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Reply to the reviewers

GENERAL COMMENTS

We thank the three reviewers for their comments on the paper.

We are pleased to see that they consider it be a comprehensive and well-executed study, which clearly establishes a previously overlooked connection between MRTF-SRF signalling and proliferation, and that its conclusions require no further experimentation.

As review 3 points out, this work has implications for cancer biology, and suggests new research routes to understand the relation between cell adhesion, proliferation, and transformation.

However, two referees raise significant concerns about its impact

Review 1 suggests that the paper lacks impact without exploration the wider biological significance of our observations, although it considers it to be a good basic cell biology study. It suggests further work extending the findings to tissue- or tumor-based systems. While we consider such studies worthwhile – indeed we are currently pursuing these directions – we consider them beyond the scope of the present paper.

Review 2 questions the novelty of our findings. We strongly disagree. This is is the first study to show that MRTF-SRF signalling is required for the proliferation of both primary and immortalised fibroblasts, and epithelial cells. We show that MRTF inactivation leads cells to enter a quiescence-like state under conditions that would permit efficient cell cycle progression in wildtype cells. The study will alter the field's perspective on the role of MRTF-SRF signalling, previously viewed as concerned with cell adhesion, morphology, and motility.

Responses to individual reviews (italic) follow in regular text.

RESPONSE TO INDIVIDUAL REVIEWS (comments in italic, response in regular, changes made)

__Reviewer #1 __

*(Evidence, reproducibility and clarity (Required)): *

*The manuscript by Neilsen et al. presents a thorough and well-structured study showing that Myocardin-related transcription factors (MRTF-A/B), via MRTF-SRF, are essential for the proliferation of both primary and immortalized fibroblasts and epithelial cells. Using a combination of knockouts/rescue experiments, cytoskeletal analysis, and transcriptomics, the authors demonstrate that MRTF-SRF signalling controls actin dynamics and contractility-key drivers of cell cycle progression. Notably, they show that the proliferative arrest caused by MRTF loss is reversible, distinguishing it from classical senescence. **

Major points*

• The link between MRTF-SRF activity, cytoskeletal organisation, and cell proliferation is clearly established. The fact that disrupting contractility phenocopies MRTF loss strengthens the case that the pathway acts through mechanical control.*
• The authors support their conclusions using multiple cell types (MEFs, primary fibroblasts, epithelial cells), a range of complementary assays (RNA-seq, traction force microscopy, adhesion/spreading), and genetic tools (CRISPR, inducible rescue).*
• The ability to restore proliferation by re-expressing MRTF-A argues against true senescence and instead suggests a quiescence-like state driven by cytoskeletal disruption.*
• This work particularly highlights how mechanical inputs feed into transcriptional programs to regulate proliferation, with implications for understanding anchorage-dependent growth.**

Suggestions While the authors argue convincingly against classical senescence, elevated SA-βGal and SASP expression suggest a more nuanced arrest state. It not really clear what this state is or is not, therefore a deeper discussion of possible hybrid or intermediate states would be helpful - maybe potential additional experiments to include or exclude potential explanations - e.g. how does it differ from G0 exit?* Our findings show that MRTF inactivation inhibits cell proliferation under conditions that would permit efficient cell cycle progression in wildtype cells, inducing a state with some features associated with classical senescence, and others conventionally associated with reversible cell cycle arrest/quiescence. The reviewer correctly points out that this raises problems with accurately defining the nature of the MRTF-null proliferation defect.

To our knowledge there are no rigorously defined unambiguous markers for senescence, quiescence, or G0. Indeed, recent studies have shown that senescence and quiescence / G0 states are not as distinct as previously assumed (Anwar et al, 2018; Ashraf et al 2023) as we reviewed in detail in Discussion p27, §2; p28 §3. We therefore do not consider it a productive endeavour to define markers for the MRTF-null state as opposed to defining its mechanistic basis. However, we agree that we should have been clearer about how the phenotypes we observe relate to classical cell arrest states.

We have therefore revised the presentation of the Results to make it clear which features of the non-proliferative state associated with MRTF inactivation are seen in classical senescence, and which are found in reversible cell cycle exit or quiescence.

Things done:

• __Results pp16-17 and Fig 1. Figure panels and presentation are reordered to present “senescence” features together before marker expression (panel G is now panel I). Text now explicitly points out that the spectrum of cell cycle markers, specifically p27 upregulation, is not that associated with classical senescence (p16, p21,etc) but previously linked to reversible arrest or quiescence. Lines 371-380 have been moved up from the succeeding paragraph; statement added re p27 and reversible cell cycle exit on lines 387-389; summary sentence added in lines 398-401). __
• Statement added that reversibility distinguishes the MRTF defect from classical senescence p20§1 line 454-455.

• Note that p27 is associated with reversible arrest included on p20§2 line 460. We also explicitly summarised the features of the phenotype at the start of the Discussion.

• Sentences added p27§1 lines 626-631.

• Emphasis that p27 protein upregulation is associated with reversible cell cycle inhibition and quiescence is added on p28 line 668-669.

• The transcriptomic data are strong, but the paper would benefit from zooming in on specific MRTF-SRF targets (e.g., actin isoforms, adhesion molecules) that directly link cytoskeletal regulation to cell cycle control.*

We have now clarified presentation of the RNAseq data in Figure 5 and the data summary tables. Figure 5B now identifies which of those genes showing deficits in MRTF-null MEFs were previously identified as direct genomic targets for MRTF-SRF, and that the majority are cytoskeletal.

• __Additional columns added in Table 1 to indicate whether genes are candidate genomic MRTF-SRF targets; Table 2 now show gene symbol lists as well as ENSMBL IDs for GO categories and NCBI Entrez IDs for GSEA categories, respectively. __
• __Figure 5B revised to point out cytoskeletal genes that are genomic MRTF-SRF targets in bold, legend clarified p40 lines 920-922. __
• Now noted____ p23 lines 527-529 that cytoskeletal genes affected include many direct MRTF-SRF targets. Our data confirms that in MEFs, MRTF inactivation affects fibroblast cell morphology, adhesion, spreading, motility and contractility (Figures 5, 6), as seen in many other settings.

A critical question remains as to whether these effects a reflect limitation in one MRTF target gene or several, and how this defect relates to proliferation.

Concerning specific MRTF-SRF gene targets:

Cells lacking cytoplasmic actins are reported to exhibit defective proliferation, (__now noted in Results p23 lines 529-532). __We are currently evaluating whether this defect has similarities with the MRTF-null proliferation phenotype (see Discussion p31, §2).

Previous findings suggest that defective cytoplasmic actin expression may underlie most MRTF knockout phenotypes (Salvany et al, 2014; Maurice et al., 2024) previously noted in the Discussion (see p31, §2).

The myoferlin gene promotes growth of liver cancer cells by inhibiting ERK activation and oncogene induced senescence. We showed that myoferlin expression does not promote proliferation of MRTF-null MEFs in the original submission (see Figure S5E). Additionally, we now point out that the RNAseq data show that myoferlin expression is not significantly affected in MRTF-null MEFs __(new text p23, lines 532-534). __

• It depends on where what target journal would be, but this is is a very well executes mechanistic study that doesn't really have an impact. Extending the discussion to human systems-or tissues where contractility is critical-could broaden the impact and applicability of the findings.*

We interpret this comment as indicating that our paper does not address the wider biological implications of our findings by extension to studies in tissue or tumour systems.

As outlined in our response to review 3, our study provides strong evidence that MRTF-SRF will be required for cell proliferation in settings where physical progression through cell cycle transitions requires high contractility, either owing to intrinsic factors or external physical constraints such as tissue stiffness, fibrosis, or tumour microenvironment.

Discussion now explicitly addresses potential roles for tissue stiffness (pp30§2 lines 717-718, and p32§1 725-727). However, we feel that resolution of this question is beyond the scope of the present paper.

• As above, the paper briefly mentions transformation, but it would be valuable to elaborate on whether MRTF-SRF acts as a barrier or enabler in tumorigenesis under different conditions. This I feel is the main weakness remaining - e.g. it would be fine with enabling different effects driven by other transcription events in emerging tumour cells (oncogenic in context of RAS, suppressive in context of p53) but I think the manuscript fails to be definitive on this points. Addressing this would make a much stronger and impactful study. I believe they have an impact peice of science that outlines how mechanical events impact cell fate decisions, but this is unlikely to be the driver - ie it facilitates cell fate decisions in context of tissue stiffness.*

We find it difficult to understand the precise points being made here.

However, transformation has long been known to bypass physical constraints on proliferation such as the requirement for adhesion. Moreover, MRTF-SRF activity is not necessarily required for proliferation of all transformed cells (Hampl et al, 2013; Medjkane et al, 2009; our unpublished data). The relation of our findings to transformation is thus an open question, which we are actively pursuing. Now noted in revised Discussion p32, lines 752-755.

MRTF-independent proliferation of tumor cells could reflect oncogenic signals substituting for MRTF-dependent ones (eg from focal adhesions), or from relief of cytoskeletal contraints on proliferation (adhesion independent proliferation). In contrast, in proliferation of DLC1-deleted cancer cells is dependent on suppression of oncogene-induced senescence by MRTF-SRF signalling (Hampl et al, 2013). These points were already made in Discussion p28, pp30-31.

Although our current work is focussed on cell transformation, we would respectfully suggest the in-depth resolution of this complex question is beyond the scope of the present paper.

See also response to (3) above.

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

*Overall *

This is a well-executed and insightful study that deepens our understanding of how cytoskeletal signals drive proliferation through MRTF-SRF. It broadens the role of this pathway beyond motility and offers new perspectives on mechanotransduction and cellular plasticity. If is weak in its demonstration of biological significance, but if the aim to to present a pure basic cell biology story it is good.

The vast majority of work with the SRF system has led to the common perception that its role is exclusively with cell motility and adhesive processes, not proliferation. The results presented in the paper, even if limited to cell culture models, are therefore novel.

Reviewer #2

(Evidence, reproducibility and clarity (Required)):

*In this manuscript, Nielsen and colleagues examine the impact of MRTF-A/B and SRF gene inactivation on cell proliferation. They performed an extensive body of work (using multiple cell types and multiple clones) to show that MRTF inactivation causes cell cycle arrest and senescence (mimicking the phenotype of SRF knockout cells) although the changes in the expression of various CDK inhibitors were cell-type specific. *

*Very interestingly, simultaneous inactivation of all three major CDK inhibitors failed to rescue MRTF knockout cells from their proliferation defect. Expectedly, MRTF knockout cells exhibited defects in actin cytoskeleton, adhesion, and contractility. Interestingly, hyperactivating Rho also failed to rescue MRTF knockout cells from proliferation defect. The main conclusion of the paper was derived from experiments which showed that inhibition of either ROCK or myosin caused wild-type cells to behave like MRTF knockout cells rather than demonstration of any molecular perturbation that could reverse the proliferation defect of MRTF knockout cells. *

While the experimental studies are thorough and rigorous, a vast majority of the core findings related to the loss-of-function of MRTF that are reported herein (i.e. defects in cell proliferation, elevation of CDK inhibitors, migration, actin cytoskeleton, contractility) are not conceptually new and have been previously reported in other cell systems by several investigators including this research group.

This is the first study showing that MRTF-SRF signalling is required for the proliferation of both primary and immortalised fibroblasts, and epithelial cells. We show that the MRTF-SRF non-proliferative state combines features of both classical senescence and reversible cell cycle exit / quiescence.

The vast majority of previous work with the SRF system has led to the common perception that its role is exclusively related to cell motility and adhesive processes and not proliferation (see Olson and Nordheim 2010). Where proliferation has been examined directly, both others and our own previous studies of the MRTFs in immune cells and cancer cells lines have revealed no direct role in proliferation (Schratt et al, 2001;Medjkane et al 2009; Maurice et al, 2024).

The results presented here are therefore novel.

In the reviewer's opinion, since the authors have not been able to identify a molecular strategy to reverse the proliferation phenotype of MRTF knockout cells, the underlying mechanisms of MRTF-dependent regulation of cell proliferation remain largely unanswered.

Indeed, our attempts to rescue the phenotype (knockouts of the CKIs, and overexpression of different downregulated factors) did not restore proliferation. We therefore now aim to attack the problem (i) through overexpression screens, and (ii) by identifying differences between MRTF-SRF dependent and -independent (eg transformed) cells. However, these are new projects that are beyond the scope of a revised paper.

• *

Other comments: Majority of the immunoblot data have not been quantified.

P16 data in Fig 1G vs Fig S1A are not similar (although the authors mention that the findings are similar)

We have addressed these issues by reorganisation and quantification the immunoblotting data as follows:

• Figure S1A has been moved to new Figure 1I, replacing the limited analysis shown in old Figure 1G. This more comprehensive, and displays data from all three WT and Mrtfab-/-
• Figure 1I data is quantified. Marker expression in each Mrtfab-/- pool is evaluated relative its mean expression in the three WT pools treated in parallel.
• A new Figure S1A shows mean marker expression across the three Mrtfab-/- pools, drawn from 5 independent analyses (not all markers included in each analysis). Different analyses of marker expression may exhibit variation, resulting from differences in handling, culture medium, plating density, relative confluence, etc. However, Mrtfab-/- cells exhibit markedly increased p27 and TLR2 expression, while expression of the other markers tested, including p16, consistently decreases.
• Spearman comparisons among the WT and Mrtfab-/- pools show that relative marker expression is indeed well correlated between the pools of each genotype. Note on quantitation added in Methods p10 lines 209-213.

Figure 1I moved from former Figure S1A, to replace former Figure 1G. New legend now includes quantitation, and reference to Spearman correlations, p44 lines 834-841.

New Figure S1A displays data from multiple independent experiments with all 3 Mrtfab-/- pools. New legend, p44 lines 997-1002.

Figure S1B legend notes correlation between relative marker expression in untreated WT and Mrtfab-/- cells, p44, lines 1005-1008.

Results text rewritten p17 lines 383-391; no reference to “similar”.

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

*This study aims to investigate a fundamental biological question of how an actin-regulated transcription machinery regulates cell proliferation and is therefore of broad significance. Strengths and limitations of this study are described above. *

Reviewer #3

*(Evidence, reproducibility and clarity (Required)): *

Summary

*The manuscript by Nielsen et al. (Treisman lab) entitled "MRTF-dependent cytoskeletal dynamics drive efficient cell cycle progression" investigates the effects on cell proliferation elicited upon cellular depletion of the transcription factors MRTF-A and MRTF-B. The MRTFs are actin-dependent co-factors of SRF, which direct the transcription of SRF target genes. The MRTF-SRF regulatory circuit defines both the functioning and the control of actin-driven cytoskeletal dynamics. *

*The work presented identifies essential molecular links that interconnect cytoskeleton-dependent cellular activities (cell-cell adhesion, cell-substrate contact, cell spreading) and cell proliferation. *

*General assessment on used methodology. *

*The presented comprehensive body of work is performed competently; it includes all relevant and necessary state-of-the-art technologies. *

• *

Reviewer #3 (Significance (Required)):

Advance

Previously published evidence by others (including the Treisman group) had indicated that SRF does not seem essential for the proliferation of some cell types (i. e., embryonic (stem) cells, activation-dependent immune cells, etc.). In regard to this, the authors discuss in the current manuscript: "Although further work is needed to elucidate the basis for these context-dependent dfferences, our data show that MRTF-SRF signalling is likely to play a more general role in proliferation than previously thought." The current manuscript already delineates this "general role": MRTF-SRF signalling impinges on cell proliferation whenever proliferative activities are dependent upon cytoskeletal dynamics.

We of course support the view that it is MRTF-SRF's role in cytoskeletal dynamics, especially contractility, that is a limiting factor for cell cycle progression in our cells; however, this may not be the cases or other cell types or settings, such adhesion-independent or transformed cells, and/or stiff tissue environments.

We have stated this view more strongly, modifying the abstract and discussion, and rewording the sentence quoted above.

The major point is that MRTF-SRF-dependent proliferation may be more common than previously thought, the field having focussed on its role in cytoskeletal dynamics rather than proliferation.

Abstract lines 48-49; Discussion p28, line 668-669; pp30-31, lines 713-714, 725-727. See also last para pp31/32, __added lines 752-755. __

*The work has implications for cancer biology. It offers new directions to investigate the regulation of proliferative activities of anchorage-independent tumor cells. **

Audience *

*The insights generated serve the wide interests of a large and diverse group of cell and tumor biologists. *

*Reviewers field of expertise (keywords). *

Cytoskeletal dynamics, transcriptional con*

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

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Referee #3

Evidence, reproducibility and clarity

Summary

The manuscript by Nielsen et al. (Treisman lab) entitled "MRTF-dependent cytoskeletal dynamics drive efficient cell cycle progression" investigates the effects on cell proliferation elicited upon cellular depletion of the transcription factors MRTF-A and MRTF-B. The MRTFs are actin-dependent co-factors of SRF, which direct the transcription of SRF target genes. The MRTF-SRF regulatory circuit defines both the functioning and the control of actin-driven cytoskeletal dynamics. The work presented identifies essential molecular links that interconnect cytoskeleton-dependent cellular activities (cell-cell adhesion, cell-substrate contact, cell spreading) and cell proliferation.

General assessment on used methodology.

The presented comprehensive body of work is performed competently; it includes all relevant and necessary state-of-the-art technologies.

Significance

Advance

Previously published evidence by others (including the Treisman group) had indicated that SRF does not seem essential for the proliferation of some cell types (i. e., embryonic (stem) cells, activation-dependent immune cells, etc.). In regard to this, the authors discuss in the current manuscript: "Although further work is needed to elucidate the basis for these context-dependent dfferences, our data show that MRTFSRF signalling is likely to play a more general role in proliferation than previously thought." The current manuscript already delineates this "general role": MRTF-SRF signalling impinges on cell proliferation whenever proliferative activities are dependent upon cytoskeletal dynamics.

The work has implications for cancer biology. It offers new directions to investigate the regulation of proliferative activities of anchorage-independent tumor cells.

Audience

The insights generated serve the wide interests of a large and diverse group of cell and tumor biologists.

Reviewers field of expertise (keywords).

Cytoskeletal dynamics, transcriptional control.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In this manuscript, Nielsen and colleagues examine the impact of MRTF-A/B and SRF gene inactivation on cell proliferation. They performed an extensive body of work (using multiple cell types and multiple clones) to show that MRTF inactivation causes cell cycle arrest and senescence (mimicking the phenotype of SRF knockout cells) although the changes in the expression of various CDK inhibitors were cell-type specific. Very interestingly, simultaneous inactivation of all three major CDK inhibitors failed to rescue MRTF knockout cells from their proliferation defect. Expectedly, MRTF knockout cells exhibited defects in actin cytoskeleton, adhesion, and contractility. Interestingly, hyperactivating Rho also failed to rescue MRTF knockout cells from proliferation defect. The main conclusion of the paper was derived from experiments which showed that inhibition of either ROCK or myosin caused wild-type cells to behave like MRTF knockout cells rather than demonstration of any molecular perturbation that could reverse the proliferation defect of MRTF knockout cells. While the experimental studies are thorough and rigorous, a vast majority of the core findings related to the loss-of-function of MRTF that are reported herein (i.e. defects in cell proliferation, elevation of CDK inhibitors, migration, actin cytoskeleton, contractility) are not conceptually new and have been previously reported in other cell systems by several investigators including this research group. In the reviewer's opinion, since the authors have not been able to identify a molecular strategy to reverse the proliferation phenotype of MRTF knockout cells, the underlying mechanisms of MRTF-dependent regulation of cell proliferation remain largely unanswered.

Other comments: Majority of the immunoblot data have not been quantified. P16 data in Fig 1G vs Fig S1A are not similar (although the authors mention that the findings are similar)

Significance

This study aims to investigate a fundamental biological question of how an actin-regulated transcription machinery regulates cell proliferation and is therefore of broad significance. Strengths and limitations of this study are described above.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

The manuscript by Neilsen et al. presents a thorough and well-structured study showing that Myocardin-related transcription factors (MRTF-A/B), via MRTF-SRF, are essential for the proliferation of both primary and immortalized fibroblasts and epithelial cells. Using a combination of knockouts/rescue experiments, cytoskeletal analysis, and transcriptomics, the authors demonstrate that MRTF-SRF signalling controls actin dynamics and contractility-key drivers of cell cycle progression. Notably, they show that the proliferative arrest caused by MRTF loss is reversible, distinguishing it from classical senescence.

Major points

1. The link between MRTF-SRF activity, cytoskeletal organisation, and cell proliferation is clearly established. The fact that disrupting contractility phenocopies MRTF loss strengthens the case that the pathway acts through mechanical control.
2. The authors support their conclusions using multiple cell types (MEFs, primary fibroblasts, epithelial cells), a range of complementary assays (RNA-seq, traction force microscopy, adhesion/spreading), and genetic tools (CRISPR, inducible rescue).
3. The ability to restore proliferation by re-expressing MRTF-A argues against true senescence and instead suggests a quiescence-like state driven by cytoskeletal disruption.
4. This work particularly highlights how mechanical inputs feed into transcriptional programs to regulate proliferation, with implications for understanding anchorage-dependent growth.

Suggestions

1. While the authors argue convincingly against classical senescence, elevated SA-βGal and SASP expression suggest a more nuanced arrest state. It not really clear what this state is or is not, therefore a deeper discussion of possible hybrid or intermediate states would be helpful - maybe potential additional experiments to include or exclude potential explanations - e.g. how does it differ from G0 exit?
2. The transcriptomic data are strong, but the paper would benefit from zooming in on specific MRTF-SRF targets (e.g., actin isoforms, adhesion molecules) that directly link cytoskeletal regulation to cell cycle control.
3. It depends on where what target journal would be, but this is is a very well executes mechanistic study that doesn't really have an impact. Extending the discussion to human systems-or tissues where contractility is critical-could broaden the impact and applicability of the findings.
4. As above, the paper briefly mentions transformation, but it would be valuable to elaborate on whether MRTF-SRF acts as a barrier or enabler in tumorigenesis under different conditions. This I feel is the main weakness remaining - e.g. it would be fine with enabling different effects driven by other transcription events in emerging tumour cells (oncogenic in context of RAS, suppressive in context of p53) but I think the manuscript fails to be definitive on this points. Addressing this would make a much stronger and impactful study. I believe they have an impact peice of science that outlines how mechanical events impact cell fate decisions, but this is unlikely to be the driver - ie it facilitates cell fate decisions in context of tissue stiffness.

Significance

Overall

This is a well-executed and insightful study that deepens our understanding of how cytoskeletal signals drive proliferation through MRTF-SRF. It broadens the role of this pathway beyond motility and offers new perspectives on mechanotransduction and cellular plasticity. If is weak in its demonstration of biological significance, but if the aim to to present a pure basic cell biology story it is good.

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

Proposed revision plan

Based on the below reviews, we propose the following revision plan. Briefly:

• We will remove the functional data on TGFβ signaling and mechanical loading/mechanosensing. We agree with the reviewers that we would need to generate additional histological and molecular data from conditional knockout mice, antibody and (ant)agonist treatments and the optogenetic model to determine their exact involvement in lining macrophage maturation. These experiments require significant time and other resources.
• We would therefore like to uncouple this question for a follow-on manuscript.We will re-focus the manuscript on the developmental data providing a molecular and cellular blueprint of lining macrophage development. This will include our data on CSF1 as a key signal. The novelty and relevance of our developmental data have been highlighted by all three reviewers, and they have also praised the rigor of these experiments and their interpretation. We thus believe that this re-focus will improve the manuscript message.
• To further enhance this, we are proposing to include additional data delineating the developmental dynamics of synovial fibroblasts. We have generated an in-depth single cell RNAsequencing dataset but did not include fibroblast-specific analyses in the original manuscript. This is not a change proposed by the reviewers, but we are proposing this because we believe this would be an impactful addition to a revised version of our study, providing data also on the maturation of the synovial (lining) macrophage niche.
• We will otherwise respond to all individual reviewer comments and implement the requested changes, unless technically not possible. Please find below detailed point-by-point answers.

Reviewer #1

Evidence, reproducibility and clarity

In their manuscript entitled "The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ-dependent but monocyte-independent process," the authors explore the developmental trajectory of synovial lining macrophages. They demonstrate that the formation of this specialized macrophage layer is age-dependent and governed by a distinct developmental program that proceeds independently of circulating monocytes. Through scRNA-Seq, the authors show that synovial lining macrophages originate locally from Aqp1⁺ macrophages and are marked by the expression of Csf1r, Tgfbr, and Piezo1. Notably, genetic ablation of each of these factors impaired the development of lining macrophages to varying degrees, suggesting differential contributions of CSF1, TGFβ, and PIEZO1 signaling pathways to their maturation and maintenance.

The manuscript is well written, and the data quality and representation is of a high standard. The authors have employed a sophisticated array of state-of-the-art mouse models and cutting-edge technologies to elucidate the developmental origin of synovial lining macrophages. Notably, the supporting scRNA-Seq datasets are of excellence and provide valuable insights that will likely be of significant interest to researchers in the field of immunology and joint biology. Accordingly, the experimental approach and interpretations regarding macrophage origin are well-founded and compelling. However, in the eye of the reviewer, the section addressing the underlying molecular mechanisms is a bit less convincing. This part of the study appears slightly underdeveloped, and some of the mechanistic claims lack sufficient experimental clarity. A more rigorous experimental investigation would be essential to reinforce the manuscript's conclusions, particularly concerning the data related to Tgfbr and Piezo1, where the current evidence appears insufficiently substantiated.

We thank the reviewer for their positive and constructive evaluation of our manuscript. We agree with them (and the other reviewers) that our functional data on the involvement of TGFβ signaling and mechanical loading/mechanosensing are comparably less convincing and substantiated than our developmental data. We are very grateful for their (and the other reviewers’) suggestions to provide more support for the involvement of these factors in lining macrophage development. However, we think that carrying this out to the same high standard will require substantial time and other resources. We have therefore decided to uncouple this from the developmental data and pursue this in follow-up work. We will re-focus the current manuscript on the developmental data. We have proposed to the editors to instead include additional data on synovial fibroblast development, to complement our macrophage data and also delineate the maturation of their niche, thereby providing a conclusive developmental atlas.

Major point:

1. The numbers of VSIG4⁺ macrophages appear either unaffected or only minimally altered in both Csf1rMerCreMer Tgfbr2floxed and Fcgr1Cre Piezo1floxed mouse models, respectively. This raises an important question: was the gene deletion efficiency sufficient in each model? Accordingly, the authors are encouraged to include quantitative data on gene deletion efficiency for both mouse models, as this information is critical for interpreting the observed phenotypic outcomes and validating the conclusions regarding gene function. Furthermore, to better assess the impact of Tgfbr2 and Piezo1 disruption, the authors should provide more comprehensive flow cytometry analyses and histological data for these mouse models. Given the apparent homogeneity of VSIG4⁺ macrophages (as shown by the authors themselves), bulk RNA-Seq of sorted Tgfbr2- and Piezo1-deficient VSIG4⁺ macrophages (or from TGFβ-treated animals) would offer valuable insights into both the effectiveness of gene deletion and the molecular pathways governed by TGFβ and PIEZO1 in lining macrophages.

As outlined above, we have decided to uncouple our functional data on TGFβ, Piezo1 and mechanical loading. The points raised here are all very valid, and we will implement your suggestions in our follow-up functional work focusing on signaling events regulating lining macrophage development. On the suggestion to perform bulk RNA sequencing for VSIG4+ macrophages: This is a good one in principle – although we will not be able to use this strategy where we want to assess the consequences of experimental treatments or genetic models on lining macrophage maturation, because acquisition of VSIG4 is a key maturation event that might be impaired in these conditions.

Minor points:

Consistent usage of Cx3cr1-GFP+ nomenclature (for instance: Fig. S1 legend "adult mouse synovial tissue, showing PDGFRα⁺ fibroblasts (yellow) and CX3CR1-GFP⁺ cells (cyan)." versus Fig. 1 legend "Automated spot detection highlights Cx3cr1-GFP⁺ macrophages)".

We will implement these changes.

Unclear Fig. 3 legend: "Representative immunofluorescence images of synovial tissue from Clec9aCre:Rosa26lsl-tdT mice at 3 weeks and in adulthood, showing and tdTomato (yellow) and stained for DAPI (blue), VSIG4 (cyan)" Check 'showing and tdTomato.'

We will implement these changes.

For greater clarity, it would have been helpful if the transcript names had been directly included within Figures 3C, S3A, and S3C.

We will implement these changes.

Page 24: "(Mki67CreERT2:Rosa26lsl-tdT)" Last bracket not superscript.

We will implement these changes.

Page 25: "we again leveraged our scRNAsequencing dataset" Missing punctuation.

We will implement these changes.

Page 27: Fig. 5C legend: " of synovial tissue of 1 week-old, 3 weeks-old and adult mice." Please specify and change to 'adult Csf1rΔFIRE/ΔFIRE mice'.

We will implement these changes.

Page 30: The outcome observed in the Acta1-rtTA:tetO-Cre:ChR2-V5fl mouse model appears to be inconclusive: "This approach resulted in an increased density of VSIG4+ and total (F4/80+) macrophages in the exposed leg of some 5 days-old pups, but others showed the opposite trend (Figure S5D)." This variability may reflect low efficiency of the model or other technical limitations (e.g. muscle contractions frequency or time point of analysis). Given this ambiguity, it is worth reconsidering whether the data are sufficiently robust to warrant inclusion. Should the authors choose to include these findings, further experimentation of appropriate depth and precision is required to allow a conclusive interpretation (either it increases the density of VSIG4+ macrophages or not). The same applies to the Yoda1-treated mice, for which additional data are needed to determine whether VSIG4⁺ macrophage density is truly affected.

We have decided to remove the data on the optogenetic mouse model and Yoda1 treatment and follow-on separately, implementing these suggestions, including proof of concept data for optogenetically induced muscle contractions.

Significance

General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This is a well-designed study that uses cutting-edge methodologies to investigate the developmental trajectory of synovial lining macrophages under homeostatic conditions. The authors present robust experimental evidence and compelling interpretations concerning synovial macrophage origin, which are both well-substantiated and impactful. Nonetheless, from the reviewer's perspective, the section exploring the molecular mechanisms underlying macrophage differentiation is comparatively less convincing. This section appears somewhat underdeveloped, as some of the mechanistic claims lack sufficient depth and experimental rigor to fully substantiate the conclusions.

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field: In contrast to earlier studies (PMID: 31391580, 32601335), the inclusion of fate-mapping experiments adds an important dimension, offering novel insight into the ontogeny of synovial macrophages. This expanded perspective may prove particularly valuable in advancing our understanding of joint immunology, especially regarding the local origins and lineage relationships of macrophage populations.

Furthermore, the authors present novel insights into the molecular pathways underlying the differentiation and development of synovial lining macrophages. By demonstrating previously unrecognized regulatory mechanisms, this work significantly deepens our understanding of the cellular and transcriptional programs that drive macrophage specialization within the joint microenvironment.

Place the work in the context of the existing literature (provide references, where appropriate): This study builds upon previous work characterizing the macrophage compartment in the joint (PMID: 31391580, 32601335), yet provides a substantially more comprehensive dataset that spans multiple developmental time points and data on the origin of this specialized macrophage subset.

State what audience might be interested in and influenced by the reported findings: Immunologist, clinicians

Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. This study falls well within the scope of the reviewer's expertise in innate immunity.

Reviewer #2

Evidence, reproducibility and clarity

In the manuscript „The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ- dependent but monocyte-independent process", Magalhaes Pinto and colleagues carefully employ a wide range of technologies including single cell profiling, imaging and an exceptional combination of fate mapping models to characterize the ontogeny and development of lining macrophages in the joint, thus dissecting their maturation during postnatal development. Over the last decade, several landmark studies highlighted the imprinting of tissue-resident macrophages by a combination of ontogenetic and tissue-specific niche factors during development. So far, the ontogeny and the tissue niche factors governing the development and maturation of lining macrophages have not been described. Therefore, the results of this study offers insights on a small highly adapted macrophage population with relevance in many disease settings in the joint. Furthermore, the findings are nicely showcasing how macrophages are specializing to even very small tissue niches across development within one bigger anatomical compartment to serve dedicated functions within this niche.

This manuscript is beautifully written and highlights many novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages.

In particular, the combination of scRNA-seq and fate mapping is providing a unique the link of transcriptional programs to ontogeny within the tissue niche. Furthermore, the integrative use of distinct fate mapping strategies, transgenic mouse lines, and treatment paradigms to elucidate key niche factors guiding the development and maturation of lining macrophages provides many interesting findings and data that are highly relevant to the field. I really enjoyed reading this manuscript.

Thank you for your complimentary and constructive assessment of our manuscript, and the detailed comments below, which are very helpful. Please find point-by-point responses below.

Major points:

The authors show dynamic regulation of VSIG4 in lining macrophages during development, therefore VSIG4 is maybe not an ideal choice for gating strategies to define lining macrophages or to show as a single markers in immunofluorescence (IF) stainings to demonstrate their abundance across development (even though it is clear that this is the reason why the F4/80 staining is shown next to it). To demonstrate the increase of lining macrophages during development in IF, it would be more helpful if the authors would show quantifications of all F4/80+ cells and additionally VSIG4+ as a proportion of F4/80+ cells (or VSIG4+ F4/80+ and all F4/80+ in a stacked bar plot). We agree with the assessment of VSIG4 not being ideal since this is a key marker of mature lining macrophages only.

We will provide these additional analyses.

In Figure 1C, the authors nicely demonstrate that the lining macrophages get closer in their distance across development to build the epithelial-like macrophage structure along the adult lining. Is the close proximity between lining macrophages already fully "matured" at 3 weeks of age and comparable to adults? Please quantify the distance in adult linings.

We will provide data for adult joints.

Can the authors explain how the grouping was performed between the analyzed human fetal joints? It is not clear why the cut was chosen between the groups at 16/17 weeks of age. Maybe it would be also beneficial if the authors would consider not grouping these samples but rather show the specific quantifications for each samples individually and estimate via linear regression the expansion over time across human development. Furthermore, can the authors give additional information about the distancing of lining macrophages in the human fetal samples, it would be great to see if they follow the same dynamics as in mouse. Maybe comparison to human juvenile/adult joints would also add on to substantiate the findings in human samples (if possible).

We will show samples ungrouped and perform linear regression analysis as suggested.

The scRNA-seq analysis leaves several questions open and some conclusions and workflows cannot be easily followed.

We appreciate this comment and the complexity of the data, and will implement the below recommendations, and clarify the issues raised.

It is not clear how and especially why the signature genes to define macrophages vs. monocytes were chosen. Especially as the signature genes for monocytes would not include patrolling monocytes and the macrophage signature genes seem to be highly regulated during development, see also Apoe expression in NB vs. adult in Figure S2e. Why did the authors not take classical markers such as Itgam, Fcgr1a, Csf1r?

Can dendritic cell signatures be excluded? Cluster 11 and 12 show indeed some DC markers, are these really macrophages?

The authors provide several figure panels showing TOP marker genes or key marker genes for the identified clusters, however it is not clear if these are TOP DE genes or if the genes were hand chosen. Somehow, the authors give the impression that the clusters were chosen and labeled not based on DE genes, but more on existing literature that previously reported these macrophage populations. DE gene lists for all annotated cell types and macrophage clusters need to be provided within the manuscript.

The authors claim that Clusters 1 and 4 are "developing" macrophages. How is this defined? Why are these developing cells compared to other clusters? And why are these clusters later on not considered as progenitors of Aqp1 macrophages and Vsig4 macrophages? Why are Aqp1+ macrophages not labeled as developing when they are later on in the manuscript shown as potential intermediate progenitors of lining macrophages?

Furthermore, it is again confusing that markers are used throughout Figure 2 which are labeled as "key marker genes" for a population and then later on they are claimed to be regulated during development within this population, see for example Figure 2D and 2H.

It is appreciated that the authors distinguished cycling clusters such as 8, 9, and 10 based on their cycling gene signature. Here it would be very exciting to see a cell cycle analysis across all clusters and time points to see when exactly the cells are expanding during development; this would also substantiate the data later shown for the Mki67-CreERT2 mouse model.

Can the authors identify certain gene modules during development of lining macrophages (and/or their progenitors) which are associated with certain functions (e.g. GO terms, GSEA enrichment)?

To determine the actual presence of the identified macrophage clusters from the scRNA-seq as macrophage populations in the joint, the authors should perform IF or FACS for key markers. Especially, Aqp1+ macrophages should be shown in the developing joint.

We will provide additional data, but would also like to reference a study by collaborators currently in revision at Immunity, which characterizes the Aqp1+ population in detail. We are hoping to have a doi available during our revision process.

The authors used a wide range of fate mapping models, which is quite unique and highly appreciated. The obtained results and the conclusions made from the models raise a couple of questions: Whereas contribution of HSC-derived/monocyte-derived macrophages to the lining compartment seems to be minor, there is still labeling across different models. Various aspects would need to be clarified.

We will clarify these data throughout as per below suggestions.

For example, the authors employ Ms4a3-Cre as a tracing model for GMP-derived monocytes, however all quantifications of the labeling efficiency are not normalized to the labeling in monocytes or another highly recombined cell population. This should be shown, similar to the other fate mapping models (Figure 3 F-I).

Labelling efficacy for Ms4a3-Cre is near complete for GMP-derived monocytes (and neutrophils) with the Rosa-lsl-tdT (aka Ai14) reporter we have used (see also PMID: 31491389 and doi: 10.1101/2024.12.03.626330); but we will include normalized data as requested.

Please show Ms4a3 expression across clusters across time points, to exclude expression in fetal-derived clusters.

We will include this in the revised supplementary information, but there is indeed very little at birth (in line with the original report for other tissues PMID: 31491389).

In line with the question raised above, if the authors can exclude a development of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages and subsequently into Vsig4 lining macrophages, the obtained data from the Ms4a3-Cre model highly suggests a correlative labeling across these clusters what could implicate a relation. However, the authors do not discuss throughout the manuscript the role of these developing macrophages. It is highly encouraged to include this into the manuscript and it would be of high relevance to understand lining macrophage development.

This is an interesting point and we agree it deserves consideration in the revised manuscript. Indeed, our trajectory analyses do not predict differentiation of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages, and hence, ultimately lining macrophages. Conversely, Aqp1+ cells might also convert into Egfr1+ and Clec4n+ developing macrophages. We will elaborate on this more in the revised manuscript.

