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Reply to the reviewers

'The authors do not wish to provide a response at this time.' The response has been included in a PDF

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Referee #3

Evidence, reproducibility and clarity

Summary:

This manuscript attempts to answer how the maternal and paternal chromosomes are organized, and probe the determinants of this organization.

The authors use two divergent strains of worms - Bristol(N2) and Hawaiian - and hybrids progeny to study this as there are large regions of sequence variants between chromosome V in the two strains, making it an ideal candidate to design specific FISH probes. The authors build on their previously published work and optimize a protocol to trace chromosomes by using a multiple-probe FISH approach to investigate chromosome architecture. This approach is well illustrated in Figure 1.

Overall, this manuscript does a good job of describing a potentially useful technique with wide application. The claims about differences (and similarities) require statistical analysis to be appreciated, and much work is necessary to make the analysis approachable to readers outside the immediate field.

Major comments:

Nearly all claims regarding the organization, compactness and pair-wise distances of chromosomes lack any statistical measures of significance. This is particularly important for the clustering and scaling analysis. This makes interpretation of the claims made in the text impossible. For example, claims such as "the step size remained virtually unchanged" or "the paternal chromosomes adopt the maternal conformation in hybrids" cannot be currently analyzed.

Throughout the manuscript (including in the abstract), the authors use the term "sister chromosomes" to (presumably) refer to the maternal and paternal chromosomes. This is a confusing term, since "sister chromatids" usually refers to the identical products of DNA replication, and "homologous chromosomes" is usually used to describe the parental chromosomes. The term would ideally be changed, and at the very least, it should be clearly defined.

Presentation: The manuscript in its current state (excluding figure one) is essentially impossible to interpret by readers who are unfamiliar with this subfield. The authors could include a blurb on the methodology behind each data type to help the manuscript reach a larger audience. The pipeline, meaning, and potential caveats of the clustering analysis should also be explicated.

Other suggestions:

The use of Hi-C-like heatmaps is good, since they are commonly used and are clear and easy to understand. However, it would be best to explain how the FISH data were used to construct the maps. The implications of the map could also be better explained (e.g., that the red cluster means relatively looser regions, and the blue means more tightly compacted ones).

The last sentence in the Introduction does not make clear sense. How does the similarity between N2 & HI open up the possibility of interrogating inheritance effects?

Several of the additional analysis that could improve the paper are: 1. Measure the nucleus diameter in N2/HI hybrid and HI/N2 hybrid. 2. Normalize the spatial distance to the nucleus size, rather than directly using the distance. 3. Explore some of the patterns in the spatial distance plots (e.g., red/blue lines and boxes). Are the sequences that are in them any different between N2 and HI in a way that might be able to account for these patterns?

Significance

The manuscript introduces a technical advance in the study of chromosomal territories - an important area of study that benefitted from recent advances in microscopy and in development of FISH approaches. However, it lacks mechanistic analysis and remains almost purely descriptive. It is also not clear what motivated the work beyond the technical feasibility. These issues make it impossible to assign biological significance to the seemingly minor differences that are documented.

However, as a report of a technical advance, it could be useful to many chromosome biologists who might apply it to diverse organisms and biological questions.

I am a chromosome biologist working on worms. However, my research does not directly deal with parental effects or with the development of novel FISH methodologies, so I did not examine claims regarding these specific points.

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Referee #2

Evidence, reproducibility and clarity

Building on their previous work using sequential fluorescent in situ hybridization (FISH) to follow the path of entire chromosomes in C. elegans (Sawh et al. 2020; Sawh and Mango 2020), the authors developed and then applied a method that distinguishes maternal and paternal homologs. In particular, they designed probes specific for ChrV in two strains, N2 and HI, and then demonstrated their effectiveness in homozygotes and hybrids. They found that HI ChrV is more compact in HI homozygotes as compared to N2 homozygotes, but decompacts in the F1 of N2 hermaphrodites x HI males. A different outcome was observed with respect to decompaction in the reverse cross, where both N2p and HIm ChrV chromosomes (p=paternal, m=maternal) exhibit decompaction. Through unsupervised clustering, the authors further found both dominant and minor patterns of chromosome-wide organization, with maternal chromosomes similar in terms of their dominant clusters regardless of the direction of the cross, but paternal chromosomes less so. Finally, the authors measured the degree to which homologous chromosomes interact, concluding that, although homologs overlap quite frequently, they rarely align (pair).

In sum, the significance of the authors' work lies in its chromosome-level observations and the application of computationally designed probes that distinguish homologs by targeting strain-specific insertions. While homolog distinction by FISH has been previously demonstrated, this study is the first to demonstrate this approach in C. elegans as well as implement it via insertions in a chromosome-wide manner. As such, the manuscripts should be of interest to a broad range of researchers, especially those in the fields of genetics, genomics, and 3D genome organization. That said, the study falls short in several ways, and we recommend the authors i) present a more thorough summary of what is known about homolog positioning across species, ii) describe how homologs have been previously distinguished by FISH and, thus, more clearly elucidate the specific advances they have enabled, iii) ground their work in more rigorous quantitation, and iii) provide a better description of the technologies (strengths as well as limitations) carried over from their previous studies so that readers can better evaluate the current study. We detail our suggestions and questions, below:

Major Comments:

1. Page 1: We suggest the authors broaden the reach of their introduction to include well-known examples outside of C. elegans of the impact of parent-of-origin on 3D genome organization. Such examples would include X-inactivation, selective silencing of paternal genomes, the physical elimination of paternal chromosomes, and the like.
2. Page 1: We also suggest the authors consider including mention of observations from the following research publications and review:

Mayer W et al. Spatial separation of parental genomes in preimplantation mouse embryos. 2003 PMC2169371

Reichmann J et al. Dual-spindle formation in zygotes keeps parental genomes apart in early mammalian embryos. 2018 PMID: 30002254

Nagele R, Freeman T, McMorrow L, Lee HY. Precise spatial positioning of chromosomes during prometaphase: evidence for chromosomal order. 1995 PMID: 8525379

Hua LL et al. Mitotic antipairing of homologous and sex chromosomes via spatial restriction of two haploid sets. 2018 PMID: 30530674

Hua LL, Casas CJ, Mikawa T. Mitotic antipairing of homologous chromosomes. 2022 PMC9731508 3. Page 5: The authors designed their strain-specific probes to target 172 insertions that are over 1 kb in size and distributed across ChrV. We ask the authors to describe these insertions in greater detail, especially as, later in the manuscript, the authors touch on the possibility that sequence differences between the two strains may account for differences in chromatin architecture. How many insertions were on the N2 and HI chromosomes, respectively? What is the range and distribution of insertion sizes for all insertions as well as specifically for the N2 and HI ChrV chromosome? Do the insertions contain repetitive sequences, or are they predominantly composed of unique sequences? What is their distribution with respect to genes, active regions, TADs? Is there an explanation for why some are clustered? If they contain genes, are the genes enriched in certain GO categories? Do the insertions differ in their characteristics across the different chromosomes? This information could be included as graphs and/or tables. 4. Page 5: Given that N2 and HI ChrV chromosomes differ by the number of insertions and, ultimately, probes, how might these differences have skewed the authors' results, especially with respect to overlap between the homologs? Here, simulations of different ratios of insertions between N2 and HI could be clarifying. 5. What difficulties were encountered when tracing the paths of overlapping homologs, and how were these difficulties accounted for and/or solved? Did the different numbers and distributions of insertions between N2 and HI exacerbate the challenge? What confidence levels accompanied their findings? 6. Page 4: It would be helpful if the authors to put their insertion-based method into the context of other studies that have developed and used FISH to distinguish homologs. 7. Pag 6: It would clarifying if the authors provided details about the mis-annotation of the Thompson genome and how it is pertains to probe design. 8. Page 6: The authors state that "...N2 and HI...harbor sequence differences, some of which are predicted to affect chromatin architecture" and that HI lacks ppw-1. We ask the authors to provide a more thorough discussion. To what extent might such predictions rest on the insertional differences between the strains? 9. Page 7: How much smaller is the HI genome and what percent of this difference is due to insertions (deletions)? Related to this, how much smaller is the HI ChrV chromosome as compared to N2? 10. Page 7 and throughout: For those readers who have not read Sawh and Mango 2020 and Sawh et al. 2020, or who are unfamiliar with the broader category of imaging technologies that support the current study, we ask the authors to provide much more background and citations to key methods. Without this information, many readers will neither sufficiently understand the strategies used for imaging acquisition, processing, and analysis nor grasp the relevance of terms such as step size, polymer step size, power-law fitting, etc. and therefore be less able to assess the authors' data and conclusions. 11. General statement: When the authors infer similarity or differences between power-law fittings, scaling exponents, step sizes, etc. what is the statistical significance of those comparisons? 12. Experiments in general: We suggest the authors provide considerably more quantitation. For example, we urge them to provide the number of trials, sample sizes, numbers of embryos examined for all experiments. Equally important would be information regarding the stage of embryos examined and, where more than one embryonic stage was involved, the number of embryos for each stage. Are there stage-specific changes? We are also concerned about the impact of mixed populations of embryos on studies using unsupervised clustering. In other words, what was the contribution of developmental stage to the outcome of the clustering? Furthermore, if not all nuclei in an embryo were captured, we ask the authors to give the percent of nuclei captured from an embryo and reasons why only a subset of nuclei were included in the analysis. 13. With regard to cluster analyses both here and elsewhere, will the authors please include statements of statistical significance whenever they note differences and/or similarities? 14. Page 8: It would be helpful if the authors explained how they implemented the nearest-neighbor approach, including caveats and limitations (success rates, drop-out rates, etc.) and providing statistical assessment wherever possible. 15. Page 8: "In some instances (6-8% of traces), traces were ambiguous and excluded from further analysis". We ask the authors to provide more detail. For example, what does ambiguous mean and, with respect to the 6-8% value, what was the total number of traces? What was the distribution of all traces (prior to filtering) with respect to percent of targets detected? Also, how coincident were the two homologs of a nucleus to each other in terms of capturing all the targets? 16. Page 8: "...we classified traces into N2 or HI based on whether the trace was located closest to a strain marking volume for N2 or HI." Will the authors please quantify "closest" and explain what this means, whether there was a cut-off and, if there had been, how it was determined and implemented? What percent of cells were problematic, and were there traces that did not overlap the strain marking volumes at all? As stated in the Materials and Methods, only a subset of traced chromosomes were analyzed for overlap - why were only a subset analyzed for overlap, how was the subset selected, and how many/what percentage did these traces represent? It would also be helpful if the authors provided a quantitative summary of the traces. 17. Page 8: Did the authors account for chromatic aberration and, if so, what protocol did they use? 18. Page 8: "...counting how often more than two N2 or HI traces were detected in one nucleus." This is puzzling, and we suggest that the authors include explanations for how this might have happened. Did the nuclei not contain signal from the other strain marking probe at all? Was there a bias for this to happen with N2 or HI chromosomes? Could this have been a consequence of biology or of the algorithm for tracing? The authors' observations are reminiscent of the many implications raised by Jia et al. (2023; A spatial genome aligner for resolving chromatin architectures from multiplexed DNA FISH. PMID: 36593410), and we ask the authors to comment on the relevance of their observations to those in this recent publication. 19. Page 8: "We found only a minority of 2% of HI traces and 7% of N2 traces were mis-assigned and excluded these from downstream analysis." 2% and 7% of what total? 20. Page 8: How do pairwise distances remain almost identical between N2 and HI and yet generate different scaling exponents? 21. Page 9: Figure 1F shows images of embryos derived from N2 hermaphrodites x HI males. It would be helpful if the authors added analogous images from the reciprocal cross as a supplementary figure. 22. Page 2, 9, 9, and 12: The authors make several comments regarding the action of factors in trans: "... factors from the mother impact chromosome folding in trans (p. 2)"; "... the HIp decompacts when subjected to the N2m environment and implies that the paternal chromosome is influenced by the maternal environment in trans (p. 9)"; "...N2 chromosomes influence HI chromosomes in trans, while N2 chromosome structure seems to be resistant to influences by the HI chromosomes (p. 9)"; and "...implicating maternal factors that act in trans (p. 12). While provocative, these statements call for more concrete consideration. Are the authors using "in trans" in lieu of "indirectly", or are they alluding to factors, such as ppw-1, or direct physical contact? Without further substantiation or argument, mention of in trans activity might best be reserved for the Discussion. 23. Page 10: "While HIp subpopulations were characterized by folding of one or the other chromosome arm, N2p clusters were more open and a subpopulation with a highly folded right arm was not present" (Figure 5CD). Was there a significant correlation between left vs. right arm folding and overall genome organization and function? 24. Page 11: Will the authors please provide a more explicit definition of alignment as well as a more detailed description of how alignment is quantified in the main text? 25. Page 11: With respect to direct physical contact, the authors mention transvection, which they conclude in the abstract is unlikely because pairing between homologs was observed to be rare. As transvection and pairing can both be short-lived, and the data are not compelling, the statement may need to be toned down considerably and/or be moved to the discussion. 26. Page 11: The authors draw a distinction between % territory overlap and physical distances between homologs. In particular, the differences between % overlap across different stages is quite interesting and potentially suggests embryo stage-specific changes. Could the authors explore this further by breaking down mean pairwise distances into different embryo stages (Figure 6B and C)? 27. Pages 2, 11-13: When the authors use "sisters", do they mean "homologs"? If the latter, we recommend they use "homologs", only, as "sisters" refers to the sister chromatids after replication. If, however, the authors are using "sisters" to mean sister chromatids, will they please explain how their data can distinguish sisters? 28. Discussion: We encourage the authors to speculate further regarding the basis of decompaction. Is it a hybrid-specific phenomenon?

Minor comments:

1. Page 1: Although the introduction focuses on C. elegans, the genome length that is mentioned (2 meters) is more aligned with that of mammalian species. The authors could cite a range of lengths or make more clear which species is being discussed.
2. Page 5: As the figures label the strains as Hawaiian and Bristol, the authors might wish to include this nomenclature in the main text. Curiously, the authors use several different spellings for the Hawaiian/Hawai'ian/Hawaiin/Hawaii strain.
3. Page 5: Will the authors please explain why the common whole-chromosome tracing probes have only one tail, while the strain-specific probes have two tails, as shown in Figure 1D?
4. Figures in general: The axes of a number graphs and heat maps need to be labeled.
5. Materials and Methods: The section on "Cluster analysis" is missing units for the resolution.
6. Page 8: Will the authors please give a reference for and explain watershed segmentation?

Significance

In sum, the significance of the authors' work lies in its chromosome-level observations and the application of computationally designed probes that distinguish homologs by targeting strain-specific insertions. While homolog distinction by FISH has been previously demonstrated, this study is the first to demonstrate this approach in C. elegans as well as implement it via insertions in a chromosome-wide manner. As such, the manuscript should be of interest to a broad range of researchers, especially those in the fields of genetics, genomics, and 3D genome organization. That said, the study falls short in several ways, and we recommend the authors i) present a more thorough summary of what is known about homolog positioning across species, ii) describe how homologs have been previously distinguished by FISH and, thus, more clearly elucidate the specific advances they have enabled, iii) ground their work in more rigorous quantitation, and iii) provide a better description of the technologies (strengths as well as limitations) carried over from their previous studies so that readers can better evaluate the current study.

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Referee #1

Evidence, reproducibility and clarity

The authors designed an elegant series of FISH probes taking advantage of insertions that are divergent between HI and N2 strains of C. elegans to identify the maternal versus paternal chromosomes in hybrid embryos. Overall, the conclusions are well supported by the data. I only have minor comments, which are numbered below in relation to each figure.

In figure 1 they demonstrate that the probes can specifically recognize their corresponding chromosome in hybrid embryos

In figure 2 they demonstrate that overall chromosome 5 adopts a similar shape from both strains.

In figure 3, they demonstrate that in the hybrids, the chromosomes are generally the same as in the homozygous embryos. However, when the HI chromosome is brought in paternally in the hybrids, it is more decompacted. In this cross, the maternal N2 chromosome is normal. In figure 5, when the N2 chromosome is now brought in paternally, it is similarly decompacted. However, in this cross, the HI chromosome that is brought in maternally is also decompacted. This appears to be the biggest difference, but is somewhat mitigated by a decrease in the scaling component, which I believe means it takes a straighter path? This is in contrast to the reciprocal cross where the maternal N2 chromosome is normal.

1. In figures 2-4, it is important to note what stages of embryos are being analyzed and whether any analysis was done to determine if the chromosomes varied with embryonic stage?
2. The authors need to clearly define chromosomal step size, scaling coefficient and pair wise distance and then describe the difference between these measurements. For example, it would be nice in relation to figure 4F if it was described exactly what it means to have an increase in step size along with a decrease in scaling exponent. This will enable the reader to more easily interpret the results.
3. Is there any way to determine if changes in the step size and scaling are significant? It would be good to know if the changes are actually significant.

In figure 5, the authors examine sub-clusters of where individual chromosomes locate. 4. In figure 5 (and maybe in earlier figures as well), it would also be helpful to mark the different clusters in the figures to show what is meant by a folder arm or a compacted central domain and refer to every panel in the text (only some descriptions in the text reference specific panels).

In figure 6, the authors determine whether the two chromosomes 5's overlap in nuclear territory and whether they the align along the length of the chromosome. From this analysis, they conclude that the chromosomes overlap a fair amount of time, but do not align. This makes it unlikely that transvection might occur. 5. In figure 6, the authors need to do a better job of describing how the data is being presented. The % overlap is being graphed by density, but density of what? Also, the text mentions the total number of nuclei that overlap, but how is that number derived from the presented data? Finally, the data are broken down by embryonic stage, but there is no mention of this in the text. It is not mentioned until the discussion. Overall, this makes it very difficult to determine what the data are showing. 6. In the discussion, the authors perhaps should spend more time interpreting their results in light of others work on the maternal and paternal inheritance of chromatin in C. elegans. For example, Arico et al 2011 Plos Genetics from the Kelly Lab. In addition to examining chromatin in the early embryo, in this paper the authors examine translocations, which might be interesting to look at using the technique presented here. Also, a number of recent papers from the Strome lab have examined chromatin inheritance from sperm. It would be interesting to interpret the finding that paternal chromosomes are influenced by the maternal environment, in light of this work.

Significance

These studies are the important extension of the elegant chromosome tracing that the Mango lab has pioneered. The authors have clearly demonstrated that the technique works well to identify individual chromosomes in a hybrid background. This provides the opportunity for the system to be used in numerous different ways, not just in C. elegans. This is the most significant advance of this paper and should be of interest to a fairly broad audience. However, using this technique, the authors also provide the initial characterization of sister chromosomes in C. elegans embryos and draw important initial conclusions, such as finding that the chromosomes do not pair, as they do in Drosophila. This makes the paper of interest to the C. elegans audience, as well as the general field of chromosomal organization. My expertise is in C. elegans biology and chromatin biology in general. I also am familiar with the field of chromosome biology. As a result, I believe I am capable of judging the significance of this paper in these areas.

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Reply to the reviewers

General Statements

Thank you for providing an initial assessment of our manuscript. We went through all the raised comments and suggestions aiming to improve our manuscript. Our manuscript will benefit from addressing them.

Our main impression is that the concerns regarding the novelty of our work by Reviewers #1 and #3 come from the fact that we apply a known flexible statistical framework (group factor analysis) to novel applications in single-cell data analysis, namely the estimation of multicellular programs and sample-level unsupervised analysis. The core methodology of our work is indeed based on the popular tool Multi-omics factor analysis (MOFA). We see the novelty of our study in the formulation of these relatively new applications within this framework, and the demonstration of the added value that this formulation provides building on MOFA’s strengths, in particular by expanding the possibilities of downstream analysis of single-cell data including the meta-analysis of distinct single-cell patient cohorts and its integration to complementary bulk and spatial data modalities.

The simultaneous estimation of multicellular programs together with sample-level unsupervised analysis is only possible with a single available tool, scITD, which is limited by its modeling strategy, based on tensor decomposition: with tensor decomposition, multicellular programs can not be estimated from distinct feature sets across cell-types, making this method less flexible and sensitive to technical effects, such as background expression. We compared our proposed methodology with scITD and showed the benefits of using group factor analysis as implemented in MOFA for this task. Moreover, as of now, no other methodology is able to estimate multicellular programs and perform sample-level unsupervised analysis, simultaneously in multiple independent single-cell atlases. We also showed how multicellular programs are traceable in bulk transcriptomics data and show that they are better fit to classify heart failure patients compared to classic cell-type deconvolution approaches.

Altogether, we believe that our current manuscript complements existing literature and puts forward an approach with distinct features to analyze single-cell atlases. We will edit the text to make more explicit the novelty and advantages of our proposed methodology, and we will emphasize that our work does not mean to propose a new method, but rather demonstrate how group factor analysis can be used for novel sample-level analysis of single-cell data. We plan to incorporate the suggestions by Reviewer #1 regarding the inclusion of additional datasets, model validations, and novel applications involving a direct modeling of cell-compositions and spatial organization of cells. Moreover, we plan to discuss perspectives on how cell communications can be incorporated in the analysis of multicellular programs as suggested by Reviewer #2. Additionally, we will correct all the figure and text typos identified by the reviewers. Finally, we will provide an R package (https://github.com/saezlab/MOFAcellulaR) and python implementations (https://liana-py.readthedocs.io/en/latest/notebooks/mofacellular.html) that facilitate the use of our approach.

Please find below the point-by-point response to the reviewers in blue, numbered for convenience.

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

Remark to authors

Flores et al. present a pipeline in which they leverage MOFA framework, a matrix factorization algorithm to infer multi-cellular programs (MCPs). Learning and using MCP has already been proposed by others. Yet, authors pursue a similar goals by using MOFA, providing a cell*sample matrix for different cell types as different views (instead of multiple modalities/views) as the input. They later apply MOFA using this data format on a series of applications to analyze acute and chronic human heart failure single-cell datasets using MCPs. Authors further try to expand their analysis by incorporating other modalities.

Major points:

1.1 As briefly outlined in the remarks, the current manuscript needs novel findings and methodology to grant a research article which I can' see here. The underlying matrix factorization is the original MOFA (literally imported in the code) with no modification to further optimize the method toward the task. While I appreciate and acknowledge the author's efforts resulting in a detailed analysis of heart samples, I think all of these could have been part of MOFA's existing tutorials.

Response 1.1 As the reviewer correctly states, we used the framework and code of MOFA. The novelty lies in its application for the unsupervised analysis of samples from cross-condition single-cell data and the inference of MCPs. MOFA is a statistical framework implementing a generalization of group factor analysis with fast inference and its current version fits the task of MCP inference and unsupervised analysis of samples across cell-types that provides a more flexible modeling alternative than current available methods (as presented in Table 1 of the manuscript). Current work on MCP inference is based on the premise of multi-view factorization with distinct statistical modeling alternatives. As mentioned in the discussion of our manuscript, three main points distinguish our discussed methodology from present alternatives and provide evidence about its relevance and uniqueness over available tools:

Simultaneous unsupervised analysis of samples across cell-types and inference of MCPs, together with comprehensive interpretable descriptions of the reconstruction of the original multi-view dataset. This is only currently possible with scITD (Mitchel et al, 2022) and is compared in the manuscript. DIALOGUE (Jerby-Arnon & Regev, 2022) is limited to the generation of MCPs and Tensor-cell2cell (Armingol et al, 2021) is only focused in cell-communications with limited interpretability.

Flexible non-overlapping feature set that handles better technical effects such as background expression, as discussed in section “__2.2 Multicellular factor analysis for an unsupervised analysis of samples in single-cell cohorts”. __Moreover, as mentioned by the reviewer in a later point (Reviewer comment 1.2), this enables joint modeling of distinct aspects of the tissue, such as cell compositions, cell communications (preliminary work: https://liana-py.readthedocs.io/en/latest/notebooks/mofatalk.htm) and spatial organization.

Joint-modeling of independent atlases that enables meta-analysis at the sample level of cross-condition single-cell data. No currently available methodology is capable of performing similar modeling. For these reasons, we believe that our work is worth being discussed and presented to the community as a research article. We will modify the discussion to put more emphasis on the added value of group factor analysis as implemented in MOFA.

Moreover, we now provide an R package (https://github.com/saezlab/MOFAcellulaR) and python implementations within our analysis framework LIANA (https://liana-py.readthedocs.io/en/latest/notebooks/mofacellular.html) that facilitates the usage of our proposed methodology. The R and python implementations are compatible with current Bioconductor and scverse pipelines, respectively.

Application of our methodology to heart failure datasets also revealed novel knowledge about heart disease processes:

In myocardial infarction, we found that our MCPs associated with cardiac remodeling capture cell-state-independent gene expression changes. This provides a novel understanding on the effect of disease contexts in the expression profiles of specialized cells. This finding was not reported in the original atlas publication.

In chronic heart failure, we identified a conserved MCP of cardiac remodeling across patient cohorts and etiologies, suggesting a common chronic phase between distinct initial causes of heart failure.

Moreover, we showed that deconvoluted chronic heart failure MCPs from bulk transcriptomics better classify patients in comparison to classic cell-type composition deconvolution of bulk data. To our knowledge, this finding was not presented in any of the manuscripts of other methodologies focused on MCPs.

Altogether, our current work shows a novel application of group factor analysis for the simultaneous estimation of MCPs and the sample-level unsupervised analysis of cross-condition single cell data. We showed the unique features compared to current available tools. Distinct post-hoc analysis in combination with other data modalities shows the biological relevance of our proposed methodology to complement the tissue-centric knowledge of disease.

1.2 How can you explain that the results in donor-level analyses are not due to technical artifacts (batch variation)? Can this be used to infer a new patient similarity map? For example, I would test this by leaving out a few patients from training, projecting them, and seeing where they would end up in the manifold or classifying disease conditions for new patients and explaining the classification by MCPs responsible for that condition.

Response 1.2 When knowledge of the technical batches is available it is possible to test for association between these labels and the factors encoding MCPs as shown in Figure 2.

In our current applications, we additionally showed the biological relevance of our estimated MCPs by mapping them to spatial and bulk data sets, which is a direct way of testing how generalizable were our findings:

In the application of MOFA to human myocardial infarction data, we mapped the gene loadings conforming the MCP associated with cardiac remodeling to paired spatial transcriptomics datasets. We showed that in general, the cell-type specific expression of the MCP of cardiac remodeling encompassed larger areas in ischemic and fibrotic samples compared to myogenic (control) samples.

In the application of MOFA to chronic human end-stage heart failure data, we mapped the gene loadings conforming the MCP associated with cardiac remodeling to 16 independent bulk transcriptomics datasets of heart failure. There we showed that the cell-type specific expression of the MCP of cardiac remodeling separates heart failure patients from control individuals. Regarding the generation of new patient similarity maps, it is possible to estimate the positions of new samples in the manifold formed by the factors representing the MCPs. As suggested by the reviewer we will show this by classifying heart failure single-cell samples using MCPs of two independent patient cohorts (presented in section 2.7).

1.3 The bulk and spatial analysis are used posthoc after running MOFA, I think since MOFA can use non-overlapping features set, it would be interesting to see if deconvoluted bulk or ST data can be encoded as another view (one view from scRNAseq data for each cell-type and another view from bulk RNA-seq or ST, you can get normalized expression per spot (for ST) or per sample (for bulk) and use them as input.

Response 1.3 Thanks for the suggestion. We agree that the possibility of using non-overlapping features opens options of complex models that include the cell-type compositional and organizational aspects of tissues. However these features must be quantified in the same sample, thus it is limited to samples profiled simultaneously at different scales.

We will present the results of a sample-level joint model of multicellular programs together with cell-proportions and spatial dependencies using the myocardial infarction dataset presented in section 2.2. For this dataset based on our previous work we have the compositions of major cell-types and their spatial relationships based on spatially contextualized models (Kuppe et al, 2022). We will run a MOFA model and show how it can be used to find factors associated with structural and molecular features of tissues.

__Minor: __

1.4 Some figure references are not correct (e.g., "the single-cell data into a multi-view data representation by estimating pseudo bulk gene expression profiles for each cell-type across samples (Figure 1b)." should be figure 2b)

Response 1.4 Thanks for pointing this out. We apologize for these mistakes and we will adjust all labels correctly.

1.5 The paper is well written, but there could be some more clarifications about what authors consider as cell-type and cell-state, condition, MCPs which I think is critical to current analysis (see here https://linkinghub.elsevier.com/retrieve/pii/S0092867423001599) for the reader not familiar with those concepts.

Response 1.5 We agree with the reviewer that it is important to introduce these concepts in more detail to avoid confusion. We will adapt the current manuscript to incorporate these definitions in the introduction.

__Reviewer #1 (Significance (Required)): __

1.6 While I find the concept of MCPs interesting, the current work seems like a series of vignettes and tutorials by simply applying MOFA on different datasets (The authors rightfully state this). However, It needs to be clarified what the novelty is since there is no algorithmic improvement to current MCP methods (because there is no new method) nor novel biological findings. Additionally, even in the current form, the applications are limited to the heart, and the generalization of this proposed analysis pipeline to other tissues and datasets is not explored. Overall, the paper lacks focus and novelty, which is required to grant a publication at this level.

Response 1.6 As mentioned in response 1.1, we show that group factor analysis as implemented in MOFA has advantages given its flexibility of the feature space, the joint-modeling of independent datasets, and the interpretability of the model. We will make these advantages clearer in the discussion, and we will explicitly mention the disadvantages and lack of functionalities of available methods.

The applications were mainly done in heart data for consistency although they represent four distinct single-cell datasets, one spatial transcriptomics dataset, and 16 independent bulk transcriptomics datasets. For completeness, as suggested by the reviewer, we will show the application of our methodology to peripheral blood mononuclear cell data of lupus samples (preliminary results: https://liana-py.readthedocs.io/en/latest/notebooks/mofacellular.html)

__expertise: Computational biology, single-cell genomics, machine learning __

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

Summary:

The authors use MOFA, an unsupervised method to analyze multi-omics data, to create multicellular programs of cross-condition multi-sample studies. First, for each cell-type, a pseudobulk expression matrix per sample is created. The cell-type now functions as the separate view, typically reserved for the different omics layers in MOFA. This then results in a latent space with a certain number of factors across samples. The factors, representing coordinated gene expression changes across cell-types, can then be checked for associations with covariates of interest across the samples.

MOFA is well-suited for this task, as it can handle missing data and it is a linear model facilitating the interpretation of the factors. Users should be aware that MOFA can estimate the number of factors, but the pseudobulk profiles require a rigorous selection of cell-type specific marker genes. The result will be most suited for downstream analysis if there is a clear association with one factor and a clinical covariate of interest. In a final step, a positive or negative gene signature can be created by setting a cut-off on the gene weights for that specific factor.

The method is applied on 3 separate data sets of heart disease, each time demonstrating that at least one of the factors is associated with a disease covariate of interest. The authors also compare the method to a competitor tool, scITD, and explore to what extent a factor mainly captures variance associated with (i) a general condition covariate or rather (ii) specific cell states.

The multicellular programs are also mapped to spatial data with spot resolution. Though this analysis does not bring any novel biological insight in the use case, it does support the claim that the programs are associated with the covariate of interest.

The most interesting applications of MOFA are in my opinion the potential for meta-analysis of single-cell studies and validation of cell-type specific gene signatures with publicly available bulkRNAseq data sets.

The authors provide various data sets and data types to support their claims and the paper is well written. The relevant code and data has been made available.

We thank the reviewer for the positive comments to our work.

__Major comments __

2.1 What is the added value of the gene signatures obtained from MOFA compared to e.g. a naive univariate approach? In theory, a similar collection of genes or gene signature could be obtained by running a differential gene expression analysis across the samples for each cell-type (e.g. myogenic vs ischemic ) and applying a set of relevant cut-offs or filters on the results. In other words, does MOFA detect genes that would otherwise be missed?

Response 2.1 Thank you for the relevant comment. The original motivation of our work is the unsupervised analysis of samples based on a manifold formed by a collection of multicellular molecular programs. We envisioned that this unsupervised analysis would be relevant in situations where a clear histological or clinical classification of samples is not possible with reliability. As mentioned by Reviewer #1 in comment 1.2, one advantage of these approaches is that they create patient similarity maps, which have been shown useful to stratify patients in a recent analogous work in multiple sclerosis (Macnair et al, 2022). The cell-type signatures obtained from relevant factors explaining the patient stratification avoid the likelihood of performing “double dipping” by avoiding the need of a direct differential expression analysis between newly formed groups.

In our applications, the generation of cell-type signatures (here called multicellular programs) associated to a specific clinical covariate (eg. control vs perturbation) are post-hoc analyses of the generated manifold. And as the reviewer correctly points out, these signatures should be similar to performing direct differential expression analysis between those patient conditions. In the related work of scITD (Mitchel et al, 2022) the authors showed high concordance between the cell-type signatures and the results of differential expression analysis. For completion, we will similarly quantify the degree of overlap between genes of our generated signatures with the ones coming from differential expression analysis.

It is relevant to mention that in complex experimental designs with multiple conditions, our approach facilitates patient ordering, which allows the understanding of one condition in the context of all the others, avoiding the need of multiple testing and the definition of multiple contrasts, as mentioned in the text.

We will incorporate these points in the discussion section of the manuscript.

2.2 Could scITD also be used for meta-analysis or could the obtained gene signatures of that method also be mapped to bulkRNAseq data? If so, it would be interesting to show the relative performance with MOFA. If not, this specific advantage should be highlighted.

Response 2.2 Thank you for pointing this out. scITD does not provide a group-based model to perform meta-analysis, and this feature is one of the main advantages of group factor analysis as currently implemented in MOFA. We will highlight this feature in Table 1 and in the discussion.

Although scITD signatures of a single study could be mapped to bulk transcriptomics data, the stringent tensor representation leads to the generation of signatures that may be influenced by technical effects as shown in the manuscript section 2.2. Thus we believe that the flexibility of the feature space in MOFA is an advantage for this task. We will add this observation to the discussion.

2.3 Users need to specify gene set signatures based on the weights for a factor of interest. This might suggest a limitation to categorical covariates of interest. If the authors see potential for a continuous covariate of interest, this should at least be highlighted in the text and if possible demonstrated on a use case.

Response 2.3 In our applications we limited ourselves to categorical variables, however, it is possible to associate factors to continuous variables. An implementation of the association with continuous variables is already available in our newly created R package “MOFAcellulaR”: https://github.com/saezlab/MOFAcellulaR/blob/main/R/get_associations.R.

The datasets we analyzed have no continuous clinical covariates to showcase this functionality, but as suggested by the reviewer we will highlight this feature in the text.

__Minor comments __

2.4 In Figure 2c the association between factor 2 and the technical factor shows a very strong outlier. Please verify that the association is still significant after applying a more robust statistical test (e.g. non-parametric test as Wilcoxon).

Response 2.4 Thanks for the observation, we will test these differences with a non-parametric test.

2.5 For mapping the cell-type specific factor signatures to bulk transcriptomics, the exact performed comparison or model is unclear. There are seven cell-type signatures for each sample in every study. Was there a t-test run for each cell-type or was a summary measure taken across the cell-types? he thresholding is also rather lenient (adj. p-val 0.1).

Response 2.5 We are sorry for not being clear about our procedure. After identifying the multicellular program associated with heart failure estimated from the two single cell studies meta-analyzed, we calculated the weighted mean expression of the seven cell-type signatures independently to every sample of the 16 bulk studies. In other words each sample within each bulk study will be represented by a vector of 7 values representing the relative expression of a cell-type specific signature (Figure 6D-left). For each bulk transcriptomics study, first, we centered the gene expression data before calculating the weighted mean.

In supplementary figure 4-e we show the results of performing a t-test of the cell-type scores between heart failure and control samples within each study. Given the relative low sample size of most of the studies (affecting the power of the test), we chose a not so stringent adjusted p-value. For completion, we will show the results of a more classical threshold (adj. p-value

2.6 typo in abstract: In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell ***atlas*** and allows for the integration of the measurements of patient cohorts across distinct data modalities

Response 2.6 Thanks for pointing out this typo. We will modify the text.

2.7 In Figure 4a it is not clear to me why on the one hand we see marker enrichment vs loading enrichment with healthy and disease.

Response 2.7 We apologize, this is a typo after editing the labels. Both should contain the marker enrichment label. We will fix this.

