we discuss our prediction on how Repli-HiC maps would appear in yeast in both scenarios of sister-forks interaction. Interestingly, we find that:

1) Fountain signals are strongly enhanced when sister forks interact.

2) Only mild replication dependent enrichment is detected when diverging forks do not interact.

These two results imply that disrupting putative sister-forks interaction would have a drastic effect on Repli-HiC if compared to HiC.

Interactions Between Convergent Forks

The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?

We thank the reviewer for this suggestion. We included in our Hi-C analysis aggregate plots around termination sites. Interestingly, no clear signature of coupling between convergent forks was detected (such as type II fountains in mammals) in vivo and in silico. Similarly, from visual inspection of individual termination sites, no fountains were clearly observed. These results can be found in the new Fig. S24 and possible mechanistic explanations are described more in detail in the Discussion (Page 15).

Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts.

In Fig.3A, a schematic illustrating the cell treatment would improve clarity. In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include:

Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks).

Altered chromatin mobility in mutants, enhancing Hi-C signal resolution.

Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included).

A scheme illustrating the experimental protocol for degron systems (CDC45-miniAID & SCC1-V5-AID) with the corresponding western blots and cell-cycle progression are shown in Fig. S26. Note that for Ctf4, we are using a KO cell line where the gene was deleted.

We do agree with the reviewer that there exist several possible explanations explaining the differences between WT fountains and those observed in mutants. In the revised manuscript, we discussed some of them in Section 2 II B (Page 8):

(1) As already suggested in the paper, asynchronization of cells may impact the intensity of the fountains due a dilution effect mediated by the cells still in G1. Therefore, possible differences in the fractions of replicating/non-relicating cells between the different experiments (new Fig. S7C) would also result in differences in the signal. Moreover, it is important to highlight that aggregate plots are normalized (Observed/Expected) by the average signal (P(s)). Therefore, as Scc1-depleted cells do not exhibit cohesin-mediated loop-extrusion (see aggregate plots around CARs in new Fig. S7B), we may expect an enhancement of signal at origins due to dividing each pixel by a lower contact frequency with respect to the one found in WT.

(2) In the new Fig. S10, we plotted the relative enrichment of Hi-C reads around origins. While we already used the same approach to compare replicon sizes between simulations and experiments (see Fig S7A and response to comment n°9 of Reviewer 3), this analysis is instructive also when comparing different experimental conditions. While we find that the experiment in WT and Scc1-depleted cells show very similar replicon sizes, we do observe a small increase in the peak height for the cohesin mutant. This may also partially motivate differences in the intensity of the fountain. For ctf4Δ, we observe significantly smaller replicons. We speculate that such a mutant might exhibit slower replication and consequently might be enriched in sister-forks contacts.

(3) Compensatory mechanisms: we now briefly discussed this in the Discussion (Page 15).

Inconsistent Figure References

Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

We thank the reviewer for this observation. We have now corrected the mismatches.

Minor:

Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.

Loops of approx 20-40 kb are found in interphase in budding yeast but only after the onset of S-phase ( ref. [52-61]). For this reason, our G1 model of yeast without loops well captures the experimental P(s) curves (Fig. S2). See also answer to point 12 of reviewer 2 .

In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

The initial use of aggregate plots was motivated by the fact that fountains are quite difficult to observe at the single origin level in the experimental Hi-C due to the strong intensity of surrounding contacts (along the diagonal). However, when dividing early-S phase maps by the corresponding G1 map, we can now observe clear correlation between origin and fountain positions on such normalized maps. We now added an example for chromosome 7 in Fig.3 indicating early/late origins.

In Fig. S8 and S9 (where we also included termination sites), we show that fountains are prominently found at origins during S-phase and are lost in G2/M.

Reviewer #2 (Significance (Required)):

The topic is relevant and the problem being addressed is very interesting. While there has been some earlier work in this area, the polymer simulation approach used here is novel. The simulation methodology is technically sound and appropriate for the problem. Results are novel. The authors compare their simulations with experimental data and explore both interacting and non-interacting replication forks. Most conclusions are supported by the data presented. Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The manuscript by D'Asaro et al. investigates the relationship between DNA replication and chromatin organization using polymer simulations. While this is primarily a simulation-based study, the authors also present relevant comparisons with experimental data and explore mechanistic aspects of replication fork interactions.

We thank Reviewer 2 for her/his positive evaluation of our work and her/his suggestions that help us to clarify many aspects in our manuscript.

The primary weakness is that many aspects are not clear from the manuscript. Below is a list of questions that the authors must clarify:

In the Model and Methods section, it is written "Arbitrarily, we choose the backbone to be divided into two equally long arms, in random directions." It is unclear what is meant by "backbone to be divided" and "two equally long arms." Does this refer to replication?

We agree with the reviewer that the term backbone may be ambiguous. In the context of the initialization of the polymer, it refers to the L/4 initial bonds used to recursively build an unknotted polymer chain of final size L using the Hedgehog algorithm (see refs [101,109]). As shown in the Fig S1A, these initial L/4 bonds define the initial backbone of each chromosome before they are recursively grown to their final size. We chose to divide them into two branches (called “arms” in the old version of the manuscript) of equal length (L/8) and with random orientations. To avoid any ambiguity between the term arm used in that context and the chromosome arms in a biological sense (sequences on the left and right with respect to centromeres), we changed it to “linear branches” to improve clarity. We highlighted in Fig. S1A two examples of such a “V-shaped” backbone.

As stated in the text, these initial configurations are artificial and just aim to generate unknotted, random structures. After initiating the structures, we then added the geometrical constraints to the centromeric, telomeric and rDNA beads. This, combined with the tendency of the polymer to explore and fill the spherical volume, determine the relaxed G1-like state (see Fig. S2) obtained after an equilibration stage (corresponding to 10^7 MCS). Only after that initialization protocol, DNA replication is activated.

In chromosome 12, since the length inside the nucleolus (rDNA) is finite, the entry and exit points should be constrained. Have the authors applied any relevant constraint in the model?

Indeed, we did not introduce any specific constraint on the relative distance between rDNA boundary monomers in our model. They can therefore freely diffuse, independently from each other, on the nucleolus surface. This point is now clarified in the text. Note that, in this paper, we did not aim to finely describe the rDNA organization and its interactions with the rest of the genome, that is why we did not explicitly model rDNA. Moreover, to the best of our knowledge, there is not available experimental data to potentially tune such additional restraints.

Previous models such as Tjong et al. (ref. [66]) and Di Stefano et al. (ref [67]) have used very similar approximations than us. In the works of Wong et al. (ref.[61]) and Arbona et al. (ref.[63]), rDNA is explicitly modelled via larger/thicker beads/segments, and thus accounts for some generic polymer-based constraints between rDNA boundary elements.

However, note that all these different models, including ours, still correctly predict the strong depletion of contacts between rDNA boundaries, indicating that there exists a spatial separation between the two boundary elements that is qualitatively well captured by our model (See Fig. S1 D and Fig. 1B).

What is the rationale for normalizing the experimental and simulation results by dividing by the respective P_intra(s = 10 kb)?

This normalization was used in Fig. 1 to obtain a rescaling between experiments and simulations. This approach assumes that simulated and experimental Hi-C maps are proportional by a factor that, in Fig 1B, was set to P_exp(s=16kb)/P_sim(s=16kb). Similar strategies are used in a number of modeling studies (for example ref. [103,106]).

We use the average contact frequency (P_intra) at this genomic scale (s in the order of 10s of kb) because our polymer simulations well capture the experimental P(s) decay above this scale. This method allows to plot the two signals with the same color scale and to give a qualitative, visual intuition on the quality of the modeling. Note that normalization has no impact on the Pearson correlation given in text. More generally, it allows to semi-quantitatively compare predicted and experimental Hi-C data.

In Fig 1D, we instead normalize the average signal between pairs of centromeres (inter-chromosomal aggregate plot off-diagonal) by the average P_intra(s=10kb). This method allows estimating how frequently centromeres of different chromosomes are in contact relative to intra-chromosomal contacts at the chosen scale (10 kb). In the new paragraph “Comparison with in vivo HiC maps in G1” (Page 22) , we describe more in detail the quantitative insights that can be recovered from such analysis.

As a comparison, such normalization is not required when computing Observed/Expected maps (Fig. 1C or aggregate plots in Fig. 2 and Fig. 3) as simulation and experimental maps are normalized by their own P(s) curves. We now clarify this aspect in the Materials in Methods under the paragraph “Comparison between on diagonal aggregate plots” (Page 22).

In the sentence "For instance, chromosomes are strictly bound by the strong potential to localize between 250 and 320 nm from the SPB," is it 320 or 325 nm? Is there a typo?

We confirm that the upper bound is indeed 325 nm as stated in Eq.2 and not 320 nm.

Please list the number of beads in each chromosome and the location of the centromere beads.

A new table (Table S2) was included to highlight beads number and centromere positions.

In Eq. 7, when the Euclidean distance between the sister forks d_ij > 50 nm, the energy becomes more and more negative. This implies that the preferred state of sister forks is at distances much greater than 50 nm. Then how is "co-localization of sister forks" maintained?

We corrected the typo sign in Eq.7. The corrected equation without the minus sign - consistently with what simulated - implies that sister forks tend to minimize their 3D distance. The term goes to zero when their distance is within 40 nm (2 nearest-neighbouring sites).

The section on "non-specific fork interactions" is unclear. You state that the interaction is between "all the replication forks in the system," but f_ij is non-zero only for second nearest-neighbors. The whole subsection needs clarification.

We corrected the text, specifying that the energy is non-zero for both first and second neighbours. In practice, two given forks do not experience any attractive energy unless their 3D distance is less than 2 nearest-neighbours. To clarify this aspect, we articulated more in the methods how non-specific fork interactions are implemented in the lattice during the KMC algorithm. We also included a new supplementary image (Fig. S15), where we schematize how forks move in 3D and how changes in their position update the table that tracks the number of forks around each lattice site.

Eq. 6 has no H_{sister-forks}. Is this a typo?

We confirm that it is a typo and the formula was corrected to H_{sister-forks}.

While discussing the published work, the authors may cite the recent paper [https://doi.org/10.1103/PhysRevE.111.054413].

The reference is now included when discussing previous polymer models of DNA replication.

It is not clear how the authors actually increase the length of new DNA in a time-dependent manner. For example, when a new monomer is added near the replication origin (green bead in Fig. 3C), what happens to the red and blue polymer segments? Do they get shifted? How do the authors take into account self-avoidance while adding a new monomer? These details are not clear.

The detailed description of the chain duplication algorithm and its systematic analysis was performed in our previous study (ref. [25]).

However, we agree with the reviewer that to improve self-consistency more details must be included in the present manuscript (see also answer to comment 1 of Reviewer 3). In particular, we now highlight in Materials and Methods that self-avoidance is indeed temporarily broken when we add a newly replicated monomer on top of the site where the fork is. Such double occupancy in the lattice rapidly vanishes due to 3D local moves. We refer to our PRX work (ref [25] and in particular to the following figure (extracted from FIG. S1 in ref.[25]) which illustrates how the bonds/segments of the two sister chromatids are consistently maintained.

How do the authors ensure that monomers get added at a rate corresponding to velocity v? The manuscript mentions "1 MCS = 0.075 msec," but in how many MC steps is a new monomer added? How is it decided?

Similarly to origin firing, replication by fork movement along the genome occurs stochastically, with a rate which we derive by converting the physiological fork speed in yeast 2.2 kb/min (ref. [41]) into a rate in (number of monomer/MCS) units. In practice, we generate a random number that, if smaller than such a rate, leads to forks duplication. We clarify this aspect in the Materials and Methods, also referring to our previous work for a more detailed summary.

The authors stress the relevance of loop extrusion. However, in their polymer simulation, the newly replicated chromatin does not form any loops. Is this consistent with what is known?

Indeed, our simulations do not have any concurrent extrusion mechanism such as cohesin-mediated loops. This choice was purposely made to isolate and characterize replication-dependent effects.

That is why we compare our predictions on chromatin fountain patterns (Fig. 3) with data obtained for the Scc1 mutant strain where cohesin is absent in order to disentangle the possible interference with loop-extruding cohesin. For subsection C where microscopy data are available only in WT condition, we cannot rule out that the observed discrepancies between experiments and predictions cannot be due to missing mechanisms including loop extrusion. It was already mentioned in the Discussion (Page 16). It is however unclear whether sparse and small loops between CARs (see Fig. S7B) in S-phase, could be sufficient to recapitulate the microscopy estimates on the sizes of replication foci and no clear signature of inter-origin loops (possibly mediated by loop extrusion) are observed in Hi-C data in WT and Scc1 deficient conditions.

Moreover, as mentioned in the Discussion, the poorly characterized mechanisms behind forks/extruding-cohesin encounters does not allow for a straightforward modelling of such processes whose accurate description/simulation would require its own study.

Please add a color bar to Fig. 4B.

The color bar was included.

In the MSD plot (Fig. 6), even though it appears to be a log-log plot, the exponents are not computed. Typically, exponents define the dynamics.

We plot the expected 0.5 exponent at smaller time-scales as mentioned in the main text in Fig. 6, previously included only in new Fig. S19A.

The dynamics will depend on the precise nature of interactions, such as the presence or absence of loop extrusion. If the authors present dynamics without extrusion, is it likely to be correct?

The reviewer is correct in highlighting how our model does not capture the potential decrease in dynamics due to cohesin mediated loop extrusion. However, our model does capture the expected Rouse regime (see Fig. 6A, S19A and ref [83]), which justify our timemapping strategy. In comment 16 of reviewer 3, we discuss more in detail the robustness of our results with respect to variation in such a mapping. In the specific context of Fig. 6A, we predict the gradual decrease in dynamics due to sister chromatids intertwining independently of any cohesin-associated activity (both loop-extruding and cohesive). As loop extrusion is also decreasing chromatin mobility overall (ref. [87]), if such a decrease in mobility is observed in WT in vivo, it may be indeed difficult to assign such a decrease to replication rather than loop extrusion. That is why in the Discussion (Page 16), we propose to compare our prediction to experiments in cohesin-depleted cells. In the context of Fig.6B&C, we don’t expect loop extrusion to be a confounding effect as the predicted decrease in dynamics is specific to forks.

Reviewer #3 (Significance (Required)):

The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interest to biophysics and molecular biology researchers; the manuscript is written such that it would suit an interdisciplinary basic research audience.

We thank Reviewer 3 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

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

The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.

We now have expanded the introduction to address some of these aspects. In particular, also as a response to comment 1 of Reviewer 4, we included additional background on the eukaryotic replication time program. We address in more detail its known interplay and correlation with crucial 3D structural features such as compartments and TADs. Finally, we add a sentence to clarify how the current work is distinct from the prior implementation and the novelty introduced here.

In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.

As very briefly summarized in the mentioned paragraph, the yeast genome is organized in the so-called Rabl organization where chromosome arms are all connected via the centromeres at the Spindle Pole Body (SPB). This is analogous to the definition of a polymer brush where several branches (the arms in this case), are grafted to a surface or to another polymer (see new Inset panel in Fig S1B). We refer in the main text to the scheme in Fig. S1B where we also include the snapshot of a single chromosome and the physical constraints that characterize this large-scale organization and extend the caption to clarify the analogy. A typical emerging feature at the single chromosome level is described in Fig. 1 B and C.

On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.

These Pearson correlations are obtained for our own modeling. We correct the values in the main text and more clearly indicate the specific correspondence with the maps used. We describe now in the Materials and Methods (new paragraph “Comparison with in vivo HiC maps in G1” and Table S2) how these values were obtained.

In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification why it will not likely change the results).

We now state more clearly in the main text the limitation of our model regarding the doubling of DNA content without any increase of nuclear size. As mentioned in the Discussion, we do not expect this approximation to strongly impact our results, which mainly focus on early S-phase.

We now also included in the Discussion how the detection of the “replication wave” should be qualitatively independent of the density regime. In fact, even in the case of growing nuclei and constant density, the polarity induced by the Rabl organization and replication timing are the main drivers of such fork redistribution.

Regarding the slowdowning in diffusion due to sister chromatids intertwinings (see response to comment 13), we instead verified that the effect is indeed density independent (new Fig S21).

Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.

This is now corrected.

In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.

We reformulate the description in the caption to make clearer that the time indicated above the plots indicate the time window used for the computation. As mentioned more in detail in the response to comment 17 below (and comment 3 of Reviewer 2), we included in the Material and Methods a more precise description on the normalization used in the case of on-diagonal aggregate plots (observed-over-expected).

In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rationale behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.

In the experimental setup, cells are arrested in G1 with alpha-factor and then released in S-phase (see Fig S26 with corresponding scheme). The release from G1 synchronisation is not immediate, and staging of cells by flow-cytometry every 5 minutes for 30 minutes after release (data not shown in the main text but provided below) proved 20 minutes to be an adequate early S-phase timepoint (Page 17 in the Materials and Methods). As a consequence, the times indicated when describing the in vivo experiment, do not correspond to the ones indicated in our in silico system, for which the onset of replication is well defined. For these reasons, we have to determine which time window among the ones used in Fig 2E, is the most appropriate to compare with the experiment (see response to comment 9 for more details).

Fig.R1: Cell cycle progression monitored by flow cytometry after the release. For the first 15 minutes, cells are still mainly in G1 and only start replicating ~20 minutes after the release.

Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.

Taken together, the G1, Cdc20 (metaphase-arrested cells), and Cdc45-depleted (early S cells but not replicated) conditions suggest that fountains reflect ongoing replication. Namely, G1-arrest shows that fountains require S-phase entry; Cdc45-depletion shows that fountains require origin firing and is not due to another S-phase event; and metaphase-arrested cells show that fountains are not permanent structures established by replication, but a transient replication-dependent structure.