The authors conclude from the pseudo bulk transcriptomic profiling of the different macrophage clusters that TdT+ and TdT- macrophages do not differ in their gene expression profile and that this is due to niche imprinting rather than origin imprinting. Even though the data supports that conclusion, the authors should verify if inkling cells early during development also show this similar gene expression profile and gene expression should be compared at the different developmental time points. Tissue niche imprinting is happening within the niche during development, most likely in a stepwise progress, and therefore there should be differences in the beginning.

This is another important point that we will address in the revised manuscript by performing additional differential gene expression analyses at the different developmental time points, including the earliest stages, as suggested.

The trajectorial analysis using different pseudotime pipelines is very interesting and nicely points out the potential role of Aqp1 macrophages as intermediates of Vsig4 lining macrophages. From my point of view, all trajectories seem to suggest that Egfr1 developing macrophages and Clec4n developing macrophages might differentiate into Aqp1 macrophages, however the authors are not exploring this further and the role of both developing macrophage clusters is not further discussed (see also comments above).

We will address and discuss this in the revised manuscript.

How was the starting point of the trajectorial analyses defined and is it the same for each pipeline used?

We will clarify this in the revised manuscript.

Are there potentially two trajectories? It looks like there is one in the beginning of postnatal life and a second one appearing from the monocyte-compartment later in life. If this is true, that would rather speak for a dual ontogeny of Vsig4+ macrophages, wouldn't it?

We will discuss this in the revised manuscript.

A heatmap (transcriptional shift) of trajectories between more clusters should be shown at least for Cluster 0,1,2, and 3. It is not sufficient to demonstrate this only between two clusters.

We will add these analyses during revision.

To show the similarity between Aqp1 macrophages and proliferating macrophage clusters, the authors should remove the cycling signature and compare these clusters to show that the cycling cells might be Aqp1 macrophages or earlier developing macrophage progenitors aka Clec4n or Egfr1 macrophages.

We will address this in the revised manuscript.

The conclusions made from the Mki67-CreERT2 data are a bit difficult to understand, whereas all progenitors (monocyte progenitors and macrophage progenitors will proliferate at the neonatal time point and no conclusions can be made if the cells expand in the niche. The authors should employ Confetti mice or other models (Ubow mice) to analyze clonal expansion in the niche.

We agree that interpretation of the Mki67-CreERT2 data is complicated by labeling of other cells, and notably, labeling observed in BM-derived cells. We will highlight this better in the revised manuscript. We have tried using Ubow mice to address this issue, but the recombination efficacy we yielded was too low to draw conclusions. We will address this during revision.

All predicted cell-cell interactions between macrophages and fibroblasts should be provided in a supplementary table. Are the interactions shown in Figure 5 chosen interactions or the TOP predicted ones? Whereas the authors show different numbers of interactions, it is most likely hand-picked and therefore biased.

We will provide a full list of all predicted interactions in the revised supplementary material in addition to a list of the full differential gene expression analysis.

The authors further aim to dissect the factors involved in the developmental niche imprinting of lining macrophages. Even though it is highly appreciated that the authors used so many experimental setups to show the reliance of lining macrophages on Csf1 and TGF-beta as well as mechanosensation, the wide range of models the different methods used and selected developmental time points make it very difficult to really interpret the data. The authors should carefully choose time points and methods (either FACS analysis across all models or IF across all, or both). Often deletion efficiencies for transgenic models and proof of concept that the inhibitors and agonists are working in the treatment paradigm are not provided. For example, Csf1rMer-iCre-Mer Tgfbr2fl/fl mice are used but no deletion efficiency is shown or different time points of analysis, maybe the macrophages are not properly targeted in the set up.

We have decided to uncouple our experimental data on Tgfb, Piezo1 and mechanosensing/mechanical loading, but are taking this into consideration for revision. In many cases, we have in fact performed flow cytometry and imaging analyses, and agree, we should be showing this consistently.

The authors have shown the role of Csf1 and Tgfbr2 only for lining macrophages, is this specific in the joint to this population of are subliming macrophages affected in a similar manner.

We will include data on sublining macrophages in the revised figure (for CSF1; Tgfb data will be uncoupled from this current manuscript).

Can the authors confirm their results in CSF1R-FIRE mice with anti-Csf1 injections or in Csf1op/op mice?

We will expand our discussion of the Csf1 findings, and will consider including anti-CSF1 data during revision. Phenotypes on other Csf1(r) deficient mice are published, if not with the same developmental resolution as our time course in Csf1rFIRE knockout mice and with simpler readouts. Csf1op/op mice are indeed deficient in synovial lining macrophages, from 2 days of age onwards (PMID: 8050349), and lining macrophages are also absent from 2-weeks-old and adult Csf1r-/- mice (PMID: 11756160).

The setup in Figure S5G is very interesting to test the role of movement and mechanical load on the joint, however, there is basically no data on the model provided showing the efficiency of the induced optogenetic muscle contractions, and only one time point is shown.

Data on mechanical loading will be uncoupled from the current manuscript and substantiated in a separate follow-up.

The results regarding the role of Piezo1 and mechanosensation vary a lot. Could it be that analyses were done too early or that actually proper weight load on the joint must be applied for the maturation of the macrophages? The authors should test this to.

We will uncouple these data from the current manuscript during revision. However, this is a possibility that we have discussed. In fact, the most appropriate experimental approach to address the involvement of mechanical loading, onset of walking and specifically, weight bearing would be a loss-of-function approach (i.e. paralysis at the newborn stage), for which we unfortunately could not obtain ethics approval from the UK Home Office.

The Rolipram experiment is shown in Figure S5G, but is not described in the result section. It only appears at some point in the discussion part. The authors should move it to results or remove it from the manuscript.

We will incorporate these data with the revised section on developing synovial macrophage populations.

Minor points:

Please reference the Figure panels in numeric order throughout the text.

We will change this where not the case.

Figure 2a and 2b are a bit out of the storyline, it is not obvious why this is shown here and maybe it would be good to move it to the supplements. Gating strategy is also not used for scRNA-seq. Therefore, it would better fit to the later analysis of joint macrophages across different transgenic mouse models and treatment paradigms. The gating strategies are changing across different experiments throughout the figures, it would be nice to have a similar gating strategy for all experiments, see also Figure 3 where the defining markers for joint macrophages are changing between models.

We will revise Figures 2, 3 and the related supplementary figures.

A lot of figure panels have very small labeling that is basically unreadable. Axes at FACS plots for example. Sometimes, it is even impossible to distinguish cluster labels especially when they have similar colors.

We will revise this, thanks for pointing it out.

In the text on page 14, many markers are named which are specifically regulated during development in lining macrophages, but these factors are not labeled anywhere in the volcano plot. It would be good to showcase at least some of these named genes in the figure panel, e.g. Trem2.

We will do this for revision.

Figure 2F and Figure S2F are really nicely showing the percentage of cells per cluster in each analyzed biological sample. Maybe the authors could additionally consider to show a stacked bar plot with the mean percentage of cells per cluster and how the clusters are distributed across time points?

We will include this in the revised manuscript.

Figure 3A: IF for adult lining macrophages and the quantification are missing.

This will be included in the revised version.

Significance

This manuscript highlights novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages. Furthermore, this study showcases in a very elegant and detailed way the adaptation of macrophage progenitors to a highly specific anatomical tissue niche.

The manuscript is of high interest to basic scientists focussing on macrophage biology and immune cell development and clinicians and clinician scientists focussing on joint diseases such as RA.

Therefore the manuscript is of interest to a wide community working in immunology.

Reviewer #3

Summary:

Magalhaes Pinto, Malengier-Devlies, and co-authors investigated the developmental origins and maturation of synovial (lining and sublining) macrophages across embryonic, newborn, and postnatal stages in mouse. The authors used multiple transgenic reporter lines, lineage tracing, scRNA-seq, 2D confocal and 3D lightsheet imaging, and perturbations to delineate the macrophage states and ontogeny. They propose a model in which the majority of the joint lining macrophages has a fetal (EMP-derived) origin and a small proportion has a definitive HSC-derived monocyte origin, which both seed and mature within the synovial space in the postnatal period in the first 3 weeks of life. Using cell-cell communication analysis on their scRNA-seq data, they identified Fgf2, Csf1, and Tgfb as candidate signaling pathways that support (lining) macrophage development and maturation. Functional experiments indicate that the process is CSF1 and TGFb-dependent and also partly dependent on mechanosensing through Piezo1.

The key conclusions on the composition of the synovial macrophages are convincing based on the presented results, and are carefully phrased. The study is very comprehensive, yet the description and organization of the results of the different mouse models could be altered to improve the storyline. Several refinements in data presentation, formulation, and minor validation experiments would further improve the clarity of the story, as well as summary recaps of the major findings throughout the text.

We thank this reviewer for their detailed review. We will be implementing the requested changes wherever technically feasible.

Major comments:

Generally, the story could be more streamlined by introducing earlier reporter lines and lineage-origin logic. Clearly state which reporter/CreERT2 lines and acrosses are used. It was unclear in Figure 2 that cells of the cross of the Cx3cr1-GFP and Ms4a3Cre:Rosa26lsl-tdT reporter lines were used for the scRNA-seq. The principle that there are fetal-derived and bone marrow (GMP)-derived monocytes and macrophages doesn't need to be "hidden" until Figure 3. For example, also the imaging of Ms4a3Cre could be introduced before the scRNA-seq.

We will revise the structure and order of the manuscript during revision.

Figure 1 could benefit from a cartoon visualizing the anatomy of the knee joint. The terms "sublining" and "synovium" are now a bit unclear, as it appears that sometimes the synovium is indicated as sublining and vice versa. Additionally, a schematic developmental timeline could be added to indicate the parallels between mouse and human development (fetal and postnatal development in mouse versus gestational age in human). Also, the various waves of hematopoiesis could be indicated in this timeline, which would be particularly helpful for Figure 3 for the lineage-tracing readouts. Lastly, the authors could end the manuscript (a new Figure 6) with a general cartoon summarizing all the results presented.

We will include illustrations as suggested.

Figure 1 could be rearranged: first introduce the markers CX3CR1 and VSIG4 (Figure 1D) and then present the quantifications (Figure 1B/E). Where possible, co-visualization CX3CR1-GFP and VSIG4 on tissue sections to strengthen the claims on the relationship between these 2 markers. Tying the scRNA-seq insights (Figure 2) to the imaging would be elegant. Moreover, it would be informative to represent the CX3CR1+ and VSIG4+ macrophages as a percentage of F4/80+ macrophages (Figure 1B/E). Similarly, for the flow cytometry data in Figure 2, the relationship between the markers CX3CR1 and VSIG4 on macrophages could be more clearly displayed and discussed.

Thanks for this remark. We will endeavor to show co-localization and analysis of both markers wherever possible. However, where we did not use Cx3cr1gfp mice, co-staining was limited by antibody choice.

The 3D imaging of the joint is a nice addition to the manuscript, as it provides more context to the anatomical structure; however, while the text suggests several newborn joints were imaged, Figure 1F visualizes (again) the knee joint. Could other joints also be represented by 3D imaging? If the knee joint is the only joint available for imaging, and previous confocal imaging focused specifically on the meniscus in the knee joint, could the meniscus also be highlighted in the lightsheet imaging?

Apologies if this was not clear from the original manuscript text, but we have only imaged the knee joint in 3D. We will clarify this during revision and consider inclusion of additional imaging data.

Clarification is requested regarding the imaging quantification representation. The M&M section under "Statistical analysis and reproducibility" states that individual data points are displayed, and bars represent the mean. However, some of the Figure legends (e.g., Figures 1B and S1C) specify that each dot corresponds to an individual mouse, with quantification based on 2-3 sections per mouse. While this appears to be a very reasonable representation of the data, does this mean that for each dot, the mean value from the 2-3 sections per mouse was calculated and plotted?

We will clarify this.

It is not clear how the differential expression analysis was performed on the Vsig4+ cells. Please specify if Cluster 0 was used for analysis, or all Vsig4-expressing cells? Not all cells in Cluster 0 have Vsig4+ expression. The authors described the expression dynamics of Aqp1 as intriguing, but lack a reasoning on why this is interesting.

We will revise this section.

Figure S3E: In line with the previous comment, can the authors justify that the tdTomato+/- comparisons are not biased by scRNA-seq dropout (scRNA-seq is zero-inflated, so some tdTomato- cells could be false negatives), and provide methodological details (thresholds, ambient RNA correction, etc.) to support this?

We will clarify this and include additional representations of the tdTomato transcript data.

Although the sex-related differences in macrophage composition and the absence of differential expression are interesting, they distract from the manuscript's main messages. Moreover, the Discussion does not elaborate on how these observations relate to joint (disease) biology. Consider removing this section or integrating it clearly into the relevant biological context.

We will remove this section as suggested.

CreERT2 transgenic lines are often not 100% efficient in recombination, also depending on whether tamoxifen or 4-OHT is used. Could the authors report the percentage of tdTomato+ cells in the joints and compare them to the recombination efficiencies in the monocytes/microglia under the same tamoxifen or 4-OHT conditions? This would help clarify how the interpret the macrophage labeling %'s.

We will report labelling efficacies and/or show normalized data in the revised manuscript.

Could the authors draw parallels between the observations in the mouse knee joint macrophage populations and literature on other joints in mouse and the knee joint in human (for example, as described in Alivernini et al., 2020 and in the very recent Raut et al., 2025)?

We will include a section on this in the revised manuscript.

Minor comments:

In general, the authors should clarify in the Results what each marker used for imaging, flow cytometry, or in the mouse reporter lines delineates. For example, mention that F4/80 is a marker for tissue-resident macrophages (correct?) in immunofluorescence, that IBA1 is a marker for macrophages on human tissue sections (Figure S1), and PDPN is GP38 (Figure S2 - align usage of marker reference across main text and figures).

We will implement this request.

For clarity in the microscopy representation, the single channels should be represented in a grey scale.

We will revise image presentation.

Figure S1B: Is CX3CR1 also restricted to the lining macrophages in human? Could a co-staining with IBA1 be performed to strengthen the species similarities?

To our knowledge, there is no antibody available that works for imaging of human CX3CR1. Moreover, CX3CR1 is only limited to the lining population in adult joints, in fetal and newborn (mouse) joints, all macrophages express this receptor, as do fetal progenitors to macrophages. However, Alivernini and colleagues have reported that TREM2high macrophages are the human counterpart of the mouse CX3CR1+ lining population (PMID: 32601335).

Adipocyte diameter quantification: Avoid plotting individual adipocytes from 2 mice without per-mouse visualization. Instead, report the mean adipocyte diameter per mouse and plot those means.

We will implement this change.

A little typo was spotted in the "Statistical analysis and reproducibility" section: it is Dunn's, not Bunn's multiple-comparison correction.

Thanks for spotting this.

Figure 2A: The gating strategy for the CX3CR1-GFP cells is missing.

We will provide this in the revised manuscript or supplementary material.

Improve the visualization of some plots. For example, Figure 2F is hard to read because of the big dot size. The dots seem to add no information to the graph and could be removed. Additionally, for comparing the clusters across the different time points, one could project the cells from the other time points in grey in the background.

We will revise the presentation of these data.

Figure S2: The dotplot is more informative than the heatmap, consider removing the heatmap.

We will do that.

Figure 3A: If technically feasible, image and visualize both the GFP and tdTomato expression. It would be informative to see the Cx3cr1+ and Ms4a3-derived cells in the same specimen.

We will thrive to show this in the revised manuscript.

Figure 3C: Highlight that tdTomato expression is visualized here.

We will do that.

Figure 3G,F: The authors should place the schematics and graphs next to each other, so the data points can be more easily compared.

We aim to do this in the revised manuscript.

Figure 4B: Which co-staining was performed for the immunofluorescence to quantify the % of tdTomato+ cells?

We co-stained for F4/80 and assessed localization in the lining or sublining. This will be clarified in the revised Figure legend.

Figure 4C: The trajectory analysis appears to have an arrow pointing from the Ccr2+ macrophages to the Ly6c+ monocytes. Please verify this directionality, as its seems against the known biology.

This will be addressed during revision.

Figure 5 mentions that the Csfr1 levels were reduced in a tissue-specific manner, but it is unclear how this tissue specificity was achieved.

We apologize for this misunderstanding. Csfr1FIRE mice are not tissue-specific knockouts, but they are more specific than global knockout mice, since only a (myeloid-specific) enhancer is affected. We will clarify this in the relevant section.

For the TGFb perturbations (Tgfbr2 KO and systemic TGFb depletion): did the authors validate reduced TGFb pathway activity in the macrophages, for example, reduced pSMAD2/3 levels? This would validate the effectiveness of the perturbations. This is an important point, and assessing signaling events downstream of TGFb is a very good suggestion.

As per above comment, we have decided to uncouple the functional data with exception of CSF1 from the revised version of the current manuscript, but we will be taking this into account for substantiating our functional data in follow-up work.

Figure 5F could benefit from a timeline of the treatment.

As for the previous point raised, we will be taking this into account for follow-up work on the uncoupled functional data.

The Methods mention that Gene Ontology analysis was performed on the single-cell data, but the results are not plotted in a figure. It would be informative to include this GO/pathway analysis in the appropriate figure(s).

We will include this in the revised (supplementary) information.

Significance:

This work provides a high temporal-resolution and "spatial" resolution reference map of the ontogeny and maturation of the synovial lining macrophages in the knee joint. It complements existing literature that demonstrated the presence of tissue-resident macrophages in the synovial space and lining (Culemann, et al., 2019 and others) by charting the embryonic-to-postnatal emergence of lining and sublining subsets. In particular, this mouse work identified some key signaling pathways in shaping this tissue compartment. This dataset serves as a robust, steady-state reference for joint pathology and can be implemented with human studies on disease biology of the knee joint (e.g., Alivernini et al., 2020; Raut et al., 2025). Insights into the exact developmental origins, mechanisms contributing to diverse or seemingly similar cell types, and distinct maturation processes are crucial to understanding disease biology, in which developmental processes can be hijacked/reactivated.

These findings will interest researchers in joint disease biology (osteoarthritis and immune-mediated arthritides such as RA and psoriasis), macrophage development (tissue-resident vs monocyte-derived lineages), the bone/joint microenvironment, and joint mechanobiology.

The reviewer's expertise is in developmental biology, mesoderm, bone biology, hematopoiesis, and monocyte/macrophage biology in disease

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Referee #3

Evidence, reproducibility and clarity

Summary:

Magalhaes Pinto, Malengier-Devlies, and co-authors investigated the developmental origins and maturation of synovial (lining and sublining) macrophages across embryonic, newborn, and postnatal stages in mouse. The authors used multiple transgenic reporter lines, lineage tracing, scRNA-seq, 2D confocal and 3D lightsheet imaging, and perturbations to delineate the macrophage states and ontogeny. They propose a model in which the majority of the joint lining macrophages has a fetal (EMP-derived) origin and a small proportion has a definitive HSC-derived monocyte origin, which both seed and mature within the synovial space in the postnatal period in the first 3 weeks of life. Using cell-cell communication analysis on their scRNA-seq data, they identified Fgf2, Csf1, and Tgfb as candidate signaling pathways that support (lining) macrophage development and maturation. Functional experiments indicate that the process is CSF1 and TGFb-dependent and also partly dependent on mechanosensing through Piezo1. The key conclusions on the composition of the synovial macrophages are convincing based on the presented results, and are carefully phrased. The study is very comprehensive, yet the description and organization of the results of the different mouse models could be altered to improve the storyline. Several refinements in data presentation, formulation, and minor validation experiments would further improve the clarity of the story, as well as summary recaps of the major findings throughout the text.

Major comments:

1. Generally, the story could be more streamlined by introducing earlier reporter lines and lineage-origin logic. Clearly state which reporter/CreERT2 lines and acrosses are used. It was unclear in Figure 2 that cells of the cross of the Cx3cr1-GFP and Ms4a3Cre:Rosa26lsl-tdT reporter lines were used for the scRNA-seq. The principle that there are fetal-derived and bone marrow (GMP)-derived monocytes and macrophages doesn't need to be "hidden" until Figure 3. For example, also the imaging of Ms4a3Cre could be introduced before the scRNA-seq.
2. Figure 1 could benefit from a cartoon visualizing the anatomy of the knee joint. The terms "sublining" and "synovium" are now a bit unclear, as it appears that sometimes the synovium is indicated as sublining and vice versa. Additionally, a schematic developmental timeline could be added to indicate the parallels between mouse and human development (fetal and postnatal development in mouse versus gestational age in human). Also, the various waves of hematopoiesis could be indicated in this timeline, which would be particularly helpful for Figure 3 for the lineage-tracing readouts. Lastly, the authors could end the manuscript (a new Figure 6) with a general cartoon summarizing all the results presented.
3. Figure 1 could be rearranged: first introduce the markers CX3CR1 and VSIG4 (Figure 1D) and then present the quantifications (Figure 1B/E). Where possible, co-visualization CX3CR1-GFP and VSIG4 on tissue sections to strengthen the claims on the relationship between these 2 markers. Tying the scRNA-seq insights (Figure 2) to the imaging would be elegant. Moreover, it would be informative to represent the CX3CR1+ and VSIG4+ macrophages as a percentage of F4/80+ macrophages (Figure 1B/E). Similarly, for the flow cytometry data in Figure 2, the relationship between the markers CX3CR1 and VSIG4 on macrophages could be more clearly displayed and discussed.
4. The 3D imaging of the joint is a nice addition to the manuscript, as it provides more context to the anatomical structure; however, while the text suggests several newborn joints were imaged, Figure 1F visualizes (again) the knee joint. Could other joints also be represented by 3D imaging? If the knee joint is the only joint available for imaging, and previous confocal imaging focused specifically on the meniscus in the knee joint, could the meniscus also be highlighted in the lightsheet imaging?
5. Clarification is requested regarding the imaging quantification representation. The M&M section under "Statistical analysis and reproducibility" states that individual data points are displayed, and bars represent the mean. However, some of the Figure legends (e.g., Figures 1B and S1C) specify that each dot corresponds to an individual mouse, with quantification based on 2-3 sections per mouse. While this appears to be a very reasonable representation of the data, does this mean that for each dot, the mean value from the 2-3 sections per mouse was calculated and plotted?
6. It is not clear how the differential expression analysis was performed on the Vsig4+ cells. Please specify if Cluster 0 was used for analysis, or all Vsig4-expressing cells? Not all cells in Cluster 0 have Vsig4+ expression. The authors described the expression dynamics of Aqp1 as intriguing, but lack a reasoning on why this is interesting.
7. Figure S3E: In line with the previous comment, can the authors justify that the tdTomato+/- comparisons are not biased by scRNA-seq dropout (scRNA-seq is zero-inflated, so some tdTomato- cells could be false negatives), and provide methodological details (thresholds, ambient RNA correction, etc.) to support this?
8. Although the sex-related differences in macrophage composition and the absence of differential expression are interesting, they distract from the manuscript's main messages. Moreover, the Discussion does not elaborate on how these observations relate to joint (disease) biology. Consider removing this section or integrating it clearly into the relevant biological context.
9. CreERT2 transgenic lines are often not 100% efficient in recombination, also depending on whether tamoxifen or 4-OHT is used. Could the authors report the percentage of tdTomato+ cells in the joints and compare them to the recombination efficiencies in the monocytes/microglia under the same tamoxifen or 4-OHT conditions? This would help clarify how the interpret the macrophage labeling %'s.
10. Could the authors draw parallels between the observations in the mouse knee joint macrophage populations and literature on other joints in mouse and the knee joint in human (for example, as described in Alivernini et al., 2020 and in the very recent Raut et al., 2025)?

Minor comments:

1. In general, the authors should clarify in the Results what each marker used for imaging, flow cytometry, or in the mouse reporter lines delineates. For example, mention that F4/80 is a marker for tissue-resident macrophages (correct?) in immunofluorescence, that IBA1 is a marker for macrophages on human tissue sections (Figure S1), and PDPN is GP38 (Figure S2 - align usage of marker reference across main text and figures).
2. For clarity in the microscopy representation, the single channels should be represented in a grey scale.
3. Figure S1B: Is CX3CR1 also restricted to the lining macrophages in human? Could a co-staining with IBA1 be performed to strengthen the species similarities?
4. Adipocyte diameter quantification: Avoid plotting individual adipocytes from 2 mice without per-mouse visualization. Instead, report the mean adipocyte diameter per mouse and plot those means.
5. A little typo was spotted in the "Statistical analysis and reproducibility" section: it is Dunn's, not Bunn's multiple-comparison correction.
6. Figure 2A: The gating strategy for the CX3CR1-GFP cells is missing.
7. Improve the visualization of some plots. For example, Figure 2F is hard to read because of the big dot size. The dots seem to add no information to the graph and could be removed. Additionally, for comparing the clusters across the different time points, one could project the cells from the other time points in grey in the background.
8. Figure S2: The dotplot is more informative than the heatmap, consider removing the heatmap.
9. Figure 3A: If technically feasible, image and visualize both the GFP and tdTomato expression. It would be informative to see the Cx3cr1+ and Ms4a3-derived cells in the same specimen.
10. Figure 3C: Highlight that tdTomato expression is visualized here.
11. Figure 3G,F: The authors should place the schematics and graphs next to each other, so the data points can be more easily compared.
12. Figure 4B: Which co-staining was performed for the immunofluorescence to quantify the % of tdTomato+ cells?
13. Figure 4C: The trajectory analysis appears to have an arrow pointing from the Ccr2+ macrophages to the Ly6c+ monocytes. Please verify this directionality, as its seems against the known biology.
14. Figure 5 mentions that the Csfr1 levels were reduced in a tissue-specific manner, but it is unclear how this tissue specificity was achieved.
15. For the TGFb perturbations (Tgfbr2 KO and systemic TGFb depletion): did the authors validate reduced TGFb pathway activity in the macrophages, for example, reduced pSMAD2/3 levels? This would validate the effectiveness of the perturbations.
16. Figure 5F could benefit from a timeline of the treatment.
17. The Methods mention that Gene Ontology analysis was performed on the single-cell data, but the results are not plotted in a figure. It would be informative to include this GO/pathway analysis in the appropriate figure(s).

Significance

This work provides a high temporal-resolution and "spatial" resolution reference map of the ontogeny and maturation of the synovial lining macrophages in the knee joint. It complements existing literature that demonstrated the presence of tissue-resident macrophages in the synovial space and lining (Culemann, et al., 2019 and others) by charting the embryonic-to-postnatal emergence of lining and sublining subsets. In particular, this mouse work identified some key signaling pathways in shaping this tissue compartment. This dataset serves as a robust, steady-state reference for joint pathology and can be implemented with human studies on disease biology of the knee joint (e.g., Alivernini et al., 2020; Raut et al., 2025). Insights into the exact developmental origins, mechanisms contributing to diverse or seemingly similar cell types, and distinct maturation processes are crucial to understanding disease biology, in which developmental processes can be hijacked/reactivated.

These findings will interest researchers in joint disease biology (osteoarthritis and immune-mediated arthritides such as RA and psoriasis), macrophage development (tissue-resident vs monocyte-derived lineages), the bone/joint microenvironment, and joint mechanobiology.

The reviewer's expertise is in developmental biology, mesoderm, bone biology, hematopoiesis, and monocyte/macrophage biology in disease

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Referee #2

Evidence, reproducibility and clarity

In the manuscript „The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ- dependent but monocyte-independent process", Magalhaes Pinto and colleagues carefully employ a wide range of technologies including single cell profiling, imaging and an exceptional combination of fate mapping models to characterize the ontogeny and development of lining macrophages in the joint, thus dissecting their maturation during postnatal development. Over the last decade, several landmark studies highlighted the imprinting of tissue-resident macrophages by a combination of ontogenetic and tissue-specific niche factors during development. So far, the ontogeny and the tissue niche factors governing the development and maturation of lining macrophages have not been described. Therefore, the results of this study offers insights on a small highly adapted macrophage population with relevance in many disease settings in the joint. Furthermore, the findings are nicely showcasing how macrophages are specializing to even very small tissue niches across development within one bigger anatomical compartment to serve dedicated functions within this niche.

This manuscript is beautifully written and highlights many novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages.

In particular, the combination of scRNA-seq and fate mapping is providing a unique the link of transcriptional programs to ontogeny within the tissue niche. Furthermore, the integrative use of distinct fate mapping strategies, transgenic mouse lines, and treatment paradigms to elucidate key niche factors guiding the development and maturation of lining macrophages provides many interesting findings and data that are highly relevant to the field. I really enjoyed reading this manuscript.

Major points:

1) The authors show dynamic regulation of VSIG4 in lining macrophages during development, therefore VSIG4 is maybe not an ideal choice for gating strategies to define lining macrophages or to show as a single markers in immunofluorescence (IF) stainings to demonstrate their abundance across development (even though it is clear that this is the reason why the F4/80 staining is shown next to it). To demonstrate the increase of lining macrophages during development in IF, it would be more helpful if the authors would show quantifications of all F4/80+ cells and additionally VSIG4+ as a proportion of F4/80+ cells (or VSIG4+ F4/80+ and all F4/80+ in a stacked bar plot).

2) In Figure 1C, the authors nicely demonstrate that the lining macrophages get closer in their distance across development to build the epithelial-like macrophage structure along the adult lining. Is the close proximity between lining macrophages already fully "matured" at 3 weeks of age and comparable to adults? Please quantify the distance in adult linings.

3) Can the authors explain how the grouping was performed between the analyzed human fetal joints? It is not clear why the cut was chosen between the groups at 16/17 weeks of age. Maybe it would be also beneficial if the authors would consider not grouping these samples but rather show the specific quantifications for each samples individually and estimate via linear regression the expansion over time across human development. Furthermore, can the authors give additional information about the distancing of lining macrophages in the human fetal samples, it would be great to see if they follow the same dynamics as in mouse. Maybe comparison to human juvenile/adult joints would also add on to substantiate the findings in human samples (if possible).

4) The scRNA-seq analysis leaves several questions open and some conclusions and workflows cannot be easily followed.

a. It is not clear how and especially why the signature genes to define macrophages vs. monocytes were chosen. Especially as the signature genes for monocytes would not include patrolling monocytes and the macrophage signature genes seem to be highly regulated during development, see also Apoe expression in NB vs. adult in Figure S2e. Why did the authors not take classical markers such as Itgam, Fcgr1a, Csf1r?

b. Can dendritic cell signatures be excluded? Cluster 11 and 12 show indeed some DC markers, are these really macrophages?

c. The authors provide several figure panels showing TOP marker genes or key marker genes for the identified clusters, however it is not clear if these are TOP DE genes or if the genes were hand chosen. Somehow, the authors give the impression that the clusters were chosen and labeled not based on DE genes, but more on existing literature that previously reported these macrophage populations. DE gene lists for all annotated cell types and macrophage clusters need to be provided within the manuscript.

d. The authors claim that Clusters 1 and 4 are "developing" macrophages. How is this defined? Why are these developing cells compared to other clusters? And why are these clusters later on not considered as progenitors of Aqp1 macrophages and Vsig4 macrophages? Why are Aqp1+ macrophages not labeled as developing when they are later on in the manuscript shown as potential intermediate progenitors of lining macrophages?

e. Furthermore, it is again confusing that markers are used throughout Figure 2 which are labeled as "key marker genes" for a population and then later on they are claimed to be regulated during development within this population, see for example Figure 2D and 2H.

f. It is appreciated that the authors distinguished cycling clusters such as 8, 9, and 10 based on their cycling gene signature. Here it would be very exciting to see a cell cycle analysis across all clusters and time points to see when exactly the cells are expanding during development; this would also substantiate the data later shown for the Mki67-CreERT2 mouse model.

g. Can the authors identify certain gene modules during development of lining macrophages (and/or their progenitors) which are associated with certain functions (e.g. GO terms, GSEA enrichment)?

5) To determine the actual presence of the identified macrophage clusters from the scRNA-seq as macrophage populations in the joint, the authors should perform IF or FACS for key markers. Especially, Aqp1+ macrophages should be shown in the developing joint.

6) The authors used a wide range of fate mapping models, which is quite unique and highly appreciated. The obtained results and the conclusions made from the models raise a couple of questions: Whereas contribution of HSC-derived/monocyte-derived macrophages to the lining compartment seems to be minor, there is still labeling across different models. Various aspects would need to be clarified.

a. For example, the authors employ Ms4a3-Cre as a tracing model for GMP-derived monocytes, however all quantifications of the labeling efficiency are not normalized to the labeling in monocytes or another highly recombined cell population. This should be shown, similar to the other fate mapping models (Figure 3 F-I).

b. Please show Ms4a3 expression across clusters across time points, to exclude expression in fetal-derived clusters.

c. In line with the question raised above, if the authors can exclude a development of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages and subsequently into Vsig4 lining macrophages, the obtained data from the Ms4a3-Cre model highly suggests a correlative labeling across these clusters what could implicate a relation. However, the authors do not discuss throughout the manuscript the role of these developing macrophages. It is highly encouraged to include this into the manuscript and it would be of high relevance to understand lining macrophage development.

d. The authors conclude from the pseudo bulk transcriptomic profiling of the different macrophage clusters that TdT+ and TdT- macrophages do not differ in their gene expression profile and that this is due to niche imprinting rather than origin imprinting. Even though the data supports that conclusion, the authors should verify if inkling cells early during development also show this similar gene expression profile and gene expression should be compared at the different developmental time points. Tissue niche imprinting is happening within the niche during development, most likely in a stepwise progress, and therefore there should be differences in the beginning.

7) The trajectorial analysis using different pseudotime pipelines is very interesting and nicely points out the potential role of Aqp1 macrophages as intermediates of Vsig4 lining macrophages. From my point of view, all trajectories seem to suggest that Egfr1 developing macrophages and Clec4n developing macrophages might differentiate into Aqp1 macrophages, however the authors are not exploring this further and the role of both developing macrophage clusters is not further discussed (see also comments above).

8) How was the starting point of the trajectorial analyses defined and is it the same for each pipeline used?

9) Are there potentially two trajectories? It looks like there is one in the beginning of postnatal life and a second one appearing from the monocyte-compartment later in life. If this is true, that would rather speak for a dual ontogeny of Vsig4+ macrophages, wouldn't it?

10) A heatmap (transcriptional shift) of trajectories between more clusters should be shown at least for Cluster 0,1,2, and 3. It is not sufficient to demonstrate this only between two clusters.

11) To show the similarity between Aqp1 macrophages and proliferating macrophage clusters, the authors should remove the cycling signature and compare these clusters to show that the cycling cells might be Aqp1 macrophages or earlier developing macrophage progenitors aka Clec4n or Egfr1 macrophages.

12) The conclusions made from the Mki67-CreERT2 data are a bit difficult to understand, whereas all progenitors (monocyte progenitors and macrophage progenitors will proliferate at the neonatal time point and no conclusions can be made if the cells expand in the niche. The authors should employ Confetti mice or other models (Ubow mice) to analyze clonal expansion in the niche.

13) All predicted cell-cell interactions between macrophages and fibroblasts should be provided in a supplementary table. Are the interactions shown in Figure 5 chosen interactions or the TOP predicted ones? Whereas the authors show different numbers of interactions, it is most likely hand-picked and therefore biased.

14) The authors further aim to dissect the factors involved in the developmental niche imprinting of lining macrophages. Even though it is highly appreciated that the authors used so many experimental setups to show the reliance of lining macrophages on Csf1 and TGF-beta as well as mechanosensation, the wide range of models the different methods used and selected developmental time points make it very difficult to really interpret the data. The authors should carefully choose time points and methods (either FACS analysis across all models or IF across all, or both). Often deletion efficiencies for transgenic models and proof of concept that the inhibitors and agonists are working in the treatment paradigm are not provided. For example, Csf1rMer-iCre-Mer Tgfbr2fl/fl mice are used but no deletion efficiency is shown or different time points of analysis, maybe the macrophages are not properly targeted in the set up.

15) The authors have shown the role of Csf1 and Tgfbr2 only for lining macrophages, is this specific in the joint to this population of are subliming macrophages affected in a similar manner.

16) Can the authors confirm their results in CSF1R-FIRE mice with anti-Csf1 injections or in Csf1op/op mice?

17) The setup in Figure S5G is very interesting to test the role of movement and mechanical load on the joint, however, there is basically no data on the model provided showing the efficiency of the induced optogenetic muscle contractions, and only one time point is shown.

18) The results regarding the role of Piezo1 and mechanosensation vary a lot. Could it be that analyses were done too early or that actually proper weight load on the joint must be applied for the maturation of the macrophages? The authors should test this to

19) The Rolipram experiment is shown in Figure S5G, but is not described in the result section. It only appears at some point in the discussion part. The authors should move it to results or remove it from the manuscript.

Minor points:

1) Please reference the Figure panels in numeric order throughout the text.

2) Figure 2a and 2b are a bit out of the storyline, it is not obvious why this is shown here and maybe it would be good to move it to the supplements. Gating strategy is also not used for scRNA-seq. Therefore, it would better fit to the later analysis of joint macrophages across different transgenic mouse models and treatment paradigms. The gating strategies are changing across different experiments throughout the figures, it would be nice to have a similar gating strategy for all experiments, see also Figure 3 where the defining markers for joint macrophages are changing between models.

3) A lot of figure panels have very small labeling that is basically unreadable. Axes at FACS plots for example. Sometimes, it is even impossible to distinguish cluster labels especially when they have similar colors.

4) In the text on page 14, many markers are named which are specifically regulated during development in lining macrophages, but these factors are not labeled anywhere in the volcano plot. It would be good to showcase at least some of these named genes in the figure panel, e.g. Trem2.

5) Figure 2F and Figure S2F are really nicely showing the percentage of cells per cluster in each analyzed biological sample. Maybe the authors could additionally consider to show a stacked bar plot with the mean percentage of cells per cluster and how the clusters are distributed across time points?

6) Figure 3A: IF for adult lining macrophages and the quantification are missing

Significance

This manuscript highlights novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages. Furthermore, this study showcases in a very elegant and detailed way the adaptation of macrophage progenitors to a highly specific anatomical tissue niche.

The manuscript is of high interest to basic scientists focussing on macrophage biology and immune cell development and clinicians and clinician scientists focussing on joint diseases such as RA

Therefore the manuscript is of interest to a wide community working in immunology.

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Referee #1

Evidence, reproducibility and clarity

In their manuscript entitled "The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ-dependent but monocyte-independent process," the authors explore the developmental trajectory of synovial lining macrophages. They demonstrate that the formation of this specialized macrophage layer is age-dependent and governed by a distinct developmental program that proceeds independently of circulating monocytes. Through scRNA-Seq, the authors show that synovial lining macrophages originate locally from Aqp1⁺ macrophages and are marked by the expression of Csf1r, Tgfbr, and Piezo1. Notably, genetic ablation of each of these factors impaired the development of lining macrophages to varying degrees, suggesting differential contributions of CSF1, TGFβ, and PIEZO1 signaling pathways to their maturation and maintenance.