2.8 IN Figure 4b it would help if the same color scheme would be maintained throughout the paper (here now black and white) and if for the cell states the boxplots would be connected per condition, emphasizing the (absence) of change across cell states within a condition.

Response 2.8 We thank the reviewer for the suggestion. We will reorganize the panels showing the gene expression per condition and fix the color scheme.

__Reviewer #2 (Significance (Required)): __

__General assessment: __

2.9 MOFA is well-suited for detecting multicellular programs because it can handle missing data and allows for easy interpretation of the factors as a linear method. It might have particular potential for meta-analysis across multiple studies and reevaluating bulkRNAseq data sets, but in the current manuscript it is unclear to what extent this is a specific advantage of MOFA or could also be done with competitors. The authors show how the obtained results and associations with clinical covariates can be validated across multiple data types. How the resulting multicellular programs can provide additional biological insight or form the starting point for additional downstream analysis (e.g. cell communication) is not covered in the paper.

Response 2.9 We thank the reviewer for highlighting the methodological advantages of group factor analysis for the estimation of multicellular programs and the unsupervised analysis of samples from cross-condition single-cell atlas. As mentioned in response 1.1 and 2.2, the added value of our methodology is the flexibility of feature views (that goes beyond gene expression) and simultaneous modeling of independent single-cell datasets, a feature not present in any of the currently available methods that facilitates the meta-analysis of datasets across modalities.

While we interpret the presented multicellular programs in the context of cellular functions and the division of labor of cell states, it is true that we did not attempt to provide mechanistic hypotheses, for example, via cell-cell communication, on how this coordination across cell-types emerges.

Previous work of the related tool Tensor-cell2cell (Armingol et al, 2021) has presented the idea of the estimation of multicellular programs from cell-cell communications and group factor analysis can also be used for this task (preliminary work: https://liana-py.readthedocs.io/en/latest/notebooks/mofatalk.html). We will discuss in the text perspectives on how the estimation of multicellular programs can be linked to the inference of cell communications from single-cell data together with analysis alternatives previously proposed by scITD and Tensor-cell2cell. However, we believe that this question requires further work and it is out of scope of our current manuscript.

__Audience: This paper will be mainly of interest to a specialized public interested in unsupervised methods for large scale multi-sample and multi-condition studies. __

__Reviewer: main background in the analysis of scRNAseq data. __

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

This manuscript by Saez-Rodriguez and colleagues proposes to repurpose Multi-Omics Factor Analysis for the use of single cell data. The initial open problem stated by the paper is the need for a framework to map multicellular programs (such as derived from factor analysis) to other modalities such as spatial or bulk data. The authors propose to repurpose MOFA for use in single cell data. Case studies involve human heart failure datasets (and focuses on spatial and bulk comparisons).

There are particular issues with clarity regarding the key methodological contribution (and assessment of it), discussed under significance.

__Reviewer #3 (Significance (Required)): __

3.1 I am very puzzled by the repeated claims the manuscript makes that their central methodological contribution and innovation is to use MOFA for single cell data. One of their citations for MOFA is to MOFA+, which is precisely that (in a relatively popular manuscript published by the original authors of MOFA and not overlapping with the present authors). I am left to wonder what I missed.

Response 3.1 We apologize for the misunderstanding, as mentioned in the response to review 1.1 and explained by reviewer 2’s summary, the main objective of our work is to use the statistical framework of group factor analysis for the inference of multicellular programs and the sample-level unsupervised analysis of cross-condition single-cell data, which is a distinct task to multimodal integration (Argelaguet et al, 2021).

While it is true that MOFA+ introduced expansions to the model for the modeling of single-cell data, namely fast inference and group-based modeling, the main focus in their applications is the multimodal integration of data, where each cell is represented by a collection of distinct collection of features (e.g. chromatin accessibility and gene expression). Unlike multimodal integration, here we propose a different approach to analyze single-cell data at the sample level instead of the cell level, without modifying the underlying statistical model (see section 2.1 of the manuscript).

In detail, what we assume is that samples of single-cell transcriptomics data (e.g. tissue from a patient) can be represented by a collection of independent vectors collecting the gene expression information of cell types composing the tissue analyzed. Decomposition of these multiple views with group factor analysis produces a manifold that captures multicellular programs (coordinated expression processes across cell-types), or shared variability across cell-types simultaneously. Altogether, this represents a novel usage of group factor analysis in an application for the inference of multicellular programs, where the main focus is not at the cell-level but at the patient level.

As a side note, Britta Velten, one of main developers of MOFA and coauthor of both the MOFA and MOFA+ papers, is a contributor and coauthor of this manuscript, and Ricard Argelaguet, who also led both versions of MOFA, gave us helpful feedback and is acknowledged as such on this work.

3.2 Multimodal integration methods are fairly numerous and even if they're not all exactly factor analyses, it's strange to argue that MOFA fills some unique conceptual gap. I agree it fills something of an interesting gap (except for MOFA+ already filling it), but it's not like the quite popular spatial to single-cell integration approaches aren't doing similar things. If this is a methods paper (as it is presented) then there would have to be very substantially more comparative evaluation to these other approaches.

Response 3.2 As presented in the previous response (3.1) our current work is not focused on multimodal integration, but rather the inference of multicellular programs and the sample-level unsupervised analysis of single-cell data. Given this, in the current manuscript we compared our proposed methodology with the only three other available methods that address at least partially the inference of multicellular programs (see Table 1 in our manuscript). In response 1.1 and 3.2 we discussed the advantages of our proposed methodology compared to available methods. In the manuscript section 2.2 we compared group factor analysis with tensor decomposition and showed that the former better deals with technical artifacts and better identifies known patient groups.

We will distinguish our work from multimodal integration explicitly in the introduction and the manuscript section 2.1 to avoid confusions.

3.3 The biological use cases are comparatively interesting and dominate the manuscript (but are still presented principally as use cases rather than a compelling biological narrative of their own).

Response 3.3 The focus of our manuscript was the reintroduction of group factor analysis for the novel applications of the inference of multicellular programs and the sample-level unsupervised analysis from single-cell data. Given the distinct possibilities of post-hoc analyses, we mainly used acute and chronic heart failure data to showcase the utility of MOFA to connect spatial and bulk modalities with single-cell data.

That said, as discussed in response 1.1, our analyses allowed to generate novel hypotheses of these datasets:

In myocardial infarction, we found that our estimated multicellular programs associated with cardiac remodeling capture cell-state-independent gene expression changes. This provides a novel understanding of the effect of disease contexts in the expression profiles of specialized cells. In other words, we found that cell-states, regardless of their specialized function, share a common response in the tissue context.

In chronic heart failure, we identified a conserved multicellular program of cardiac remodeling across patient cohorts and etiologies, suggesting a common chronic phase between distinct initial causes of heart failure, which again may be linked to the dominating response to the tissue context that is shared across etiologies.

These two results support the observation that deconvoluted chronic heart failure multicellular programs from bulk transcriptomics better classify patients in comparison to classic cell-type composition deconvolution of bulk data. To our knowledge, this finding was not presented in any of the manuscripts of other methodologies focused on MCPs. We summarize these results in the third paragraph of the discussion in the manuscript:

“In an application to a collection of public single-cell atlases of acute and chronic heart failure, we found evidence of dominant cell-state independent transcriptional deregulation of cell-types upon myocardial infarction. This may suggest that while certain functional states within a cell-type are more favored in a disease context, most of the cells of a specific type have a shared transcriptional profile in disease tissues. If part of this shared transcriptional profile is interpreted as a signature of the tissue microenvironment that drives cells in tissues towards specific functions, this result may also indicate that a major source of variability across tissues, besides cellular composition, is the degree in which the homeostatic transcriptional balance of the tissue is disturbed. By combining the results of multicellular factor analysis with spatial transcriptomics datasets, we explored this hypothesis and identified larger areas of cell-type-specific transcriptional alterations in diseased tissues. Given these observations on global alterations upon myocardial infarction, we meta-analyzed single-cell samples from two additional studies of healthy and heart failure patients with multiple cardiomyopathies. Here, we found a conserved transcriptional response across cell-types in failing hearts, despite technical and clinical variability between patients. Further, we could find traces of these cell-type alterations in independent bulk data sets. These observations suggest that our approach can estimate cell-type-specific transcriptional changes from bulk data that, together with changes in cell-type compositions, describe tissue pathophysiology. Altogether, these results highlight how MOFA can be used to integrate the measurements of independent single-cell, spatial, and bulk datasets to measure cell-type alterations in disease.”

To fully assess the relevance of these observations, they should be investigated in more datasets and analyses, where shared functional cell-states across distinct heart failure etiologies are identified and then compared at their compositional and molecular level. This, in our opinion, represents an independent study on its own.

3.4 Altogether, I found the framing of this manuscript very puzzling. It is possible the result would be more clearly presented if the use case was the major focus rather than the more conceptual point about factor analysis.

Response 3.4 Thanks for the suggestion. The major aim of this manuscript is to highlight the versatility of the generalization of group factor analysis as implemented in MOFA for novel applications in single-cell data analysis, beyond multimodal integration of single cells. The definition of multicellular programs from single-cell data and its sample-level unsupervised analysis are relatively new analyses in the field, and thus we believe that it is timely to show how a known statistical framework can be used for these applications.

We believe that a detailed analysis of single-cell datasets of heart failure deserves its own focus and it is out of scope of our current objective with this manuscript. We apologize for the apparent misunderstanding of the objective of our methodology. We will add these distinctions in the introduction of the manuscript.

References

Argelaguet R, Cuomo ASE, Stegle O & Marioni JC (2021) Computational principles and challenges in single-cell data integration. Nat Biotechnol 39: 1202–1215

Armingol E, Baghdassarian H, Martino C, Perez-Lopez A, Knight R & Lewis NE (2021) Context-aware deconvolution of cell-cell communication with Tensor-cell2cell. BioRxiv

Jerby-Arnon L & Regev A (2022) DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat Biotechnol 40: 1467–1477

Kuppe C, Ramirez Flores RO, Li Z, Hayat S, Levinson RT, Liao X, Hannani MT, Tanevski J, Wünnemann F, Nagai JS, et al (2022) Spatial multi-omic map of human myocardial infarction. Nature 608: 766–777

Macnair W, Calini D, Agirre E, Bryois J, Jaekel S, Kukanja P, Stokar-Regenscheit N, Ott V, Foo LC, Collin L, et al (2022) Single nuclei RNAseq stratifies multiple sclerosis patients into three distinct white matter glia responses. BioRxiv

Mitchel J, Gordon MG, Perez RK, Biederstedt E, Bueno R, Ye CJ & Kharchenko P (2022) Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals. BioRxiv

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Referee #3

Evidence, reproducibility and clarity

This manuscript by Saez-Rodriguez and colleagues proposes to repurpose Multi-Omics Factor Analysis for the use of single cell data. The initial open problem stated by the paper is the need for a framework to map multicellular programs (such as derived from factor analysis) to other modalities such as spatial or bulk data. The authors propose to repurpose MOFA for use in single cell data. Case studies involve human heart failure datasets (and focuses on spatial and bulk comparisons).

There are particular issues with clarity regarding the key methodological contribution (and assessment of it), discussed under significance.

Significance

1. I am very puzzled by the repeated claims the manuscript makes that their central methodological contribution and innovation is to use MOFA for single cell data. One of their citations for MOFA is to MOFA+, which is precisely that (in a relatively popular manuscript published by the original authors of MOFA and not overlapping with the present authors). I am left to wonder what I missed.
2. Multimodal integration methods are fairly numerous and even if they're not all exactly factor analyses, it's strange to argue that MOFA fills some unique conceptual gap. I agree it fills something of an interesting gap (except for MOFA+ already filling it), but it's not like the quite popular spatial to single-cell integration approaches aren't doing similar things. If this is a methods paper (as it is presented) then there would have to be very substantially more comparative evaluation to these other approaches.
3. The biological use cases are comparatively interesting and dominate the manuscript (but are still presented principally as use cases rather than a compelling biological narrative of their own).

Altogether, I found the framing of this manuscript very puzzling. It is possible the result would be more clearly presented if the use case was the major focus rather than the more conceptual point about factor analysis.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors use MOFA, an unsupervised method to analyze multi-omics data, to create multicellular programs of cross-condition multi-sample studies. First, for each cell-type, a pseudobulk expression matrix per sample is created. The cell-type now functions as the separate view, typically reserved for the different omics layers in MOFA. This then results in a latent space with a certain number of factors across samples. The factors, representing coordinated gene expression changes across cell-types, can then be checked for associations with covariates of interest across the samples. MOFA is well-suited for this task, as it can handle missing data and it is a linear model facilitating the interpretation of the factors. Users should be aware that MOFA can estimate the number of factors, but the pseudobulk profiles require a rigorous selection of cell-type specific marker genes. The result will be most suited for downstream analysis if there is a clear association with one factor and a clinical covariate of interest. In a final step, a positive or negative gene signature can be created by setting a cut-off on the gene weights for that specific factor. The method is applied on 3 separate data sets of heart disease, each time demonstrating that at least one of the factors is associated with a disease covariate of interest. The authors also compare the method to a competitor tool, scITD, and explore to what extent a factor mainly captures variance associated with (i) a general condition covariate or rather (ii) specific cell states. The multicellular programs are also mapped to spatial data with spot resolution. Though this analysis does not bring any novel biological insight in the use case, it does support the claim that the programs are associated with the covariate of interest. The most interesting applications of MOFA are in my opinion the potential for meta-analysis of single-cell studies and validation of cell-type specific gene signatures with publicly available bulkRNAseq data sets. The authors provide various data sets and data types to support their claims and the paper is well written. The relevant code and data has been made available.

Major comments

• What is the added value of the gene signatures obtained from MOFA compared to e.g. a naive univariate approach? In theory, a similar collection of genes or gene signature could be obtained by running a differential gene expression analysis across the samples for each cell-type (e.g. myogenic vs ischemic ) and applying a set of relevant cut-offs or filters on the results. In other words, does MOFA detect genes that would otherwise be missed?
• Could scITD also be used for meta-analysis or could the obtained gene signatures of that method also be mapped to bulkRNAseq data? If so, it would be interesting to show the relative performance with MOFA. If not, this specific advantage should be highlighted.
• Users need to specify gene set signatures based on the weights for a factor of interest. This might suggest a limitation to categorical covariates of interest. If the authors see potential for a continuous covariate of interest, this should at least be highlighted in the text and if possible demonstrated on a use case.

Minor comments

• In Figure 2c the association between factor 2 and the technical factor shows a very strong outlier. Please verify that the association is still significant after applying a more robust statistical test (e.g. non-parametric test as Wilcoxon).
• For mapping the cell-type specific factor signatures to bulk transcriptomics, the exact performed comparison or model is unclear. There are seven cell-type signatures for each sample in every study. Was there a t-test run for each cell-type or was a summary measure taken across the cell-types? he thresholding is also rather lenient (adj. p-val 0.1).
• typo in abstract: In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlas and allows for the integration of the measurements of patient cohorts across distinct data modalities
• In Figure 4a it is not clear to me why on the one hand we see marker enrichment vs loading enrichment with healthy and disease.
• IN Figure 4b it would help if the same color scheme would be maintained throughout the paper (here now black and white) and if for the cell states the boxplots would be connected per condition, emphasizing the (absence) of change across cell states within a condition.

Significance

General assessment:

MOFA is well-suited for detecting multicellular programs because it can handle missing data and allows for easy interpretation of the factors as a linear method. It might have particular potential for meta-analysis across multiple studies and reevaluating bulkRNAseq data sets, but in the current manuscript it is unclear to what extent this is a specific advantage of MOFA or could also be done with competitors. The authors show how the obtained results and associations with clinical covariates can be validated across multiple data types. How the resulting multicellular programs can provide additional biological insight or form the starting point for additional downstream analysis (e.g. cell communication) is not covered in the paper.

Audience: This paper will be mainly of interest to a specialized public interested in unsupervised methods for large scale multi-sample and multi-condition studies.

Reviewer: main background in the analysis of scRNAseq data.

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Referee #1

Evidence, reproducibility and clarity

Remark to authors

Flores et al. present a pipeline in which they leverage MOFA framework, a matrix factorization algorithm to infer multi-cellular programs (MCPs). Learning and using MCP has already been proposed by others. Yet, authors pursue a similar goals by using MOFA, providing a cell*sample matrix for different cell types as different views (instead of multiple modalities/views) as the input. They later apply MOFA using this data format on a series of applications to analyze acute and chronic human heart failure single-cell datasets using MCPs. Authors further try to expand their analysis by incorporating other modalities.

Major points:

As briefly outlined in the remarks, the current manuscript needs novel findings and methodology to grant a research article which I can' see here. The underlying matrix factorization is the original MOFA (literally imported in the code) with no modification to further optimize the method toward the task. While I appreciate and acknowledge the author's efforts resulting in a detailed analysis of heart samples, I think all of these could have been part of MOFA's existing tutorials.

How can you explain that the results in donor-level analyses are not due to technical artifacts (batch variation)? Can this be used to infer a new patient similarity map? For example, I would test this by leaving out a few patients from training, projecting them, and seeing where they would end up in the manifold or classifying disease conditions for new patients and explaining the classification by MCPs responsible for that condition.

The bulk and spatial analysis are used posthoc after running MOFA, I think since MOFA can use non-overlapping features set, it would be interesting to see if deconvoluted bulk or ST data can be encoded as another view (one view from scRNAseq data for each cell-type and another view from bulk RNA-seq or ST, you can get normalized expression per spot (for ST) or per sample (for bulk) and use them as input.

Minor:

Some figure references are not correct (e.g., "the single-cell data into a multi-view data representation by estimating pseudo bulk gene expression profiles for each cell-type across samples (Figure 1b)." should be figure 2b)

The paper is well written, but there could be some more clarifications about what authors consider as cell-type and cell-state, condition, MCPs which I think is critical to current analysis (see here https://linkinghub.elsevier.com/retrieve/pii/S0092867423001599) for the reader not familiar with those concepts.

Significance

While I find the concept of MCPs interesting, the current work seems like a series of vignettes and tutorials by simply applying MOFA on different datasets (The authors rightfully state this). However, It needs to be clarified what the novelty is since there is no algorithmic improvement to current MCP methods (because there is no new method) nor novel biological findings. Additionally, even in the current form, the applications are limited to the heart, and the generalization of this proposed analysis pipeline to other tissues and datasets is not explored. Overall, the paper lacks focus and novelty, which is required to grant a publication at this level.

expertise: Computational biology, single-cell genomics, machine learning

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Reply to the reviewers

Point-by-point response to reviewers, including our plans for the revision:

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

* Summary: In this manuscript by the Sanson group, Lye and colleagues try to definitively answer the question of whether pulling forces from the ventral mesoderm have significant effects on convergent extension in the Drosophila germband (germband extension). While germband extension does occur in mutant embryos lacking mesoderm invagination, it has long been an open question in the field as to whether ventral pulling forces from the mesoderm have significant effects (positive or negative) on cell intercalation during germband extension. To definitely address this question, Lye and colleagues generated high-quality, directly comparable datasets from wild-type and twist mutant embryos, and then systematically assessed nearly all aspects of cell intercalation, myosin recruitment, and tissue elongation over time. They demonstrate that pulling forces from the ventral mesoderm have negligible impacts on the course of germband extension. While there are indeed some interesting differences between wild-type and twist embryos with respect to cell intercalation and myosin recruitment, such differences are relatively minor. They conclude that the events of germband extension neither require nor are strongly affected by external forces from the mesoderm. While this is largely a negative results paper, I believe that it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo, and it suggests that tissues are largely autonomous developmental units that are buffered from outside mechanical inputs.*

• * *Major comments: *

* It seems to me that the one obvious omission from this paper is a general measure of convergent extension over time. I think it would be useful to the reader to include some measure of change in tissue aspect ratio over time between wild-type and twist embryos. This could be included in Figure 5 or 6. *

• *

We are happy to include a graph with what we call “tissue strain rate”, which measures the deformation of the germ-band in the direction of extension (along AP) over time, and propose to add it as a panel in Supplementary Figure 6. Note that in our measures, the “tissue” strain rate is decomposed into contributions from two cell behaviors, the “cell intercalation” strain rate and the “cell shape” strain rate (Blanchard et al., 2009). “Tissue” and “cell shape” strain rate are directly measured, and “cell intercalation” strain rate is what remains when “cell shape” strain rate is removed from “tissue” strain rate. The “cell intercalation” strain rate calculated in that way is a “continuous” measure of cell intercalation, measuring the progressive shearing of cells during convergent extension. We also use a “discrete” measure of cell intercalation, which measures the number of cell neighbor exchanges, also called T1 swaps. We found that both “continuous” and “discrete” measures of cell intercalation are unchanged in twist mutant compared to wild-type embryos (Fig. 6F and 6E, respectively). In contrast, we find that the “cell shape” strain rate is increased in twist mutants (Fig. 5B and Fig. 5S1A). Consistent with this finding, the “tissue” strain rate is also increased in twist mutants (see graph below).

Otherwise, I have no major comments on the experimental approach or the findings of this manuscript. It seems to me a straightforward and systematic approach for determining whether mesoderm invagination affects germband extension. I do have several minor comments that should be addressed prior to publication (below).

*Minor comments: *

*I understand why cells would initially stretch more along the DV axis in wild-type embryos compared with twist embryos, but why do cells become so much more stretched along the AP axis (and become smaller apically) after 10 minutes of GBE in wild type compared with twist (Figure 2C and E). *

*I think this is an interesting and non-intuitive result that would warrant a bit of explanation/conjecture. *

This is not what Fig. 2C and E show, and we realize now that our schematics on the graphs might have been confusing. We will work on those to improve their clarity (or remove them), and also review our text.

Figure 2C shows how cells deform along DV (cell shape strain rate projected onto the DV axis). So the graph does not show that the cells are elongating in AP, as only the DV component of the strain rate is shown in this figure. In the wild type, the DV strain rate is positive (the cells are elongating in DV) at developmental times when the mesoderm invaginate (from about -10 minutes to until 7.5 minutes). The DV strain shows an acceleration until about 5 mins, then decelerates, crossing the x-axis to become negative at 7.5 minutes. From this timepoint and until the end of GBE, the DV strain rate is negative (the cells are contracting along DV). Mirroring the positive section of the curve, the DV contraction of the cells accelerate until about 12 mins and then slows down. The strong rate of DV contraction between 7.5 and 20 mins could in part be due to the endoderm invagination pulling in the orthogonal direction (AP) and helping the cells regaining a more isotropic shape. We could add a mention about this in the discussion.

In Figure 2E, the rate of change in cell area follows a similar time course in the wild type, showing that the cells are increasing their areas until about 10 mins (positive values) and then reduce their areas again until the end of GBE (negative values). Note that the graph does not show raw (instantaneous) cell areas as suggested by the comment, but rather a rate of change.

So in wild type, the cells get stretched by the invaginating mesoderm, and once the mesoderm is not pulling anymore, the cells appear to relax back. As there is no stretching in twist mutants, there is no equivalent relaxation of the cells along DV. Note that in twist, there is a milder increase in cell area in the first 15 mins of GBE (Fig. 2E). This could again be caused by the pull from endoderm invagination stretching the cells along AP, which, as we have shown before, increases both cell shape strain rates along AP and cell areas (Butler et al., 2009). So the pull from endoderm invagination (along AP) will have an impact on cell area rates of change and possibly also, indirectly, on DV cell shape strain rates, in both twist and wild type embryos, during most of GBE. Therefore cell area and DV cell shape strain rates are affected by more than one process during GBE. In this paper, we are focusing on the impact of mesoderm invagination, which happens around the start of GBE, so have focused our analysis of the graphs in the results section to this period, and the differences between wildtype and *twist. *

*I don't understand how you are defining cell orientation in Figure 2G. How are you choosing the cell axis that you are then comparing with the body axis? Is it the long axis, or something more complicated than that? I think you should briefly provide this information in the results section. If it is included in the methods, I wasn't able to locate it. *

Yes, it is the orientation of the long axis of the cell relative to the antero-posterior embryonic axis. We will clarify this in the text, in particular in the Methods, and also try improve our schematics.

Figure 2: Since you have the space, it might help the reader if you simply wrote out "strain rate" for panels B, D, and F, rather that used the abbreviation "SR." Thank you for this suggestion, we will reduce use of abbreviations where space permits.

*Please ensure that all axis labels are fully visible in the final figures. In several figures, the Y-axis labels were cut off (e.g., Fig 2I, 4A, 4D, 6B, 6C). *

These were visible to us in our submitted version, but of course we will ensure everything is visible on the final version.

*Where space permits, I would suggest using fewer abbreviations in axis labels to increase readability of the figures (e.g., in Figures 3H or 4D). *

Thank you for this suggestion, will do.

* In Figure 7, I would move the wild-type panels to the left and the twist panels to the right. I think it is more conventional to describe the normal wild-type scenarios first, and then contrast the mutant state.*

Will do.

To be consistent with the literature, "wildtype" should be hyphenated (wild-type) when used as an adjective, or two separate words (wild type) when used as a noun. Thank you, we will change this.

Review*er #1 (Significance (Required)): *

* Advance: The advances in this manuscript are largely methodological, but the experiments and analyses are quite rigorous and allow the authors to make strong conclusions concerning their hypotheses. Their findings are based on a high-quality collection of movies from control and twist mutant embryos expressing a cell membrane marker and knock-in GFP-tagged myosin. Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm. *

*

Audience: The primary audience for this article will be basic science researchers working in the early Drosophila embryo who are interested in the interplay between the germband and neighboring tissues. Secondary audiences will include developmental biologists more broadly who are interested in biomechanical coupling (or in this case decoupling) of neighboring tissues. *

*

Describe your expertise: I have been a Drosophila developmental geneticist for over twenty years, and I have been working directly on Drosophila germband extension for over a decade. I have published numerous papers and reviews in this field, and I am very familiar with the genetic backgrounds and types of experimental analyses used in this manuscript. Therefore, I believe I am highly qualified to serve as a reviewer for this manuscript.*

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

*

In the present manuscript, Lye et al. describe a highly detailed quantification of cell shape changes during germband extension in Drosophila melanogaster early embryo. During this process, ectodermal tissue contracts along the dorso-ventral axis, simultaneously expanding along the perpendicular antero-posterior direction, migrating from the ventral to the dorsal surface of the embryo as it extends. This important morphogenetic event is preceded by ventral furrow formation when mesodermal tissue (located in the ventral part of the embryo) contracts along the dorso-ventral axis and invaginates into the embryonic interior. The study compares cell shape dynamics in the wildtype Drosophila with that in the twist mutant, which largely lacks mesoderm and does not form ventral furrow. The major motivation of the study is to examine whether cellular behaviors and myosin recruitment in the ectoderm is cell autonomous, or if those cellular behaviors depend on mechanical interactions between mesoderm and ectoderm.*

• The authors first examine whether transcriptional patterning of key genes involved in germband extension is different between the wildtype and the twist mutant and find no significant difference. Next, the authors thoroughly quantify cellular behaviors and patterns of myosin recruitment in the two genetic backgrounds. A number of different measures are investigated, notably the rate of change in the degree of cellular asymmetry, rate of cell area change, rate of change of cell orientation, differences in myosin recruitment to cell edges of various orientation, as well as the rates of growth, shrinkage, and re-orientation of the various cellular interfaces. It is thoroughly documented how these quantities change as a function of developmental timing and spatial position within the embryo. These data serve basis for quantitative comparison between cellular dynamics in the two genetic backgrounds considered.*

• Overall, the study shows that cellular behaviors observed in the ectoderm are largely the same during the period of time following ventral furrow formation, as would be expected if those cellular behaviors were predominantly cell autonomous and not dependent on stresses generated in the mesoderm.*

• The data presented in the manuscript are of excellent quality and presentation is very clear.

Minor comments: none *

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

* I find that the study provides a thorough quantification of cell behaviors in a widely studied important model of morphogenesis. The work may be of particular interest for future model-to-data comparison, perhaps providing a basis for future modeling work. I therefore certainly think that this work warrants publication.*

• However, the results of the study largely parallel previous findings and do not appear novel or surprising. It is well established that in snail mutant that lack mesoderm entirely, germband extension proceeds largely normally. This well-established fact suggests that since tissue dynamics in complete absence of mesoderm are largely unaffected, behaviors of individual cells are likely to not be affected either*.

*The work is pretty much entirely observational, and for most part provides a more detailed documentation/quantification of previous findings. I do not think it is appropriate for high profile publication. *

We are not sure which evidence the reviewer is referring to here specifically. We agree that the single mutants twist or snail, or the double twist snail mutants do extend their germ-band. However, the question we are asking here, is how well do they extend their germband and to answer this question, quantitation is needed. The first quantitation of GBE were performed by (Irvine and Wieschaus, 1994). While they quantified GBE in various mutant contexts, they did not perform quantitation for snail, twist, or twist snail mutants. Instead, they refer to these mutants once in p839, with the following sentence: ”Additionally, twist and snail mutant embryos, which lack mesoderm, extend their germbands almost normally (Leptin and Grunewald, 1990; Simpson, 1983)*.” *

Following these earlier qualitative observations, various studies have quantified different aspects of GBE in mesoderm invagination mutants, with contradictory results. For example, some studies, including from our own lab, report a reduction in cell intercalation in the absence of mesoderm invagination (Butler et al., 2009; Wang et al., 2020), but there have also been reports that tissue extension and T1-transistions occur normally (Farrell et al., 2017)(see also introduction of our manuscript). These contradictory results have motivated our present study, and we have implemented rigorous comparison between wild type and mesoderm invagination mutants, being careful i) to check that the regions analyzed were comparable in terms of cell fate, and ii) to control for any confounding effects between experiments (see also response to reviewer 4, main question 2). We have also considered which mesoderm invagination mutants to use. We rejected snail or twist snail mutants because the absence of snail means that the mesodermal cells do not contract and thus stay at the surface of the embryo, which changes the spatial configuration of the embryo considerably and would make a fair quantitative comparison very difficult. Instead, we decided to use twist mutants, as in those, cell contractions still happen so the cells do not take as much space at the surface of the embryo, but the contractions are uncoordinated which means that there is no invagination (and we demonstrate here, no significant pulling on the ectoderm). We note that reviewer 1 highlights the merit of settling the question of the impact of mesoderm invagination on GBE and the pertinence of choosing twist mutants versus the alternatives (see also response to reviewer 4, suggestion 1).

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

During morphogenesis, the final shape of the tissue is not only dictated by mechanical forces generated within the tissue but can also be impacted by mechanical contributions from surrounding tissues. The way and extent to which tissue deformation is influenced by tissue-extrinsic forces are not well understood. In this work, Lye et al. investigated the potential influence of Drosophila mesoderm invagination on germband extension (GBE), an epithelial convergent extension process occurring during gastrulation. Drosophila GBE is genetically controlled by the AP patterning system, which determines planar polarized enrichment of non-muscle myosin II along the DV-oriented adherens junctions. Myosin contractions drive shrinking of DV-oriented junctions into 4-way vertices, followed by formation of new, AP-oriented junctions. This process results in cell intercalation, which causes tissue convergence along the DV-axis and extension along the AP-axis. In addition, GBE is facilitated by tissue-extrinsic pulling forces produced by invagination of the posterior endoderm. Interestingly, some recent studies suggest that the invagination of the mesoderm, which occurs immediately prior to GBE, also facilitates GBE. In the proposed mechanism, invaginating mesoderm pulls on the germband tissue along the DV-axis; the resulting strain of the germband cells generates a mechanotransduction effect that promotes myosin II recruitment to the DV-oriented junctions, thereby facilitating cell intercalation. Here, the authors revisited this proposed mechanotransduction effect using quantitative live imaging approaches. By comparing the wildtype embryos with twist mutants that fail to undergo mesoderm invagination, the authors show that although the DV-oriented strain of the germband cells was greatly reduced in the absence of mesoderm pulling, this defect had a negligible impact on junctional myosin density, myosin planar polarity, the rate of junction shrinkage or the rate of cell intercalation during GBE. A mild increase in the rate of new junction extension and a slight defect in cell orientation were observed in twist mutants, but these differences did not cause obvious defects in cell intercalation. The authors conclude that myosin II-mediated cell intercalation during GBE is robust to the extrinsic mechanical forces generated by mesoderm pulling.

• * *Overall, I found that the results described here are very interesting and of high quality. The data acquisition and analyses were elegantly performed, statistics were appropriately used, and the manuscript was clearly written. However, there are a few points where some further explanation or clarification is necessary, as detailed below: *

• The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:*

• -For myosin quantification, the authors state that "Background signal was subtracted by setting pixels of intensities up to 5 percentile set to zero for each timepoint" [Line826]. The rationale for selecting 5 percentile as the threshold for background should be explained. Also, how does this background value change over time? *

• *

For our normalization method, we stretched the intensity histogram of images to use the full dynamic range for quantification and enable meaningful comparison of intensities between different movies. The 5th percentile was chosen to set to zero intensity as this removed background signal without removing any structured Myosin signal (i.e., non-uniform, low level fluorescence - this was assessed by eye). We will provide some before and after normalization images at different timepoints to illustrate this (See reviewer 3, minor point 4 below). Since the cytoplasmic signal is uniform, it is difficult to discern from true ‘background’, therefore some cytoplasmic signal might be set to zero with this method, but all medial and junctional Myosin structures will still be visible and have none-zero intensity values. However, since cytoplasm takes up a large majority of pixels in the image, and we only set 5% of pixels to zero, the majority of the cytoplasm will have non-zero pixel values. ‘Background’ changes increases slightly as Myosin II levels increase in general over time, as expected from the embryo accumulating Myosin II as they develop.

-The authors mention that "Intensities varied slightly between experiments due to differences in laser intensity and therefore histograms of pixel intensities were stretched" [Line828]. The method of intensity justification should be justified. For example, does this normalization result in similar cytoplasmic myosin intensity between control and twist mutant embryos?

• *

As stated above, we stretched the intensity histogram of images to enable meaningful comparison of intensities between different movies, as stretching the histograms would bring Myosin II structures of similar intensities into the same pixel value range. We chose to stretch histograms using a reference timepoint (30 minutes, the latest timepoint analyzed), rather than on a per timepoint basis, because we saw a general increase in Myosin II over time, and we wanted to ensure that this increase was preserved in our analysis.

• *

Note that we quantify Myosin from 2 µm above to 2 µm below the level of the adherens junctions (see Methods), not throughout the entire cell, and therefore we have no true measure of cytoplasmic Myosin. However, we can plot non-membrane Myosin from this same apicobasal position in the cell. Non-membrane Myosin will include both the cytoplasmic signal and the Myosin II medial web (see above). When plotting these, we find that Myosin II intensities in this pool are similar in wildtype and twist (see graph below, dotted lines show standard deviations), confirming that that we are not inappropriately brightening one set of images compared to the other (e.g., twist versus wildtype).

Finally, our observations of rate of junction shrinkage and intercalation are consistent with our Myosin II quantification results (see Figures 4A, 4D and 6F). This further validates our methods.

• *

• *

- A previous study demonstrates that the accumulation of junctional myosin is substantially reduced in twist mutant embryos compared to the wild type (Gustafson et al., 2022). In that work, junctional myosin was quantified as (I_junction - I_cytoplasm)/I_cytoplasm. In contrast, the cytoplasmic myosin intensity does not appear to be subtracted from the quantification in this study. How much of the difference in the conclusions of the two studies can be explained by this difference in myosin quantification?

        As explained above, we choose to normalize our data by stretching histograms, rather than subtracting and dividing intensities between different pools of Myosin. The setting pixels of intensities up to 5 percentiles set to zero for each will have a similar effect to subtracting a small fraction of the cytoplasmic pool. We note that the intensity measurements in (Gustafson et al., 2022) are in the apical-top 5µm of the cell, and therefore their ‘cytoplasmic’ signal is likely to also include the apical medial web of Myosin. Also, after subtraction they use division by the cytoplasmic intensity in an attempt to bring pixel intensities between different movies into a comparable range, whereas we do this by stretching the histograms themselves (see above).  We carefully designed our method to preserve the increase in Myosin levels that we see over time in our post-normalization data. This is something that their method of normalization would not be predicted to capture, if their ‘cytoplasmic’ signal increase over time as well as their junctional signal.  Indeed, in FigS6D of their paper, Myosin II levels do not appear to increase over time in these (presumably normalized) images.