This demonstrates that the emerging signal is not trivially dependent on (1) the presence of the second sister chromatids; or on (2) potential overlaps between origin positions and barriers (CARs) to loop extrusion (see also comment 12 of Reviewer 2). A sentence at the end of II-a was added to clarify the different information gained with the two strains.

We discuss again the cdc20 and cdc45 mutants in II-b to highlight how the results in II-a do not exclude potential interplay between cohesin-mediated loop-extrusion in presence forks progression. These considerations motivated our experiment in Scc1-depleted cells during early S-phase.

At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.

As mentioned above in comment 7, we cannot clearly define a “t=0” when replication starts in vivo as the release from the G1-arrest is not immediate and perfectly synchronous. On the other hand, the times indicated within the text are those following the onset of polymer self-duplication in our simulations. Note that the mean replication time (MRT) shown in Fig.2B does not represent an absolute time, but rather an average relative timing along S-phase (signal rescaled between 0 and 1).

For all these considerations, we think that the most reliable strategy to compare fountains in vivo and in silico is to look at the replicon size via the enrichment in raw contacts around early origins, as illustrated in Fig S7A. In practice, looking at the relative counts of contacts around early origins we have a proxy for the average replicon size that we can match by computing the same analysis on simulated signals (Fig S7A). As a result, we find that the best simulated time window is between 5 and 7.5 minutes, compatible with early-S phase and with an approximate duration of G1 after release of 15 minutes as observed in other studies (ref. [61]).

Note that our conclusions are robust with respect to modulating this mapping method. In particular in Fig. S7, we thoroughly investigated how several confounding factors (such as time window used or partial synchronization) may impact the quantitative nature of our prediction without affecting the qualitative insights.

We included a more precise reference to the Supplementary Materials, where the approach is described and clarified.

In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).

This issue seems to appear using the default PDF viewer on Mac OS. We have corrected the problem and no more black boxes should appear in the main text and in the Supplementary Material.

In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?

We included a more accurate description in the main text about why Saner et al. (ref [20]) make these assumptions in their estimates. We highlight possible inconsistencies such as the presence of termination events which, in our formalism, break sister forks interactions and lead to single forks to be detected. We also clarify the latter point when describing Fig 5B and describe in more detail replication bubbles merging events in the Materials and Methods.

Fig 6B and C, it would be useful if the same scale was used on both plots.

We now use the same scale when plotting Fig 6B and C.

Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.

We included a small paragraph to better explain how intertwining of sister chromatids occurs, and more clearly refer to a snapshot in supplementary figure S19D (Page 14). As correctly mentioned by the reviewer, replication bubbles by construction are always unknotted during their growth (see example in Fig. 2C). As we thoroughly characterize in our previous work (ref. [25]), when several replication bubbles merge, the random orientation of sister chromatids potentially lead to catenation points and intertwined structures. We show below a scheme from our previous work (ref [25]). While in this past work, we demonstrated that the center of mass of the two sister chromatids show subdiffusive behaviour due to the additional topological constraints of their intertwining, this new analysis in the present work suggests that possible effects may also be observed when tracking the MSD (mean square displacement at the locus level) in a more realistic scenario where we included correct replication timing, chromosome sizes and Rabl-organization.

On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?

To the best of our knowledge, there is no other candidate protein of the replisome that has been directly associated to sister-fork pairing in previous studies (as Ctf4). However, components of the replisome such as Cdt1, that have the capacity to oligomerize/self-interact, could be good candidates. We now mention this possibility in the Discussion (Page 15).

As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.

We rephrased the mentioned passages to clarify the differences in detecting such “replication wave” at the population vs single cell level. In video S3 and S4, we can still observe an enrichment of forks at the SPB and later in S-phase a shift towards the equatorial plane. However, the stochasticity of polymer dynamics and 1D replication strongly hinder the ability to clearly visualize such redistribution.

In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.

Indeed, we choose to map the experimental MSD measured in ref [83] by simulating a homopolymer 5% volume fraction and in periodic boundary conditions for consistency to previous work in the group (ref. [102-106]) and our previous replication model (ref.[25]). Moreover, this intermediate density regime also lies in between the minimal (3%) and maximal (6%) densities present in our system. When redoing the time mapping with the G1 MSD plotted in Fig 6A and new Fig S19A, we obtain a very similar value of approx. 1MC=0.6ms. Note that the time mapping aims to obtain a rough estimation of real times as several factors, such as active processes, non-constant density, cell-cycle progression may all contribute to chromatin diffusion in vivo (see also comment 15 to Reviewer 2). In the context of our formalism, differences in time mapping do not affect the 1D replication dynamics as all the parameters to model the 1D process are rescaled by the same factor. Moreover, as we characterized in more depth in our previous work (ref [25]), a crucial aspect that defines self-replicating polymers is the relationship between fork progression and the polymer relaxation dynamics. In physiological conditions, we remain in the regime where forks progress almost quasi-statically to allow the bubbles to re-equilibrate. Therefore, small discrepancies in the time mapping will not modify this regime and our results should remain robust.

On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?

In the raw - unbalanced - data, we observe an artificial increase in contacts around origins in S-phase for both simulation and experiments. This is simply due to the presence of the second Sister chromatids and the fact that contacts between distinct DNA segments are mapped to a single bin.

In the new Fig. S25, we illustrate this effect by computing aggregate plots around early origins using single-chromosome simulations. We demonstrate that the ICE normalization corrects for the variations in copy number due to replication and thus for such artificial increases in contacts during S-phase. We show that such a normalization is equivalent to explicitly divide each bin by the average copy-number of the corresponding segments.

We have now included a sentence in the Materials and Methods to clarify this. Moreover, a detailed description of the other alternative strategies used to compare experiments and simulations were presented in response to comment 3 to Reviewer 2 and two new paragraphs were added in the Materials and Methods.

The references section has an unusual formatting with journal names underlined.

We updated the formatting.

Reviewer #4 (Significance (Required)):

D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condensin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

We thank Reviewer 4 for her/his positive evaluation of our work and her/his comments that help us to greatly improve the manuscript.

Reviewer Comments / Significance

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors D’Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Major Comments

There is a tremendous amount of work coupling RT domains to 3D genome architecture, especially deriving from the ENCODE and 4D Nucleome consortia. These studies are not adequately highlighted in the introduction and discussion of this manuscript, and this treatment of the literature would ideally be amended in any revised manuscript.

We include new sentences in the introduction to discuss more in detail the correlation between 3D genome architecture and replication timing program, and advancement in this field in the last decades. We also included additional citations to reviews and publications (ref [8-16]). These references were also included at the end of the Discussion where we address the exciting perspective of employing our model in higher eukaryotes and potentially tackle the complex interplay between 3D nuclear compartmentalization and replication dynamics (see also response 1 to Reviewer 1).

S. cerevisiae origins of replication differ from metazoan origins of replication in that they are sequence-defined and are known to fire in a largely deterministic pattern (see classic study PMID11588253). From the methods of the authors it is not clear that the known deterministic firing pattern is being used here, but instead a stochastic sampling method? Please clarify in the manuscript. Specifically, it would be good to understand how the Initiation Probability Landscape Signal correlates with what is already known about origin firing timing.

In our model, the positions of origins are stochastically sampled proportionally to the IPLS which was inferred directly from experimental MRT (ref. [63]) and RFD (ref. [44]). This modeling approach allows reproducing with a very high accuracy the known replication timing data (correlation of 0.96) and Fork directionality data (correlation of 0.91) (see ref. [71]). Origins were defined as the peaks in the IPLS signal. In Fig S3, we extensively compare these origins and the known ARS positions from the Oridb database. For example, most of our early origins (96%) are located close to known, confirmed ARS. Moreover, even if our algorithm is stochastic for origin firing, we remark that each early origin will fire in 90 % of the simulations, coherent with the quasi-deterministic pattern of origin firing and experimental MRT and RFD data. We now have added such statistics of firing in the revised manuscript (Page 4).

It seems possible that experimental sister chromatid Hi-C data (PMID32968250) and nanopore replicon data (PMID35240057) could be used to further ascertain the validity of some of the findings of this paper. Specifically, could the authors demonstrate evidence in sister chromatid Hi-C data that the replisome is in fact extruding sister chromatids? Moreover, are the interactions being measured specifically in cis (as opposed to trans sister contacts)? For the nanopore replicon data, how do replicon length, replication timing, and position along the replication 'wave' correlate?

We thank the reviewer for the suggestions.

Hopelessly there is currently no Sister-C data available during S-phase. In the seminal study (PMID32968250), cells were arrested in G2/M via nocodazole treatment. For a different unpublished work, we already analysed in detail the SisterC dataset and we did not observe clear fountain-like signature, consistent with our own G2/M Hi-C maps (cdc20) where fountains were absent. Note that, in the present work, in order to compare our predictions with standard HiC data, we included all contacts (cis and trans chromatids), mapping pairwise contacts from distinct replicated sequences/monomers to a single bin (see also response to comment 17 to Reviewer 3 and new Fig. S25).

We now mention in the Discussion that Sister-C data during S-phase could help monitoring the role of replisomes on relative sister-chromatids organization (Page 15).

Main results from the nanopore replicon data study include the observed high symmetry between sister forks and their linear progression, as the density of replicons appears to be uniform with respect to their length. Since these two specific constraints are already present in the framework of Arbona et al. (ref. [63]), our model is able to reproduce these features of DNA replication captured by the nanopore data.

Moreover, as we model with very high accuracy replication timing data (see response to comment 2) and forks positioning, we can assume that our formalism well captures replicon positioning and lengths observed in vivo.

As this study does not include any additional exploration or variation of the parameters inferred by Arbona et al. (ref. [63]), we consider a quantitative comparison with the nanopore replicon data to be beyond the scope of this paper.

Minor Comments:

The paper is in most places easy to follow. However, Section C bucked this trend and in general was quite difficult to follow. We would recommend that the authors try to revise this section to make clearer the actual physical parameters that govern a 'replication wave' and the formation of replication foci - how many forks, the extent to which the sisters are coordinated, etc for early vs. late replicating regions.

We now state more clearly with a sentence in the main text the driving forces behind the formation of such a “replication wave”. We believe that the several additions and clarifications following the various comments, improved the clarity of the manuscri

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Referee #4

Evidence, reproducibility and clarity

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

Reviewer Comments / Significance

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Major Comments

• There is a tremendous amount of work coupling RT domains to 3D genome architecture, especially deriving from the ENCODE and 4D Nucleome consortia. These studies are not adequately highlighted in the introduction and discussion of this manuscript, and this treatment of the literature would ideally be amended in any revised manuscript.
• S. cerevisiae origins of replication differ from metazoan origins of replication in that they are sequence-defined and are known to fire in a largely deterministic pattern (see classic study PMID11588253). From the methods of the authors it is not clear that the known deterministic firing pattern is being used here, but instead a stochastic sampling method? Please clarify in the manuscript. Specifically, it would be good to understand how the Initiation Probability Landscape Signal correlates with what is already known about origin firing timing.
• It seems possible that experimental sister chromatid Hi-C data (PMID32968250) and nanopore replicon data (PMID35240057) could be used to further ascertain the validity of some of the findings of this paper. Specifically, could the authors demonstrate evidence in sister chromatid Hi-C data that the replisome is in fact extruding sister chromatids? Moreover, are the interactions being measured specifically in cis (as opposed to trans sister contacts)? For the nanopore replicon data, how do replicon length, replication timing, and position along the replication 'wave' correlate?

Minor Comments:

• The paper is in most places easy to follow. However, Section C bucked this trend and in general was quite difficult to follow. We would recommend that the authors try to revise this section to make clearer the actual physical parameters that govern a 'replication wave' and the formation of replication foci - how many forks, the extent to which the sisters are coordinated, etc for early vs. late replicating regions.

Significance

Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

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Referee #3

Evidence, reproducibility and clarity

The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

1. The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.
2. In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.
3. On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.
4. In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification was to why it will not likely change the results).
5. Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.
6. In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.
7. In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rational behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.
8. Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.
9. At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.
10. In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).
11. In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?
12. Fig 6B and C, it would be useful if the same scale was used on both plots.
13. Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.
14. On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?
15. As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.
16. In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.
17. On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?
18. The references section has an unusual formatting with journal names underlined.

Significance

The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interested to biophysics and molecular biology researchers; the manuscript is written such that it would suit a interdisciplinary basic research audience.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by D'Asaro et al. investigates the relationship between DNA replication and chromatin organization using polymer simulations. While this is primarily a simulation-based study, the authors also present relevant comparisons with experimental data and explore mechanistic aspects of replication fork interactions.

The primary weakness is that many aspects are not clear from the manuscript. Below is a list of questions that the authors must clarify:

1. In the Model and Methods section, it is written "Arbitrarily, we choose the backbone to be divided into two equally long arms, in random directions." It is unclear what is meant by "backbone to be divided" and "two equally long arms." Does this refer to replication?
2. In chromosome 12, since the length inside the nucleolus (rDNA) is finite, the entry and exit points should be constrained. Have the authors applied any relevant constraint in the model?
3. What is the rationale for normalizing the experimental and simulation results by dividing by the respective P_intra(s = 10 kb)?
4. In the sentence "For instance, chromosomes are strictly bound by the strong potential to localize between 250 and 320 nm from the SPB," is it 320 or 325 nm? Is there a typo?
5. Please list the number of beads in each chromosome and the location of the centromere beads.
6. In Eq. 7, when the Euclidean distance between the sister forks d_ij > 50 nm, the energy becomes more and more negative. This implies that the preferred state of sister forks is at distances much greater than 50 nm. Then how is "co-localization of sister forks" maintained?
7. The section on "non-specific fork interactions" is unclear. You state that the interaction is between "all the replication forks in the system," but f_ij is non-zero only for second nearest-neighbors. The whole subsection needs clarification.
8. Eq. 6 has no H_{sister-forks}. Is this a typo?
9. While discussing the published work, the authors may cite the recent paper [https://doi.org/10.1103/PhysRevE.111.054413].
10. It is not clear how the authors actually increase the length of new DNA in a time-dependent manner. For example, when a new monomer is added near the replication origin (green bead in Fig. 3C), what happens to the red and blue polymer segments? Do they get shifted? How do the authors take into account self-avoidance while adding a new monomer? These details are not clear.
11. How do the authors ensure that monomers get added at a rate corresponding to velocity v? The manuscript mentions "1 MCS = 0.075 msec," but in how many MC steps is a new monomer added? How is it decided?
12. The authors stress the relevance of loop extrusion. However, in their polymer simulation, the newly replicated chromatin does not form any loops. Is this consistent with what is known?
13. Please add a color bar to Fig. 4B.
14. In the MSD plot (Fig. 6), even though it appears to be a log-log plot, the exponents are not computed. Typically, exponents define the dynamics.
15. The dynamics will depend on the precise nature of interactions, such as the presence or absence of loop extrusion. If the authors present dynamics without extrusion, is it likely to be correct?

Significance

1. The topic is relevant and the problem being addressed is very interesting. While there has been some earlier work in this area, the polymer simulation approach used here is novel.
2. The simulation methodology is technically sound and appropriate for the problem. Results are novel.
3. The authors compare their simulations with experimental data and explore both interacting and non-interacting replication forks.
4. Most conclusions are supported by the data presented.

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Referee #1

Evidence, reproducibility and clarity

By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) T The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks. This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation. However, the following revisions could strengthen the manuscript:

Major:

1. Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.
2. Validation with Repli-HiC or Time-Resolved Techniques The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.
3. Interactions Between Convergent Forks The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?
4. Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts In Fig.3A, a schematic illustrating the cell treatment would improve clarity.

In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include: Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks). Altered chromatin mobility in mutants, enhancing Hi-C signal resolution. Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included). 5. Inconsistent Figure References Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

Minor:

1. Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.
2. In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

Significance

General assessment:

This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

Audience: broad interests; including DNA replication, 3D genome structure, and basic research

Expertise: DNA replication and DNA damage repair within the chromatin environment.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Quétin et al "Transient hypoxia followed by progressive reoxygenation is required for efficient skeletal muscle repair through Rev-ERBα modulation" describes the nature of muscle stem cell (MuSC) differentiation within its hypoxic niche using in vivo, ex vivo and in vitro methodologies. Approaches to limit oxygen in a regenerating model of muscle injury showed that muscle oxygenation is necessary for proper muscle repair. They found that the lack of oxygen is associated with the formation of hypotrophic myofibers, due to the inability of MuSCs to differentiate and fuse. Their findings show that the phenotype was independent of HIF-1α. However, RNA-seq of MuSCs 7 day post injury from prolonged hypoxia was shown to have significantly increased circadian clock gene Rev-erbα expression. Pharmacological inhibition of Rev-erbα during hypoxia rescued the myogenic phenotype. Contrarily, the use of Rev-erbα agonist in normoxia impaired the fusion capacity of MuSCs and decreases the number of large mature myofibres. This manuscript is well written and very easy to follow. Though, there are certain shortcomings outlined below. Sometimes the evidence provided does not support the conclusions made. For example, more rigour should be performed to state that there is a self-renewal phenotype.