The manuscript is well written, and the data quality and representation is of a high standard. The authors have employed a sophisticated array of state-of-the-art mouse models and cutting-edge technologies to elucidate the developmental origin of synovial lining macrophages. Notably, the supporting scRNA-Seq datasets are of excellence and provide valuable insights that will likely be of significant interest to researchers in the field of immunology and joint biology. Accordingly, the experimental approach and interpretations regarding macrophage origin are well-founded and compelling. However, in the eye of the reviewer, the section addressing the underlying molecular mechanisms is a bit less convincing. This part of the study appears slightly underdeveloped, and some of the mechanistic claims lack sufficient experimental clarity. A more rigorous experimental investigation would be essential to reinforce the manuscript's conclusions, particularly concerning the data related to Tgfbr and Piezo1, where the current evidence appears insufficiently substantiated.

Major point:

• The numbers of VSIG4⁺ macrophages appear either unaffected or only minimally altered in both Csf1rMerCreMer Tgfbr2floxed and Fcgr1Cre Piezo1floxed mouse models, respectively. This raises an important question: was the gene deletion efficiency sufficient in each model? Accordingly, the authors are encouraged to include quantitative data on gene deletion efficiency for both mouse models, as this information is critical for interpreting the observed phenotypic outcomes and validating the conclusions regarding gene function. Furthermore, to better assess the impact of Tgfbr2 and Piezo1 disruption, the authors should provide more comprehensive flow cytometry analyses and histological data for these mouse models. Given the apparent homogeneity of VSIG4⁺ macrophages (as shown by the authors themselves), bulk RNA-Seq of sorted Tgfbr2- and Piezo1-deficient VSIG4⁺ macrophages (or from TGFβ-treated animals) would offer valuable insights into both the effectiveness of gene deletion and the molecular pathways governed by TGFβ and PIEZO1 in lining macrophages.

Minor points:

• Consistent usage of Cx3cr1-GFP+ nomenclature (for instance: Fig. S1 legend "adult mouse synovial tissue, showing PDGFRα⁺ fibroblasts (yellow) and CX3CR1-GFP⁺ cells (cyan)." versus Fig. 1 legend "Automated spot detection highlights Cx3cr1-GFP⁺ macrophages)"
• Unclear Fig. 3 legend: "Representative immunofluorescence images of synovial tissue from Clec9aCre:Rosa26lsl-tdT mice at 3 weeks and in adulthood, showing and tdTomato (yellow) and stained for DAPI (blue), VSIG4 (cyan)" Check 'showing and tdTomato.'
• For greater clarity, it would have been helpful if the transcript names had been directly included within Figures 3C, S3A, and S3C.
• Page 24: "(Mki67CreERT2:Rosa26lsl-tdT)" Last bracket not superscript.
• Page 25: "we again leveraged our scRNAsequencing dataset" Missing punctuation.
• Page 27: Fig. 5C legend: " of synovial tissue of 1 week-old, 3 weeks-old and adult mice." Please specify and change to 'adult Csf1rΔFIRE/ΔFIRE mice'.
• Page 30: The outcome observed in the Acta1-rtTA:tetO-Cre:ChR2-V5fl mouse model appears to be inconclusive: "This approach resulted in an increased density of VSIG4+ and total (F4/80+) macrophages in the exposed leg of some 5 days-old pups, but others showed the opposite trend (Figure S5D)." This variability may reflect low efficiency of the model or other technical limitations (e.g. muscle contractions frequency or time point of analysis). Given this ambiguity, it is worth reconsidering whether the data are sufficiently robust to warrant inclusion. Should the authors choose to include these findings, further experimentation of appropriate depth and precision is required to allow a conclusive interpretation (either it increases the density of VSIG4+ macrophages or not). The same applies to the Yoda1-treated mice, for which additional data are needed to determine whether VSIG4⁺ macrophage density is truly affected.

Significance

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

This is a well-designed study that uses cutting-edge methodologies to investigate the developmental trajectory of synovial lining macrophages under homeostatic conditions. The authors present robust experimental evidence and compelling interpretations concerning synovial macrophage origin, which are both well-substantiated and impactful. Nonetheless, from the reviewer's perspective, the section exploring the molecular mechanisms underlying macrophage differentiation is comparatively less convincing. This section appears somewhat underdeveloped, as some of the mechanistic claims lack sufficient depth and experimental rigor to fully substantiate the conclusions. - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field:

In contrast to earlier studies (PMID: 31391580, 32601335), the inclusion of fate-mapping experiments adds an important dimension, offering novel insight into the ontogeny of synovial macrophages. This expanded perspective may prove particularly valuable in advancing our understanding of joint immunology, especially regarding the local origins and lineage relationships of macrophage populations. Furthermore, the authors present novel insights into the molecular pathways underlying the differentiation and development of synovial lining macrophages. By demonstrating previously unrecognized regulatory mechanisms, this work significantly deepens our understanding of the cellular and transcriptional programs that drive macrophage specialization within the joint microenvironment. -Place the work in the context of the existing literature (provide references, where appropriate):

This study builds upon previous work characterizing the macrophage compartment in the joint (PMID: 31391580, 32601335), yet provides a substantially more comprehensive dataset that spans multiple developmental time points and data on the origin of this specialized macrophage subset. - State what audience might be interested in and influenced by the reported findings:

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

This study falls well within the scope of the reviewer's expertise in innate immunity.

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Reply to the reviewers

Reply to the reviewers

We would like to thank the reviewers for their overall positive evaluations of our manuscript and for their invaluable suggestions that will allow us to reinforce our conclusions. We acknowledge that there is some work to be done and are ready to address most of the reviewers' comments as detailed in our replies below.

Reviewer #1

1. The findings that mmDicer is proviral in bat cells relies exclusively on the observation that the depletion of Dicer in M. myotis cells leads to a reduced accumulation of SFV and SINV at the RNA and protein levels (figure 2). Heterologous expression of mmDicer in HEK 293T NoDice doesn't lead to an increase permissivity to viral infections (figure 1) and the accumulation of Dicer foci is only observed in M. myotis cells but not when mmDicer is expressed in HEK 293 NoDice cells (figure 6). Given that the key finding of this manuscript relies on these knockdown experiments, the authors should ensure that the impact on viral infections is due to the specific silencing of mmDicer and not caused by off-target effects of their siRNA-mediated approach. The authors designed a siRNA pool to efficiently knock-down mmDicer. They should validate their findings by using individual Dicer siRNA and verify whether the decrease SFV/SINV accumulation is observed with at least two individual siRNAs targeting Dicer. It would also strengthen their findings if they could show a complementation experiment in which a mmDicer (designed to not be affected by the siRNA-mediated silencing) is introduced exogenously in Dicer-depleted cells and show that it rescues the observed decrease in viral accumulation to demonstrate that the proviral role is strictly dependent on mmDicer. Alternatively, the authors could consider a CRISPR/Cas9 genome editing approach to knockout Dicer in bat cells to test whether this proviral effect is confirmed.

Reply: We agree with this reviewer that it is important to provide evidence for the specificity of the knock-down and to rule out any off-target effect of the siRNAs. This is the reason for using the siTool technology, which relies on the use of a pool of 30 siRNAs that are transfected at a final concentration of 3 nM. This means that each individual siRNA in the pool is at a concentration of 0.1 nM, so the possibility of off-target effect is largely avoided and the efficiency of silencing is boosted by the cooperative activity of many siRNAs (see https://www.sitoolsbiotech.com/documents/sipools/siPOOLBrochure2019_Web.pdf for more details). This being said, we agree that it would be better to confirm that the observed effect can be recapitulated using a single siRNA and that a complementation experiment would definitely strengthen our findings. For this reason, we will test two individual siRNAs targeting the 3' UTR of mmDicer, which will allow us to complement the knock-down by transfecting a cDNA construct. Regarding the CRISPR/Cas9 genome editing approach, we will give it a try, but Dicer is notoriously difficult to knock-out, so we cannot be sure that this will be successful.

Figure 2: the authors knock-downed Dicer in M. myotis nasal epithelial cells and carried out infections with SINV-GFP and SFV. The authors conclude that Dicer is proviral as its depletion causes a decrease in SINV-GFP and SFV accumulation. While this conclusion is supported by the decrease levels of viral RNA and protein levels upon Dicer depletion (figure 2D, 2E, 2G), the effect on the viral titers is non-significant for both viruses (Figure 2C and 2F) based on the statistical analysis. This reviewer appreciates that the titers are lower upon Dicer knockdown, which support the authors' findings at the viral RNA and protein levels. However, as these results are central to the core message of the manuscript, the authors should provide evidence that this proviral effect observed is statistically significant on viral titers by perhaps providing additional repeats and/or comment on this observation.

Reply: Indeed, we agree that even if the effect of Dicer knockdown results in a lowering of the viral titer, it would be better to have a statistically significant effect. We will repeat the experiment to increase the number of replicates and the power of the statistical test.

a) *In figure 4 and 5, the authors nicely show that mmDicer accumulate to cytoplasmic foci in M. myotis cells upon infection with SFV and SINV and these foci co-localise with double-stranded RNA. The authors used a commercial polyclonal antibody against Dicer (A301-937A, Bethyl according to the Material and Methods section) which is specific to human Dicer to carry out their immunostaining in bat cells. The authors should provide evidence that this antibody indeed recognises/crossreacts with mmDicer as well and that the staining shown is indeed specific to mmDicer localisation especially because the heterologous expression of HA-tagged version of mmDicer in HEK 293T NoDice cells did not show this accumulation of cytoplasmic foci. The authors should verify the specificity of their mmDicer immunostaining by performing the same labelling in bat cells in which Dicer is knock-downed (or knock out) by individual and validated siRNA against mmDicer. The decrease signal of bat Dicer staining using the anti-human Dicer antibody would indicate specificity. *

Reply: the reviewer is correct in its assertion and it is important to provide evidence that the protein that is detected by the anti-human Dicer antibody in bat cells is indeed Dicer. We will perform the suggested experiment and do an immunostaining using the Dicer antibody in bat cells upon Dicer knockdown.

b) Another complementary approach would be to test their Dicer staining between HEK NoDice cells (no Dicer present) versus NoDice complemented with either mmDicer or human Dicer constructs, which would then indicate how much the anti-human Dicer antibody recognises bat Dicer.

Reply: this complementary approach should yield even cleaner result than the previous one as there will be no expression of Dicer at all in the HEK NoDice cells. Therefore, we should be able to measure the increase of signal in the IF upon expression of either human or bat Dicer. We will perform this experiment together with the other one suggested above. In addition, since the constructs are tagged, we might be able to do a double-staining and verify the colocalization of the two signals.

c) In addition, the authors should overexpress HA-tagged mmDicer in M. myotis nasal epithelial cells and test whether HA-mmDicer accumulate into foci upon infection using an anti-HA immunostaining. This would confirm that these accumulation into foci indeed is specific to mmDicer but also would reinforce the authors' findings that host factors within bat cells are important for this formation into foci since mmDicer expression in HEK 293T No Dice cells didn't show this phenotype upon infection (figure 6). OPTIONAL: it would be interesting to overexpress HA-tagged human Dicer into M. myotis nasal epithelial cells as well to then test using anti-HA staining whether human Dicer in presence of host factors from the bat can accumulate into cytoplasmic foci or not upon viral infection.

Reply: we could perform the suggested experiment, but we might face the issue that transfected cells might mount an immune response, which makes them resistant to the infection. We have observed indeed that we needed to use a higher MOI to infect cells after they have been transfected. Since we will have controls in place, this might not be too much of a problem, but we will have to keep it in mind. Alternatively, we will perform a lentiviral transduction of the cells.

This reviewer appreciates that this might be judged as beyond the scope of this study since it is focused on the role of Dicer in M. myotis. However, the observation that mmDicer accumulates into foci containing as well viral dsRNA is very interesting and it would significantly improve the manuscript if the authors would provide further indications that this phenotype is related to the lack of antiviral activity of mmDicer compared to what has been previously shown in other bat species (P.alecto and T. brasiliensis). In other words, is this accumulation of mmDicer into foci responsible for its different impact on virus infection? It would therefore be insightful to compare Dicer localisation upon infection in M. myotis versus P.alecto and/or T. brasiliensis bat cells in which Dicer was shown to be antiviral and test whether this accumulation in foci is only observed in bat cells in which Dicer is proviral (M. myotis) but not in the other bat cells in which Dicer is antiviral (P.alecto and/or T. brasiliensis).

Reply: this is something that we have been wondering about and we have therefore started to look for the cell lines that have been described in the two published studies. While it proved difficult to find the PaKi cells from P. alecto bats, which is not commercially available, we have obtained the Tblu cells from T. brasiliensis and will look at Dicer localization in this model. However, we have to pay attention to the fact that the published data reported a contribution of RNAi in this cell line upon SARS-CoV-2 infection and that we will be using SINV. In addition, we do not know yet whether the anti-Dicer antibody will cross react with the T. brasiliensis Dicer protein.

OPTIONAL: Given the difference between the provial role of mmDicer compared to the antiviral activity of Dicer in cells from P.alecto and T. brasiliensis bat cells, it would strengthen the authors' findings. if additional experiments would be conducted in parallel using M. myotis, P.alecto and/or T. brasiliensis cells. Notably knocking down Dicer in both M. myotis, P.alecto and/or T. brasiliensis cells, compare the impact on viral infections with SINV, SFV, VSV and correlate any observed difference in phenotype with putative variations in the formation of foci.

Reply: it would indeed be really nice to be able to do the Dicer knockdown experiment in several bat cell lines and to correlate the phenotype with the formation of foci. This experiment might take a long time and we are not sure to be able to realize it in a reasonable amount of time. It could however be the subject of another manuscript further down the line.

*Minor comments *

• • Figure 2I: The authors performed a knockdown of Dicer in M. myotis nasal epithelial cells and monitor the impact on VSV-GFP infection. They found that knocking down Dicer leads to an increase in GFP protein and RNA levels suggesting an antiviral role of Dicer while, in contrast, no effect is observed on the production of infectious particles (figure 2H). On the western blot there is only a slight/weak increase of GFP protein level observed upon Dicer knockdown. Yet, the quantification of the band intensity shows a 4-fold increase relative to tubulin and compared to cells treated with siRNA control. This 4-fold increase seems exaggerated given the low increase in the intensity shown on the blot. This discrepancy is most likely due to the lower intensity of tubulin in the western blot analysis of siDicer-treated cells compared to siNeg-treated cells. The authors should reload their western blot with equal amount of protein extract loaded to ensure that the results shown on the western blot are in line with the quantification.*

Reply: the signal quantification for this experiment was done across several replicates, but we agree that the observed effect seems exaggerated when compared to the signal seen on the blot. We observed important variations between replicates, but we will make sure that this was not due to a problem in the analysis and reload the western blot if needed.

• • Figure 3D: the authors mention that in both HEK293T cells and M. myotis nasal epithelial cells infected with SINV-GFP, there was an enrichment of 22-nucleotides (nt) paired positive and negative sense reads that overlapped with a 2-nt overhang, typical of Dicer cleavage. In Figure 3D, the data shows indeed that the duplexes are enriched for reads of 22-nt but it is unclear how this analysis reveals a 3' 2nt overhang within these duplexes. Can the authors clarify this point and if the data provided in that particular analysis indeed doesn't allow to detect these overhangs, please rephrase accordingly or provide additional analysis to support that point. *

Reply: In Figure 3D, the graphs show the probability of pairing of all 22 nucleotides sequence mapping either to the plus or the minus strand of the viral RNA. Thus, for each sequence mapping to the plus strand, the number of sequences mapping to the minus strand with a full or partial overall is counted. A corresponding probability of pairing and Z score is calculated for each number of overlapping nucleotides (for more information on the calculation see Antoniewski (2014) Computing siRNA and piRNA Overlap Signatures. In Animal Endo-SiRNAs: Methods and Protocols, Werner A (ed) pp 135-146. New York, NY: Springer). The Z score peaks for an overlap of 20 nt in both HEK293T and M. myotis nasal epithelial cells infected with SINV. This means that there is a higher probability of two 22 nt sequence to pair along 20 nt, and thus that there are two unpaired nucleotides at the extremities of the duplexes. This higher Z score at 20 nt is not seen in VSV-infected cells. We will rephrase the text in the manuscript to make this point clearer.

• • Typo: page 5, line 152: the authors mention that Dicer knock down had an antiviral effect against VSV-GFP infection at the RNA and protein levels. However, the data in Figure 2I and 2J show an increase in both GFP RNA and proteins levels upon knockdown of Dicer. Although this data suggests that Dicer is antiviral against VSV, the knockdown of Dicer itself is not antiviral but rather proviral/increase virus accumulation. Please rephrase this sentence to avoid confusions. *

Reply: thank you for spotting this typo. We have corrected it accordingly.

Reviewer #2.

1. Figure 1 relies on transduction of cells and antibiotic selection to obtain mmDicer-expressing cells. Although we would expect that every cell expresses the construct of interest, this is not always the case, depending on the cell type and toxicity of the construct. As the constructs are tagged, I suggest that the authors use flow cytometry to measure expression levels in a single cell manner. While doing so, they can infect with SINV-GFP and correlate GFP signal with construct expression in each cell, providing a more accurate measurement of mmDicer effect on viral infection. Alternatively, the authors could use live microscopy, as done in Fig 2, to obtain similar data.

Reply: the reviewer is correct that we did not go for monoclonal selection of our mmDicer-expressing cells and therefore that there could be some cell-to-cell variation in expression. However, we have done immunostaining of Dicer in these cells and did not see drastic differences in expression, so we do not think this should impact SINV-GFP expression in a major way. We will provide these images and a quantification of the Dicer signal as a supplementary figure.

For Fig 1C and 1F, it would be great to have growth curves with two different MOIs, instead of a single time point, to ensure that a putative antiviral effect is not missed. Same goes for Fig 2C, especially when the authors document quite a big defect on GFP expression (a proxy for SINV infection) when Dicer is knocked down (Fig 2B). There may be a bigger difference in titers at earlier time points. This matter runs throughout the manuscript. I do not suggest that the authors should provide growth curves every time viral titers are measured, but it is still worth doing it for the 2-3 key experiments of the paper.

Reply: we will perform growth curves of virus infection for the key experiments in the manuscript as suggested. We already have done kinetic measurements of GFP accumulation at different MOIs, which we can provide as supplementary data, but we agree with the reviewer that GFP signal should not been used as the only proxy for the infection and that measuring viral titers by plaque assay is important as well.

Figure 4, could the authors provide a proof that the Dicer antibody is specific in the bat context? This can be done by staining Dicer in bat cells knocked down for Dicer and infected with SINV. The apparition of foci upon anti-Dicer antibody staining should be abbrogated or severely impaired by the knock-down.

Reply: see our reply to point 3 of Reviewer 1.

Fig 5C, please provide a quantification of the images.

Reply: these microscopy images have not been quantified because they have been obtained with an epifluorescence microscope. Indeed, the Pearson correlation coefficient can only be obtained using a confocal microscope. In fact, we have tried to use a confocal microscope to take pictures of these FISH images, but the SINV gRNA signal was too weak or the dots too small to be properly visualized. Furthermore, there is a very large difference in signal intensity between HEK293T and M. myotis cells, making it difficult to define a signal threshold compatible for both cell lines.

l.263, when comparing this work with the recent publications on bat antiviral RNAi, the authors could also provide the percentage identity between Dicers from different species.

Reply: this is a valid point, we have looked at the percentage identity between Dicer proteins from different bat species but we did not include this in our manuscript. We will provide this analysis in the revised version together with a comparison of Dicer from other mammals as a reference point.

Reviewer 3.

1. • Without direct comparison to the other bat species Dicers (especially where RNAi activity has been suggested as antiviral in previous publications) there is little in this paper that can be concluded about global aspects of bat dicer/RNAi.*

Reply: see our reply to point 4 of Reviewer 1. We are planning to look at least in Tblu cells whether there is also a relocalization of Dicer upon SINV infection. So far, we could not obtain PaKi cells, but we are still looking and should we get those, we will test them as well.

*Minor *

What rules out that the mmDicer re-localization observed in the immortalized mm nasal epithelial is due simply to greater expression levels over the NoDice cells heterologously expressing mmDicer?

Reply: we will provide an immunoblot to show the level of Dicer expression between HEK NoDice + mmDicer and M. myotis nasal epithelial cells as suggested below to address this point.

• Although partially addressed in the text stating the generally long half-life of miRNAs, it seems the simplest explanation for this observation is due to some activity of a shorter-lived miRNA is required for optimal alphavirus replication is the mm nasal epithelial cells. *

Reply: this is an interesting hypothesis that would prove difficult to test in a reasonable amount of time. We thank the reviewer and will mention this possibility in the discussion of the revised manuscript.

*Suggestions that could enhance the magnitude of conclusions that can be drawn from this work. *

*Major *

• • Making NoDice cells expressing other bat species Dicers, including those with claims that RNAi is antiviral, would address how universal these current observations are to bats/cell lines.*

Reply: this could be an alternative to the use of P. alecto or T. brasiliensis cell lines that we have mentioned above. We will try to clone Dicer from the Tblu cells that we have in the laboratory. Since we do not have PaKi cells at the moment, it will be more complicated for the Pteropus Dicer, but one possibility could be to synthesize it. However, Dicer is a big gene so it could prove tricky.

• • Including an immunoblot showing that mm cells express mmDicer no more abundantly than the heterologous NoDice cells would allow ruling out the trivial explanation that foci occur at a certain critical mass of Dicer*

Reply: yes, we will provide this piece of data as stated in reply to point 2.

*Minor *

• • I believe line 151 " In contrast, Dicer * *knock down had an ANTIVIRAL effect against VSV-GFP infection at the RNA and protein *

*levels, but no difference in titers was found (Fig. 2H-J)." should be " In contrast, Dicer *

*knock down had an PROVIRAL effect against VSV-GFP infection at the RNA and protein *

*levels, but no difference in titers was found (Fig. 2H-J)." *

Reply: thank you for spotting this error, which was also mentioned by Reviewer 1, we have corrected this in the text.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript by Gaucherand and colleagues, the authors demonstrate that heterologous expression of Myotis myotis Dicer into 293 derivative Dicer KO cells did not produce antiviral effects. The authors further demonstrate that knockdown of Dicer in SV40 immortalized M myotis nasal epithelial cells results in reduced alphavirus infection. Finally, they show a correlation where mmDicer changes subcellular localization co-localizing with likely alphavirus replication foci. The manuscript is clearly written, and the conclusions drawn as stated are accurate.

Strengths

• This is an overall topical area of research: how bat antiviral responses differ from other mammals - - with enormous general interest in host-pathogen interfaces, and particular relevance to the role of RNAi.
• The manuscript is clearly written and does not overstate the conclusions.
• The team are well-qualified experts in this area with an excellent track record of findings from the Pfeffer lab in the years preceding this work

Critiques

Major

1. Without direct comparison to the other bat species Dicers (especially where RNAi activity has been suggested as antiviral in previous publications) there is little in this paper that can be concluded about global aspects of bat dicer/RNAi. Minor
2. What rules out that the mmDicer re-localization observed in the immortalized mm nasal epithelial is due simply to greater expression levels over the NoDice cells heterologously expressing mmDicer?
3. Although partially addressed in the text stating the generally long half-life of miRNAs, it seems the simplest explanation for this observation is due to some activity of a shorter-lived miRNA is required for optimal alphavirus replication is the mm nasal epithelial cells.

Suggestions that could enhance the magnitude of conclusions that can be drawn from this work.

Major

• Making NoDice cells expressing other bat species Dicers, including those with claims that RNAi is antiviral, would address how universal these current observations are to bats/cell lines.
• Including an immunoblot showing that mm cells express mmDicer no more abundantly than the heterologous NoDice cells would allow ruling out the trivial explanation that foci occur at a certain critical mass of Dicer Minor
• I believe line 151 " In contrast, Dicer knock down had an ANTIVIRAL effect against VSV-GFP infection at the RNA and protein levels, but no difference in titers was found (Fig. 2H-J)." should be " In contrast, Dicer knock down had an PROVIRAL effect against VSV-GFP infection at the RNA and protein levels, but no difference in titers was found (Fig. 2H-J)."

Significance

As written, this work would be significant to aficionados of bat RNAi. With a little extra work, this could have broader significance regarding more global aspect of Dicer in the the bat antiviral response.

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Referee #2

Evidence, reproducibility and clarity

This study by the Pfeffer lab interrogates the role of Dicer during RNA virus infection in bats. This is an interesting and important topic, as bats are well-documented to be a reservoir of viruses that can target humans. The field of bat immunology is gaining momentum, but there is still a lot to be done. This study is thus particularly timely. It also explores more of a niche pathway when it comes to immunity: antiviral RNAi and its entry point, Dicer. This work comes after two recent studies, cited by the authors (Dai 2024, Owolabi 2025), that also explore this concept. Here though, the Pfeffer lab comes to an opposite conclusion, as their results advocate against the existence of antiviral RNAi in bats. As discussed by the authors, discrepancies between their study and the two others may be linked to differences in experimental systems. It nonetheless brings a novel, interesting take on the topic of Dicer & antiviral RNAi in bats, and will be of interest to immunologists and virologists. Altogether, I find the manuscript well-written and clear. Experiments are to the point and well interpreted. Below are a few suggestions that will help bolster the authors' conclusions.

Figure 1 relies on transduction of cells and antibiotic selection to obtain mmDicer-expressing cells. Although we would expect that every cell expresses the construct of interest, this is not always the case, depending on the cell type and toxicity of the construct. As the constructs are tagged, I suggest that the authors use flow cytometry to measure expression levels in a single cell manner. While doing so, they can infect with SINV-GFP and correlate GFP signal with construct expression in each cell, providing a more accurate measurement of mmDicer effect on viral infection. Alternatively, the authors could use live microscopy, as done in Fig 2, to obtain similar data.

For Fig 1C and 1F, it would be great to have growth curves with two different MOIs, instead of a single time point, to ensure that a putative antiviral effect is not missed. Same goes for Fig 2C, especially when the authors document quite a big defect on GFP expression (a proxy for SINV infection) when Dicer is knocked down (Fig 2B). There may be a bigger difference in titers at earlier time points. This matter runs throughout the manuscript. I do not suggest that the authors should provide growth curves every time viral titers are measured, but it is still worth doing it for the 2-3 key experiments of the paper.

Figure 4, could the authors provide a proof that the Dicer antibody is specific in the bat context? This can be done by staining Dicer in bat cells knocked down for Dicer and infected with SINV. The apparition of foci upon anti-Dicer antibody staining should be abbrogated or severely impaired by the knock-down.

Fig 5C, please provide a quantification of the images.

l.263, when comparing this work with the recent publications on bat antiviral RNAi, the authors could also provide the percentage identity between Dicers from different species.

Significance

This study by the Pfeffer lab interrogates the role of Dicer during RNA virus infection in bats. This is an interesting and important topic, as bats are well-documented to be a reservoir of viruses that can target humans. The field of bat immunology is gaining momentum, but there is still a lot to be done. This study is thus particularly timely. It also explores more of a niche pathway when it comes to immunity: antiviral RNAi and its entry point, Dicer. This work comes after two recent studies, cited by the authors (Dai 2024, Owolabi 2025), that also explore this concept. Here though, the Pfeffer lab comes to an opposite conclusion, as their results advocate against the existence of antiviral RNAi in bats. As discussed by the authors, discrepancies between their study and the two others may be linked to differences in experimental systems. It nonetheless brings a novel, interesting take on the topic of Dicer & antiviral RNAi in bats, and will be of interest to immunologists and virologists. Altogether, I find the manuscript well-written and clear. Experiments are to the point and well interpreted. Below are a few suggestions that will help bolster the authors' conclusions.

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Referee #1

Evidence, reproducibility and clarity

Bats acts a reservoir for many viruses. While some of these viruses can be pathogenic for humans and other animals, infected bats tolerate these viruses and show little to no pathogenesis. It is therefore key to characterise which immune pathways are active in bats and how do they differ from other mammals to understand how bats can sustain these virus infections. RNA interference (RNAi) acts as an antiviral mechanism in plants, invertebrates and was recently shown to be active in a cell type-dependent manner as a defence mechanism in mammals. Notably, recent findings show that antiviral RNAi activity is high in cells lines from two bats species (P.alecto and T. brasiliensis) and that this pathway might play an important role in bat viral tolerance. In this study, the authors investigate the antiviral role of Dicer in another bat species, Myotis myotis. First they express M. myotis Dicer (mmDicer) or human Dicer (hDicer) in a human epithelial kidney (HEK) 293T cell line knockout for Dicer (NoDice cells) and show that, in a human cell line, expression of mmDicer or hDicer doesn't restrict infections with either Sindbis virus (SINV) or vesicular stomatitis virus (VSV). The authors then tested the role of endogenous bat Dicer in M. myotis nasal epithelial cells and found that mmDicer has a proviral activity since its knockdown reduced the replication of SINV and Semliki Forest virus (SFV), but not of VSV. The authors also show by small RNA deep sequencing analysis that there was only a modest RNAi signature in both HEK293T and M. myotis infected with SINV suggesting that mmDicer does not have increased RNAi activity compared to human cells. Interestingly, the authors then found that in M. myotis cells infected with SINV, SFV but not VSV, mmDicer accumulates into cytoplasmic foci, which also contain double-stranded RNA (dsRNA) derived from viral replication. Finally, the authors showed that this relocalisation of mmDicer into foci was dependent on host factors from M. myotis cells as there was no change in localisation in SINV-infected HEK 293T NoDice cells complemented with mmDicer.

Major comments

• The findings that mmDicer is proviral in bat cells relies exclusively on the observation that the depletion of Dicer in M. myotis cells leads to a reduced accumulation of SFV and SINV at the RNA and protein levels (figure 2). Heterologous expression of mmDicer in HEK 293T NoDice doesn't lead to an increase permissivity to viral infections (figure 1) and the accumulation of Dicer foci is only observed in M. myotis cells but not when mmDicer is expressed in HEK 293 NoDice cells (figure 6). Given that the key finding of this manuscript relies on these knockdown experiments, the authors should ensure that the impact on viral infections is due to the specific silencing of mmDicer and not caused by off-target effects of their siRNA-mediated approach. The authors designed a siRNA pool to efficiently knock-down mmDicer. They should validate their findings by using individual Dicer siRNA and verify whether the decrease SFV/SINV accumulation is observed with at least two individual siRNAs targeting Dicer. It would also strengthen their findings if they could show a complementation experiment in which a mmDicer (designed to not be affected by the siRNA-mediated silencing) is introduced exogenously in Dicer-depleted cells and show that it rescues the observed decrease in viral accumulation to demonstrate that the proviral role is strictly dependent on mmDicer. Alternatively, the authors could consider a CRISPR/Cas9 genome editing approach to knockout Dicer in bat cells to test whether this proviral effect is confirmed.
• Figure 2: the authors knock-downed Dicer in M. myotis nasal epithelial cells and carried out infections with SINV-GFP and SFV. The authors conclude that Dicer is proviral as its depletion causes an decrease in SINV-GFP and SFV accumulation. While this conclusion is supported by the decrease levels of viral RNA and protein levels upon Dicer depletion (figure 2D, 2E, 2G), the effect on the viral titers is non-significant for both viruses (Figure 2C and 2F) based on the statistical analysis. This reviewer appreciates that the titers are lower upon Dicer knockdown, which support the authors' findings at the viral RNA and protein levels. However, as these results are central to the core message of the manuscript, the authors should provide evidence that this proviral effect observed is statistically significant on viral titers by perhaps providing additional repeats and/or comment on this observation.
• In figure 4 and 5, the authors nicely show that mmDicer accumulate to cytoplasmic foci in M. myotis cells upon infection with SFV and SINV and these foci co-localise with double-stranded RNA. The authors used a commercial polyclonal antibody against Dicer (A301-937A, Bethyl according to the Material and Methods section) which is specific to human Dicer to carry out their immunostaining in bat cells. The authors should provide evidence that this antibody indeed recognises/crossreacts with mmDicer as well and that the staining shown is indeed specific to mmDicer localisation especially because the heterologous expression of HA-tagged version of mmDicer in HEK 293T NoDice cells did not show this accumulation of cytoplasmic foci. The authors should verify the specificity of their mmDicer immunostaining by performing the same labelling in bat cells in which Dicer is knock-downed (or knock out) by individual and validated siRNA against mmDicer. The decrease signal of bat Dicer staining using the anti-human Dicer antibody would indicate specificity. Another complementary approach would be to test their Dicer staining between HEK NoDice cells (no Dicer present) versus NoDice complemented with with either mmDicer or human Dicer constructs, which would then indicate how much the anti-human Dicer antibody recognises bat Dicer. In addition, the authors should overexpress HA-tagged mmDicer in M. myotis nasal epithelial cells and test whether HA-mmDicer accumulate into foci upon infection using an anti-HA immunostaining. This would confirm that these accumulation into foci indeed is specific to mmDicer but also would reinforce the authors' findings that host factors within bat cells are important for this formation into foci since mmDicer expression in HEK 293T No Dice cells didn't show this phenotype upon infection (figure 6). OPTIONAL: it would be interesting to overexpress HA-tagged human Dicer into M. myotis nasal epithelial cells as well to then test using anti-HA staining whether human Dicer in presence of host factors from the bat can accumulate into cytoplasmic foci or not upon viral infection.
• This reviewer appreciates that this might be judged as beyond the scope of this study since it is focused on the role of Dicer in M. myotis. However, the observation that mmDicer accumulates into foci containing as well viral dsRNA is very interesting and it would significantly improve the manuscript if the authors would provide further indications that this phenotype is related to the lack of antiviral activity of mmDicer compared to what has been previously shown in other bat species (P.alecto and T. brasiliensis). In other words, is this accumulation of mmDicer into foci responsible for its different impact on virus infection? It would therefore be insightful to compare Dicer localisation upon infection in M. myotis versus P.alecto and/or T. brasiliensis bat cells in which Dicer was shown to be antiviral and test whether this accumulation in foci is only observed in bat cells in which Dicer is proviral (M. myotis) but not in the other bat cells in which Dicer is antiviral (P.alecto and/or T. brasiliensis).
• OPTIONAL: Given the difference between the provial role of mmDicer compared to the antiviral activity of Dicer in cells from P.alecto and T. brasiliensis bat cells, it would strengthen the authors' findings. if additional experiments would be conducted in parallel using M. myotis, P.alecto and/or T. brasiliensis cells. Notably knocking down Dicer in both M. myotis, P.alecto and/or T. brasiliensis cells, compare the impact on viral infections with SINV, SFV, VSV and correlate any observed difference in phenotype with putative variations in the formation of foci.

Minor comments

• Figure 2I: The authors performed a knockdown of Dicer in M. myotis nasal epithelial cells and monitor the impact on VSV-GFP infection. They found that knocking down Dicer leads to an increase in GFP protein and RNA levels suggesting an antiviral role of Dicer while, in contrast, no effect is observed on the production of infectious particles (figure 2H). On the western blot there is only a slight/weak increase of GFP protein level observed upon Dicer knockdown. Yet, the quantification of the band intensity shows a 4-fold increase relative to tubulin and compared to cells treated with siRNA control. This 4-fold increase seems exaggerated given the low increase in the intensity shown on the blot. This discrepancy is most likely due to the lower intensity of tubulin in the western blot analysis of siDicer-treated cells compared to siNeg-treated cells. The authors should reload their western blot with equal amount of protein extract loaded to ensure that the results shown on the western blot are in line with the quantification.
• Figure 3D: the authors mention that in both HEK293T cells and M. myotis nasal epithelial cells infected with SINV-GFP, there was an enrichment of 22-nucleotides (nt) paired positive and negative sense reads that overlapped with a 2-nt overhang, typical of Dicer cleavage. In Figure 3D, the data shows indeed that the duplexes are enriched for reads of 22-nt but it is unclear how this analysis reveals a 3' 2nt overhang within these duplexes. Can the authors clarify this point and if the data provided in that particular analysis indeed doesn't allow to detect these overhangs, please rephrase accordingly or provide additional analysis to support that point.
• Typo: page 5, line 152: the authors mention that Dicer knock down had an antiviral effect against VSV-GFP infection at the RNA and protein levels. However, the data in Figure 2I and 2J show an increase in both GFP RNA and proteins levels upon knockdown of Dicer. Although this data suggests that Dicer is antiviral against VSV, the knockdown of Dicer itself is not antiviral but rather proviral/increase virus accumulation. Please rephrase this sentence to avoid confusions.

Significance

The findings from this study are interesting as they provide further insights into the role of RNAi towards virus infections. Notably, it highlights a putative proviral role of Dicer in M. myotis bat cells in contrast to the antiviral role in mammals (including other bat species) as well as in plants and invertebrates. Another exciting finding of this study is the observation that mmDicer accumulates in cytoplasmic foci upon viral infection and that these foci also contain viral dsRNA replication intermediates. These accumulation of Dicer into foci only appear in bat cells infected with viruses producing large amounts of dsRNA such as SFV and SINV but not with VSV infection where no dsRNA was detected.

While these findings are novel and interesting, this study, as it stands, is rather descriptive and doesn't provide mechanistic insights into the proviral activity of mmDicer and its localisation into cytoplasmic foci upon infections. The importance of the authors' findings would greatly improve if there were some experiments addressing whether this localisation of mmDicer into foci is responsible or at least correlate with its proviral activity/its lack of antiviral activity. Comparative studies between M. myotis cells in which Dicer is proviral and/or P.alecto and T. brasiliensis cells where RNAi was previously shown to be antiviral would likely provide key mechanistic insights.

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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

RESPONSE TO REVIEWERS

We thank the reviewers for their thoughtful and constructive feedback, which has been instrumental in improving the overall quality of our manuscript.