Additionally, we note that in (Gustafson et al., 2022), not all Myosin II is fluorescently tagged since they use a sqhGFP transgene located on the balancer chromosome. This means that the line they use will have a pool of exogeneous Myosin tagged with GFP (expressed from the CyO balancer) and a pool of endogenous Myosin (expressed from the sqh gene on the X chromosome. It is not known whether endogenous and exogeneous GFP-tagged Myosin II will be recruited equally to cell junctions when in competition with each other. Therefore, in their genetic background, the ratio of junctional/cytoplasmic sqhGFP might not reflect the true ratio. To avoid this potential caveat, in our study we have used a new knock-in of Myosin, which tags the sqh gene at the endogenous locus (Proag et al., 2019). The line is homozygous viable and thus all the molecules of Myosin II Regulatory Light Chain (encoded by sqh), and thus the Myosin II mini-filaments, are labelled with GFP.

Additionally, we note that when comparing their images of Myosin II in wildtype and twist (Figure 5D and D’), the overall Myosin signal appears reduced in twist mutants (including in the head and posterior midgut, which is outside the area that they are claiming Myosin II is recruited in response to mesoderm invagination). This suggests that Myosin II is generally reduced in their twist mutants (or images thereof), which is not expected and might indicate issues with their methods.

Therefore differences in the methods may explain the discrepancies between studies. Importantly, we have quantified junctional shrinkage rates and intercalation, and our analysis of these rates is consistent with our Myosin II quantification results (see above).

-The authors used the tissue flow data to register the myosin channel and the membrane channel, which were acquired at slightly different times. The accuracy of this channel registration should be demonstrated.

As stated in our methods: “the channel registration was corrected post-acquisition in order that information on the position of interfaces in the Gap43 channel could be used to locate them in the Myosin channel. Therefore the local flow of cell centroids between successive pairs of time frames in the Gap43 channel is used to give each interface/vertex pixel a predicted flow between frames. A fraction of this flow is applied, equal to the Myosin II to Gap43 channel time offset, divided by the frame interval. Because cells deform as well as flow, the focal cell’s cell shape strain rate is also applied, in the same fractional manner as above.”

The images in Figure 3C and C’ show the Myosin II, with quantified membrane Myosin superimposed on the image as a color-code. Images in Figure 3B and B’ show the (normalized) Myosin II. Comparison of these images demonstrates that the channel registration is accurate. We will add a reference to these images in the methods.

• The authors show that cell intercalation is not influenced in twist mutant embryos. However, a previous study demonstrates that the speed of GBE is substantially reduced in twist mutants (Gustafson et al., 2022). It would be interesting to see whether a similar reduction in the speed of GBE was observed in this study. *

We do not see a reduction in the speed of GBE as reported by (Gustafson et al., 2022), we will add “tissue strain rate” graphs to demonstrate this. On the contrary, we find a slight increase in the “tissue strain rate”, because there is a slight increase in the “cell shape strain rate” contributing to extension (while “cell intercalation strain rate” is unchanged). See also response to Reviewer 1 (major comment) .

• It has been previously shown that contractions of medioapical myosin in germband cells also contribute to cell intercalation. The authors should explain why medioapical myosin was not included in the comparison between wildtype and twist mutant embryos. *

• *

Indeed, it has been shown that there is a flow of medial Myosin towards the junctions (Rauzi et al., 2010). However, and as described in that paper, this flow ‘feeds’ the enrichment of Myosin II at shrinking junctions, and thus the junctional Myosin II can be taken as a readout of polarized Myosin II behavior. Additionally, medial flows are more technically challenging to quantify, especially when quantification is required in a large number of cells as is the case for our study.

Importantly, our junctional Myosin II and junctional shrinkage rate results are consistent with each other, therefore it is very unlikely that analyzing medial Myosin II would lead us to form a different conclusion. We will add a sentence to explain why we chose to quantify junctional, and not medial, Myosin II.

*Minor points: *

1. * Fig. 1-S1 panel C: the number of cyan cells changes non-monotonically. It first decreases from -10 min to 10 min, then increases from 10 min to 20 min. This is confusing since in theory the number of tracked cells should not increase over time if the cells are tracked from the beginning of the movie. *
2. *

The cyan cells highlight tracked mesodermal and mesectodermal cells, which are not included in the analysis. The low number of mesodermal cells highlighted at 10mins germband extension is because mesodermal and mesectodermal cells are not always tracked successfully at this time. Note that the legend includes a note that ‘”Unmarked cells are poorly tracked and excluded from the analysis”. Also see Methods: “Note on number of cells in movies, for notes on changes to the number of tracked ectodermal cells throughout the timecourse of the movies.”

• Fig. 1-S2: the vnd band in panel A appears to be much narrower than in panel B. *

• *

These are fixed embryos, therefore this could be (at least partially) due to slight differences in exact developmental age of the embryo. Note that we wanted to check that vnd and ind are expressed in the correct places in the ectoderm. We were motivated to check this because the width of mesoderm is reduced in twist, so we thought it was important to verify that there is not a population of ‘ectodermal’ cells with a strange fate (i.e., negative for both vnd and ind). Our experiments show that vnd abuts the mesoderm/mesectoderm in twist as in wildtype, and that the cells immediately lateral to the vnd cell population express ind as expected.

It is possible that there is a slight difference in the number of vnd cells in twist mutants compared to wildtype, but we see no differences in Myosin II bipolarity that would coincide with the vnd/ind boundary (Fig3-S1). Therefore, this would not change the interpretation of our results. Counting the number of rows of vnd cells prior to any cell intercalation (the number of rows will reduce as cells intercalate) would be technically challenging as the lateral border of vnd expression is hard to discern at this time due to lower levels of vnd expression laterally within the vnd expression domain.

• The schematic in Fig. 2J suggests that at the onset of mesoderm pulling the germband cells have a uniform angle of rotation (towards bottom right). Is this the case?*

• *

No, this schematic is purely supposed to show that as cells stretch, they also reorient. Note that we will review our schematics in Fig. 2 to increase clarity (see response to reviewer 1, first minor comment).

• The description of myosin intensity normalization in the Methods section is somewhat difficult to follow [Line 829 - 832]. It would be helpful if the authors can show one or two images before and after intensity normalization as examples. *

We will add some examples of before and after normalization images to this section. We will also review the Methods to improve the text’s clarity.

• Line 704: "Z-stacks for each channel were collected sequentially" - the step size in Z-axis should be reported. *

Thank you for this, the step size was 1µm. We will add this information.

• Fig. 4C: what are the thin, black lines in the image? *

This image is a 2D representation of the Gap43Cherry signal at the level of the adherens junctions extracted for tracking, not a simple confocal z-slice. When viewing these representations, you can see lines showing borders between where information from different z-stacks was used for the tracking layer. Unfortunately, our software does not allow us to remove these lines, but they do not affect tracking, quantification etc.

Reviewer #3 (Significance (Required)):

While most previous work on tissue mechanics and morphogenesis focuses on tissue-intrinsic mechanical input, recent studies have started to emphasize the contribution of tissue-extrinsic forces. An important challenge in understanding the function of tissue-extrinsic forces lies in the difficulties in properly comparing the wild type and the mutant samples that disrupt extrinsic forces, in particular when cell fate specification is altered in the mutants. In this work, the authors addressed this challenge by employing a number of approaches to warrant a parallel comparison between genotypes, including examining the AP- and DV-patterning of the tissue, selecting sample regions with comparable cell fate for analysis, and carefully aligning the stage of the movies. With these approaches, the authors provide compelling evidence to support their main conclusions. By teasing apart the role of the intrinsic genetic program and the extrinsic tissue forces, the work provides important clarifications on the function of mesoderm pulling in GBE and adds new insights into this well-studied tissue morphogenetic process. This work should be of interest to the broad audience of epithelial morphogenesis, tissue mechanics and myosin mechanobiology.

• *

Review____er #4 (Evidence, reproducibility and clarity (Required)):

*Lye and colleagues investigate the impact of tissue-tissue interactions on morphogenesis. Specifically, they ask how disrupting mesoderm internalization affects convergence and extension of the ectoderm (germband) in Drosophila embryos. Using twi mutants in which mesoderm invagination fails, the authors find that the invagination of the mesoderm deforms germband cells, but does not significantly contribute to patterning, cell alignment, myosin polarization and cell-cell contact disassembly (which drive germband convergence). The authors find modest effects of mesoderm invagination on new junction formation and orientation (which drive extension), but these changes do not have a significant effect on germband elongation. The authors conclude that germband extension is robust to external forces from the invagination of the mesoderm. *

*MAIN 1. The authors clearly show that myosin density is not different in wild-type and twi mutant embryos, and subsequently argue that the pulling force from the mesoderm does not elicit a mechanosensitive response in early germband extension. But if the cell density is constant, doesn't that mean that the longer, DV-oriented interfaces in the wild type accumulate more total myosin than their shorter counterparts in twi mutants? Assuming that the total number of myosin molecules per cell is not greater in the wild type, wouldn't increased total myosin at the membrane suggest a response to the increased deformation? Certainly the cells are able to maintain the same cell density despite the pulling force from the mesoderm, so can the authors rule out a mechanosensing mechanism? *

• *

We do not rule out a mechanosensing mechanism. We agree the total Myosin at stretched interfaces is higher than at unstretched interfaces and proposed a homeostatic mechanism to maintain Myosin II density on the cortex upon rapid stretching (summarized in Fig. 7). Indeed it is possible that this mechanism could itself be due to mechanosensitive recruitment of Myosin II (though there are also other possibilities). We have tried to address this in our discussion (under “Mechanisms regulating Myosin II density at the cortex and consequences for cell intercalation” and “Restoration of DV cell length after being stretched by mesoderm invagination”), but we will amend the wording the make the possibility of mechanosensitive recruitment of Myosin II to maintain cortical density more explicit.

*What happens to the Gap43mCherry signal? From Figure 2A, it seem to be diluted ventrally in the wild type as compared to twi mutants? Comparing myosin and Gap43 dynamics may shed light on whether myosin accumulates more or less than one would expect simply on the basis of having longer contacts. *

We quantify the density of Myosin, rather than the total amount. Therefore, the length of the contact should not matter. The suggestion of comparing Myosin density to Gap43Cherry density is in principle a good one, as it would allow us to compare a protein which is not diluted as cell contact length increases (Myosin) to one which appears to be (Gap43). However, it is not essential for the conclusions that we make. However, in practice quantifying the Gap43Cherry signal would not be straightforward on our existing movies due to the imaging parameters used. We capture the Gap43Cherry channel (but not the Myosin channel) with a ‘spot noise reducer’ tuned on in the camera software, due to very occasional bright spot noise, which confuses the tracking software. Therefore, our Gap43Cherry signal is manipulated during acquisition and to quantify from these images would not be appropriate. Therefore, we would have to acquire, track and quantify some new movies, which is not possible within the timeframe of a revision.

In summary, we think that we have sufficient evidence from our analysis that Myosin II is not diluted upon junctional stretching without comparing to quantification of Gap43Cherry, and the time investment required to quantify the Gap43Cherry would not be worthwhile as it would require more data to be acquired and processed.

• The authors previously argued that mesoderm invagination was required for the fast phase of cell intercalation [Butler et al., 2009]. However, here the authors interpret that loss of twi does not significantly slow down interface contraction, but accelerates the elongation of junctions and cells along the AP axis, which overall would mean that mesoderm invagination is (slightly) detrimental for axis elongation. The discrepancy between their previous and current results should be discussed. *

We are happy to add more information about these discrepancies in the discussion. In a nutshell, we think that these discrepancies arise from the challenges of comparing wildtype and twist mutant embryos relative to each other, and as a consequence we have made various improvements to our methods since (Butler et al., 2009). These improvements included using markers that would be expressed at the same levels in wildtype and twist embryos. Additionally, we did not use overexpressed cadherin-FPs (namely, the ubi-CadGFP transgene), which may have confounding effects, and we used a knock-in sqhGFP to ensure we could all Myosin II molecules were labelled by GFP. We also carefully controlled the temperature at which we acquired the movies, standardized the level at which to track cells and quantify Myosin between movies, as well as improving the accuracy of our image segmentation and cell type identification since our previous study (Butler et al., 2009). See also response to reviewer 2.

• Related to the previous point, it is surprising that the differences shown in Figure 4A-B are not significant. This is particularly troubling when in Figure 5B the authors claim a significant difference in cell elongation rate, which is higher in twi mutants (but only in very short time intervals and actually switches sign at the end of germband extension). These are just two examples, but I think the analysis of significance on a per-time point basis is problematic. *

*Have the authors considered analyzing their results as time series rather than comparing individual time points? Or perhaps integrating the different metrics over the duration of germband extension (e.g. using areas under the curve)? That way they would not have to arbitrarily decide if significant differences in a few time points should or not be interpreted as significant overall differences. *

• *

For graphs plotted against time of germband extension, we do not think it is appropriate to analyze as a time series rather than comparing individual time points, since different developmental events (such as mesoderm invagination) occur at different times. For graphs plotted against time to/from cell neighbor swap, these can also change over time (e.g., ctrd-ctrd orientation, Fig6D). Therefore we do not feel that it appropriate to run statistical analyses as a timeseries for these comparisons either. Statistically cut-offs are by their nature arbitrary. We have tried to highlight non-significant trends throughout the text (including for Fig4A&B), in addition to stating where we see significant differences to highlight where there may be minor (but not significant) differences.

---

• While the number of cells analyzed is impressive, the number of embryos is relatively low, particularly for the wild type (only four embryos analyzed). If I understood correctly (if not, please clarify) the authors ran their statistics using cells and not embryos as their measurement unit. But I could not find any evidence that cells from the same embryo can be considered as independent measurements. This could be easily done by demonstrating that the variance of any of the measurements (e.g. elongation, area change rate, etc.) for cells in an embryo is comparable to that calculated when mixing cells from different embryos. *

• *

We do not simply use the number of cells as an n for our experiments. We use a mixed effects model for our statistics as previously (Butler et al., 2009; Finegan et al., 2019; Lye et al., 2015; Sharrock et al., 2022; Tetley et al., 2016). This estimates the P value associated with a fixed effect of differences between genotypes, allowing for random effects contributed by differences between embryos within a given genotype. We will make sure that this is clear in the Methods.

MINOR 1. Figure 4D: the authors show no difference in the proportion of neighbor swaps per minute between wild-type and twi- mutant embryos. But how about the absolute number of neighbour swaps per minute? Does that change in twi mutants (and if so, why?).

The number of interfaces involved in a T1 swap are expressed as a proportion of the total number of DV-oriented interfaces for all tracked ectodermal germband cells, to take account of differences in the number of tracked cells between different timepoints and different movies. Presenting the absolute number of swaps per minute could lead to misleading interpretations.

• I was a bit confused about the reason why in Figure 4A the authors measure the rate of interface contraction in units of “proportion/min”, but in Figure 5A they measure interface elongation in units of “um/min”. Unless there is a good reason not to, these two metrics should be reported using the same units. Is there a difference in the rate of interface contraction when measured in absolute units (um/min)? *

Thank you, we will amend so that both measures are expressed in the same units.

• The discussion of previous work on cell deformation within the mesoderm (page 16, first paragraph) should probably include recent work from Adam Martin's lab (e.g. [Heer et al., 2017]; or [Denk-Lobnig et al., 2021]). *

Thank you, and apologies for this oversight, we will add these references__.__

SUGGESTIONS 1. While I appreciate the arguments that the authors provide to use twi mutants rather than sna mutants or twi sna double mutants, as the authors indicate, in twi mutants there is still contractility in the mesoderm (albeit not ratcheted). Therefore, it is possible that contractile pulses from the mesoderm in twi mutants could still facilitate cell alignment and polarization of myosin in the germband. Given the previous results from the Zallen lab using twi sna double mutants (see above) this is unlikely to be the case, but the findings in this manuscript would be significantly stronger if they included similar analysis in the double mutants.

We had concerns about using sna or twi sna double mutants due to the large amount of space the un-internalized mesoderm takes up on the exterior of the embryo. This concern is also shared by reviewer 1 “Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm.” * In addition to this concern, imaging of snail or twist snail* embryos by confocal imaging to include the ventral midline (which is required to define embryonic axes) is problematic as the un-constricted mesodermal cells occupy virtually all the field of view, leaving very few ectodermal cells to analyze.

Whilst we acknowledge that there are some (un-ratcheted) contractions of mesodermal cells in twist mutants, we have clearly shown that there is no DV stretch and very little reorientation of cells. Therefore, any residual contractile activity in the mesodermal cells of twist mutants does not appear to have a mechanical impact on the ectoderm. We cannot exclude the possibility that there is some transmission of forces between contracting cells of the mesoderm and the ectoderm in twist mutants. However, our evidence suggests that the large tissue scale force that transmits to the ectoderm from the invaginating mesoderm is missing in twist mutants, and it was the effects of that force that we wished to investigate (See also response to reviewer 2).

Review*er #4 (Significance (Required)): *

*This is an interesting study, with careful quantitative analysis of cellular and subcellular dynamics. The results follow previous findings from Jennifer Zallen and the authors themselves. The Zallen lab showed that cell alignment, myosin polarization and germband extension are normal in sna twi mutants [Fernandez-Gonzalez et al., 2009], a result that the authors fail to cite. The results in the present manuscript are similar, but the analysis is much more in depth here, so the findings by Lye and colleagues certainly warrant publication. *

We did not specifically cite this result from (Fernandez-Gonzalez et al., 2009), because the subject of their study is the formation of multicellular rosettes, not whether a pull from mesoderm affects Myosin II polarity and cell intercalation. The formation of multicellular rosettes occurs later in germband extension, and therefore these results are not directly relevant to our study. Additionally, their measures of alignment are defined as linkage to other approximately DV oriented interfaces, rather than directly measuring orientation compared to the embryonic axes as we do here, as a different question is being addressed. Specifically, the quoted sna twi experiment is interpreted as extrinsic forces from the mesoderm not being required for linkage of Myosin enriched DV-oriented interfaces together. Myosin II quantification is more rudimentary with edges being assigned as Myosin positive or Myosin negative, as opposed to quantifying the density of Myosin on each interface and we cannot see any comparison of Myosin II quantification between wildtype and twist embryos.­

So, although the results are consistent with each other, they are not directly comparable due to methods used and we are happy that the reviewer acknowledges that our analysis is more in depth, which was necessary to address the specific questions that we investigate in our study.

        In general, there have been inconsistencies in results between previous studies, leading reviewer one to recognize that *“…it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo.”  *The high amount of conflicting information in the literature led us to not exhaustively describe individual findings, but we will ensure the results from the Zallen lab are appropriately cited.

However, there are a number of experimental points that I think need to be addressed to solidify the manuscript, particularly in terms of statistical analysis.

Please see more details above (main points 3 and 4) regarding specific concerns about experimental points and statistics. Additionally, we note that reviewer 3 states “statistics were appropriately used”, and our statistical methods are the same as we have used in previous studies comparing live imaging data (Butler et al., 2009; Finegan et al., 2019; Lye et al., 2015; Sharrock et al., 2022; Tetley et al., 2016).

• *

__REFERENCES

__

Blanchard, G. B., Kabla, A. J., Schultz, N. L., Butler, L. C., Sanson, B., Gorfinkiel, N., Mahadevan, L. and Adams, R. J. (2009). Tissue tectonics: morphogenetic strain rates, cell shape change and intercalation. Nat Methods 6, 458-464.

Butler, L. C., Blanchard, G. B., Kabla, A. J., Lawrence, N. J., Welchman, D. P., Mahadevan, L., Adams, R. J. and Sanson, B. (2009). Cell shape changes indicate a role for extrinsic tensile forces in Drosophila germ-band extension. Nat Cell Biol 11, 859-864.

Farrell, D. L., Weitz, O., Magnasco, M. O. and Zallen, J. A. (2017). SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics. Development 144, 1725-1734.

Fernandez-Gonzalez, R., Simoes Sde, M., Roper, J. C., Eaton, S. and Zallen, J. A. (2009). Myosin II dynamics are regulated by tension in intercalating cells. Dev Cell 17, 736-743.

Finegan, T. M., Hervieux, N., Nestor-Bergmann, A., Fletcher, A. G., Blanchard, G. B. and Sanson, B. (2019). The tricellular vertex-specific adhesion molecule Sidekick facilitates polarised cell intercalation during Drosophila axis extension. PLoS Biol 17, e3000522.

Gustafson, H. J., Claussen, N., De Renzis, S. and Streichan, S. J. (2022). Patterned mechanical feedback establishes a global myosin gradient. Nat Commun 13, 7050.

Irvine, K. D. and Wieschaus, E. (1994). Cell intercalation during Drosophila germband extension and its regulation by pair-rule segmentation genes. Development 120, 827-841.

Leptin, M. and Grunewald, B. (1990). Cell shape changes during gastrulation in Drosophila. Development 110, 73-84.

Lye, C. M., Blanchard, G. B., Naylor, H. W., Muresan, L., Huisken, J., Adams, R. J. and Sanson, B. (2015). Mechanical Coupling between Endoderm Invagination and Axis Extension in Drosophila. PLoS Biol 13, e1002292.

Proag, A., Monier, B. and Suzanne, M. (2019). Physical and functional cell-matrix uncoupling in a developing tissue under tension. Development 146.

Rauzi, M., Lenne, P. F. and Lecuit, T. (2010). Planar polarized actomyosin contractile flows control epithelial junction remodelling. Nature 468, 1110-1114.

Sharrock, T. E., Evans, J., Blanchard, G. B. and Sanson, B. (2022). Different temporal requirements for tartan and wingless in the formation of contractile interfaces at compartmental boundaries. Development 149.

Simpson, P. (1983). Maternal-Zygotic Gene Interactions during Formation of the Dorsoventral Pattern in Drosophila Embryos. Genetics 105, 615-632.

Tetley, R. J., Blanchard, G. B., Fletcher, A. G., Adams, R. J. and Sanson, B. (2016). Unipolar distributions of junctional Myosin II identify cell stripe boundaries that drive cell intercalation throughout Drosophila axis extension. Elife 5.

Wang, X., Merkel, M., Sutter, L. B., Erdemci-Tandogan, G., Manning, M. L. and Kasza, K. E. (2020). Anisotropy links cell shapes to tissue flow during convergent extension. Proc Natl Acad Sci U S A 117, 13541-13551.

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Referee #4

Evidence, reproducibility and clarity

Lye and colleagues investigate the impact of tissue-tissue interactions on morphogenesis. Specifically, they ask how disrupting mesoderm internalization affects convergence and extension of the ectoderm (germband) in Drosophila embryos. Using twi mutants in which mesoderm invagination fails, the authors find that the invagination of the mesoderm deforms germband cells, but does not significantly contribute to patterning, cell alignment, myosin polarization and cell-cell contact disassembly (which drive germband convergence). The authors find modest effects of mesoderm invagination on new junction formation and orientation (which drive extension), but these changes do not have a significant effect on germband elongation. The authors conclude that germband extension is robust to external forces from the invagination of the mesoderm.

Main

1. The authors clearly show that myosin density is not different in wild-type and twi mutant embryos, and subsequently argue that the pulling force from the mesoderm does not elicit a mechanosensitive response in early germband extension. But if the cell density is constant, doesn't that mean that the longer, DV-oriented interfaces in the wild type accumulate more total myosin than their shorter counterparts in twi mutants? Assuming that the total number of myosin molecules per cell is not greater in the wild type, wouldn't increased total myosin at the membrane suggest a response to the increased deformation? Certainly the cells are able to maintain the same cell density despite the pulling force from the mesoderm, so can the authors rule out a mechanosensing mechanism? What happens to the Gap43mCherry signal? From Figure 2A, it seem to be diluted ventrally in the wild type as compared to twi mutants? Comparing myosin and Gap43 dynamics may shed light on whether myosin accumulates more or less than one would expect simply on the basis of having longer contacts.
2. The authors previously argued that mesoderm invagination was required for the fast phase of cell intercalation [Butler et al., 2009]. However, here the authors interpret that loss of twi does not significantly slow down interface contraction, but accelerates the elongation of junctions and cells along the AP axis, which overall would mean that mesoderm invagination is (slightly) detrimental for axis elongation. The discrepancy between their previous and current results should be discussed.
3. Related to the previous point, it is surprising that the differences shown in Figure 4A-B are not significant. This is particularly troubling when in Figure 5B the authors claim a significant difference in cell elongation rate, which is higher in twi mutants (but only in very short time intervals and actually switches sign at the end of germband extension). These are just two examples, but I think the analysis of significance on a per-time point basis is problematic. Have the authors considered analyzing their results as time series rather than comparing individual time points? Or perhaps integrating the different metrics over the duration of germband extension (e.g. using areas under the curve)? That way they would not have to arbitrarily decide if significant differences in a few time points should or not be interpreted as significant overall differences.
4. While the number of cells analyzed is impressive, the number of embryos is relatively low, particularly for the wild type (only four embryos analyzed). If I understood correctly (if not, please clarify) the authors ran their statistics using cells and not embryos as their measurement unit. But I could not find any evidence that cells from the same embryo can be considered as independent measurements. This could be easily done by demonstrating that the variance of any of the measurements (e.g. elongation, area change rate, etc.) for cells in an embryo is comparable to that calculated when mixing cells from different embryos.

Minor

1. Figure 4D: the authors show no difference in the proportion of neighbor swaps per minute between wild-type and twi-mutant embryos. But how about the absolute number of neighbour swaps per minute? Does that change in twi mutants (and if so, why?).
2. I was a bit confused about the reason why in Figure 4A the authors measure the rate of interface contraction in units of "proportion/min", but in Figure 5A they measure interface elongation in units of "um/min". Unless there is a good reason not to, these two metrics should be reported using the same units. Is there a difference in the rate of interface contraction when measured in absolute units (um/min)?
3. The discussion of previous work on cell deformation within the mesoderm (page 16, first paragraph) should probably include recent work from Adam Martin's lab (e.g. [Heer et al., 2017]; or [Denk-Lobnig et al., 2021]).

Suggestions

1. While I appreciate the arguments that the authors provide to use twi mutants rather than sna mutants or twi sna double mutants, as the authors indicate, in twi mutants there is still contractility in the mesoderm (albeit not ratcheted). Therefore, it is possible that contractile pulses from the mesoderm in twi mutants could still facilitate cell alignment and polarization of myosin in the germband. Given the previous results from the Zallen lab using twi sna double mutants (see above) this is unlikely to be the case, but the findings in this manuscript would be significantly stronger if they included similar analysis in the double mutants.

Significance

This is an interesting study, with careful quantitative analysis of cellular and subcellular dynamics. The results follow previous findings from Jennifer Zallen and the authors themselves. The Zallen lab showed that cell alignment, myosin polarization and germband extension are normal in sna twi mutants [Fernandez-Gonzalez et al., 2009], a result that the authors fail to cite. The results in the present manuscript are similar, but the analysis is much more in depth here, so the findings by Lye and colleagues certainly warrant publication. However, there are a number of experimental points that I think need to be addressed to solidify the manuscript, particularly in terms of statistical analysis.

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Referee #3

Evidence, reproducibility and clarity

During morphogenesis, the final shape of the tissue is not only dictated by mechanical forces generated within the tissue but can also be impacted by mechanical contributions from surrounding tissues. The way and extent to which tissue deformation is influenced by tissue-extrinsic forces are not well understood. In this work, Lye et al. investigated the potential influence of Drosophila mesoderm invagination on germband extension (GBE), an epithelial convergent extension process occurring during gastrulation. Drosophila GBE is genetically controlled by the AP patterning system, which determines planar polarized enrichment of non-muscle myosin II along the DV-oriented adherens junctions. Myosin contractions drive shrinking of DV-oriented junctions into 4-way vertices, followed by formation of new, AP-oriented junctions. This process results in cell intercalation, which causes tissue convergence along the DV-axis and extension along the AP-axis. In addition, GBE is facilitated by tissue-extrinsic pulling forces produced by invagination of the posterior endoderm. Interestingly, some recent studies suggest that the invagination of the mesoderm, which occurs immediately prior to GBE, also facilitates GBE. In the proposed mechanism, invaginating mesoderm pulls on the germband tissue along the DV-axis; the resulting strain of the germband cells generates a mechanotransduction effect that promotes myosin II recruitment to the DV-oriented junctions, thereby facilitating cell intercalation. Here, the authors revisited this proposed mechanotransduction effect using quantitative live imaging approaches. By comparing the wildtype embryos with twist mutants that fail to undergo mesoderm invagination, the authors show that although the DV-oriented strain of the germband cells was greatly reduced in the absence of mesoderm pulling, this defect had a negligible impact on junctional myosin density, myosin planar polarity, the rate of junction shrinkage or the rate of cell intercalation during GBE. A mild increase in the rate of new junction extension and a slight defect in cell orientation were observed in twist mutants, but these differences did not cause obvious defects in cell intercalation. The authors conclude that myosin II-mediated cell intercalation during GBE is robust to the extrinsic mechanical forces generated by mesoderm pulling.

Overall, I found that the results described here are very interesting and of high quality. The data acquisition and analyses were elegantly performed, statistics were appropriately used, and the manuscript was clearly written. However, there are a few points where some further explanation or clarification is necessary, as detailed below:

1. The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:
   • For myosin quantification, the authors state that "Background signal was subtracted by setting pixels of intensities up to 5 percentile set to zero for each timepoint" [Line826]. The rationale for selecting 5 percentile as the threshold for background should be explained. Also, how does this background value change over time?
   • The authors mention that "Intensities varied slightly between experiments due to differences in laser intensity and therefore histograms of pixel intensities were stretched" [Line828]. The method of intensity justification should be justified. For example, does this normalization result in similar cytoplasmic myosin intensity between control and twist mutant embryos?
   • A previous study demonstrates that the accumulation of junctional myosin is substantially reduced in twist mutant embryos compared to the wild type (Gustafson et al., 2022). In that work, junctional myosin was quantified as (I_junction - I_cytoplasm)/I_cytoplasm. In contrast, the cytoplasmic myosin intensity does not appear to be subtracted from the quantification in this study. How much of the difference in the conclusions of the two studies can be explained by this difference in myosin quantification?
   • The authors used the tissue flow data to register the myosin channel and the membrane channel, which were acquired at slightly different times. The accuracy of this channel registration should be demonstrated.
2. The authors show that cell intercalation is not influenced in twist mutant embryos. However, a previous study demonstrates that the speed of GBE is substantially reduced in twist mutants (Gustafson et al., 2022). It would be interesting to see whether a similar reduction in the speed of GBE was observed in this study.
3. It has been previously shown that contractions of medioapical myosin in germband cells also contribute to cell intercalation. The authors should explain why medioapical myosin was not included in the comparison between wildtype and twist mutant embryos.

Minor points:

1. Fig. 1-S1 panel C: the number of cyan cells changes non-monotonically. It first decreases from -10 min to 10 min, then increases from 10 min to 20 min. This is confusing since in theory the number of tracked cells should not increase over time if the cells are tracked from the beginning of the movie.
2. Fig. 1-S2: the vnd band in panel A appears to be much narrower than in panel B.
3. The schematic in Fig. 2J suggests that at the onset of mesoderm pulling the germband cells have a uniform angle of rotation (towards bottom right). Is this the case?
4. The description of myosin intensity normalization in the Methods section is somewhat difficult to follow [Line 829 - 832]. It would be helpful if the authors can show one or two images before and after intensity normalization as examples.
5. Line 704: "Z-stacks for each channel were collected sequentially" - the step size in Z-axis should be reported.
6. Fig. 4C: what are the thin, black lines in the image?

Significance

While most previous work on tissue mechanics and morphogenesis focuses on tissue-intrinsic mechanical input, recent studies have started to emphasize the contribution of tissue-extrinsic forces. An important challenge in understanding the function of tissue-extrinsic forces lies in the difficulties in properly comparing the wild type and the mutant samples that disrupt extrinsic forces, in particular when cell fate specification is altered in the mutants. In this work, the authors addressed this challenge by employing a number of approaches to warrant a parallel comparison between genotypes, including examining the AP- and DV-patterning of the tissue, selecting sample regions with comparable cell fate for analysis, and carefully aligning the stage of the movies. With these approaches, the authors provide compelling evidence to support their main conclusions. By teasing apart the role of the intrinsic genetic program and the extrinsic tissue forces, the work provides important clarifications on the function of mesoderm pulling in GBE and adds new insights into this well-studied tissue morphogenetic process. This work should be of interest to the broad audience of epithelial morphogenesis, tissue mechanics and myosin mechanobiology.

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Referee #2

Evidence, reproducibility and clarity

In the present manuscript, Lye et al. describe a highly detailed quantification of cell shape changes during germband extension in Drosophila melanogaster early embryo. During this process, ectodermal tissue contracts along the dorso-ventral axis, simultaneously expanding along the perpendicular antero-posterior direction, migrating from the ventral to the dorsal surface of the embryo as it extends. This important morphogenetic event is preceded by ventral furrow formation when mesodermal tissue (located in the ventral part of the embryo) contracts along the dorso-ventral axis and invaginates into the embryonic interior. The study compares cell shape dynamics in the wildtype Drosophila with that in the twist mutant, which largely lacks mesoderm and does not form ventral furrow. The major motivation of the study is to examine whether cellular behaviors and myosin recruitment in the ectoderm is cell autonomous, or if those cellular behaviors depend on mechanical interactions between mesoderm and ectoderm. The authors first examine whether transcriptional patterning of key genes involved in germband extension is different between the wildtype and the twist mutant and find no significant difference. Next, the authors thoroughly quantify cellular behaviors and patterns of myosin recruitment in the two genetic backgrounds. A number of different measures are investigated, notably the rate of change in the degree of cellular asymmetry, rate of cell area change, rate of change of cell orientation, differences in myosin recruitment to cell edges of various orientation, as well as the rates of growth, shrinkage, and re-orientation of the various cellular interfaces. It is thoroughly documented how these quantities change as a function of developmental timing and spatial position within the embryo. These data serve basis for quantitative comparison between cellular dynamics in the two genetic backgrounds considered. Overall, the study shows that cellular behaviors observed in the ectoderm are largely the same during the period of time following ventral furrow formation, as would be expected if those cellular behaviors were predominantly cell autonomous and not dependent on stresses generated in the mesoderm.

The data presented in the manuscript are of excellent quality and presentation is very clear.

Minor comments: none

Significance

I find that the study provides a thorough quantification of cell behaviors in a widely studied important model of morphogenesis. The work may be of particular interest for future model-to-data comparison, perhaps providing a basis for future modeling work. I therefore certainly think that this work warrants publication. However, the results of the study largely parallel previous findings and do not appear novel or surprising. It is well established that in snail mutant that lack mesoderm entirely, germband extension proceeds largely normally. This well-established fact suggests that since tissue dynamics in complete absence of mesoderm are largely unaffected, behaviors of individual cells are likely to not be affected either. The work is pretty much entirely observational, and for most part provides a more detailed documentation/quantification of previous findings. I do not think it is appropriate for high profile publication.

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Referee #1

Evidence, reproducibility and clarity

Summary: In this manuscript by the Sanson group, Lye and colleagues try to definitively answer the question of whether pulling forces from the ventral mesoderm have significant effects on convergent extension in the Drosophila germband (germband extension). While germband extension does occur in mutant embryos lacking mesoderm invagination, it has long been an open question in the field as to whether ventral pulling forces from the mesoderm have significant effects (positive or negative) on cell intercalation during germband extension. To definitely address this question, Lye and colleagues generated high-quality, directly comparable datasets from wild-type and twist mutant embryos, and then systematically assessed nearly all aspects of cell intercalation, myosin recruitment, and tissue elongation over time. They demonstrate that pulling forces from the ventral mesoderm have negligible impacts on the course of germband extension. While there are indeed some interesting differences between wild-type and twist embryos with respect to cell intercalation and myosin recruitment, such differences are relatively minor. They conclude that the events of germband extension neither require nor are strongly affected by external forces from the mesoderm. While this is largely a negative results paper, I believe that it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo, and it suggests that tissues are largely autonomous developmental units that are buffered from outside mechanical inputs.

Major comments:

It seems to me that the one obvious omission from this paper is a general measure of convergent extension over time. I think it would be useful to the reader to include some measure of change in tissue aspect ratio over time between wild-type and twist embryos. This could be included in Figure 5 or 6.