Major issues

1. In Figure 1, why were these timepoints chosen? Is the hypoxia more severe between days 0 and 5 (i.e. when MuSCs begin their activation).
2. "From 5 to 28 dpi, pimonidazole adduct intensity gradually declined, demonstrating a progressive reoxygenation after transient hypoxia during muscle repair (Fig. 1E and 1F) that correlates with progressive restoration of the vascular network (Fig. 1C) and MuSC return into quiescence (Fig. 1B and 1D)." For this statement, correlating these events to MuSC returning to quiescence might not be appropriate. As Figure 1D shows all the Pax7+ cells, it does not reflect whether they are quiescent. Thus, the timelines might not actually match up with the proportion of self-renewed MuSCs?
3. The manuscript cites far too many review articles (at least half) and not primary sources. Also, some citations are misrepresented. For example: Reference #13 does not show that HIF-1alpha level increases during muscle injury in rodents, Reference #15 shows fusion is impaired in hypoxic c2c12 cells, not promotion of quiescence, Reference #22 does not support the claim that hypoxia induces myostatin expression, only that myostatin inhibits MyoD expression.
4. Figure 1E and 1F, does the dye intensity change with it being more accessible to the muscle during early injury as opposed to later recovery. Also, when using the probe for hypoxia determination, the whole tissue is fluorescing intensely suggesting potential non specificity. It would be prudent to use markers of hypoxia on western blots or gene expression to corroborate this data.
5. a) It is well known that CTX injury does not cause damage to the vasculature but directly to the muscle (Tatsumi et al doi:10.1002/stem.2639; Ramadasan-Nair et al doi:10.1074/jbc.M113.493270; Ohtsubo et al. doi: 10.1016/j.biocel.2017.02.005; Wang et al doi: 10.3390/ijms232113380). How do the authors reconcile their findings that there is vasculature damage with CTX (Fig. 1C).

b) Moreover, the endothelial cell staining (Fig. 1B) appears to be unchanged in the time course of injury. To prove vascular damage this data should be corroborated, for example with lectin perfusion. 6. Problems with Figure 3J. There are data points with zero clusters/isolated myofibres suggesting that the hypoxic environment caused MuSCs to not activate from quiescence. There are several outliers for example at 1% there is a zero reading that makes the data significant. 7. In Figure 1G, Loxl2 after 14 days appears to be significant, as the error bars at 0 and 14 days do not overlap and thus it does not return to normal. An n=3 is not sufficient, as one of the data points at 14 days appears to be an outlier (the data stretching from 1500 to 3000). 8. In Fig. 2C and 2D, there are no control CSA and myofiber diameter experiments for keeping the mice in hypoxia over 14 and 28 days without injury. 9. For Figure 3K, how can self-renewing MuSCs be distinguished from MuSCs that never activated? Especially in the 1% O2 condition where few clusters formed. How does hypoxia influence activation? A 4hr or 8hr timepoint is necessary, as well as 24hrs. Also, for Figure 5E and 5F, it is possible that HIFcKO allowed the cells to activate normally, thus explaining the shift from quiescence to activation in the read-outs. This further highlights the importance of analyzing earlier timepoints. One cannot state that these cells are self-renewing or returning to quiescence without performing experiments on earlier timepoints. 10. The data for Figure 4 does not suggest that transient reoxygenation is required "for proper skeletal muscle repair" as stated by the authors only that reoxygenation has rescued the phenotype in the primary myoblasts. There is no hypoxia in the control (8% O2) for regeneration to occur (Fig. 2B). 11. One cannot rule out metabolic dysregulation. It's true that glycolytic fibers are generally larger than oxidative, it is likely that that alone does not explain the difference in fiber size. However, the fact that the fibers are more glycolytic does suggest a metabolic shift in the muscle (which was the aim of the experiment), which could also shift MuSC character altering their behaviour. How are MuSCs metabolically responding to hypoxia? 12. In Figure 2, how can one be sure that reoxygenation is blocked by the hypoxic chamber? Reduced O2 levels will induce hypoxia, but one cannot state that it blocks reoxygenation without further validation such as using pimonidazole as in Fig. 1E. If reoxygenation is blocked, then pimonidazole staining should remain consistent throughout the injury. 13. For Figure 3G, is a sum appropriate for the graph? Proportions would be more appropriate as cell number is not equal as shown in figure 3E. Can Pax7+/MyoD+ be defined as differentiated? By day 7, many MuSCs will have fused and be expressing MyoG, which is not accounted for by these definitions. Did systemic hypoxia increase self-renewal or impair activation? How can you distinguish these two? 14. In Figure 6A, while it is interesting that Pax7 levels are elevated in hypoxia and differentiation and fusion markers are down at 7days, it does not necessarily mean that self-renewal is increased. It might suggest that the hypoxic cells might have never activated or might have differentiated precociously. Are any cell cycle genes down regulated? Any other genes involved in quiescence altered? 15. The use of pimonidazole in Fig. 1E shows the staining within fibers (many with centrally located nuclei). These nuclei are differentiating and not representative of expanding MuSCs. How do the authors reconcile these MuSCs as part of their population.

Minor Problems

1. In the introduction, the line "Vascular alterations result in reduced oxygen (O2) levels, disrupting cell homeostasis and contributing to many diseases" is not always true as vascular alterations do not always result in reduced oxygen levels. For example, in angiogenesis there is no reduction of O2. This line should better reflect this.
2. In the introduction, Paragraph 2, line 9 change "quiescence thought HIF-1α" to "quiescence through HIF-1α".
3. Paragraph 3, line 8: "lead" instead of "leads"
4. It is not sure how important the connection between capillary density and Pax7+ cell number is. Both are presumed to occur at the same time in muscle, so both will recover concurrently. To state that it is a coupled response is overstating the evidence presented.
5. Figure 1B the colour-labels for Pax7 and Dapi over lap with the border.
6. In the Introduction, the following sentence does not follow the previous sentence: "In vivo, Majmundar and colleagues show that HIF-1a in MuSCs negatively regulates myogenesis by decreasing myogenic differentiation".
7. In the Introduction, the following statement is not accurate "Hypoxia can also alter myogenic differentiation and myotube formation by inhibiting p21 (as known as p21 and CDKN1A) that leads to an accumulation of the retinoblastoma protein Rb24", for what was found in the reference. The authors should correct this statement.
8. Paragraph 3, line 5: "as known as p21 and CDKN1A" should perhaps read "also known as CDKN1A"
9. The following statement is not supported by the results: "Strikingly, the most abundant and intense pimonidazole staining is detected on CTX-injured TAs at 5 dpi, indicating that myogenic cell expansion is initiated in a hypoxic environment in situ (Fig. 1D-1F)." MuSCs are activated and expanding from time zero to 5 days according to Figure 1D.
10. "....Since glycolytic fibers are larger than oxidative fibers, ...." citation missing
11. An inconsistent finding is that the authors show that protein synthesis rates are normal between normoxia and hypoxia of regenerating muscle (suppl. Fig. 1E), yet the capacity of protein synthesis is found to be higher in oxidative muscle fibres compared to glycolytic fibers (Van Wessel et al, doi: 10.1007/s00421-010-1545-0), which are formed during regeneration (Fig. 2G and 2H).
12. Some figure legends that describe graphs do not denote the number of samples or mice used.
13. In Figure 1C, 1D and 1F what is being compared to obtain statistical significance?
14. The font size of many figures is too small to follow.
15. Confusion for the results of figure 3G. Labels in the text do not reflect the labels in figure (which cannot be read anyway because the font is too small). Why is Ki67 used as a marker for activation versus proliferation.
16. The physiological O2 concentration is 8%, do the authors know what the hypoxic O2 concentration is in the injured environment. Why did they choose hypoxic O2 concentration at 1% for ex vivo and invitro experiments? Why did they choose 10% for the in vivo experiment?
17. For Figure 2H it is not appropriate to state that type IIA ratio was reduced with hypoxia, as the results show no statistical significance.
18. For Figure legend 3K, are the cell number/fiber the sums per one mouse or the sum from all mice combined for each condition?
19. For Figure 3B and 3E "concomitantly with their proliferation peak" seems to imply that hypoxia in Pax7+ cells peaks alongside proliferation, but the evidence doesn't support that conclusion. More timepoints would be needed to show that 5 dpi is truly the peak of hypoxia in Pax7+ cells.
20. For Figure legend 4E, should read "MHC" not "MCH"
21. In Figure 4C there is no gap between the significance bar.
22. In Figure legend 5G, "Experience design" should read "Experimental design"
23. Representative images Fig 3I and 5E are poor quality.
24. Confusing statement "In the same way, this presence of smaller myofibers under prolonged hypoxia could not be explain by the glycolytic fiber-type switch from type-IIA to type-IIB, as observed in pathological context of COPD or peripheral arterial disease (PAD), since type-IIB are the largest myofibers in mice."

Referees cross-commenting

I agree with the thoughtful reviews and issues raised by Reviewers 1 and 2. I do not have anything more to add.

Significance

General Assessment: This manuscript is well written and easy to follow. It rigorously investigates the influence of oxygenation on MuSC behaviour. The authors utilize in vivo, ex vivo, and in vitro models to support their study and executed their work to a high degree. A limitation is that all experiments are only performed in mice and might not be applicable in humans. In addition, some claims made by the authors were over-reaching. The study can be improved by further validating some of the authors' claims, as has been suggested in the review.

Advance: This study is the first to report the effect of hypoxia on MuSCs in an ex vivo culture and in vivo injury model using a hypoxia chamber. This study helps clarify the role of HIF-1α on MuSC behaviour by suggesting that it does have a role in MuSC fate decisions. Finally, the authors make a novel link between circadian rhythm and MuSC behaviour in hypoxia.

Audience: A specialized audience that is interested in myogenesis, muscle stem cells, and/or hypoxia will be interested in this study. It highlights the important role of oxygen in muscle regeneration and may help researchers understand the role of oxygen in MuSC fate decisions.

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Referee #2

Evidence, reproducibility and clarity

The manuscript, Transient hypoxia followed by progressive reoxygenation is required for efficient skeletal muscle repair through Rev-ERBa modulation, revisits the role of hypoxia in skeletal muscle regeneration after acute injury. They first nicely demonstrate, using the pimonidazole hypoxia probe, that during regeneration skeletal muscle is transiently hypoxic at 5 days post injury (DPI). Then they show skeletal muscle regeneration is impaired in mice housed in a hypoxic (10% 02) chamber; the regenerated muscle mass is smaller, due to smaller regenerated myofibers and there is a shift in myofiber type so that there are more IIB myofibers. In addition, at 7 DPI when mice are raised in a hypoxic environment there is a shift in muscle stem cells so that they are more proliferative and fewer have differentiated. Ex vivo experiments culturing muscle stem cells in association with EDL myofibers in 1% 02, as compared with 8% 02, also led to fewer differentiated Pax7-MyoD+ cells, but could be restored if 02 was subsequently increased to 8%. They also found that low oxygen inhibited myoblast fusion in vitro. They then tested, via Pax7CreERT2/+;HIF-1afl/fl, whether HIF-1a signaling mediated the response of muscle stem cells to hypoxia in vivo. Surprisingly, they found that loss of HIF-1 did not impair myofiber regeneration in normoxic or hypoxic conditions, but they do provide some data suggesting that HIF-1a is required for the hypoxic-induced increase in Pax7+MyoD- muscle stem cells. Bulk RNA-seq analysis of 7 DPI muscle from mice housed in normoxic versus hypoxic conditions uncovered the interesting mis-regulation of circadian rhythm associated genes - in particular, the circadian clock repressor Rev-ERBa. Using a pharmacological antagonist of Rev-ERBa they show in culture that blocking Rev-ERBa (in contrast to loss of HIF-1a) rescues the fusion defect of muscle stem cells cultured in 1% 02. Conversely, they show that a Rev-ERBa agonist inhibits fusion in 8% 02. Altogether, the paper provides interesting new data on the controversial role of hypoxia and HIF-1a as well as data suggesting a connection between hypoxia and circadian rhythm genes. The data is logical and well presented, and the paper will be of strong interest to the regeneration and skeletal muscle research communities. I have two major comments and a list of smaller suggestions to improve the manuscript.

Major comments:

1. In vivo experiments (presented in Figures 2, 3, 5, 6, 7) house mice in hypoxic (10% oxygen) chambers, and the authors suggest that this blocks the progressive reoxygenation of skeletal muscle during regeneration. Surprisingly, the authors do not test when the mice are in hypoxic chambers whether, in fact, skeletal muscle is hypoxic at homeostasis and whether during regeneration muscle experiences prolonged hypoxia. The obvious experiment would be to use the pimonidazole probe on skeletal muscle sections of muscle at homeostasis and at 0, 5, 6, 14, and 28 DPI CTX injury in mice housed in hypoxic chambers. Without some demonstration that skeletal muscle oxygenation is changed when the mice are housed in hypoxic chambers, it is impossible to interpret these experiments.

2. The authors claim that reducing reoxygenation by maintaining the mice under systemic hypoxia impairs skeletal muscle repair by limiting the differentiation and fusion capacity of MuSCs in HIF-1a-independent manner, while it favors their return into quiescence through HIF-1a activation. They provide some in vitro evidence that Hif1ais required for the high levels Pax7+MyoD- muscle stem cells in 1% O2. They should also show that the elevated levels of Pax7+ muscle stem cells at 7 DPI (seen in Fig. 3D-G) requires HIF1a via analysis of Pax7CreERT2/+;HIF-1afl/fl mice.

Minor comments:

1. Please provide a reference for the pimonidazole probe. Reference 26, Hardy et al., is not the right one.

2. Please provide references that Loxl-2, Pdgfb, and Ang2 are HIF-inducible target genes.

3. Fig. 2C shows changes in average myofiber diameter. How was this calculated? Is this the largest diameter? Is there a reason that cross-sectional area was not measured (the more standard measurement)? Also, generally this type of data is shown as bar graphs - which is how these data are shown in Fig. 5C. Please also show the data in Fig. 2C as bar graphs.

4. Please provide reference for 8% 02 being physioxia in culture.

5. Fig.5 should also quantify the number of centronuclei/myofiber (as in Fig. 2I) for Pax7CreERT2/+;HIF-1afl/fl mice 14 and 28 DPI - to further demonstrate that differentiation defects in hypoxia are HIF-1a independent.

6. Please provide a graphical model of your research findings.

7. There are many typos and verb tense issues. Please fix these. The most amusing is Stinkingly in the Discussion.

Referees cross-commenting

I think several important issues are raised by myself and reviewer 3. First, the authors need to explain and support their use of 10% O2 hypoxia in vivo chambers and 1% O2 for hypoxic in vitro experiments. Second, the authors have not demonstrated that reoxygenation of muscle is prevented in mice raised in hypoxic chamber. There are questions about how well the pimonidazole probe is working (the widespread expression at 5 dpi in Fig. 1E suggests there may be specificity issues) and this probe is also not shown for muscle from mice living in hypoxic chambers. Another method of demonstrating hypoxia in muscle tissue would be useful.

Significance

The paper provides interesting new data on the controversial role of hypoxia and HIF-1a as well as data suggesting a connection between hypoxia and circadian rhythm genes.

This paper will be of interest to researchers studying the role of hypoxia on regeneration and also to researchers studying muscle regeneration.

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Referee #1

Evidence, reproducibility and clarity

SUMMARY

Quétin et al investigated the dynamics of oxygen levels during the skeletal muscle regeneration following sterile damage and its impact on muscle repair. They combined in vivo and ex-vivo model systems, together with genetic and pharmacological manipulations. They found results consistent with the fact that a dynamic oxygeneation process, hypoxia during the early phase followed by reoxygenation, are involved in muscle repair. Prolonged hypoxia leads to defective myogenesis and muscle repair. These activities apper to be meadiated by modulation of Rev-ERBα levels. Collectively, the study provide intriguing insight regarding the role of oxygen in muscle repair.

MAJOR COMMENTS

1. In Figure 1, the 5 days post CTX injury is too late to claim that "myogenic cell expansion is initiated in a hypoxic environment". Indeed, at day 5 myofibers are already regenerated, although immature. To support their claim, the authors should perform analyses and quantification of Pax7+, Pax7+Ki67+ and hypoxia at earlier timepoints.

2. In Figure 2B, a larger number of mononuclear cells is present in hypoxia mice. Is hypoxia affecting the number/activity of extra-muscular cells important for muscle regeneration like for example FAPs, macrophages, etc?

3. In Figure 5H, the myotubes formed by HIF-1α cKO appear thinner than control myotubes. Is myotube size affected by lack of HIF1 α?

4. The choice of the 7 days post CTX for the RNA-seq is odd. Indeed, at that timepoint there are obvious histological abnormalities in hypoxia mice. Hence, it is highly likely that many DEGs are simply secondary to the defect in regeneration and not directly linked to hypoxia exposure. This is probably the reason why the authors found so many (close to 4K) DEGs. To focus on the genes closely-associated to the primary defect, the authors should have performed the RNA-seq at an earlier timepoint, in which minimal histological defects were present. While repeating the RNA-seq would be costly and time consuming, the authors could at least address this issue by RT-qPCR. Are muscle stem cell fate, repair, and circadian clock genes significantly altered 3 and 5 days after CTX injury in hypoxia vs normoxia?

5. Given that compounds have frequently off-target effects, the authors must independently support their Rev-ERBα findings by performing genetic manipulations, at least ex-vivo.

6. A recent study (PMID: 38333911), which was not cited by the authors, reports muscle atrophy and weakness, impaired muscle regeneration, and increased fibrosis in hypoxia exposed mice. Intriguingly, this was due to impaired MuSC proliferation and differentiation following HIF-2α stabilization under hypoxia. Hence, the authors should investigate if HIF-2α plays any role in the phenotypes they describe. For example, is HIF-2α a regulator of circadian clock genes expression?

Referees cross-commenting

The other reviewers raised very relevant issues and I fully agree with their comments. In particular, I concur with Reviewer #3 that in several instances the evidence provided by the authors does not support the conclusions made.