In response, we have undertaken a substantial revision that includes new experimental data, refined analyses, and clearer presentation of our findings. Specifically, we have addressed concerns about RNAi efficiency and protein-level validation, expanded our genetic models to include loss-of-function contexts, and clarified the interpretation of mitochondrial morphology using both confocal and electron microscopy. We also incorporated new data on Cyclin E regulation and mitochondrial membrane potential to strengthen the mechanistic link between dPGC1 depletion and Yki-driven tumorigenesis. These revisions not only address the specific points raised by the reviewers but also enhance the coherence and impact of the study. We are confident that the revised manuscript presents a more robust and compelling case for the role of dPGC1 as a context-dependent tumor suppressor and that it will be of broad interest to the fields of developmental biology, cancer metabolism, and mitochondrial dynamics.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Sew et al. examine the master regulator of mitochondrial biogenesis, dPGC1, in the context of Drosophila wing and larval development. They primarily use confocal imaging to probe the interplay between dPGC1 and an overactive Hippo pathway, driven by overexpression of the main effector protein, Yki. In their study, they find that tumors, driven by overactivity of Yki grow larger when dPGC1 is downregulated, implicating the mitochondrial biogenesis pathway in tumor suppression. Furthermore, in the context of Yki overexpression, they find that levels of Mfn or Opa1 modulate tumor size. Lastly, they show a role of cyclin E in controlling the size of tumors formed by Yki OE + dPGC1 RNAi. The potential role of dPGC1 as a tumor suppressor is interesting because it highlights an emerging recognition of mitochondria in the aetiology of cancer. However, before publication, much of the data in this manuscript should be strengthened by a refinement in the methods/analysis and an increase in orthogonal approaches.

We addressed concerns regarding RNAi efficiency and wing development by incorporating data from a dPGC1 mutant allele and using a ubiquitous driver for qPCR validation of transgene efficiency. We clarified the rationale for EM use. The manuscript now avoids overinterpretation of mitochondrial morphology and focuses on fusion-specific regulators. We also revised the narrative arc to maintain coherence and added loss-of-function models to support our conclusions.

Below, we address each of the reviewer’s points in detail.

Major comments:

The authors indicate that for example, in lines127-28, that neither downregulating or overexpressing dPGC1 affects wing size. However, the quantification in Fig. 1C shows a significant decrease in wing size following RNAi treatment. This decrease is modest, but it is nevertheless significant. It is worth pointing out, too, that the efficiency of the RNAi in Fig. S1C suggests that the conclusions drawn are premature. While a roughly 55% drop in mRNA levels may be statistically significant, it is unclear whether this drop in transcripts corresponds to a commensurate depletion of protein. Moreover, it is unclear, in this context, how much dPGC1 may indeed be necessary to drive a relatively normal program of mitochondrial biogenesis in wing development. To obtain a clear result, it is necessary to show significant depletion of the dPGC1 protein. (Ultimately, if it is the case that dPGC1 is unnecessary for wing development and function, a more coherent line of inquiry would be to find out the reason for this rather than to pivot the story to studying tumorigenesis in larva.)

We agree that the interpretation of the RNAi efficiency data requires clarification.

The qPCR analysis shown in former Fig. S1C was performed using wing discs from flies expressing UAS-dPGC1-RNAi under the control of the MS1096-Gal4 driver. However, as shown in current Fig. 1C, MS1096-Gal4 is not expressed uniformly across the wing disc. Some regions remain RFP-negative, indicating that the RNAi construct is not active in all cells. As a result, the measured mRNA levels likely underestimate the true knockdown efficiency. This is because the qPCR includes mRNA from both RNAi-expressing and non-expressing cells, diluting the apparent reduction in transcript levels.

To address this limitation and more accurately assess RNAi efficiency, we repeated the qPCR analysis using a ubiquitous driver (actin-Gal4) to ensure uniform expression of the RNAi construct. Under these conditions, we observed a more substantial knockdown, with dPGC1 mRNA levels reduced to approximately 25% of control levels (this is shown in current Fig S2). This result indicates that the RNAi line is more effective than initially suggested by the MS1096-Gal4-based analysis.

To complement our RNAi-based analysis, we additionally used a mutant strain carrying a characterized allele of dPGC1 (dPGC11, also known as dPGC1KG08646; see FlyBase: https://flybase.org/reports/FBal0148128). This genetically distinct approach allowed us to validate and strengthen our findings regarding dPGC1 function. Flies homozygous for this allele exhibited a modest but statistically significant reduction in both wing disc and adult wing size. These results support the conclusion that dPGC1 is required for normal wing growth and development. The new data are now included in Figure 1 and referenced in the main text (lines 144-153).

Additionally, as suggested by the reviewer, we have revised the relevant section to maintain a coherent line of inquiry. The updated text can be found in lines 163–172.

In Figure 3H-K, it is not clear why the authors used electron microscopy to evaluate mitochondrial morphology. The very good confocal images in Figure 3C-G show a clear change in mitochondrial morphology following the knockdown of Mfn, Opa1, and Miro. While it is clear from the electron micrographs in Figure H that the mitochondria are enlarged, it is not obvious that this increase in length is a result of increased mitochondrial fusion. Indeed, if the mean form factor were used to quantify the shape, it is likely that in both conditions, the value would be close to 1, indicating more of a round object, and it not obvious whether there would be a difference between the Yki OE versus the YkI OE + dPGC1 RNAi. Therefore, from this data alone, it cannot be concluded that the YkI OE + dPGC1 RNAi condition leads to mitochondrial hyperfusion.

Our rationale for including electron microscopy (EM) was to overcome specific limitations in imaging mitochondrial morphology within the main epithelium of the wing disc, where Yki-driven tumors arise. These tumors were generated using ap-Gal4, which drives expression specifically in the main epithelium and is not active in the peripodial membrane. This is an important distinction, as the peripodial membrane—used in Figures 3C–G—has a squamous architecture and larger cytoplasmic volume, making it ideal for high-resolution confocal imaging and for assessing the effects of manipulating dMfn, Opa1, and miro. However, because ap-Gal4 is not expressed in the peripodial membrane, this tissue could not be used to analyze mitochondrial morphology in the actual tumorous context.

To directly evaluate mitochondria in the main epithelium, we employed EM, which provides the resolution necessary to visualize ultrastructural changes that are not easily captured by confocal microscopy in this densely packed tissue. While EM does not directly measure fusion events, it allowed us to detect changes in mitochondrial size and shape that support our broader findings.

We acknowledge that mitochondrial enlargement alone does not definitively demonstrate hyperfusion. However, the EM data were interpreted alongside additional evidence: the upregulation of mitochondrial fusion genes (dMfn and Opa1) in Yki + dPGC1-RNAi tumors, and functional data showing that overexpression of these genes promotes fusion in the peripodial membrane. Together, these findings suggest that dPGC1 depletion enhances mitochondrial fusion in Yki-driven tumors.

To further clarify this point, we also imaged mitochondria in the main epithelium using confocal microscopy. However, the resolution was considerably lower than that achieved with EM, limiting our ability to assess fine mitochondrial structures. We have prepared a representative figure for the reviewer (below), showing representative confocal images of wing discs from three genotypes: (A) ap-Gal4, UAS-GFP (control), (B) ap-Gal4, UAS-Yki, and (C) ap-Gal4, UAS-Yki, UAS-dPGC1-RNAi. We used anti-ATP-synthase (Abcam, ab14748, dilution 1:200), to label the mitochondria for this Figure. Despite the lower resolution, mitochondria in the Yki + dPGC1-RNAi tumors appear elongated (yellow arrows) compared to those in the other conditions, consistent with the changes observed by EM. We believe this example illustrates the limitations of confocal imaging in this tissue and reinforces the need for EM to accurately assess mitochondrial morphology in the tumorous epithelium.

While our EM analyses reveal mitochondrial enlargement in wing discs co-expressing Yki and PGC1-RNAi, we acknowledge that these structural features alone do not conclusively demonstrate mitochondrial hyperfusion. To address this, we have revised the manuscript to avoid overinterpreting the EM data and instead emphasize the functional relevance of mitochondrial fusion regulators such as dMfn and Opa1 in promoting tumor growth.

Taken together, the EM analysis provides structural validation in the tumorous epithelium (Fig 4), while the confocal imaging and functional manipulation of fusion genes in the peripodial membrane offer mechanistic insight (Fig 3). This integrated approach strengthens the conclusion that PGC1 depletion in a Yki-overexpressing context promotes changes in mitochondrial morphology and contributes to tumorigenesis, independent of whether these changes reflect hyperfusion.

Figure 4. refers to changes in mitochondrial fusion and fission in tumor formation; however, the authors do not attempt to alter mitochondrial fission factors, so it is not accurate to mention a role of mitochondrial fission, in this context.

As we did not directly manipulate fission-related factors in our experiments, we agree that it would be inappropriate to draw conclusions about the role of mitochondrial fission in this context. Our revised figure (current Fig 5) and accompanying text now focus exclusively on the effects of mitochondrial fusion and the genes directly involved in regulating this process.

It must be noted, too, that the authors have not demonstrated that their genetic interventions have actually affected mitochondrial morphology in these experiments. As noted in the previous figure, the Yki OE + dPGC1 RNAi condition showed enlarged mitochondria, but not necessarily hyperfused organelles. Therefore, the downregulation of Mfn or Opa1 in this set of experiments may not necessarily have altered mitochondrial morphology. Perhaps suppression of Mfn or Opa1 would normalize the areas of these evidently swollen mitochondria, but this is unclear without images. Furthermore, it should be appreciated that both Opa1 and Mfn exhibit pleiotropic attributes - e.g., Opa1 not only regulates IMM fusion, but it also modulates the shape and tightness of cristae membranes, specialized sites of oxidative phosphorylation as well as sequestration of cytochrome c, the release of which influences apoptosis (Frezza et al., 2006). At least in mammalian cells, Mfn2 is thought to regulate contacts between mitochondria and endoplasmic reticulum (Naon et al., 2023), which may serve other functions than OMM fusion, such as stabilization of the MAM.

To directly address this point, we performed EM to assess mitochondrial ultrastructure in Yki + dPGC1-RNAi wing disc tumors, with and without dMfn1 downregulation, the most upregulated mitochondrial fusion gene in this tumor context. In Yki + dPGC1-RNAi tumors, mitochondria appeared more elongated, consistent with increased fusion. Upon dMfn1 depletion, we observed a dramatic shift in mitochondrial morphology: mitochondria became larger and more rounded, with disrupted cristae and onion-like structures, indicative of compromised mitochondrial integrity and function (see current Fig. 4).

As the reviewer rightly notes, these morphological changes are consistent with the pleiotropic roles of Mfn and Opa1, which extend beyond outer and inner membrane fusion to include regulation of cristae architecture and ER-mitochondria contacts (Frezza et al., 2006; Naon et al., 2023). We now discuss these broader roles in the revised manuscript (lines 493–497). Taken together, our EM and confocal analyses, combined with targeted genetic manipulations, provide evidence that mitochondrial morphology is indeed altered in response to dPGC1 depletion and fusion gene deregulation in the wing disc.

Figure 5 highlights a connection between dysregulation of mitochondria and Cyclin E, which allows cells to prematurely enter S phase. The data presented here do not offer clarity on whether the enlargement of the tumors results from increase cellular proliferation and/or cell size. The role of the cell cycle adds a layer of complexity to these results, because it is thought that mitochondria undergo fragmentation during the cell cycle to promote an even distribution of the organelle population after mitosis (Taguchi et al., 2007); however, in this manuscript, the authors contend that the downregulation dPGC1 is promoting mitochondrial hyperfusion. It is unclear how and whether cellular division and proliferation would proceed at an accelerated rate in a situation with mitochondrial hyperfusion.

To address this point, we started by analyzing whether Yki + dPGC1-RNAi tumors exhibit increased proliferation compared to tumors expressing Yki alone. We quantified mitotic activity using the phospho-Histone H3 (PH3) marker of mitotic cells and observed a significant increase in PH3-positive cells in the Yki + dPGC1-RNAi condition. These results indicate an elevated proliferation rate in these tumors and are now presented in Fig 2O–Q. In the text, can be found in lines 221-228.

We agree with the reviewer that our findings challenge the conventional view that mitochondrial fragmentation is a prerequisite for mitosis, as we observe increased expression of gene promoting mitochondrial fusion in the context of dPGC1 downregulation alongside signs of accelerated cell cycle entry. It is important to note that we also show that the levels of the oncogene Cyclin E, a key driver of cell cycle progression and S-phase entry, were elevated in Yki + dPGC1-RNAi tumors compared to those expressing Yki alone, suggesting that the increased proliferation observed is at least in part driven by enhanced cycle activity. To further probe Cyclin E’s role, we used the CycE-05306 heterozygous mutant allele, which reduces Cyclin E levels by ~50% without affecting normal development. Notably, this partial reduction strongly suppressed tumor growth in the Yki + dPGC1-RNAi background (Fig 6), underscoring Cyclin E’s functional importance in supporting oncogenic growth in this context.

These findings support the notion that defects in the expression of mitochondrial genes involved in mitochondrial morphology induced by dPGC1 depletion do not impair but rather coincide with accelerated cell division.

Minor comments:

Lines 69-72 contrast the roles of PGC1α and β. It is not clear whether the comparison is of their respective roles in cancer or in normal physiology. In either case, it is important to note that PGC1β has been shown to drive mitochondrial fusion as well as biogenesis through its control of MFN2, among other factors (Liesa et al., 2008).

In response, we have clarified the comparison between PGC1α and PGC1β in the introduction to specify that it refers to their roles in cancer. Additionally, we now acknowledge that PGC1β has been shown to promote mitochondrial biogenesis and fusion, notably through the regulation of MFN2, as demonstrated by Liesa et al. (2008). This reference has been added to provide a more balanced and accurate representation of PGC1β’s functions. In the text it can be found in lines 77-81.

Although this study focuses on PGC1, the authors do not seem to site the original literature from the Spiegelman lab.

In response to the reviewer’s comment, we have added a new section in the introduction that cites key foundational studies from the Spiegelman lab. This addition can be found in the introduction in lines 68-73.

There are 10-20 grammatical errors throughout the text.

We apologize for this. We have carefully revised the text, and we are very confident those errors have been corrected.

**Referee Cross-commenting**

There is agreement among the referees that the potential role of PGC1 as a tumor suppressor is interesting and significant. However, various aspects of this work require attention prior to publication. For example, there needs to be a complete knock down of PGC1 to come to any conclusion as to its role in wing development. The methods for analyzing mitochondrial morphology need to be clarified and be consistent with standards in the field of mitochondrial dynamics. Also, the authors need to quantify their Western blots to obtain accurate assessments of protein levels. Generally, the study relies too heavily on overexpression experiments; understanding the potential role of mitochondria in regulating the Hippo pathway should include various knockdown and/or knockout models.

Reviewer #1 (Significance (Required)):

Overall, the authors show an interesting dampening effect of dPGC1 on growth of Yki-driven tumors. This data could be relevant for elucidating how dysregulation of the Hippo signalling pathway can underlie tumorigenesis.

The narrative arc of the study, however, appears to lack a focused line of inquiry. Figure 1 highlights an attempt to modulate Drosophila wing size and/or structure by downregulating dPGC1, but to no effect. Although examination of the efficiency of the RNAi revealed that the transcripts were still present in significant quantities; so, the conclusion that dPGC1 is dispensable for wing formation is premature. To have clarity on this point, it would be necessary to completely knockdown the gene, preferably by showing a total loss of protein. This should be feasible for the authors, since they showed Western blotting in Figure 5A. In any event, it seems that this negative data led the authors to study the Hippo pathway in the larval stage. This transition from Figure 1 to 2 seemed somewhat arbitrary and leads to a rather disjointed sense of the main line of inquiry around dPGC1.

It is important to note, too, that the authors highlight a role of mitochondrial dynamics in the pathway of Yki-driven tumor formation; however, they only directly evaluate mitochondrial dynamics in this context in a single assay, namely, Figure 3H-K, and this quantification is likely inaccurate because the mitochondria in the Yki OE + dPGC1 RNAi condition seem to be substantially enlarged, circular structures. It is critical to keep in mind that mitochondrial enlargement does not necessarily stem from hyperfusion. It could come from a decrease in the activity of Drp1 or result from an imbalance between mitochondrial biogenesis and mitophagy.

As noted in our responses above, we have addressed these concerns by clarifying the limitations of our mitochondrial morphology analysis. Additionally, we have expanded the discussion (lines 498-504) to explicitly acknowledge that mitochondrial enlargement does not necessarily indicate hyperfusion. In that paragraph, we consider alternative explanations such as reduced fission or imbalances in mitochondrial biogenesis and mitophagy, and we outline the need for future studies using dynamic assays and additional markers to more precisely dissect mitochondrial remodeling in Yki-driven tumors.

A marked limitation of this study is the overuse of rather artificial manipulations of transcriptional regulatory pathways. The study would benefit a lot from investigation of the loss of function of components of the Hippo pathway rather than just OE of Yki.

We performed additional experiments using Warts (Wts) mutant clones to assess the role of dPGC1 in a loss-of-function context within the Hippo pathway. While our initial analyses were based on Yki overexpression, which allowed us to robustly probe the interaction between Yki and dPGC1, we agree that this approach may not fully reflect physiological conditions. By generating Wts mutant clones, which endogenously activate Yki through loss of upstream inhibition, we were able to evaluate the impact of dPGC1 depletion in a more physiologically relevant setting. These new results confirm and extend our previous findings, showing that dPGC1 limits tissue overgrowth even when Yki is activated through loss of Wts, thereby strengthening the biological relevance of our conclusions.

These results are presented in Fig 2F-I. In the text, those results are presented in lines 181-189.

My expertise is in mitochondrial biology, with specialization in super-resolution imaging, mitochondrial dynamics and membrane architecture. I have also worked in the interface between mitochondrial physiology and cancer. With this perspective, I think that the authors uncover a potentially interesting role of PGC1 as a tumor suppressor.

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

Summary In this manuscript the authors the investigate the role of the mitochondrial regulatory transcription factor dPGC1 in tissue growth and oncogenic transformation. They show that dPGC1 limits hyperplasia mediated by overexpression of Yki in the Drosophila wing disc, while having no effect on normal growth. dPGC1 depletion in discs overexpressing Yki results neoplastic overgrowth and hyperfused mitochondria, which was dependent on the increased expression of genes involved in promoting mitochondrial fusion. Additionally, the authors show that dPGC1 limits CycE levels post-transcriptionally in Yki tumors.

In the revised version of our manuscript, we have clarified the relationship between our findings and prior work by Nagaraj et al., including new experiments that demonstrate the specificity of dPGC1’s role in Yki-driven growth. Specifically, we show that dPGC1 depletion does not enhance tissue overgrowth in EGFR or InR contexts, nor does it affect Yki expression or activity. Furthermore, we tested dPGC1 overexpression in Yki-overexpressing tissues and observed no significant changes in growth or mitochondrial fusion gene expression. Additional controls confirmed that Cyclin E upregulation is specific to the Yki + dPGC1 depletion condition, reinforcing the context-dependent nature of our findings.

Each of the reviewer’s comments is addressed below.

Major comments 1) The authors mention several times in passing in the results a manuscript from the Banerjee lab (Nagaraj et al 2012), which shows that many of the genes the authors of the present manuscript show are upregulated upon Yki overexpression + dPGC1-RNAi compared with Yki overexpression alone are in fact upregulated upon Yki overexpression alone compared with control (dMfn/marf, opa1, miro - while interestingly dPGC1 itself is not affected). Nagaraj et al further show that Yki-overexpressing discs have longer mitochondria suggesting increased fusion even in the absence of dPGC1 depletion. The findings from Nagaraj et al should be mentioned explicitly in the introduction and the relationship between this manuscript and the present work clearly outlined in the discussion.

In the revised manuscript, we have now explicitly referenced the findings of Nagaraj et al. (2012) in the Introduction (lines 106-118), Results (lines 355-360) and Discussion (lines 466-468) sections.

In the revised Introduction, we summarize their key observations that Yki overexpression alone upregulates mitochondrial fusion genes such as dMfn and Opa1, and leads to mitochondrial elongation, while not affecting dPGC1 expression.

In the revised Results section, we mention that, building on that work, our study demonstrates that dPGC1 depletion further amplifies this effect, leading to enhanced mitochondrial elongation and tumor growth.

In the revised Discussion, we now explicitly reference the findings by Nagaraj et al. (2012), which demonstrated that Yki overexpression promotes mitochondrial fusion and upregulates key fusion genes. We build upon this work by showing that dPGC1 depletion in a Yki-overexpressing background further enhances mitochondrial fusion gene expression and tumor growth. This supports a model in which dPGC1 acts as a safeguard against Yki-induced mitochondrial remodeling and oncogenesis, reinforcing its role as a context-dependent tumor suppressor.

Importantly, we show that this effect is context-dependent and not observed in otherwise normal tissues, highlighting a sensitized mitochondrial response to Yki activation when dPGC1 is lost. These additions help delineate the novel contribution of our study in identifying dPGC1 as a critical modulator of mitochondrial dynamics and tumorigenesis downstream of Yki.

2) Given that Yki overexpression alone induces mitochondrial fusion and that dMfn/marf and opa1 depletion suppresses Yki-induced overgrowth (Nagaraj et al), does dPGC1 overexpression also suppress Yki-induced overgrowth?

If so, is this correlated with reduction in dMfn/marf and opa1 compared with Yki overexpression alone?

In response, we performed additional experiments to assess whether dPGC1 overexpression influences Yki-driven overgrowth. We also analyzed the expression of mitochondrial fusion genes (dMfn and Opa1) in this context. As shown in new Fig. S8, dPGC1 overexpression in Yki-overexpressing wing discs did not significantly affect tissue growth, nor did it alter the mRNA levels of key fusion regulators, dMfn and Opa1. These findings suggest that the transcriptional upregulation of mitochondrial fusion genes observed upon dPGC1 depletion is not a general consequence of altered dPGC1 levels, but rather a specific response that emerges in the context of Yki activation. We now present and discuss these results in the revised manuscript (lines 278-285), highlighting the sensitized nature of mitochondrial remodeling in an oncogenic environment driven by Yki signaling.

3) One important question raised by this study is: how specific is the effect of dPGC1 depletion to Yki-driven overgrowth? As Yki-driven overgrowth already have increased mitochondrial length, it is possible that Yki-expressing cells are already sensitised to the effects of dPGC1 depletion. Interestingly, Nagaraj et al show that mitochondrial morphology is not affected upon EGFR activation (hyperplasia) or upon scrib and avl depletion (neoplasia). The authors should therefore test if dPGC1 depletion can potentiate the growth of other hyperplasia drivers such as activated EGFR and InR in the wing disc.

We tested whether the growth-suppressive effect of dPGC1 depletion was specific to Yki-driven overgrowth or could also potentiate tissue growth in other oncogenic contexts. Specifically, we downregulated dPGC1 in wing discs overexpressing either EGFR or InR. In both cases, we did not observe any enhancement of tissue overgrowth upon dPGC1 depletion, in contrast to what we observed in Yki-overexpressing discs. These results suggest that the sensitivity to dPGC1 depletion is specific to Yki-driven overgrowth and is not a general feature of hyperplastic growth induced by other oncogenes.

These results are shown in Fig S4 and in lines 195-202.

4) There are a few simple control experiments the authors should provide to clarify the relationship between Yki and dPGC1: - Are Yki levels affected by dPGC1 depletion?

To address the potential regulation of Yki by dPGC1, we performed quantitative PCR (qPCR) analysis to measure the expression levels of yki and its well-established transcriptional targets—Cyclin E, Diap1, and bantam—in wing discs depleted of dPGC1. As shown in Fig. S3, we did not detect significant changes in the transcript levels of yki or its target genes, suggesting that the enhanced phenotype observed upon dPGC1 depletion is unlikely to be driven by increased Yki expression or activity. These results indicate that dPGC1 does not strongly influence Yki expression or activity. These new results are presented in lines 190-194.

• Does dPGC1 knockdown alone modify the expression of the genes tested in Fig.3A? In other words, is this upregulation specific of the Yki-overexpression context?

We have conducted this analysis, and the results are now presented in new Fig S7. While the trend is similar to that observed in tumors with both Yki depletion and dPGC1 depletion, the magnitude of change is smaller compared to the context of Yki overexpression. This is described in the text in lines 273-277.

• Does dPCG1 knockdown also stabilise CycE in the absence of Yki overexpression or does the stabilisation of CycE occur only in Yki tumors?

To address this, we examined Cyclin E levels in wing imaginal discs mutant for dPGC1 alone. Our analysis did not reveal any detectable changes in Cyclin E levels under these conditions. These findings suggest that the upregulation of Cyclin E is not a general consequence of dPGC1 loss, but rather a feature specific to the context of Yki overactivation. The corresponding data are now included in Fig S14 of the revised manuscript. In the text, it can be found in lines 442-448.

5) Figure 3C-G: it is not clear how the authors can quantify the length of 3D structures like mitochondria from 2D TEM images (unless they have done volume reconstruction from consecutive sections) and no details are provided in the methods. The quantification of mitochondrial length has to be performed rigorously as it is a key part of the paper.

We agree that TEM provides only 2D profiles of 3D mitochondrial structures, and that this does not allow for precise volumetric reconstruction. In our study, we measured the longest axis of mitochondria visible in thin TEM sections, which is a commonly used 2D proxy for mitochondrial length in the literature (e.g., PMID: 36367943 and PMID: 38637532). To avoid misunderstandings, we have clarified in the Material and Methods section that the reported values represent apparent mitochondrial length in 2D sections, not true 3D length. To enhance the accuracy of these estimates, we measured more than three tissues per genotype, multiple regions per tissue, several cells per region, and various fields of view per cell.

Minor Comments:

1) Line 51: "Mitochondria are highly dynamics organelles." should be "Mitochondria are highly dynamic organelles."

We have corrected that mistake. Thanks!

2) Introduction: the authors should summarise the known physiological functions of PGC1α in order to put their findings in context.

We have added a section in the introduction (lines 66-81) summarizing the known physiological functions of PGC1α

3) lines: 121-3: "...depletion of dPGC1...did not have a major impact on adult wing size and shape (Fig 1B, C)." There is a small but statistically significant difference so the authors should state this in the text.

We have revised the text to acknowledge that dPGC1 depletion leads to a modest but statistically significant reduction in wing size. In addition to the original analysis, we have now included further experiments to strengthen this point. Specifically, we analyzed wings from flies homozygous for the dPGC11 allele (also known as dPGC1KG08646; see FlyBase: https://flybase.org/reports/FBal0148128) and confirmed a small but significant reduction in both wing disc and adult wing size compared to controls (this can be found in Fig. 1 and Fig. S1). These results support the conclusion that, although dPGC1 is dispensable for viability and gross morphology, it contributes to normal wing growth. These new results can be found in lines 144-153.

4) Figure 5A (Cyclin E western blot): the authors should show molecular weight markers. In the revised version of our manuscript, we are including the molecular markers as indicated by the reviewer. These can be found in Fig S12.

Reviewer #2 (Significance (Required)):

The manuscript by Sew et al builds on the previous work by Nagaraj et al to explore the role of mitochondrial function in tumors driven by disruption of the Hippo pathway. In particular, the authors identify dPGC1 as a transcription factor that limits Yki-driven mitochondrial fusion and tissue growth. Interestingly, they further show that Yki/PGC1-depleted tumors are highly sensitive to Cyclin E levels, due to post-transcriptional Cyclin E increase. These results further our knowledge of how Yki drives growth and how mitochondria participate in oncogenic transformation. With appropriate revision as outlined above (for example exploring whether the mechanism proposed is Yki-specific), the manuscript will be of broad interest to developmental and cancer biologists.

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

The manuscript presents compelling evidence that dPGC1 acts as a context-dependent tumor suppressor in Drosophila by modulating mitochondrial dynamics and limiting Yorkie (Yki)-induced oncogenic growth. By leveraging the Drosophila wing imaginal disc as a model, the authors investigate how dPGC1 depletion exacerbates Yki-driven tissue overgrowth, mitochondrial hyperfusion, Cyclin E upregulation, and DNA damage, leading to tumorigenesis. The study provides valuable insights into the interplay between mitochondrial dynamics and cancer, with implications for understanding metabolic regulation in oncogenesis. While the findings are significant and well-aligned with the field, certain aspects of the experimental design, data presentation, and mechanistic insights require further attention to enhance clarity, reproducibility, and impact. Below, I outline my major concerns and recommendations.

We addressed concerns about RNAi efficiency and protein-level validation with new qPCR data and mutant analysis. We provided EM and confocal evidence of mitochondrial changes. We clarified non-autonomous effects and quantified Mmp1 and F-actin and added data on miro and Opa1 manipulations. Cyclin E quantification was expanded using multiple Western replicates and a validated mutant allele, and we included new data on mitochondrial membrane potential to assess functional consequences.

Our detailed responses to each point raised by the reviewer are provided below.

Major Points

1. One point is the knock-down efficiency of dPGC1 on the mRNA level, which is between 30 to >50% (Fig. S1C). This is not too strong, so the question arises how severly the protein levels are affected. If possible, an antibody staining with quantification should be performed. From these data it cannot be concluded dPGC1 is not required for normal development, half the dose could be sufficient. How do wings look like when the ap-GAL4 driver is used for dPGC1 knock-down, as this is the driver used in the subsequent experiments? Reviewer 1 also raised concerns about the potential inefficiency of the RNAi treatment in revealing a function during normal wing growth. We agree with both reviewers that the interpretation of the RNAi efficiency data requires clarification.

The qPCR analysis shown in former Fig. S1C was performed using wing discs from flies expressing UAS-dPGC1-RNAi under the control of the MS1096-Gal4 driver. However, as shown in current Fig. 1C, MS1096-Gal4 is not expressed uniformly across the wing disc. Some regions remain RFP-negative, indicating that the RNAi construct is not active in all cells. As a result, the measured mRNA levels likely underestimate the true knockdown efficiency. This is because the qPCR includes mRNA from both RNAi-expressing and non-expressing cells, diluting the apparent reduction in transcript levels.

To address this limitation and more accurately assess RNAi efficiency, we repeated the qPCR analysis using a ubiquitous driver (actin-Gal4) to ensure uniform expression of the RNAi construct. Under these conditions, we observed a more substantial knockdown, with dPGC1 mRNA levels reduced to approximately 25% of control levels (this is shown in current Fig S2). This result indicates that the RNAi line is more effective than initially suggested by the MS1096-Gal4-based analysis.

To complement our RNAi-based analysis, we additionally used a mutant strain carrying a characterized allele of dPGC1 (dPGC11, also known as dPGC1KG08646; see FlyBase: https://flybase.org/reports/FBal0148128). This genetically distinct approach allowed us to validate and strengthen our findings regarding dPGC1 function. Flies homozygous for this allele exhibited a modest but statistically significant reduction in both wing disc and adult wing size. These results support the conclusion that dPGC1 is required for normal wing growth and development. The new data are now included in Figure 1 and referenced in the main text (lines 144-151).

Unfortunately, we cannot perform antibody staining due to the unavailability of antibodies against dPGC1.

How does the wing disc look like when dPGC1 is overepressed together with Yki?

In response, we performed additional experiments to assess whether dPGC1 overexpression influences Yki-driven overgrowth. We also analyzed the expression of mitochondrial fusion genes (dMfn and Opa1) in this context. As shown in new Fig. S8, dPGC1 overexpression in Yki-overexpressing wing discs did not significantly affect tissue growth, nor did it alter the mRNA levels of key fusion regulators, dMfn and Opa1. These findings suggest that the transcriptional upregulation of mitochondrial fusion genes observed upon dPGC1 depletion is not a general consequence of altered dPGC1 levels, but rather a specific response that emerges in the context of Yki activation. We now present and discuss these results in the revised manuscript (lines 278-285), highlighting the sensitized nature of mitochondrial remodeling in an oncogenic environment driven by Yki signaling.

In Fig 2D (but also in Fig. 2C) not only cells in the dorsal but also in the ventral comparmtent seem to overproliferate. Either this is a mis-conception or it is a non-autonomous effect from interfering with Yki and dPGC1 in the vertrnal compartment. In either cases, this has to be clarified.

Ventral cells are not labelled by GFP. Fig 3D shows a tumor in which GFP-negative cells are not present, suggesting that they are not overproliferating but rather being eliminated. This phenomenon is consistent with cell competition, a well-characterized process in which transformed or tumorigenic cells outcompete and eliminate neighboring wild-type cells. We have previously described this behavior in wing disc tumors (PMID: 26853367; DOI: 10.1016/j.cub.2015.12.042), and it likely contributes to the expansion of the tumor mass by removing surrounding normal tissue also in this context.

In Fig. 2F-H quantification of Mmp1 and F-actin is missing. Mmp1 is a JNK target, so the authors could do in addition an anti-phospho JNK antibody staining.

In response, we have performed those quantifications. They are now included in Fig 2M, N.

In Fig. 3: how does the mitochondrial network look like in the wing disc periopodial epithelium using the Gug>Yki+dPGC1 genotype? Is it similar to Gug>dMfn or Gug>miro?

We attempted to perform this analysis; however, Yki overexpression under the control of Gug-GAL4 resulted in larval lethality, likely due to GAL4 activity in essential tissues such as the central nervous system. As a result, we were only able to induce transgene expression for 24 hours before lethality occurred.

At this early point, no detectable changes in mitochondrial morphology were observed in the peripodial membrane, likely because the duration of transgene expression was insufficient to elicit phenotypic alterations in this specific tissue. Therefore, while we aimed to compare this genotype to Gug>dMfn and Gug>miro, the technical limitations prevented a conclusive analysis.

We have prepared a representative figure for the reviewer (below), showing representative confocal images of wing discs showing mito-GFP and Dapi in the three genotypes indicated in the Fig.

In Fig. 3I: what is really the mitochondrion? It would be good to outline the region(s) that was/were measured.

To improve clarity, we have repeated the electron microscopy (EM) analysis and now provide representative images that more clearly illustrate mitochondrial morphology in the different genotypes analyzed. These updated images presented in Fig 4 better highlight the structural alterations observed upon genetic manipulation and help clarify the basis for our morphological assessments.

We have extended our analysis and have assessed mitochondrial ultrastructure in Yki + dPGC1-RNAi wing disc tumors, with and without dMfn1 downregulation—the most upregulated mitochondrial fusion gene in this tumor context. In Yki + dPGC1-RNAi tumors, mitochondria appeared more elongated, consistent with increased fusion. Upon dMfn1 depletion, we observed a dramatic shift in mitochondrial morphology: mitochondria became larger and more rounded, with disrupted cristae and onion-like structures, indicative of compromised mitochondrial integrity and function (see new Fig 4).

A quantification of RNAi and overexpression efficiencies of the different transgenes in Fig. 3 is required.

To assess the efficiency of RNAi-mediated knockdown and transgene overexpression, we performed quantitative PCR (qPCR) using the ubiquitous Actin-Gal4 driver. While we acknowledge that this driver does not replicate the spatial specificity of the periodic membrane Gal4 driver used in the experiments shown in Figure 3 (Gug-Gal4), the latter targets a very limited number of cells within the imaginal disc, making reliable qPCR quantification unfeasible.

Using Actin-Gal4 allows us to obtain a relative and informative measure of transgene efficiency across the different constructs. These data confirm effective knockdown and overexpression of the relevant genes and are now included in Figure S2.

In Fig. 4: what is the phenotype when miro is over-expressed in combination with Yki? Or when it is knocked down in the ap>Yki-dPGC1 background? This was the gene tested in Fig. 3 with a clear mitochondrial phenotype

To address whether miro contributes to Yki-mediated tumor growth, we performed the requested experiments and now include the results in the revised manuscript (see updated Results section, lines 374-377, and new Fig. S11).

Our data show that overexpression of miro in combination with Yki does not lead to a significant increase in tissue growth or tumor-like phenotypes, in contrast to the effects observed with dMfn or Opa1 overexpression. Similarly, knockdown of miro in the ap>Yki-dPGC1-RNAi background did not suppress tumor growth, indicating that miro is not required for the enhanced proliferation observed in this context.

These findings suggest that, although miro influences mitochondrial morphology in normal wing discs (as shown in Fig. 3), its role in tumorigenesis is distinct from that of dMfn and Opa1. We have revised the manuscript to clarify the gene-specific contributions of mitochondrial fusion regulators to Yki-driven tumorigenesis. This distinction underscores the complexity of mitochondrial dynamics and highlights that not all fusion-related genes exert the same functional impact in oncogenic settings.

How does the mitochondrial morphology in the wing disc peripodial epithelium look like in Gug>Opa1RNAi or Gug>Opa1 discs?

To assess the impact of Opa1 on mitochondrial morphology in the peripodial epithelium of the wing disc, we used the Gug-GAL4 driver to either overexpress or knock down Opa1. Our analysis revealed that Opa1 overexpression led to slightly elongated mitochondria, but did not result in extensive network formation, suggesting a modest enhancement of inner membrane fusion. In contrast, Opa1 knockdown caused clear mitochondrial fragmentation, closely resembling the phenotype observed upon dMfn depletion. These results shown in Fig 3 are consistent with the distinct roles of Opa1 and dMfn in regulating mitochondrial fusion: Opa1 primarily modulates inner membrane fusion and cristae architecture, while dMfn drives outer membrane fusion and network connectivity.

The corresponding data are presented in Figure 3F, G, and quantified in Figure S9, alongside experiments manipulating other genes involved in mitochondrial dynamics.

Why have the authors switched between the ap>Yki+dPGCRNAi and the ap>Yki+dPGC1shRNA lines? It would be important to have this series of experiments in the same backgrounds, as KD efficiencies are different (Fig. S1C).

The primary reason for switching between the dPGC1-RNAi and dPGC1-shRNA lines was practical: the chromosomal insertion sites of the transgenes made certain genetic combinations more feasible with one line over the other. This flexibility significantly facilitated our experimental design and analysis.

To address concerns regarding knockdown efficiency, we performed a comparative analysis using the ubiquitous actin-GAL4 driver, rather than MS1096-GAL4, which exhibits patchy and dynamic expression in the wing imaginal disc. This allowed us to obtain a more consistent and interpretable measure of mRNA downregulation for both transgenes. Our results show that both lines achieve comparable levels of knockdown, as shown in Figure S2.

Fig. 5A: proper quantification of Western Blot signals is required. I do not agree that Cyclin E protein levels are elevated in ap>Yki or ap>Yki+dPGC1 discs. Even at the mRNA levels the increase in expression is rather weak. From these results nothing can be concluded.

We have repeated the Western blot analysis using seven independent membranes to ensure robust quantification of Cyclin E levels in ap>Yki and ap>Yki+dPGC1-RNAi wing discs (Fig 6).

Although the increase in Cyclin E protein levels is subtle, it is consistent across replicates and statistically significant. We have now included the quantification of these Western blot signals in the revised Figure 6, which supports the conclusion that Cyclin E levels are elevated in ap>Yki+dPGC1 discs.