Otherwise, I have no major comments on the experimental approach or the findings of this manuscript. It seems to me a straightforward and systematic approach for determining whether mesoderm invagination affects germband extension. I do have several minor comments that should be addressed prior to publication (below).

Minor comments:

I understand why cells would initially stretch more along the DV axis in wild-type embryos compared with twist embryos, but why do cells become so much more stretched along the AP axis (and become smaller apically) after 10 minutes of GBE in wild type compared with twist (Figure 2C and E). I think this is an interesting and non-intuitive result that would warrant a bit of explanation/conjecture.

I don't understand how you are defining cell orientation in Figure 2G. How are you choosing the cell axis that you are then comparing with the body axis? Is it the long axis, or something more complicated than that? I think you should briefly provide this information in the results section. If it is included in the methods, I wasn't able to locate it.

Figure 2: Since you have the space, it might help the reader if you simply wrote out "strain rate" for panels B, D, and F, rather that used the abbreviation "SR."

Please ensure that all axis labels are fully visible in the final figures. In several figures, the Y-axis labels were cut off (e.g., Fig 2I, 4A, 4D, 6B, 6C).

Where space permits, I would suggest using fewer abbreviations in axis labels to increase readability of the figures (e.g., in Figures 3H or 4D).

In Figure 7, I would move the wild-type panels to the left and the twist panels to the right. I think it is more conventional to describe the normal wild-type scenarios first, and then contrast the mutant state.

To be consistent with the literature, "wildtype" should be hyphenated (wild-type) when used as an adjective, or two separate words (wild type) when used as a noun.

Significance

Advance: The advances in this manuscript are largely methodological, but the experiments and analyses are quite rigorous and allow the authors to make strong conclusions concerning their hypotheses. Their findings are based on a high-quality collection of movies from control and twist mutant embryos expressing a cell membrane marker and knock-in GFP-tagged myosin. Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm.

Audience: The primary audience for this article will be basic science researchers working in the early Drosophila embryo who are interested in the interplay between the germband and neighboring tissues. Secondary audiences will include developmental biologists more broadly who are interested in biomechanical coupling (or in this case decoupling) of neighboring tissues.

Describe your expertise: I have been a Drosophila developmental geneticist for over twenty years, and I have been working directly on Drosophila germband extension for over a decade. I have published numerous papers and reviews in this field, and I am very familiar with the genetic backgrounds and types of experimental analyses used in this manuscript. Therefore, I believe I am highly qualified to serve as a reviewer for this manuscript.

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Reply to the reviewers

1. General Statements

We thank the editors for sending our manuscript for peer review and the reviewers for careful reading and their critical comments to improve the manuscript. Below, we describe the experiments that have been carried out in response to the reviewers and incorporated in the preliminary revision. We also describe our plan for the revisions that will address the remaining comments of the reviewers. Most of the comments are addressable with additional experiments (some of which are already ongoing) and these experiments will surely strengthen the study reported in this manuscript without affecting the fundamental findings. We would require up to 4-6 weeks to complete these experiments.

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)):

­Summary: The authors used a conditional transgenic mouse model to demonstrate that deletion of serum response factor (SRF) from adult astrocytes provides neuroprotection in various insult/ diseases contexts without promoting any obvious phenotypic deficiencies. The work builds on the group’s previous study where SRF was embryonically deleted from astrocytes and their precursor cells. Given the role of SRF in promoting glial cell differentiation, the adult conditional KO used in the current study was designed to circumvent the limitations of the previous approach. The authors used a variety of complementary approaches (including immunohistochemistry, electrophysiology, transcriptomics, and behavior) to demonstrate the therapeutic potential of their approach. However, I have questions regarding the validity of the behavioral analyses as well as some of the imaging results that dampen my overall enthusiasm.

Major Comment #1

The synaptogenic factors probed in Figure 3C (e.g. glypicans, thrombospondins, etc.) are not likely to play major roles in the adult brain in a non-injury context, so I do not know that these analyses provide any significant insight into potential functional changes in the mutant mice. Along the same lines, the analysis of synapse count (Figure 3D-E) seems inconsequential given that SRF was knocked out well after the period of developmental synaptogenesis. It would have been much more interesting to have performed these analyses following insult (such as the kainate injury model used by the authors) or in one of the disease models presented later in the manuscript. As it stands, I don't think they add very much to the study.

Response: We are grateful to the reviewer for the careful reading of the manuscript. Astrocytes are known to regulate the formation, maintenance, and elimination of synapses. It has been previously shown that LPS-induced reactive astrocytes exhibit reduced expression of several synaptogenic factors, were unable to promote synapse formation and showed reduced phagocytic activity (PMID: 28099414). We wanted to determine whether the SRF-deficient reactive-like astrocytes were likely compromised in their ability to produce pro-synaptogenic factors and/or adversely affect synapse maintenance. We agree with the reviewer that analysis of synapses in the adult brain may not address the role of these mutant astrocytes in synaptogenesis. But our results indicate that the mutant astrocytes are likely not affecting synapse maintenance or exhibit altered phagocytotic activity that would result in increased or decreased synapse numbers. We will make this clearer in the revised manuscript.

Minor Comment #2:

The authors should note that the use of GluA1 as a postsynaptic marker will not identify silent synapses (i.e. structurally "normal" but functionally inert).

Response: We agree with the reviewer that GluA1 will not identify silent synapses. To study silent vs functional synapses, we will stain for Piccolo (presynaptic) and NMDA receptor NR1 subunit (post-synaptic) to label all synapses and compare this with Piccolo/GluA1 co-localized synapses to identify the functional synapses.

Reviewer #2 (Significance (Required):

The manuscript addresses the important area of the cellular mechanisms that underlie neuroprotection. The ms adds to our understanding of genetic control of neuroprotection and should be of significant interest to others in the field. The experimental approach systematic and the data presented are generally of high quality and believable. While the ms presents quite a bit of overall cellular data that underlies various areas of neuronal and brain function that may be affected by loss of SRF, it is still somewhat descriptive. It is unclear what aspect of astrocyte reactivity is determinative, how mechanistically in normal cells SRF suppresses reactivity, and how SRF -negative reactive astrocytes confer such broad neuroprotection. While the latter is well beyond the scope of this study, the authors do propose SRF may be involved in regulating oxidative stress and amyloid plaque clearance as a potential pathway to account for SRF's role, however a more systematic discussion based on the gene expression data and known pathways would be welcome. Overall, this is a high quality ms that should be of interest to the field that identifies a SRF as a novel player in neuroprotection.

Response: We thank the reviewer for the careful reading of the manuscript and for the positive comments. We will include a more detailed discussion on the genes and pathways based on our gene expression data that may provide insights into how SRF may regulate astrocyte reactivity and neuroprotection.

Additional considerations:

1. Quantification of the extent of SRF loss in astrocytes in conditional tamoxifen knockout would strengthen the quality of the data.

Response: We will provide this data in the revised manuscript.

While the authos did use a Sholl analysis to show hypertophic changes in SRF negative astrocytes, given that SRF is an important regulator of actin and other cytoskeletal related proteins in other cell types, and that cytoskeletal components can play an important role in cell signaling, it is somewhat surprising that the gene array analysis did not include actin and other cytoskeletal proteins, nor did the authors consider a more careful analysis of intracellular cytoskeletal changes and the potential mechanistic implications of this for observed reactivity and neuroprotection.

Response: We agree with the reviewer that SRF is a well-established regulator of actin cytoskeleton. However, we did not any significant changes in gene expression for actin or actin-regulatory proteins. We would have expected a decrease in astrocyte morphology similar to the neurite/axon defects exhibited by SRF-deficient neurons. It is unclear whether the hypertrophic morphology is due to transcriptional regulation of actin/actin-binding proteins or due to astrocyte reactivity. This would be a very interesting question and we will investigate these aspects in future studies.

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

Summary: The study by Thumu et al., suggests that astrocytic specific deletion of SRF in mice results in morphological changes in these cells that does not affect neuronal survival, synapse number, plasticity or cognition. However, in in vivo mouse models of excitotoxic damage and neurodegenerative disease, deletion of SRF reduced neurotoxicity. The authors provide sufficient evidence to suggest that astrocytic SRF contributes to neurotoxicity in various models however some claims are made that are currently not supported by evidence.

Major comments:

2) The authors claim that SRF KO astrocytes undergo hypertrophy. However, the quantification of the number of intersections gives information about morphology rather than hypertrophy. Quantification of cell size (area of S100B staining) should be provided.

Response: We will provide the data suggested by the reviewer.

6) For the RNAseq of isolated astrocytes did the authors confirm that other cell types (e.g microglia) did not contaminate their samples?

Response: We will provide the information requested by the reviewer.

Reviewer #3:

Minor comments:

1) The authors say that in Figure 1B many astrocytes did not show any SRF expression. However, overall averages of SRF intensity are plotted in Figure 1C. It would support their claim to instead to calculate the percentage of SRF expressing cells above a certain threshold in each condition, rather than plotting the mean intensity. As a control for their method of quantifying SRF intensity in Figure 1B, demonstrating no change in SRF in neurons would provide confidence for the specificity of the knockout.

Response: We will provide the quantification of the extent of SRF loss in astrocytes (percent astrocytes that are deleted for SRF) as suggested by Reviewer 2. We will also provide SRF intensity from neurons as suggested by the reviewer.

2) The authors use the term "reactivation" throughout the manuscript. This could be misconstrued as re-activation and so I would suggest using the terms "reactivity" or "reactive transformation". Furthermore, only one region is quantified in Figure 1C while in later figures multiple regions are quantified. The authors should justify this decision or update the figures with data from missing regions.

Response: We will make this change in using the term “reactivity” as suggested by the reviewer.

3) In Figure S2 the authors should provide a positive control for their staining.

Response: We will provide the positive control data for this experiment.

4) Can the authors explain the large amount of variability in number of synapses in 15 mpi in Figure 3E?

Response: We will perform more immunostainings and update the data presented in this figure.

5) Images in Figure 2C are poorly visible and should be improved in terms of either quality or magnification.

Response: We will provide better quality image for Figure 2C.

8) The authors should provide a list of differentially expressed genes from RNAseq of SRF KO mice. No information is currently given in the text about the number of differentially expressed genes in the conditional knockout.

Response: We will include this information in the revised manuscript.

9) In figure 5A data would be better illustrated as a volcano plot (similar to Fig. S7C).

Response: We will provide this in the revised manuscript.

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

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer #1: Major Comment #2

There is considerable variability in the behavioral results, particularly the fear conditioning and Barnes maze tasks (Figures 4F-G). Given the extremely low sample size for mouse behavior (n=5 in on group, n=7 in the other), it is highly likely that the behavioral tests were done with a single cohort of animals (which would be far from ideal) and that these experiments are significantly underpowered. Furthermore, it does not appear that the fear conditioning task was properly optimized. For example, in the control mice in context A, there were two animals that were at or very close to 0 percent freezing; these were likely outliers, or even an indication that the foot shock conditioning protocol was not working as it should. The highest percent freezing of either group was ~70%, which would have been an ideal starting place as an average for the control group. In addition, sex of the animals was not reported for these experiments. If the authors combined sexes as they did in other analyses in this paper, it is possible that they missed reaching the appropriate reaction threshold for the foot shock for some of the animals, as sex differences have previously been demonstrated in mice (DOI: 10.1037/bne0000248). Given the age at which the animals are assessed with these tasks, these specific revisions would require greater than 6 months to complete. However, as currently presented, there simply are not enough data points to make any conclusions regarding behavior.

Response: We have performed the behavioural experiments with an additional cohort of animals for both control and mutant groups and reanalysed the data. We now have n=11 for control and n=9 for mutant group. Only males were used for the behaviour experiments, and we do not see any significant difference in behaviour between the two groups. These results are included in revised Figure 4E-G in the Preliminary Revision of the manuscript. However, we are waiting to perform the remote recall memory for the fear conditioning experiment and will include this date in the revised manuscript.

Minor Comment #1:

The representative GFAP images (Figure 1 E/G) do not appear to have been taken at the same magnification. This was particularly apparent in the comparison between the control and CKO hippocampus at 12mpi. It is difficult to say with certainty, due to the lack of fiducial markers in many of the images. Inclusion of a nuclear stain (DAPI) would be highly beneficial to allow the reader to make a more informed comparison.

Response: These images were taken at the same magnification. We have included the DAPI staining for these images in Suppl. Figure 2 in the Preliminary Revision of the manuscript.

**Referees cross-commenting**

After reading the comments of the other reviewer, I think we're in agreement that the cellular and molecular data, while descriptive, is of mostly excellent quality. Moreover, the significance of the study is high, and the potential readership broad. However, I stand by my initial assessment of the behavioral data and find the manuscript quite lacking in this regard. Proper revisions would take at least half a year or more, so the authors may be disinclined to go this route. That being said, if the behavioral data were to be excised, I would be happy to sign off on the rest of the manuscript provided that the other major criticisms are addressed.

Response: We thank the reviewer for the appreciation of our work. We have increased the number of animals in the behavioural experiments and do not see any significant difference between the two groups. These results are included in revised Figure 4E-G in the Preliminary Revision of the manuscript.

In response cross-comment of Rev 2:

Agreed that if properly conducted and presented, the behavioral data would indeed provide a nice functional correlate to the cellular work. In its current state, I'm afraid that it is instead a hindrance to the study and I would recommend that they just remove it if they choose not to address my concerns with the quality (particularly the extreme variability and the complete lack of freezing by several of the animals, especially in the controls).

Response: We hope that the revised behaviour data would provide a strong functional correlate to the other findings in the study.

Additional cross-comments:

I agree with the added criticisms raised by Reviewer #3, and I think that the manuscript would be greatly improved by revisions that address those and the original criticisms from myself and Reviewer #2. I still think that the behavioral data should be omitted, provided that the authors are not capable or willing to appropriately address those concerns within a reasonable time frame.

Response: We will address all the concerns raised by the reviewers with the required experiments to further strengthen the findings in this study.

Reviewer #3

Major Comment

3) In Figure S1 the authors provide evidence showing lack of B-gal in cell types other than astrocytes (neurons/OPCs). However, microglia are missing, which could be important as later they show that microglia undergo changes in the SRF knockout model. This staining should be provided.

Response: We have performed double immunostaining for b-gal and IbaI and do not see any overlap between IbaI and b-gal, suggesting that there is no Cre expression in microglia. We have included this data in revised Figure S1F in the Preliminary Revision of the manuscript.

5) The authors claim in the text that microglia have thicker processes and an amoeboid shape however no evidence of this is provided in Figure S5.

Response: We have provided data to show larger microglia area and morphology in revised Figure S5 in the Preliminary Revision of the manuscript.

7) In the text "Enrichment analysis of Gene Ontology terms for Biological Process (GO BP) revealed that Srf deficient astrocytes showed enrichment of pathways related to cellular response to beta amyloid and beta-amyloid clearance." This is not shown in fig 5. It would be more accurate to say that there is a downregulation of genes involved in B amyloid metabolic process.

Response: We apologize for the omission in showing that this data was presented in Suppl. Fig. S8E. We have now indicated this in the main text.

Minor Comments:

4) Figure 1E is missing body weight data noted in the figure legend.

Response: We apologize for this oversight. This data was actually included in Suppl. Figure S3E and not in Figure 1. We have made the appropriate correction to Figure legend 1.

6) In Figure 2B figure labels are missing.

Response: We thank the reviewer for pointing out this omission. We have added the missing labels.

7) Details of houskeeping gene normalisation are missing from qPCR data.

Response: We apologize for not providing this information. We have included this in the revised Methods section.

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

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

Reviewer #3, Major Comment 1:

1) The title of the manuscript is "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration". It would be more accurate to say that SRF is involved in neurotoxicity in these models. To make a comment on the role of SRF in neuroprotection, experiments should be performed in spinal cord injury or ischaemia, where deficiency of SRF would be hypothesised to worsen recovery.

Response: We disagree with the reviewer with this assessment. There is no evidence to suggest that SRF is involved in neurotoxicity. What our data suggests is that SRF deficiency results in a reactive astrocyte state that is neuroprotective in these models. We hypothesize that in injury/infection/disease conditions that would result in generation of neuroprotective astrocytes, SRF expression or function may be negatively regulated. It would be interesting to see whether the SRF-deficient astrocytes alleviate or exacerbate pathology and recovery following spinal cord injury and ischaemia.

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Referee #3

Evidence, reproducibility and clarity

Summary: The study by Thumu et al., suggests that astrocytic specific deletion of SRF in mice results in morphological changes in these cells that does not affect neuronal survival, synapse number, plasticity or cognition. However, in in vivo mouse models of excitotoxic damage and neurodegenerative disease, deletion of SRF reduced neurotoxicity. The authors provide sufficient evidence to suggest that astrocytic SRF contributes to neurotoxicity in various models however some claims are made that are currently not supported by evidence.

Major comments: The title of the manuscript is "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration". It would be more accurate to say that SRF is involved in neurotoxicity in these models. To make a comment on the role of SRF in neuroprotection, experiments should be performed in spinal cord injury or ischaemia, where deficiency of SRF would be hypothesised to worsen recovery.

The authors claim that SRF KO astrocytes undergo hypertrophy. However, the quantification of the number of intersections gives information about morphology rather than hypertrophy. Quantification of cell size (area of S100B staining) should be provided.

In Figure S1 the authors provide evidence showing lack of B-gal in cell types other than astrocytes (neurons/OPCs). However, microglia are missing, which could be important as later they show that microglia undergo changes in the SRF knockout model. This staining should be provided.

Can the authors explain the large amount of variability in number of synapses in 15 mpi in Figure 3E?

The authors claim in the text that microglia have thicker processes and an amoeboid shape however no evidence of this is provided in Figure S5.

For the RNAseq of isolated astrocytes did the authors confirm that other cell types (e.g microglia) did not contaminate their samples?

In the text "Enrichment analysis of Gene Ontology terms for Biological Process (GO BP) revealed that Srf deficient astrocytes showed enrichment of pathways related to cellular response to betaamyloid and beta-amyloid clearance." This is not shown in fig 5. It would be more accurate to say that there is a downregulation of genes involved in B-amyloid metabolic process.

OPTIONAL: Figure 6 would be greatly strengthened by functional in vivo experiments showing reversal of motor/ cognitive phenotypes.

OPTIONAL: The study would be improved by isolating astrocytes from the models used in figure 6 and performing RNAseq to provide information about how SRF knockout affects astrocyte reactivity in these models.

Minor comments: The authors say that in Figure 1B many astrocytes did not show any SRF expression. However, overall averages of SRF intensity are plotted in Figure 1C. It would support their claim to instead to calculate the percentage of SRF expressing cells above a certain threshold in each condition, rather than plotting the mean intensity. As a control for their method of quantifying SRF intensity in Figure 1B, demonstrating no change in SRF in neurons would provide confidence for the specificity of the knockout.

The authors use the term "reactivation" throughout the manuscript. This could be misconstrued as re-activation and so I would suggest using the terms "reactivity" or "reactive transformation".

Furthermore, only one region is quantified in Figure 1C while in later figures multiple regions are quantified. The authors should justify this decision or update the figures with data from missing regions.

In Figure S2 the authors should provide a positive control for their staining.

Figure 1E is missing body weight data noted in the figure legend.

Images in Figure 2C are poorly visible and should be improved in terms of either quality or magnification.

In Figure 2B figure labels are missing.

Details of houskeeping gene normalisation are missing from qPCR data.

The authors should provide a list of differentially expressed genes from RNAseq of SRF KO mice. No information is currently given in the text about the number of differentially expressed genes in the conditional knockout. In figure 5A data would be better illustrated as a volcano plot (similar to Fig. S7C).

Significance

The strength of the manuscript is that the authors demonstrate in more than one model that astrocyte specific knockout of SRF rescues neuronal death, implicating SRF in astrocyte mediated neurotoxicity. The limitations of the study are that the mechanism by which SRF deletion reduces excitotoxicity is not addressed and there is no supporting data beyond neuronal survival in the excitotoxicity/OHDA models or plaque density in the APP/PS1 model.

This study adds SRF to an expanding understanding of the neurotoxic capacity of astrocytes in certain reactive states. It will be of broad interest to the astrocyte reactivity field.

My field of expertise is in astrocyte and microglia interactions in neurodegenerative diseases.

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Referee #2

Evidence, reproducibility and clarity

The manuscript, "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration" by Thumu et al., describes the observation astrocyte specfic SRF deficient mice exhibit neuroprotection against a broad range of brain pathologies. The current ms follows up on previous work done by the corresponding author Ramanan and colleagues in which they showed that astrocyte-specific deletion of SRF early during mouse development resulted in persistent reactive-like astrocytes throughout the postnatal mouse brain. In the current ms the authors present data that adult astrocyte specific conditional deletion of serum response factor results in reactive-like hypertrophic astrocytes that localize throughout the mouse brain. They further show that SRF deficient astrocytes do not affect neuron survival, synapse numbers, synaptic plasticity or learning and memory. Strikingly, they further show that brains of Srf knockout mice exhibit protection against neurodegenerative disease related pathologies including induced excitotoxic cell death and that SRF-deficient astrocytes abrogate dopaminergic neuron death and reduce beta-amyloid plaques in mouse models of Parkinson's and Alzheimer's disease. Based on their results, the authors proposes that SRF is a key molecular switch for the generation of reactive astrocytes with neuroprotective functions can attenuate neuronal injury in the setting of neurodegenerative diseases.

Referees cross-commenting

Reviewer #1 raises an important concern regarding the quality of the behavioral studies. I would also agree that the ms is still strong and the findings are significant without them, although they do extend the functional dimensions of the overall study.

Significance

The manuscript addresses the important area of the cellular mechanisms that underlie neuroprotection. The ms adds to our understanding of genetic control of neuroprotection and should be of significant interest to others in the field. The experimental approach systematic and the data presented are generally of high quality and believable. While the ms presents quite a bit of overall cellular data that underlies various areas of neuronal and brain function that may be affected by loss of SRF, it is still somewhat descriptive. It is unclear what aspect of astrocyte reactivity is determinative, how mechanistically in normal cells SRF suppresses reactivity, and how SRF -negative reactive astrocytes confer such broad neuroprotection. While the latter is well beyond the scope of this study, the authors do propose SRF may be involved in regulating oxidative stress and amyloid plaque clearance as a potential pathway to account for SRF's role, however a more systematic discussion based on the gene expression data and known pathways would be welcome. Overall, this is a high quality ms that should be of interest to the field that identifies a SRF as a novel player in neuroprotection.

Additional considerations:

1. Quantification of the extent of SRF loss in astrocytes in conditional tamoxifen knockout would strengthen the quality of the data.
2. While the authos did use a Sholl analysis to show hypertophic changes in SRF negative astrocytes, given that SRF is an important regulator of actin and other cytoskeletal related proteins in other cell types, and that cytoskeletal components can play an important role in cell signaling, it is somewhat surprising that the gene array analysis did not include actin and other cytoskeletal proteins, nor did the authors consider a more careful analysis of intracellular cytoskeletal changes and the potential mechanistic implications of this for observed reactivity and neuroprotection.

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Referee #1

Evidence, reproducibility and clarity

Summary: The authors used a conditional transgenic mouse model to demonstrate that deletion of serum response factor (SRF) from adult astrocytes provides neuroprotection in various insult/diseases contexts without promoting any obvious phenotypic deficiencies. The work builds on the group's previous study where SRF was embryonically deleted from astrocytes and their precursor cells. Given the role of SRF in promoting glial cell differentiation, the adult conditional KO used in the current study was designed to circumvent the limitations of the previous approach. The authors used a variety of complementary approaches (including immunohistochemistry, electrophysiology, transcriptomics, and behavior) to demonstrate the therapeutic potential of their approach. However, I have questions regarding the validity of the behavioral analyses as well as some of the imaging results that dampen my overall enthusiasm.

Major Comments:

1. The synaptogenic factors probed in Figure 3C (e.g. glypicans, thrombospondins, etc.) are not likely to play major roles in the adult brain in a non-injury context, so I do not know that these analyses provide any significant insight into potential functional changes in the mutant mice. Along the same lines, the analysis of synapse count (Figure 3D-E) seems inconsequential given that SRF was knocked out well after the period of developmental synaptogenesis. It would have been much more interesting to have performed these analyses following insult (such as the kainate injury model used by the authors) or in one of the disease models presented later in the manuscript. As it stands, I don't think they add very much to the study.
2. There is considerable variability in the behavioral results, particularly the fear conditioning and Barnes maze tasks (Figures 4F-G). Given the extremely low sample size for mouse behavior (n=5 in on group, n=7 in the other), it is highly likely that the behavioral tests were done with a single cohort of animals (which would be far from ideal) and that these experiments are significantly underpowered. Furthermore, it does not appear that the fear conditioning task was properly optimized. For example, in the control mice in context A, there were two animals that were at or very close to 0 percent freezing; these were likely outliers, or even an indication that the foot shock conditioning protocol was not working as it should. The highest percent freezing of either group was ~70%, which would have been an ideal starting place as an average for the control group. In addition, sex of the animals was not reported for these experiments. If the authors combined sexes as they did in other analyses in this paper, it is possible that they missed reaching the appropriate reaction threshold for the foot shock for some of the animals, as sex differences have previously been demonstrated in mice (DOI: 10.1037/bne0000248). Given the age at which the animals are assessed with these tasks, these specific revisions would require greater than 6 months to complete. However, as currently presented, there simply are not enough data points to make any conclusions regarding behavior.

Minor Comments:

1. The representative GFAP images (Figure 1 E/G) do not appear to have been taken at the same magnification. This was particularly apparent in the comparison between the control and CKO hippocampus at 12mpi. It is difficult to say with certainty, due to the lack of fiducial markers in many of the images. Inclusion of a nuclear stain (DAPI) would be highly beneficial to allow the reader to make a more informed comparison.
2. The authors should note that the use of GluA1 as a postsynaptic marker will not identify silent synapses (i.e. structurally "normal" but functionally inert).

Referees cross-commenting

After reading the comments of the other reviewer, I think we're in agreement that the cellular and molecular data, while descriptive, is of mostly excellent quality. Moreover, the significance of the study is high, and the potential readership broad. However, I stand by my initial assessment of the behavioral data and find the manuscript quite lacking in this regard. Proper revisions would take at least half a year or more, so the authors may be disinclined to go this route. That being said, if the behavioral data were to be excised, I would be happy to sign off on the rest of the manuscript provided that the other major criticisms are addressed.

In response cross-comment of Rev 2:

Agreed that if properly conducted and presented, the behavioral data would indeed provide a nice functional correlate to the cellular work. In its current state, I'm afraid that it is instead a hindrance to the study and I would recommend that they just remove it if they choose not to address my concerns with the quality (particularly the extreme variability and the complete lack of freezing by several of the animals, especially in the controls).

Additional cross-comments:

I agree with the added criticisms raised by Reviewer #3, and I think that the manuscript would be greatly improved by revisions that address those and the original criticisms from myself and Reviewer #2. I still think that the behavioral data should be omitted, provided that the authors are not capable or willing to appropriately address those concerns within a reasonable time frame.

Significance

General assessment: Overall strengths of this study are the implications of SRF as a broad spectrum anti-neurodegeneration agent and the variety of techniques used. Limitations of this study include a lack of meaningful synaptic comparisons and underpowered behavioral assays.

Advance: Provided the above limitations are addressed, this study would provide a meaningful advance in our understanding of controlled reactive astrogliosis as a potential therapeutic strategy for neuroprotection.

Audience: This study would be of interest to a wide audience, particularly neuro- and gliobiologists as well as clinicians who deal with brain disorders and injury.

Expertise: imaging; behavior; synaptic development

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Reply to the reviewers

We appreciate the valuable suggestions and the overall highly positive review of our manuscript. We have now included many suggestions provided by the reviewers, which have made our manuscript much stronger and more rigorous. One reviewer acknowledged, “This study uncovers sex-dependent mechanisms underlying cold sensitivity between male and female mice. The detailed IHC analysis of MHCII expression in DRG neurons is a clear strength of this study and supports flow cytometry results as well as existing literature. The specificity of MHCII expression on small diameter is well characterized and supported by conditional knockout mouse models of MHCII in TRVPV1-lineage neurons.”

R1: It is not, yet, possible to conclude that all experiments are adequately powered as N's for some studies are not provided.

All experiments include N’s both in the text and in the figure legend.

R1: It is unclear what is meant by "novel" expression, used throughout the manuscript.

MHCII is traditionally thought to be constitutively expressed on antigen-presenting cells (APCs) and induced by inflammation on some non-APCs, including endothelial, epithelial, and glial cells (van Velzen et al., 2009). RNA seq data sets (Nguyen et al., 2021, Tavares- Ferreira et al., 2022, Usoskin et al., 2015, Lopes et al., 2017) demonstrate that mouse and human DRG neurons express transcripts for MHCII and MHCII-associated genes. However, there are no reports to date that demonstrate MHCII protein expression in terminally differentiated neurons. To the best of our knowledge, we are the first group to show that MHCII protein is expressed in DRG neurons.

R1: The statement at the end of the abstract, "and that neuronal MHCII may also contribute to many other neurological disorders" seems premature, beyond the scope of the present study.

We agree with the reviewer’s comment and have changed the sentence to the following: “Collectively, our results demonstrate expression of MHCII on DRG neurons and a functional role during homeostasis and inflammation” (pg. 1).

R1: While cold allodynia (hypersensitivity) is a clinically important feature of CIPN, especially in CIPN associated with the platinum based chemotherapeutic agents, it is less so taxane CIPN. Do 60% of patients with PTX CIPN express cold allodynia or does that number refer to CIPN in general?

This statistic is based on a study that conducted a meta-analysis of CIPN incidence and prevalence with paclitaxel, bortezomib, cisplatin, oxaliplatin, vincristine or thalidomide. However, we now include another reference (PMID: 15082135) that demonstrates patients receiving PTX experience cold hypersensitivity (pg.3).

R1: Again, the future direction of expanding studies of the role of MHCII in other aspects of the CIPN phenotype might bear mention.

We have included future directions regarding other aspects of CIPN phenotype in the discussion. We state, “Reducing the expression of MHCII in TRPV1-lineage neurons exacerbated PTXinduced cold hypersensitivity in both male and female mice. Future studies will evaluate the role of MHCII in PTX-induced mechanical hypersensitivity, another prominent feature of CIPN” (pg. 29).

R1: Is there any evidence that IL-4 and/or IL-10 influence cold sensitivity?

IL-10 and IL-4 have been shown to suppress spontaneous activity from sensitized nociceptors (Krukowski et al., 2016; Laumet et al., 2020; Chen et al., 2020) and to reduce neuronal hyperexcitability (Li et al., 2018), respectively. In addition, IL-10 has been shown to reduce mechanical hypersensitivity (Krukowski et al., 2016); however, cold sensitivity has not been evaluated. IL-4 KO mice do not have an increase in tactile allodynia or cold sensitivity after CCI; however, there is an increase in anti-inflammatory cytokines, specifically IL-10, and opioid receptors, which may be a compensatory mechanism that protects against enhanced pain after injury (Nurcan Üçeyler et al. 2011).

R1: Are these experiments run blinded?

Yes, this is discussed in the materials and methods section (pg. 31).

R1: The term "directly contacts" is unclear. No synaptic structure is identified. It might be more accurate to estimate the actual proximity between the two cells, especially as direct contact would not be necessary for the type of intercellular communication they are studying. This is not an EM study.

We agree with the reviewer’s comment and have changed the wording to “in close proximity” (pgs. 1,5, 7, 27).

R1: Two abbreviations are used for immunohistochemistry, ICC and IHC.

IHC refers to immunohistochemistry, and ICC refers to immunocytochemistry. We accidently wrote ICC in the immunohistochemistry section in the materials and methods section. We have now corrected it to say IHC (pg. 32).

R1: In some figure, group sizes are not indicated (e.g., Fig. 4D).

All group sizes are indicated in the text and figure legends.

R1: "small non-nociceptive neurons" - seems to refer to TRPV1+ neurons. There are, however, TRPV1-nociceptors. "Therefore, the majority of MHCII+ neurons in the DRG of naïve female mice were not TRPV1- lineage neurons but non-nociceptive C-LTMRs." Could use some clarification here. Are the authors suggesting that being TRPV1- defines a neuron a non-nociceptive?

We never said small non-nociceptive neurons are TRPV1+ neurons. We crossed TRPV1 lineage mice with td-tomato to label TRPV1 lineage neurons, which include TRPV1 neurons, IB4, and a subset of Aẟ neurons. We found that TRPV1 lineage neurons comprise about 65% of small diameter neurons, so 35% of small diameter neurons are not TRPV1 lineage cells. These non- TRPV1 lineage small diameter neurons are non-nociceptive LTMRs, most likely TH and MrgB4 neurons.

R2: The most pressing concern regarding this study is a lack of a vehicle control group. It is not appropriate to be comparing paclitaxel treated mice to naïve mice. Please include a vehicle treatment (cremophor:ethanol 1:1 diluted 1:3 in PBS) group for all experiments involving paclitaxel.

We believe the most appropriate control to paclitaxel treatment is the naïve control because clinically, paclitaxel is always administered to the patient in a formulation of 50% Cremophor and 50% ethanol. In clinical studies, the controls are healthy no-pain individuals and patients receiving paclitaxel without pain. However, the percentage of patients receiving paclitaxel that do not develop CIPN is low, emphasizing the need for healthy individuals not taking paclitaxel.

R2: Figure 1A only includes representative images of a small number of T cells in presumable contact with DRG neurons in female Day 14 paclitaxel mice but does not include images from other groups. Similarly, B-D show a single CD4+ T cell in contact with DRG neurons in Day 14 paclitaxel and naïve female mice. Please include quantification of the frequency of CD4+ T cells interacting with DRG neurons in the different experimental groups utilized in this study.

We have now quantified the number of CD4+ T cells per mm2 of DRG tissue, which is found in the text (pg. 5) and figures (Fig. S1 and Fig. 1A). We plan to add the quantification of CD4+ T cells per mm2 of DRG tissue for naïve and day 14 PTX-treated male mice. This data will be included in the text (pg. 5) and in Fig. S1.

R2: Please include entire blot for Figure 2A (or at least more of the blot). There is plenty of space in the figure and as it currently appears is not free from apparent manipulation.

We included a larger area of the western blot in Fig 2A (pg. 9).

R2: The authors conclude that MHCII helps to suppress chemotherapy-induced peripheral neuropathy, resolving cold allodynia following paclitaxel treatment. To support this conclusion, I think it is necessary to include a time-course experiment highlighting whether cKO of MHCII in TRPV1 neurons indeed increases the duration for cold hypersensitivity to resolve following paclitaxel treatment.

We conclude that neuronal MHCII suppresses cold hypersensitivity in naïve male mice and reduces the severity of PTX-induced cold hypersensitivity at the peak of the response (day 6) (pg. 1-2). In addition, knocking out one copy of MHCII in male TRPV1-lineage mice reduced total neuronal MHCII in naïve and PTX-treated mice (day 7 and 14) (pgs. 21-22; Fig.7). Moreover, knocking out one copy of MHCII in female TRPV1-lineage mice reduced surface- MHCII in female 7 days post-PTX (pgs. 19-20; Fig.6). Future studies will investigate the distinct roles of surface and intracellular neuronal MHCII and the contribution of MHCII to the resolution of CIPN.

R2: The graphical abstract is misleading. The authors suggest paclitaxel is acting exclusively via TLR4 and that signaling is resolved at Day 14 which their data does not support. Please adjust to reflect findings from the experiments included in this study.

We have removed TLR4 from our graphical abstract as we do not investigate the role of TLR4 in this manuscript. However, we do not suggest paclitaxel is acting exclusively through TLR4. We modified our wording to indicate both pro-inflammatory cytokines and PTX act on neurons to induce hyperexcitability and neurotoxicity: “Pro-inflammatory cytokines and PTX act on DRG neurons inducing hyperexcitability (Li et al., 2018, Boehmerle et al., 2006, Li et al., 2017) and neurotoxicity (Goshima et al., 2010, Flatters and Bennett, 2006), which manifests as pain, tingling, and numbness in a stocking and glove distribution (Rowinsky et al., 1993)” (pg. 9).

R2: Figure 4 and 6 MHCII labelling is oversaturated in most of the images, creating a blurry hue in the representative images. This should be fixed.

The signal intensity of immune cell MHCII is >5 times greater than neuronal MHCII; therefore, in order to visualize neuronal MHCII, the immune cell MHCII is oversaturated. We reference this in the discussion (pg. 26).