Significance

SIGNIFICANCE

There is a limited knowledge regarding the role of oxygen supply during tissue differentiation and repair. In the muscle field, there are conflicting reports in the literature. This study combines genetic, pharmacological and oxygen manipulations both in vivo and ex-vivo to investigate the role of oxygen during regeneration following sterile skeletal muscle injury. The results are very intriguing and potentially relevant both for muscle, but possibly also for other tissue repair. Aspects of the study that must be improved concern the role of HIF-1a and HIF-2α in the process, and the characterization of the molecular mechanism through which Rev-ERBα is regulated by oxygen and regulates muscle repair.

• AUDIENCE: specialized, basic research, translational research; results could potentially extend beyond the muscle field.

• FIELD OF EXPERTISE: muscle differentiation, muscular dystrophy, gene expression regulation.

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

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Referee #2

Evidence, reproducibility and clarity

SUMMARY OF THE PRESENTED FINDINGS

Abstract

1. LCOR (Ligand-dependent corepressor), which suppresses tumor growth by inducing the antigen presentation machinery (APM) of the tumor cells and constrains cellular plasticity.
2. poly β-(amino esters) (pBAE) nanoparticles (NPs).. Our results show optimal endosomal escape, which results in high transfection efficiency in vitro and in vivo
3. the combination of Lcor mRNA-loaded NPs with anti-PDL1 or anti-CTLA4 immunotherapies eradicated most of the tumors in our preclinical TNBC model.

Introduction

a. These structures facilitate endosomal escape due to protonation of tertiary amines at lower pH7.

Results

b. In human models MDAMB-231 and MCF7 cells, the NPs also showed high eGFP mRNA transfection efficiency

c. The efficiency of eGFP mRNA-loaded pBAE-NPs to transfect mRNA into different mouse breast cancer cells (AT3, 4T07, EO771, EMT6, 66cl4, EpRAS, and 4T1) was tested using NPs encapsulating eGFP mRNA,

d. Synthetic Lcor mRNA contained a Cap1, 5' and 3' untranslated regions (UTR) and a standard polyA tail (Fig. S2A), and all uracil were replaced for 5-methoxyuracil (5-moU) to avoid immunogenic reactions27,28. First, we measured and detected high levels of Lcor mRNA by qRT-PCR

e. NPs were stable at 25ºC for 24 h (Fig. S2C). In contrast, under conditions simulating the physiological environment (37ºC), a decrease in FRET signaling was detected ... indicating disassembly of the NPs after 2 h (Fig. S2C).

f. Lcor mRNA NPs, induces the expression of APM genes in AT3 and 4T07 cell lines

g. AT3 cells that constitutively overexpress ovalbumin (OVA). In these cells, OVA is cleaved, generating the SIINFEKL antigen peptide presented in the H-2Kb context. This can be used to measure APM activity using the anti-SIINFEKL antibody via flow cytometry.

h. We also observed a time- and dose-dependent effect regarding APM induction.

i. When tumors reached 0.5 x 0.5 cm2, we treated them intratumorally with pBAE-NPs loaded with 5 ug of synthetic FLuc or eGFP mRNA. We detected BLI at 3 h, meaning that tumor cells had taken up the mRNA-loaded NPs and translated a luciferase active protein within 3 h. In both models, expression peaked around 6 to 10 hours after administration

j. After local administration of 5 μg of Lcor mRNA-loaded NPs, we observed a rapid increase in Lcor mRNA in the tumor tissue, followed by a decrease, reaching baseline levels after 24 h (Fig. 3C). ..To unravel the protein dynamics, we used ... LCOR-HA protein and uniquely detect the ectopic protein using anti-HA by IF. As expected, LCOR-HA protein expression was delayed, peaking 3 h after administration (Fig. 3D). Linked to protein expression, at 3 h and 6 h after administration, we detected an increase in APM genes by RT-qPCR (Fig. 3E and S3D).

k. the combination of Lcor mRNA-loaded NPs with anti-PDL1 therapy not only reduced tumor growth but also led to tumor eradication in 5 out of 7 mice.

l. The combination of Lcor mRNA-loaded NPs with different ICIs showed high efficiency in preclinical models, thus supporting the feasibility of starting clinical studies and thus bringing the treatment closer to patients.

Major points

L. 277: "NPs were stable at 25ºC for 24 h (Fig. S2C). In contrast, under conditions simulating the physiological environment (37ºC), a decrease in FRET signaling was detected ... indicating disassembly of the NPs after 2 h (Fig. S2C)." - The disassembly of the NPs after 2 h is key to the performance of the chosen approach.

L. 296: "The results showed an increased number of cells with higher OVA-SIINFEKL presentation, indicating the enhanced activity of the APM induced by the Lcor mRNA-loaded pBAE-NPs... demonstrate the efficiency of this mRNA nanotechnology to rescue the function of the LCOR TF in inducing tumor cell immunogenicity and thus modulating tumor phenotypes." - There is a key difference between activating antigen-presenting machinary and inducing immunogenicity, i.e. recognition by the immune system and activation of effector cells. There is no indication on how effective endogenous immune responses (e.g. antibody titers, TIL infiltration, cytokine release) are to the administration of Lcor mRNA-loaded NPs.

L. 325: "Based on these results, we estimated an optimal therapeutic regimen of Lcor-mRNA-loaded pBAE-NPs administration in our preclinical experimental models would be every 3 days." - It is highly unclear how the authors came to this conclusion, as it should be based on the time frame of optimal immune responses.

L. 332: "Lcor mRNA-loaded NPs were administered at a dose of 250 μg/kg by intratumoral (i.t.) injection twice a week" - This possibly is the strongest limitation of this study. Intratumor injections of largely unfeasible/unrealistic in clinical setting. Even more, the management of metastatic disease appears out of question.

L. 337: "the results revealed that Lcor mRNA monotherapy was enough to reduce 4T07 tumor 338 growth." - These effects appear rather limited (Fig. 4A,B) and are not statistically significant in Fig. S4B and Fig. S5A.

L. 338: "the combination of Lcor mRNA-loaded NPs with anti-PDL1 therapy not only reduced tumor growth but also led to tumor eradication in 5 out of 7 mice" - Fig. 4A bottom left panel. Three of the tumor growth curves abruptly stop at below 200 mm3. Typically, this is mouse death. This reduces the tumor pool to four xenografts. Among these, we notice two complete responses and two tumor progressions. Two tumor progressions are seen also in the combination Lcor mRNA+ α-PD-L1 group. We are unsure about the statistics of this experiment.

L. 350: "The combination of Lcor mRNA-loaded NPs with different ICIs showed high efficiency in preclinical models, thus supporting the feasibility of starting clinical studies and thus bringing the treatment closer to patients."

• Please see comment on L. 332. It appears unrealistic to consider clinical studies in patients unless a systemic administration of Lcor mRNA-loaded NPs is tackled and corresponding therapeutic efficacy is shown.

Significance

General assessment:strengths and limitations.

The identification of a candidate therapeutic means, by supplying Lcor mRNA for induction of antigen-presenting molecules is of potential interest. As this is not a basic science study, but aims at developing feasible therapeutics, it falls short in this respect, as most likely unfeasible in patients. The combined effect with anti-immune blockade agents is of interest. However, if one assumes that effective immunostimulation was indeed induced by Lcor mRNA, its overall impact on tumor growth is per se weak, if any. Maybe only antigen presentation is induced, but this is in the absence of costimulatory signals? This needs to be investigated.

Advance

This article is based on good papers that were published years ago. The science novelty is limited. As the idea is to develop a novel therapeutic approach, the lack of realistic feasibility severely limits merits.

Audience

Scientists involved in preclinical studies.

Reviewer expertise

This reviewer and his research group have cloned the genes and biochemically characterized novel tumor drivers. He identified their function as stimulators of tumor cell growth and of metastatic spreading, together with roles in cell-cell adhesion, signal transduction and local cancer invasion. This led to the discovery of their prognostic / predictive relevance in human cancer. Two murine models of rare genetic diseases were generated by ablating the corresponding murine genes. He then pioneered the development of software for the identification of fusion oncogenes and of transcription factor-DNA binding sites. This reviewer fostered novel anti-cancer immunotherapies. He generated anti-cancer cytotoxic T lymphocytes, by the use of in vitro engineered antigen presenting cells. Using proprietary discovery platforms, this reviewer developed novel anti-cancer monoclonal antibodies, that selectively target cancer cells. This led to the engineering of humanized antibody-drug conjugates, bispecific anti-CD3/activated Trop-2 antibodies and innovative CAR-T designs. ADCs are now being tested in clinical trials in cancer patients.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Serra-Mir et al investigate the therapeutic potential of delivering the mRNA of LCOR transcription factor via nanoparticles to enhance the efficacy of immune checkpoint inhibitors. The authors show that the mRNA delivery mediated by H and R-nanoparticles was efficient in multiple breast cancer cell lines in vitro. Moreover, using mouse models, they show that LCOR mRNA delivery may improve the efficacy of the treatment with anti-PDL1 or anti-CTLA4 checkpoint inhibitors against tumors. Although this proof-of-concept study has promising aspects, there are significant weaknesses that should be addressed. Details below.

Major points:

1. In vitro delivery of LCOR appears to be effective in both AT3 and 4T07 cell lines when continuously exposed to the mRNA loaded nanoparticles. However, the impact of LCOR on antigen presentation machinery (APM) is rather mixed and not very convincing. The expression pattern and kinetics of several APM genes are inconsistent with LCOR kinetics and at several timepoints the expression in LCOR samples is essentially the same as in mutant LCOR negative controls (Figure 2D). Moreover, the APM reporter assay experiments show that APM in LCOR transduced 4T07 cells is induced rather modestly at best (Figure 2E). The APM effect needs to be demonstrated more rigorously to be convincing.
2. Considering that previous studies by the authors suggest a role for LCOR in regulating stem cell properties in normal and malignant mammary cells (Celia-Terrassa et al Nat Cell Bio 2017; Perez-Nunez et al Nat Can 2022), it is important to address whether transduced LCOR mRNA impacts these properties. Moreover, other autocrine cell functions such as proliferation and apoptosis are also relevant and should be analyzed.
3. The impact of LCOR delivery on immune responses in mouse models could be more rigorous. Analysis of APM genes shows rather modest difference in these gene after LCOR transduction (Figure 3E). Is this sufficient to induce effective anti-tumor immune response? What is the status of T cell activity or exhaustion? Furthermore, LCOR may regulate cytokines and chemokines that are critical for modulation of the immune environment. Did the authors measure any immune-modulating cytokines in the tumor microenvironment, following LCOR expression? Finally, whereas the study focuses on APM and its function, LCOR may directly modulate expression of checkpoint activators on cancer cells. The impact of LCOR transduction on PD-L1, PD-L2 and CTLA-4 expression in cancer cells should be determined.
4. In line with point nr 2, it would be important to analyze the impact of delivered LCOR mRNA on cell functions such as proliferation and apoptosis in the mouse tumors. Even if LCOR delivery sensitizes tumors to checkpoint inhibitors, it cannot be assumed that the impact of LCOR is primarily due to induction of the APM.
5. The experiments analyzing treatment efficacy in the 4T07 model in mice show lack of consistency and a substantial variation between mice that are treated in the same manner. Even the group treated with PBS and Ctr-mRNA contains mice with tumors that regress (Figure 5A). This inconsistency suggests that more mice are required to generate a convincing pattern. Furthermore, the inclusion of a second model would provide a stronger case for a broad applicability of the LCOR treatment with checkpoint inhibitors. Indeed, it is surprising that the authors did not use the AT3 model in vivo considering that mRNA delivery and LCOR expression is substantially more efficient in AT3 compared to 4T07.
6. Following the injection of LCOR nanoparticles to the tumor, the proportion and spatial distribution of LCOR expressing cells should be determined. This is particularly relevant in light of the almost complete elimination of the tumors treated with combination therapy (Figures 4 and 5). Is this striking impact on tumors in spite of mRNA being delivered only to a small portion of cells within the tumor?
7. The in vivo results indicate that expression levels of Fluc mRNA decline rapidly post-treatment, returning to baseline within 24 hours after peaking at 10 hours (Supplementary Figure 3). Although the investigators treat mice every 3rd day with LCOR nanoparticles in their therapeutic experiments, the analysis of durability of immune responses after single injection should be done and can provide important practical insights to guide therapeutic design.

Minor points:

1. The authors mention that LCOR mRNA delivery synergizes with checkpoint inhibitor treatment. However, synergy has a specific meaning when drug interaction is analyzed. This was not really addressed or calculated.
2. There seems to be a mistake in the text (lines 261-263). Based on Figure 1C the mRNA delivery efficiency is higher in AT3 cells compared to 4T07 cells (very difficult to determine anything from Figure 3D, since the cell density is not visible).
3. It is surprising how little expression of luciferase is observed in the 4T07 model (Figure S3), even if almost 60% of cancer cells and 40% of stromal cells are positive (Figure 3A). What could explain this discrepancy?
4. Representative FACS plots from Figure 3 should be shown.
5. There are issues with the figure legends of Figure 3 (from 3C onwards) and Figure S2 (from 2D onwards) that need to be fixed.

Significance

The study is a proof-of-concept investigation addressing whether LCOR mRNA can be delivered by nanoparticles to sensitize tumors to immunotherapy. This approach aims to overcome the limitations and difficulties of targeting transcription factors for therapeutic purposes. However, although the delivery of LCOR mRNA appears to be sufficient, further characterization of the resulting impact needs to be done. This includes both impact on immune responses as well as cell-autonomous impact on cancer cell proliferation and apoptosis.

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Reply to the reviewers

Manuscript number: RC-2025-02946

Corresponding author(s): Margaret, Frame

Roza, Masalmeh

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Reviewer #1

Evidence, reproducibility and clarity

Review of Masalmeh et al. Title: "FAK modulates glioblastoma stem cell energetics..."

Previous studies have implicated FAK and the related tyrosine kinase PYK2 in glioblastoma growth, cell migration, and invasion. Herein, using a murine stem cell model of glioblastoma, the authors used CRISPR to inactivate FAK, FAK-null cells selected and cloned, and lentiviral re-expression of murine FAK in the FAK-null cells (termed FAK Rx) was accomplished. FAK-/- cells were shown to possess epithelial characteristics whereas FAK Rx cells expressed mesenchymal markers and increased cell migration/invasion in vitro. Comparisons between FAK-/- and FAK Rx cells showed that FAK re-expressed increased mitochondrial respiration and amino acid uptake. This was associated with FAK Rx cells exhibiting filamentous mitochondrial morphology (potentially an OXPHOS phenotype) and decreased levels of MTFR1L S235 phosphorylation (implicated in mito morphology fragmentation). Mito and epithelial cell morphology of FAK-/- cells was reversed by treatment with Rho-kinase inhibitors that also increased mito metabolism and cell viability. Last, FAK-dependent glioblastoma tumor growth was shown by comparisons of FAK-/- and FAK Rx implantation studies.

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

Some questions that would enhance potential impact. 1. Cell generation. Please describe the analysis of FAK-/- clones in more detail. The "low viability" phenotype needs further explanation with regard to clonal expansion and growth characteristics?

Response:

• We included a better description and a supplementary figure in our revised manuscript to indicate that we have examined several FAK -/- clones and confirmed that our observations were not due to clonal variation; multiple clones displayed similar morphological changes (Figure S1D). We also show that the elongated mesenchymal-like morphology was observed at 48 h after nucleofecting the cells with the FAK‑expressing vector, before beginning G418 selection to enrich for cells expressing FAK (Figure S1C). We also included experiments to acutely modulate FAK signalling (detaching and seeding cells on fibronectin) (Figure S2D, E, F and Figure S3) to exclude the possibility that the profound effects are due to protocols/selection we used for generating FAK-deleted cells.
• Regarding the term “low viability”, we have clarified in the text that there is no significant difference in cell number (Figure S1A) or ‘cell viability’ when it is assessed by trypan blue exclusion (a non-mitochondria-dependent read-out) (Figure S1B) between FAK-expressing FAK Rx and FAK-/- cells cultured for three days under normal conditions. Therefore, we agree the term ‘cell viability’ in this context could be confusing and have replace "cell viability” with “metabolic activity as measured by Alamar Blue.” in Figure 1D and Figure 5B, and the corresponding text in the original manuscript. This wording more accurately reflects the data.

Figure 1F: need further support of MET change upon FAK KO and EMT reversion.

Response: We have added a heatmap (Figure S1E) illustrating the changes in protein expression of core-enriched EMT/MET genes products (by proteomics) after FAK gene deletion (EMT genes as defined in Howe et al., 2018) ; this strengthens the conclusion that the MET reversion morphological phenotype is accompanied by recognised MET protein changes.

Fig. 2: Need further support if FAK effects impact glycolysis or oxidative phosphorylation in particular as implicated by the stem cell model.

Response: We show that FAK impacts both glycolysis (Figure 2A, 2E, and 2F) and mitochondrial oxidative phosphorylation on the basis of the oxygen consumption rate (OCR) (Figure 2B, and 2D), showing both are contributing pathways to FAK-dependent energy production. We have clarified this in the text.

Is there a combinatorial potential between FAKi and chemotherapies used for glioblastoma. Need to build upon past studies.

Response: Yes, previous studies suggest that inhibiting FAK can sensitize GBM cells to chemotherapy (Golubovskaya et al., 2012; Ortiz-Rivera et al., 2023). We have included a paragraph in the discussion section to make sure this is clearer. Although it is not the subject of this study, we appreciate it is useful context.

The notation of changes in glucose transporter expression should be followed up with regard to the potential that FAK-expressing cells may have different uptake of carbon sources and other amino acids. Altered uptake could be one potential explanation for increase glycolysis and glutamine flux.

Response: We agree with the reviewer that glucose uptake could be contributing and we include data that 2 glucose transporters are indeed FAK-regulated namely Glucose transporter 1 (GLUT1, encoded by Slc2a1 gene) and Glucose transporter 3 (GLUT 3, encoded by Slc2a3 gene) (shown in Figure S2B and C).