We hope this additional data addresses the reviewer’s concern and strengthens the interpretation of our results.

Knock-down efficiencies for dap and CycE needs to be quantifiec (Fig. 5H-N). Although the rescue experiment with CycE knock down is from the phenotype convincing, it is nonetheless puzzling, as CycE is accodring to Fig. 5A+B hardly upregulated. An independent CycE RNAi line would be useful.

We have quantified the knockdown efficiency of the dap-RNAi line, and the results are included in Figure S13.

Regarding Cyclin E, we would like to clarify that we did not use an RNAi line in this experiment. Instead, we employed the CycE-05306 mutant allele in a heterozygous background, which is expected to reduce Cyclin E levels by approximately 50%. The CycE-05306 allele in Drosophila melanogaster is a loss-of-function allele of the Cyclin E gene. This allele carries a P-element insertion in the first intron of the CycE gene, which disrupts normal transcription and reduces Cyclin E expression. In a heterozygous background, as used in your experiments, CycE-05306/+ is expected to reduce Cyclin E levels by approximately 50%, which is typically sufficient to observe genetic interactions or sensitized phenotypes without affecting normal development. This makes it a valuable tool for studying gene dosage effects, particularly in tumor models where Cyclin E activity may be rate-limiting.

Importantly, this partial reduction does not impair normal tissue growth, but it strongly limits tumor growth in the context of Yki overexpression combined with dPGC1 downregulation, as shown in Figure 6. This selective sensitivity highlights the functional importance of Cyclin E in supporting oncogenic growth driven by Yki and dPGC1 depletion. We believe this provides compelling evidence for Cyclin E’s role in this tumor model.

Reviewer #3 (Significance (Required)):

Strengths and Limitations of the Study Strengths Innovative Focus on Mitochondrial Dynamics and Oncogenesis: The study provides compelling evidence linking mitochondrial dynamics, particularly hyperfusion, to tumorigenesis in Drosophila. The identification of dPGC1 as a context-dependent tumor suppressor adds novel insights into the interplay between metabolism and oncogenesis. Comprehensive Use of Drosophila as a Model System: The study leverages the genetic tractability of Drosophila, allowing precise manipulation of mitochondrial regulators and signaling pathways. The use of wing imaginal discs as a model for tumor growth is well-established and appropriate. Integration of Morphological and Genetic Data: The manuscript combines confocal imaging, electron microscopy, and genetic tools to demonstrate the role of dPGC1 in regulating mitochondrial dynamics, Cyclin E levels, and tissue overgrowth. Relevance to Cancer Biology: The findings address key hallmarks of cancer, including deregulated metabolism, genomic instability, and cell cycle misregulation. The study's exploration of these processes in a simple model organism provides a strong basis for translating findings to mammalian systems.

Limitations Validation of RNAi and Overexpression Efficiency: The knockdown efficiency of dPGC1 on the mRNA level is only moderate (30-50%), and protein-level validation is missing. Without this, the study cannot conclusively demonstrate the role of dPGC1 in normal development or tumorigenesis. Incomplete Mechanistic Insights: The manuscript identifies Cyclin E as a potential driver of tumor growth but does not adequately explore how mitochondrial hyperfusion leads to Cyclin E regulation (e.g., post-transcriptional mechanisms or protein stability). Inconsistencies in Experimental Backgrounds: The study uses different RNAi/shRNA lines and driver combinations inconsistently across experiments, making it difficult to compare results directly. This variability undermines the robustness of the conclusions. Limited Functional Analysis of Mitochondria: While mitochondrial morphology is well-characterized, functional assays (e.g., membrane potential or ATP production) are missing. These would confirm the impact of hyperfusion on cellular energetics and oncogenesis.

In the revised manuscript, we have addressed each of the concerns raised.

In addition to that, in the revised version of the manuscript, we have included new experiments to assess mitochondrial functionality in tumors co-expressing Yki and dPGC1-RNAi. Specifically, we analyzed the Mitochondrial Membrane Potential (MMP). We used TMRE staining to evaluate MMP, a key indicator of mitochondrial integrity and oxidative phosphorylation capacity. Our analysis revealed no significant differences in MMP between Yki tumors and Yki + dPGC1-RNAi tumors, suggesting that mitochondrial membrane potential is preserved despite the observed morphological abnormalities. These results are shown in Fig S6. In the text it is discussed in lines 233-243.

Contribution to Existing Literature The study makes a significant contribution to the growing body of literature on the metabolic regulation of cancer by identifying dPGC1 as a tumor suppressor modulating mitochondrial dynamics. Previous work has established the dual roles of mammalian PGC1α in promoting or suppressing cancer depending on context. This study adds depth by demonstrating similar context-dependent effects in a simpler model organism, facilitating further exploration of the molecular mechanisms involved.

By linking mitochondrial fusion, Yki signaling, and Cyclin E regulation, the manuscript aligns with and expands upon research on Hippo pathway regulation, cancer metabolism, and mitochondrial biology. The findings highlight the importance of integrating metabolic and signaling networks in understanding oncogenesis.

Community Selection The current form of the manuscript is best suited for a specialized audience, particularly mitochondrial biologists, Drosophila researchers, and Hippo pathway specialists. To engage a broader community, additional work linking these findings to mammalian models or human cancer biology would be necessary.

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Referee #3

Evidence, reproducibility and clarity

The manuscript presents compelling evidence that dPGC1 acts as a context-dependent tumor suppressor in Drosophila by modulating mitochondrial dynamics and limiting Yorkie (Yki)-induced oncogenic growth. By leveraging the Drosophila wing imaginal disc as a model, the authors investigate how dPGC1 depletion exacerbates Yki-driven tissue overgrowth, mitochondrial hyperfusion, Cyclin E upregulation, and DNA damage, leading to tumorigenesis. The study provides valuable insights into the interplay between mitochondrial dynamics and cancer, with implications for understanding metabolic regulation in oncogenesis. While the findings are significant and well-aligned with the field, certain aspects of the experimental design, data presentation, and mechanistic insights require further attention to enhance clarity, reproducibility, and impact. Below, I outline my major concerns and recommendations.

Major Points

1. One point is the knock-down efficiency of dPGC1 on the mRNA level, which is between 30 to >50% (Fig. S1C). This is not too strong, so the question arises how severly the protein levels are affected. If possible, an antibody staining with quantification should be performed. From these data it cannot be concluded dPGC1 is not required for normal development, half the dose could be sufficient. How do wings look like when the ap-GAL4 driver is used for dPGC1 knock-down, as this is the driver used in the subsequent experiments?
2. How does the wing disc look like when dPGC1 is overepressed together with Yki?
3. In Fig 2D (but also in Fig. 2C) not only cells in the dorsal but also in the ventral comparmtent seem to overproliferate. Either this is a mis-conception or it is a non-autonomous effect from interfering with Yki and dPGC1 in the vertrnal compartment. In either cases, this has to be clarified.
4. In Fig. 2F-H quantification of Mmp1 and F-actin is missing. Mmp1 is a JNK target, so the authors could do in addition an anti-phospho JNK antibody staining.
5. In Fig. 3: how does the mitochondrial network look like in the wing disc periopodial epithelium using the Gug>Yki+dPGC1 genotype? Is it similar to Gug>dMfn or Gug>miro?
6. In Fig. 3I: what is really the mitochondrion? It would be good to outline the region(s) that was/were measured.
7. A quantification of RNAi and overexpression efficiencies of the different transgenes in Fig. 3 is required.
8. In Fig. 4: what is the phenotype when miro is over-expressed in combination with Yki? Or when it is knocked down in the ap>Yki-dPGC1 background? This was the gene tested in Fig. 3 with a clear mitochondrial phenotype. How does the mitochondrial morphology in the wing disc peripodial epithelium look like in Gug>Opa1RNAi or Gug>Opa1 discs? Why have the authors switched between the ap>Yki+dPGCRNAi and the ap>Yki+dPGC1shRNA lines? It would be important to have this series of experiments in the same backgrounds, as KD efficiencies are different (Fig. S1C).
9. Fig. 5A: proper quantification of Western Blot signals is required. I do not agree that Cyclin E protein levels are elevated in ap>Yki or ap>Yki+dPGC1 discs. Even at the mRNA levels the increase in expression is rather weak. From these results nothing can be concluded.
10. Knock-down efficincies for dap and CycE needs to be quantifiec (Fig. 5H-N). Although the rescue experiment with CycE knock down is from the phenotype convincing, it is nonetheless puzzling, as CycE is accodring to Fig. 5A+B hardly upregulated. An independent CycE RNAi line would be useful.

Significance

Strengths and Limitations of the Study

Strengths

Innovative Focus on Mitochondrial Dynamics and Oncogenesis: The study provides compelling evidence linking mitochondrial dynamics, particularly hyperfusion, to tumorigenesis in Drosophila. The identification of dPGC1 as a context-dependent tumor suppressor adds novel insights into the interplay between metabolism and oncogenesis. Comprehensive Use of Drosophila as a Model System: The study leverages the genetic tractability of Drosophila, allowing precise manipulation of mitochondrial regulators and signaling pathways. The use of wing imaginal discs as a model for tumor growth is well-established and appropriate. Integration of Morphological and Genetic Data: The manuscript combines confocal imaging, electron microscopy, and genetic tools to demonstrate the role of dPGC1 in regulating mitochondrial dynamics, Cyclin E levels, and tissue overgrowth. Relevance to Cancer Biology: The findings address key hallmarks of cancer, including deregulated metabolism, genomic instability, and cell cycle misregulation. The study's exploration of these processes in a simple model organism provides a strong basis for translating findings to mammalian systems.

Limitations

Validation of RNAi and Overexpression Efficiency: The knockdown efficiency of dPGC1 on the mRNA level is only moderate (30-50%), and protein-level validation is missing. Without this, the study cannot conclusively demonstrate the role of dPGC1 in normal development or tumorigenesis. Incomplete Mechanistic Insights: The manuscript identifies Cyclin E as a potential driver of tumor growth but does not adequately explore how mitochondrial hyperfusion leads to Cyclin E regulation (e.g., post-transcriptional mechanisms or protein stability). Inconsistencies in Experimental Backgrounds: The study uses different RNAi/shRNA lines and driver combinations inconsistently across experiments, making it difficult to compare results directly. This variability undermines the robustness of the conclusions. Limited Functional Analysis of Mitochondria: While mitochondrial morphology is well-characterized, functional assays (e.g., membrane potential or ATP production) are missing. These would confirm the impact of hyperfusion on cellular energetics and oncogenesis.

Contribution to Existing Literature

The study makes a significant contribution to the growing body of literature on the metabolic regulation of cancer by identifying dPGC1 as a tumor suppressor modulating mitochondrial dynamics. Previous work has established the dual roles of mammalian PGC1α in promoting or suppressing cancer depending on context. This study adds depth by demonstrating similar context-dependent effects in a simpler model organism, facilitating further exploration of the molecular mechanisms involved.

By linking mitochondrial fusion, Yki signaling, and Cyclin E regulation, the manuscript aligns with and expands upon research on Hippo pathway regulation, cancer metabolism, and mitochondrial biology. The findings highlight the importance of integrating metabolic and signaling networks in understanding oncogenesis.

Community Selection

The current form of the manuscript is best suited for a specialized audience, particularly mitochondrial biologists, Drosophila researchers, and Hippo pathway specialists. To engage a broader community, additional work linking these findings to mammalian models or human cancer biology would be necessary.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this manuscript the authors the investigate the role of the mitochondrial regulatory transcription factor dPGC1 in tissue growth and oncogenic transformation. They show that dPGC1 limits hyperplasia mediated by overexpression of Yki in the Drosophila wing disc, while having no effect on normal growth. dPGC1 depletion in discs overexpressing Yki results neoplastic overgrowth and hyperfused mitochondria, which was dependent on the increased expression of genes involved in promoting mitochondrial fusion. Additionally, the authors show that dPGC1 limits CycE levels post-transcriptionally in Yki tumors.

Major comments

1. The authors mention several times in passing in the results a manuscript from the Banerjee lab (Nagaraj et al 2012), which shows that many of the genes the authors of the present manuscript show are upregulated upon Yki overexpression + dPGC1-RNAi compared with Yki overexpression alone are in fact upregulated upon Yki overexpression alone compared with control (dMfn/marf, opa1, miro - while interestingly dPGC1 itself is not affected). Nagaraj et al further show that Yki-overexpressing discs have longer mitochondria suggesting increased fusion even in the absence of dPGC1 depletion. The findings from Nagaraj et al should be mentioned explicitly in the introduction and the relationship between this manuscript and the present work clearly outlined in the discussion.
2. Given that Yki overexpression alone induces mitochondrial fusion and that dMfn/marf and opa1 depletion suppresses Yki-induced overgrowth (Nagaraj et al), does dPGC1 overexpression also suppress Yki-induced overgrowth? If so, is this correlated with reduction in dMfn/marf and opa1 compared with Yki overexpression alone?
3. One important question raised by this study is: how specific is the effect of dPGC1 depletion to Yki-driven overgrowth? As Yki-driven overgrowth already have increased mitochondrial length, it is possible that Yki-expressing cells are already sensitised to the effects of dPGC1 depletion. Interestingly, Nagaraj et al show that mitochondrial morphology is not affected upon EGFR activation (hyperplasia) or upon scrib and avl depletion (neoplasia). The authors should therefore test if dPGC1 depletion can potentiate the growth of other hyperplasia drivers such as activated EGFR and InR in the wing disc.
4. There are a few simple control experiments the authors should provide to clarify the relationship between Yki and dPGC1:
   • Are Yki levels affected by dPGC1 depletion?
   • Does dPGC1 knockdown alone modify the expression of the genes tested in Fig.3A? In other words, is this upregulation specific of the Yki-overexpression context?
   • Does dPCG1 knockdown also stabilise CycE in the absence of Yki overexpression or does the stabilisation of CycE occur only in Yki tumors?
5. Figure 3C-G: it is not clear how the authors can quantify the length of 3D structures like mitochondria from 2D TEM images (unless they have done volume reconstruction from consecutive sections) and no details are provided in the methods. The quantification of mitochondrial length has to be performed rigorously as it is a key part of the paper.

Minor Comments:

1. Line 51: "Mitochondria are highly dynamics organelles." should be "Mitochondria are highly dynamic organelles."
2. Introduction: the authors should summarise the known physiological functions of PGC1α in order to put their findings in context.
3. lines: 121-3: "...depletion of dPGC1...did not have a major impact on adult wing size and shape (Fig 1B, C)." There is a small but statistically significant difference so the authors should state this in the text.
4. Figure 5A (Cyclin E western blot): the authors should show molecular weight markers.

Significance

The manuscript by Sew et al builds on the previous work by Nagaraj et al to explore the role of mitochondrial function in tumors driven by disruption of the Hippo pathway. In particular, the authors identify dPGC1 as a transcription factor that limits Yki-driven mitochondrial fusion and tissue growth. Interestingly, they further show that Yki/PGC1-depleted tumors are highly sensitive to Cyclin E levels, due to post-transcriptional Cyclin E increase. These results further our knowledge of how Yki drives growth and how mitochondria participate in oncogenic transformation. With appropriate revision as outlined above (for example exploring whether the mechanism proposed is Yki-specific), the manuscript will be of broad interest to developmental and cancer biologists.

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Referee #1

Evidence, reproducibility and clarity

Sew et al. examine the master regulator of mitochondrial biogenesis, dPGC1, in the context of Drosophila wing and larval development. They primarily use confocal imaging to probe the interplay between dPGC1 and an overactive Hippo pathway, driven by overexpression of the main effector protein, Yki. In their study, they find that tumors, driven by overactivity of Yki grow larger when dPGC1 is downregulated, implicating the mitochondrial biogenesis pathway in tumor suppression. Furthermore, in the context of Yki overexpression, they find that levels of Mfn or Opa1 modulate tumor size. Lastly, they show a role of cyclin E in controlling the size of tumors formed by Yki OE + dPGC1 RNAi. The potential role of dPGC1 as a tumor suppressor is interesting because it highlights an emerging recognition of mitochondria in the aetiology of cancer. However, before publication, much of the data in this manuscript should be strengthened by a refinement in the methods/analysis and an increase in orthogonal approaches.

Major comments:

The authors indicate that for example, in lines127-28, that neither downregulating or overexpressing dPGC1 affects wing size. However, the quantification in Fig. 1C shows a significant decrease in wing size following RNAi treatment. This decrease is modest, but it is nevertheless significant. It is worth pointing out, too, that the efficiency of the RNAi in Fig. S1C suggests that the conclusions drawn are premature. While a roughly 55% drop in mRNA levels may be statistically significant, it is unclear whether this drop in transcripts corresponds to a commensurate depletion of protein. Moreover, it is unclear, in this context, how much dPGC1 may indeed be necessary to drive a relatively normal program of mitochondrial biogenesis in wing development. To obtain a clear result, it is necessary to show significant depletion of the dPGC1 protein. (Ultimately, if it is the case that dPGC1 is unnecessary for wing development and function, a more coherent line of inquiry would be to find out the reason for this rather than to pivot the story to studying tumorigenesis in larva.)

In Figure 3H-K, it is not clear why the authors used electron microscopy to evaluate mitochondrial morphology. The very good confocal images in Figure 3C-G show a clear change in mitochondrial morphology following the knockdown of Mfn, Opa1, and Miro. While it is clear from the electron micrographs in Figure H that the mitochondria are enlarged, it is not obvious that this increase in length is a result of increased mitochondrial fusion. Indeed, if the mean form factor were used to quantify the shape, it is likely that in both conditions, the value would be close to 1, indicating more of a round object, and it not obvious whether there would be a difference between the Yki OE versus the YkI OE + dPGC1 RNAi. Therefore, from this data alone, it cannot be concluded that the YkI OE + dPGC1 RNAi condition leads to mitochondrial hyperfusion.

Figure 4. refers to changes in mitochondrial fusion and fission in tumor formation; however, the authors do not attempt to alter mitochondrial fission factors, so it is not accurate to mention a role of mitochondrial fission, in this context. It must be noted, too, that the authors have not demonstrated that their genetic interventions have actually affected mitochondrial morphology in these experiments. As noted in the previous figure, the Yki OE + dPGC1 RNAi condition showed enlarged mitochondria, but not necessarily hyperfused organelles. Therefore, the downregulation of Mfn or Opa1 in this set of experiments may not necessarily have altered mitochondrial morphology. Perhaps suppression of Mfn or Opa1 would normalize the areas of these evidently swollen mitochondria, but this is unclear without images. Furthermore, it should be appreciated that both Opa1 and Mfn exhibit pleiotropic attributes - e.g., Opa1 not only regulates IMM fusion, but it also modulates the shape and tightness of cristae membranes, specialized sites of oxidative phosphorylation as well as sequestration of cytochrome c, the release of which influences apoptosis (Frezza et al., 2006). At least in mammalian cells, Mfn2 is thought to regulate contacts between mitochondria and endoplasmic reticulum (Naon et al., 2023), which may serve other functions than OMM fusion, such as stabilization of the MAM.

Figure 5 highlights a connection between dysregulation of mitochondria and Cyclin E, which allows cells to prematurely enter S phase. The data presented here do not offer clarity on whether the enlargement of the tumors results from increase cellular proliferation and/or cell size. The role of the cell cycle adds a layer of complexity to these results, because it is thought that mitochondria undergo fragmentation during the cell cycle to promote an even distribution of the organelle population after mitosis (Taguchi et al., 2007); however, in this manuscript, the authors contend that the downregulation dPGC1 is promoting mitochondrial hyperfusion. It is unclear how and whether cellular division and proliferation would proceed at an accelerated rate in a situation with mitochondrial hyperfusion.

Minor comments:

Lines 69-72 contrast the roles of PGC1α and β. It is not clear whether the comparison is of their respective roles in cancer or in normal physiology. In either case, it is important to note that PGC1β has been shown to drive mitochondrial fusion as well as biogenesis through its control of MFN2, among other factors (Liesa et al., 2008).

Although this study focuses on PGC1, the authors do not seem to site the original literature from the Spiegelman lab.

There are 10-20 grammatical errors throughout the text.

Referee Cross-commenting

There is agreement among the referees that the potential role of PGC1 as a tumor suppressor is interesting and significant. However, various aspects of this work require attention prior to publication. For example, there needs to be a complete knock down of PGC1 to come to any conclusion as to its role in wing development. The methods for analyzing mitochondrial morphology need to be clarified and be consistent with standards in the field of mitochondrial dynamics. Also, the authors need to quantify their Western blots to obtain accurate assessments of protein levels. Generally, the study relies too heavily on overexpression experiments; understanding the potential role of mitochondria in regulating the Hippo pathway should include various knockdown and/or knockout models.

Significance

Overall, the authors show an interesting dampening effect of dPGC1 on growth of Yki-driven tumors. This data could be relevant for elucidating how dysregulation of the Hippo signalling pathway can underlie tumorigenesis.

The narrative arc of the study, however, appears to lack a focused line of inquiry. Figure 1 highlights an attempt to modulate Drosophila wing size and/or structure by downregulating dPGC1, but to no effect. Although examination of the efficiency of the RNAi revealed that the transcripts were still present in significant quantities; so, the conclusion that dPGC1 is dispensable for wing formation is premature. To have clarity on this point, it would be necessary to completely knockdown the gene, preferably by showing a total loss of protein. This should be feasible for the authors, since they showed Western blotting in Figure 5A. In any event, it seems that this negative data led the authors to study the Hippo pathway in the larval stage. This transition from Figure 1 to 2 seemed somewhat arbitrary and leads to a rather disjointed sense of the main line of inquiry around dPGC1.

It is important to note, too, that the authors highlight a role of mitochondrial dynamics in the pathway of Yki-driven tumor formation; however, they only directly evaluate mitochondrial dynamics in this context in a single assay, namely, Figure 3H-K, and this quantification is likely inaccurate because the mitochondria in the Yki OE + dPGC1 RNAi condition seem to be substantially enlarged, circular structures. It is critical to keep in mind that mitochondrial enlargement does not necessarily stem from hyperfusion. It could come from a decrease in the activity of Drp1 or result from an imbalance between mitochondrial biogenesis and mitophagy.

A marked limitation of this study is the overuse of rather artificial manipulations of transcriptional regulatory pathways. The study would benefit a lot from investigation of the loss of function of components of the Hippo pathway rather than just OE of Yki.

My expertise is in mitochondrial biology, with specialization in super-resolution imaging, mitochondrial dynamics and membrane architecture. I have also worked in the interface between mitochondrial physiology and cancer. With this perspective, I think that the authors uncover a potentially interesting role of PGC1 as a tumor suppressor.

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Reply to the reviewers

Manuscript number: RC- 2025-03073

Corresponding author(s): Shaul Yogev

1. General Statements [optional]

We kindly thank our reviewers for their enthusiasm, thoughtful feedback, and constructive suggestions on how to strengthen our manuscript. Below, we provide a point-by-point response to reviewer comments and outline the experiments we will do to address every concern that has been raised.

2. Description of the planned revisions

• *

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

This interesting study uses an unbiased genetic screen in C. elegans to identify SAX-1/NDR kinase as a regulator of dendritic branch elimination. Loss of SAX-1 results in an excess branching phenotype that is striking and highly penetrant. The authors identify several additional regulators of branch elimination (SAX-2, MOB-1, RABI-1, RAB-11.2) by using a candidate genetic screen aimed at factors that interact physically or genetically with SAX-1. They propose that SAX-1 acts by promoting membrane retrieval based on the nature of these interactors and the results of an imaging-based in vivo assay for endocytic puncta.

Major comments.

1. My biggest concern is that the phenotypes are only observed in temperature-sensitive dauer-constitutive mutant backgrounds, and not in wild-type dauers. That is, wild-type animals exiting dauer do not require SAX-1 for dendrite elimination. While this does not undermine the importance of the results, it does require more explanation. The authors write that "the requirement for sax-1... relies on specific physiological states of the dauer stage," but I do not understand what this means. Are they saying that daf-7 and daf-2 dauers are in a different "physiological state" than wild-type dauers? In what way? What is the evidence for this? A more rigorous explanation is needed. We agree that this is puzzling, and we thank the reviewer for recognizing that this does not undermine the importance of the results. There is ample evidence that daf-2 and daf-7 differ from starvation-induced dauers. For example, a recent preprint finds that the transcriptomes of these two mutants at dauer cluster much closer to each other than to starvation-induced dauers (Corchado et al. 2024). Older work has noted other differences, such as the time the dauer entry decision is made (Swanson and Riddle 1981), the synchronicity of dauer exit, the ability to force dauer entry in daf-d mutants, as well as additional dauer-unrelated phenotypes (reviewed in Karp 2018). We agree with the reviewer that this merits further clarifications and will perform the experiments suggested by the reviewer below:

To me, the simplest genetic explanation is that daf-7 and daf-2 are partially required for branch retraction in a manner redundant with sax-1, and the ts mutants are not fully wild-type at 15C. Thus, the sax-1 requirement is revealed only in these mutant backgrounds. Can the authors examine starvation-induced dauers of daf-7 or daf-2 raised continuously at 15C?

We will do this experiment.

daf-7 and daf-2 ts strains can form "partial dauers" that have a dauer-like appearance but are not SDS resistant. Could the difference between partial dauers and full dauers account for the difference in sax-1-dependence? The authors could use SDS selection of the daf-7 strain at 25C to ensure they are examining full dauers.

We tested daf-7 mutants with 1% SDS when we set up the system – they are fully dauer at 25°C and are SDS sensitive after exit. We will repeat this important control with daf-7; sax-1 double mutants.

The Bargmann lab has created a daf-2 FLP-OUT strain (ky1095ky1087) that allows cell-type-specific removal of daf-2. Could this be used to test for a cell-autonomous role of daf-2 in IL2Q related to branch elimination?

We can attempt this experiment. However, since IL2 promoters turn on prior to dauer, the interpretation would not be straightforward – it would be hard to exclude that a cell autonomous defect in dauer entry does not account for the IL2 dauer exit phenotype, even if branching appears normal.

These ideas are not a list of specific experiments the authors need to complete, rather they are meant to illustrate some possible approaches to the question. Whatever approach they use, it is important for them to more rigorously explain why SAX-1 is not required for branch removal in wild-type animals.

We completely agree. We will carry out the 15°C experiment, examine morphological characteristics and test SDS resistance. In addition, we will test neuronal markers that differ between dauers and non-dauers to determine whether the mutants are full or partial dauers at the relevant timepoints.

The SAX-2 localization (Fig. 4) and endocytosis assay (Fig. 6) results were not clear to me from the data shown. Overall a more rigorous analysis and presentation of the data would be important to make these conclusions convincing. This may involve refining the data presentation in the figures, modifying the claims (e.g., "we propose" vs "we find"), or saving some of the data to be more fully explored in a future paper. In my view, these figures are the biggest weak point of the manuscript and also are not important for the central conclusions (which are well supported and convincing), indeed these results are barely mentioned in the Abstract or last paragraph of Introduction.

We agree that the analysis and presentation of Figures 4 and 6 need to be improved. The presentation has already been updated, and the figures are clearer now. In the revision, we will increase sample size to provide stronger conclusions, consolidate some of the analysis and further improve presentation. While we agree with the reviewer that conclusions from these figures are not as strong as those drawn from genetic experiments, they do complement and support the conclusions of those other figures.

• In Fig. 4D, why is SAX-2 visible throughout the entire neuron and why is the "punctum" marked with an arrow also seen in the tagRFP channel? One gets the impression that some of the puncta may be background, bleed-through, or artifacts due to cell varicosities.

There is no bleed-through: this is most evident by looking at the brightest signals in the cell body (now labelled with an asterisk in a zoomed-out image) and noting that they do not bleed between channels. In sax-1 mutants, the SAX-2::GFP puncta are very obvious and distinguishable from the tagRFP channel. In control, SAX-2::GFP is very faint in the dendrite, so we increased the contrast to allow visualization. The reviewer is correct that under these conditions, some puncta look like the cytosolic fill. In the revision, we will re-analyze the data and will not consider these as bona-fide SAX-2 puncta, but rather cytosolic SAX-2 that accumulates due to constrictions and varicosities in the dendrite.

• Related to both Fig. 4 and Fig. 6, where does SAX-1 localize in IL2Q in dauer and post-dauer? Does its expression or localization change during branch retraction? Does it co-localize with SAX-2 or endocytic puncta?

We generated an endogenously tagged sax-1 with a 7xspGFP11 tag; however, this was below detection in the IL2s. For the revisions, we can test an overexpressed cDNA construct.

**Referee cross-commenting**

I think we all touched on similar points. I wanted to follow up on Reviewer 3's comment, "Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults." I thought this was an excellent point. It made me wonder if that might explain why the defect is only seen in daf-7 and daf-2 mutant backgrounds - maybe these strains retain partial dauer traits even after exit. Is there a specific experiment that they could do? Did you have specific characteristics of dauer morphology in mind for them to check? (Ideally something in the nervous system that can be scored quantitatively.)

Please see response to point #1 regarding experiments we will do to confirm the “dauer state” of daf-7 and daf-7; sax-1 double mutants.

Reviewer #1 (Significance (Required)):

A major strength of this work is the pioneering use of a novel system to study neuronal branch retraction. C. elegans has provided a powerful model for studying how dendrite branches form, but much less attention has been paid to how excess neuronal branches are removed. The post-dauer remodeling of IL2Q neurons provides an exciting and dramatic physiological example to explore this question.

This paper is notable for taking the first steps towards developing this innovative model. It does exactly what is needed at the outset of a new exploration - a forward genetic screen to discover the main regulators of the process. Using a combination of classical and modern genetic approaches, the authors bootstrap their way to a sizeable list of factors and a solid understanding of the properties of this system, for example that retraction of higher vs lower order dendrites show different genetic requirements.

We thank the reviewer for recognizing the novelty and significance of our work.

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

In this manuscript, the authors establish C. elegans IL2 neurons as a system in which to study dendrite pruning. They use the system to perform a genetic screen for pruning regulators and find an allele of sax-1. Unexpectedly sax-1 is only required for post-dauer pruning in two different genetic backgrounds that induce dauer formation, but not starvation-induced dauer formation. Sax-1/NDR kinase reduction has previously been associated with increased outgrowth and branching in other systems, so this is a new role for this protein. However, the authors show that proteins that work with Sax-1 in other systems, like sax-2/fry, also play a role in this pathway. The genetic experiments are beautiful and the findings are all clearly explained and strongly supported. The authors also examine sax-2 localization, which localizes sax-1 in other systems, and show it in puncta in dendrites that increase with dauer exit, consistent with function at the time of pruning. They also show that membrane trafficking regulators associated with NDR kinases function in the same pathway here, hinting that endocytosis may play a role during pruning as in Drosophila. The link to endocytosis was a little weak (see Major point below). Overall, this study describes a new system to study pruning and identifies NDR/fry/Rabs as regulators of pruning during dauer exit. The work is very high quality and both the imaging and genetics are extremely well done.

We thank the reviewer for their positive assessment of the manuscript.

Major points

1. The only place where there were any questions about the data was the last figure (6G and I). Here they use uptake of GFP secreted from muscle as a readout of endocytosis in IL2 neurons. They nicely show that more internalized puncta accumulate as animals exit dauer. The claim that this is reduced in sax-1 mutants doesn't seem to match the images shown well. In the image there are many more puncta in the GFP channel and much more accumulation of the RFP-tagged receptor everywhere. It seems like some additional analysis of this data is important to fully capture what is going on and whether this really represents an endocytic defect. We agree and will provide additional data in Figure 6. The specific discrepancy between the image and the quantification is because we showed a single focal plane rather than a projection. This does not capture all the puncta in a neurite. The current version shows a projection, making it evident that the mutants has fewer puncta compared to the control.

Reviewer #2 (Significance (Required)):

Neurite pruning is important in all animals with neurons. Genetic approaches have primarily been applied to the problem using Drosophila, so identifying a new model system in which to study it is an important step. Using this system, a pathway known to function in a different context is linked to pruning. Thus the study provides new insights into both pruning and this pathway.

We thank the reviewer for the positive assessment of our study’s significance.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

Summary: Figueroa-Delgado et al. use a C. elegans neuro plasticity model to examine how dendrites are eliminated upon recovery from the stress induced larval stage, dauer. The authors performed a mutagenesis screen to identify novel regulators of dendrite elimination and revealed some surprising results. Branch elimination mechanism varies between 2{degree sign}, 3{degree sign}, and 4{degree sign} branches. The NDR kinase, SAX-1 and it's interactors (SAX-2 and MOB-2) are required for elimination of second and third order branches but not fourth order branches. Interestingly they showed that branch elimination varies depending on the stimulus of dendrite outgrowth such that the NDR kinase is required for branch elimination after genetically inducing the dauer stage but is not required if dauers are produced through food deprivation. The authors go a step further to include a small candidate screen looking at various pathways of membrane remodeling and identify additional regulators of dendrite elimination related to membrane trafficking including RABI-1, RAB-8, RAB-10, and RAB-11.2.

We thank the reviewer for their time and suggestions below

Major comments:

• While I find the data promising and exciting, several of the experiments have concerningly low sample sizes. Fig 3G, Fig 4G, Fig 5J and L, and Fig 6I all contain data sets that are fewer than 10 animals. Sample sizes should be stated specifically in the figure legends for all data represented in the graphs. We thank the reviewer for finding the data exciting. We agree that the sample sizes in some panels is low and will increase it in the revised version. Sample sizes are now specifically listed in the figure legends.

• All statements based on data not shown should be amended to include the data as a supplemental figure or edited to omit the statement based on withheld data. We agree. Some “not shown” data are already added to the current version of the manuscript and the rest will be added to the fully revised version, or the statements will be omitted.

• Rescue experiments (Fig 2J) should demonstrate failure to rescue from neighboring tissue types (hypodermis and muscle) to conclude cell autonomous rescue rather than a broadly acting factor. Thank you for the suggestion. We will use a hypodermal promoter and a muscle promoter driving SAX-1 cDNA expression to strengthen the claim of cell autonomy.

• Fig 4 needs quantification of higher order branches and SAX-2 proximity to branch nodes as these are discussed in the text. We will add this quantification.

Minor comments:

• Fig 1C-F, It appears like the shy87 allele produces animals of significantly different body sizes. It would improve rigor to normalize the dendrite coverage to body size in the quantification. We do not see a biologically meaningful size difference between shy87 and control, it may be the specific image shown. We will confirm this by measuring animal size for the final revision.

• Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults. This important point was also raised by Reviewer 1. We will test SDS sensitivity, morphological markers, and molecular markers to determine the dauer “state” of the mutants used in this study. The results will be included in the final revision.

• The text references multiple transgenic lines tested in Fig 2I-J but only one line is shown. Additional lines were visually examined under a fluorescent compound microscope but not imaged or quantified. We will add this quantification to the final revision.

• Fig 4F, Additional timepoints would enhance the sax-1 localization result and might provide insight into mechanism of action for sax-1. We will add the localization in post-dauer adults.

• Fig 6I Control and sax-1(ky491) example images should be provided in the supplement. We will add these images to the final revision.

**Referee cross-commenting**

I agree that we shared many of the same concerns.

There are several general assays for dauer characteristics that could be used here to determine if the post-dauer animals retain other characteristics of the dauer stage in addition to IL2 branches (SDS resistance, alae remodeling, pharyngeal bulb morphology, nictation behavior). The nictation behavior has been connected very nicely with IL2 neurons (Junho Lee's group). Additionally, FLP dendrites occupy the same space as the IL2 branches and outgrowth in post-dauers occurs in coordination with IL2 branch elimination - this might be another optional experiment, to check if FLP growth is impeded by persistent IL2 branches. All of these could be quantified similar to how the authors have already established with their IL2 model (FLP dendrite branches) or with a binary statistic.

Please see responses to Reviewer 1 and 3 above for the list of experiments to determine whether the animals fail to completely enter or exit dauer.

Reviewer #3 (Significance (Required)):

SIGNIFICANCE ============ These results describe a new role for the NDR kinase complex in dendrite pruning that has clinical significance to our understanding of human brain development and human health concerns in which pruning is dysregulated, such as observed in the case of autism. The authors use an established neuro-plasticity, C. elegans model (Schroeder et al. 2013) which provides a tractable and reproduceable platform for discovering the mechanism of dendrite pruning. These results would influence future work in the fields of cell biology of the neuron and disease models of brain development.

My expertise is in the field of C. elegans neuroscience and stress biology and have sufficient expertise to evaluate all aspects of this work.

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

Reviewer #1

• In Fig. 4C, the distinction between puncta in the primary or higher-order dendrites is not clear to me, and several puncta that I would have scored as primary are marked as higher-order.

We apologize for a mistake in the arrowhead color and overall presentation of this figure. It has been fixed in the current version.

• Related to this, in Fig. 4B are the two arrows meant to be white as in the top panel, or yellow as in the bottom panel?

We thank Reviewer #1 for their observation, and we apologize for our oversight. We fixed this in the current version.

• In Fig. 4, where in the head are we looking? It would help to show a more low-magnification view of the entire cell.

We added zoomed-out images and indicated where the zoomed in insets are taken from. We thank the reviewer for helping us improve the clarity of the data.

• The main sax-1 phenotype is increased SAX-2 puncta in dauer, but the branch retraction defect is in post-dauers. How is this relevant to the phenotype?

This is a very good point. The increase in SAX-2 puncta in sax-1 mutants is stronger during dauer-exit than in dauer, consistent with this being the time when SAX-1 functions. We agree that some earlier activity of SAX-1 cannot be excluded, and we do not assume that the effect on SAX-2 completely accounts for the pruning defects. This is now acknowledged in the text. However, given that both proteins function together in pruning, and given that the effect is strongest during dauer exit, we do believe that this data is informative and worth showing.

• The number of SAX-2 puncta in sax-1 mutants decreases almost to normal in post dauers. Is there a correlation between the number of remaining branches and the number of SAX-2 puncta? That is, do the many wild-type animals with "excess" SAX-2 puncta also fail to retract branches?

There is no correlation. In other words, the number of SAX-2 puncta does not instruct the extent of pruning. Please note the quantifications underestimate the number of SAX-2 puncta in the mutants, since they were only done on the primary dendrite. This is necessary because the mutant and control have different arbor size, so only branch order that can be appropriately compared are primary dendrites.

• The control post-dauer data in Fig. 4F and 4H are identical (re-used data) but the corresponding control dauer data in Fig. 4F and 4G are different. What is going on here?

We thank the reviewer for raising this point and apologize for the oversight in data presentation. In the revised manuscript, we now show all control and experimental data integrated into a single graph, ensuring that each dataset is represented accurately to provide a comparison between dauer and post dauer recovery conditions.