R2: The effects of the PTX cHET group are very mild in both the male and female cohorts, and specific to 1 trial. R3: Furthermore, the behavioral effect is seemingly variable, with only one of the three trials being significantly different between groups. This variable response needs to be discussed further.

This behavioral assay was developed by the UNE COBRE Behavior Core, under the guidance of Dr. Tamara King, who has extensive experience in using learning and memory measures to determine changes in pain such as development of thermal hypersensitivity (1-3, King et al, Nat Neuro 2009). Methodologically, the process is as follows: In the temperature placed preference assay, mice are placed on the reference plate (25 °C) to begin each 3-minute trial. For the habituation trial, both the test and reference plates are set to 25 °C, and the mice are allowed to explore for 3 minutes. The following 3 trials are the acquisition trials where the reference plate is set to 25 °C and the test plate to 20 °C. If the animals have cold hypersensitivity, modeling cold allodynia, then they will demonstrate faster acquisition of a learned avoidance response compared to the WT controls. For the results, we will clarify our findings, which are outlined below: 1) We will change the axis labels to better distinguish BL/habituation trial from reference trials in the graphs. 2) We will add graphs comparing naïve versus PTX for male and female WT mice. 3) The changes in the graphs will now reflect 3 key findings: First, we note that PTX-treated mice learn to avoid the cold test plate faster than the naive controls in the WT mice reflecting PTX-induced cold hypersensitivity. Of interest, both males and females demonstrate learned avoidance by trial 2 and that the percent of time on the cold plate continued to decline only in the PTX-treated mice. We had not graphed this in the original figure and plan to add graphs for both male and female WT mice. These graphs are important to include as it validates that this TPP can capture the expected PTX-induced cold hypersensitivity in WT mice. Second, in terms of the naïve cHET mice, these data show that both female and male cHET mice demonstrate faster learning to avoid the cold (20 °C) plate compared to the WT mice (Fig. 8A, B. We note that the males demonstrate a more robust effect, (faster learned avoidance of the cold plate) with significant avoidance to the cold plate emerging in the cHET mice by trial 3 compared to trial 4 in the females (sig diff compared to BL trial). Third, we observed that cHET mice treated with PTX demonstrate even more accelerated learning to avoid the cold plate compared to WT mice treated with PTX. This observation suggests that PTX-treated cHET mice have heightened cold allodynia compared to the WT mice.

R2: The statistical analysis (for the behavior) should also have been a mixed-effects repeated measures between groups ANOVA.

We agree and re-analyzed our behavior data using repeated measures mixed-effects model (REML) with Dunnett’s multiple comparison test comparing trials 2-4 to trial 1 within same group, and Sidak’s multiple tests for significance between groups at the same trial (pgs. 23-25; Fig. 8)

R3: Presented in Figure 3, the authors present data to show surface expression of MHCII, along with the ability of MHCII to present OVA peptide, on naïve and PTX-treated DRG neurons. These data are probably the most relevant in terms of expression as they look at the surface expression of MHCII along with the potential of MHCII to function; therefore, it is unclear why the authors only conducted this analysis on female neurons, and not both male and female neurons. Given the claims of the paper in terms of sex differences for MHC expression, I strongly suggest this is done in order to put the other observations into context.

We completely agree and have added male mice data in Figs. 2 and 3. By western blot, we show that PTX increased the amount of MHCII protein 14 days post-PTX in DRG neurons from female mice, but there’s no change in MHCII protein after PTX in male mice (Fig. 2). In agreement with the western blot, surface-MHCII determined by flow cytometry did not increase after PTX on DRG neurons from male mice (Fig. 3B). Moreover, the frequency of DRG neurons from male mice with surface-MHCII (determined by ICC) and OVA peptide did not change after PTX treatment (Fig. 3D, E). However, the percent area with polarized MHCII on DRG neurons from male mice increased 14 days post-PTX, indicating a modest PTX-induced response in males (Fig. 3F). We have now included the frequency of surface-MHCII on DRG neurons from male and female mice after PTX treatment, and again there was no change in surface-MHCII in male mice (Fig. 6). Collectively, our data demonstrates that neuronal MHCII in male mice is not strongly regulated by PTX treatment.

R3: Given the data presented in Figure 3, it is not clear what the relevance of investigating the subcellular puncta expression of MHCII neurons is, particularly when considering the sex differences observed, and how this was not been performed for surface expression.

We now include surface and total MHCII quantification for male and female WT and cHET mice (Figs. 6,7). In the text, we describe the significance of surface versus endosomal MHCII. “While endosomal MHCII can promote TLR signaling events(Liu et al., 2011), expression of MHCII on the cell surface is required to activate CD4+ T cells.” (pg. 10). “Although the major role for surface MHCII is to activate CD4+ T cells, cAMP/PKC signaling occurs in the MHCII-expressing cell(Harton, 2019). In addition, it has recently been shown that endosomal MHCII plays an important role in promoting TLR responses(Liu et al., 2011), and since DRG neurons are known to express TLRs (Lopes et al., 2017, Wang et al., 2020, Cameron et al., 2007, Barajon et al., 2009, Xu et al., 2015, Zhang et al., 2018), this suggests the potential for T-independent responses in MHCII+ neurons. Knocking out one copy of MHCII in TRPV1- lineage neurons (cHET) from female mice did not change total MHCII 7 days post-PTX but reduced surface-MHCII. Accordingly, PTX-treated cHET female mice were more hypersensitive to cold than PTX-treated WT female mice, suggesting a role for neuronal MHCII in CD4+ T cell activation and/or neuronal cAMP/PKC signaling. In contrast, knocking out one copy of MHCII in TRPV1-lineage neurons (cHET) from male mice did not change surface-MHCII in naïve or PTX-treated mice but reduced total MHCII, indicating endosomal MHCII and potentially a role in TLR signaling. Future studies are required to delineate MHCII surface and endosomal signaling mechanisms in naïve and PTX-treated female and male mice.” (pg. 28).

R3: Furthermore, the authors should provide details of what the abundant non-neuronal structures are within the DRG images that appear positive for MHCII staining.

We now include an image of the high MHCII+ cells in mouse DRG co-stained with macrophage and dendritic cell markers (CD11b/c), indicating the presence of immune cells (Fig. S6).

R3: The behavioral data presented in Figure 7 is somewhat confusing. Can the authors confirm how many alleles of MHCII were knocked out from the Trpv1-lineage neurons for these experiments? In Figure 7, it states cKO Het, which suggests that only one allele was deleted within the Trpv1 population. If this is the case, this needs to be clearly outlined within the results section and not simply referred to as "knocking out MHCII in Trpv1-lineage neurons". In addition, an explanation as to why heterozygous cKO were used rather than homozygous cKO needs to be provided. This is particularly relevant when discussing potential sex differences.

The mouse behavior is performed in wild type and TRPV1lin MHCII+/- heterozygote mice (Fig 8). Instead of saying we knocked out MHCII, we changed the text to “knocking out one copy of MHCII in TRPV1-lineage neurons” (pgs. 23, 29). In the methods section, we state that “cHET×MHCIIfl/fl crosses only yielded 8% cKO mice (4% per sex) instead of the predicted 25% (12.5% per sex) based on normal Mendelian genetics. Thus, cKO mice were only used to validate MHCII protein in small nociceptive neurons” (pg. 30) (Fig 7).

R3: A significant gap in the current manuscript is the functional assessment of MHCII protein expressed on DRG neurons in terms of T cell activity. I would suggest the authors consider performing a co-culture DRG-T cell (i.e. Treg) assay where anti-inflammatory cytokine release can be measured in the presence and absence of MHCII on DRG neurons.

The functional implication of MHCII protein on DRG neurons in terms of T cell activity is out of the scope of this manuscript. We currently have another manuscript in progress investigating CD4+ T cell signaling and cytokine production when co-cultured with DRG neurons. R3: Within the first paragraph of the results section, the authors reference Goode et al, 2022, stating that they have previously shown that CD4+ T cells in the DRG secrete anti-inflammatory cytokines. I have read this paper and could not find any data that showed increased secretion of cytokines, only that there is an increase in T-cell populations that contain anti-inflammatory markers. Please consider rewording to reflect the observations made in the original paper. We have changed “secrete” to “produce” (pg. 5) because we detected anti-inflammatory cytokines (IL-10 and IL-4) within CD4+ T cells using intracellular staining and multi-color flow cytometry.

R3: Figure 1A states that it is "day 14 PTX", however, there is no reference to this in the corresponding text - please state what Figure 1A is showing in the main text and legend regarding PTX treatment.

We have now included text and Fig. 1. legend that states that the images in Fig1A are of DRG tissue collected from female mice 14 days after PTX treatment (pg. 5).

R3: Throughout the results section (Figure 3-Figure 6), the authors provide percentage changes in observed difference in expression, however, in addition to this, it would be valuable to have the actual number of neurons analysed for each group and sex.

We now report in the materials and methods section the number of neurons that were analyzed (pg. 33).

R3: For Figure 5, can the authors confirm whether this was performed on tissue sections or dissociated cell culture?

This analysis was performed in DRG tissue sections. The legend now states, “Gaussian distribution of the diameter of MHCII+ DRG neurons in DRG tissue from naïve (pink), day 7 (orange) and day 14 PTX-treated (blue) (A) female and (E) male mice (n=8/sex, pooled neurons).”

R3: Can the authors comment on why surface expression for MHCII was not performed on the these reporter neurons?

In the future, we plan to delineate which subsets of neurons express MHCII by co-staining for MHCII and specific neuronal markers. However, these studies are beyond the scope of the current manuscript.

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Referee #3

Evidence, reproducibility and clarity

This manuscript sets out to investigate the mechanism by which the previously reported infiltration CD4+ T cells into the DRG parenchyma can mediate analgesia in the paclitaxel (PTX) model of chemotherapy-induced neuropathic pain (CIPN). The authors provide good rationale for the purpose of the study and make a number of interesting observations, including the expression of MHCII on DRG neurons, the effect of PTX on MHCII expression on DRG neurons, and the effect of targeted deletion of MHCII on Trpv1-expressing putative nociceptive neurons in exacerbating the effect of PTX-induced cold hypersensitivity. These data culminate to a hypothesis that MHCII expression on DRG neurons may drive T-cell mediated anti-inflammatory effects (and analgesia) in models where their recruitment is notable. Overall I enjoyed reading the manuscript, however, I believe there are a number of points that need to be considered further.

Major comments.

• Presented in Figure 3, the authors present data to show surface expression of MHCII, along with the ability of MHCII to present OVA peptide, on naïve and PTX-treated DRG neurons. These data are probably the most relevant in terms of expression as they look at the surface expression of MHCII along with the potential of MHCII to function; therefore, it is unclear why the authors only conducted this analysis on female neurons, and not both male and female neurons. Given the claims of the paper in terms of sex differences for MHC expression, I strongly suggest this is done in order to put the other observations into context.
• Given the data presented in Figure 3, it is not clear what the relevance of investigating the subcellular puncta expression of MHCII neurons is, particularly when considering the sex differences observed, and how this was not been performed for surface expression. Furthermore, the authors should provide details of what the abundant non-neuronal structures are within the DRG images that appear positive for MHCII staining.
• The behavioural data presented in Figure 7 is somewhat confusing. Can the authors confirm how many alleles of MHCII were knocked out from the Trpv1-lineage neurons for these experiments? In Figure 7, it states cKO Het, which suggests that only one allele was deleted within the Trpv1 population. If this is the case, this needs to be clearly outlined within the results section and not simply referred to as "knocking out MHCII in Trpv1-lineage neurons". In addition, an explanation as to why heterozygous cKO were used rather than homozygous cKO needs to be provided. This is particularly relevant when discussing potential sex differences. Furthermore, the behavioural effect is seemingly variable, with only one of the three trials being significantly different between groups. This variable response needs to be discussed further.
• A significant gap in the current manuscript is the functional assessment of MHCII protein expressed on DRG neurons in terms of T cell activity. I would suggest the authors consider performing a co-culture DRG-T cell (i.e. Treg) assay where anti-inflammatory cytokine release can be measured in the presence and absence of MHCII on DRG neurons.

Minor comments.

• Within the first paragraph of the results section, the authors reference Goode et al, 2022, stating that they have previously shown that CD4+ T cells in the DRG secrete anti-inflammatory cytokines. I have read this paper and could not find any data that showed increased secretion of cytokines, only that there is an increase in T-cell populations that contain anti-inflammatory markers. Please consider rewording to reflect the observations made in the original paper.
• Figure 1A states that it is "day 14 PTX", however, there is no reference to this in the corresponding text - please state what Figure 1A is showing in the main text and legend regarding PTX treatment.
• Throughout the results section (Figure 3-Figure 6), the authors provide percentage changes in observed difference in expression, however, in addition to this, it would be valuable to have the actual number of neurons analysed for each group and sex.
• For Figure 5, can the authors confirm whether this was performed on tissue sections or dissociated cell culture? In addition, can the authors comment on whey surface expression for MHCII was not performed on the these reporter neurons?

Significance

This paper presents interesting data on the expression of MHCII on DRG neurons, which corroborates existing and published RNA expression data from the literature. In addition, this paper builds on our current understanding of how T-cells may be able to interact with DRG neurons in order to modulate their responses in instances of nerve injury. However, there are significant gaps in the data presented which prevent a more informative conclusion being drawn regarding the role of MHCII in modulating neuronal responses following PTX-induced CIPN.

Audience: I would suggest basic scientists working within the field of pain and neuroimmunology would be interested in this work.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Whitaker EE and co-authors implicate MHCII expression in DRG neurons in the resolution of pain following paclitaxel treatment. The authors demonstrate that CD4 T cells closely interact with DRG neurons, which also express MHCII proteins. They further characterize neuronal MHCII expression in naïve and paclitaxel treated mice in small diameter TRPV1+ neurons. Utilizing genetic animal models with MHCII knockout in TRPV1-lineage neurons, the authors highlight that loss of MHCII in TRPV1 neurons exaggerates cold sensitivity in naïve male mice, and in both sexes following paclitaxel treatment.

Major concerns:

The most pressing concern regarding this study is a lack of a vehicle control group. It is not appropriate to be comparing paclitaxel treated mice to naïve mice. Please include a vehicle treatment (cremophor:ethanol 1:1 diluted 1:3 in PBS) group for all experiments involving paclitaxel. This would also improve statistics as unpaired T tests comparing naïve vs paclitaxel is not convincing.

Figure 1A only includes representative images of a small number of T cells in presumable contact with DRG neurons in female Day 14 paclitaxel mice, but does not include images from other groups. Similarly, B-D show a single CD4+ T cell in contact with DRG neurons in Day 14 paclitaxel and naïve female mice. Please include quantification of the frequency of CD4+ T cells interacting with DRG neurons in the different experimental groups utilized in this study.

Please include entire blot for Figure 2A (or at least more of the blot). There is plenty of space in the figure and as it currently appears is not free from apparent manipulation.

The authors conclude that MHCII helps to suppress chemotherapy-induced peripheral neuropathy, resolving cold allodynia following paclitaxel treatment. To support this conclusion, I think it is necessary to include a time-course experiment highlighting whether cKO of MHCII in TRPV1 neurons indeed increases the duration for cold hypersensitivity to resolve following paclitaxel treatment.

Minor concerns:

The graphical abstract is misleading. The authors suggest paclitaxel is acting exclusively via TLR4 and that signaling is resolved at Day 14 which their data does not support. Please adjust to reflect findings from the experiments included in this study.

Figure 4 and 6 MHCII labelling is oversaturated in most of the images, creating a blurry hue in the representative images. This should be fixed

The effects of the PTX cHET group are very mild in both the male and female cohorts, and specific to 1 trial. I believe these assessments were conducted at Day 6 post injection. Why was this timepoint chosen considering differences in MHCII expression in small neurons was only present at Day 14 relative to naïve? The statistical analysis should also have been a mixed-effects repeated measures between groups ANOVA.

Significance

This study uncovers sex-dependent mechanisms underlying cold sensitivity between male and female mice. The detailed IHC analysis of MHCII expression in DRG neurons is a clear strength of this study, and supports flow cytometry results as well as existing literature. The specificity of MHCII expression on small diameter is well characterized and supported by conditional knockout mouse models of MHCII in TRVPV1-lineage neurons. The clear limitations of this study is the lack of a vehicle control group and limited behavioral analysis. They undermine the conclusions made by the author, and in extension, the significance of this study.

This study adds to the understanding of neuro-immune signaling in peripheral neuropathic pain. As far as I am aware, this is the first study to investigate MHCII expression in DRGs in relation to development of chemotherapy-induced peripheral neuropathy. Thus this study provides an incremental advance in neuroimmune mechanisms contributing to the development of chemotherapy-induced peripheral neuropathy in mice.

This study would be of interest to basic researchers interested in neuropathic pain, with particularly researchers with a focus on neuroimmunology and chemotherapy-induced peripheral neuropathy models. The sex differences observed in naïve mice would also be of interest to basic researchers within the wider pain field. Given the preliminary nature of the findings, I do not think this would be of interest to broader neuroimmunology or clinical audiences.

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Referee #1

Evidence, reproducibility and clarity

Section A:

The key conclusions of this study are quite robust and compelling.

While no claims need qualification clarification of some conclusions could improve the impact of this study.

Additional experiments are not essential to support the claims of this study.

Sufficient details are provided to allow reproduction of the key findings of this study.

It is not, yet, possible to conclude that all experiments are adequately powered as N's for some studies are not provided.

Significance

Section B:

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

This study should be of broad interest not only in the field of the neurobiology of pain but in broader issues related to neuroimmunology.<br /> - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I am a clinician-scientist with clinical responsibilities in immunologic disorders and a basic scientist with expertise in the area of pain, including chemotherapy-induced painful peripheral neuropathies and neuroimmune mechanisms.<br /> - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).

The authors provide compelling evidence that MHCII expression of MHCII in primary sensory neurons, is regulated in painful chemotherapy-induced peripheral neuropathy (CIPN) induced by the commonly used taxane class of chemotherapy drugs, paclitaxel (PTX).

The present studies build on recent literature demonstrating that PTX CD4+ T cells in DRG. This is key to their hypothesis as T cells and anti-inflammatory cytokines protect against CIPN. In the present study these investigators studied how CD4+ T cells are activated role of cytokines released from these cells on CIPN. To key findings of the present study: the expression of functional MHCII protein in DRG neurons and the proximity of the DRG neurons and CD4+ T cells. While the MHCII protein was expressed in small-diameter, nociceptive, DRG neurons, in male mice, in females it was induced by PTX. Compatible with the hypothesis that the anti-inflammatory CD4+ T cells attenuate CIPN. Finally, in support of the contribution of this mechanism to CIPN pain, they demonstrated that attenuation of MHCII protein from nociceptors produced the predicted increase in cold hypersensitivity. Taken together their findings support suppression of CIPN by MHCII

While the experiments are well designed and executed and the results clearly presented, I have some relatively minor concerns that, if addressed, might improve the ability of a general scientific audience to appreciate the impact of the findings presented (possibly a penultimate paragraph covering caveats and limitations of the present study).

It is unclear what is meant by "novel" expression, used throughout the manuscript.

The statement at the end of the abstract, "and that neuronal MHCII may also contribute to many other neurological disorders" seems premature, beyond the scope of the present study.

While cold allodynia (hypersensitivity) is a clinically important feature of CIPN, especially in CIPN associated with the platinum based chemotherapeutic agents, it is less so taxane CIPN. Do 60% of patients with PTX CIPN express cold allodynia or does that number refer to CIPN in general? Again, the future direction of expanding studies of the role of MHCII in other aspects of the CIPN phenotype might bear mention. Is there any evidence that IL-4 and/or IL-10 influence cold sensitivity? Are these experiments run blinded?

The term "directly contacts" is unclear. No synaptic structure is identified. It might be more accurate to estimate the actual proximity between the two cells, especially as direct contact would not be necessary for the type of intercellular communication they are studying. This is not an EM study.

Two abbreviations are used for immunohistochemistry, ICC and IHC.

In some figure, group sizes are not indicated (e.g., Fig. 4D).

"small non-nociceptive neurons" - seems to refer to TRPV1+ neurons. There are, however, TRPV1-nociceptors.

"Therefore, the majority of MHCII+ neurons in the DRG of naïve female mice were not TRPV1-lineage neurons but non-nociceptive C-LTMRs." Could use some clarification here. Are the authors suggesting that being TRPV1- defines a neuron a non-nociceptive?

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Reply to the reviewers

We would like to thank all reviewers for their highly valuable comments, reviewing the article, and suggesting changes to improve its overall structure, clarity, and comprehension. Please find below our point-by-point responses to each reviewer’s comments. Lines, figures, and tables we refer to in the responses correspond to the clean copy of the revised manuscript.

Reviewer #1:

1) In the introduction the authors list 4 databases of ultra-conserved non-coding elements, and choose to work with the UCNEbase that detects UCNE regions by comparing human and chicken, as they state that it is one of the most comprehensive resources. Can the authors comment on the extent of overlap of UCNE regions identified in this database compared to other databases. It would also be helpful to add an explanation in the Introduction on the advantage of comparing the human genome to the chicken genome, e.g., is chicken the most distant vertebrate with a high quality genome, or another reason? And can the authors comment on the extent of conservation of the UCNEs compared to other species - in the UCNEbase paper, orthologues for the UCNEs are identified in 18 vertebrate species including reptiles, amphibians and fish.

We have addressed these comments and incorporated them in the introduction (lines 46-47; 49-52). As stated, it is not straightforward to evaluate to which extent UCNE regions overlap with those collected in other databases due to the different scopes and methods of these resources. We clarified why we selected this database, which was precisely based on the comments mentioned by the reviewer, i.e., sufficient evolutionary distance to identify functional regions confidently and high quality genome assemblies. Regarding the extent to which UCNEs are conserved in other species, the UCNEdatabase indeed provides additional information with respect to UCNE orthologues in other vertebrate species, including reptiles, amphibians, and fish. As we consider that this comparison is beyond the scope of the present study, it was not included in the main manuscript. However, to address this interesting point raised by the reviewer, we evaluated the proportion of UCNEs found in different species with respect to those annotated for human-chicken. As show in the figure below, UCNEs are conserved to a larger extent up to Xenopus tropicalis, for which most of the UCNEs annotated for human-chicken have corresponding orthologues in this species. Although to a minor extent, UCNEs are also conserved across more distant species (e.g., fish), for which approximately half of the UCNEs annotated for human-chicken have orthologues.

2) The authors briefly describe how potential target genes were assigned to UCNEs in the Method section (section begins on page 21, line 407 and on lines 379-381). They note that they used the Genomic Regions Enrichment of Annotations Tool (GREAT). Can the authors provide additional details on what this method does and also provide a high level sentence in the Results section on page 8, lines 144-146, on how target genes were assigned to UCNEs, as well as in the Discussion on page 16, line 289. Based on the Methods description on lines 409-414, a UCNE was associated with any gene within 1Mb of the UNCE that was expressed in retina and whose curated regulatory domains overlapped the UNCE. Is this correct? How were the regulatory domains curated (line 411-412)? Can the authors please clarify this important point.

Additional details about the GREAT algorithm have been included in the Methods section (lines 410-424) as well as a high-level sentences in the Results (lines 140-141) and Discussion (lines 276-280). It is correct that a UCNE was associated with any gene within 1Mb of the UCNE that was expressed in retina and whose curated regulatory domains overlapped the UCNE. As stated now (lines 420-424), these regulatory domains are supported by experimental evidence demonstrating that a gene is directly regulated by an element located beyond of its putative regulatory domain. We specified these domains for the utilized version of GREAT as well.

3) There are other approaches for linking putative transcriptionally active genomic regions with target genes in specific tissues or cell types through correlation analysis of ATAC-seq peaks (chromatin accessibility) and gene expression in RNA-seq data. A commonly used peak-to-gene linkage method is implemented in ArchR (Granja et al, 2021, PMID: 33633365). The authors note that they used scATAC-seq gene scores and scRNA-seq gene expression to further characterize the proposed target genes of UCNEs. It is not clear what the authors mean by "scATAC-seq gene scores". Please define "scATAC-seq gene score" and add a reference. Can the authors compare the target gene mapping to UCNEs using GREAT and gene expression filtering to that using peak-to-gene linkage based on retina tissue or single cell ATAC-seq and RNA-seq data?

We have defined explicitly what scATAC-seq gene scores are in the Methods section (lines 436-437) along with its reference. We have also addressed this important point and compared the overlap between the set of target genes predicted by GREAT and those assigned by the peak-to-gene linkage method implemented by ArchR. Details of this analysis, its results, interpretation are included in the Methods (lines 441-446), Results (lines 158-163; Supplementary Table 4), and Discussion (lines 280-282) sections.

4) For gene expression filtering (line 419) the authors quantify transcript expression of retina from FASTQ samples using the Kallisto method, and then note that they did the filtering on gene expression levels (TPM<0.5). Please add details on how you went from transcript expression levels to gene expression levels for the filtering, or was the filtering performed at the transcript level?

We have added details on how transcript-level quantification estimates were summarized at gene level, for which the filtering was performed (lines 429-430).

5) The authors use the words "active UCNE", first mentioned in the Results on line 144. Can the authors define what they mean by "active UCNE". What information/evidence is used to ascertain that a UCNE is indeed active. Overlap of a UCNE with a chromatin accessibility region from ATAC-seq or DNAase-Seq would only suggest that the UCNE may be active. Intersection with enhancer activity measured with in vivo enhancer reporter assays in transgenic mice from the VISTA enhancer browser provides stronger evidence of transcriptional activity. The authors might want to distinguish between putatively actively and active based on the functional support.

We thank the reviewer for this relevant comment to address the nuances of defining active UCNEs. The reviewer’s assumptions are correct and hence these terms were clarified throughout the entire text. The term functional is now only used when referring to UCNEs for which there is functional support (e.g., PAX6-associated UCNE in line 193) .

6) The authors assessed the significance of depletion of common variation (MAF>1%) in the UCNE regions compared to a background of randomly selected genomic regions. In generating the random distribution of regions, did the authors match on the distribution of distances of the UNCEs to the TSS of genes in the randomly selected regions? This may be a confounder. Also, in the legend of Figure 3, lines 191-192, it is stated: "Variant population frequencies within putative retinal UCNEs normalized to a background composed of randomly selected sequences (see Methods).", but we did not find a description of this analysis in the Methods section.

Evaluating the potential confounding effect of the genomic background was indeed a very important point. We have now incorporated the details showing the suitability of such background well as a detail description of how such background was generated (lines 479-481; Figure S1). Additionally, to further support our analysis demonstrating the depletion of common variation within UCNEs, we have included an evaluation of the distribution of genome-wide residual variation intolerance score (gwRVIS) values (PMID: 33686085) compared to this background of randomly selected genomic regions in a human reference cohort (lines 173-178; 487-495; Fig. 3C).

7) In regards to intersecting UCNEs with epigenetic marks that detect active or repressed enhancers in retina, the authors used data from Aldiri et al 2017 that measured epigenetic changes during retinal development. Did this dataset contain epigenetic measurements in adult retina? The authors might want to consider using the epigenetic marks/ChIP-seq data from adult human retina in Cherry et al. PNAS 2020 (PMID: 32265282)

We have incorporated the adult-stage data suggested by the reviewer to provide a more comprehensive characterization. Details about the integration of this dataset as well as the results and their corresponding interpretation have been incorporated in the Methods (lines 372-374), Results (lines 115-117; Supplementary Table 2), and Discussion (lines 271-273) sections accordingly.

8) With respect to the examination of rare variants that may be associated with rare eye disease in retina active UCNEs, for the interpretation of the results, it would be helpful to get more information on the distribution of rare variants found in UCNEs associated in this study to known IRD genes in all affected individuals in families, if this information is available in the 100,000 Genomes Project.

Although it is indeed a relevant point, this information cannot be retrieved in the 100,000 Genomes Project. As it is a restricted research environment, we are only allowed to query sequencing data corresponding to participants enrolled within the framework of our specific sub-domain, namely “Hearing and Sight”, and therefore evaluating the distribution of rare variants in all affected individuals is not feasible.

9) In the Methods section on lines 450-451, the authors mention that they performed variant screening of retinal disease genes, referencing the Genomics England Retinal Disorder panel and Martin et al., 2019. Can the authors add to the Methods and Results sections how many retinal disease genes were initially tested. Also, to get a sense of the specificity of the overlap of rare variants in the 100k Genome Project cohort with UNCE regions, it would be informative to show a distribution of the number of rare variants <0.5% that passed the filtering in gnomAD per eye disease gene before the overlap with UNCEs.

We specified the number of retinal genes that were tested in the Method section (line 471). In addition, as suggested by the reviewer, we generated allele frequency distributions for all variants retrieved within a selection of 25 disease-gene associated loci and their corresponding UCNEs in order to assess the specificity of the overlap between rare variants and UCNE regions (lines 181-182, 496-501; Figure S2).

10) The authors found "an ultrarare SNV (chr11:31968001T>C) within a candidate cis-regulatory UCNE located ~150kb upstream of PAX6. This variant was found in four individuals of a family segregating autosomal dominant foveal abnormalities". They tested the functional effect of this element with a reporter assay in zebrafish and found that the UNCE affects expression in the eye, forebrain, and neural tube. It would add further value if the authors were to test the effect of this SNV in the UCNE on the reporter expression pattern, using CRISPR/cas9?

That is a very relevant point. We have tested the effect of this SNV in the UCNE on the reported expression pattern using the same experimental setup that we used for testing the wild-type construct, namely transgenic enhancer zebrafish assays. However, we could not obtain conclusive results, most likely due to the limitations posed by testing these regions outside their native genomic context. Therefore, additional experimental work (e.g., CRISPR-based) should be performed to address this question. This is, however, beyond the scope of the present study, for which the main focus was the identification and functional annotation of ultraconserved cCREs. We have incorporated the details, results, and interpretation of the assays performed mutant construct in the Methods (lines 525-527; Supplementary Table 12), Results (lines 235-238; Supplementary Table 10), and Discussion (lines 350-353) sections.

11) The authors found rare variants in UCNEs linked to 45 IRD genes. Can the authors provide additional information on the functional genomic annotations of these UCNEs and distance to the target genes. The UCNEs were characterized with respect to their genic features in the original paper (UCNEbase, Dimitrieva et al., NAR, 2013), e.g., intergenic, intronic and 3'/5' UTR. Also, it would be useful for clinical applications to provide the start and end positions of the UCNEs that contain the rare variants associated with their 45 IRD genes in Supplementary Table 6.

Additional functional genomic annotations, genic features following those of the original UCNE paper, and the distance to the TSS of these 45 target disease-associated genes have been incorporated in (new) Supplementary Table 5. The start and end positions of the UCNEs that contain the rare variants have also been indicated in new Supplementary Table 7.

12) A total of 724 target genes were assigned to 1,487 UCNEs that displayed candidate cis-regulatory activity. Given the interest in using UCNEs to help identify potential pathogenic mutations that lead to IRDs, can the authors note in the Results section how many of the 724 target genes are IRDs.

We thank the reviewer for this important point. From the total of 724, a total of 27 genes are annotated as IRD genes, of which (interestingly) 23 were kept as found to be expressed in the retina. This has been clarified in the Results section (line 166-168).

13) In the Discussion on page 15 line 259, can the authors clarify if variation found in UCNEs were only associated with rare disease or also with common diseases.

We have clarified that variation found in UCNEs has only been associated with rare diseases (line 247).

Minor edits:

1) In abstract, the authors might consider changing the words "rare eye diseases" on lines 20 and 22 to "rare retina degeneration diseases", and on lines 88-89.

We thank the reviewer for this comment. However, we consider that rare eye diseases is a more suitable term for our purpose as it includes diseases primarily characterized by stationary and non-progressive phenotypes such as North Carolina Macular Dystrophy and fovea hypoplasia.

2) In the Introduction on line 49, there seems to be a typo in the number of UCNE regions reported. 4,135 UCNE regions is supposed to be 4,351, based on the original paper (https://academic.oup.com/nar/article/41/D1/D101/1057253).

We have corrected this typo accordingly.

3) In the introduction on lines 75-76, these references: Lyu et al., Cell Reports 2021, PMID: 34788628, and Zhang et al., Trends in Genetics 2023, PMID: 37423870, could be added to the following sentence to provide additional: "This cellular complexity is the result of spatiotemporally controlled gene expression programs during retinal development”.

We have now included these relevant references.

4) On lines 77 and 84, I would write IRD as plural: IRDs.

This has been amended in the new version.

5) In introduction on lines 89-90, it can be further added that you provide experimental support for an ultra rare SNV in a cis regulatory UCNE affecting PAX6.

We have explicitly stated that we provided functional evidence for this UCNE.

6) On line 98, the authors refer to Figure 1A when noting that the integration of UCNEs with multi-omics data in human retina allows to evaluate the regulatory capacity of UCNEs across the major developmental stages of human retina. However panel A in figure 1 does not seem to show this point. It shows the comparison of elements across species. Please make appropriate changes to the main text and figure legend.

We have made the appropriate changes and located the reference to this figure in a more relevant part of the text (line 87).

7) Please explain what the names appended to the gene symbols in the first column "UCNE ID" in Supplementary Tables 1 and 2 refer to.

We have clarified what these refer to.

8) On line 145, can the authors clarify what they mean by "active gene expression in the retina". Is this just another way of referring to genes found to be expressed in retina? If so, it might be clearer just to write: "We annotated the identified active UCNEs to assign them potential target genes and thus assess their association with genes expressed in the retina"

We indeed meant genes found to be expressed in retina. As this phrasing might not be completely clear, we have now changed to the wording suggested by the reviewer.

9) One line 156, I would write "regulation of transcription" as listed in the gene ontology terms in Figure 2C, instead of "regulation of gene expression". The authors might want to add this to the Discussion. Can the authors include the full gene set enrichment results from Enrichr in a supplementary table at Padj<0.05 since only the top gene sets are shown in Fig. 2C (at P<1E-13)?

We changed the term to “regulation of transcription” to keep the nomenclature consistent to that of Figure 2C. We have also provided a full gene set enrichment from Enrichr as well (Supplementary Table 3).

10) On page 12, line 214, what does "EH38E1530321" Stand for? It seems to specify a distal enhancer-like signatures in bipolar neurons, but I couldn't find this ID in any database.

This refers to the identifier of ENCODE:

https://screen-v2.wenglab.org/search/?q=EH38E1530321&assembly=GRCh38

Additionally, when mentioning a specific UCNE, VISTA enhancer, or ENCODE cCRE (as in this case), we have explicitly included its corresponding identifier.

11) In the Methods section on lines 391-392, can the authors give some high-level explanation of the unconstrained integration method: "Single-nucleus RNA-seq of the same tissue and timepoints (GSE183684) were integrated using the unconstrained integration method". Also, can they comment on how retinal cell class identities were assigned (line 393). Was it based on known markers or on previous identification of cell classes and highly variable genes between clusters?

We have included a high-level explanation of the unconstrained method in the Methods section (lines 387-392). We also clarified that the assignment of cell class identities was based on known markers (line 394).

12) In the integration of UCNEs with bulk and single cell ATAC-seq and Dnase hypersentitivity regions, can the authors note in the Methods section (lines 400-404) what peak width was used to test for overlap with the UCNEs.

We have specified the peak widths that were used for the overlap with UCNEs (lines 397 and 403).

13) On line 436, the word 'and' is missing between "(SNVs, and indels < 50bp)" and "large structural variants".

This has been corrected.

14) On lines 443-444, please provide references to the computational tools listed. Please note if default settings/parameters were used.

We have specified that default parameters were used in the analysis (line 464).

15) In the following sentence in the Methods section on lines 447-449, it is not clear in the Results section how this was used in the flow of the analysis, and how many cases showed such a similarity in phenotype: "For each candidate variant, we compared the similarities between the participant phenotype (HPO terms) and the ones known for its target gene through literature search and clinical assessment by the recruiting clinician when possible." Can the authors add more detail to the Results section.

As the evaluation of the candidate variants was essentially performed on a case-by-case basis, we opted to include a rather general description of the workflow, which indeed included a comparison of the reported phenotypes with those associated with the putative target gene. An example of such comparison has been included in the Results (lines 186-187) section regarding the cases for which a NCMD-like phenotype was suspected.