It would be helpful to support the confocal microscopy of mitos with EM.

Response:

We are concerned (and in our experience) that Electron microscopy (EM) may introduce artefacts during sample preparation. In contrast, immunofluorescence sample preparation is less susceptible to artefacts. The SORA system we used is not a conventional point-scanning confocal microscope, but is a super-resolution module based on a spinning disk confocal platform (CSU-W1; Yokogawa) using optical pixel reassignment with confocal detection. This method enhances resolution in all dimensions with resolution in our samples measured at 120nm. This has been instructive in defining a new level of changes in mitochondrial morphology upon FAK gene deletion.

Lack of FAK expression with increased MTFR1 phosphorylation is difficult to interpret.

Response: We do not directly show that this phosphorylation event is causal in our experiments; however, we think it important to document this change since it has been published that phosphorylation of MTFR1 has been causally linked to the mitochondrial morphology we observed in other systems (Tilokani et al., 2022).

Need to have better support between loss of FAK and the increase in Rho signaling. Use of Rho kinase inhibitors is very limited and the context to FAK (and or Pyk2) remains unclear. Past studies have linked integrin adhesion to ECM as a linkage between FAK activation and the transient inhibition of RhoA GTP binding. Is integrin signaling and FAK involved in the cell and metabolism phenotypes in this new model?

Response: To better support the antagonistic effect of FAK on Rho-kinase (ROCK) signalling, we included a new experiment in which the integrin-FAK signalling pathway has been disrupted by treating FAK WT cells with an agent that causes detachment from the substratum, Accutase, and growing the cells in suspension in laminin-free medium. We present ROCK activity data, as judged by phosphorylated MLC2 at serine 19 (pMLC2 S19), relating this to induced FAK phosphorylation at Y397 (a surrogate for FAK activity) that is supressed after integrin disengagement. These measurements have been compared with conditions whereby integrin-FAK signalling is activated by growing the cells on laminin coated surfaces. We observed a time-dependent decrease in pFAK(Y397) levels (normalised to total FAK) in suspended cells compared to those spread on laminin, while pMLC2(S19) levels increased in a reciprocal manner over time in detached cells relative to spread cells (S4A and B). There is therefore an inverse relationship between integrin-FAK signalling and ROCK-MLC2 activity, consistent with findings from FAK gene deletion experiments. In the former case, we do not rely on gene deletion cell clones.

Significance

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

__Response: __

Deleting the gene encoding FAK in mouse embryonic fibroblasts leads to elevated Pyk2 expression (Sieg, 2000). However, in the GBM stem cell model we used here, Pyk2 was not expressed (determined by both transcriptomics and proteomics). We have included Figure S1E to show that PYK2 expression was undetectable in FAK -/- and FAK Rx cells at the RNA level (Figure S1F). We conclude that there is no compensatory increase in Pyk2 upon FAK loss in these cells. In the transformed neural stem cell model of GBM, we do not consistently or robustly detect nuclear FAK.

Review #2

Masalmeh and colleagues employ a neural stem/progenitor cell-based glioma model (NPE cells) to investigate the role of Focal Adhesion Kinase (FAK) in GBM, with a focus on potential links between the regulation of morphological/adhesive and metabolic GBM cell properties. For this, the authors employ wt cells alongside newly generated FAK-KO and -reexpressing cells, as well as pharmacological interventions to probe the relevance of specific signaling pathways. The authors´ main claims are that FAK crucially modulates glioma cell morphology, cell-cell and cell-substrate interactions and motility, as well as their metabolism, and that these effects translate to changes to relevant in vivo properties such as invasion and tumor growth.

My main issues are with the model chosen by the authors.

As per the methods section, generation of FAK-KO and -"Rx" NPE cells entailed protracted selection/expansion processes, which may have resulted in inadvertent selection for cellular/molecular properties unrelated to the desired one (loss or gain of FAK expression) and which may have had cascading effects on NPE cells. The authors nonetheless repeatedly claim the parameters they quantify, such as mitochondrial or cytoskeletal properties or metabolic features, to have directly resulted from FAK loss or reintroduction. Examples of such causal inferences are to be found in lines 123, 134/135, 165, 181. Such causal claims are, in my view, unsupported.

Acute perturbation of FAK expression/activity, genetically or pharmacologically, followed by a rapid assessment of the processes under investigation, would be needed to begin to assess causality, even if acute genetic perturbations may be technically challenging as sufficient gene expression reduction or restoration to physiologically relevant levels may be hard to achieve.

Response:

We would like to first comment on the model we used here, which we think will clarify the validity of our approach. The model is a transformed stem cell model of GBM that was published in (Gangoso et al., Cell, 2021) and is now used regularly in the GBM field. As mentioned in the response to Reviewer 1, we have added text (page 4 and 5 in the revised manuscript) and a new supplementary figure (Figure S1D) clarifying that the morphological changes we observed were consistent across multiple FAK -/- clones, showing this was not due to any inter-clonal variability. We also added images showing that the morphological changes were apparent at 48 h after nucleofecting FAK -/- cells with the FAK‑expressing vector specifically (not the empty vector), prior to starting G418 selection to enrich for FAK‑expressing cells (Figure S1C), addressing the worry that clonal variation and selection was the cause of the FAK-dependent phenotypes we observed. We believe that our model provides a type of well controlled, clean genetic cancer cell system of a type that is commonly used in cancer cell biology, allowing us to attribute phenotypes to individual proteins.

We have also carried out a more acute treatment by using the FAK inhibitor VS4718 to perturb FAK kinase activity and assessed the effects on glycolysis and glutamine oxidation after 48h treatment (Figure S2D, E and F). We found that treating the transformed neural stem cells (parental population) with FAK inhibitor (300nM VS4718) decreases glucose incorporation into glycolysis intermediates and glutamine incorporation into TCA cycle intermediates, consistent with a role for FAK’s kinase activity in maintaining glycolysis and glutamine oxidation.

The employed pharmacological modulation of ROCK activity is the only approach that, given the presumably acute nature of the treatment, may have allowed the authors to probe the proposed functional links. The methods section of the manuscript does not however comprise details as to the duration of these treatments, which leaves open the possibility of long-term treatment having been carried out (data shown in Figure 5B refers to 72hr treatment).

__Response: __

We have added the duration of the treatment to the Methods section and Figure Legends, to clarify that cells were treated with ROCK inhibitors for 24h, before assessing the effects on mictochondria (Figure 4C, D, S4C and D) and glutamine oxidation (Figure 5A, and S5). For metabolic activity by AlamarBlue assay, cells were treated with ROCK inhibitors for 72h (Figure 5B).

Even in the case of ROCK inhibitor experiments, it is however unclear if and how the effects on cell morphology and adhesion, mitochondrial organization and metabolic activity may be connected to each other and, if at all, to FAK expression.

Given the above uncertainties due to the nature of the model and experimental approaches, it is hard to assess the reliability and thus the relevance of the findings.

Response:

FAK suppresses ROCK activity (as judged by pMLC2 S19, Figure 4A and B). Treating FAK -/- cells with two different ROCK inhibitors restored mesenchymal-like cell morphology, mitochondrial morphology and glutamine oxidation. As mentioned above, to strengthen our evidence for the antagonistic role of FAK in ROCK-MLC2 signalling, we have now introduced an experiment whereby integrin-FAK signalling was disrupted through treatment with a detachment agent (Accutase), and subsequently maintaining the cells in suspension in laminin-free medium. We assessed pMLC2 S19 levels (a measure of ROCK activity) relating this to FAK phosphorylation that is supressed after integrin disengagement. These results were evaluated relative to spread wild type cells growing on laminin where Integrin-FAK signalling was active (Figure S4A and B). We observed an inverse relationship between Integrin-FAK signalling and ROCK-MLC2 activity in keeping with our conclusions (Figure 4A and B).

Experimental support for the ability of cell-substrate interaction modulation to concomitantly impact cellular metabolism and motility/invasion would be significant both in terms of advancing our understanding of glioma cell biology and of its translational potential, but the evidence being provided is at best compatible with the proposed model.

Response: We carried out a new experiment to support the ability of cell-substrate interaction modulation to impact metabolism; specifically, we inhibited cell-substrate interactions by plating the cells on Poly-2-hydroxyethyl methacrylate (Poly 2-HEMA)-coated dishes. This suppressed FAK phosphorylation at Y397, as expected, with concomitant reduction in glutamine utilisation in the TCA cycle (Figure S3A, B and C).

My background/expertise is in developmental and adult neurogenesis, in vivo modelling of gliomagenesis and cell fate control/reprogramming, with a focus on molecular mechanisms of differentiation and quantitative aspects of lineage dynamics; molecular details of the control of cellular metabolism, cell-cell adhesion and cytoskeletal dynamics are not core expertise of mine.

We appreciate this reviewer’s expertise are not necessarily in the cancer cell biology and genetic intervention aspects of our study. We hope that the explanations we have provided satisfy the reviewer that our conclusions are valid.

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Referee #2

Evidence, reproducibility and clarity

Masalmeh and colleagues employ a neural stem/progenitor cell-based glioma model (NPE cells) to investigate the role of Focal Adhesion Kinase (FAK) in GBM, with a focus on potential links between the regulation of morphological/adhesive and metabolic GBM cell properties. For this, the authors employ wt cells alongside newly generated FAK-KO and -reexpressing cells, as well as pharmacological interventions to probe the relevance of specific signaling pathways. The authors´ main claims are that FAK crucially modulates glioma cell morphology, cell-cell and cell-substrate interactions and motility, as well as their metabolism, and that these effects translate to changes to relevant in vivo properties such as invasion and tumor growth. My main issues are with the model chosen by the authors.

As per the methods section, generation of FAK-KO and -"Rx" NPE cells entailed protracted selection/expansion processes, which may have resulted in inadvertent selection for cellular/molecular properties unrelated to the desired one (loss or gain of FAK expression) and which may have had cascading effects on NPE cells. The authors nonetheless repeatedly claim the parameters they quantify, such as mitochondrial or cytoskeletal properties or metabolic features, to have directly resulted from FAK loss or reintroduction. Examples of such causal inferences are to be found in lines 123, 134/135, 165, 181. Such causal claims are, in my view, unsupported. Acute perturbation of FAK expression/activity, genetically or pharmacologically, followed by a rapid assessment of the processes under investigation, would be needed to begin to assess causality, even if acute genetic perturbations may be technically challenging as sufficient gene expression reduction or restoration to physiologically relevant levels may be hard to achieve.

The employed pharmacological modulation of ROCK activity is the only approach that, given the presumably acute nature of the treatment, may have allowed the authors to probe the proposed functional links. The methods section of the manuscript does not however comprise details as to the duration of these treatments, which leaves open the possibility of long-term treatment having been carried out (data shown in Figure 5B refers to 72hr treatment). Even in the case of ROCK inhibitor experiments, it is however unclear if and how the effects on cell morphology and adhesion, mitochondrial organization and metabolic activity may be connected to each other and, if at all, to FAK expression.

Significance

Given the above uncertainties due to the nature of the model and experimental approaches, it is hard to assess the reliability and thus the relevance of the findings.

Experimental support for the ability of cell-substrate interaction modulation to concomitantly impact cellular metabolism and motility/invasion would be significant both in terms of advancing our understanding of glioma cell biology and of its translational potential, but the evidence being provided is at best compatible with the proposed model.

My background/expertise is in developmental and adult neurogenesis, in vivo modelling of gliomagenesis and cell fate control/reprogramming, with a focus on molecular mechanisms of differentiation and quantitative aspects of lineage dynamics; molecular details of the control of cellular metabolism, cell-cell adhesion and cytoskeletal dynamics are not core expertise of mine.

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Referee #1

Evidence, reproducibility and clarity

Review of Masalmeh et al.

Title: "FAK modulates glioblastoma stem cell energetics..."

Previous studies have implicated FAK and the related tyrosine kinase PYK2 in glioblastoma growth, cell migration, and invasion. Herein, using a murine stem cell model of glioblastoma, the authors used CRISPR to inactivate FAK, FAK-null cells selected and cloned, and lentiviral re-expression of murine FAK in the FAK-null cells (termed FAK Rx) was accomplished. FAK-/- cells were shown to possess epithelial characteristics whereas FAK Rx cells expressed mesenchymal markers and increased cell migration/invasion in vitro. Comparisons between FAK-/- and FAK Rx cells showed that FAK re-expressed increased mitochondrial respiration and amino acid uptake. This was associated with FAK Rx cells exhibiting filamentous mitochondrial morphology (potentially an OXPHOS phenotype) and decreased levels of MTFR1L S235 phosphorylation (implicated in mito morphology fragmentation). Mito and epithelial cell morphology of FAK-/- cells was reversed by treatment with Rho-kinase inhibitors that also increased mito metabolism and cell viability. Last, FAK-dependent glioblastoma tumor growth was shown by comparisons of FAK-/- and FAK Rx implantation studies.

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

Some questions that would enhance potential impact.

1. Cell generation. Please describe the analysis of FAK-/- clones in more detail. The "low viability" phenotype needs further explanation with regard to clonal expansion and growth characteristics?
2. Figure 1F: need further support of MET change upon FAK KO and EMT reversion.
3. Fig. 2: Need further support if FAK effects impact glycolysis or oxidative phosphorylation in particular as implicated by the stem cell model.
4. Is there a combinatorial potential between FAKi and chemotherapies used for glioblastoma. Need to build upon past studies.
5. The notation of changes in glucose transporter expression should be followed up with regard to the potential that FAK-expressing cells may have different uptake of carbon sources and other amino acids. Altered uptake could be one potential explanation for increase glycolysis and glutamine flux.
6. It would be helpful to support the confocal microscopy of mitos with EM.
7. Lack of FAK expression with increased MTFR1 phosphorylation is difficult to interpret.
8. Need to have better support between loss of FAK and the increase in Rho signaling. Use of Rho kinase inhibitors is very limited and the context to FAK (and or Pyk2) remains unclear. Past studies have linked integrin adhesion to ECM as a linkage between FAK activation and the transient inhibition of RhoA GTP binding. Is integrin signaling and FAK involved in the cell and metabolism phenotypes in this new model?

Significance

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

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Reply to the reviewers

'Please download the Response to Reviewers file, which contains Reviewers comments and authors responses and new data'

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Referee #3

Evidence, reproducibility and clarity

In this work, the authors investigate the cytoplasmatic roles of Mei2, an RNA-binding protein in fission yeast, in particular its interactions with processing bodies (PBs) in the cytoplasm. The manuscript rests heavily on microscopy data, using a combination of time-resolved microscopy and molecular mutation and tagging techniques.

Mei2 is known for its role in the nucleus of zygotic cells. Here, it is shown that Mei2 co-localizes with the PB markers Dcp2 and Edc3. This happens in zygotes but not in gametes (e.g. when fusion is blocked in fus1 mutants) (Fig 4E). <br /> This co-localization in PBs is counteracted by Pat1-driven phosphorylation of Mei2. Phosphorylation by Pat1 is known to suppress Mei2 activity. Mei3 inhibits Pat1; in a mei3 mutant Mei2 cannot accumulate in PBs, the same happens with a non-phosphorylatable mei2 allele (Fig. 5). In a pat1Δ mutant, constitutively active Mei2 is compatible with growth if it stays in the nucleus (mei2-NLS), but not if Mei2 is forced to the cytoplasm (mei2-NES) (Fig. 3G). This indicates that it is the cytoplasmic function of Mei2 that is critical.

Forcing Pat1 to be cytoplasmic (Pat1-NES) allowed normal vegetative growth and mating (Fig. 3A-C), whereas nuclear Pat1 (Pat1-NLS) produced premature mating (Fig. 3A,B). Thus, cytoplasmic Pat1 phosphorylation of Mei2 is critical for controlling the transition from mitotic growth to fusion and zygote formation.

Mei2 shuttles between the nucleus and cytoplasm, and one of its RNA-binding domains (RRM1) drives nuclear import, while both RRM1 and RRM3 are required for export to the cytoplasm (Fig. 2 and S2). Little was known previously of the role of RRM1.

They present evidence that this localization to PBs is required for development. Knocking out the RNA helicase Ste13 (ortholog of S. cerevisiae Dhh1 which is a PB component) reduces PB formation (Fig. 6A). Even a non-phosphorylatable mei2 allele (i.e. it cannot be inactivated by Pat1) is incapable of driving sporulation in a ste13Δ background (Fig. 6B-D). This demonstrates that Mei2 activity is dependent on PBs.

The study is well conceived and performed, and the conclusions mostly well backed by data. Experimental and statistical procedures are well described, and the number of replicates is sufficient.

There are some minor questions however:

In the literature, Mei2 is described as appearing as a nuclear dot in zygotic cells, but invisible in mitotic cells. Here, the authors demonstrate a Mei2 dot already 30 minutes before fertilization (Fig. 2A). Is the reason for this a more sensitive microscopic technique, or something else?

The authors claim that the RRM1 RNA-binding region of Mei2 is essential for cytoplasmic Mei2 function and recruitment to PBs. This contrasts with previous publications (Watanabe 1994, Watanabe 1997, Otsubo 2014), as pointed out by the authors, where RRM1 appears to be dispensable for development. How do the authors argue about this discrepancy?

Significance

Overall, this paper presents major advances in our understanding of the cytoplasmic functions of this intensely studied RNA-binding protein, Mei2, in the transitions between the mitotic and meiotic cell cycles.