• Why are sample sizes so small for both strains in Fig. 4G compared to Fig. 4F and 4H?

We sincerely apologize for this mistake, some of the data was erroneously grouped in the original submission. The revised version contains an updated number of neurons, presented on the same graph, and in the final revision we will further increase sample size. We apologize again for this error.

• In Fig. 6C, why are the tagRFP (blue) puncta larger than the neurite? Aren't these meant to represent vesicles inside the surrounding neurite? One gets the impression that this is bleed-through from the GFP channel.

Based on EM, both an endocytic punctum and the diameter of the neuron are smaller than a single pixel. The apparent difference in size in fluorescence microscopy is because the puncta are brighter (they contain more membrane) and thus appear larger. In the current version, the improved presentation of the figure contains zoomed out images that clearly show that there is no bleed-through.

• In Fig. 6E and 6F, why are there no tagRFP (blue) puncta? Is CD8 not endocytosed at all if it lacks the nanobody sequence? One would expect the tagRFP (blue) signal to be the same in both strains and simply to lack yellow if the nanobody is not present.

CD8 lacks clear endocytosis motifs, which is why it is advantageous for labelling neurites and testing endocytosis when paired with an endocytic signal (Lee and Luo 1999; Kozik et al. 2010). Conversely, extracellular GFP binding to a membrane GFP antibody can induce endocytosis (for example, see (Tang et al., 2020)), likely by inducing clustering, although we are not familiar with work that explored the mechanism. In the updated version we included a rare example of an mCD8 punctum.

• The authors report a decrease in endocytic events in sax-1, but qualitatively it looks like there are vastly more puncta inside the neuron in Fig. 6H than in 6G.

We apologize for the presentation in the original version of Figure 6. This impression was because we showed single focal planes that only captured some of the signal. In the revised version we show projections, which makes it evident that there are fewer endocytic events in the mutant.

• In Fig. 6E and 6H, why are there so many GFP (yellow) puncta outside the neuron? What are these structures and why are they absent in the strain with the nanobody?

These puncta are secreted or muscle-associated GFP that has not been internalized by IL2Q neurons. They are present in all strains in this figure, this can be clearly seen in the zoomed-out images that have been added to the updated figure.

• What is the large central blue structure in Fig. 6H - is this the soma? - and why are puncta in this region not counted?

This is indeed the soma. In the updated version this can be clearly seen in the zoom-out. The large puncta in the soma were not counted because they may arise from the fusion of an unknown number of smaller puncta, and their precise number cannot be determined at the resolution of fluorescence microscopy.

• minor: there is text reading "40-" in the bottom panel of Fig. 6H. It is visible when printed but not on screen - adjust levels in Photoshop to reveal it.

We thank the reviewer for catching this oversight, it is now fixed.

Minor points:

1. At several points the authors emphasize the relationship of neurite remodeling to stress, e.g. Abstract and Discussion: "we adapted C. elegans IL2 sensory dendrites as a model [of...] stress-mediated dendrite pruning". It seems unnecessary and potentially misleading to treat this as a neuronal stress response. First, it conflates organismal and cellular stress - there is no reason to think that IL2 neurons are under cellular stress in dauer. In fact parasitic nematodes go through dauer-like stages as part of healthy development and probably have similar remodeling of IL2. Second, dendrite pruning occurs during dauer exit, which is the opposite of a stress response - it reflects a return to favorable conditions. We agree. We modified the abstract and discussion to avoid conflating organismal stress (the alleviation of which is relevant for triggering pruning) and cellular stress. Thank you for pointing this out.

In Fig. 1A, C. elegans is shown going directly from L1 to dauer in response to unfavorable conditions, which is incorrect. Animals proceed through L2 (in many cases actually an alternative L2d pre-dauer) and then molt into dauer (an alternative L3 stage) after completing L2.

We updated the schematic to include the L2d stage where commitment to dauer entry or resumption to reproductive development is made.

In Fig. 1B, please check if it is correct that hypodermis contacts the pharynx basement membrane as drawn. The schematic in the top panel makes it look like there is a single secondary branch and the quaternary branches are similar in length to the primary dendrite. The schematic in the bottom panel makes it look like the entire neuron is a small fraction of the length of the pharynx. Could these be drawn closer to scale?

The hypodermis does contact the pharynx basement membrane. We redrew the schematic for clarity.

Reviewer #2

For context, it might be helpful to know whether branching of other dendrites is increased in sax-1 mutants (as expected based on phenotypes in other animals) or decreased like IL2 neurons.

We examined the branching pattern of PVD, a polymodal nociceptive neuron (new Supplemental Figure 3). We find no significant difference between control and sax-1 or sax-2 mutants, suggesting that these genes function in the context of pruning. Recent work (Zhao et al. 2022) confirms that sax-1 is not required for PVD branching.

Minor:

"shy87 mutant dauers showed a minor reduction in secondary and tertiary branches compared to control (Figure 1G). These results indicate that shy87 is specifically required for the elimination of dauer-generated dendrite branches." Maybe temper the specificity claim some as the reduction in branches is definitely there.

We agree, the claim was tempered.

"three complimentary approaches" should be complementary

Thank you for noticing. We fixed this.

"In control animals, SAX-2 was mostly concentrated in the cell body (data not shown)" It might be nice to include some overview images that show the cell body for completeness.

We added zoomed-out images to the revised figure, thank you for the suggestion.

Reviewer #3

Minor comments:

• Fig 1G-H, are shy87 second and third order branch counts statistically different between dauer and post dauer adults? This comparison would strengthen the claim that these order branches fail to eliminate all together rather than undergo a partial elimination. We added this to Figure S2. The shy87 mutants show a complete failure in eliminating secondary branches (i.e. no difference between dauer and post-dauer) and a strong but incomplete defect in eliminating tertiary branches.

• Fig 4B-E Indicate branch order in the images, this is unclear and a point that is focused on in the text. Done.

• Discussion of Fig 1G from the text claims that shy87 is specifically required for branch elimination yet the data shows significant defects in branch outgrowth as well. This raises the question, are the branches abnormally stabilized that results in early underdevelopment and late atrophy? Authors should acknowledge alternative hypotheses. We agree and will revise the text accordingly. The difference between shy87 and control dauers, while statistically significant, is relatively minor and can only be detected by careful quantification, it is not apparent from looking at the images (in contrast for example to rab-8 and rab-10 mutants, where we acknowledge in the text that their branching defects might affect subsequent pruning.

• Authors reference a branch elimination process but don't outline what this would entail and where their results fit in. We apologize for being unclear. Given that sax-1 and sax-2 function together, one would intuitively expect to see SAX-2 being reduced in sax-1 mutants, yet the opposite is observed. On potential explanation is that SAX-1 does not directly control SAX-2 abundance, but that clearance of SAX-2 is part of the pruning process that both proteins regulate. This would explain the enrichment of SAX-2 in sax-1 mutants. However, additional models cannot be excluded, and we acknowledge this in the revised text.

References:

Corchado, Johnny Cruz, Abhishiktha Godthi, Kavinila Selvarasu, and Veena Prahlad. 2024. “Robustness and Variability in Caenorhabditis Elegans Dauer Gene Expression.” Preprint, bioRxiv, August 26. https://doi.org/10.1101/2024.08.15.608164.

Karp, Xantha. 2018. “Working with Dauer Larvae.” WormBook, August 9, 1–19. https://doi.org/10.1895/wormbook.1.180.1.

Kozik, Patrycja, Richard W Francis, Matthew N J Seaman, and Margaret S Robinson. 2010. “A Screen for Endocytic Motifs.” Traffic (Copenhagen, Denmark) 11 (6): 843–55. https://doi.org/10.1111/j.1600-0854.2010.01056.x.

Lee, T., and L. Luo. 1999. “Mosaic Analysis with a Repressible Cell Marker for Studies of Gene Function in Neuronal Morphogenesis.” Neuron 22 (3): 451–61.

Swanson, M. M., and D. L. Riddle. 1981. “Critical Periods in the Development of the Caenorhabditis Elegans Dauer Larva.” Developmental Biology 84 (1): 27–40. https://doi.org/10.1016/0012-1606(81)90367-5.

Tang, Rui, Christopher W Murray, Ian L Linde, et al. n.d. “A Versatile System to Record Cell-Cell Interactions.” eLife 9: e61080. https://doi.org/10.7554/eLife.61080.

Zhao, Ting, Liying Guan, Xuehua Ma, Baohui Chen, Mei Ding, and Wei Zou. 2022. “The Cell Cortex-Localized Protein CHDP-1 Is Required for Dendritic Development and Transport in C. Elegans Neurons.” PLOS Genetics 18 (9): e1010381. https://doi.org/10.1371/journal.pgen.1010381.

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

Figueroa-Delgado et al. use a C. elegans neuro plasticity model to examine how dendrites are eliminated upon recovery from the stress induced larval stage, dauer. The authors performed a mutagenesis screen to identify novel regulators of dendrite elimination and revealed some surprising results. Branch elimination mechanism varies between 2{degree sign}, 3{degree sign}, and 4{degree sign} branches. The NDR kinase, SAX-1 and it's interactors (SAX-2 and MOB-2) are required for elimination of second and third order branches but not fourth order branches. Interestingly they showed that branch elimination varies depending on the stimulus of dendrite outgrowth such that the NDR kinase is required for branch elimination after genetically inducing the dauer stage but is not required if dauers are produced through food deprivation. The authors go a step further to include a small candidate screen looking at various pathways of membrane remodeling and identify additional regulators of dendrite elimination related to membrane trafficking including RABI-1, RAB-8, RAB-10, and RAB-11.2.

Major comments:

• While I find the data promising and exciting, several of the experiments have concerningly low sample sizes. Fig 3G, Fig 4G, Fig 5J and L, and Fig 6I all contain data sets that are fewer than 10 animals. Sample sizes should be stated specifically in the figure legends for all data represented in the graphs.
• All statements based on data not shown should be amended to include the data as a supplemental figure or edited to omit the statement based on withheld data.
• Rescue experiments (Fig 2J) should demonstrate failure to rescue from neighboring tissue types (hypodermis and muscle) to conclude cell autonomous rescue rather than a broadly acting factor.
• Fig 4 needs quantification of higher order branches and SAX-2 proximity to branch nodes as these are discussed in the text.

Minor comments:

• Fig 1C-F, It appears like the shy87 allele produces animals of significantly different body sizes. It would improve rigor to normalize the dendrite coverage to body size in the quantification.
• Fig 1G-H, are shy87 second and third order branch counts statistically different between dauer and post dauer adults? This comparison would strengthen the claim that these order branches fail to eliminate all together rather than undergo a partial elimination.
• Discussion of Fig 1G from the text claims that shy87 is specifically required for branch elimination yet the data shows significant defects in branch outgrowth as well. This raises the question, are the branches abnormally stabilized that results in early underdevelopment and late atrophy? Authors should acknowledge alternative hypotheses.
• Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults.
• The text references multiple transgenic lines tested in Fig 2I-J but only one line is shown.
• Fig 4B-E Indicate branch order in the images, this is unclear and a point that is focused on in the text.
• Fig 4F, Additional timepoints would enhance the sax-1 localization result and might provide insight into mechanism of action for sax-1.
• Authors reference a branch elimination process but don't outline what this would entail and where their results fit in.
• Fig 6I Control and sax-1(ky491) example images should be provided in the supplement.

Referee cross-commenting

I agree that we shared many of the same concerns.

There are several general assays for dauer characteristics that could be used here to determine if the post-dauer animals retain other characteristics of the dauer stage in addition to IL2 branches (SDS resistance, alae remodeling, pharyngeal bulb morphology, nictation behavior). The nictation behavior has been connected very nicely with IL2 neurons (Junho Lee's group). Additionally, FLP dendrites occupy the same space as the IL2 branches and outgrowth in post-dauers occurs in coordination with IL2 branch elimination - this might be another optional experiment, to check if FLP growth is impeded by persistent IL2 branches. All of these could be quantified similar to how the authors have already established with their IL2 model (FLP dendrite branches) or with a binary statistic.

Significance

These results describe a new role for the NDR kinase complex in dendrite pruning that has clinical significance to our understanding of human brain development and human health concerns in which pruning is dysregulated, such as observed in the case of autism. The authors use an established neuro-plasticity, C. elegans model (Schroeder et al. 2013) which provides a tractable and reproduceable platform for discovering the mechanism of dendrite pruning. These results would influence future work in the fields of cell biology of the neuron and disease models of brain development.

My expertise is in the field of C. elegans neuroscience and stress biology and have sufficient expertise to evaluate all aspects of this work.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors establish C. elegans IL2 neurons as a system in which to study dendrite pruning. They use the system to perform a genetic screen for pruning regulators and find an allele of sax-1. Unexpectedly sax-1 is only required for post-dauer pruning in two different genetic backgrounds that induce dauer formation, but not starvation-induced dauer formation. Sax-1/NDR kinase reduction has previously been associated with increased outgrowth and branching in other systems, so this is a new role for this protein. However, the authors show that proteins that work with Sax-1 in other systems, like sax-2/fry, also play a role in this pathway. The genetic experiments are beautiful and the findings are all clearly explained and strongly supported. The authors also examine sax-2 localization, which localizes sax-1 in other systems, and show it in puncta in dendrites that increase with dauer exit, consistent with function at the time of pruning. They also show that membrane trafficking regulators associated with NDR kinases function in the same pathway here, hinting that endocytosis may play a role during pruning as in Drosophila. The link to endocytosis was a little weak (see Major point below). Overall, this study describes a new system to study pruning and identifies NDR/fry/Rabs as regulators of pruning during dauer exit. The work is very high quality and both the imaging and genetics are extremely well done.

Major points

1. The only place where there were any questions about the data was the last figure (6G and I). Here they use uptake of GFP secreted from muscle as a readout of endocytosis in IL2 neurons. They nicely show that more internalized puncta accumulate as animals exit dauer. The claim that this is reduced in sax-1 mutants doesn't seem to match the images shown well. In the image there are many more puncta in the GFP channel and much more accumulation of the RFP-tagged receptor everywhere. It seems like some additional analysis of this data is important to fully capture what is going on and whether this really represents an endocytic defect.
2. For context, it might be helpful to know whether branching of other dendrites is increased in sax-1 mutants (as expected based on phenotypes in other animals) or decreased like IL@ neurons.

Minor:

"shy87 mutant dauers showed a minor reduction in secondary and tertiary branches compared to control (Figure 1G). These results indicate that shy87 is specifically required for the elimination of dauer-generated dendrite branches." Maybe temper the specificity claim some as the reduction in branches is definitely there.

"three complimentary approaches" should be complementary

"In control animals, SAX-2 was mostly concentrated in the cell body (data not shown)" It might be nice to include some overview images that show the cell body for completeness.

Significance

Neurite pruning is important in all animals with neurons. Genetic approaches have primarily been applied to the problem using Drosophila, so identifying a new model system in which to study it is an important step. Using this system, a pathway known to function in a different context is linked to pruning. Thus the study provides new insights into both pruning and this pathway.

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Referee #1

Evidence, reproducibility and clarity

This interesting study uses an unbiased genetic screen in C. elegans to identify SAX-1/NDR kinase as a regulator of dendritic branch elimination. Loss of SAX-1 results in an excess branching phenotype that is striking and highly penetrant. The authors identify several additional regulators of branch elimination (SAX-2, MOB-1, RABI-1, RAB-11.2) by using a candidate genetic screen aimed at factors that interact physically or genetically with SAX-1. They propose that SAX-1 acts by promoting membrane retrieval based on the nature of these interactors and the results of an imaging-based in vivo assay for endocytic puncta.

Major comments.

1. My biggest concern is that the phenotypes are only observed in temperature-sensitive dauer-constitutive mutant backgrounds, and not in wild-type dauers. That is, wild-type animals exiting dauer do not require SAX-1 for dendrite elimination.

While this does not undermine the importance of the results, it does require more explanation. The authors write that "the requirement for sax-1... relies on specific physiological states of the dauer stage," but I do not understand what this means. Are they saying that daf-7 and daf-2 dauers are in a different "physiological state" than wild-type dauers? In what way? What is the evidence for this? A more rigorous explanation is needed.

To me, the simplest genetic explanation is that daf-7 and daf-2 are partially required for branch retraction in a manner redundant with sax-1, and the ts mutants are not fully wild-type at 15C. Thus, the sax-1 requirement is revealed only in these mutant backgrounds. Can the authors examine starvation-induced dauers of daf-7 or daf-2 raised continuously at 15C?

daf-7 and daf-2 ts strains can form "partial dauers" that have a dauer-like appearance but are not SDS resistant. Could the difference between partial dauers and full dauers account for the difference in sax-1-dependence? The authors could use SDS selection of the daf-7 strain at 25C to ensure they are examining full dauers.

The Bargmann lab has created a daf-2 FLP-OUT strain (ky1095ky1087) that allows cell-type-specific removal of daf-2. Could this be used to test for a cell-autonomous role of daf-2 in IL2Q related to branch elimination?

These ideas are not a list of specific experiments the authors need to complete, rather they are meant to illustrate some possible approaches to the question. Whatever approach they use, it is important for them to more rigorously explain why SAX-1 is not required for branch removal in wild-type animals. 2. The SAX-2 localization (Fig. 4) and endocytosis assay (Fig. 6) results were not clear to me from the data shown. Overall a more rigorous analysis and presentation of the data would be important to make these conclusions convincing. This may involve refining the data presentation in the figures, modifying the claims (e.g., "we propose" vs "we find"), or saving some of the data to be more fully explored in a future paper. In my view, these figures are the biggest weak point of the manuscript and also are not important for the central conclusions (which are well supported and convincing), indeed these results are barely mentioned in the Abstract or last paragraph of Introduction.

• In Fig. 4, where in the head are we looking? It would help to show a more low-magnification view of the entire cell.
• In Fig. 4D, why is SAX-2 visible throughout the entire neuron and why is the "punctum" marked with an arrow also seen in the tagRFP channel? One gets the impression that some of the puncta may be background, bleed-through, or artifacts due to cell varicosities.
• In Fig. 4C, the distinction between puncta in the primary or higher-order dendrites is not clear to me, and several puncta that I would have scored as primary are marked as higher-order.
• Related to this, in Fig. 4B are the two arrows meant to be white as in the top panel, or yellow as in the bottom panel?
• The main sax-1 phenotype is increased SAX-2 puncta in dauer, but the branch retraction defect is in post-dauers. How is this relevant to the phenotype?
• The number of SAX-2 puncta in sax-1 mutants decreases almost to normal in post dauers. Is there a correlation between the number of remaining branches and the number of SAX-2 puncta? That is, do the many wild-type animals with "excess" SAX-2 puncta also fail to retract branches?
• The control post-dauer data in Fig. 4F and 4H are identical (re-used data) but the corresponding control dauer data in Fig. 4F and 4G are different. What is going on here?
• Why are sample sizes so small for both strains in Fig. 4G compared to Fig. 4F and 4H?
• In Fig. 6C, why are the tagRFP (blue) puncta larger than the neurite? Aren't these meant to represent vesicles inside the surrounding neurite? One gets the impression that this is bleed-through from the GFP channel.
• In Fig. 6E and 6F, why are there no tagRFP (blue) puncta? Is CD8 not endocytosed at all if it lacks the nanobody sequence? One would expect the tagRFP (blue) signal to be the same in both strains and simply to lack yellow if the nanobody is not present.
• In Fig. 6E and 6H, why are there so many GFP (yellow) puncta outside the neuron? What are these structures and why are they absent in the strain with the nanobody?
• What is the large central blue structure in Fig. 6H - is this the soma? - and why are puncta in this region not counted?
• The authors report a decrease in endocytic events in sax-1, but qualitatively it looks like there are vastly more puncta inside the neuron in Fig. 6H than in 6G.
• minor: there is text reading "40-" in the bottom panel of Fig. 6H. It is visible when printed but not on screen - adjust levels in Photoshop to reveal it.
• Related to both Fig. 4 and Fig. 6, where does SAX-1 localize in IL2Q in dauer and post-dauer? Does its expression or localization change during branch retraction? Does it co-localize with SAX-2 or endocytic puncta?

Minor points:

1. At several points the authors emphasize the relationship of neurite remodeling to stress, e.g. Abstract and Discussion: "we adapted C. elegans IL2 sensory dendrites as a model [of...] stress-mediated dendrite pruning". It seems unnecessary and potentially misleading to treat this as a neuronal stress response. First, it conflates organismal and cellular stress - there is no reason to think that IL2 neurons are under cellular stress in dauer. In fact parasitic nematodes go through dauer-like stages as part of healthy development and probably have similar remodeling of IL2. Second, dendrite pruning occurs during dauer exit, which is the opposite of a stress response - it reflects a return to favorable conditions.
2. In Fig. 1A, C. elegans is shown going directly from L1 to dauer in response to unfavorable conditions, which is incorrect. Animals proceed through L2 (in many cases actually an alternative L2d pre-dauer) and then molt into dauer (an alternative L3 stage) after completing L2.
3. In Fig. 1B, please check if it is correct that hypodermis contacts the pharynx basement membrane as drawn. The schematic in the top panel makes it look like there is a single secondary branch and the quartenary branches are similar in length to the primary dendrite. The schematic in the bottom panel makes it look like the entire neuron is a small fraction of the length of the pharynx. Could these be drawn closer to scale?

Referee cross-commenting

I think we all touched on similar points. I wanted to follow up on Reviewer 3's comment, "Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults." I thought this was an excellent point. It made me wonder if that might explain why the defect is only seen in daf-7 and daf-2 mutant backgrounds - maybe these strains retain partial dauer traits even after exit. Is there a specific experiment that they could do? Did you have specific characteristics of dauer morphology in mind for them to check? (Ideally something in the nervous system that can be scored quantitatively.)

Significance

A major strength of this work is the pioneering use of a novel system to study neuronal branch retraction. C. elegans has provided a powerful model for studying how dendrite branches form, but much less attention has been paid to how excess neuronal branches are removed. The post-dauer remodeling of IL2Q neurons provides an exciting and dramatic physiological example to explore this question.

This paper is notable for taking the first steps towards developing this innovative model. It does exactly what is needed at the outset of a new exploration - a forward genetic screen to discover the main regulators of the process. Using a combination of classical and modern genetic approaches, the authors bootstrap their way to a sizeable list of factors and a solid understanding of the properties of this system, for example that retraction of higher vs lower order dendrites show different genetic requirements.

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Reply to the reviewers

We thank the reviewers for their positive comments. Our manuscript is to our knowledge the first to investigate the role of VAIL (V-ATPase—ATG16L1 induced LC3 lipidation), a form of CASM (Conjugation of ATG8s to single membranes) in SARS-CoV-2 replication. We demonstrate that SARS-CoV-2 Envelope (E) induces VAIL and this contributes to viral replication, including by using a reverse genetics system to make an E mutant virus. There have been many high quality studies examining the role of canonical autophagy in SARS-CoV-2 replication and our manuscript does not argue that all or even most LC3 lipidation during infection is via VAIL. We will try to make this point more clearly in the text. We do not think this detracts from the novelty and importance of our manuscript.

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

• Figueras-Novoa et al present a short report demonstrating the induction of LC3 lipidation on single membranes by SARS-CoV-2 through a noncanonical autophagy pathway referred to as VAIL. The authors utilize elegant genetic tools to show that the induction of LC3 lipidation upon viral infection is mainly due to VAIL rather than canonical autophagy. They demonstrate that the activity of the viral E protein that can cause neutralization of acidic vesicles leads to the activation of non-canonical LC3 lipidation on single membranes. Interestingly, the authors also conclude that the impairment of VAIL leads to a reduction of viral load as a result of a defect in later stages of viral infection, although the underlying mechanism was not further explored. *

• Overall, this is an elegant and well controlled study that provides a clear conclusion. I only have some minor comments.*

We thank the reviewer for their assessment of our manuscript.

In some experiments, LC3 lipidation does not appear to be fully disrupted upon VAIL inhibition (e.g. Fig.'s 1H, 3D, 4A). As other labs have shown that SARS-CoV2 blocks autophagic flux, this could be further clarified in this manuscript as both VAIL and autophagy may be co-induced upon viral infection.

We agree with the reviewer that there is a contribution of canonical macroautophagy to the LC3B lipidation observed in SARS-CoV-2. We will extend the discussion in the manuscript to clarify this point for the readers.

Can the authors test the induction of LC3 lipidation in cells expressing K490 mutant of ATG16L1 in ATG16L1 KO cells to compare them with ATG16L1-ATG13 double knockouts?

The western blot in figure 3F (quantified in Figure 3G) shows LC3B lipidation in response to E expression in ATG16L1-ATG13 double knock out cells reconstituted with wild type ATG16L1 but not in cells complimented with ATG16L1 K490A mutant. We agree that the referee’s suggestion to perform these experiments in the context of infection would be informative. However in spite of numerous attempts, we have so far been unable to generate a cell clone fully devoid of ATG16L1 in a cell line that can be productively infected with SARS-CoV-2. For reasons unclear to us there appears to be a very low level of residual ATG16L1 activity despite multiple different CRISPR/Cas9 targeting attempts. The suggested complementation experiments might still be informative in the context of low level ATG16L1 expression so we will pursue this. Alternatively, as a contingency we can try to produce SARS-CoV-2 infectable cells with mutations in ATG16L1’s binding partner V1H, this interaction is required for VAIL. A further contingency could be to assess LC3B lipidation during infection and treatment with a Vps34 inhibitor, which inhibits canonical autophagy.

Minor points: * * The difference between Fig. 1F&G is unclear and why the authors are including both analyses. Similarly figures 4G&H.

We included both metrics to show that the decrease in LC3B lipidation in cells expressing SopF during infection is robust and observed in two separate readouts. While spot area measures the area of infected cells covered by GFP-LC3B fluorescence, spot intensity is a reading of the intensity of the area defined in an infected cell as being LC3 positive. Theoretically, these measurements could change in different ways. For example, if the same amount of lipidated LC3 were to distribute over a larger area of the cell. We prefer to keep both measurements in the manuscript.

The authors should show boxed colocalisation of all images, including negative controls. For examples, the authors have shown boxed magnifications in only the lowest panel in Figure 2A but not the upper two panels. Figures 4E&F should include boxed examples. This serves to clarify both positive and negative colocalisation events.

Boxed magnifications will be added to all images.

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

• Overall an elegant and well controlled study demonstrating the induction of non-canonical LC3 conjugation on single membranes (VAIL) during SARS-CoV2 infection. A further exploration of canonical autophagy (as previously published by others) in addition to VAIL would enhance this study.*

As the reviewer noted, several excellent studies have explored canonical autophagy during SARS-CoV-2 infection, many of which we cite in our manuscript. Our focus, however, is to demonstrate that SARS-CoV-2 E induces LC3 lipidation via VAIL. We believe that exploring the diverse roles of canonical autophagy mechanisms in SARS-CoV-2 infection is beyond the scope of this study.

*This study is of interest to researchers studying autophagy, viruses, immunology, single membrane LC3 lipidation, and lysosomes as well as potentially clinicians treating SARS-CoV2 infecteted individuals. *

• This reviewer is experienced in autophagy research.*

We thank the reviewer for this assessment of our manuscript.

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

• Major Comments *

• Figure 1D does not very clearly show an overlap between V1D and LC3B. Both proteins seem broadly present across the cell and there is no easily identifiable change in V1D distribution upon infection. As such the overlay may be purely stochastic. The authors should quantify the observed co-localization events across multiple cells and biological replicates and compare them to other protein(s) with a similar cellular distribution pattern.*

We agree there is no obvious change in V1D staining on infection. The images in Figure 1D are purely intended to illustrate that LC3 and the V-ATPase can colocalise, not to demonstrate a change in V-ATPase distribution or to suggest a direct interaction. We will make this point more clearly in the text. We will also carry out analyses of the kind (see also response to the first two Minor Comments). We would be happy to provide an alternative method of visualising the V-ATPase (we could use any suitable antibody to the V-ATPase, or the bacterial effector SidK) if required. In response to reviewer 3’s comments, we will carry out a pull-down experiment to test the association of the V-ATPase and ATG16L1 during E expression, as this is a key interaction during VAIL activation.

Based on Figure 2F the authors suggest that virus entry is unaffected by the inhibition of VAIL in early timepoints. However, according to the figure legend, the timepoint used is 7hpi, while 2D uses 24hpi. Some SARS-CoV-2 papers suggest 7-10 hours is sufficient time to release new virions (Ban-On et al., 2020). As such 7hpi can not necessarily be seen as an early time point. Did the authors test earlier ones? Also, based on this, would it be possible that the effects observed at 24hpi are actually secondary infections, meaning that the virus utilizes pathway components for virion production and a lack thereof reduces infectivity of newly formed virions? In this case it would be interesting to set up an assay that can distinguish between primary and secondary infection to study both individually more closely.

Whereas 7 hours may be sufficient to release new virions, it is not sufficient to establish infections in other cells – this is why we chose that time point. The observation that there is no difference in the percentage of infected cells at 7 h p.i. (figure 2F) led us to suggest that viral entry is unaffected . We then confirmed this through the pseudovirus assay in Figure 2G, where no difference is found between SopF and mCherry expressing cells. For this assay, GFP-expressing, replication incompetent, lentiviral particles pseudotyped with Spike from different SARS-CoV-2 lineages were used to transduce mCherry and SopF expressing cells. A change in the percentage of GFP-positive cells would indicate an effect on viral entry, but no such change was observed in SopF-expressing cells.

We agree with the reviewer that the effects observed at 24 hpi are likely due to a defect in subsequent rounds of infection, since no difference was observed at 7 hpi or with our pseudovirus assay. We will attempt to make this point in the text as clearly as possible.

The authors nicely show in their study an involvement of VAIL in SARS-CoV-2 mediated LC3 lipidation. However, the observed effects are relatively moderate in several experiments, indicating that there may be another contributor to the observed phenotype. It would be nice to highlight this in the discussion and debate potential mechanisms that are causing the observed effects during infection.

We agree with the reviewer’s analysis. We have discussed the contribution of canonical autophagy in the second paragraph of the discussion, but we will expand on this in a revised manuscript. E expression levels are moderate during infection, other structural proteins such as N and M are present in much higher amounts. Since E is the key protein in VAIL initiation, a moderate effect of VAIL inhibition in perhaps expected. Nonetheless this still plays a crucial role in the viral life cycle.

*Minor Comments *

• The re-localization events shown in Fig 3A should be quantified.*

This quantification of GFP-LC3 relocalisation will be carried out and included.

• The co-localization events displayed in Fig 4A should be quantified.*

The quantification of V1D, E and GFP-LC3 will be carried out and included.

For Figure 2H-K the authors perform KDs of ATG16L1 and ATG13. While the results for the two specific proteins are certainly convincing, the authors would strengthen their argument by testing additional proteins in the autophagy pathway to support their claim that VAIL but not autophagy affects protein abundance of N (OPTIONAL).

As discussed in response to reviewer 1, we will attempt to infect ATG16L1 KO cells reconstituted with a K490A ATG16L1 mutant, which is an established tool and has been validated to be deficient in VAIL but not canonical autophagy.

***Referee cross-commenting** *

• Overall I agree with the comments of my co-reviewers and I think the suggested experiments/comments are sensible. *
• I in part already eluted to it my analysis, but I tend to agree with reviewer 3 on the limited effect VAIL seems to have on LC3b lipidation.*

As outlined above in response to reviewer 1 and below to reviewer 3, we agree that there is a modest contribution of VAIL to overall LC3 lipidation, which correlates with a modest amount of E expression in SARS-CoV-2 infection. VAIL is clearly important for the viral life cycle, thus whatever the proportion of LC3 lipidation attributable to this pathway it must be biologically significant.

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

• While previous publications have shown interaction between SARS-CoV2 and autophagy, the authors of this manuscript demonstrate that V-ATPase-ATG16L1 induced LC3 lipidation (VAIL) is activated during infection and affects viral replication. *

• This study provides an interesting new aspect to host-SARS_CoV-2 interactions. *

• The manuscript is of interest for people studying virus-host cell interaction, as well as for researchers in the fields of infectious diseases, specifically SARS-CoV2, and autophagy/VAIL*.

We thank the reviewer for their assessment of our manuscript.

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

• The interaction of SARS-CoV-2 with canonical autophagy has been well documented. However, whether SARS-CoV-2 infection induces and benefits from non-canonical autophagy is unclear. In this manuscript, the authors demonstrated that SARS-CoV-2 infection induces V-ATPase-ATG16L1-induced LC3 lipidation (VAIL), a form of non-canonical autophagy in which LC3 is conjugated to single membranes. The SARS-CoV-2 envelope protein, through its ion channel activity, triggers the V-ATPase proton pump and induces VAIL during SARS-CoV-2 infection. Inhibiting VAIL during SARS-CoV-2 infection with SopF, a Salmonella effector, attenuates SARS-CoV-2 egress. *

• While these findings are interesting and demonstrate that SARS-CoV-2 infection triggers VAIL for its own benefit, the mechanism by which VAIL promotes SARS-CoV-2 replication remains unclear. Moreover, the contribution of VAIL to LC3 lipidation during SARS-CoV-2 infection appears to be minimal, as blocking VAIL through SoPF expression only marginally reduced LC3B lipidation (Fig. 1H). Therefore, the contribution of VAIL to LC3 lipidation during SARS-CoV-2 infection is minimal.*

We thank the reviewer for their assessment of our manuscript. As we have already alluded to in our response, we agree that only part of the LC3 lipidation observed during infection can be attributed to VAIL. There is a reproducible effect on viral replication which we have demonstrated in multiple ways, therefore the contribution of VAIL is of biological importance.

*Comments: *

• The authors show that the ion channel activity of E is essential for VAIL induction during SARS-CoV-2 infection. Since V-ATPase recruits the ATG16L complex to induce VAIL, and to clarify how SARS-CoV-2 infection triggers VAIL, the authors should examine whether SARS-CoV-2 infection or the expression of E induces V-ATPase-ATG16L interaction and whether this interaction is disrupted when SopF is expressed.*

We agree with the reviewer that this would be an informative experiment. We can carry out this experiment in an E expression system, rather than infection. This is due to the difficulty of getting enough material to carry out this kind of pull-down experiment in infected cells (at the time of writing these experiments still have to be carried out in CL3).

• Since the authors suggest that expression of SopF attenuates viral exit, one would expect that the number of N-positive cells will increase in SopF-expressing cells compared to the mCherry control cells. However, as shown in Figure 2D, this is not the case. Could the authors discuss why N-positive cells will be reduced in SopF-expressing cells when viral egress is impeded in these cells*?

This is a reflection of multi-cycle kinetics. N is still very strongly expressed in infected cells, even after virions have egressed. SARS-CoV-2 can infect VAIL-deficient cells and expresses the same levels of N prior to subsequent rounds of infection (at 7 hours after infection for example). Egress in VAIL-deficient, SopF-expressing cells is defective. Therefore, fewer cells will be infected in subsequent rounds of infection in SopF expressing cells, resulting in fewer N-positive cells in the SopF expressing cell population (most obvious after 24 hours).

Figure 2H. The authors show that knockdown of ATG16L1 reduces the expression of N during SARS-CoV-2 infection compared to the controls. To confirm that knockdown of ATG16L1, which is required for both canonical autophagy and VAIL, reduces N staining via VAIL, the authors should examine the impact of SopF expression on N levels in ATG16L KD cells. This experiment will confirm if the reduction in N staining in ATG16L1 KD cells is due to VAIL.

As stated in the response to reviewer 1, we can attempt this experiment in an ATG16L1 KO system complemented with K490A ATG16L1, which is deficient in VAIL and not canonical autophagy.

• Figure 2J. The quality of the Western blot data is poor.*

In this western the exposure is deliberately turned up to show that minimal ATG13 was left after knock down. We will also show the full blot with less exposure – this will demonstrate high quality.

Also, N appears as a single band in Figure 2J, but appears as double bands in Figures 2A and H. Could the authors explain this?

An extra band can be seen in 2J for N. However, as the reviewer points out, the intensity of the lower band is fainter than in 2A or 2H. The biology of SARS-CoV-2 N is interesting and complicated, with different truncated isoforms and phosphorylation patterns observed (see for example Mears et al., 2025 PMID:39836705). We observed changes in abundance of the second band between experiments, but this did not obviously depend on VAIL. We therefore consider this to be beyond the scope of this investigation.

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

• This manuscript proposes a role for VAIL in LC3 lipidation during SARS-CoV-2 infection. While the findings are interesting, VAIL only marginally contributes to LC3 lipidation during SARS-CoV-2 infection. Therefore, the significance of VAIL to LC3B lipidation during SARS-CoV-2 infection is unclear.*

Our experiments show unambiguously that VAIL contributes to viral replication. Therefore even if As alluded to above, we do not think a further investigation of canonical macroautophagy and SARS-CoV-2 would enhance the quality of our manuscript. We will try to make our description of the contribution of macroautophagy clearer in the revised manuscript (without providing a full literature review). We also do not think that exploring the nature of the multiple N bands on western blot is within the scope of this paper.

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Referee #3

Evidence, reproducibility and clarity

The interaction of SARS-CoV-2 with canonical autophagy has been well documented. However, whether SARS-CoV-2 infection induces and benefits from non-canonical autophagy is unclear. In this manuscript, the authors demonstrated that SARS-CoV-2 infection induces V-ATPase-ATG16L1-induced LC3 lipidation (VAIL), a form of non-canonical autophagy in which LC3 is conjugated to single membranes. The SARS-CoV-2 envelope protein, through its ion channel activity, triggers the V-ATPase proton pump and induces VAIL during SARS-CoV-2 infection. Inhibiting VAIL during SARS-CoV-2 infection with SopF, a Salmonella effector, attenuates SARS-CoV-2 egress.

While these findings are interesting and demonstrate that SARS-CoV-2 infection triggers VAIL for its own benefit, the mechanism by which VAIL promotes SARS-CoV-2 replication remains unclear. Moreover, the contribution of VAIL to LC3 lipidation during SARS-CoV-2 infection appears to be minimal, as blocking VAIL through SoPF expression only marginally reduced LC3B lipidation (Fig. 1H). Therefore, the contribution of VAIL to LC3 lipidation during SARS-CoV-2 infection is minimal.

Comments:

The authors show that the ion channel activity of E is essential for VAIL induction during SARS-CoV-2 infection. Since V-ATPase recruits the ATG16L complex to induce VAIL, and to clarify how SARS-CoV-2 infection triggers VAIL, the authors should examine whether SARS-CoV-2 infection or the expression of E induces V-ATPase-ATG16L interaction and whether this interaction is disrupted when SopF is expressed.