16) It would be helpful to have a table that describes the different omics datasets used in the paper, with some basic annotations (tissue type, sample size, reference).

This has now been incorporated in Supplementary Table 11.

17) Can the authors add references to their sentence in the Discussion on page 17 lines 299-301: "As has been shown before, the phenotype caused by a coding mutation of a developmental gene can be different from the phenotype caused by a mutation in a CRE controlling spatiotemporal expression of this gene."

We clarified that this phrase referred to the case of PRDM13, for which bi-allelic coding pathogenic variants are linked to hypogonadotropic hypogonadism and perinatal brainstem dysfunction in combination with cerebellar hypoplasia (Whittaker et al., 2021), while non-coding variants within its regulatory regions are associated with NCMD.

Reviewer #2:

Minor discretionary suggestions for improving the presentation:

1) Wherever a specific UCNE, Vista enhancer or ENCODE cCRE is mentioned, the element should be identified by name or accession code: For instance (Iine 212): "this variant is located within a UCNE (PAX6_Veronica) that is catalogued as a cCRE in ENCODE (EH38E1530321)". UCNE names are particularly important, because they are systematically used as identifiers in the supplemental Tables and thus would enable the reader to easily find additional information about the element mentioned in the main text.

We have now explicitly included all corresponding identifiers throughout the text.

2) I also recommend inclusion of the UCNE, Vista and ENCODE cCRE tracks in all genome browser screen shots. The UCNE track is currently included only in Figure 1. Vista and ENCODE cCRE tracks are missing in all browser views.

We have now included UCNE, VISTA, and ENCODE cCREs tracks in the main genome browser figures (Figures 1 and 4).

3) Supplementary Table 6: It would be useful to indicate for each variant, the type of ophthalmological disorder (Table S5, column C) it is associated with.

We agree this is a relevant point. However, due to limitations in the (bulk) export of clinical information from the protected Research Environment of Genomics England, inclusion of this type of information is not feasible.

4) Fig S2 and supplementary Table 3 are not referred to in the main Text.

We have corrected this and updated the figure and table accordingly.

5) Supplementary Table 8: The Table caption should be expanded.

The contents of each column should be explained. For instance, column F: what means Homo_sapiens|M01298_1.94d|Zoo_01|2337? Where does this information come from, what data and software resources were used?

We have expanded the caption of this table to clarify this output, which is derived from the QBiC-Pred tool, a software used for predicting quantitative TF binding changes due to nucleotide variants.

6) Line 401 probable typo: 103-105 days (103-125?)

Indeed, this typo has now been corrected.

Reviewer #3:

1) Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.

We agree that restricting our analysis to these particular regions is one of the limitations of the study, as stated in the Discussion section (line 304-311). However, one of our main aims was to provide a strategy to reduce the search space for pathogenic variants with a potential regulatory effect. Given the substantial body of literature supporting a regulatory role for these regions and, particularly, the availability of already-existing functional data, we considered that this set of regions could represent a suitable target for such analysis. Indeed, the features evaluated, and the methods presented in this study could be extrapolated and applied in other settings involving other candidate regulatory regions and/or tissues of interest, and their associated disease-phenotypes, for which, in any case, the overall contribution of regulatory pathogenic variation to disease might vary greatly.

2) I am wondering how many UCNE overlaps with open chromatin regions specific to the fetal retina and how many UCNE overlaps with adult only. Are UCNEs enriched for developmental genes? If so, how many patients are due to developmental defect?

We have now integrated into our analysis epigenetic measurements in adult retina, in particular the candidate cis-regulatory elements nominated by Cherry et al. PNAS 2020 (PMID: 32265282) based on accessible chromatin and enrichment for active enhancer-related histone modifications in adult human retina. Details about the integration of this dataset as well as the results and their corresponding interpretation have been incorporated in the Methods (lines 372-374), Results (115-117; Supplementary Table 2), and Discussion (lines 271-273) sections accordingly. In particular, out of the 111 UCNEs identified to display the active enhancer mark H3K27ac, 33 were found to maintain this signature at adult stage. Regarding the specific question from the reviewer, the majority of UCNEs overlapping with open chromatin regions are specific to the fetal retina (1,346), with only 7 UCNEs overlapping with open chromatin regions exclusively in adult state. This indeed further supports the major role of the identified candidate cis-regulatory UCNEs in the regulation of developmental gene expression programs, which was already suggested by the Gene Ontology enrichment analysis performed using the set of UCNE target genes as input (Figure 2C; new Supplementary Table 3). As far as the number of patients with development defects that were included in this study, these included: corneal abnormalities (n=62), Leber congenital amaurosis (n=142), ocular coloboma (n=111), developmental foveal and macular dystrophy (n=230), developmental glaucoma (n=94), anophthalmia or microphthalmia (n=120).

3) I am wondering if the 431 ultrarare variants found in the UCNEs is higher than expected. This can be tested by comparing patients without visual disorders.

Although it is indeed a relevant point, retrieving sequencing data from patients without visual disorders is not feasible for us. As it is a restricted research environment, we are only allowed to query sequencing data corresponding to participants enrolled within the framework of our specific sub-domain, namely “Hearing and Sight”, and therefore evaluating additional patients from other sub-domains is not doable. Based on previous studies and our observations, common variants are precisely the ones depleted within UCNEs, while ultrarare variation seems to occur at levels comparable to those observed elsewhere in the genome. Therefore, it is reasonable to speculate that this amount of ultrarare variants is not higher than expected as compared to patients without visual disorders. To further demonstrate the high intolerance of UCNEs to common variation, we have included an evaluation of the distribution of genome-wide residual variation intolerance score (gwRVIS) values compared to a set of randomly selected genomic regions in a human reference cohort (lines 173-178; 487-495; Fig. 3C). Additionally, to address this question further, we have also generated allele frequency distributions for all variants retrieved within a selection of 25 disease-gene associated loci and their corresponding UCNEs in order to assess the specificity of the overlap between rare variants and UCNE regions (lines 181-182, 496-501; Figure S2).

4) It seems that the ultrarare variants listed in sup table 6 are more abundant in a small number of genes. Is this due to the number/size of UCNEs is larger in these genes?

Indeed, the clustering of UCNEs in genomic regions containing genes coding for transcription factors and developmental regulators (e.g., OTX2, PAX6, ZEB2) seems to be one of their intrinsic properties, hence the observed enrichment for a small number of genes. One reason can be that these neighboring UCNEs cooperate to achieve higher degrees of tissue-specific regulatory accuracy needed for these genes.

5) The variant in the putative Pax6 gene regulatory element is intriguing. It would be much more informative if the enhancer with and without the variant is tested in parallel in fish.

That is a very relevant point. We have now tested the effect of this SNV in the UCNE on the reported expression pattern using the same experimental setup that we used for testing the wild-type construct, namely transgenic enhancer zebrafish assays. However, we could not obtain conclusive results, most likely due to the limitations posed by testing these regions outside their native genomic context. We have incorporated the details, results, and interpretation of the assays performed mutant construct in the Methods (lines 525-527; Supplementary Table 12), Results (lines 235-238; Supplementary Table 10), and Discussion (lines 350-353) sections.

6) (optional) it would be quite interesting to check the phenotype in fish or mice with the element repressed via technique such as CRISPRi.

Indeed, we fully agree that CRISPR-based techniques would be the ideal experimental approaches to further validate the functionality of the PAX6-associated UCNE and the identified variant in their native genomic context. Conducting these detailed and focused mechanistic studies is, however, beyond the scope of the present work, for which the main focus was the identification and functional annotation of ultraconserved cCREs.

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Referee #3

Evidence, reproducibility and clarity

In the report, the authors combined bulk and single cell multi-omics data from the retina to identify ultraconserved noncoding elements (UCNE) that might function as regulatory elements based on chromatin openness, DNAse sensitive regions, and histone marks. The candidate UCNEs are intersected with whole genome sequencing data from 3.220 patients with rare eye disease to identify potential rare variants that might affect the activity of UNCE. The goal of the project is intriguing.

My comments are listed below:

1. Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.
2. I am wondering how many UCNE overlaps with open chromatin regions specific to the fetal retina and how many UCNE overlaps with adult only. Are UCNEs enriched for developmental genes? If so, how many patients are due to developmental defect?
3. I am wondering if the 431 ultrarare variants found in the UCNEs is higher than expected. This can be tested by comparing patients without visual disorders.
4. It seems that the ultrarare variants listed in sup table 6 are more abundant in a small number of genes. Is this due to the number/size of UCNEs is larger in these genes?
5. The variant in the putative Pax6 gene regulatory element is intriguing. It would be much more informative if the enhancer with and without the variant is tested in parallel in fish.
6. (optional) it would be quite interesting to check the phenotype in fish or mice with the element repressed via technique such as CRISPRi.

Significance

Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.

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Referee #2

Evidence, reproducibility and clarity

Starting with a comprehensive analysis of multi-omics data, the authors identify a subset of ultra-conserved non-coding elements (UCNEs) likely to play a role in retinal development. Restricting subsequent analyses to these genomic regions, they identify ultra-rare mutations associated with eye disease, using data from the 100k genome project. They then follow-up on one newly discovered, disease associated UCNE and a presumably causal mutation within this UCNE. The follow-up experiments involve fine mapping of the spatiotemporal expression pattern of a UCNE-driven reporter gene in zebra fish, as well as a re-examination of the disease phenotype in carriers of the mutation.

The computational pipeline used for prioritization of UCNEs is sound. The evidence supporting the claim that the identified ultra-rare SNV is causal is highly convincing. This study also constitutes proof of concept for a novel methodology to search for causal disease associated mutations in the nowadays still under-investigated non-coding part of the genome.

The paper is clearly written. Methods are described in enough detail to allow for reproduction of the results. Overall, this study is of high scientific quality, self-contained, and complete. Publication in a peer-reviewed journal should not be delayed by additional, perhaps interesting but non-essential experiments.

However, if the authors intend to undertake similar studies in the future, I would recommend to carry out the reporter-gene assays in zebrafish with both the wild-type and the mutant version of the UCNE. Comparison of the spatiotemporal expression patterns of the two alleles could provide valuable insights into the mechanism of action of the deleterious mutation under investigation.

Minor discretionary suggestions for improving the presentation:

Wherever a specific UCNE, Vista enhancer or ENCODE cCRE is mentioned, the element should be identified by name or accession code: For instance (Iine 212):

"this variant is located within a UCNE (PAX6_Veronica) that is catalogued as a cCRE in ENCODE (EH38E1530321)"

UCNE names are particularly important, because they are systematically used as identifiers in the supplemental Tables and thus would enable the reader to easily find additional information about the element mentioned in the main text.

I also recommend inclusion of the UCNE, Vista and ENCODE cCRE tracks in all genome browser screen shots. The UCNE track is currently included only in Figure 1. Vista and ENCODE cCRE tracks are missing in all browser views.

Supplementary Table 6: It would be useful to indicate for each variant, the type of ophthalmological disorder (Table S5, column C) it is associated with.

Fig S2 and supplementary Table 3 are not referred to in the main Text.

Supplementary Table 8: The Table caption should be expanded. The contents of each column should be explained. For instance, column F: what means Homo_sapiens|M01298_1.94d|Zoo_01|2337? Where does this information come from, what data and software resources were used?

Line 401 probable typo: 103-105 days (103-125?)

Significance

This work is of interest to different research communities: Biomedical researchers working on neural disorders, human geneticists engaged in GWAS studies, computational biologists trying to make sense out of omics data, molecular biologists exploring the "dark matter" of the genome, and finally the small community tackling the enigma of UCNEs. As with many omics papers, the most valuable parts of this study are in the supplemental tables, in particular tables 1,4, and 6. It can be hoped that some prospective readers will follow up on the leads presented in these tables.

The detailed computational and experimental characterization of a likely causal ultra-rare disease associated mutation may serve as a guiding and motivating example for medical geneticists working on other syndromes.

Back to UCNEs: They are enigmatic entities, which so far have largely resisted molecular and physiological characterization. It took 10 years to finally uncover a phenotype in ko mice missing one or several UCNEs, after the surprising initial observation that such mice were viable and fertile. The difficulties in studying the function of UCNEs may be due to their conjectured pleiotropic activity in different cell types at different developmental stages, their apparent cooperative interactions with many other control elements (limiting the power of reporter gene assays with single elements), and their putative involvement in morphogenetic processes (minimizing the relevance of epigenetic data collected from cell lines). In view of these considerations, I consider UCNE research starting from human disease phenotypes more promising, than ab initio approaches using reverse genetics in model organisms.

The impact of this paper is potentially very high. Note the following statement in the paper:

"For each instance for which only the UCNE variant remained as candidate, we placed a clinical collaboration request with Genomics England."

We thus can expect more exiting stories from the same team. The strategy and computational pipeline introduced here are of course applicable to other congenital diseases, and it can reasonably be hoped that researchers inspired by this study will apply components of the methodology in other contexts. The prioritization of UCNEs in studying the "dark matter" of the genome and the "missing heredity" would likely lead to new insights into the function of these enigmatic elements and the reasons for their extreme conservation.

My background: I'm a bioinformatician with first training in molecular genetics. My research focus is on gene regulation: promoters, enhancers, transcription factor binding sites. I also made some contributions to the UCNE field, having co-developed UNCEbase with Slavica Dimitrieva. On the other hand, I don't claim to be an expert in medical genetics, and more specifically, I know very little about eye diseases and retinal development.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript entitled "Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci", Soriano et al. explore the role of ultraconserved noncoding elements (UCNEs) as candidate cis-regulators (cCREs) in developing and adult human retina by integrating UCNEs with multi-omic data from fetal and adult human retina. They further examine the potential contribution of UCNEs to rare eye diseases by testing for rare variants that fall in UCNEs that are potentially active in retina in patients with inherited retina degeneration diseases (IRDs), using whole genome sequencing (WGS) data from Genomics England. This work is based on the assumption that genomic regions under strong evolutionary constrain are good predictors of important functionality. The authors use 4,135 UCNEs identified in Dimitrieva and Bucher, 2013, defined as sequences with more than 95% identity between human and chicken with >200bp in length. By integrating bulk tissue RNA-Seq, single cell (sc)RNA-Seq, DNAase-Seq, scATAC-Seq and ChIP-Seq from retinal tissues from different developmental stages and adulthood with the 4,135 UCNEs, they identified one third of UCNEs (1,487) that may be associated with various retinal development stages. Using bioinformatic approaches they identify potential target genes for 724 UCNEs that may be regulated in cis. Based on ATAC-Seq, the authors show that 834 UCNEs fall in open chromatin regions, and of these 111 have active histone markers, supporting an active functional role for the identified UCNEs. The authors demonstrate an application of this approach in identifying potential pathogenic noncoding variants for rare eye diseases. Using WGS from 100,000 Genomes project the authors identify 45 UCNEs that were bioinformatically associated with mendelian IRD genes that contain rare variants, proposing potential solutions for unsolved cases. They demonstrate the utility of their analysis by highlighting an ultra-rare SNP they identified in a UCNE candidate CRE located upstream of PAX6 that belongs to a family of genes with foveal abnormalities. They confirm the effect of the UCNE 150k upstream of PAX6 on gene expression in the eye, forebrain and neuronal tube using an enhancer reporter assay in Zebrafish.

The authors demonstrate a strategy for narrowing down the genomic space to putative functional noncoding regions involved in development and that may contribute to rare disease. This work that integrates various genomic modalities is of broad interest and can be applied to other diseases and tissues. There are several points that should be addressed to assess the robustness of the results and strengthen the conclusions of the paper, and in some places to better clarify the analyses conducted.

Major edits:

1. In the introduction the authors list 4 databases of ultra-conserved non-coding elements, and choose to work with the UCNEbase that detects UCNE regions by comparing human and chicken, as they state that it is one of the most comprehensive resources. Can the authors comment on the extent of overlap of UCNE regions identified in this database compared to other databases. It would also be helpful to add an explanation in the Introduction on the advantage of comparing the human genome to the chicken genome, e.g., is chicken the most distant vertebrate with a high quality genome, or another reason? And can the authors comment on the extent of conservation of the UCNEs compared to other species - in the UCNEbase paper, orthologues for the UCNEs are identified in 18 vertebrate species including reptiles, amphibians and fish.
2. The authors briefly describe how potential target genes were assigned to UCNEs in the Method section (section begins on page 21, line 407 and on lines 379-381). They note that they used the Genomic Regions Enrichment of Annotations Tool (GREAT). Can the authors provide additional details on what this method does and also provide a high level sentence in the Results section on page 8, lines 144-146, on how target genes were assigned to UCNEs, as well as in the Discussion on page 16, line 289. Based on the Methods description on lines 409-414, a UCNE was associated with any gene within 1Mb of the UNCE that was expressed in retina and whose curated regulatory domains overlapped the UNCE. Is this correct? How were the regulatory domains curated (line 411-412)? Can the authors please clarify this important point.

There are other approaches for linking putative transcriptionally active genomic regions with target genes in specific tissues or cell types through correlation analysis of ATAC-seq peaks (chromatin accessibility) and gene expression in RNA-seq data. A commonly used peak-to-gene linkage method is implemented in ArchR (Granja et al, 2021, PMID: 33633365). The authors note that they used scATAC-seq gene scores and scRNA-seq gene expression to further characterize the proposed target genes of UCNEs. It is not clear what the authors mean by "scATAC-seq gene scores". Please define "scATAC-seq gene score" and add a reference. Can the authors compare the target gene mapping to UCNEs using GREAT and gene expression filtering to that using peak-to-gene linkage based on retina tissue or single cell ATAC-seq and RNA-seq data?

For gene expression filtering (line 419) the authors quantify transcript expression of retina from FASTQ samples using the Kallisto method, and then note that they did the filtering on gene expression levels (TPM<0.5). Please add details on how you went from transcript expression levels to gene expression levels for the filtering, or was the filtering performed at the transcript level?<br /> 3. The authors use the words "active UCNE", first mentioned in the Results on line 144. Can the authors define what they mean by "active UCNE". What information/evidence is used to ascertain that a UCNE is indeed active. Overlap of a UCNE with a chromatin accessibility region from ATAC-seq or DNAase-Seq would only suggest that the UCNE may be active. Intersection with enhancer activity measured with in vivo enhancer reporter assays in transgenic mice from the VISTA enhancer browser provides stronger evidence of transcriptional activity. The authors might want to distinguish between putatively actively and active based on the functional support.<br /> 4. The authors assessed the significance of depletion of common variation (MAF>1%) in the UCNE regions compared to a background of randomly selected genomic regions. In generating the random distribution of regions, did the authors match on the distribution of distances of the UNCEs to the TSS of genes in the randomly selected regions? This may be a confounder.

Also, in the legend of Figure 3, lines 191-192, it is stated: "Variant population frequencies within putative retinal UCNEs normalized to a background composed of randomly selected sequences (see Methods).", but we did not find a description of this analysis in the Methods section.<br /> 5. In regards to intersecting UCNEs with epigenetic marks that detect active or repressed enhancers in retina, the authors used data from Aldiri et al 217 that measured epigenetic changes during retinal development. Did this dataset contain epigenetic measurements in adult retina? The authors might want to consider using the epigenetic marks/ChIP-seq data from adult human retina in Cherry et al. PNAS 2020 (PMID: 32265282)<br /> 6. With respect to the examination of rare variants that may be associated with rare eye disease in retina active UCNEs, for the interpretation of the results, it would be helpful to get more information on the distribution of rare variants found in UCNEs associated in this study to known IRD genes in all affected individuals in families, if this information is available in the 100,000 Genomes Project.<br /> 7. In the Methods section on lines 450-451, the authors mention that they performed variant screening of retinal disease genes, referencing the Genomics England Retinal Disorder panel and Martin et al., 2019. Can the authors add to the Methods and Results sections how many retinal disease genes were initially tested. Also, to get a sense of the specificity of the overlap of rare variants in the 100k Genome Project cohort with UNCE regions, it would be informative to show a distribution of the number of rare variants <0.5% that passed the filtering in gnomAD per eye disease gene before the overlap with UNCEs.<br /> 8. The authors found "an ultrarare SNV (chr11:31968001T>C) within a candidate cis-regulatory UCNE located ~150kb upstream of PAX6. This variant was found in four individuals of a family segregating autosomal dominant foveal abnormalities". They tested the functional effect of this element with a reporter assay in zebrafish and found that the UNCE affects expression in the eye, forebrain, and neural tube. It would add further value if the authors were to test the effect of this SNV in the UCNE on the reporter expression pattern, using CRISPR/cas9?<br /> 9. The authors found rare variants in UCNEs linked to 45 IRD genes. Can the authors provide additional information on the functional genomic annotations of these UCNEs and distance to the target genes. The UCNEs were characterized with respect to their genic features in the original paper (UCNEbase, Dimitrieva et al., NAR, 2013), e.g., intergenic, intronic and 3'/5' UTR. Also, it would be useful for clinical applications to provide the start and end positions of the UCNEs that contain the rare variants associated with their 45 IRD genes in Supplementary Table 6.<br /> 10. A total of 724 target genes were assigned to 1,487 UCNEs that displayed candidate cis-regulatory activity. Given the interest in using UNCEs to help identify potential pathogenic mutations that lead to IRDs, can the authors note in the Results section how many of the 724 target genes are IRDs.<br /> 11. In the Discussion on page 15 line 259, can the authors clarify if variation found in UNCEs were only associated with rare disease or also with common diseases.

Minor edits:

1. In abstract, the authors might consider changing the words "rare eye diseases" on lines 20 and 22 to "rare retina degeneration diseases", and on lines 88-89.
2. In the Introduction on line 49, there seems to be a typo in the number of UCNE regions reported. 4,135 UCNE regions is supposed to be 4,351, based on the original paper (https://academic.oup.com/nar/article/41/D1/D101/1057253).
3. In the introduction on lines 75-76, these references: Lyu et al., Cell Reports 2021, PMID: 34788628, and Zhang et al., Trends in Genetics 2023, PMID: 37423870, could be added to the following sentence to provide additional: "This cellular complexity is the result of spatiotemporally controlled gene expression programs during retinal development,"
4. On lines 77 and 84, I would write IRD as plural: IRDs.
5. In introduction on lines 89-90, it can be further added that you provide experimental support for an ultra rare SNV in a cis regulatory UCNE affecting PAX6.
6. On line 98, the authors refer to Figure 1A when noting that the integration of UCNEs with multi-omics data in human retina allows to evaluate the regulatory capacity of UCNEs across the major developmental stages of human retina. However panel A in figure 1 does not seem to show this point. It shows the comparison of elements across species. Please make appropriate changes to the main text and figure legend.
7. Please explain what the names appended to the gene symbols in the first column "UCNE ID" in Supplementary Tables 1 and 2 refer to.
8. On line 145, can the authors clarify what they mean by "active gene expression in the retina". Is this just another way of referring to genes found to be expressed in retina? If so, it might be clearer just to write: "We annotated the identified active UCNEs to assign them potential target genes and thus assess their association with genes expressed in the retina"
9. One line 156, I would write "regulation of transcription" as listed in the gene ontology terms in Figure 2C, instead of "regulation of gene expression". The authors might want to add this to the Discussion. Can the authors include the full gene set enrichment results from Enrichr in a supplementary table at Padj<0.05 since only the top gene sets are shown in Fig. 2C (at P<1E-13)?
10. On page 12, line 214, what does "EH38E1530321" Stand for? It seems to specify a distal enhancer-like signatures in bipolar neurons, but I couldn't find this ID in any database.
11. In the Methods section on lines 391-392, can the authors give some high-level explanation of the unconstrained integration method: "Single-nucleus RNA-seq of the same tissue and timepoints (GSE183684) were integrated using the unconstrained integration method". Also, can they comment on how retinal cell class identities were assigned (line 393). Was it based on known markers or on previous identification of cell classes and highly variable genes between clusters?
12. In the integration of UCNEs with bulk and single cell ATAC-seq and DNase hypersentitivity regions, can the authors note in the Methods section (lines 400-404) what peak width was used to test for overlap with the UCNEs.
13. On line 436, the word 'and' is missing between "(SNVs, and indels < 50bp)" and "large structural variants".
14. On lines 443-444, please provide references to the computational tools listed. Please note if default settings/parameters were used.
15. In the following sentence in the Methods section on lines 447-449, it is not clear in the Results section how this was used in the flow of the analysis, and how many cases showed such a similarity in phenotype: "For each candidate variant, we compared the similarities between the participant phenotype (HPO terms) and the ones known for its target gene through literature search and clinical assessment by the recruiting clinician when possible." Can the authors add more detail to the Results section.
16. It would be helpful to have a table that describes the different omics datasets used in the paper, with some basic annotations (tissue type, sample size, reference)
17. Can the authors add references to their sentence in the Discussion on page 17 lines 299-301: "As has been shown before, the phenotype caused by a coding mutation of a developmental gene can be different from the phenotype caused by a mutation in a CRE controlling spatiotemporal expression of this gene."

Significance

General assessment: This is the first study to our knowledge to integrate ultraconserved noncoding elements (UNCEs) with a range of multi-omic data to identify UNCEs that may be active and their target genes in a specific tissue, in this case human retina. They also experimentally test one of the UNCE predicted to affect the expression of a given gene in retina (and potentially other tissues) in Zebrafish and confirm its transcriptional activity in the eye, to provide some functional support to their strategy. This study also demonstrates the potential value of inspecting UCNEs in prioritizing pathogenic mutations for rare disease. One limitation is the lack of statistical significance assessment of the potential causal effect of a rare variant found in a UCNE on the expression of its predicted target gene, which is a rare eye disease gene, since the linkage of the UCNE to the disease gene was performed based on bioinformatic analysis of multi-omic data. Experimental testing of the effect of some of these mutations on their target gene expression could provide additional support.

Advance: This study addresses an important unsolved problem in the field of human genetics and rare diseases, namely the challenge of identifying pathogenic mutations that lead to rare Mendelian diseases, in particular, in noncoding regions in unsolved cases. This is the first study to consider UCNEs together with tissue or cell type specific expression, epigenetics and chromatin accessibility in detecting pathogenic mutations for retina degeneration diseases. See for example Ellingford et al., Genome Medicine 2023 (PMID: 35850704). Their demonstration of rare variants in UCNE associated with inherited retinal degeneration diseases in patients with IRD in the 100k Genomics Project suggests that the role of UCNEs in disease should be further investigated and functionally tested. This work could have important clinical implications, and also proposes a strategy for integrating UCNEs with multi-omic and genomic data.

Audience: This work will be of interest to the human genetics and genomics community, in particular to researchers interested in uncovering the genetic basis and causal mechanisms of rare diseases, and to scientists interested in clinical applications of genetic variation. This work will also be of interest to scientists interested more broadly in understanding the regulatory effects of the noncoding regions in the genome.

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Reply to the reviewers

Reviewer #1:

1. If doable, image dynein and dynactin simultaneously in the Halo-DYNC1H1/DCTN4-SNAP iNeurons. Co-movement of dynein and dynactin towards the somatodendritic compartment and their separate movement in the anterograde direction along the axon would provide the most convincing evidence for the key claims of the manuscript.

Please see the planned revision section for our response

Reviewer #2:

Major comment (requires additional experimentation)

1. While the data presented do certainly suggest that dynein and Lis1 are transported anterogradely on separate vesicular cargoes from dynactin and Ndel1, the study would be much stronger if supported by dual imaging of dynein and dynactin to prove that these proteins do indeed move in association with separate vesicular populations. I would like to see dual-color kymograph traces showing that the proteins move independently. The authors should be able to accomplish this using their dual Halo-DYNC1H1/DCTN4-SNAP hESC line. To acquire and analyze this data might take several months, but it would greatly strengthen this paper. If the authors do this experiment, they may also be able to address the mechanism of reversal of anterograde cargoes which they speculate about in the Discussion, which would add even more interest and insight.

Please see the planned revision section for our response

Minor comments (addressable without additional experimentation)

1. The authors deduce that 1-4 Halo fluorochromes corresponds to 1-2 dynein molecules. This implies that the cells are homozygous for the Halo tag, but I do not see this addressed explicitly. The authors should state explicitly whether the lines generated for their study are heterozygous or homozygous for the tag. If the cells are heterozygous, which would seem most likely, then they may be underestimating the number of dyneins per spot and should take this into account.

We have added whether lines are homozygous or heterozygous to the manuscript. We also include a new Supplementary Figure panel (Fig S6) showing the genotyping data. In summary, all lines are homozygous except for PAFAH1B1-Halo (hESCs) which is heterozygous.

1. Why are the moving spots lower in intensity than the NEM-treated static spots. It appears to suggest that they may be associated with different structures. This should be clarified and discussed.

Our data suggest that the fast-moving spots have fewer dyneins than NEM treated static spots. We suggest this is because the fast-moving cargos are smaller than the average cargo and therefore have fewer dyneins on them. This is also supported by the smaller number of dyneins reported previously on endosomes as compared to the large lysosomes. We have clarified this in the discussion (page 7-8).

1. The authors state in the Results that most of the dynein spots were diffusing, often along microtubules, but they do not visualize microtubules so how do they know this? They may need to remove the phrase "often along microtubules".

This has been removed.

1. At the end of the Introduction the authors state that their data "allow us to understand how the dynein machinery drives long-range transport in the axon". This is an overstatement. The "how" in this sentence is not addressed in this study.

We have softened the sentence by adding the phrase “better understand”.

1. The conclusion that dynein binds to cargos stably throughout their transport along the axon is based on measurements of the fastest moving cargoes but the authors do not provide data on the distribution of velocities for the entire population of retrograde cargoes. It is not valid to extrapolate the behavior of a small number of cargoes to the entire population. The average may be much slower than the fastest cargoes. Moreover, even for the fastest organelles the authors cannot say that the dynein is stably bound because they did not track single cargoes and thus do not know that the cargoes moved continuously in one single bout of movement for 500 µm; it is possible that the cargoes moved in multiple consecutive bouts interrupted by brief pauses and dynein motors may have exchanged between bouts.

We have added a section to the discussion to highlight that other cargos may behave differently from the fastest ones (page 7). We have also clarified the assumptions that lead us to expect a slower arrival time of the first signal (page 5).

1. The authors say that "it is clear that at least some dyneins remain on cargoes throughout their transport along the axon". As explained above, the data do not prove this so this statement should be removed.

We have softened this sentence from “it is clear” to “our results suggest” and explained in more detail why we make this conclusion

1. The authors note that most of the dynein spots were not moving processively and state that this is consistent with prior studies showing that only a subset of dynein is actively involved in transport. However, as they note elsewhere, dynein is both motor and cargo and most axonal dynein is transported at slow average velocities so maybe they should be more explicit about what they mean by "involved in transport".

We have clarified we mean fast axonal transport and thank the reviewer for highlighting this point.

1. When the authors note that most of the dynein in axons is transported in the slow component of axonal transport, they should also cite the work of Pfister and colleagues who were the first to show this (PMID 8824315 and 8552592).

This was an omission on our part. The references have now been added.

1. The authors propose that dynein and Lis1 are transported together but there were significantly fewer anterogradely transported Lis1 particles than dynein particles. This should be discussed.

We have added more information to the discussion. Although we cannot rule out this effect being due to the heterozygous tagging of our LIS1 cell line, we do not witness the same decrease in events in the retrograde direction. Therefore, we believe there is a subset of anterogradely moving dynein lacking LIS1. As discussed in the manuscript, this subset may already be bound to dynactin and therefore not require LIS1.

1. For the statistical analysis, the authors should provide p values in the legends for the comparisons that are judged to be "not significant". The authors should also be consistent in how they label differences that are not significant - they mark them as "ns" in Fig. 1, but in the other figures they do not, leaving some ambiguity about whether particular comparisons were not tested or were found to be not significant. For example, in Fig. 4C the average speed of the dynactin is about 0.5 µm/s greater than for the other proteins and the spread in the data suggest that this could be significant, but no significance is indicated on the plot, implying p>0.05. It is not clear how confident we can be that there is no difference.

We have now included all p values in the figure legends and have removed the “ns” in Fig 1D. In our revised manuscript, only significant differences are highlighted in the figures.

Reviewer #3:

• if I look at the kymographs, trajectories appear rather complex, pausing, standing still, moving and everything mixed. The explanation of how actual trajectories are extracted and on what basis is very short, too short for me. I think the authors should expand this. Furthermore, I think it would be good if the authors would present, in their kymographs examples of the tracked (and also the not included) tracks. Maybe in supplementary info.

The analysis of this data used the Trackmate Fiji plugin. This tracks spots frame to frame in a movie and then outputs the data of the tracks. No data was extracted from kymographs but they were used as a graphical illustration of the moving spots. To better explain our analysis pipeline, we have expanded our methods section and have added an example of a tracked movie (Video 15) as well as highlighted the tracked spots in one kymograph example (Figure 7S).

• I found 'velocity' ill defined. I get the impression, judging from the number of points (compared to the other parameters) that the authors determine the average velocity of each individual trajectory. That is an important parameter (but should indeed be called 'trajectory averaged' velocity), but might not be the only one useful to learn from the data, where trajectories do not always appear to have constant speeds (pausing, etc.). Why do the authors not determine point-to-point velocities and plot histograms of those for all the trajectories (simply plot histograms of all the displacements between subsequent data points in trajectories)? This might provide great insight into the actual maximum velocity and the fraction of pausing or moving in opposite direction etc., providing much more molecular detail than currently extracted from the data.

The reviewer is correct. We have measured the average velocity of the spots from the beginning of the track to the end. We have clarified this in the text. Furthermore, as stated above in the revision plan, we are currently doing the additional analysis and will include it in the final revision

• I was a bit surprised to read that the authors have gone to the effort to create a dual-color labeled cell line, but did not do actual correlative two-color measurements (or at least show them). It would be so insightful to see dynein and dynactin move separately in the anterograde direction.

Please see the planned revision section for our response.

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Referee #3

Evidence, reproducibility and clarity

Fellows and coauthors present a signle-molecule study toward dynein regulation in axons. They observe that dynein in vivo makes very long runs and that regulators LIS1 and NDEL1 cotransport with dynein all the (retrograde way). Remarkably, different components of the dynein complex appear to be transported in different ways/velocities in the antorograde direction. Overall experiments are well conducted, I only have a couple of important questions regarding data analysis. Some aspects should be explained better, more steps should be shown and here and there I think the authors could, with minimal effort, obtain much more out of their data (see below). Nevertheless, I think this is an important study, on of the first single-molecule efforts to understand axonal transport in the cell (see below). Key findings are important for our understanding of dynein regulation.

My concerns:

• if I look at the kymographs, trajectories appear rather complex, pausing, standing still, moving and everything mixed. The explanation of how actual trajectories are extracted and on what basis is very short, too short for me. I think the authors should expand this. Furthermore, I think it would be good if the authors would present, in their kymographs examples of the tracked (and also the not included) tracks. Maybe in supplementary info.
• I found 'velocity' ill defined. I get the impression, judging from the number of points (compared to the other parameters) that the authors determine the average velocity of each individual trajectory. That is an important parameter (but should indeed be called 'trajectory averaged' velocity), but might not be the only one useful to learn from the data, where trajectories do not always appear to have constant speeds (pausing, etc.). Why do the authors not determine point-to-point velocities and plot histograms of those for all the trajectories (simply plot histograms of all the displacements between subsequent data points in trajectories)? This might provide great insight into the actual maximum velocity and the fraction of pausing or moving in opposite direction etc., providing much more molecular detail than currently extracted from the data.
• I was a bit surprised to read that the authors have gone to the effort to create a dual-color labeled cell line, but did not do actual correlative two-color measurements (or at least show them). It would be so insightful to see dynein and dynactin move separately in the anterograde direction.

Referee Cross-Commenting

I think we agree on the key points:<br /> - in principle, great study<br /> - quantification / tracking could go a bit further and should be explained better<br /> - manuscript / conclusions would be strengthened substantially if the authors could do some 2-color experiments to correlated dynein / dynactin movements in anterograde vs retrograde direction.

Significance

I think this is an important and exciting manuscript. As an in vivo single-molecule biophysicist with great interest in intracellular transport, I have been astonished in the lack of people trying to take single-molecule data on the motor involved, in particular neurons. I believe this is the only way to find out how transport actually works and what role motors play. Mutants is not enough, bulk data is not enough, in vitro is not enough. This is what the field needs (and many in the field do not seem to be aware of this...). Great that Fellows and coauthors took on this task and show some really exciting data. I am not an expert on their stem-cell labeling approach so cannot judge on that. The imaging seems to be done well. As discussed above, I think there might be much more in the data than the authors now get out, so I would encourage them to do some additional analysis. But overall, this effort is important and I think the conclusions will stand and provide important new insights in dynein regulation in the cell.