It builds on the original observations of Mei2 as an essential protein for fusion and meiosis (Watanabe EMBO J 1988), being RNA-binding (Watanabe Cell 1994), and forming a nuclear dot in meiotic cells (Yamashita Cell 1998). These were followed by e.g. reports how Pat1 phosphorylation regulates Mei2 degradation (Matsuo J Cell Sci 2007) and its binding to RNA (Shen J Mol Cell Biol 2022). The present manuscript gives a broader view of the functions of Mei2 beyond its previously described role in the nucleus, and characterizes its interactions with the other players in fusion and meiosis.

These findings will be of great interest not only to the fission yeast community, but to a wide range of scientists specializing in meiosis and fertilization, and to the RNA biologists at large. Since Mei2 is conserved across many branches of the eukaryotic tree as an RNA-binding protein, albeit with somewhat different functions in e.g. plants, the work has general relevance.

I have read this manuscript with a background in general yeast cell and molecular biology, including post-transcriptional regulation. I am no microscopy expert, however I find the experimental setup with fluorescent tagging, combinations of mutations in key components in the pathway, and high resolution microscopy data from time series, convincing.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Araoyinbo et al. present a wealth of detailed data analyzing the cellular behavior of mainly three proteins, the RNA-binding protein Mei2, the kinase Pat1 and its inhibitor the protein Mei3 during mating and subsequent initiation of meiosis in fission yeast. This analysis involve also the detailed testing of potential models about how these protein act on each other to fulfil their different functions, such as to block remating on zygotes, the initiation of zygotic S-phase and the initiation of meiosis and sporulation. These data converge to a model whereby Pat1 inhibition by Mei3 expression upon cell fusion unleashes Mei2 function in the cytoplasm. This is due to the subsequent dephosphorylation of Mei2, and its RNA-recognition motif RRM1 interacting with and recruiting Mei2-bound RNAs to P-bodies, where their translation is most likely repressed (at least the translation of a synthetic mRNA - Mei2 pair is repressed when the pair is targeted to P-bodies). Together, this study provides detailed insights into how the meiotic cycle is induced upon mating of fission yeast cells but not in gametes).

Overall, this is a very carefully controlled study and the data are very convincing and very interesting. It makes a compelling case for the model proposed and makes many original observations and far reaching observations, such as the role of nucleo-cytoplasmic compartmentalization and P-bodies in implementing developmental decisions. Since the notion that P-bodies have a function at all has been strongly questioned in recent years, this study will be very useful for the field.

The only limitations that I have concerns the readability of the manuscript. It is extremely dense and that makes it a laborious read. Furthermore, the manuscript is not particularly well motivated, such that it is not very obvious what questions the authors are after. This becomes more or less clear only slowly as the reader progresses, or in the second read. Therefore, this very nice piece of work may escape people who are not working on fission yeast mating and meiosis, which would be a pity. I therefore recommend working on better motivating the study and its different parts for a general audience, streamlining the fission yeast intricacies and explaining more precisely what is conceptually learnt from these studies, on a broad sense and possibly in a way that would be relevant beyond the model used. This paper is opening a reach area of research and it would be unfortunate to not make that point more clearly.

Significance

Overall, this is a very carefully controlled study and the data are very convincing and very interesting. It makes a compelling case for the model proposed and makes many original observations and far reaching observations, such as the role of nucleo-cytoplasmic compartmentalization and P-bodies in implementing developmental decisions. Since the notion that P-bodies have a function at all has been strongly questioned in recent years, this study will be very useful for the field.

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Referee #1

Evidence, reproducibility and clarity

Summary

The study by Araoyinbo et al. explores the role of the RNA-binding protein Mei2 in fission yeast zygotic development. It highlights Mei2's cytosolic functions, its interaction with P-bodies, and nucleocytoplasmic shuttling. Mei2's regulation by Mei3 and Pat1, and the importance of its RNA recognition motifs (RRM1 and RRM3) are also discussed.

The main conclusion of the manuscript is somewhat unexpected from previous studies about Mei2. Particularly, the cytoplasmic function of Mei2 is a novel point in this field.

Lots of experiments have been done to make the scenario of the manuscript. The experiments and results are technically sound, and I potentially agree with the interpretation by the authors. It would require some more explanation as well as additional experiments to conclude in the way the authors wish to do.

Major points

1. Page 4. "Taken together, these results show that fertilization, and Mei3 expression in particular, promote Mei2 nuclear export." It is also possible that Mei2-NLS-GFP was degraded somewhere in the cell (as Mei2 may be still shuttling even if NLS was fused) upon mating (120 min onwards in Fig2D) rather than exported to the cytoplasm. In mei3∆ (Fig 2E) Mei2-NLS-GFP might be somehow escaped from the degradation. Also, nuclear signal of Mei2 is very bright but cytosolic signal seems vague. I wonder the entire results in the manuscript could be interpreted from the viewpoint of degradation/protein stability/protein amount, rather than regulation of localization such as nuclear import and export.
2. Page 4. "We conclude that RRM1 promotes nuclear import of Mei2." This may be true, but is it also possible that RRM1 inhibits nuclear export of Mei2? This type of possible dual explanation can be applied to the entire manuscript. This is expected to be neutralized or clarified at each point.
3. Page 5. "Thus, diminishing nuclear Pat1 levels does not compromise its roles during growth and mating." It is interesting for me to find that Pat1-NLS induced ectopic meiosis. This is a fine finding. I wonder just addition of NLS (basic residues) at the C-terminus of Pat1 might deteriorate the activity of Pat1, apart from localization shift. Is it possible to exclude this possibility by making NES-Pat1-NLS-3GFP fusion, in which NLS and NES are fused doubly and distally, because proximal double fusion such as Pat1-NLS-NES-3GFP might just mutually cancel the NLS NES activities.
4. In general in the Results section. What confused me is when each event occurred. Nutritional conditions, -N but not yet conjugated, after conjugation, premeiotic S or meiotic prophase (or even later). It is particularly hard to catch the story when the timing issue and the location issue (nuclear and cytosolic localization, NLS and NES...) are discussed at the same time. Explanation in chronological order, hopefully at the earlier stages such as explanation for Figures 2 and 3, would be appreciated. The model shown in Figure 8 is quite helpful for my understanding.

Minor points

1. "Fertilization" in the title, and "Mei2 is expressed in gametes" in the main text on pare 2. Authors try to generalize fission yeast mating as fertilization of higher organisms as both are events in which two haploids conjugate. I personally do not agree with this type of explanation. This is mainly because S. pombe conjugation (mating) is a part of sexual differentiation and therefore is biologically distinct from fertilization of higher organisms. S. pombe grows and divides in the haploid state, which is distinct from general gametes. To avoid such confusion, I would propose authors to neutralize expression throughout the manuscript.
2. I found quite a few "surprising(ly)", which are hopefully neutralized, as it is somewhat emotional.

Significance

General assessment: strengths and limitations:

Strengths: It provides novel understanding of molecular mechanisms of meiotic initiation of fission yeast. Technically sound. Lots of experiments. Limitations: The story is very confusing and difficult to catch. Explanation can be simplified.

Advance: compare the study to existing published knowledge: does it fill a gap? What kind of advance does it make (conceptual, clinical, fundamental, methodological, incremental,,,,)? It is a big advancement. It is conceptually novel regarding how meiosis is initiated in fission yeast.

Audience: which communities will be interested/influenced, what kind of audience (broad, specialized, clinical, basic research, applied science, fields and subfields,,,) It is mainly for audience of basic research, biology, molecular mechanism of gene explanation, meiosis or yeast cellular events. For non-yeast researchers, this manuscript is probably very hard to read/understand, although the authors tried to generalize yeast-specific events with general words.

Describe your expertise:

Yeast genetics, Meiosis, Cell biology, Gene expression regulation

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

Summary

This manuscript presents a large-scale comparative genomics analysis of Salmonella genomes to identify and characterize the repertoire of Type VI Secretion System (T6SS) effectors. The authors combine bioinformatic predictions with experimental validation of one novel toxin domain (Tox-Act1), revealing a unique catalytic activity not previously reported in bacterial toxins. While the study is comprehensive and offers valuable insights into T6SS diversity, the insufficient description of computational methods and limited accessibility of underlying data reduce reproducibility and impact.

Major comments

1. The computational methods are inadequately described in the Materials and Methods section, and the authors did not provide the underlying datasets. These omissions make it impossible to reproduce the analysis or to apply the approach to other organisms.
2. The criteria used to distinguish between T6SS effectors and non-effectors are unclear. The reliance on proximity to structural genes ("guilt-by-association") is insufficient and may have led to the omission of cargo effectors not proximal to these structural genes.
3. No information is provided in the Materials and Methods section about the graph-based clustering strategy mentioned in the main text (Rows 109-111), including the Jaccard index and Louvain algorithm.
4. The definition and identification of T6SS subtypes, including the use of the term "orphan," are not explained (Rows 111-112).
5. The phylogenetic analysis of the newly identified domain Tox-Act1 lacks consistency and detail. For example, Rows 324-326 state: "To predict the function of Tox-Act1, we sought to understand its evolutionary relationship by constructing a phylogenetic tree using the sequences of Tox-Act1, TseH and additional permuted members, such as LRAT and YiiX." However, this contradicts Rows 342-344 and Figure 4A, which describe the phylogenetic tree as being built from permuted NlpC/P60 members, and indicate that a single query was used for PSI-BLAST, marked with a red star. It is unclear whether Tox-Act1, TseH, or another sequence was used as the initial PSI-BLAST query.
6. The Tox-Act1 domain investigated is labeled as an acyltransferase, but the evidence presented supports only phospholipid-degrading activity. In my opinion, the naming should better reflect the activity demonstrated by the data.
7. Table S1 should include representative protein accessions for each T6SS toxin domain. This is essential for evaluating the novelty of the identified domains and for enabling their use in future analyses. The repeated use of "This study" (96 times) as a reference, without further detail, is confusing and unhelpful. In my view, referencing the current study is appropriate only when the manuscript provides sufficient information on the corresponding domain.
8. In general, the authors should place greater emphasis on ensuring that the proteins and genomes analyzed in this study can be reliably identified. Genomic accessions and locus tags should be traceable in public databases such as NCBI, and the supplemental information must correspond accurately to the main text. For example, I was unable to find information on FD01543424_00914, which was used as the query for the alignment of STox_15 (the name used in the supplemental information, while in the main text it is referred to as Tox-Act1; see related comment below).
9. A supplementary table listing all Salmonella effectors and their domain annotations is missing. This is essential for transparency, reproducibility, and future use of the data.
10. The GitHub repository contains a large volume of data and code but lacks detailed documentation and clear instructions, including example files. This greatly limits reproducibility and usability. The current organization of the repository makes it difficult to locate specific results; for example, Tox-Act1 is referred to as STox_15 in the GitHub files, but this is not mentioned in the manuscript. The authors should improve data organization and provide a README file for clarity.

Minor comments

1. The introduction should discuss previous work on Salmonella T6SS effectors, including Blondel et al. (2023) (ref 71 in the manuscript), Amaya et al. (2022), and Amaya et al. (2024).
2. In Figure 1C, genomic examples should include strain names and locus tags.
3. In Figure 1F, 'ND' should be replaced with 'Unknown' or 'Not Determined'.
4. Figure 1E is overly complex and, in my opinion, does not add value, especially since the accompanying text is sufficient on its own. Moreover, the authors acknowledge that their initial analysis missed the similarity between Tox-Act1 and both DUF4105 and the TseH effector, which raises concerns about the accuracy and usefulness of this graph.
5. Figure 3D lacks information about the number of replicates (n=?).
6. Discrepancies in domain annotations:
   • Row 232: STox_47 is missing from Table S1.
   • Row 233: STox_18 is pore-forming and STox_53 is a nuclease (per Table S1), which contradicts the main text.
7. Multiple grammatical and typographical errors exist throughout the text, including:
   • Row 41: "provide" should be "provides"
   • Rows 131, 222: "immunities" should be "immunity proteins"
   • Rows 170, 253, 288: "thee" should be "three"
   • Row 388: "corresponds" should be "correspond"
   • Row 389: "chomatogram" should be "chromatogram"
8. Rows 257-259: The claim that PAAR and RHS domains assist in translocation across the bacterial inner membrane is presented as fact, but this is only a hypothesis and should be stated more cautiously.
9. Figure 3A: The selection of representative genomic loci is unclear. For example, FD01843896 is shown in the figure, but cloning was performed using FD01848827, and the HHPred analysis was based on FD01543424. The rationale for using different sequences at each step should be clarified.
10. Rows 296-299: The absence of a secretion assay in the study is notable. If this is due to the inability to activate the SPI-6 T6SS of Salmonella enterica serovar Typhimurium, as discussed in these lines, it should be explicitly mentioned in the text.
11. Figure 4C (sequence logo) is not described in the Materials and Methods section.
12. Row 467: The retrieval date of the gff files from the 10KSG database is missing.
13. Rows 474-476: The domain models used for T6SS cluster prediction are not described.

Significance

This is a comprehensive study involving a large number of Salmonella genomes, potentially identifying many new T6SS effectors and toxic activities. One new domain analyzed in this work is experimentally investigated and shown to have a unique catalytic activity not previously observed in toxins. However, the bioinformatic methods are not described in sufficient detail, making it difficult to assess or reproduce the work. Protein accession numbers are missing, even for representative toxins, and locus tags are not traceable, making the identified effectors not readily accessible. There are many inaccuracies throughout the text and supplemental data. The Tox-Act1 domain investigated is labeled as an acyltransferase, but the evidence only supports phospholipid-degrading activity. While the study includes many graphs and histograms, they often obscure the main findings. Consequently, the audience is likely to be limited.

Nevertheless, despite these concerns, I believe this is an important work that could be valuable to the broad community once a more thorough revision is undertaken, not only by addressing the specific comments raised, but also by rechecking the analyses, reorganizing the presentation, and ensuring that all data and annotations are clearly accessible and traceable.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The manuscript titled "Genome-directed study reveals the diversity of Salmonella T6SS effectors and identifies a novel family of lipid-targeting antibacterial toxins" presents a comprehensive in silico analysis of T6SS-associated effector and immunity genes across approximately 10,000 Salmonella genomes. In addition, the authors selected one of the newly identified effectors, Tox-Act1, for detailed biochemical characterization. To my knowledge, this study represents the most extensive genome-wide mining effort to date for T6SS-associated effectors and immunity proteins in Salmonella, employing a range of state-of-the-art computational prediction tools. The in vitro enzymatic characterization of Tox-Act1 further validates the in silico approach and adds a novel functional perspective to the dataset. Overall, the study provides a rich and comprehensive dataset. However, for readers without a strong bioinformatics background, the logic and workflow of the in silico prediction pipeline may be challenging to follow. Consequently, my comments focus primarily on the biochemical analysis of Tox-Act1, rather than the computational aspects of the study.

Major comments:

1. In Figure 3, the authors first demonstrated that Tox-Act1 and Imm-Act1 constitute a functional antibacterial toxin-immunity pair using a heterologous E. coli expression system. They then proceeded to an in vivo mouse colonization model, showing that prey cells lacking the tox-act1/imm-act1 locus exhibited reduced competitiveness when co-infected with a Salmonella strain carrying the endogenous tox-act1, compared to a ∆tssL mutant. As this is the first report identifying and characterizing Tox-Act1 function in Salmonella, the authors should provide additional experimental evidence addressing the following key points: (i) Whether Tox-Act1 is secreted by Salmonella in a T6SS-dependent manner; (ii) Whether target cells lacking imm-act1 (in either Salmonella or E. coli) can be intoxicated by Salmonella secreting Tox-Act1; (iii) Whether the observed competitive advantage in vitro conferred by Tox-Act1 is dependent on its phospholipase activity. Given that Salmonella T6SS can be activated by hns deletion, such experiments should be feasible and are crucial for the functional validation of any newly identified T6SS effector. Addressing these points would substantially strengthen the mechanistic basis of the study and reinforce the biological importance and relevance of Tox-Act1.
2. In Figure 4, the authors present the evolutionary relationship between Tox-Act1 and the previously identified T6SS effector TseH from Vibrio, and they propose that these two effectors may share similar enzymatic activities and overlapping cellular targets. Given the ongoing debate and unresolved questions regarding the biochemical function of TseH, the authors should leverage their established in vitro phospholipase assay to test whether TseH exhibits phospholipase activity similar to that of Tox-Act1. Demonstrating such activity would not only substantiate the proposed functional conservation but also provide critical biochemical insight into a long-standing question in the T6SS field.
3. In Figures 5C and 5D, the authors performed lipidomic analyses on E. coli cells heterologously expressing Tox-Act1 and reported that specific phospholipid species are altered in a manner dependent on Tox-Act1's phospholipase activity. However, the data presented in Figure 5D only include changes in the abundance of PG, FFA, LPG, and LPE. To provide a comprehensive overview of the lipidomic alterations, the authors should present the full dataset of all identified phospholipid species. This is essential to evaluate the extent and specificity of lipid remodeling induced by Tox-Act1. It is currently unclear whether the observed reduction in PG is the only statistically significant change or if additional lipid species were similarly affected but not shown. Furthermore, the authors claim that Tox-Act1 functions as a phospholipase A1. However, in Figures 5A and 5B, the signal corresponding to intact phospholipids remains relatively high, raising concerns about the apparent weak enzymatic activity in this assay. This observation contrasts with previously characterized phospholipase toxins in the antibacterial toxin field, such as Tle1 from Burkholderia, which exhibit robust activity under in vitro conditions. To substantiate the enzymatic potency of Tox-Act1 and clarify this discrepancy, the authors should include a side-by-side comparison using the same in vitro assay with a well-established phospholipase toxin (e.g., Tle1) as a positive control. This would allow for a direct evaluation of the relative enzymatic strength of Tox-Act1 and support the interpretation of its lipid-targeting function.