Since the authors suggest that expression of SopF attenuates viral exit, one would expect that the number of N-positive cells will increase in SopF-expressing cells compared to the mCherry control cells. However, as shown in Figure 2D, this is not the case. Could the authors discuss why N-positive cells will be reduced in SopF-expressing cells when viral egress is impeded in these cells?

Figure 2H. The authors show that knockdown of ATG16L1 reduces the expression of N during SARS-CoV-2 infection compared to the controls. To confirm that knockdown of ATG16L1, which is required for both canonical autophagy and VAIL, reduces N staining via VAIL, the authors should examine the impact of SopF expression on N levels in ATG16L KD cells. This experiment will confirm if the reduction in N staining in ATG16L1 KD cells is due to VAIL.

Figure 2J. The quality of the Western blot data is poor. Also, N appears as a single band in Figure 2J, but appears as double bands in Figures 2A and H. Could the authors explain this?

Significance

This manuscript proposes a role for VAIL in LC3 lipidation during SARS-CoV-2 infection. While the findings are interesting, VAIL only marginally contributes to LC3 lipidation during SARS-CoV-2 infection. Therefore, the significance of VAIL to LC3B lipidation during SARS-CoV-2 infection is unclear.

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Referee #2

Evidence, reproducibility and clarity

Major Comments

Figure 1D does not very clearly show an overlap between V1D and LC3B. Both proteins seem broadly present across the cell and there is no easily identifiable change in V1D distribution upon infection. As such the overlay may be purely stochastic. The authors should quantify the observed co-localization events across multiple cells and biological replicates and compare them to other protein(s) with a similar cellular distribution pattern.

Based on Figure 2F the authors suggest that virus entry is unaffected by the inhibition of VAIL in early timepoints. However, according to the figure legend, the timepoint used is 7hpi, while 2D uses 24hpi. Some SARS-CoV-2 papers suggest 7-10 hours is sufficient time to release new virions (Ban-On et al., 2020). As such 7hpi can not necessarily be seen as an early time point. Did the authors test earlier ones? Also, based on this, would it be possible that the effects observed at 24hpi are actually secondary infections, meaning that the virus utilizes pathway components for virion production and a lack thereof reduces infectivity of newly formed virions? In this case it would be interesting to set up an assay that can distinguish between primary and secondary infection to study both individually more closely.

The authors nicely show in their study an involvement of VAIL in SARS-CoV-2 mediated LC3 lipidation. However, the observed effects are relatively moderate in several experiments, indicating that there may be another contributor to the observed phenotype. It would be nice to highlight this in the discussion and debate potential mechanisms that are causing the observed effects during infection.

Minor Comments

The re-localization events shown in Fig 3A should be quantified.

The co-localization events displayed in Fig 4A should be quantified.

For Figure 2H-K the authors perform KDs of ATG16L1 and ATG13. While the results for the two specific proteins are certainly convincing, the authors would strengthen their argument by testing additional proteins in the autophagy pathway to support their claim that VAIL but not autophagy affects protein abundance of N (OPTIONAL).

Referee cross-commenting

Overall I agree with the comments of my co-reviewers and I think the suggested experiments/comments are sensible. I in part already eluted to it my analysis, but I tend to agree with reviewer 3 on the limited effect VAIL seems to have on LC3b lipidation.

Significance

While previous publications have shown interaction between SARS-CoV2 and autophagy, the authors of this manuscript demonstrate that V-ATPase-ATG16L1 induced LC3 lipidation (VAIL) is activated during infection and affects viral replication.

This study provides an interesting new aspect to host-SARS_CoV-2 interactions.

The manuscript is of interest for people studying virus-host cell interaction, as well as for researchers in the fields of infectious diseases, specifically SARS-CoV2, and autophagy/VAIL.

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Referee #1

Evidence, reproducibility and clarity

Figueras-Novoa et al present a short report demonstrating the induction of LC3 lipidation on single membranes by SARS-CoV-2 through a noncanonical autophagy pathway referred to as VAIL. The authors utilize elegant genetic tools to show that the induction of LC3 lipidation upon viral infection is mainly due to VAIL rather than canonical autophagy. They demonstrate that the activity of the viral E protein that can cause neutralization of acidic vesicles leads to the activation of non-canonical LC3 lipidation on single membranes. Interestingly, the authors also conclude that the impairment of VAIL leads to a reduction of viral load as a result of a defect in later stages of viral infection, although the underlying mechanism was not further explored.

Overall, this is an elegant and well controlled study that provides a clear conclusion. I only have some minor comments.

In some experiments, LC3 lipidation does not appear to be fully disrupted upon VAIL inhibition (e.g. Fig.'s 1H, 3D, 4A). As other labs have shown that SARS-CoV2 blocks autophagic flux, this could be further clarified in this manuscript as both VAIL and autophagy may be co-induced upon viral infection. Can the authors test the induction of LC3 lipidation in cells expressing K490 mutant of ATG16L1 in ATG16L1 KO cells to compare them with ATG16L1-ATG13 double knockouts?

Minor points:

The difference between Fig. 1F&G is unclear and why the authors are including both analyses. Similarly figures 4G&H.

The authors should show boxed colocalisation of all images, including negative controls. For examples, the authors have shown boxed magnifications in only the lowest panel in Figure 2A but not the upper two panels. Figures 4E&F should include boxed examples. This serves to clarify both positive and negative colocalisation events.

Significance

Overall an elegant and well controlled study demonstrating the induction of non-canonical LC3 conjugation on single membranes (VAIL) during SARS-CoV2 infection. A further exploration of canonical autophagy (as previously published by others) in addition to VAIL would enhance this study.

This study is of interest to researchers studying autophagy, viruses, immunology, single membrane LC3 lipidation, and lysosomes as well as potentially clinicians treating SARS-CoV2 infecteted individuals.

This reviewer is experienced in autophagy research.

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Reply to the reviewers

We appreciate the constructive and supportive feedback on our manuscript. All three reviewers acknowledged the significance and novelty of our work on bacterial telomere protection. In response to their suggestions, we have conducted the requested experiments and revised the manuscript accordingly. These changes have enhanced the rigor of our study and clarified our interpretations and explanations.

Moreover, we characterized an additional truncation mutant of TelN (TelN Δ445–631), which lacks the two C-terminal domains. Despite this deletion, the mutant retained protection activity (Supplementary Figure S4B), indicating that the remaining regions of the protein are sufficient to confer efficient protection in this assay.

Finally, we removed three sequence alignments (previously Supplementary Figures S6A and S7), as we recognized that the high degree of sequence divergence could hinder proper alignment and potentially lead to misinterpretation.

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

This study addresses how the bacterial telomere protein TelN protects telomere ends against the action of the Mre11-Rad50 nuclease (MR). This protection is essential for the stability of hairpin-ended linear plasmid and chromosomes in bacteria but had not been explored before. The authors demonstrate that TelN is necessary and sufficient to block MR-dependent DNA cleavage when bound to its specific telomere sequence. By combining elegant genetics and biochemical approaches, it convincingly shows that TelN-dependent inhibition likely involves a specific interaction between TelN and the MR complex. The manuscript is well written, easy to read and focused on the relevant information. The claims and the conclusions are supported by the data. There is no over-interpretation.

Comments: - Figure 1B, unnormalized transformation efficiency would be useful to show in SI

The unnormalized B. subtilis transformation efficiency has now been added as new figure panel S1B.

• Figures 2B, 2C, 3C, 3D, 4C, 5A and 5B: quantification of independent experiments should be added

While these DNA protection experiments show a clearly reproducible pattern of DNA degradation, the exact response to TelN titration varies somewhat between experimental replicates. We initially included the quantification of remaining full-length DNA because the corresponding band is hard to discern in the gel image due to pixel saturation. However, we realize now that this may mislead readers to think that the degradation occurs always with the exact same dosage response.

To avoid this, we have decided to remove the quantification and instead show the relevant part of the gel also at higher contrast to better visualize the loss of full-length DNA due to DNA degradation. In addition, we have included replicate experiments carried out at the same MR concentration (125 nM M₂R₂) or at higher concentration (500 nM M₂R₂) in the supplementary material. These examples demonstrate the general reproducibility of the assay.

**Referee cross-commenting**

Perfect for me. It seems that there is a consensus.

Reviewer #1 (Significance (Required)):

This pioneering study provides a very strong basis for a new understanding of telomeres in bacteria and offers fascinating evolutionary perspectives when compared to similar mechanisms active at telomeres in eukaryotic cells.

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

The paper is well-presented and well-written throughout. The paper shows convincingly that TelN protects hairpin DNA ends from the activity of SbcCD, presumably providing a protection mechanism for N15 phage DNA in vivo. Furthermore, this protection activity is shown not to require the catalytic (resolvase) activity of TelN, nor its poorly characterised C-terminal domain. The paper also suggests that this inhibition acts both at the level of competition for the DNA hairpin end and at the level of a direct protein:protein interaction between TelN and MR. An (acknowledged) weakness is that there is no real insight into the protein:protein interaction suggested by the experiments shown in Figure 5. Ideally, the protein:protein interaction interface would be identified and mutations in this interface would be shown to reduce hairpin protection.

Specific comments/questions

(1) What pathway (in vivo) leads to inactivation of linear hairpin DNA - one suspects that cleavage by SbcCD at the hairpins is probably not the full story. Presumably SbcCD cleavage facilitates further processing by other long range resection systems such as RecBCD, Exo1, RecQ/J etc. Would it be appropriate to view the hairpin as an adaption to protect against these nucleases, which then must be complemented with a mechanism to suppress SbcCD?

The reviewer's suggestion that hairpin ends represent a first layer of adaptation against nucleolytic processing is compelling. Hairpin structures inherently resist many exonucleases due to their covalently closed nature (absence of free 3’ or 5’ ends) but remain vulnerable to MR processing (Connelly et al, 1998, 1999; Saathoff et al, 2018). This creates a scenario where effective telomere protection requires both the structural barrier provided by the hairpin and an active mechanism to suppress MR activity. We have added this perspective to the relevant paragraph in the discussion.

(2) Section starting "Direct inhibition of MR by TelN in vitro". What is the word direct supposed to convey here? To me it suggests that the inhibition is via direct interaction of TelN with MR (rather than, for example, a result of competition for the hairpin DNA end) which is not shown here. Suggest either defining or removing the word direct. This point gains more importance considering that differentiating between inhibition mechanisms becomes a focus of later parts of the paper.

By "direct inhibition," we meant that TelN blocks MR nuclease activity without requiring additional cofactors, as demonstrated in this minimal reaction system containing only TelN, MR complex, DNA substrate, and ATP. To avoid ambiguity, we have reworded the corresponding headline and paragraph.

(3) Figure 2B - Why no control lane without MR? - this is a basic control to show that he degradation we are seeing in the absence of TelN is MR-dependent. Formally, as shown, the degradation could be caused by the ATP stock.

We have now included ATP-only control lanes (without MR complex), which show no substrate degradation, confirming that ATP stocks do not contain contaminating nucleases and that the observed degradation is indeed MR-dependent. These controls are included in the supplementary data (Figure S3A) along with additional replicate experiments. Notably, the dose-dependent protection observed at low TelN concentrations (where MR activity is not fully inhibited) provides additional evidence for the specificity of the MR-TelN interaction system, as non-specific nuclease contamination would result in complete substrate degradation regardless of TelN concentration.

(4) Why not use B. subtilis SbcCD for the species specificity experiment? Also, is it not surprising that TelN yielded zero protection against MRX given that the DNA sequence specificity experiments above suggest competition for DNA substrate is part of the inhibition mechanism?

We agree that this would be a great addition. We attempted but were unable to purify active B. subtilis SbcCD protein despite multiple attempts. The yeast MRX experiment serves the same purpose of demonstrating species specificity and represents a more evolutionarily distant comparison, which strengthens our conclusions about bacterial-specific inhibition.

(5) If the authors felt it appropriate, I thought there was scope for further discussion/introductory material. There are strong parallels here with mechanisms used by phage to protect themselves from the activities of RecBCD, which include both proteins that protect DNA ends like T4 gene 2, we well as proteins that bind directly to RecBCD to inactivate it like lambda Gam. As such, the work here will appeal as much to those interested in bacterial defence systems / phage:host interactions as it does to those interested in telomere biology. Especially significant is the inhibition of DNA end processing factors by lambda Gam since this protein is reported to interact with both RecBCD and SbcCD (PMID: 2531105).

We agree that there are obvious parallels between lambda Gam and TelN as counter-defence factors. This was likely largely missed in previous work because the telomere resolution activity of TelN masked its function in counter-defence. We have added a statement on this matter at the end of the discussion.

(6) Just a gripe really: it seems to be 'de rigeur' at the moment to re-name bacterial proteins for their human orthologues, presumably to elevate the perceived importance of the work(?), but it is not a practice I think is terribly helpful as it causes issues when searching literature. Minimally it would be great if the authors could ensure they add SbcCD as a keyword for search purposes.

We appreciate the reviewer's concern about nomenclature inconsistencies in the literature. We have chosen MR over SbcCD as a more generic term that covers eukaryotes, archaea and lately also bacteria and will hopefully contribute to a more consistent terminology in the literature across the domains of life in the future. Our choice to use "Mre11-Rad50" (MR) for the E. coli SbcCD complex is also consistent with prominent recent publications (Käshammer et al., 2019; Gut et al., 2022), explicitly referring to the E. coli system as "Mre11-Rad50" while acknowledging the bacterial designation. To link to previous literature, we made sure that both "SbcCD" and "Mre11-Rad50" are mentioned in the abstract. And, as suggested, we have now also added “SbcCD” to our keyword list to facilitate comprehensive literature searches.

**Referee cross-commenting**

I have nothing to add. The reviewers' comments are all broadly positive and consistent.

Reviewer #2 (Significance (Required):

This is an excellent paper unveiling a phage encoded "counter-defence" mechanism designed to protect phage DNA from degradation. It will be of special interest to those studying telomere biology of phage:host interactions.

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Reviewer #3

The authors investigate how the N15 phage protelomerase TelN protects linear chromosomes that terminate in hairpin structures (a sort of telomere). In E. coli and B. subtilis cells, removal or truncation of telN reduces transformation/survival of linear DNA, whereas complementation with full-length or a catalytically inactive TelN restores viability, consistent with TelN playing a nonenzymatic capping function.

In vitro, TelN binds hairpin substrates with moderate affinity and protects them from the nuclease activity of the Mre11/Rad50 complex. The authors propose that TelN originated as an early, sequence specific barrier against MR mediated DNA end processing, establishing fundamental principles of telomere protection that persist from bacteria to eukaryotes.

Major comments:

The manuscript convincingly shows that TelN can functionally block the Mre11Rad50 (MR) nuclease on a hairpin DNA end in a sequence specific manner (suggesting a physical interaction), but it doesn't directly demonstrate this. A simple pull-down or equilibrium binding method would be useful in proving a physical interaction.

We agree that this would be a valuable addition to the study. We have made several attempts to detect direct interaction by co-immunoprecipitation. However, without success so far. We do not have sufficient material for equilibrium binding methods (yet).__ ____ __

The MR complex requires ATP hydrolysis for resection of DNA ends. It would be a nice addition to the manuscript if the effect of TelN of Rad50 ATPase activity was tested.

We have tested the effect of TelN on Rad50 ATPase activity and found no significant impact under our experimental conditions, possible in line with the lack of stable interaction.

The bar plot on Fig 3B indicates that the experiments are performed in triplicate. The statistical significance of the differences between conditions should be determined. The same general comment could be made regarding the quantification of the polyacrylamide gels - how reproducible are these values?

We performed paired t-test analysis for the following figures and now indicate the p-values wherever significant (below 0.05): Figures 1D, 1E, 3B, 4B and S4B. We used paired t-tests to generally compare linear vs circular plasmid transformation efficiency for each condition. In Figure 4B, which included two different linear DNA constructs, we compared the two linear DNA constructs directly to each other. [Given that our experimental design included multiple control conditions with known expected outcomes to validate assay performance, rather than many independent exploratory comparisons, we report uncorrected p-values as the primary analysis. The inclusion of multiple controls with predictable outcomes reduces the likelihood of false positive interpretations.]

As stated in response to reviewer 1, while the exact values for the DNA degradation profile vary somewhat between experiments (likely due to variations in band quantification – see also response to comment below), the general trends are robust as for example indicated by similar experiments performed with higher MR concentration (500 nM instead of 125 nM M₂R₂ concentrations for all TelN variants) demonstrating reproducibility across different conditions. For Figure 5, however, we are unable to provide additional repeat experiments due to limitations in reagent availability. Considering the robust effect seen with Ec MR controls and the presence of multiple samples in the dilution series, we are nevertheless confident about the conclusion.

Minor comments:

A better explanation of how the gels were quantified should be provided. Were the products included in the analysis, or was it just the decrease in the substrate band that was measured?

As also stated above, we have removed the band quantification and instead show the bands also at different contrast settings.

In our original approach, gel band quantification was performed using ImageQuant TL software (version 8.2.0, GE Healthcare). For each gel, individual lanes were defined using either fixed-width boundaries (95-103 pixels) or automatic edge detection, depending on the gel quality and band definition. Band volumes were calculated using rolling ball background subtraction (radius 180 pixels) with automatic band detection. Substrate degradation was assessed by measuring the integrated density (volume) of the remaining full-length (or near full-length) substrate bands under different treatment conditions. The band volume values were plotted directly to compare substrate levels across treatment groups.

We now present the data as two gel panels: an exposure showing the full reaction profile, and another exposure focusing on the substrate bands to clearly demonstrate dose-dependent protection. Additional replicate experiments including ATP-only controls (confirming no contamination from ATP stocks) and experiments at 500 nM M₂R₂ concentrations, are provided in the supplementary data. This approach provides more direct visualization of the biological phenomenon with comprehensive control validation.

I felt like the Results jump rather abruptly from B. subtilis chromosome assays to E. coli plasmid experiments. Maybe the addition of a few linking sentences would improve this transition.

---

Upon re-reading the manuscript we agree with this assertion and have added further information to provide a smoother transition.

A comment on the stoichiometry of TelN and genome ends during phage replication would be useful.

Our in vitro data suggest that effective protection can be achieved at relatively low TelN:DNA ratios in vitro, consistent with the notion of formation of stable, protective nucleoprotein structures. We unfortunately do not currently have information on the copy number of TelN per cell or per hairpin end. It is not easy to obtain reliable values for these numbers. However, we can speculate that multiple TelN proteins are present due to the presence of three copies of a DNA sequence motif (binding to CTD1) in each telomeric DNA, consistent with the formation of stable, protective nucleoprotein structures.

Reviewer #3 (Significance (Required)):

General assessment:

Strengths: A nice combination of genetics and biochemistry convincingly demonstrates that TelN protects linear chromosomes/replicons from MR-dependent degradation independent of its cleavage-ligase activity. It does this by binding to the hairpin DNA ends in a sequence specific fashion and the species specificity suggests a direct physical interaction, which likely inhibits the nuclease activity of the MR complex

Limitations: The lack of characterization of the putative physical interaction between TelN and the MR complex is considered a weakness.

Advance: The manuscript fills in a mechanistic gap between protelomerase-mediated telomere formation and maintenance by demonstrating a protective/capping role. This is the first quantitative analysis of DNA-end protection from MR nuclease activity by TelN.

Audience: Readers interested in bacterial chromosome biology, DNA repair, the parallels to eukaryotic shelterin will be interesting to the broader telomere and genome stability communities.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors investigate how the N15 phage protelomerase TelN protects linear chromosomes that terminate in hairpin structures (a sort of telomere). In E. coli and B. subtilis cells, removal or truncation of telN reduces transformation/survival of linear DNA, whereas complementation with full‑length or a catalytically inactive TelN restores viability, consistent with TelN playing a non‑enzymatic capping function.

In vitro, TelN binds hairpin substrates with  moderate affinity and protects them from the nuclease activity of the Mre11/Rad50 complex. The authors propose that TelN originated as an early, sequence‑specific barrier against MR‑mediated DNA end processing, establishing fundamental principles of telomere protection that persist from bacteria to eukaryotes.

Major comments:

The manuscript convincingly shows that TelN can functionally block the Mre11‑Rad50 (MR) nuclease on a hair‑pin DNA end in a sequence specific manner (suggesting a physical interaction), but it doesn't directly demonstrate this. A simple pull-down or equilibrium binding method would useful in proving a physical interaction.

The MR complex requires ATP hydrolysis for resection of DNA ends. It would be a nice addition to the manuscript if the effect of TelN of Rad50 ATPase activity was tested.

The bar plot on Fig 3B indicates that the experiments are performed in triplicate. The statistical significance of the differences between conditions should be determined. The same general comment could be made regarding the quantification of the polyacrylamide gels - how reproducible are these values?

Minor comments:

A better explanation of how the gels were quantified should be provided. Were the products included in the analysis, or was it just the decrease in the substrate band that was measured?

I felt like the Results jump rather abruptly from B. subtilis chromosome assays to E. coli plasmid experiments. Maybe the addition of a few linking sentences would improve this transition.

A comment on the stoichiometry of TelN and genome ends during phage replication would be useful.

Significance

General assessment:

Strengths: A nice combination of genetics and biochemistry convincingly demonstrates that TelN protects linear chromosomes/replicons from MR-dependent degradation independent of its cleavage-ligase activity. It does this by binding to the hairpin DNA ends in a sequence specific fashion and the species specificity suggests a direct physical interaction, which likely inhibits the nuclease activity of the MR complex

Limitations: The lack of characterization of the putative physical interaction between TelN and the MR complex is considered a weakness.

Advance: The manuscript fills in a mechanistic gap between protelomerase‑mediated telomere formation and maintenance by demonstrating a protective/capping role. This is the first quantitative analysis of DNA-end protection from MR nuclease activity by TelN.

Audience: Readers interested in bacterial chromosome biology, DNA repair, the parallels to eukaryotic shelterin will be interesting to the broader telomere and genome‑stability communities.

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Referee #2

Evidence, reproducibility and clarity

The paper is well-presented and well-written throughout. The paper shows convincingly that TelN protects hairpin DNA ends from the activity of SbcCD, presumably providing a protection mechanism for N15 phage DNA in vivo. Furthermore, this protection activity is shown not to require the catalytic (resolvase) activity of TelN, nor its poorly characterised C-terminal domain. The paper also suggests that this inhibition acts both at the level of competition for the DNA hairpin end and at the level of a direct protein:protein interaction between TelN and MR. An (acknowledged) weakness is that there is no real insight into the protein:protein interaction suggested by the experiments shown in Figure 5. Ideally, the protein:protein interaction interface would be identified and mutations in this interface would be shown to reduce hairpin protection.

Specific comments/questions

(1) What pathway (in vivo) leads to inactivation of linear hairpin DNA - one suspects that cleavage by SbcCD at the hairpins is probably not the full story. Presumably SbcCD cleavage facilitates further processing by other long range resection systems such as RecBCD, Exo1, RecQ/J etc. Would it be appropriate to view the hairpin as an adaption to protect against these nucleases, which then must be complemented with a mechanism to suppress SbcCD?

(2) Section starting "Direct inhibition of MR by TelN in vitro". What is the word direct supposed to convey here? To me it suggests that the inhibition is via direct interaction of TelN with MR (rather than, for example, a result of competition for the hairpin DNA end) which is not shown here. Suggest either defining or removing the word direct. This point gains more importance considering that differentiating between inhibition mechanisms becomes a focus of later parts of the paper.

(3) Figure 2B - Why no control lane without MR? - this is a basic control to show that he degradation we are seeing in the absence of TelN is MR-dependent. Formally, as shown, the degradation could be caused by the ATP stock.

(4) Why not use B. subtilis SbcCD for the species specificity experiment? Also, is it not surprising that TelN yielded zero protection against MRX given that the DNA sequence specificity experiments above suggest competition for DNA substrate is part of the inhibition mechanism?

(5) If the authors felt it appropriate, I thought there was scope for further discussion/introductory material. There are strong parallels here with mechanisms used by phage to protect themselves from the activities of RecBCD, which include both proteins that protect DNA ends like T4 gene 2, we well as proteins that bind directly to RecBCD to inactivate it like lambda Gam. As such, the work here will appeal as much to those interested in bacterial defence systems / phage:host interactions as it does to those interested in telomere biology. Especially significant is the inhibition of DNA end processing factors by lambda Gam since this protein is reported to interact with both RecBCD and SbcCD (PMID: 2531105).

(6) Just a gripe really: it seems to be 'de rigeur' at the moment to re-name bacterial proteins for their human orthologues, presumably to elevate the perceived importance of the work(?), but it is not a practice I think is terribly helpful as it causes issues when searching literature. Minimally it would be great if the authors could ensure they add SbcCD as a keyword for search purposes.

Referee cross-commenting

I have nothing to add. The reviewers comments are all broadly positive and and consistent.

Significance

This is an excellent paper unveiling a phage encoded "counter-defence" mechanism designed to protect phage DNA from degradation. It will be of special interest to those studying telomere biology of phage:host interactions.

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Referee #1

Evidence, reproducibility and clarity

This study addresses how the bacterial telomere protein TelN protect telomere ends against the action of the Mre11-Rad50 nuclease (MR). This protection is essential for the stability of hairpin-ended linear plasmid and chromosomes in bacteria but had not been explored before. The authors demonstrates that TelN is necessary and sufficient to block MR-dependent DNA cleavage when bound to its specific telomere sequence. By combining elegant genetics and biochemical approaches, it convincingly shows that TelN-dependent inhibition likely involves a specific interaction between TelN and the MR complex. The manuscript is well written, easy to read and focused on the relevant information. The claims and the conclusions are supported by the data. There is no over-interpretation.

Comments:

• Figure 1B, unnormalized transformation efficiency would be useful to show in SI
• Figures 2B, 2C, 3C, 3D, 4C, 5A and 5B: quantification of independent experiments should be added

Referee cross-commenting

Perfect for me. It seems that there is a consensus.

Significance

This pioneering study provides a very strong basis for a new understanding of telomeres in bacteria and offers fascinating evolutionary perspectives when compared to similar mechanisms active at telomeres in eukaryotic cells.

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Reply to the reviewers

We are very grateful for the positive feedback from all three reviewers. Below, we address each point in detail and outline proposed experiments and revision plans, with changes indicated by an underscore.

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

In this paper "Magnesium depletion unleashes two unusual modes of colistin resistance with different fitness costs," the authors examine how Pseudomonas aeruginosa evolves resistance to colistin, a last-resort antibiotic for multidrug-resistant Gram-negative infections. Although colistin resistance is a major clinical challenge, its underlying mechanisms, particularly under nutrient-limited conditions typical of infections, are not fully understood. The study shows that under low magnesium (Mg²_⁺_) conditions-mimicking infection or biofilm stress-P. aeruginosa can develop colistin resistance via two distinct genetic pathways, each with unique fitness costs. The first involves mutations in genes such as htrB2 and lpxO2, granting strong resistance but compromising the outer membrane and increasing susceptibility to other antibiotics. The second involves regulatory mutations (e.g., in the oprH/phoP/phoQ promoter) that confer resistance with minimal membrane defects and generally lower fitness costs. These resistance strategies lead to different trade-offs: membrane-compromising mutations reduce bacterial fitness without colistin, while regulatory mutations typically avoid these penalties, with context-dependent effects. The study underscores clinical relevance, noting that in infections-such as in cystic fibrosis-other microbes like Candida albicans may deplete magnesium, indirectly promoting resistance evolution. Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

Work is timely and important. Colistin resistance represents an urgent threat as colistin is a last-resort antibiotic used against multidrug-resistant Gram-negative pathogens. Insights into mechanisms evolving under nutrient limitation are highly relevant given the prevalence of such environmental conditions during infection and microbial biofilm growth. The study reveals two previously uncharacterized pathways to colistin resistance in P. aeruginosa triggered by magnesium (Mg²_⁺_) depletion, each with distinct genetic signatures and trade-offs. This finding directly impacts the understanding of polymicrobial infection dynamics, especially where magnesium sequestration by fungi/ or other microbes may occur. The identification of fitness costs and pleiotropic effects associated with specific resistance mutations provides crucial guidance for clinicians considering antibiotic stewardship and combination therapy strategies.

__

We thank the reviewer for their summary of our study and its potential impact.

__Drawbacks

• Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.__

In our revision, we now explicitly point out that the magnesium limitation we have observed broadly applies to Gram-negative bacteria, as we demonstrated in our previous PLOS Biology paper. Therefore, we expect the same themes (and even genes, which are broadly conserved) to apply to Gram-negative bacteria in general. However, a full-fledged experimental study of other Gram-negative pathogens is outside the scope of our current study, which required a 90-day experimental evolution.

__Strengths

• Experimental evolution: This work uses laboratory evolution under controlled Mg²_⁺_-limited conditions to simulate selection pressures relevant to infection microenvironments. • Genetics: Systematic identification and functional validation of key mutations-particularly in htrB2, lpxO2, and the oprH/phoP/phoQ promoter-give mechanistic depth to the findings. • Two distinct resistance modes: Evidence for (i) one pathway leading to colistin resistance via htrB2 mutations, resulting in high resistance but significant membrane integrity loss and increased susceptibility to other antibiotics. (ii) a second pathway providing resistance without compromising membrane integrity, highlighting evolutionary flexibility and ecological implications. • Fitness assessments: measurement of the costs associated with each resistance strategy, both in terms of membrane integrity and susceptibility to other agents. • Relevance: Connection to natural scenarios, such as magnesium sequestration by fungi (e.g. Candida albicans) in polymicrobial environments, underscores the ecological and clinical significance. • This manuscript is well written with clearly logical hypothesis testing__

We thank the reviewer for their appraisal, especially for recognizing the rigor and broader biological implications of our study.

__Drawbacks

• Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.__

We agree with the reviewer's point about broader applicability in other Gram-negative bacteria, as many of the lipid A biosynthesis genes are conserved among diverse bacterial lineages. We will include this point in our revised Discussion to suggest relevance to other Gram-negative bacteria:

"We previously showed that magnesium sequestration by fungi applies not only to P. aeruginosa but to other Gram-negative bacteria as well (ref). Our current study lays a foundation for developing evolution-guided strategies to combat multidrug-resistant P. aeruginosa and other Gram-negative bacteria that can also acquire colistin resistance. Since many other antibiotic mechanisms are similarly dependent on metal ions (refs), our work suggests that nutritional competition for metal ions may alter initial antibiotic resistance in Gram-negative bacteria and potentiate new evolutionary pathways of antibiotic resistance."

• __ Mechanistic depth: Some inferred mechanisms (e.g., the precise molecular impact of late-occurring adaptive mutations) merit deeper biochemical analysis.__ We will emphasize in our Revision that the MS data of endpoint clones and triple mutants reveal that their lipid A structures are identical. This suggests that the role of other late-occurring mutations in enhancing resistance is likely through lipid A-independent pathways.

• __ Results Lines 414- 423: While correlation is most what makes sense for some drugs, causality is implied (membrane defects increase susceptibility), but could be strengthened by directly measuring antibiotic uptake (e.g., fluorescence) or membrane permeability for these 3 antibiotics.__ We thank the reviewer for highlighting the issue of causality. For the three antibiotics tested, the most direct way to measure their effect is by measuring their impact on bacterial growth directly, which is what we have done. Our membrane permeability assay using NpN uptake operates under the same conditions suggested by the reviewer and directly measures molecular uptake. Moreover, only fluorescently labeled vancomycin is commercially available among the three antibiotics tested. Since it binds to the cell wall, its utility to measure membrane defects is more limited than the NpN assay we have already used. However, in response to this comment, we will make clear in our revision that we infer that increased susceptibility to other antibiotics is due to their increased membrane permeability.

__ o Effect is mild and mostly not significant. It is also not clear whether authors only tested a handful of mutants shown in Fig. 7B-D or whether other clones were also tested. The sample of endpoints (P2, P5, P8) covers well-characterized lineages, but additional evolved clones or a broader panel could boost generality about other antibiotics. The authors note "significantly lower MICs" statistical treatment is implied; explicit statistical values and replicate numbers should be given in the text or figures.__

We slightly disagree with the reviewer that the results are not significant. Even two-to-three-fold differences in MICs translate to large differences in microbial competition. These three endpoint clones are representative of all eight evolved strains after 90-day evolution experiments. Moreover, we will emphasize in the Revision that we have tested all the mutations found in the endpoint clones; we know what these are from whole genome sequencing of multiple endpoint clones. In addition, we will explicitly state the p-value in the legend of Figure 7.

• __ The structural or physiological nature of "mild" vs. "severe" membrane defects could be better defined/quantified.__ Although we agree with the reviewer's suggestion, the variability of the SEM assay makes the classification of membrane defects based on cell morphology hard to quantify. We therefore only use the SEM images as representative of the various defects observed. For a more quantitative assay of the membrane defects, we instead rely on the standard NpN uptake assay to quantify membrane permeability as a quantifiable readout for membrane defects.

• __ Quantitative limits: Authors should add in the discussion that statistical robustness could be strengthened-for example, by including longer-term evolutionary predictions.__ We are not sure what the reviewer means and so cannot address this point completely. We ask the reviewer to rephrase this point, and we will address it to the best of our abilities.

• __ in vivo relevance: While the ecological context is discussed, direct in vivo confirmation (e.g., in animal infection models) of the observed resistance trajectories would increase translational impact and relevance.__ We agree with the reviewer's point. However, it is not trivial to directly perform evolution experiments of microbes in animal models. There are only a handful of labs worldwide that have working CF-relevant animal models. However, the colistin resistance mutations we identified provide a tool to look deeper into how colistin-resistant P. aeruginosa can evolve in vivo.

• __ Some sections are repetitive or overly detailed; condense where possible (especially on mutation lists and background for each claim).__

We will condense our manuscript as the reviewer suggested in our revision. Adding a graphical summary as suggested will also allow us to be more succinct in our description.

__Other comments

• Authors should provide clarification on how the Mg²_⁺_ concentrations used in vitro compare to those found in clinically relevant infection settings. This would be helpful to enhance significance.__

We thank the reviewer for raising this good point. Based on our previous work, we know the Mg2+ levels in our model (0.3-0.45mM) are within the physiological range of Mg2+ in infection settings (0.1-0.8mM). We will highlight this point in the introduction.

• __ Authors should explicitly report statistical methods (e.g., types of tests, adjustments for multiple comparisons) in figure legends for reproducibility.__

We will include the details of our statistical tests in each panel of figures both in the main text and the supplement.

• __ Nomenclature for key mutations and their position within the genetic context (e.g., htrB2 mutation specifics) could be more detailed in figures or supplemental materials.__

We will name each of the particular mutations tested to be specific about the nature of all the evolved mutations in our figure legends.

• __ The manuscript could benefit from a graphical summary illustrating the two distinct evolutionary pathways and their respective fitness landscapes.__ We thank the reviewer for this suggestion to enhance the clarity of our work. We will make a new graphical summary highlighting two different evolutionary pathways as a new figure.

• __ A brief discussion of therapeutic implications-such as combining colistin with agents that target membrane integrity-would help bridge the gap from mechanism to clinical management.__ In our discussion, we have suggested that collateral sensitivity (line 446-453) and PhoPQ kinase inhibitors (line 512-515) could be exploited to combat colistin resistance. To make this point more clearly, we will slightly expand our Discussion to include the therapeutic implications of our study.

• __ Additional discussion on whether the fitness costs are reversible or can be compensated by further adaptation would be valuable for long-term dynamics.__ We thank the reviewer for raising this interesting point. The evolution trajectory of P8 suggests that fitness costs can be compensated by later-occurring mutations during evolution. We will further discuss this point to highlight the importance of understanding the mutational dynamics of antibiotic resistance evolution.

• __ It would be valuable for the authors to comment on, or further analyze, whether there is a direct association between specific fitness costs and sensitivity to other antibiotics. Such information could inform on evolutionary constraints and possible trade-offs relevant to clinical settings.__

We will include a supplemental figure showing the correlation between fitness costs and antibiotic susceptibility for P2, P5, and P8.

__ Main figures and support for claims

The main and supplementary figures comprehensively illustrate the evolutionary trajectories, genetic bases, and phenotypic outcomes associated with colistin resistance under magnesium depletion in P. aeruginosa. The figures effectively detail: • Genetic pathways involved including the experimental evolution design (colistin selection under Mg²_⁺_ depletion), whole-genome sequencing results, and timelines of observed mutations (e.g., in htrB2, lpxO2, oprH/phoP/phoQ promoter, PA4824). • Phenotypes and biochemical analyses such as lipid A structure (via mass spectrometry), minimum inhibitory concentration (MIC) assays, and epistasis analyses between mutations are depicted. • Fitness trade-offs are demonstrated using bacterial survival, membrane integrity (e.g., scanning electron microscopy images), membrane permeability assays (NPN uptake), and competitive fitness assays. • Mechanistic claims about the necessity of early mutations, the requirement of the PhoPQ pathway at different evolutionary stages, and the fitness cost imposed by certain resistance mutations. To further enhance the rigor and clarity of the manuscript, the authors should implement the following improvements: • Labelling consistency: In some instances, figure legends could provide more granular detail about specific mutations (e.g., positions of amino acid changes). • Graphical summary: A schematic summary figure that visually integrates the three main evolutionary resistance trajectories, the mutational order, corresponding lipid A changes, and fitness costs, would enhance readability. • Replicates: Plots should more thoroughly indicate the number of replicates and show individual data points (not just means {plus minus} SD), add number of replicates in each experiment. • Supplementary: figures referenced in the text (e.g., lipid A structures or mutation reversion outcomes) should be made more prominent or better cross-referenced from the main results section. Authors should highlight when supplementary data provide critical functional confirmation (e.g., confirming mutation function or fitness reversal).__

We thank the reviewer for their appreciation of our work and constructive feedback.

__Statistics

The authors have appropriately incorporated statistical analyses throughout the figures. To enhance the robustness and credibility of their findings, authors should also cross-check • Tests in legends: Every figure and supplementary figure should clearly state the type of statistical test used, how many biological replicates, and any corrections for multiple comparisons.__

As mentioned above, we will provide more details about the statistical tests of each panel.

• __ Effect sizes: Where appropriate, reporting effect sizes-rather than just p values-would contextualize the biological impact.__ We agree with the reviewer; we will mention the magnitude of MIC changes in the corresponding figure legends.

• __ Raw data accessibility: For full transparency, consider sharing underlying raw data and analysis scripts.