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Referee #2

Evidence, reproducibility and clarity

Summary - The authors use a CRISPR knock-in gene editing strategy to label endogenous dynein, dynactin (p62 or Arp11) and dynein regulators (Ndel1 and Lis1) with Halo or SNAP tags. They do this in human iPSC and ESC cell lines engineered to express doxycycline-inducible NGN2 cloned into a "safe harbor" site of the genome. They induce the cells to differentiate into iNeurons using doxycycline and image the tagged proteins in axons with single molecule sensitivity using HILO illumination. The paper is clearly written, the description of the methods is thorough, and the data and figures (including the videos) are of good quality. The use of gene editing to knock the tags into the endogenous gene loci is a superior strategy to classic overexpression strategies. The authors also make effective use of microfluidic chambers to ensure the axons are uniformly orientated and coaligned over a distance of 500µm.

Major comment (requires additional experimentation)

1. While the data presented do certainly suggest that dynein and Lis1 are transported anterogradely on separate vesicular cargoes from dynactin and Ndel1, the study would be much stronger if supported by dual imaging of dynein and dynactin to prove that these proteins do indeed move in association with separate vesicular populations. I would like to see dual-color kymograph traces showing that the proteins move independently. The authors should be able to accomplish this using their dual Halo-DYNC1H1/DCTN4-SNAP hESC line. To acquire and analyze this data might take several months, but it would greatly strengthen this paper. If the authors do this experiment, they may also be able to address the mechanism of reversal of anterograde cargoes which they speculate about in the Discussion, which would add even more interest and insight.

Minor comments (addressable without additional experimentation)

1. The authors deduce that 1-4 Halo fluorochromes corresponds to 1-2 dynein molecules. This implies that the cells are homozygous for the Halo tag, but I do not see this addressed explicitly. The authors should state explicitly whether the lines generated for their study are heterozygous or homozygous for the tag. If the cells are heterozygous, which would seem most likely, then they may be underestimating the number of dyneins per spot and should take this into account.
2. Why are the moving spots lower in intensity than the NEM-treated static spots. It appears to suggest that they may be associated with different structures. This should be clarified and discussed.
3. The authors state in the Results that most of the dynein spots were diffusing, often along microtubules, but they do not visualize microtubules so how do they know this? They may need to remove the phrase "often along microtubules".
4. At the end of the Introduction the authors state that their data "allow us to understand how the dynein machinery drives long-range transport in the axon". This is an overstatement. The "how" in this sentence is not addressed in this study.
5. The conclusion that dynein binds to cargos stably throughout their transport along the axon is based on measurements of the fastest moving cargoes but the authors do not provide data on the distribution of velocities for the entire population of retrograde cargoes. It is not valid to extrapolate the behavior of a small number of cargoes to the entire population. The average may be much slower than the fastest cargoes. Moreover, even for the fastest organelles the authors cannot say that the dynein is stably bound because they did not track single cargoes and thus do not know that the cargoes moved continuously in one single bout of movement for 500 µm; it is possible that the cargoes moved in multiple consecutive bouts interrupted by brief pauses and dynein motors may have exchanged between bouts.
6. The authors say that "it is clear that at least some dyneins remain on cargoes throughout their transport along the axon". As explained above, the data do not prove this so this statement should be removed.
7. The authors note that most of the dynein spots were not moving processively and state that this is consistent with prior studies showing that only a subset of dynein is actively involved in transport. However, as they note elsewhere, dynein is both motor and cargo and most axonal dynein is transported at slow average velocities so maybe they should be more explicit about what they mean by "involved in transport".
8. When the authors note that most of the dynein in axons is transported in the slow component of axonal transport, they should also cite the work of Pfister and colleagues who were the first to show this (PMID 8824315 and 8552592).
9. The authors propose that dynein and Lis1 are transported together but there were significantly fewer anterogradely transported Lis1 particles than dynein particles. This should be discussed.
10. For the statistical analysis, the authors should provide p values in the legends for the comparisons that are judged to be "not significant". The authors should also be consistent in how they label differences that are not significant - they mark them as "ns" in Fig. 1, but in the other figures they do not, leaving some ambiguity about whether particular comparisons were not tested or were found to be not significant. For example, in Fig. 4C the average speed of the dynactin is about 0.5 µm/s greater than for the other proteins and the spread in the data suggest that this could be significant, but no significance is indicated on the plot, implying p>0.05. It is not clear how confident we can be that there is no difference.

Referee Cross-Commenting

There seems to be agreement among all three reviewers that the authors should perform dual imaging of dynein and dynactin to prove that these proteins do indeed move together in the retrograde direction but separately in the anterograde direction. This would strengthen the study greatly.

Significance

General assessment - There are now multiple papers that have analyzed axonal transport of cargoes in iPSC-derived neurons, but this one appears to be the first to do it by tagging dynein motors and with single-molecule sensitivity. The principal conclusions are (1) that dynein is capable of long-range movement in axons and (2) that dynein moves dynein/Lis1 complexes are transported anterogradely in association with distinct cargoes from dynactin/Ndel1 complexes. The former is a modest conclusion and is entirely expected so not very impactful, but the latter is interesting and novel. The difference between the average velocities for the four proteins in the anterograde and retrograde directions is striking. All four move at similar velocities in the retrograde direction but in the anterograde direction, dynein and Lis1 move significantly faster than dynactin and Ndel1. Given these data, it is reasonable to infer that these proteins are being transported in two separate sets of cargoes. As the authors note in their Discussion, this is important because it could provide a mechanism for transporting dynein components anterogradely in a less active form that could then be activated when the components come together in the distal axon. However, I feel that one critical experiment is missing, which is to perform dual labeling of anterogradely transported dynein and dynactin in the same cells (see major comment). Without this experiment, the evidence is indirect.

Audience - If confirmed by the dual labeling experiment, the authors' conclusions would represent a conceptual and mechanistic insight into the mechanism of bidirectional transport in axons that would be of broad interest to neuronal cell biologists studying neuronal trafficking.

Expertise - This reviewer has expertise in the neuronal cytoskeleton, live imaging and axonal transport and has some experience working with iPSC-derived neurons.

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Referee #1

Evidence, reproducibility and clarity

To image dynein in the axon at a single-molecule level, Fellows et al. used neuron-inducible human stem cell lines to Halo/SNAP tag endogenous dynein components by gene editing, and visualized fluorescently labeled protein molecules in differentiated neurons in microfluidic chambers by HILO microscopy-based live imaging. Using those cutting edge technologies, the authors demonstrate that in the axon, not only dynein and dynactin but also the dynein regulators LIS1 and NDEL1 can move long distance retrogradely towards the somatodendritic compartment. They also show that dynein /LIS1 move faster than dynactin/NDEL1 in the anterograde direction, suggesting that they are delivered separately to the distal end of the axon. The approach to study subcellular motility of endogenous dynein/dynactin is creative, the data are solid. I would like to suggest one experiment to support more strongly the authors' conclusions:<br /> If doable, image dynein and dynactin simultaneously in the Halo-DYNC1H1/DCTN4-SNAP iNeurons. Comovement of dynein and dynactin towards the somatodendritic compartment and their separate movement in the anterograde direction along the axon would provide the most convincing evidence for the key claims of the manuscript.

Referee Cross-Commenting

I agree with Reviewer 2 that the authors should clarify whether the knockin lines for dynein are homozygous. I also agree with both Reviewers 2 and 3 that the authors should do more analysis of the kymographs to obtain more information.

Significance

This is an elegant study on dynein motility and transport in vivo. The experimental approaches and findings presented in this manuscript are very valuable contributions to the field of dynein/dynactin and axonal transport. The results showing that dynein/dynactin can move long-range retrogradely in the axon are in good agreement with previous findings that dynein-driven cargo transport is highly processive, and the data suggesting that dynein and dynactin/NDEL1 are trafficked separately to the distal tip of the axon provide new insights into the regulatory mechanisms for the subcellular distribution and activity of molecular motors. Together these findings provide conceptual advances for understanding axonal transport. They will be of great interest to not only scientists in the field of intracellular transport but also those in cellular neurobiology.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

REVIEW COMMENT

The article titled "The tRNA thiolation-mediated translational control is essential for plant immunity" by Zheng et al. highlights the critical role of tRNA thiolation in Arabidopsis plant immunity through comprehensive analysis, including genetics, transcriptional, translational, and proteomic approaches. Through their investigation, the authors identified a cbp mutant, resulting in the knockout of ROL5, and discovered that ROL5 and CTU2 form a complex responsible for catalyzing the mcm5s2U modification, which plays a pivotal role in immune regulation. The findings from this study unveil a novel regulatory mechanism for plant defense. Undoubtedly, this discovery is innovative and holds significant potential impact. However, before considering publication, it is necessary for the authors to address the various questions raised in the manuscript concerning the experiments and analysis to ensure the reliability of the study's conclusions.

Response: Thank you very much for your support and suggestions!

Here is Comments:

Line 64-65:

The author mentioned that 'While NPR1 is a positive regulator of SA signaling, NPR3 and NPR4 are negative regulators.' However, several recent discoveries are suggesting that it may not be a definitive fact that NPR3 and NPR4 are negative regulators. Therefore, I recommend the authors to review this section in light of the findings from recent papers and make necessary edits to reflect the most current understanding.

Response: Thank you for your feedback. Since we mainly focused on NPR1 in this study, we removed this sentence to avoid confusion. We provided additional information about NPR1 in the Introduction section to emphasize the importance of NPR1 (Line 64-68).

Line 182- & Figure 4:

The author conducted RNA-seq, Ribo-seq, and proteome analysis. Describing the analysis as "transcriptional and translational" using RNA-seq and proteome data seems not entirely accurate. Proteome data compared with RNA-seq not only reflects translational changes but may also encompass post-translational regulations that contribute to the observed differences. To maintain precision, the title of this section should either be modified to "transcriptional and protein analysis" or, alternatively, compare RNA-seq and Ribo-seq data to demonstrate the transcriptional and translational changes more explicitly.

Responses: Thank you for your suggestions. We agree with you and thus change the description accordingly throughout the manuscript.

Line 229-235 and Figure 5C:

The interpretation of Figure 5C's polysome profiling results is inconclusive. There does not seem to be a noticeable difference in polysomal fractions between the cab mutant and CAM. The observed differences in the overlay of multiple polysome fractions between cab and COM could be primarily influenced by baseline variations rather than a significant decrease in the polynomial fractions in cpg. Therefore, it is necessary to carefully review other relevant papers that discuss polysome fraction data and their analysis. By doing so, the authors can make the appropriate corrections to ensure accurate interpretations.

Responses: Thank you for your comments. We agree that the difference between cgb and COM was not dramatic visually. This is a common feature of polysome profiling assay (e.g. Extended Data Fig. 1f in Nature 545: 487–490; Fig. 1c in Nature Plants, 9: 289–301). In our case, the difference between polysome fractions was unlikely due to the baseline variation for two reasons. First, baseline variation affects monosome and polysome fractions in the same way. However, our results showed the monosome fraction of cgb is higher than that of COM, whereas the polysome fraction of cgb is lower than that of COM. Second, this result was repeatedly detected. For better visualization, we adjusted the scale of Y axis in the revised manuscript (Figure 5D).

Line 482 Ion Leakage assay:

I could not find the ion leakage assay in this manuscript, so I wonder why it is mentioned.

Response: We are sorry for the mistake. The Ion leakage data were included in previous visions of the manuscript. We removed the data but forgot to remove the corresponding method in the present version.

Materials and Methods:

To enhance the reproducibility of the study, the authors should provide a more detailed description of the materials and methods, especially for critical experiments like the Yeast-two-hybrid assays. Clear documentation of specific reagents, strains, and protocols used, along with information on controls, will bolster the validity of the results and facilitate future research in this area.

Response: Thank you for your suggestions. We provided more details in the methods. For yeast two-hybrid assays, the vector information was included in “Vector constructions” section.

Minor Point:

Line 61: There is a space between ')' and '.', which needs to be edited.

Response: The space was deleted.

Reviewer #1 (Significance): This study holds significant importance within the field of plant immunity research. The authors have made valuable contributions through their comprehensive analysis, encompassing genetics, transcriptional, translational, and proteomic approaches, to elucidate the critical role of tRNA thiolation in plant immunity. One of the major strengths of this study lies in its ability to shed light on a previously unknown regulatory mechanism for plant defense. By identifying the cbp mutant and investigating the role of ROL5 and CTU2 in catalyzing the mcm5s2U modification, the authors have unveiled a novel aspect of plant immune regulation. This innovative discovery provides a deeper understanding of the intricate molecular processes governing immunity in plants.

Moreover, the study's findings are not limited to the immediate field of plant immunity but also have broader implications for the scientific community. By employing diverse methodologies, the authors have demonstrated how tRNA thiolation exerts control over both transcriptional and translational reprogramming, revealing intricate links between these processes. This integrative approach sets a precedent for future research in the field of plant molecular biology and opens up new avenues for investigating other aspects of immune regulation.

In terms of its relevance, the study's findings have the potential to captivate researchers across various disciplines, such as plant biology, molecular genetics, and translational research. The insights gained from this study may inspire researchers to explore further the role of tRNA in other regulation.

Response: Thank you very much for your positive comments and support!

Reviewer #2 (Evidence, reproducibility and clarity): The authors presented an intriguing and previously unknown mechanism that the tRNA mcm5s2U modification regulates plant immunity through the SA signaling pathway, specifically by controlling NPR1 translation. The manuscript was well-written and logically structured, allowing for a clear understanding of the research. The authors provided strong and persuasive data to support their key claims. However, further improvement is required to strengthen the conclusion that mcm5s2U regulates plant immunity by controlling NPR1 translation.

Response: Thank you very much for your positive comments and support!

Major comments:

1. NPR1 translation should be examined to verify the Mass Spec (Figure 5B) and polysome profiling data (Figure 5D) by checking the NPR1 protein and mRNA level using antibodies and qPCR, respectively, in the cgb mutant background to establish a concrete confirmation of CGB regulation in NPR1 translation.

Response: This is a very constructive suggestion. We performed these experiments and found that the transcription levels of NPR1 were similar between COM and cgb both before and after PsmES4326 infection (Figure S2), which is consistent with RNA-Seq data. Consistent with the Mass Spec and polysome profiling data, the NPR1 protein level was much higher in COM than that in cgb(Figure 5C) after Psm ES4326 infection. Together, these data further supported our conclusion that translation of NPR1 is impaired in cgb.

1. Analyzing the genetic epistasis of CGB and NPR1 to check if CGB regulates plant immunity through the NPR1-dependent SA signal pathway. If the authors' claim is valid, I would expect no addictive effect on bacterial growth in the cgb/npr1 double mutant compared to the single mutants. Due to the broad impact of CGB on plant signaling (Figures 4E and 4F), the SA protection assay, which concentrates on the SA signal pathway, needs to be tested in WT, cgb and npr1 plants as an alternative assay to the genetic epistasis analysis. I expect that the SA-mediated protection is also compromised in cgb mutant background.

Response: Thank you for your suggestions. We did examine the growth of Psm ES4326 in the cgb npr1_double mutant and found that _cgb npr1 was significantly more susceptible than npr1 and cgb (Figure below). Although the additive effects were observed, this result was not against our conclusion for the following reasons. First, the translation of NPR1 was reduced rather than completely blocked in cgb. In other words, NPR1 still has some function in cgb. But in the cgb npr1 double mutant, the function of NPR1 is completely abolished, which explains why cgb npr1 was more susceptible than cgb. Second, in addition to NPR1, some other immune regulators (such as PAD4, EDS5, and SAG101) were also compromised in cgb(Figure 5B), which explained why cgb npr1 was more susceptible than npr1. Since the result of the genetic analysis was not intuitive, we decided not to include it in the manuscript.

SA signaling is known to regulate both basal resistance and systemic acquired resistance (SA-mediated protection). We have shown that cgb is defective in the defect of basal resistance, which cgb is sufficient to support our conclusion that the tRNA thiolation is essential for plant immunity. We agree that it is expected that the SA-mediated protection is also compromised in cgb. We will test this in the future study.

1. Could the authors comment on why using COM instead of WT as a control to perform the majority of the experiments?

Response: Thank you for your comments. In addition to ROL5, the cgb mutant may have other mutations compared with WT.COM is a complementation line in the cgb background. Therefore, the genetic background between COM and cgb may be more similar than that of WT and cgb.

1. In Figure 5E, why does ACTIN2 have an enhanced translation while NPR1 shows a compromised one in cgb mutant? How does the mcm5s2U distinguish NPR1 and ACTIN2 codons? Does mcm5s2U modification have both positive and negative roles in regulating protein translation?

Response: Thank you for raising this question. As previously reported, loss of the mcm5s2U modification causes ribosome pausing at AAA and CAA codons. Therefore, the translation of the mRNAs with more GAA/CAA/AAA codons (called s2 codon) is likely to be affected more dramatically in cgb. We have analyzed the percentage of s2 codon at whole-genome level (Figure below). The average percentage is 8.5%, while NPR1 contains 10.1% s2 codon and actin contains only 4.5% s2 codon. When fewer ribosomes are used for translation of the mRNAs with high s2 codon percentage, more ribosomes are available for translation of the mRNAs with low s2 codon percentage, which may account for the enhanced translation efficiency. To focus on NPR1 and to avoid confusion, we removed the ACTIN data in the revised manuscript.

1. Specify the protein amount used for the in vitro pull-down assay and agrobacteria concentration used for the tobacco Co-IP assay in the protocol section.

Response: We added this information in Method section in the revised manuscript.

4. Delete the SA quantification and Ion leakage assay in the protocol, which are not used in the study.

Response: We are sorry for the mistake. The SA quantification and ion leakage data were included in previous visions of the manuscript. We removed the data but forgot to remove the corresponding method in the present version. We deleted them in the revised manuscript.

1. The strain Pst DC3000 avrRPT2 was not used in this study. Please remove it.

Response: We are sorry for the mistake. The strain Pst DC3000 avrRPT2 was used for ion leakage assay in previous visions of the manuscript. We deleted it in the revised manuscript.

1. In Figure 5F, did the 59 genes tested overlap with the 366 attenuated proteins in the cgb mutant? Were the 59 genes translationally regulated?

Response: Thank you for your suggestion. Venn diagram analysis revealed that 12 genes (about 20%) are also attenuated proteins, suggesting that the mcm5s2U modification regulates the translation of some SA-responsive genes.

Reviewer #2 (Significance): The authors' study is significant as it establishes the first connection between tRNA mcm5s2U modification and plant immunity, specifically by regulating NPR1 protein translation. This research expands our understanding of the biological role of tRNA mcm5s2U modification and highlights the importance of translational control in plant immunity. It is likely to captivate scientists working in this field.

Response: Thank you very much for your positive comments and support!

Reviewer #3 (Evidence, reproducibility and clarity):

In this manuscript, the authors identified a cgb mutant that carries a mutation in the ROL5 gene Both the cgb mutant and the newly created rol5-c mutant are susceptible to the bacterial pathogen Psm. The authors showed that ROL5 interacts with CTU2, the Arabidopsis homologous protein of the yeast tRNA thiolation enzyme NCS2. A ctu2-1 mutant is also susceptible to Psm, suggesting the tRNA thiolation may play a role in plant immunity. Indeed, tRNA mcm5S2U levels are undetectable in rol5-c and ctu2-1 mutants. The authors found that the cgb mutation significantly attenuated basal and Psm-induced transcriptome and proteome changes. Furthermore, it was found that the translation efficiency of a group of SA signaling-related proteins including NPR1 is compromised.

The manuscript provides solid evidence for the involvement of ROL5 and CTU2 in plant immunity using the rol5 and ctu2 mutants. The authors may consider the following suggestions and comments to improve the manuscript.

Response: Thank you very much for your support and suggestions!

1. The function of the Elongator complex in tRNA modification/thiolation has been extensively studied. In Arabidopsis Elongator mutants, mcm5S2U levels are very low, similar to the levels in the rol5 and ctu2 mutants (Mehlgarten et al., 2010, Mol Microbiology, 76, 1082-1094; Leitner et al., 2015 Cell Rep). In elp mutants, the PIN protein levels are reduced without reduced mRNA levels (Leitner et al., 2015), indicating that Elongator-mediated tRNA modification is involved in translation regulation. The Elongator complex plays an important role in plant immunity, though the reduced mcm5S2U levels in elp mutants were not proposed as the exclusive cause of the immune phenotypes. In fact, it would be difficult to establish a cause-effect relationship between tRNA modification and immunity. These results should be discussed in the manuscript.

Response: Thank you very much for your insightful comment on the role of the ELP complex in tRNA modification and plant immunity. We added a paragraph discussing the ELP complex in the revised manuscript (Line 280-295).

In addition to tRNA modification, the ELP complex has several other distinct activities including histone acetylation, α-tubulin acetylation, and DNA demethylation. Therefore, it is difficult to dissect which activity of the ELP complex contributes to plant immunity. However, the only known activity of ROL5 and CTU2 is to catalyze tRNA thiolation. Considering that the elp, rol5, and ctu2 mutants are all defective in tRNA thiolation, it is likely the tRNA modification activity of the ELP complex underlies its function in plant immunity.

1. The interaction between CTU2 and ROL5 in Y2H has previously been reported (Philipp et al., 2014). The same report also showed reduced tRNA thiolation in the ctu2-2 mutant using polyacrylamide gel. These results should be mentioned/discussed in the manuscript.

Response: Thank you for pointing them out. We added this information in the revised version (Line 146-147).

1. tRNA modification unlikely plays a unique role in plant immunity. It can be inferred that mutations affecting tRNA modification (rol5, ctu2, elp, etc.) would delay both internal and external stimulus-induced signaling including immune signaling.

Response: We agree with you that tRNA modification has other roles in addition to plant immunity. In the Discussion section, we have mentioned that “it was found that tRNA thiolation is required for heat stress tolerance (Xu et al., 2020). ……It will also be interesting to test whether tRNA thiolation is required for responses to other stresses such as drought, salinity, and cold.” (Line276-279).

1. It would be interesting to conduct statistical analyses on the genetic codons used in the CDSs whose translation was attenuated as described in the manuscript. Do these genes including NPR1 use more than average levels of AAA, CAA, and GAA codons? If not, why their translation is impaired?

Response: Thank you for your suggestion. We called GAA/CAA/AAA codons s2 codon. We have analyzed the percentage of s2 codon at whole-genome level (Figure below). NPR1 does contain more s2 codon (10.1%) than the average level (8.5%). We are preparing another manuscript, which will report the relationship between s2 codon and translation.

Referees cross-commenting

It is important to put current research in the context of available knowledge in the field. The digram in Figure 3C shows that the Elongator complex functions upstream of ROL5 & CTU2 in modifying tRNA. The function of Elongator in plant immunity has been well established. The similarities and differences should be discussed. Additionally, it may no be a good idea to claim that the results are novel.

Response: Thank you for your comments. We added a paragraph discussing the ELP complex in the revised manuscript (Line 280-295). The ELP complex catalyzes the cm5U modification, which is the precursor of mcm5s2U catalyzed by ROL5 and CTU2. In addition to tRNA modification, the ELP complex has several other distinct activities including histone acetylation, α-tubulin acetylation, and DNA demethylation. Therefore, it is difficult to dissect which activity of the ELP complex contributes to plant immunity. However, the only known activity of ROL5 and CTU2 is to catalyze tRNA thiolation. Considering that the elp, rol5, and ctu2 mutants are all defective in tRNA thiolation, it is likely the tRNA modification activity of the ELP complex underlies its function in plant immunity. Therefore, our study improved our understanding of the ELP complex in plant immunity. We have deleted the words “new” and “novel” throughout the manuscript.

Reviewer #3 (Significance): The manuscript provides solid evidence for the involvement of ROL5 and CTU2 in plant immunity. However, the authors did not acknowledge the existing results about the Elongator complex that functions in the same pathway in modifying tRNA. The involvement of Elongator in plant immunity has been well established. The cause-effect relationship between tRNA modification and plant immunity is difficult to demonstrate.

Response: We think that the cause-effect relationship between the activities of the ELP complex and plant immunity is difficult to demonstrate because the ELP complex has several distinct activities other than tRNA modification. However, since the only known activity of ROL5 and CTU2 is to catalyze tRNA thiolation, the cause-effect relationship between tRNA thiolation and plant immunity is clear, which indicated that the tRNA modification activity of the ELP complex contributes to plant immunity.

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

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, the authors identified a cgb mutant that carries a mutation in the ROL5 gene Both the cgb mutant and the newly created rol5-c mutant are susceptible to the bacterial pathogen Psm. The authors showed that ROL5 interacts with CTU2, the Arabidopsis homologous protein of the yeast tRNA thiolation enzyme NCS2. A ctu2-1 mutant is also susceptible to Psm, suggesting the tRNA thiolation may play a role in plant immunity. Indeed, tRNA mcm5S2U levels are undetectable in rol5-c and ctu2-1 mutants. The authors found that the cgb mutation significantly attenuated basal and Psm-induced transcriptome and proteome changes. Furthermore, it was found that the translation efficiency of a group of SA signaling-related proteins including NPR1 is compromised.

The manuscript provides solid evidence for the involvement of ROL5 and CTU2 in plant immunity using the rol5 and ctu2 mutants. The authors may consider the following suggestions and comments to improve the manuscript.

1. The function of the Elongator complex in tRNA modification/thiolation has been extensively studied. In Arabidopsis Elongator mutants, mcm5S2U levels are very low, similar to the levels in the rol5 and ctu2 mutants (Mehlgarten et al., 2010, Mol Microbiology, 76, 1082-1094; Leitner et al., 2015 Cell Rep). In elp mutants, the PIN protein levels are reduced without reduced mRNA levels (Leitner et al., 2015), indicating that Elongator-mediated tRNA modification is involved in translation regulation. The Elongator complex plays an important role in plant immunity, though the reduced mcm5S2U levels in elp mutants were not proposed as the exclusive cause of the immune phenotypes. In fact, it would be difficult to establish a cause-effect relationship between tRNA modification and immunity. These results should be discussed in the manuscript.
2. The interaction between CTU2 and ROL5 in Y2H has previously been reported (Philipp et al., 2014). The same report also showed reduced tRNA thiolation in the ctu2-2 mutant using polyacrylamide gel. These results should be mentioned/discussed in the manuscript.
3. tRNA modification unlikely plays a unique role in plant immunity. It can be inferred that mutations affecting tRNA modification (rol5, ctu2, elp, etc.) would delay both internal and external stimulus-induced signaling including immune signaling.
4. It would be interesting to conduct statistical analyses on the genetic codons used in the CDSs whose translation was attenuated as described in the manuscript. Do these genes including NPR1 use more than average levels of AAA, CAA, and GAA codons? If not, why their translation is impaired?

Referees cross-commenting

It is important to put current research in the context of available knowledge in the field. The digram in Figure 3C shows that the Elongator complex functions upstream of ROL5 & CTU2 in modifying tRNA. The function of Elongator in plant immunity has been well established. The similarities and differences should be discussed. Additionally, it may no be a good idea to claim that the results are novel.

Significance

The manuscript provides solid evidence for the involvement of ROL5 and CTU2 in plant immunity. However, the authors did not acknowledge the existing results about the Elongator complex that functions in the same pathway in modifying tRNA. The involvement of Elongator in plant immunity has been well established. The cause-effect relationship between tRNA modification and plant immunity is difficult to demonstrate.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

The authors presented an intriguing and previously unknown mechanism that the tRNA mcm5s2U modification regulates plant immunity through the SA signaling pathway, specifically by controlling NPR1 translation. The manuscript was well-written and logically structured, allowing for a clear understanding of the research. The authors provided strong and persuasive data to support their key claims. However, further improvement is required to strengthen the conclusion that mcm5s2U regulates plant immunity by controlling NPR1 translation.

Major comments:

1. NPR1 translation should be examined to verify the Mass Spec (Figure 5B) and polysome profiling data (Figure 5D) by checking the NPR1 protein and mRNA level using antibodies and qPCR, respectively, in the cgb mutant background to establish a concrete confirmation of CGB regulation in NPR1 translation.
2. Analyzing the genetic epistasis of CGB and NPR1 to check if CGB regulates plant immunity through the NPR1-dependent SA signal pathway. If the authors' claim is valid, I would expect no addictive effect on bacterial growth in the cgb/npr1 double mutant compared to the single mutants. Due to the broad impact of CGB on plant signaling (Figures 4E and 4F), the SA protection assay, which concentrates on the SA signal pathway, needs to be tested in WT, cgb and npr1 plants as an alternative assay to the genetic epistasis analysis. I expect that the SA-mediated protection is also compromised in cgb mutant background.

Minor comments:

1. Could the authors comment on why using COM instead of WT as a control to perform the majority of the experiments?
2. In Figure 5E, why does ACTIN2 have an enhanced translation while NPR1 shows a compromised one in cgb mutant? How does the mcm5s2U distinguish NPR1 and ACTIN2 codons? Does mcm5s2U modification have both positive and negative roles in regulating protein translation?
3. Specify the protein amount used for the in vitro pull-down assay and agobacterial concentration used for the tobacco Co-IP assay in the protocol section.
4. Delete the SA quantification and Ion leakage assay in the protocol, which are not used in the study.
5. The strain Pst DC3000 avrRPT2 was not used in this study. Please remove it.
6. In Figure 5F, did the 59 genes tested overlap with the 366 attenuated proteins in the cgb mutant? Were the 59 genes translationally regulated?

Significance

The authors' study is significant as it establishes the first connection between tRNA mcm5s2U modification and plant immunity, specifically by regulating NPR1 protein translation. This research expands our understanding of the biological role of tRNA mcm5s2U modification and highlights the importance of translational control in plant immunity. It is likely to captivate scientists working in this field.

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 article titled "The tRNA thiolation-mediated translational control is essential for plant immunity" by Zheng et al. highlights the critical role of tRNA thiolation in Arabidopsis plant immunity through comprehensive analysis, including genetics, transcriptional, translational, and proteomic approaches. Through their investigation, the authors identified a cbp mutant, resulting in the knockout of ROL5, and discovered that ROL5 and CTU2 form a complex responsible for catalyzing the mcm5s2U modification, which plays a pivotal role in immune regulation. The findings from this study unveil a novel regulatory mechanism for plant defense. Undoubtedly, this discovery is innovative and holds significant potential impact. However, before considering publication, it is necessary for the authors to address the various questions raised in the manuscript concerning the experiments and analysis to ensure the reliability of the study's conclusions.

Here is Comments:

Line 64-65:<br /> The author mentioned that 'While NPR1 is a positive regulator of SA signaling, NPR3 and NPR4 are negative regulators.' However, several recent discoveries are suggesting that it may not be a definitive fact that NPR3 and NPR4 are negative regulators. Therefore, I recommend the authors to review this section in light of the findings from recent papers and make necessary edits to reflect the most current understanding.

Line 182- & Figure 4:<br /> The author conducted RNA-seq, Ribo-seq, and proteome analysis. Describing the analysis as "transcriptional and translational" using RNA-seq and proteome data seems not entirely accurate. Proteome data compared with RNA-seq not only reflects translational changes but may also encompass post-translational regulations that contribute to the observed differences. To maintain precision, the title of this section should either be modified to "transcriptional and protein analysis" or, alternatively, compare RNA-seq and Ribo-seq data to demonstrate the transcriptional and translational changes more explicitly.

Line 229-235 and Figure 5C:<br /> The interpretation of Figure 5C's polysome profiling results is inconclusive. There does not seem to be a noticeable difference in polysomal fractions between the cab mutant and CAM. The observed differences in the overlay of multiple polysome fractions between cab and COM could be primarily influenced by baseline variations rather than a significant decrease in the polynomial fractions in cpg. Therefore, it is necessary to carefully review other relevant papers that discuss polysome fraction data and their analysis. By doing so, the authors can make the appropriate corrections to ensure accurate interpretations.

Line 482 Ion Leakage assay:

I could not find the ion leakage assay in this manuscript, so I wonder why it is mentioned.

Materials and Methods:

To enhance the reproducibility of the study, the authors should provide a more detailed description of the materials and methods, especially for critical experiments like the Yeast-two-hybrid assays. Clear documentation of specific reagents, strains, and protocols used, along with information on controls, will bolster the validity of the results and facilitate future research in this area.

Minor Point:

Line 61: There is a space between ')' and '.', which needs to be edited.

Significance

This study holds significant importance within the field of plant immunity research. The authors have made valuable contributions through their comprehensive analysis, encompassing genetics, transcriptional, translational, and proteomic approaches, to elucidate the critical role of tRNA thiolation in plant immunity. One of the major strengths of this study lies in its ability to shed light on a previously unknown regulatory mechanism for plant defense. By identifying the cbp mutant and investigating the role of ROL5 and CTU2 in catalyzing the mcm5s2U modification, the authors have unveiled a novel aspect of plant immune regulation. This innovative discovery provides a deeper understanding of the intricate molecular processes governing immunity in plants.

Moreover, the study's findings are not limited to the immediate field of plant immunity but also have broader implications for the scientific community. By employing diverse methodologies, the authors have demonstrated how tRNA thiolation exerts control over both transcriptional and translational reprogramming, revealing intricate links between these processes. This integrative approach sets a precedent for future research in the field of plant molecular biology and opens up new avenues for investigating other aspects of immune regulation.

In terms of its relevance, the study's findings have the potential to captivate researchers across various disciplines, such as plant biology, molecular genetics, and translational research. The insights gained from this study may inspire researchers to explore further the role of tRNA in other regulation.

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Reply to the reviewers

General Statements

In this manuscript, we describe a LINC complex-dependent centrosome positioning mechanism that takes place during the early stages of mitotic spindle assembly. We are grateful to the reviewers for their comments and suggestions and hope the proposed revision plan addresses all concerns raised. We are pleased that reviewers recognize the excellent technical quality of the experiments and the significance of the work presented in this manuscript.

Description of the planned revisions

Reviewer 1

• “Moreover, we demonstrate this mechanism is altered in cancer cells, leading to increased chromosome segregation errors. » Here the authors infer that the identified mechanism is absent in cancer cells and that its absence contributes to chromosome segregation errors. Both conclusions are not supported by the presented data. First, the authors did not test whether any members of the LINC complex or dynactin is present at lower levels on the nuclear membranes of the cancer cells. Such a direct validation would be essential to make such a strong statement. Second, the authors conclude that this mechanism prevents chromosome segregation errors, based on the fact that depletion or impairment of the LINC complex (shSUN1, shSUN2, DN-KASH) results in chromosome segregation errors. These perturbations lead, however, as noted by the authors themselves to pleiotropic effects, including insufficient retraction of nuclear membrane, which can all contribute to chromosome segregation errors. It is therefore impossible to estimate the contribution of the centrosome positioning mechanism to these segregation errors using this type of perturbations. One could even argue that this mechanism might not be that important, since depletion of SUN2, which also impairs centrosome positioning has no significant effect on chromosome segregation.

We agree with the reviewer that an analysis of the levels of LINC complex components and dynactin in cancer cells is lacking. For this reason, we propose to analyze the levels of SUN1, SUN2, dynactin and Nesprins by immunofluorescence in all cell lines. In addition, we have now re-written the manuscript regarding the chromosome segregation phenotype, to clarify that the observed phenotypes are not necessarily due to centrosome positioning defects.

Reviewer 2

“The authors need some other NE protein as a control to show that the reduction of dynein by DN-KASH is a specific defect and not a broad impact on the NE. The dynein data in Figs. 5J-L need to be extended to SUN1/2”.

We thank the reviewer for these suggestions. To clarify this point, we will analyze the levels of lamin B following expression of DN-KASH or DPPPL-KASH. This will allow us to determine whether expression of the DN-KASH construct only affects dynein and not other NE proteins. In addition, we will analyze dynactin levels following SUN1 and SUN2 depletion.

Reviewer 3:

“Fig. 3: I suggest to quantify the lamin B1 and LBR overexpression levels”.

According to the reviewer´s suggestion, we will perform a WB analysis of the cells overexpressing lamin B1 and LBR and quantify its levels.