Minor Comments:

1. Line 32: Please specify "Type VI Secretion System (T6SS)" when first introducing the term in the abstract, to ensure clarity for a broad readership.
2. There are inconsistencies between the numerical values reported in the main text and those shown in the figures. For instance, the manuscript repeatedly states that approximately 10,000 Salmonella genomes were analyzed in the in silico search, whereas Figure 1 indicates a total of 10,419 genomes. Similarly, Line 108 mentions 42,560 genomic sites, yet Figure 1 displays a count of 49,080. Please ensure that all numerical data are consistent across the manuscript and figures to avoid confusion or misinterpretation.
3. The definition of "Orphan clusters" is not provided. Please specify the criteria used to define these clusters and clarify the rationale for grouping them separately from the other clusters (i1-i4) shown in Figure 1A. It would be helpful to explicitly state how they differ from the canonical clusters.
4. Lines 114-119: The sentence structure in this section is overly long and difficult to follow. Please revise this portion for clarity and conciseness to ensure that the intended message is clearly conveyed.
5. The color coding in Figure 1C is incomplete; only a few categories are indicated in the legend. Please revise the legend to include all color codes used in the figure for accurate interpretation.
6. Lines 278-280: The authors state that "cells lysed without losing their rod shape, which suggests that the peptidoglycan was not affected... indicating that this is not the target of Tox-Act1." Please provide appropriate references or supporting evidence for this interpretation. Clarification is needed to explain the morphological criteria being used to infer peptidoglycan integrity.
7. Please define "competitive index" in the legend of Figure 3D to ensure the metric is clearly understood by readers unfamiliar with the term.
8. It is unclear to me why the author use (data not shown) in Line 315. Please provide evidence to support the claim in the paragraph.
9. In Figure 4D, the authors compare the activity of wild-type and catalytic mutant Tox-Act1, but protein expression levels are not shown. Please include immunoblot or other relevant data to confirm equivalent expression of both constructs, to rule out differential expression as a confounding factor.

Referee cross-commenting

I agree with Reviewer #3 that the authors should provide more details on their search for better reproducibility.

Significance

This manuscript presents a large-scale in silico analysis of Salmonella T6SS effectors and immunity proteins, accompanied by the biochemical characterization of a novel phospholipase effector, Tox-Act1. The genome-wide dataset is comprehensive, representing the most extensive mining effort of its kind to date. The study is strengthened by in vitro validation of Tox-Act1 activity and its role in interbacterial competition. However, the manuscript would benefit from additional experimental data to confirm key mechanistic aspects, including T6SS-dependent secretion of Tox-Act1, its toxicity toward target cells lacking immunity, and the contribution of phospholipase activity to its antibacterial function. Comparative assays with established T6SS phospholipases (e.g., Tle1) are recommended to clarify enzymatic potency. Further, the authors should apply their phospholipase assay to test TseH activity and resolve long-standing questions in the field. Several areas also require clarification or correction, including inconsistencies in reported genome counts, incomplete figure legends, unclear terminology (e.g., "Orphan clusters"), and missing experimental controls (e.g., protein expression levels, full lipidomic dataset). Minor edits to improve clarity and consistency are also suggested. Overall, the study is significant and of high potential impact but requires additional experimental validation and revisions to improve clarity and completeness.

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Referee #1

Evidence, reproducibility and clarity

Summary: In this study, authors used in silico approaches to analyse 10,000 bacterial genomes and identified 128 candidate effectors secreted via the T6SS of Salmonella. Among these, Tox-Act1 was selected for detailed characterisation. The authors demonstrated that Tox-Act1 harbours a permuted NlpC/P60 catalytic domain with phospholipase activity, targeting key membrane lipids. Furthermore, they confirmed that Tox-Act1 is secreted in a T6SS-dependent manner and enhances bacterial competitiveness during gut colonisation in mice, providing new insights into lipid-targeting toxin domains in interbacterial interactions. My concerns raised are all minor and should be readily addressable by the authors.

Minor Concerns:

Line 279-280: The statement that the peptidoglycan is not a target of Tox-Act1 is somewhat strong at this stage of the manuscript. The preservation of cell shape does not necessarily imply that the peptidoglycan remains unaltered at a subcellular level. Given that Tox-Act1 belongs to the NlpC/P60 family, members of which include known peptidases, the authors should moderate this assertion. Replacing "is not" with "is likely not" or using conditional phrasing would be more appropriate here.

Lines 328-331: The conclusion that the Tox-Act1 clade is deployed in biological conflicts is not fully explained or substantiated. The authors are encouraged to provide a brief rationale to support this conclusion.

Figure 4D: There appears to be a labelling inconsistency. The immunity protein is referred to as "Slmm15," which may relate to the original name of Tox-Act1 (i.e., STox_15), but the correct label should likely be "Imm-Act1."

Line 401 and elsewhere: The correct spelling is "L-arabinose" with a capital "L". The manuscript should be checked for consistency in this regard.

Throughout the text and figures: Bacterial species names are often incorrectly formatted, e.g., "S. Panama" (Line 226) should be written in scientific style as S. panama, with italics and the species name in lowercase. A systematic revision of species names is recommended to enhance rigour.

Figure 3D: The X-axis labelling is somewhat confusing. The use of terms such as "attackers" and "prey" is misleading in this context, as the experiment tests the in vivo survival capacity of different Salmonella strains (WT or T6SS mutants mixed with toxin/immunity double mutants) in a mouse model, rather than a direct bacterial killing assay. Clarifying this would greatly improve readability.

Significance

Overall, this study is well-executed. The approach used to identify a previously uncharacterised diversity of T6SS effectors in Salmonella is robust and provides a valuable framework that could be extended to other systems involved in interbacterial competition. This renders the work relevant and of interest for publication. While the manuscript occasionally lacks clarity in explaining the rationale behind certain experimental choices, the narrative remains generally accessible.

Field of expertise: Secretion systems, interbacterial competition, bacterial predation, live-cell imaging, protein network

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Reply to the reviewers

Manuscript number: RC-2023-02191

Corresponding author: Jan Rehwinkel

1. General Statements

The authors wish to thank all three reviewers and the Review Commons team for carefully evaluating our study. We have addressed all points raised as detailed below.

We have thoroughly revised our bulk RNAseq analysis, which is now performed at the transcript level using the latest GENCODE release. We have updated Figure 3 and associated supplementary figures and tables. This change from gene to transcript level was important for accurate motif analysis as requested by reviewer 2: matching promoters to individual IFN-regulated transcripts – rather than aggregating all promoters per gene – avoids significant signal dilution. This strategy yields higher-resolution expression data and is biologically preferable. Indeed, several well characterised IFN-regulated RNAs (e.g., the ADAR1-202 transcript encoding the p150 isoform) originate from promoters located far from the constitutive promoters of their host genes. In our revised manuscript, we now provide in the new supplementary figure 13 the requested promoter motif analysis. Using two computational approaches – de novo motif search and analysis of a curated motif database – we find strong enrichment of interferon-stimulated response elements (ISREs) in promoters of type I IFN regulated transcripts. No other motifs reached similarly high levels of enrichment, and our analysis did not reveal differences between different type I IFNs. These new data show that all type I IFNs engage a common regulatory pathway, supporting our overall conclusion that different type I IFNs do not induce qualitatively different responses in PBMCs.

Regrettably, in the process of analysing the bulk RNAseq data at transcript level, we noticed that our original lncRNA analysis contained numerous false positives. Closer inspection showed that many “differentially expressed” LNCipedia models were likely not full-length transcripts and commonly shared a single IFN-induced set of exons that artificially inflated expression estimates for every overlapping model. To correct this issue, we replaced LNCipedia with the latest high-quality non-coding RNA catalogue from GENCODE, most entries of which were defined by full-length RNA sequencing [1]. We also tightened our filtering criteria and now report only transcripts that are robustly expressed in our dataset and are either classified as high-confidence by GENCODE or robustly supported at every splice junction by our RNAseq.

We hope our manuscript is sufficiently improved and suitable for publication in PLoS Biology. New or revised text is highlighted in green in our revised manuscript.

2. Point-by-point description of the revisions

Reviewer #1

Evidence, reproducibility and clarity:

The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN differ in the way they engage IFN signals to produce transcriptome changes. The data lead the authors to conclude that any differences are of quantitative rather than qualitative nature.

The authors' conclusions are based on a mass cytometry approach to phenotype STAT activation in different cell types, bulk RNA sequencing to study ISG expression in PBMC, and single cell sequencing to study ISG responses in individual cell types. The data are solid, clear and reproducible in biological replicates (eg different blood donors).

Significance: While some of the data can be considered confirmatory, the comprehensive analysis of cell-type specificity and IFN-I subtype specificity advances the field and provides a reference for future analyses. The study is complete and there is no obvious lack of a critical experiment. The number of scientists interested in the multitude of open questions around type I IFN is large, thus the study is likely to attract a broad readership.

We thank the reviewer for her/his positive assessment of our study.

The biggest limitation is to my opinion the low sequencing depth of scRNAseq which is clearly the downside of this technology. Using 11 hematopoietic cell types and bulk RNA sequencing the total number of ISG was determined to be 975 by Mostafavi et al. and the core ISG numbered 166. This is in stark contrast to this studies' 10 core ISG. The authors limitations paragraph should discuss the fact that scRNAseq reduces the overall ISG number that can be analyzed.

Thank you for this valid comment. We amended the limitations paragraph as requested. We agree that the Mostafavi et al. 2016 Cell paper [2] is important but note that there are many differences to our study: Mostafavi et al. use mice, a seemingly very high IFN dose (10,000 Units) and microarrays (not RNAseq).

A minor point concerns the 25 supplementary figures of the study. There must be a better way to support the conclusions with the necessary data.

We agree that our supplementary materials are extensive. However, this is not unusual for studies reporting multiple large datasets. We would be delighted to organise our supplementary information differently in due course according to journal guidelines.

Reviewer #2

Evidence, reproducibility and clarity:

The manuscript entitled “Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

We thank the reviewer for evaluating our study and the suggestions made.

*Major Comments: *

• Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859) We thank the reviewer for the suggestion to study the promoters of genes regulated by type I IFNs. The analyses outlined below were performed by A. Fedorov, who is now a new co-author of our study. To investigate promoter features that might underlie the observed transcriptional responses across type I IFNs, we first performed a de novo*motif search using STREME [3] on our bulk RNAseq dataset (Figure 3). Specifically, we compared the promoters of transcripts that were up- or down-regulated by each IFN subtype (e.g., IFN-β-induced) either with one another or with promoters of robustly expressed RNAs that remained unresponsive to any treatment. No significant motifs emerged from these comparisons, except when we compared promoters of IFN-induced transcripts to the background set of unresponsive RNAs. This comparison consistently yielded strong enrichment of interferon-stimulated response element (ISRE)-like motifs in the promoters of up-regulated RNAs (new Figure S13a).

Next, we conducted a complementary analysis using known transcription factor (TF) motifs from the JASPAR database [4]. We screened all promoters of annotated RNAs using clustered JASPAR motifs and Z-standardised motif scores relative to all high-confidence GENCODE RNAs, including those not expressed in PBMCs. We reasoned that TFs actively mediating IFN responses would likely bind promoters with high motif scores (Z ≥ 2), while promoters with low scores (Z ≤ -1) would represent an unregulated background. This approach produced two sets of RNAs per TF cluster: putatively regulated and unregulated. We then restricted each set to RNAs expressed in our dataset and associated each transcript with its estimated fold change in response to each type I IFN, regardless of statistical significance. Next, we compared median fold changes between the likely regulated and unregulated sets across all TF clusters and IFN subtypes (Figure S13b). Among all tested TF motifs, only the ISRE-like cluster showed strong and consistent associations with transcriptional changes across all IFN subtypes. We also observed statistically significant but much weaker associations for other TFs, including a known negative regulator of innate antiviral signaling, NRF1 [5]. However, effect sizes for these motifs were dwarfed by those of ISRE-like motifs, suggesting that no JASPAR TFs other than those within the ISRE-like cluster play a major role in PBMCs under our conditions. Overall, these findings support the idea that all type I IFNs engage a common regulatory pathway, differing primarily in the magnitude rather than the nature of their transcriptional effects.

How do they relate to the activation of kinases by IFN subtypes?

We did not analyse the activation of the canonical kinases (i.e., TYK2 and JAK1) downstream of IFNAR. This would be interesting and may be possible using phospho-specific antibodies to these kinases in our CyTOF setup. However, this would require a very large investment of time and resources to identify specific antibodies, optimise a new CyTOF staining panel and to acquire and analyse new datasets. We therefore believe this should be pursued as a separate future study.

*Are there distinct features that dictate differential responses in monocytes and lymphocytes? *

Following the computational approach described above, we applied STREME to identify DNA motifs that could distinguish promoters associated with monocyte- and lymphocyte-specific ISGs. Regrettably, this analysis did not yield any significant motifs, likely due in part to the limited number of genes in each category.

• Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.*

Thank you for this suggestion. We tried using the same scale for all heatmaps. However, given that the values for pSTAT1 are higher than those for other pSTATs, the resulting heatmaps did not show differences for the other pSTATs well. We therefore decided to leave these panels unchanged. Please also note that Figures 2b and S3b provide comparison between pSTATs (and other markers) using the same scale.

Minor Comments:

• The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.* Thank you for this suggestion. We have amended sub-headers accordingly.

• Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.*

The scales have been enlarged for better visibility as requested.

• Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb. *

Thank you for the suggestion. We combined these panels.

• Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types. *

Figure 5 and several accompanying supplementary figures already depict ISGs unique to IFN subtypes or cell types. Whilst we appreciate the suggestion, we prefer not to add additional figures to avoid redundancies.

• The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes. *

Thank you for this comment. We changed the presentation of the GO analysis in Fig S11 by sorting on p-value (instead of % of hits in category). We hope this shows more clearly that GO category enrichment amongst genes encoding IFN-induced transcripts had high statistical significance (log10 p-values of about -5 or lower for many categories).

Significance:* ** The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset. *

Audience: *** This is a valuable resource for immunologists, virologists, and bioinformaticians.*

Thank you for these encouraging comments.

Reviewer #3

Evidence, reproducibility and clarity:

*Summary *

Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

Thank you for your positive assessment of our work and for recommending publication.

*Major comments: *

• *

*Two of the key conclusions are not very convincing. *

• *

First, the authors claim that the magnitude of the responses varied between the 5 types of IFNs, however, as they point out in the 'limitation' paragraph, doses of the different IFNs were normalized using bioactivity. Knowing that this bioactivity is based on assays performed on A549 lung cells, this normalization likely induces a bias. How do the authors explain similar antiviral bioactivity but differing magnitudes of modulation of ISG expression? Would the authors expect the same differences of expression between the several IFNs tested in A549 cells? We thus recommend being very cautious when comparing magnitude of the response between the 5 types of IFNs.

We thank the reviewer for this important point and included the following reasoning in our discussion:

“An important technical consideration for our study was the normalisation of type I IFN doses used to treat cells (see also ‘Limitations of the study’ below). We relied on bioactivity (U/ml) that is measured by the manufacturer of recombinant type I IFNs using a cytopathic effect (CPE) inhibition assay. In brief, the lung cancer cell line A549 is treated with type I IFN and is infected with the cytopathic encephalomyocarditis virus (EMCV). Control cells not treated with IFN are killed by EMCV, whereas cells treated with sufficient IFN survive. How, then, is it possible that different type I IFNs induce differing magnitudes of STAT phosphorylation and ISG expression despite being used at the same bioactivity? Cell survival in the CPE inhibition assay may be due to one or a few ISGs. Indeed, single ISGs can mediate powerful antiviral defence. For example, MX1 is crucial for host defence against influenza A virus [6]. Thus, similar bioactivity of different IFNs in A549 cells against EMCV-triggered cell death may not reflect the breadth of effects on many ISGs. Moreover, IFN-induced survival of A549 cells following EMCV infection is a binary readout. Induction of the relevant ISG(s) mediating protection beyond a threshold required for cell survival is unlikely to register in this assay. Thus, similar antiviral bioactivity (in the CPE inhibition assay) and differing magnitudes of modulation of ISG expression (at transcriptome level) are compatible.”

We believe inclusion of this paragraph demonstrates an appropriate level of caution in our data interpretation. Further, we would expect to make similar observations if we were to apply transcriptomic analysis to A549 cells treated with different type I IFNs. However, given our focus in this study on primary, normal cells, we decided not to pursue work with the transformed and lab adapted A549 cell line.

Second, the qualitatively different responses to type I IFN subtypes claimed by the authors were not apparent. This seems true at the level of the bulk population (Fig. S10) but not at cell-type level (Fig. S15/S16).

We believe there may be a misunderstanding here. In relation to Figure S10, we do not claim “qualitatively different responses to type I IFN subtypes”. Instead, we conclude that “differences in expression between the different type I IFNs were quantitative” (page 8; lines 229-230, now: 238-239). Moreover, Figures S15/S16 (now: S16/S17) do not refer to analyses of responses to different type I IFN subtypes.

The authors state (line 311-312) that 'Consistent with our bulk RNAseq data, differences were again quantitative rather than qualitative' at the cell-type level. The response between cell types seems very different to us since a core set of only 10 ISGs are shared by all cell types and all 5 type I IFNs. Knowing that the expression of hundreds, sometimes thousands of genes, are induced by IFN, this seems like a rather small overlap (and thus qualitatively different responses). Fig S15 and S16 nicely illustrate that the responses are qualitatively different between cell-type. Please modify this conclusion accordingly.

Thank you for highlighting this. The statement in lines 311-312 does not refer to differences between cell types but to differences between type I IFN subtypes. We are sorry this was not clear and changed this sentence (now lines 357-358). Furthermore, we have made it clearer in the revised text that qualitative differences were observed between cell types (e.g. lines 329 and 350-352).