__ We will provide the raw data of each panel.

__Overall, the main and supplementary figures effectively illustrate and substantiate the key claims-particularly the alternative molecular pathways, phenotypic trade-offs, and the role of environmental magnesium in mediating colistin resistance. Statistical analysis is generally robust and appropriately presented throughout, though improvements could include more explicit reporting, additional controls, and accessible raw data. The visual and quantitative data in the figures provide support for the authors' conclusions about the evolution of antibiotic resistance under nutrient limitation in microbial environments. Understanding these alternative pathways is important for designing better treatment strategies and for predicting how resistance might evolve under varying clinical and environmental conditions.

__

We thank the reviewer for their positive assessment.

__ Reviewer #1 (Significance (Required)):

Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.__

We sincerely thank the reviewer for constructive and thoughtful feedback and the acknowledgement of our figure presentation and experimental design. We feel very encouraged by the reviewer's perspective that our study provides unique insights into resistance evolution in polymicrobial environments and may inform therapeutic strategies.

__My expertise: Gut microbiome, gut microbiota resilience, ecology, and evolution in microbial communities, antimicrobial resistance, high-throughput drug-bacteria interactions

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

Summary: The paper by Hsieh and colleagues unravels the molecular basis of colistin resistance in Pseudomonas aeruginosa under low magnesium (Mg2+) conditions. Colistin is a last resort antibiotic that compromises bacterial cell wall integrity. Bacteria can respond (phenotypically and genotypically) to colistin by modifying membrane-anchored lipopolysaccharides. Mg2+ depletion can trigger similar responses. In their study, Hsieh et al. find that Mg2+ depletion (induced by a co-infecting fungal pathogen, Candida albicans) leads to evolutionary trajectories and resistance mechanisms that differ from those observed under Mg-rich conditions. The authors conducted a series of detailed genetic, chemical and fitness-based experiments to elucidate the molecular, physiological and evolutionary basis of these new resistance mechanisms.__

We thank the reviewer for their summary of our study.__

Major comments: __ 1. The authors reconstituted key mutations observed during experimental evolution in the ancestral background. Moreover, they took clones from the final stage of the evolution experiment and restored the ancestral state of the mutated genes. This dual approach is extremely strong and allows to decipher the causal effects of colistin resistance. I like to applaud the authors for this rigorous approach.

We thank the reviewer's appreciation about the rigor and comprehensive analyses of our study.

2. I understand that this work focusses on evolved mutants isolated from a previous experiment. The focus is on Mg2+ limitation. However, it would still have been nice to include a characterised colistin resistent strain featuring more standard resistance mechanisms. How different would such a strain be in the analyses shown in Fig. 3? Would morphological changes (Fig. 5A), fitness trade-offs (Fig. 6) and collateral sensitivity (Fig. 7) also occur in such a mutant. I do not regard it as imperative to include data from such a strain. But putting the new data into context (at least in the discussion) would clearly increase the overall impact of this work.

We thank the reviewer for raising this fascinating and vital point. We will address the point in our Revision using the monoculture (high Mg2+) evolved strains, which acquired many known mutations for colistin resistance, as our reference. We will provide a supplemental figure about the membrane permeability, fitness costs, and collateral sensitivity of monoculture evolved strains. We will also contrast their difference from co-culture evolved strains in the revised Discussion.__

1. I recommend to discuss the findings in the context of the work conducted by Jochumsen et al. 2016 Nature Communications https://doi.org/10.1038/ncomms13002. To me, this is one of the most insightful papers on the genetic basis and epistasis of colistin resistance.__

We thank the reviewer for pointing out this important reference. We will include this reference and its findings in the Discussion.

__Minor comments:

1. First section of results and Fig. 1. It is unclear what parts are repetition from the ref. 37 and what is new. Please clarify.__

We thank the reviewer for this suggestion. Figures 1A and 1B summarize the previous paper; all other panels are new data. We will make this clear in the revised text and figure legend.

5. MIC-data (e.g. Fig. 2) come in discrete categories (based on the underlying dilution series). This comes with some challenges for statistical analysis. First, linear models like ANOVAs are based on normally distributed residuals. This is violated with discrete data distributions. Second, there is often no within-treatment variation (e.g., Fig. 2B), which makes statistical analyses obsolete. These points need to be addressed. Moreover, how is it possible to have subtle variations in MIC (e.g., Fig. 2A, P2 endpoint clone) with classic dilution series (as indicated on the y-axis, 128, 256, 512)? Please explain.

We agree with the reviewer that statistical analysis of MIC data is not straightforward. ANOVAs are not well-suited for this type of discrete data, and the lack of variation within replicates reduces the power of non-parametric tests such as the Mann-Whitney U test. To improve the statistical reporting of MIC data, we will apply non-parametric tests and include effect size measurements, as recommended by Reviewer 1.

Moreover, the design of dilution series may underestimate the true nature of antibiotic susceptibility. To address these issues, we have also performed survival assays to assess colistin resistance in both the endpoint and reversion strains; we will also include statistics to assess the significance of their different survival frequencies.

We thank the reviewer for highlighting the point about subtle variations in a classical dilution series. Our endpoint strains grew robustly in media containing 192 μg/mL colistin-the highest concentration used in our evolution experiment. To more accurately determine and compare their maximum MICs, we expanded the colistin concentration range using finer fold increases (1.5×, 2×, 2.5×, 3×, 3.5×, and 4×) from 192 to 768 μg/mL. We will update these details in the Materials & Methods.

__ Lines 264-269. This analysis focusses on enzyme impairment. However, mutations could also change enzyme activity. Could any of these mutations have such an effect?__

The answer is "yes". As evolved strains with lpxA mutation still have lipid A, we suspect this mutation does not altogether abolish lipid A synthesis. However, this mutation could affect the amount of lipid A or change enzyme specificity. These are interesting ideas for further investigation, but they fall beyond the scope of our current study. We will, however, include the requested detail in the discussion.

__ Figure 5A. Some arrows seem to be out of place and point at void spaces. Please check.__

We thank the reviewer for pointing out this error, which we will correct.

8. The use of polymyxin B is not well justified (Fig. 5 and Fig. S13). Did the authors aim to test whether there is cross-resistance to other antimicrobial peptides?

We will more clearly justify our choice of using polymyxin B for directly assaying binding of polymyxin antibiotics to bacterial cells using fluorescence-labeled polymyxins, since no such reagents exist for colistin and since previous studies (including ours) have shown similarity of susceptibility to colistin and polymyxin B:

"Although P2 and P5 endpoint clones have more permeable membranes, they exhibited greater resistance to polymyxin antibiotics, including colistin (polymyxin E) (Fig. 5D), and polymyxin B (Fig. S13A) than WT cells. To investigate how membrane-compromised cells gain increased resistance to antibiotics that target the outer membrane, we used dansyl-labeled polymyxin B [51] to quantify the binding of polymyxins to P. aeruginosa; dansyl-labeled polymyxin fluoresces upon binding the hydrophobic portion of bacterial membranes. We used polymyxin B binding as a surrogate for how bacterial cells bind to all polymyxin antibiotics, including colistin."

__ Line 564. Please indicate the dilution factor used.__

Thank you for pointing out this inadvertent omission. We will update our Materials & Methods accordingly, as in response to the Reviewer 2's comment 5.

__Reviewer #2 (Significance (Required)):

This is a very strong and well designed study. It provides novel and relevant insights into the resistance mechanisms against an important last resort antibiotic.__

We sincerely thank the reviewer for their thoughtful summary and generous evaluation of our work.

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

This manuscript reports on biologically interesting and clinically-relevant findings, that upon passaging in the presence of spent media from C. albicans, P. aeruginosa develops resistance to colistin through lipid A modifications. The authors thoroughly characterize novel lipid A structures seen in their resistant mutants, and test a variety of genetically constructed mutants to determine the contributions of specific mutant alleles to resistance.__

We thank the reviewer for the appreciation of our experimental design and comprehensive genetic and biochemical analyses of our evolved strains.

However, additional experiments are needed to demonstrate the specific role and necessity of the lipid modifications for colistin resistance.

We are also grateful for the reviewer's feedback and constructive criticisms to improve the clarity and impact of our manuscript. We have listed detailed responses to the reviewer below.

1. __ Evidence that the lipid A mutations are causal for colistin resistance is sparse:
2. Both the htrB2 mutations (in P2 and P5) are posited to be loss-of-function alleles. However, the phenotypes of the individual alleles are different (shown in Fig 2A and 2B). While the mutation in P2 shows a ~2x increase in resistance, the mutation in P5 does not. Thus it is not clear that the specific lipid A modifications seen in the htrB2 mutants are sufficient to confer colistin resistance. Can the authors test a clean deletion mutant of htrB2? Further, reversion of the htrB2 mutation in P2 has only a mild effect on colistin resistance, while reversion in P5 leads to a ~3-4x reduction in colistin resistance (Fig. S3), once again making it hard to parse out the exact effect of the lipid A modifications seen in the htrB2 mutants.
3. Similarly, a single lpxO2 mutation does not have any effect on colistin resistance (in P5), indicating that the modifications seen in this mutant are not sufficient to lead to resistance.__ We thank the reviewer for making this suggestion. The reviewer is correct that a clean deletion will directly assess the effects of htrB2 mutations. We will make htrB2 deletion in WT and the triple mutants and endpoint clones of P2 and P5 to check the effect of htrB2 deletion on colistin resistance.

Additionally, as Reviewer 2 pointed out, both mutation reconstruction and reversion experiments are required for understanding the roles of each mutation and interactions among different mutations in contributing to resistance. Combining all the results of htrB2 and lpxO2 mutations in these two orthogonal genetic experiments, it is the synergistic interactions among these mutations that lead to enhanced resistance after evolution. This explains why we saw genetic background effects of htrB2 mutation (P2 vs P5) and why each single mutation is required for resistance but doesn't contribute to resistance significantly by itself.

- In P8, the effect of a single lpxA mutation is not tested. Further, the resistance of a P-oprH + lpxA mutant is the same as that of just the P-oprH mutant, indicating that the lpxA mutation likely does not directly alter colistin resistance. It is possible that mutations in lpxA were selected to compensate for fitness defects resulting from the other mutations, or for adaptation to some other component of the media conditions.

This is an excellent suggestion. We will assess the MIC and fitness of reconstructed strains with the lpxA mutation to update the role of this mutation.

- While reversion of the htrB2 and lpxO2 mutations do lead to ~3-4x reduced resistance in P5 indicating some contribution of these mutations, it is specific to this population, and thus not clear whether it is due to the specific lipid A modifications (some of which are seen in the other populations too). A specific combination of lipid A modifications may confer colistin resistance, but this needs to be demonstrated by generating just those clean deletion mutants and showing an effect on resistance.

In response to this comment and comment 1, we will make lpxO2 deletions in WT, the triple mutant and the endpoint clone of P5 to test colistin resistance. However, our results of reverting single htrB2 or lpxO2 mutation to WT are robust and use two independent assays, including the standard MIC test and colistin survival assay. So, we are confident that each mutation is necessary for enhancing colistin resistance.

__ Overall, given the high levels of colistin resistance still exhibited by single mutant revertants (Fig. S3) and the absence of double or triple revertants, it is hard to come to any conclusions regarding causality. This is especially the case for P8 but also true of P2 and P5. What are the other mutations in these populations, and what role do they play in colistin resistance?__

We respectfully disagree with the reviewer on this point. One point that we have made and will re-emphasize in our Revision is that we have assayed all the mutations in these populations; this is one of the advantages of our experimental evolution and genome sequencing strategy. All the mutations that could play a role in colistin resistance have therefore been tested. Furthermore, due to genetic epistasis of mutations in different evolutionary lineages, we do not necessarily expect that a single revertant would altogether abolish colistin resistance, as has been demonstrated in several previous studies. As Reviewer 2 pointed out, combining mutation reconstruction and reversion is the best way to establish causality, and we have done so. Therefore, it is not correct to say that we cannot come to 'any conclusions regarding causality'.

__ Figure 4 is titled "The PhoPQ pathway synergizes with early-arising mutations to confer colistin resistance.", but instead what this figure shows is that the mutation upstream of oprH increases PhoP activity. I'm not sure what the synergy here is. The same is true for the section starting on line 276. Further, the first sentence of that section states "We next investigated why the mutations conferring robust colistin resistance in low Mg2+ conditions are not observed in Mg2+ replete conditions.". However, there are no experiments there testing whether the mutations conferred resistance in Mg2+ conditions, instead the authors just test whether the mutations they are studying increase PhoP activity, and require PhoPQ to confer resistance.__

We thank the reviewer for raising this point. We apologize for the unclear writing. We will use this opportunity to improve the clarity of this section by rewriting it to focus on two points: 1. Evolved resistance is PhoPQ-dependent, instead of PmrAB-dependent. 2. Two lineages evolved enhanced resistance by boosting PhoPQ activity in both high and low Mg2+ conditions. We will also remove the statement highlighted by the reviewer from this section that obfuscates the motivation of this section. We feel this approach will more clearly show how lipid A-related mutations contribute to resistance in low Mg2+.

__ The authors claim that the identified mutations did not appear in the high magnesium conditions because they had a fitness cost under those conditions, but figure 6A shows that the evolved strains have fitness costs in low magnesium conditions as well. Further, the authors suggest that because the studied mutations act via increased PhoPQ activity, they do not lead to resistance under high magnesium conditions (lines 376-379). However, the increased PhoPQ activity is mediated by the P-oprH mutation in the isolates which likely increases PhoPQ activity even in high magnesium conditions. Overall, it is not clear why the mutations in the low magnesium condition were not selected for under high magnesium conditions.__

The reviewer is correct about the fitness cost in high Mg2+ and low Mg2+ conditions. These fitness experiments were carried out in the absence of colistin, which explains the finding that there are fitness defects in both conditions. As is well known, evolution for antibiotic resistance will ultimately select for resistant mutants, despite their fitness costs. In contrast, colistin MIC of these endpoint strains in high Mg2+ conditions was still much lower than the colistin concentration we applied during evolution (Fig. S15), indicating it is much less likely for these mutations to be selected for in high Mg2+. We will clarify this point in our revised Results and Discussion.

We agree with the reviewer about the P-oprH mutations (PhoPQ expression) and will note that, unlike the other mutations, it is not clear why these emerge only in the low Mg2+ condition.

__ The authors used C. albicans spent BHI media as their low magnesium condition, but this condition has a lot of other C. albicans metabolites that may be affecting the results. It is possible that what the authors are observing is not related to magnesium at all, and the authors should test the phenotypes in normal BHI medium depleted for magnesium or some defined medium where magnesium levels can be controlled.__

We thank the reviewer for mentioning this important point. In our prior PLOS Biology paper (https://doi.org/10.1371/journal.pbio.3002694.g005), we demonstrated that supplementing Mg2+ in evolved co-culture populations reduces colistin resistance, suggesting this evolved resistance is Mg2+ dependent. We also know that the MIC of our endpoint strains in C. albicans-spent BHI with supplemented Mg2+ (MIC of all three endpoint clones is less than 48 mg/mL colistin) is much lower than in C. albicans-spent BHI. We will mention this detail in the paper and include the data in our revision if the reviewer and editor require it.

Other comments: - The authors use MIC assays as well as % survival to measure resistance against colistin, and sometimes use both in the same figure (e.g. Figure 2). This makes direct comparisons difficult. It would be better to consistently use one assay, preferably the MIC, at least in all the main figures. If the survival data needs to be included, it could go in the supplementary figures.

We thank the reviewer for this suggestion. We will move the MIC data of mutation-reversion strains to the main Fig. 2D-F.

- While the mutations seen in the low and high magnesium conditions were shown in the previous manuscript, given the extensive dissection here, it would be useful for readers if the authors gave some details about the serial passaging and evolution experiment, identification of mutations, and some mention of what mutations were seen in high Mg populations.

We will add these details in the introduction.

- Given that oprH is present in an operon, it would be more accurate to call that mutation as being in the promoter of the oprH-phoP-phoQ operon rather than it being an oprH mutation (at least in the text, e.g. lines 127-129).

We agree. We will change this as the reviewer requested.

- Unlike what is stated on lines 287-290, deletion of oprH in P2 leads to a greater than 2x reduction in colistin MIC, suggesting that OprH is playing a role (albeit a smaller role than phoP) - Line 50 has a typo, remove "160". - Line 122: Specify which Pa and Ca strain backgrounds were used. - Line 132: Were representative isolates derived from terminal passages? This should be defined.

We will change these points according to the reviewer's suggestions; we thank them for these suggestions.

- Line 215-219: It is interesting that Pa WT grown in spent medium additionally results in lipid A that is hexa-acylated. Is this sufficient to alter colistin resistance on its own?

We find that WT PAO1 in low Mg2+ conditions has PagP-mediated acylation, which can slightly increase colistin resistance, but not to the extent of resistance as our evolved strains.

- It would be useful to see a PCA plot for the samples shown in figures S6 and S7.

We will include such a plot in Figures S6 and S7

- Fig. S11: What are the colistin MICs of pmrA and phoP deletions in the WT background?

MIC of pmrA and phoP deletions in WT is 1.5ug/mL. We will include these data in the Revision.

- Instead of qualitative data, can the authors quantify cell length and perhaps some measure of cell shape (instead of just showing images in Fig. 5A and S12).

We thank the reviewer for raising this point. A similar comment was raised by Reviewer 1. As it's challenging to quantify membrane changes from the morphological data obtained through SEM (a point which we will now clarify in our Revision), we used a quantifiable NpN uptake assay to quantify membrane defects of our evolved strains.

- What is the WT MIC in high magnesium conditions? Please show that in Fig. S15.

We will include this detail in Fig. S15

- I am not an expert in lipid modifications and structures, but in figure S5, P2 and P4 show high peaks with lower m/z that seem specific to low magnesium conditions, but they are not labeled or discussed. What are these peaks?

We thank the reviewer for bringing up this concern. The unlabeled lipids in these spectra are cardiolipin, not lipid A. These peaks are present in all the samples, and the reason they appear larger in the P1 and P4 low magnesium conditions is that both spectra are scaled to the relative intensity of one another. It is important to note that MALDI-TOF MS is not a quantitative technique, and the relative intensity of the peak heights between two samples should not be used to compare the amounts of lipids in one sample versus another. Therefore, we cannot say that these lipids are present in greater quantities in low magnesium conditions versus high magnesium conditions.

- Lines 357-358 state that "mutant cells minimally bind polymyxin B (Fig. S13B)", but the figure shows increased binding compared to the WT. The legend of the figure also says something similar. Are the phoP pmrA mutants expected to bind more polymyxin B because they can't modify lipid A?

We thank the reviewer for pointing out this substantial error. We will change 'minimally bind' to 'demonstrate increased binding'.

- Given the fitness defects in just regular medium, is the data shown in Figure 7 specific collateral sensitivity to the antibiotics tested? Are there other conditions where P2 and P5 do not show increased sensitivity?

These are all the antibiotics we have tested. It is conceivable that P2 and P5 might not show increased sensitivity to other antibiotics that use the same mode of action as colistin or polymyxin B.

__Reviewer #3 (Significance (Required)):

This study aims to dissect novel mechanisms of colistin resistance in P. aeruginosa that arise upon passaging in C. albicans spent media. While the authors identify novel lipid A modifications associated with the evolved strains, the significance of the modifications for resistance, and the mechanisms for why these evolutionary trajectories were not selected for in high magnesium are not clear from the data presented.__

We thank the reviewer for recognizing the integrity of our work and for the constructive feedback on improving the clarity of our writing. We understand that some concerns may stem from a lack of clarity in our original submission, but that additional genetic experiments are necessary. We have already identified all mutations that arose independently across different lineages and characterized their contributions to resistance, which we believe supports a robust inference of causality. To strengthen our conclusions, we will incorporate additional experiments, including htrB2 deletion, lpxO2 deletion, and lpxA mutation, to better dissect the roles of these genes and mutations in colistin resistance. We hope this revision plan will ameliorate the reviewer's concerns.

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Referee #3

Evidence, reproducibility and clarity

This manuscript reports on biologically interesting and clinically-relevant findings, that upon passaging in the presence of spent media from C. albicans, P. aeruginosa develops resistance to colistin through lipid A modifications. The authors thoroughly characterize novel lipid A structures seen in their resistant mutants, and test a variety of genetically constructed mutants to determine the contributions of specific mutant alleles to resistance. However, additional experiments are needed to demonstrate the specific role and necessity of the lipid modifications for colistin resistance.

1. Evidence that the lipid A mutations are causal for colistin resistance is sparse:
   • Both the htrB2 mutations (in P2 and P5) are posited to be loss-of-function alleles. However, the phenotypes of the individual alleles are different (shown in Fig 2A and 2B). While the mutation in P2 shows a ~2x increase in resistance, the mutation in P5 does not. Thus it is not clear that the specific lipid A modifications seen in the htrB2 mutants are sufficient to confer colistin resistance. Can the authors test a clean deletion mutant of htrB2? Further, reversion of the htrB2 mutation in P2 has only a mild effect on colistin resistance, while reversion in P5 leads to a ~3-4x reduction in colistin resistance (Fig. S3), once again making it hard to parse out the exact effect of the lipid A modifications seen in the htrB2 mutants.
   • Similarly, a single lpxO2 mutation does not have any effect on colistin resistance (in P5), indicating that the modifications seen in this mutant are not sufficient to lead to resistance.
   • In P8, the effect of a single lpxA mutation is not tested. Further, the resistance of a P-oprH + lpxA mutant is the same as that of just the P-oprH mutant, indicating that the lpxA mutation likely does not directly alter colistin resistance. It is possible that mutations in lpxA were selected to compensate for fitness defects resulting from the other mutations, or for adaptation to some other component of the media conditions.
   • While reversion of the htrB2 and lpxO2 mutations do lead to ~3-4x reduced resistance in P5 indicating some contribution of these mutations, it is specific to this population, and thus not clear whether it is due to the specific lipid A modifications (some of which are seen in the other populations too). It is possible that a specific combination of lipid A modifications confers colistin resistance, but this needs to be demonstrated by generating just those clean deletion mutants and showing an effect on resistance.
2. Overall, given the high levels of colistin resistance still exhibited by single mutant revertants (Fig. S3) and the absence of double or triple revertants, it is hard to come to any conclusions regarding causality. This is especially the case for P8 but also true of P2 and P5. What are the other mutations in these populations, and what role do they play in colistin resistance?
3. Figure 4 is titled "The PhoPQ pathway synergizes with early-arising mutations to confer colistin resistance.", but instead what this figure shows is that the mutation upstream of oprH increases PhoP activity. I'm not sure what the synergy here is. The same is true for the section starting on line 276. Further, the first sentence of that section states "We next investigated why the mutations conferring robust colistin resistance in low Mg2+ conditions are not observed in Mg2+ replete conditions.". However, there are no experiments there testing whether the mutations conferred resistance in Mg2+ conditions, instead the authors just test whether the mutations they are studying increase PhoP activity, and require PhoPQ to confer resistance.
4. The authors claim that the identified mutations did not appear in the high magnesium conditions because they had a fitness cost under those conditions, but figure 6A shows that the evolved strains have fitness costs in low magnesium conditions as well. Further, the authors suggest that because the studied mutations act via increased PhoPQ activity, they do not lead to resistance under high magnesium conditions (lines 376-379). However, the increased PhoPQ activity is mediated by the P-oprH mutation in the isolates which likely increases PhoPQ activity even in high magnesium conditions. Overall, it is not clear why the mutations in the low magnesium condition were not selected for under high magnesium conditions.
5. The authors used C. albicans spent BHI media as their low magnesium condition, but this condition has a lot of other C. albicans metabolites that may be affecting the results. It is possible that what the authors are observing is not related to magnesium at all, and the authors should test the phenotypes in normal BHI medium depleted for magnesium or some defined medium where magnesium levels can be controlled.

Other comments:

• The authors use MIC assays as well as % survival to measure resistance against colistin, and sometimes use both in the same figure (e.g. Figure 2). This makes direct comparisons difficult. It would be better to consistently use one assay, preferably the MIC, at least in all the main figures. If the survival data needs to be included, it could go in the supplementary figures.
• While the mutations seen in the low and high magnesium conditions were shown in the previous manuscript, given the extensive dissection here, it would be useful for readers if the authors gave some details about the serial passaging and evolution experiment, identification of mutations, and some mention of what mutations were seen in high Mg populations.
• Given that oprH is present in an operon, it would be more accurate to call that mutation as being in the promoter of the oprH-phoP-phoQ operon rather than it being an oprH mutation (at least in the text, e.g. lines 127-129).
• Unlike what is stated on lines 287-290, deletion of oprH in P2 leads to a greater than 2x reduction in colistin MIC, suggesting that OprH is playing a role (albeit a smaller role than phoP)
• Line 50 has a typo, remove "160".
• Line 122: Specify which Pa and Ca strain backgrounds were used.
• Line 132: Were representative isolates derived from terminal passages? This should be defined.
• Line 215-219: It is interesting that Pa WT grown in spent medium additionally results in lipid A that is hexa-acylated. Is this sufficient to alter colistin resistance on its own?
• It would be useful to see a PCA plot for the samples shown in figures S6 and S7.
• Fig. S11: What are the colistin MICs of pmrA and phoP deletions in the WT background?
• Instead of qualitative data, can the authors quantify cell length and perhaps some measure of cell shape (instead of just showing images in Fig. 5A and S12).
• What is the WT MIC in high magnesium conditions? Please show that in Fig. S15.
• I am not an expert in lipid modifications and structures, but in figure S5, P2 and P4 show high peaks with lower m/z that seem specific to low magnesium conditions, but they are not labeled or discussed. What are these peaks?
• Lines 357-358 state that "mutant cells minimally bind polymyxin B (Fig. S13B)", but the figure shows increased binding compared to the WT. The legend of the figure also says something similar. Are the phoP pmrA mutants expected to bind more polymyxin B because they can't modify lipid A?
• Given the fitness defects in just regular medium, is the data shown in Figure 7 specific collateral sensitivity to the antibiotics tested? Are there other conditions where P2 and P5 do not show increased sensitivity?

Significance

This study aims to dissect novel mechanisms of colistin resistance in P. aeruginosa that arise upon passaging in C. albicans spent media. While the authors identify novel lipid A modifications associated with the evolved strains, the significance of the modifications for resistance, and the mechanisms for why these evolutionary trajectories were not selected for in high magnesium are not clear from the data presented.

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Referee #2

Evidence, reproducibility and clarity

Summary: The paper by Hsieh and colleagues unravels the molecular basis of colistin resistance in Pseudomonas aeruginosa under low magnesium (Mg2+) conditions. Colistin is a last resort antibiotic that compromises bacterial cell wall integrity. Bacteria can respond (phenotypically and genotypically) to colistin by modifying membrane-anchored lipopolysaccharides. Mg2+ depletion can trigger similar responses. In their study, Hsieh et al. find that Mg2+ depletion (induced by a co-infecting fungal pathogen, Candida albicans) leads to evolutionary trajectories and resistance mechanisms that differ from those observed under Mg-rich conditions. The authors conducted a series of detailed genetic, chemical and fitness-based experiments to elucidate the molecular, physiological and evolutionary basis of these new resistance mechanisms.

Major comments:

1. The authors reconstituted key mutations observed during experimental evolution in the ancestral background. Moreover, they took clones from the final stage of the evolution experiment and restored the ancestral state of the mutated genes. This dual approach is extremely strong and allows to decipher the causal effects of colistin resistance. I like to applaud the authors for this rigorous approach.
2. I understand that this work focusses on evolved mutants isolated from a previous experiment. The focus is on Mg2+ limitation. However, it would still have been nice to include a characterised colistin resistent strain featuring more standard resistance mechanisms. How different would such a strain be in the analyses shown in Fig. 3? Would morphological changes (Fig. 5A), fitness trade-offs (Fig. 6) and collateral sensitivity (Fig. 7) also occur in such a mutant. I do not regard it as imperative to include data from such a strain. But putting the new data into context (at least in the discussion) would clearly increase the overall impact of this work.
3. I recommend to discuss the findings in the context of the work conducted by Jochumsen et al. 2016 Nature Communications https://doi.org/10.1038/ncomms13002. To me, this is one of the most insightful papers on the genetic basis and epistasis of colistin resistance.

Minor comments:

1. First section of results and Fig. 1. It is unclear what parts are repetition from the ref. 37 and what is new. Please clarify.
2. MIC-data (e.g. Fig. 2) come in discrete categories (based on the underlying dilution series). This comes with some challenges for statistical analysis. First, linear models like ANOVAs are based on normally distributed residuals. This is violated with discrete data distributions. Second, there is often no within-treatment variation (e.g., Fig. 2B), which makes statistical analyses obsolete. These points need to be addressed. Moreover, how is it possible to have subtle variations in MIC (e.g., Fig. 2A, P2 endpoint clone) with classic dilution series (as indicated on the y-axis, 128, 256, 512)? Please explain.
3. Lines 264-269. This analysis focusses on enzyme impairment. However, mutations could also change enzyme activity. Could any of these mutations have such an effect?
4. Figure 5A. Some arrows seem to be out of place and point at void spaces. Please check.
5. The use of polymyxin B is not well justified (Fig. 5 and Fig. S13). Did the authors aim to test whether there is cross-resistance to other antimicrobial peptides?
6. Line 564. Please indicate the dilution factor used.

Significance

This is a very strong and well designed study. It provides novel and relevant insights into the resistance mechanisms against an important last resort antibiotic.

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Referee #1

Evidence, reproducibility and clarity

In this paper "Magnesium depletion unleashes two unusual modes of colistin resistance with different fitness costs," the authors examine how Pseudomonas aeruginosa evolves resistance to colistin, a last-resort antibiotic for multidrug-resistant Gram-negative infections. Although colistin resistance is a major clinical challenge, its underlying mechanisms, particularly under nutrient-limited conditions typical of infections, are not fully understood.

The study shows that under low magnesium (Mg²⁺) conditions-mimicking infection or biofilm stress-P. aeruginosa can develop colistin resistance via two distinct genetic pathways, each with unique fitness costs. The first involves mutations in genes such as htrB2 and lpxO2, granting strong resistance but compromising the outer membrane and increasing susceptibility to other antibiotics. The second involves regulatory mutations (e.g., in the oprH/phoP/phoQ promoter) that confer resistance with minimal membrane defects and generally lower fitness costs. These resistance strategies lead to different trade-offs: membrane-compromising mutations reduce bacterial fitness without colistin, while regulatory mutations typically avoid these penalties, with context-dependent effects. The study underscores clinical relevance, noting that in infections-such as in cystic fibrosis-other microbes like Candida albicans may deplete magnesium, indirectly promoting resistance evolution. Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

Work is timely and important. Colistin resistance represents an urgent threat as colistin is a last-resort antibiotic used against multidrug-resistant Gram-negative pathogens. Insights into mechanisms evolving under nutrient limitation are highly relevant given the prevalence of such environmental conditions during infection and microbial biofilm growth. The study reveals two previously uncharacterized pathways to colistin resistance in P. aeruginosa triggered by magnesium (Mg²⁺) depletion, each with distinct genetic signatures and trade-offs. This finding directly impacts the understanding of polymicrobial infection dynamics, especially where magnesium sequestration by fungi/ or other microbes may occur. The identification of fitness costs and pleiotropic effects associated with specific resistance mutations provides crucial guidance for clinicians considering antibiotic stewardship and combination therapy strategies.

Strengths

• Experimental evolution: This work uses laboratory evolution under controlled Mg²⁺-limited conditions to simulate selection pressures relevant to infection microenvironments.
• Genetics: Systematic identification and functional validation of key mutations-particularly in htrB2, lpxO2, and the oprH/phoP/phoQ promoter-give mechanistic depth to the findings.
• Two distinct resistance modes: Evidence for (i) one pathway leading to colistin resistance via htrB2 mutations, resulting in high resistance but significant membrane integrity loss and increased susceptibility to other antibiotics. (ii) a second pathway providing resistance without compromising membrane integrity, highlighting evolutionary flexibility and ecological implications.
• Fitness assessments: measurement of the costs associated with each resistance strategy, both in terms of membrane integrity and susceptibility to other agents.
• Relevance: Connection to natural scenarios, such as magnesium sequestration by fungi (e.g. Candida albicans) in polymicrobial environments, underscores the ecological and clinical significance.
• This manuscript is well written with clearly logical hypothesis testing

Drawbacks

• Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.
• Mechanistic depth: Some inferred mechanisms (e.g., the precise molecular impact of late-occurring adaptive mutations) merit deeper biochemical analysis.
• Results Lines 414- 423: While correlation is most what makes sense for some drugs, causality is implied (membrane defects increase susceptibility), but could be strengthened by directly measuring antibiotic uptake (e.g., fluorescence) or membrane permeability for these 3 antibiotics.
  • Effect is mild and mostly not significant. It is also not clear whether authors only tested a handful of mutants shown in Fig. 7B-D or whether other clones were also tested. The sample of endpoints (P2, P5, P8) covers well-characterized lineages, but additional evolved clones or a broader panel could boost generality about other antibiotics. The authors note "significantly lower MICs" statistical treatment is implied; explicit statistical values and replicate numbers should be given in the text or figures.
• The structural or physiological nature of "mild" vs. "severe" membrane defects could be better defined/quantified.
• Quantitative limits: Authors should add in the discussion that statistical robustness could be strengthened-for example, by including longer-term evolutionary predictions.
• in vivo relevance: While the ecological context is discussed, direct in vivo confirmation (e.g., in animal infection models) of the observed resistance trajectories would increase translational impact and relevance.
• Some sections are repetitive or overly detailed; condense where possible (especially on mutation lists and background for each claim).

Other comments

• Authors should provide clarification on how the Mg²⁺ concentrations used in vitro compare to those found in clinically relevant infection settings. This would be helpfu to enhance significance.
• Authors should explicitly report statistical methods (e.g., types of tests, adjustments for multiple comparisons) in figure legends for reproducibility.
• Nomenclature for key mutations and their position within the genetic context (e.g., htrB2 mutation specifics) could be more detailed in figures or supplemental materials.
• The manuscript could benefit from a graphical summary illustrating the two distinct evolutionary pathways and their respective fitness landscapes.
• A brief discussion of therapeutic implications-such as combining colistin with agents that target membrane integrity-would help bridge the gap from mechanism to clinical management.
• Additional discussion on whether the fitness costs are reversible or can be compensated by further adaptation would be valuable for long-term dynamics.
• It would be valuable for the authors to comment on, or further analyze, whether there is a direct association between specific fitness costs and sensitivity to other antibiotics. Such information could inform on evolutionary constraints and possible trade-offs relevant to clinical settings.

Main figures and support for claims

The main and supplementary figures comprehensively illustrate the evolutionary trajectories, genetic bases, and phenotypic outcomes associated with colistin resistance under magnesium depletion in P. aeruginosa. The figures effectively detail: - Genetic pathways involved including the experimental evolution design (colistin selection under Mg²⁺ depletion), whole-genome sequencing results, and timelines of observed mutations (e.g., in htrB2, lpxO2, oprH/phoP/phoQ promoter, PA4824). - Phenotypes and biochemical analyses such as lipid A structure (via mass spectrometry), minimum inhibitory concentration (MIC) assays, and epistasis analyses between mutations are depicted. - Fitness trade-offs are demonstrated using bacterial survival, membrane integrity (e.g., scanning electron microscopy images), membrane permeability assays (NPN uptake), and competitive fitness assays. - Mechanistic claims about the necessity of early mutations, the requirement of the PhoPQ pathway at different evolutionary stages, and the fitness cost imposed by certain resistance mutations. To further enhance the rigor and clarity of the manuscript, the authors should implement the following improvements: - Labelling consistency: In some instances, figure legends could provide more granular detail about specific mutations (e.g., positions of amino acid changes). - Graphical summary: A schematic summary figure that visually integrates the three main evolutionary resistance trajectories, the mutational order, corresponding lipid A changes, and fitness costs, would enhance readability. - Replicates: Plots should more thoroughly indicate the number of replicates and show individual data points (not just means {plus minus} SD), add number of replicates in each experiment. - Supplementary: figures referenced in the text (e.g., lipid A structures or mutation reversion outcomes) should be made more prominent or better cross-referenced from the main results section. Authors should highlight when supplementary data provide critical functional confirmation (e.g., confirming mutation function or fitness reversal).

Statistics

The authors have appropriately incorporated statistical analyses throughout the figures. To enhance the robustness and credibility of their findings, authors should also cross-check - Tests in legends: Every figure and supplementary figure should clearly state the type of statistical test used, how many biological replicates, and any corrections for multiple comparisons. - Effect sizes: Where appropriate, reporting effect sizes-rather than just p values-would contextualize the biological impact. - Raw data accessibility: For full transparency, consider sharing underlying raw data and analysis scripts.

Overall, the main and supplementary figures effectively illustrate and substantiate the key claims-particularly the alternative molecular pathways, phenotypic trade-offs, and the role of environmental magnesium in mediating colistin resistance. Statistical analysis is generally robust and appropriately presented throughout, though improvements could include more explicit reporting, additional controls, and accessible raw data. The visual and quantitative data in the figures provide support for the authors' conclusions about the evolution of antibiotic resistance under nutrient limitation in microbial environments. Understanding these alternative pathways is important for designing better treatment strategies and for predicting how resistance might evolve under varying clinical and environmental conditions.

Significance

Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

My expertise:

Gut microbiome, gut microbiota resilience, ecology, and evolution in microbial communities, antimicrobial resistance, high-throughput drug-bacteria interactions

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Reply to the reviewers

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

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

Thank you for your positive feedback.

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Significance (Required)):

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

Thank you very much for your supportive remarks.

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

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

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

*Major concerns: *

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

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

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

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

*Minors: *

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

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

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

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

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

We have added the numbers to the corresponding legends.

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

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

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

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

Reviewer #2 (Significance (Required)):

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

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

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

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

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

Thank you for your remarks.

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

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

*Major concerns *

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

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

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

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

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

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

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

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

*Minor concerns *

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

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

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

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

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

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

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

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

Reviewer #3 (Significance (Required)):

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

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

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

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Referee #3

Evidence, reproducibility and clarity

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

Major concerns

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

Minor concerns

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

Significance

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

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

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

Major concerns:

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

Minors:

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

Significance

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

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

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

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

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

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

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

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

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

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

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

Significance

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

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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.

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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.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

This 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.

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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.

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

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

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: 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.

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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.

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 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

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

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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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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Reply to the reviewers

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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Reply to the reviewers

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.*

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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.

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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.

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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

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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.