*No additional experiments are needed to support the claims. However, we believe that two additional analyses could provide useful information. *

• *

The levels of IFNAR1 and IFNAR2 expressed at the plasma membrane probably vary between cell types and may thus influence the magnitude of the IFN response. While it would be difficult to measure these levels by flow cytometric analysis on the different cell types, could the authors extract information from their scRNAseq analysis on the expression level of IFNAR1/2 in all cell types? This would give a hint about potential differences in expression (and thus in magnitude).

We analysed IFNAR1/2 transcript levels in our scRNAseq dataset (Figure R1 below). Unfortunately, for many cells, IFNAR1 and IFNAR2 transcripts were not detected (see width of violin plots at zero), probably due to low sequencing depth inherent to scRNAseq analysis. We therefore prefer not to draw conclusions from these data.

Could the authors investigate further the expression of lncRNAs at the single-cell levels? It would be useful to also define a core set of lncRNAs that are shared between cell types and IFN subtypes. If such a core set does not exist (since lncRNAs are less conserved than coding genes), it would be nice to mention it.

Thank you for this suggestion. The expression of lncRNAs is generally lower than protein-coding genes, resulting in high drop-out rates in 10X datasets. Indeed, Zhao et al. comment that “current development of single-cell technologies may not yet be optimized for lncRNA detection and quantification” [7]. We only detected a small number of lncRNAs in our scRNAseq analysis, and only four lncRNAs were significantly differentially expressed between cell types. We thus could not perform a meaningful analysis of lncRNAs in our scRNAseq dataset. This is now mentioned in the limitations paragraph at the end of the manuscript.

Minor comments:

There is a typo in line 355 Fig.4C =>6C.

Thank you for spotting this.

***Referees cross-commenting** *

We agree with Reviewer 1 that the low sequencing depth of scRNAseq restricts the analysis and must be discussed in the 'limitation' paragraph. This would explain why the authors identified only 10 ISGs that are common to all cell types and all 5 IFN subtypes. Of note, as a comparison, Shaw et al (10.1371/journal.pbio.2004086) identified a core set of 90 ISGs that are upregulated upon IFN treatment in cells isolated mainly from kidney and skin of nine mammalian species ("core mammalian ISGs"). It is thus expected that stimulated blood cells isolated from a single mammalian species share more than 10 ISGs.

We amended the limitations section as requested. Shaw et al. [8] used a single type I IFN (universal or IFNα, depending on species) at a very high dose (1000 U/ml). Taken together with the use of bulk RNAseq in this study, it is unsurprising that our work identified fewer core ISGs. We believe our small list of core ISGs is nonetheless both a high confidence and a high utility set of ISGs: these genes are induced by multiple type I IFNs, in all major cell types in blood and their regulation can be measured even when sequencing depth is low.

Significance (Required)

*Multiple single-cell RNAseq analysis of PBMCs, stimulated or not, have been previously performed in multiple contexts (for instance with PBMCs isolated from the blood of patients infected with influenza virus or SARS-CoV-2). The technical advance is thus limited. *

• *

*However, the work represents a conceptual advance for the field since it provides the first single-cell atlas of PBMCs stimulated with five type-I IFN subtypes. The generated datasets represent a great resource for anyone interested in innate immunity (virologists, immunologists and cancerologists). *

• *

Of note, we are studying innate immunity in the context of RNA virus infection but we have no expertise on scRNA sequencing. We may thus have missed a flaw in the analyses.

We thank the reviewer for their positive assessment of the advances of our study and the value of our IFN resource.

A

B

C

D

Figure R1. IFNAR1/2 expression in scRNAseq data.

Violin plots showing expression of IFNAR1 (A,C) or IFNAR2 (B,D) in different cell types. In (A,B), data were pooled across conditions. In (C,D), data are shown separately for unstimulated control cells and cells stimulated with different type I IFNs.

References

Kaur G, Perteghella T, Carbonell-Sala S, Gonzalez-Martinez J, Hunt T, Madry T, et al. GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing. bioRxiv. 2024. Epub 20241031. doi: 10.1101/2024.10.29.620654. PubMed PMID: 39554180; PubMed Central PMCID: PMCPMC11565817. Mostafavi S, Yoshida H, Moodley D, LeBoite H, Rothamel K, Raj T, et al. Parsing the Interferon Transcriptional Network and Its Disease Associations. Cell. 2016;164(3):564-78. Epub 2016/01/30. doi: 10.1016/j.cell.2015.12.032. PubMed PMID: 26824662; PubMed Central PMCID: PMCPMC4743492. Bailey TL. STREME: accurate and versatile sequence motif discovery. Bioinformatics. 2021;37(18):2834-40. doi: 10.1093/bioinformatics/btab203. PubMed PMID: 33760053; PubMed Central PMCID: PMCPMC8479671. Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R, Castro-Mondragon JA, Ferenc K, Kumar V, et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic acids research. 2024;52(D1):D174-D82. doi: 10.1093/nar/gkad1059. PubMed PMID: 37962376; PubMed Central PMCID: PMCPMC10767809. Zhao T, Zhang J, Lei H, Meng Y, Cheng H, Zhao Y, et al. NRF1-mediated mitochondrial biogenesis antagonizes innate antiviral immunity. The EMBO journal. 2023;42(16):e113258. Epub 20230706. doi: 10.15252/embj.2022113258. PubMed PMID: 37409632; PubMed Central PMCID: PMCPMC10425878. Grimm D, Staeheli P, Hufbauer M, Koerner I, Martinez-Sobrido L, Solorzano A, et al. Replication fitness determines high virulence of influenza A virus in mice carrying functional Mx1 resistance gene. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(16):6806-11. Epub 20070410. doi: 10.1073/pnas.0701849104. PubMed PMID: 17426143; PubMed Central PMCID: PMCPMC1871866. Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J. 2022;20:4381-9. Epub 20220804. doi: 10.1016/j.csbj.2022.08.003. PubMed PMID: 36051880; PubMed Central PMCID: PMCPMC9403499. Shaw AE, Hughes J, Gu Q, Behdenna A, Singer JB, Dennis T, et al. Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses. PLoS Biol. 2017;15(12):e2004086. Epub 2017/12/19. doi: 10.1371/journal.pbio.2004086. PubMed PMID: 29253856.

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Referee #3

Evidence, reproducibility and clarity

Summary

Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

Major comments:

Two of the key conclusions are not very convincing.

First, the authors claim that the magnitude of the responses varied between the 5 types of IFNs, however, as they point out in the 'limitation' paragraph, doses of the different IFNs were normalized using bioactivity. Knowing that this bioactivity is based on assays performed on A549 lung cells, this normalization likely induces a bias. How do the authors explain similar antiviral bioactivity but differing magnitudes of modulation of ISG expression? Would the authors expect the same differences of expression between the several IFNs tested in A549 cells? We thus recommend being very cautious when comparing magnitude of the response between the 5 types of IFNs.

Second, the qualitatively different responses to type I IFN subtypes claimed by the authors were not apparent. This seems true at the level of the bulk population (Fig. S10) but not at cell-type level (Fig. S15/S16). The authors state (line 311-312) that 'Consistent with our bulk RNAseq data, differences were again quantitative rather than qualitative' at the cell-type level. The response between cell types seems very different to us since a core set of only 10 ISGs are shared by all cell types and all 5 type I IFNs. Knowing that the expression of hundreds, sometimes thousands of genes, are induced by IFN, this seems like a rather small overlap (and thus qualitatively different responses). Fig S15 and S16 nicely illustrate that the responses are qualitatively different between cell-type. Please modify this conclusion accordingly.

No additional experiments are needed to support the claims. However, we believe that two additional analyses could provide useful information.

The levels of IFNAR1 and IFNAR2 expressed at the plasma membrane probably vary between cell types and may thus influence the magnitude of the IFN response. While it would be difficult to measure these levels by flow cytometric analysis on the different cell types, could the authors extract information from their scRNAseq analysis on the expression level of IFNAR1/2 in all cell types? This would give a hint about potential differences in expression (and thus in magnitude).

Could the authors investigate further the expression of lncRNAs at the single-cell levels? It would be useful to also define a core set of lncRNAs that are shared between cell types and IFN subtypes. If such a core set does not exist (since lncRNAs are less conserved than coding genes), it would be nice to mention it.

Minor comments:

There is a typo in line 355 Fig.4C =>6C.

Referees cross-commenting

We agree with Reviewer 1 that the low sequencing depth of scRNAseq restricts the analysis and must be discussed in the 'limitation' paragraph. This would explain why the authors identified only 10 ISGs that are common to all cell types and all 5 IFN subtypes. Of note, as a comparison, Shaw et al (10.1371/journal.pbio.2004086) identified a core set of 90 ISGs that are upregulated upon IFN treatment in cells isolated mainly from kidney and skin of nine mammalian species ("core mammalian ISGs"). It is thus expected that stimulated blood cells isolated from a single mammalian species share more than 10 ISGs.

Significance

Multiple single-cell RNAseq analysis of PBMCs, stimulated or not, have been previously performed in multiple contexts (for instance with PBMCs isolated from the blood of patients infected with influenza virus or SARS-CoV-2). The technical advance is thus limited.

However, the work represents a conceptual advance for the field since it provides the first single-cell atlas of PBMCs stimulated with five type-I IFN subtypes. The generated datasets represent a great resource for anyone interested in innate immunity (virologists, immunologists and cancerologists).

Of note, we are studying innate immunity in the context of RNA virus infection but we have no expertise on scRNA sequencing. We may thus have missed a flaw in the analyses.

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Referee #2

Evidence, reproducibility and clarity

The manuscript entitled "Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

Major Comments:

1. Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859) How do they relate to the activation of kinases by IFN subtypes? Are there distinct features that dictate differential responses in monocytes and lymphocytes?
2. Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.

Minor Comments:

1. The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.
2. Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.
3. Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb.
4. Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types.
5. The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes.

Significance

Significance:

The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset.

Audience:

This is a valuable resource for immunologists, virologists, and bioinformaticians.

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Referee #1

Evidence, reproducibility and clarity

The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN differ in the way they engage IFN signals to produce transcriptome changes. The data lead the authors to conclude that any differences are of quantitative rather than qualitative nature. The authors' conclusions are based on a mass cytometry approach to phenotype STAT activation in different cell types, bulk RNA sequencing to study ISG expression in PBMC, and single cell sequencing to study ISG responses in individual cell types. The data are solid, clear and reproducible in biological replicates (eg different blood donors).

Significance

While some of the data can be considered confirmatory, the comprehensive analysis of cell-type specificity and IFN-I subtype specificity advances the field and provides a reference for future analyses. The study is complete and there is no obvious lack of a critical experiment. The number of scientists interested in the multitude of open questions around type I IFN is large, thus the study is likely to attract a broad readership.

The biggest limitation is to my opinion the low sequencing depth of scRNAseq which is clearly the downside of this technology. Using 11 hematopoietic cell types and bulk RNA sequencing the total number of ISG was determined to be 975 by Mostafavi et al. and the core ISG numbered 166. This is in stark contrast to this studies' 10 core ISG. The authors limitations paragraph should discuss the fact that scRNAseq reduces the overall ISG number that can be analyzed.

A minor point concerns the 25 supplementary figures of the study. There must be a better way to support the conclusions with the necessary data.

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Reply to the reviewers

1. General Statements

We would like to thank all reviewers assigned by Review Commons for their thoughtful and constructive feedback, which helped us to further improve the quality and clarity of our manuscript. In this study, we developed a novel fluorescence-based live-cell imaging platform for detecting mitochondria-endoplasmic reticulum contact sites (MERCS), which we named MERCdRED. This system enables quantitative analysis of MERCS dynamics in living cells by combining stable gene expression of dimerization-dependent fluorescent proteins with single-cell cloning. Using this tool, we uncovered a nutrient-dependent regulatory mechanism of MERCS formation mediated by the ER-localized tethering protein PDZD8. We appreciate that all the reviewers acknowledged the methodological robustness of this work. In response to reviewers' comments, we will significantly improve the manuscript by adding the live-cell imaging to assess the reversible propertyof MERCdRED, and investigating the physiological impacts of MERCS remodeling in regulating metabolism in response to nutrient starvation. We believe that both the methodological advance and the biological findings presented in this study will be of broad interest to the cell biology community.

1. Description of the planned revisions

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

Summary: In this study, the authors successfully established a stable cell line expressing MERCdRED, a dimerization-dependent fluorescent protein (ddFP)-based sensor for monitoring mitochondrial-ER contact sites (MERCS). Through light and electron microscopy analyses, they demonstrated that MERCS formation is regulated by nutrient availability and requires PDZD8. While the work is technically sound and well-presented, the biological implications of nutrient-dependent MERCS regulation remain underexplored.

Major Concerns: Although the manuscript is methodologically robust and suitable for a Methods-type article, its biological significance is limited. The findings primarily serve as proof-of-concept for the MERCdRED tool, without substantially advancing our understanding of MERCS regulation.

We appreciate the reviewer for acknowledging the methodological robustness of our study. We would like to respectfully emphasize that, using the MERCdRED cell system, we uncovered the distinct features of MERCS dynamics by comparing structures of various sizes (Figure 4A-D). Furthermore, we discovered an unexpected biological finding: nutrient starvation leads to a reduction in MERCS formation, which contrasts with previous reports using cell lines (former Figure 4E-H). Additionally, we revealed that PDZD8 mediates nutrient-dependent MERCS regulation (former Figure 4E-H).

To clarify these findings, we have now separated the former Figure 4 into two distinct figures (now Figure 4 and 5). Furthermore, to assess the functional relevance of PDZD8-mediated MERCS regulation upon nutritional change, we will perform rescue experiments by overexpressing PDZD8 in starved cells, along with a metabolomic analysis in these conditions. We will add these new data in Figure 6.

Taken together, we believe that our data provide novel mechanistic insights into how MERCS are modulated and utilized for the regulation of metabolism under physiological stress, thereby contributing to a deeper understanding of the roles and regulation of MERCS beyond the scope of a mere proof-of-concept study.

Reviewer #1 (Significance (Required)):

To enhance the impact of the study, the authors could use this sensor to investigate novel biological questions-such as the molecular pathways linking nutrient sensing to MERCS dynamics-or explore downstream activities of nutrient-dependent MERCS formation. Deeper mechanistic insights would significantly strengthen the work's contribution to the field.

We thank the reviewer for their constructive suggestions. We fully agree that the MERCdRED cell system has great potential for investigating upstream signaling pathways regulating MERCS dynamics, as well as the downstream consequences of nutrient-dependent MERCS modulation. As mentioned above, this study already presents important findings, including the discovery of PDZD8 as a key protein linking the nutrient starvation and MERCS remodeling, and a relationship between MERCS dynamics and contact site size.

To further assess the biological consequence of the MERCS remodeling, we will perform metabolomics analysis in PDZD8-overexpressing cells under starved conditions.

Additionally, to further reinforce the utility of MERCdRED and extend the findings presented in this study, we performed live-cell imaging experiments using MERCdRED. The preliminary results demonstrated dynamic and reversible changes in MERCS in response to nutrient starvation and subsequent recovery (Please see the response to Reviewer 3 below, Reviewer-only Figure 1).

These new data will significantly strengthen the contribution of this study to the field.

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

This manuscript entitled "Live-cell imaging reveals nutrient-dependent dynamics of ER-mitochondria contact formation via PDZD8" by Saeko Aoyama-Ishiwatari et al., describes a novel methodology for visualizing contacts between mitochondria and the endoplasmic reticulum (MERCs) by fluorescence microscopy. Inter-organelle contacts, defined as membrane proximities below ~30 nm, fall below the diffraction limit of conventional light microscopy. The method developed by Hirabayashi's laboratory leverages dimerization-dependent fluorescence complementation to create a reporter capable of both visualizing and quantifying ER-mitochondria contacts (MERCs).

Reviewer #2 (Significance (Required)):

This timely study provides a valuable and innovative approach to overcoming a longstanding technical limitation in the field, enabling dynamic analysis of ER-mitochondria contacts.

We appreciate the reviewer for recognizing the timeliness and innovation of our work.

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

In this manuscript, the authors develop a new system to study mitochondrial-ER contact sites in living mouse embryonic fibroblast cells and explore the impact that nutritent starvation has on these contact sites in real time. By stably expressing a bicistroic reporter construct of a dimerization-dependent fluorescent protein that will generate a signal once the two moities, one anchored in the ER by Sec61b, and the other anchored in the outer membrane of mitochondria, via TOM20, comme in close apposition. This cell model is validated using sofisticated CLEM experiments and via the ablation of known regulators of MERCs, such as PDZD8 and FKBP8.

Major comments

The authors claim to have developed a new for the study of MERCs. They indeed have benchmarked this system using very sophisticated CLEM approaches and through the ablation of known regulators of MERCs, all of which is very carefully performed and convincing.

We appreciate the reviewer for acknowledging our efforts in the development and validation of the MERCdRED system presented in this study.

They argue that the generation of a stable cell line via lentiviral delivery is an improvement over the transient transfection approaches that have been applied in the past (see cited references), which I would generally agree. However, they have not contrasted or compared their system to the widly-used SLPICs system from the Tito Cali group (Vallese, F. et al. An expanded palette of improved SPLICS reporters detects multiple organelle contacts in vitro and in vivo. Nat. Commun. 11, 6069 (2020)) which measures bi and tri-partite interactions with other membrane contacts, including mitochondria and ER at two specific distances, which in my opinion has been more extensivley used to study cell and tissue physiology. They accurately point out that the reversability of this and other systems is challenging and it would be important to define highlight whether the current system allows the study of reversible MERCs. It does not appear as though the reversability of MERCs has been explored in this study.