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

Reviewer 1

• “The authors conclude based on three cell lines that the centrosome positioning mechanisms is present in non-transformed cells and not in cancerous cells. The authors have, however, only analysed 1 non-cancerous cell line, and they compare cells originating from vastly different tissues (retina, bones and breast) and origins (epithelial vs. mesenchymal cartilage cells). Such a general statement is not possible, without a systematic comparison of several healthy cells vs cancerous cells from the same tissue”.

We agree with this reviewer´s comment, which is also shared by the other reviewers. Accordingly, we have now extensively rewritten the manuscript to tone down this statement and focus on the role of the LINC complex in determining centrosome positioning.

• “While the data showing that centrosome positioning depends on the LINC complex is solid and robust, some of the "negative" examples identified by the authors are less convincing. One the process the authors study is cell rounding. Based on the fact that Rap1 transfection or treatment with Calyculin A does not lead to differences that are statistically different, the authors conclude that cell rounding is not involved. However, absence of statistical difference does not mean that there is no difference. Indeed, when comparing the raw data in Figure 2L and 2Q to the positive hit shSun2 in Figure 4J, one could conclude that cell rounding does make a difference, and that this statistical difference would emerge if the authors would count a high number of cells. Therefore the authors should interpret these results in a more differentiated manner, and also instead of just stating nonsignificant, state also the real p-values for the different experiment”.

According to the reviewer´s suggestion, we have now added all p values to the respective graphs and interpreted these results in a more step-by-step manner. Moreover, while we understand the reviewer`s comment regarding our sample size, it should be noted that this is a single-cell, high-resolution imaging approach which, in combination with certain treatments makes it very challenging to obtain data for a high number of cells. In this regard, we point out that interfering with cell rounding was extremely difficult to achieve. When highly overexpressed, Rap1* completely impairs mitotic cell de-adhesion, and this blocks mitotic entry (Marchesi et al., 2014). Furthermore, CalA treatment induces a fast and drastic rounding, which makes it very challenging to accurately track centrosome and nuclear positions. Nevertheless, we filmed additional cells treated with CalA and added the data to the figures. Our results still confirm that interfering with cell rounding does not significantly change centrosome positioning during this stage. It should be noted that the sample size in all conditions is within the range normally used when performing single-cell high resolution imaging.

• The second major concerns emerges when looking at the data in Figure 5, when the authors test for the abundance of the dynein complex on the nuclear envelope in cells treated with DPPPL-KASH or DN-KASH. Yes, there is a statistical difference, but the absolute difference is tiny (I estimated a normalized intensity of 1.44 vs 1.35). This is a difference of less than 10%. How do the authors think that such a small change in dynein could have such a strong effect on centrosome positioning? Would a partial dynactin depletion by 10% give an equivalent result? Does the depletion of other proteins involved in the late recruitment of dynein at the NE also affect centrosome positioning?

We thank the reviewer for this important point. Originally, we quantified dynactin intensity by selecting three unbiased random regions of the NE. However, this approach might underestimate the overall fluorescence intensity across the entire structure. For this reason, we have now measured dynactin fluorescence intensity over the entire NE using the same dataset. We have replaced Fig. 5K and L with this data and a description of the method has been added to the Materials and Methods section. As can be seen from the new graph, there is a reduction of approximately 50% in dynactin NE fluorescence intensity.

The reviewer also asks whether depletion of other proteins involved in the late recruitment of dynein at the NE would also affect centrosome positioning. However, extensive previous work done by us and others, has shown that depletion of either BicD2 or NudE/NudEL, which are the main adaptors for dynein loading during the G2/M transition, significantly affect prophase centrosome positioning, since they detach centrosomes from the NE (Splinter et al., 2010; Bolhy et al., 2011; Hu et al., 2013; Baffet et al., 2015; Nunes et al., 2020). Once detached, centrosomes are no longer able to orient according to nuclear cues. Therefore, we do not believe such an approach would provide additional information regarding the role of the LINC complex in this process.

Reviewer 2:

• “Figure 1 is insufficiently explained. The authors have to describe in an understandable way how they measured centrosome-centrosome angle and centrosome-nucleus angle. They should show a cartoon in which these angles are clearly shown. The small cartoons in Fig. 1C are not helpful at all; they are also not explained. The authors should explain the meaning of the black dots (are these centrosomes?) and the even smaller dots. The short nuclear axis should be indicated, e.g., by a red line”.

We apologize for the lack of sufficient explanation in Figure 1. We have now re-written the text. We have also added a scheme explaining how centrosome-nucleus and centrosome-centrosome angles are quantified, according to the reviewer´s suggestion. We have added this to Fig. S1. We believe this makes our data more understandable and easier to follow.

“On the first page of the manuscript: "Consequently, at the NEP, centrosomes are positioned on the shortest nuclear axis (Fig. 1C) as can be seen in Fig. 1A. This means that the centrosome-nucleus angle relative to the shortest nuclear axis should be 0. However, in Fig. 1C, this angle is between 45 and 90 degrees. This is also the case for Fig. 1D. Please clarify”.

We thank the reviewer for noticing this error. In fact, the graphs reflect positioning of centrosomes relative to the longest nuclear axis. Therefore, when the values are close to 90º, this means they are oriented on the shortest nuclear axis. We understand this could be confusing to the readers. We have now clarified this information throughout the text.

• “I find it confusing that in Fig. 1, depending on the subfigure, the short or longest nuclear axis is used as a reference point: Fig. 1C: shortest; D: shortest; F: shortest; G: longest; I: shortest; J: longest. Thus, even within the same cell line, the reference point is changing. What is the rational for this variation”?

Again, we refer to the point above. The reference point is always the shortest nuclear axis. However, we apologize for the lack of clarity. This has all been changed, according to the explanation provided in the previous point.

• Fig. 4K, L, M: in figure, y-axis: "shortest nuclear axis". In legend: "relative to the longest nuclear axis". I guess the longest nuclear axis is correct. Same in Fig. 5D and E. Fig. 5C lacks the WT control.

This information has been clarified in the text and panels have been corrected accordingly. Regarding Fig. 5C, we believe the correct control is the expression of PPPL-KASH, since it has been shown extensively that Nesprins localize to the NE in control, unmanipulated cells. Nevertheless, we have added a WT control to Supplementary Figure 5, showing localization of Nesprins in unmanipulated prophase cells.

“The cells in Fig. 5J are not comparable: one has a monopolar spindle, the other a bipolar. The authors need some other NE protein as a control to show that the reduction of dynein by DN-KASH is a specific defect and not a broad impact on the NE. The dynein data in Figs. 5J-L need to be extended to SUN1/2”.

We agree with the reviewer´s comment that the cell in the top panel might appear as a monopolar. However, it is not. In fact, this cell has centrosomes on the top and bottom of the nucleus, in a vertical configuration (check Magidson et al., Cell, 2011). To clarify this, we have now added lateral projections of all cells, highlighting the centrosomes to clearly show they are positioned on opposite sides of the nucleus. The other points related to the effects of DN-KASH on other NE proteins and dynactin levels following shSUN1 and shSUN2 are being addressed (please see comments above in the section “description of planned reviews”).

“The title of the paper is misleading: the authors do not provide any indication for a nuclear signal in prophase that determines centrosome positioning”.

We have changed the title of the manuscript according to the reviewer´s suggestion.

“It would make sense to use the same time scale in Figs. 1A and B (either min.sec. or sec.) to allow direct comparison”.

We have now changed the time scale to seconds in all figures to allow direct comparison.

“2nd section: Mitotic cell rounding "The authors state: Given that cancer cells failed... I would be careful with this generalization; only one cancer cell was used in this study”.

Given the limited number of cells that we used, and following the concern raised by all reviewers, we have now re-written the text to avoid generalizations. Instead, we now focus on the role of the LINC complex in determining centrosome positioning.

“The authors say: "However, they did not place the centrosomes at the shortest nuclear axis (Figure 4K-M)." Centrosomes are still on the shortest nuclear axis but not as frequent as in control”.

This has been corrected.

“The white color in Fig. 6B cannot be seen and needs to be changed to something else”.

We apologize for this oversight. During the upload and pdf conversion process, we did not realize the color of this bar, corresponding to the DN-KASH group had changed to white. This has now been corrected.

The paper has neither line nor page numbers.

This has been added.

Reviewer 3

“it would make sense to indicate the test used for each p-value in all the figure legends”.

We have now added the statistical test used and the p-value in the figure legends.

“Figure legends are quite repetitive and could be shortened. E.g. in Fig. 1 the description for E, F, H and I repeats what has been explained for B and C. Same applies between figure legends. The authors might refer to previous legends if the analysis was done in a similar way”.

The legends have been simplified.

“How is nuclear solidity defined and analyzed in Fig S3D”?

Nuclear solidity was analyzed using Fiji. In short, nuclei are outlined using the polygon tool and nuclear area is measured. To calculate nuclear solidity, the nuclear area is then divided by the corresponding nuclear convex hull area. Irregular nuclei will typically show a lower nuclear solidity value. This information was added to the text.

“The references to Fig S3 in figure legend 3 ("see Fig S3") do not enlighten the message and could be removed. The same applies to Fig5 - here it is not clear why the author refer to Fig S4”.

We agree with this reviewer´s comment. We have now removed these references from the legends.

“Fig. 5: Consider reordering the panel: Start with the current panel C (as in the text) as it is the necessary control prior to the experimental data”.

We have now changed the order of the panel according to the reviewer´s suggestion.

“Fig 5 I: what means "before"? Can the authors give a time window they use for analysis”.

We have now replaced the term “before” with a defined time.

“Page 20: "... shortest nuclear axis (Fig. 1C, 5D-G; n.s. - not significant). However, DN-KASH-expressing cells showed compromised separation and positioning of centrosome (Fig. 5D-G, * p=0.0155 and * p=0.0237, respectively). - rather point to the specific panels, i.e. Fig. 1C, 5D and F as well as and Fig. 5E and G”.

We have now clarified these points in the text.

“Fig 6B. The DN- KASH bars are on my pdf not visible - use a darker grey”.

As mentioned above, we apologize for this oversight. We did not realize that during the pdf conversion process the bar corresponding to the DN-KASH group had changed to white. We have now corrected this.

“Fig S6, albeit mentioned in the text, is not included in the supplementary info”.

We apologize for this error. In fact, where it reads Fig. S6, should be Fig. S5. We have now corrected this.

“a. GlutaMAX instead of GlutaMAXE (page 29)

b. What means "as described previously"? No reference is given. Do you refer to the upper part of the method section? (page 30)

c. 20 nM HEPES should most probably read 20 mM (page 32)

d. "1:50 protease inhibitor; 1:100 Phenylmethylsulfonul fluoride" - which protease inhibitor (mixture)? Rather phenylmethylsulfonyl fluoride.

e. exact composition of the cytoskeleton buffer used to prepare 4% paraformaldehyde could be given”.

All these suggestions/corrections have been introduced in the text.

Description of analyses that authors prefer not to carry out.

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Reply to the reviewers

Manuscript number: RC-2023-02111

Corresponding author(s): Moira O’Bryan

1. General Statements

We thank the Review Commons editor and the three reviewers for their overall positive responses in assessing this manuscript. Further, we appreciate and would like to reiterate the similarities across our three reviewers’ comments regarding the significance of this work, where our examination of epsilon tubulin (TUBE1) during mammalian spermatogenesis will be valuable for both microtubule/cytoskeletal and developmental/ reproductive fields. Below, we have made point-by-point responses to the reviewers’ comments, and outlined by the revisions we plan to make, or have made. All line numbers refer to the transferred manuscript file with tracked changes.

2. Description of the planned revisions

Reviewer 1:* The authors claim that because the TUBE1 knockout mouse have abnormal centrosome numbers during meiosis, there is a role for TUBE1 in suppressing supernumerary centriole formation. While this is one possibility, it's also possible that abnormal centrosome numbers arose as a result of cell division defects, especially because binucleate cells are present in mutants. The authors should edit the text to state that abnormal centrosome numbers may arise from either supernumerary centriole formation (by the templated or de novo pathways) or from failure to complete cell division. *

*OPTIONAL: to test these possibilities, the authors may choose to 1) count the number of centrioles in meiosis with two different centriole markers 2) stain for markers of mature centrioles, such as Cep164, to determine the number of parental centrioles. *

Response: This is a good point. Published data indicates that the Stra8-cre is active within a subset of undifferentiated spermatogonia, and in differentiated spermatogonia through to pre-leptotene spermatocytes (Sadate-Ngatchou et al., 2008). This raises the possibility that the increase in centriole numbers could be due to a failure to complete cell division if cre is active in mitotically active spermatogonia populations. The text has been appropriately modified in lines 207-209 and 352 to reflect these insights. We appreciate the Reviewer’s optional suggestion to perform additional immunolabeling experiments and intend to examine the number of parental centrioles in spermatocytes during meiotic division using a marker of the distal or subdistal appendages. This data will be included in the final revised document.

Reviewer 2:* Considering the suggested non-canonical function of Epsilon tubulin outside the centriole in mice sperm, it is critical to know the localization of the protein in spermatocytes during meiosis and spermatids during differentiation. *

Response: We agree with Reviewer 2 that determining the localization of TUBE1 in spermatocytes and spermatids would be desirable. However, we are yet to find an appropriate available antibody for this. We have previously assessed the specificity of a TUBE1 antibody (PA5-56917, Invitrogen), however, this antibody was not suitable for use in our mouse model. This aside, we have recently acquired a new TUBE1 antibody which we plan to evaluate its specificity during this revision period. If it appears to bind specifically to TUBE1, we will perform the requested localization experiments.

For clarification we have previously defined the location of TUBE1 in spermatids to the manchette and basal body in elongating spermatids (lines 72-74) (Dunleavy et al., 2017). Unfortunately, the antibody used in this study is now discontinued. The phenotypes observed as a consequence of TUBE1 loss of function in this study are, however, consistent with these patterns of localization.

Reviewer 2:* Localization of Epsilon tubulin is needed to distinguish between mutant sperm cells and those that are not Epsilon tubulin mutants in the Tube1GCKO/GCKO mice. E.g., are the 28.07% of Tube1GCKO/GCKO tubules that showed a Sertoli cell only (SCO) phenotype the one where all the cells are mutants? *

Response: As per our response to Reviewer 2’s comment above, we plan to test a new TUBE1 antibody to determine TUBE1 localization in this model. Outlined in our response to Reviewer 2 below, we also plan to sequence DNA from mature epididymal sperm from our mutant mice to further confirm the deletion of Tube1 exon 3.

Reviewer 2:* The generated conditional germ cell-specific mutants are demonstrated by mRNA expression spermatocytes. It would help if DNA sequencing, western, and Immunohistochemical staining were used to show the gene and protein are affected. *

Response: We thank Reviewer 2 for their suggestions. Should we successfully validate an appropriate TUBE1 antibody for use in our model, we will perform immunohistochemical staining during the revision process. Our qPCR results from purified spermatocytes however, strongly suggest that the Tube1 gene is deleted in our model, noting that such purifications are on average 81% pure with the major contaminants being Sertoli cells and spermatids (Dunleavy et al., 2019). To further confirm the deletion of Tube1 exon 3, we plan to sequence DNA from mature epididymal sperm from our mutant mice.

Reviewer 2:* "Suggesting a core TUBE1 function that can be supplemented by either z-tubulin or TUBD1." Can you test what happens to mice Z and D tubulin isoforms in the mutant? Did their level increase in the centrioles? This is informative since there is no clear centriolar phenotype (other than centriole number that may be due to cell division failure) in mice spermatogenesis and the paper's central hypothesis in the introduction. *

Response: We appreciate this question by Reviewer 2. Zeta tubulin is not present in the mouse genome as outlined in our introduction (lines 38-39). We do acknowledge that exploring Tubd1 will be informative in our mutant and thereby plan to examine its expression in round spermatids.

Reviewer 2: The authors looked at the Metaphase stage cells to assess meiosis. It would be more interesting to look at the meiosis prophase I. Since the Stra8 acts very early leptotene stage, it would be interesting to see if meiosis is defective from the very beginning. Also, some suggest that the manchette is nucleated at the pachytene stage. Is the manchette defective from the very early stage of nucleation?

Response: We thank Reviewer 2 for this suggestion. To this end, we plan to examine juvenile mouse testes at days 10 and 17 post-partum where leptotene and pachytene spermatocytes are the most mature germ cells respectively.

In regard to the Reviewer’s comment of the manchette being nucleated in pachytene stage spermatocytes, we acknowledge that the precise mechanism of manchette nucleation has not been confirmed. We are aware of the alternative hypothesis introduced by Moreno and Schatten (2000), which postulates manchette microtubules may be nucleated prior to pachytene period, through their examination of bovine male germ cells. This hasn’t, however, been supported by evidence and with more recent data, others have suggested that the manchette is nucleated at the centrosomal adjunct (Lehti and Sironen, 2016). Indeed, our unpublished data suggests this is the case (another study). Regardless, the origin of the microtubule seeds that ultimately extend to form the manchette is not relevant to the hypothesis we have proposed. As we note that in our manuscript and mouse model, manchettes appear to assemble normally in step 8 spermatids. Rather, their movement and disassembly is abnormal i.e. TUBE1 serves critical roles more manchette movement and disassembly rather than manchette formation.

Reviewer 2:* Is the acetylation of manchette microtubules affected in the absence of TUBE1? *

Response: Reviewer 2 raises an interesting question, which we plan to answer through immunolabeling of testis sections for acetylated tubulin in our control and mutant groups.

Reviewer 3: *Minor points, a substantial percentage of sperm produced had a normal head shape in the KO (Figure 1I), which undermine the function of tube1 in nuclear shaping, the author should address this point in their manuscript. It is also curious whether there are phenotype in other tissues, can the authors comment on that? *

Response: We thank Reviewer 3 for highlighting this point. As reported in Fig. 1I, 28.5% of sperm from Tube1GCKO/GCKO epididymides have abnormal nuclear shape. This is a 4.4-fold increase over that seen in wild type sperm. These data clearly highlight the role of TUBE1 in defining nuclear morphology. Variations between cells does not undermine this conclusion. It appears that prior to sperm release from the testis, the majority of TUBE1 null spermatids heads are abnormally shaped. However, in the epididymis there appears to be an increase in the proportion of normally shaped heads. We thus hypothesize that the high rates of spermiation failure in the TUBE1 null mice reflect the preferential removal of abnormally shaped sperm by Sertoli cells, thus enriching for normally shaped heads that are released. During the revision process, we will quantify the percentage of spermatids with normal versus abnormally shaped heads prior to spermiation in testis sections. All Tube1 null mice were sterile.

To Reviewer 3’s second point - we have not examined other tissues in this conditional male germ cell knockout mouse model, as the cre used in this manuscript is only expressed in the testis (Sadate-Ngatchou et al., 2008). Consistent with the specificity of the deletion, null male mice are overtly healthy, with the exception of male fertility, and exhibit normal body weight as detailed on line 123 and in Fig S1D.

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

Reviewer 1:* In figure 5, based on quantification of fluorescence intensity, the authors conclude that loss of epsilon-tubulin results in an increase in the levels of KATNAL1, KATNAL2, and KATNB1. Given the inherent variability in immunofluorescence staining, the authors should at a minimum normalize their intensity measurements to those of an unrelated control protein stained in the same cell (ex: alpha-tubulin). It would be more convincing to quantify the levels of these proteins by Western blot (again, normalized to a control protein or to total cellular protein), which should be feasible given that the authors can isolate elongating spermatids. *

Response: We thank Reviewer 1 for this suggestion to better account for any potential variability between immunofluorescence staining in cells. In this instance, alpha-tubulin would be a related protein in our model, making it unsuitable for normalization - the longer manchette phenotypes in our mutant spermatids indicate more tubulin present in mutant cells. We have therefore normalized the fluorescence intensity in our cells to DNA content (DAPI staining). This has provided comparable results to our initial analysis, and we have edited our text accordingly at lines 303, 307-310, 563-564, 845, 850 and Fig. 5. We respectfully disagree that western blotting would be informative, as the point is that katanin proteins are accumulating abnormally on the elongating sperm manchette. This does not necessarily mean that overall katanin levels will be increased. This aside, given the low numbers of elongating spermatids in the Tube1GCKO/GCKO mice, obtaining sufficient materials of western blotting is prohibitive. With the severity of germ cell loss indicated by our daily sperm production calculations, we predict the isolated spermatids of up to 5 Tube1GCKO/GCKO animals would be required to make up one biological replicate. It would not be feasible to collect the large number of animals required for at least three biological replicates in the revision timeframe.

Reviewer 1:* A major claim of the paper is that epsilon-tubulin plays a different role within mammalian germ cells (abstract, line 22; p9, lines 167-168; p15 lines 315-316), because the Tube1GCKO/GCKO mice can form some sperm with relatively normal ciliary ultrastructure, whereas ciliates lacking epsilon-tubulin fail to form cilia. However, it's unclear whether the centrioles that templated these normal cilia were formed before or after epsilon-tubulin loss. Given that centrioles are inherited from one generation to the next, it's possible that the few normal cilia may be templated by relatively normal parental centrioles. These parental centrioles would have been present in spermatogonia prior to Cre expression/epsilon-tubulin deletion, and inherited by a fraction of sperm after the mitotic and meiotic divisions, resulting in sperm with normal ciliary ultrastructure. Other spermatocytes may have inherited centrioles formed in the absence of epsilon-tubulin, resulting in aberrant centrioles similar to those reported in human somatic cells, but these would not form any sperm flagella due to a loss of cell viability, as has been reported for acentriolar cells in a p53+ background. Underscoring this point, Chlamydomonas and human somatic mutant cells constitutively lack epsilon-tubulin. In these systems, the parental centrioles were diluted from the population over many cell divisions, and phenotypic analysis would only include the centrioles that formed in the absence of epsilon-tubulin. To make their major claim, the authors need to demonstrate that the basal bodies of sperm flagella with normal ultrastructure were formed in the absence of epsilon-tubulin, and were not normal parental centrioles. Given the difficulty of this experiment, the authors may instead choose to remove their claim that epsilon-tubulin plays a different role within mammalian germ cells. *

Response: The authors thank Reviewer 1 for their detailed input regarding TUBE1’s centriolar importance across species. From their feedback, we recognize the need to modulate our interpretation of this result. We have also added a line to our manuscript highlighting that the normal axonemal structure observed may be due to the inheritance of normal centrioles (lines 328-329). We note however, that sperm produced within the null animals were immotile and that motility could not be recovered by the addition of exogenous ATP thus revealing that TUBE1 is required to form functional sperm tails.

Reviewer 2:* It will help if the introduction summarizes the knowledge on Epsilon tubulin in spermatogenesis with emesis on its localization and the method used to find the localization. *

Response: We have modified the introduction accordingly in lines 72-73.

Reviewer 2:* How many independent mutant animals were studied, and what was the elfishness of generating mutants with a complete mutant testis? From Fig s1c, it appears all mutants generated were total mutations in almost all cells - is this correct? *

Response: We have updated the number of animals studied as per the comment below. Regarding the mutant status of our mouse model, we used Stra8-Cre which is active between early (postnatal day 3) spermatogonia to pre-leptotene spermatocytes (Sadate-Ngatchou et al., 2008) thus all spermatocytes, spermatids, and sperm will carry the deletion. As shown in Fig. S1C we measured a 90.1% reduction in Tube1 mRNA expression from purified spermatocytes. As mentioned above, we note that the purified germ cells always contain a low percentage of contaminating cells. Using our optimized Staput method we obtain isolated germ cell populations of high purity, where in spermatocyte populations we calculate 19% contamination with other testicular cell types (e.g. somatic Sertoli/interstitial cells, spermatogonia, spermatids) (Dunleavy et al., 2019). We therefore believe the 9.9% Tube1 mRNA expression detected in our Tube1GCKO/GCKO group are the origin of that residual mRNA. We have included this information in the materials and methods section (lines 491-493).

Reviewer 2:* Add a definition to "ZED-tubulins." *

Response: A definition to the ZED-tubulins can be found on line 32.

Reviewer 2:* From the paper, it is unclear if Epsilon tubulin is dispensable for centriole function only in sperm cells or if the same is true in mice somatic cells in vivo. *

Response: In this study we have used a conditional male germ cell knockout mouse model to examine TUBE1’s function specifically in male germ cells. As mentioned in our introduction, the function of TUBE1 has not been examined in murine somatic cells in vivo (lines 68-70). To avoid confusion, we have reiterated this point in lines 356-358 of our discussion.

Reviewer 2:* Fig. S1 and other figures: "n {greater than or equal to} 3 samples/genotype" - this is unclear - please indicate the number of independent animals tested. *

Response: We have modified the figure legends accordingly in lines 11-13 and 33-35 of the transferred supplementary information file and lines 787-788 and 810-811 of the transferred manuscript file.

Reviewer 2:* "suppressing supernumerary centriole formation" is this due to access centriole formation or failed mitosis? *

Response: We acknowledge Reviewer 2’s comment is similar to the comment made by Reviewer 1 above and note we have modified the associated text in lines 207-209 in response to the above comment.

Reviewer 2:* The KATNAL1, KATNAL2, and KATNB1 staining in Fig 5 show multiple foci in the nucleus. Are these foci-specific staining or nonspecific? It is surprising to see such a large complex. *

Response: As outlined in the materials and methods and the Fig. 5 legend, Fig. 5 displays three-dimensional (3D) z-stack images of whole elongating spermatids presented as 2D maximum intensity projections. The katanin subunit staining is around the nucleus rather than inside of it, however the flattening of the image from 3D to 2D make the foci appear inside the nucleus. To clarify this, we have modified the Fig. 5 legend in lines 845 and 848.

Reviewer 2:* How the staging of spermatids was performed needs to be explained in the method. *

Response: We have included additional explanation the materials and methods section (lines 513-514).

Reviewer 3: The experimental part is of the highest quality and the manuscript is very well written. My only reservation with the manuscript is concerning the model proposed for manchette migration in the Discussion section (Figure 6). I find the proposed model highly speculative and pre-mature, not supported enough by data, as even admitted by the authors (lines 415-427). Having it as a figure and concluding remark gives it too match weight, my suggestion would be to remove figure 6 and tone down the discussion.

Response: The authors thank Reviewer 3 for their complimentary overview of our manuscript. We agree that some unanswered questions remain in our proposed model of manchette migration. This study has however, added several critical missing pieces. With respect, we prefer to keep Figure 6 in the manuscript as explaining manchette function to non-experts is very difficult without a visual aide. To ensure transparency with the audience that our model is indeed hypothetical, we have edited our discussion and Figure 6 legend to reflect this (lines 406, 417, 428, 435, 463, 860, 863, 869).

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

None

References

DUNLEAVY, J. E., GRAFFEO, M., WOZNIAK, K., O’CONNOR, A. E., MERRINER, D. J., NGUYEN, J., SCHITTENHELM, R. B., HOUSTON, B. J. & O’BRYAN, M. K. 2022. Male mammalian meiosis and spermiogenesis is critically dependent on the shared functions of the katanins KATNA1 and KATNAL1. bioRxiv, 2022.11.11.516072.

DUNLEAVY, J. E. M., O’CONNOR, A. E. & O’BRYAN, M. K. 2019. An optimised STAPUT method for the purification of mouse spermatocyte and spermatid populations. Molecular Human Reproduction.

DUNLEAVY, J. E. M., OKUDA, H., O’CONNOR, A. E., MERRINER, D. J., O’DONNELL, L., JAMSAI, D., BERGMANN, M. & O’BRYAN, M. K. 2017. Katanin-like 2 (KATNAL2) functions in multiple aspects of haploid male germ cell development in the mouse. PLOS Genetics, 13.

LEHTI, M. S. & SIRONEN, A. 2016. Formation and function of the manchette and flagellum during spermatogenesis. Reproduction, 151__,__ R43-54.

MORENO, R. D. & SCHATTEN, G. 2000. Microtubule configurations and post-translational alpha-tubulin modifications during mammalian spermatogenesis. Cell Motil Cytoskeleton, 46__,__ 235-46.

SADATE-NGATCHOU, P. I., PAYNE, C. J., DEARTH, A. T. & BRAUN, R. E. 2008. Cre recombinase activity specific to postnatal, premeiotic male germ cells in transgenic mice. Genesis, 46__,__ 738-42.

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Referee #3

Evidence, reproducibility and clarity

In this study Stathatos et al looked at the function of epsilon tubulin (tube1), specifically in male germ cells. Previous work showed that tube1 is an important member of the tubulin family but its function is more enigmatic compared to alpha, beta and gamma tubulin. The authors produced a mouse KO line of tube1 and the data presented in this manuscript concerns the effects on spermatogenesis. They found that tube1 is essential for multiple microtubule dependent functions, including meiosis, nuclear shaping and sperm motility.

The experimental part is of the highest quality and the manuscript is very well written. My only reservation with the manuscript is concerning the model proposed for manchette migration in the Discussion section (Figure 6). I find the proposed model highly speculative and pre-mature, not supported enough by data, as even admitted by the authors (lines 415-427). Having it as a figure and concluding remark gives it too match weight, my suggestion would be to remove figure 6 and tone down the discussion. Minor points, a substantial percentage of sperm produced had a normal head shape in the KO (Figure 1I), which undermine the function of tube1 in nuclear shaping, the author should address this point in their manuscript. It is also curious whether there are phenotype in other tissues, can the authors comment on that?

Significance

The observations reported are novel and will be highly valuable specifically for the sperm biology field but also very interesting to the microtubule field in general.

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Referee #2

Evidence, reproducibility and clarity

The paper "Epsilon tubulin is an essential determinant of microtubule-based structures in male germ cells" provides the first insight into the essential function of Epsilon tubulin. TUBE1 (epsilon tubulin) is a non-canonical tubulin localized at the pericentriolar material of somatic and germ cell centrosome. TUBE1 has been primarily studied in unicellular organisms and cell lines, and multiple studies have shown its role in ciliogenesis and flagellum formation. However, its role in mammals, specifically in fertility, is unknown. Here, Stathatos et al address the critical question of whether TUBE1 plays a role in mammalian spermatogenesis and fertility. The authors show by germline inactivation of TUBE1 that the mice lacking TUBE1 are sterile, defective in meiosis, form abnormal manchette, and sperms are nonmotile. The authors further correlate that the TUBE1 functions together with KATNAL-1, KATNAL-1, and KATNB1, the microtubule severing protein. As little is known about the role of non-canonical tubulin like TUBE1 in fertility, this manuscript addresses a significant knowledge gap and generates an exciting hypothesis that TUBE1 regulates the KATNAL1-KATNB1 and KATNAL2-KATNB1 dynamic at manchette microtubules and perinuclear ring to control the manchette microtubule severing and migration.

Overall, the paper suggests that Epsilon tubulin is essential for multiple complex microtubule arrays, including the meiotic spindle, axoneme, and manchette; however, in the absence of Epsilon tubulin localization data, it is unclear which microtubule array is affected directly and which indirectly (e.g., is the axoneme defect is due to Epsilon tubulin in the axoneme or centriole?). In particular, it is interesting that in mice sperm, Epsilon tubulin is dispensable for centriole-mediated axoneme formation, its primary function in single-cell organisms (can this be due to compensation by the other tubulin isoforms?). Once the concerns below are resolved, the paper will be significant for the cytoskeleton and reproductive research fields.

Major comment

• Considering the suggested non-canonical function of Epsilon tubulin outside the centriole in mice sperm, it is critical to know the localization of the protein in spermatocytes during meiosis and spermatids during differentiation.
• Localization of Epsilon tubulin is needed to distinguish between mutant sperm cells and those that are not Epsilon tubulin mutants in the Tube1GCKO/GCKO mice. E.g., are the 28.07% of Tube1GCKO/GCKO tubules that showed a Sertoli cell only (SCO) phenotype the one where all the cells are mutants?

Minor comment

• It will help if the introduction summarizes the knowledge on Epsilon tubulin in spermatogenesis with emesis on its localization and the method used to find the localization.
• The generated conditional germ cell-specific mutants are demonstrated by mRNA expression spermatocytes. It would help if DNA sequencing, western, and Immunohistochemical staining were used to show the gene and protein are affected.
• How many independent mutant animals were studied, and what was the elfishness of generating mutants with a complete mutant testis? From Fig s1c, it appears all mutants generated were total mutations in almost all cells - is this correct?
• Add a definition to "ZED-tubulins."
• "Suggesting a core TUBE1 function that can be supplemented by either z-tubulin or TUBD1." Can you test what happens to mice Z and D tubulin isoforms in the mutant? Did their level increase in the centrioles? This is informative since there is no clear centriolar phenotype (other than centriole number that may be due to cell division failure) in mice spermatogenesis and the paper's central hypothesis in the introduction.
• From the paper, it is unclear if Epsilon tubulin is dispensable for centriole function only in sperm cells or if the same is true in mice somatic cells in vivo.
• Fig. S1 and other figures: "n {greater than or equal to} 3 samples/genotype" - this is unclear - please indicate the number of independent animals tested.
• "suppressing supernumerary centriole formation" is this due to access centriole formation or failed mitosis?
• The KATNAL1, KATNAL2, and KATNB1 staining in Fig 5 show multiple foci in the nucleus. Are these foci-specific staining or nonspecific? It is surprising to see such a large complex.
• How the staging of spermatids was performed needs to be explained in the method.
• The authors looked at the Metaphase stage cells to assess meiosis. It would be more interesting to look at the meiosis prophase I. Since the Stra8 acts very early leptotene stage, it would be interesting to see if meiosis is defective from the very beginning. Also, some suggest that the manchette is nucleated at the pachytene stage. Is the manchette defective from the very early stage of nucleation?
• Is the acetylation of manchette microtubules affected in the absence of TUBE1?

Significance

Overall, the paper suggests that Epsilon tubulin is essential for multiple complex microtubule arrays, including the meiotic spindle, axoneme, and manchette; however, in the absence of Epsilon tubulin localization data, it is unclear which microtubule array is affected directly and which indirectly (e.g., is the axoneme defect is due to Epsilon tubulin in the axoneme or centriole?). In particular, it is interesting that in mice sperm, Epsilon tubulin is dispensable for centriole-mediated axoneme formation, its primary function in single-cell organisms (can this be due to compensation by the other tubulin isoforms?). Once the concerns are resolved, the paper will be significant for the cytoskeleton and reproductive research fields.

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Referee #1

Evidence, reproducibility and clarity

The ZED (zeta-, epsilon-, and delta-) tubulins are important, yet understudied, members of the tubulin superfamily. Here, Stathatos et al. build upon previously published work and leverage their expertise to uncover the roles of epsilon-tubulin in mouse male germ cells. The authors create a germ cell-specific Tube1 knockout mouse, using Stra8-Cre, which is active in spermatogonia before the meiotic divisions. The authors report that knockout of Tube1 results in a range of defects during spermatogenesis, including: 1) a loss of male germ cells 2) sperm motility defects 3) abnormally shaped sperm heads 4) abnormal meiotic spindle morphology and abnormal centrosome numbers 5) some defects in sperm axoneme ultrastructure, 6) disrupted manchette migration 7) increased levels of katanin subunits at the manchette. Most of the experiments are convincing and well done, and based on this work, the authors propose a novel model for regulation of the manchette. I believe this work is of interest and should be published with revisions addressing the following major and minor comments.

Major comment:

1. A major claim of the paper is that epsilon-tubulin plays a different role within mammalian germ cells (abstract, line 22; p9, lines 167-168; p15 lines 315-316), because the Tube1GCKO/GCKO mice can form some sperm with relatively normal ciliary ultrastructure, whereas ciliates lacking epsilon-tubulin fail to form cilia. However, it's unclear whether the centrioles that templated these normal cilia were formed before or after epsilon-tubulin loss. Given that centrioles are inherited from one generation to the next, it's possible that the few normal cilia may be templated by relatively normal parental centrioles. These parental centrioles would have been present in spermatogonia prior to Cre expression/epsilon-tubulin deletion, and inherited by a fraction of sperm after the mitotic and meiotic divisions, resulting in sperm with normal ciliary ultrastructure. Other spermatocytes may have inherited centrioles formed in the absence of epsilon-tubulin, resulting in aberrant centrioles similar to those reported in human somatic cells, but these would not form any sperm flagella due to a loss of cell viability, as has been reported for acentriolar cells in a p53+ background. Underscoring this point, Chlamydomonas and human somatic mutant cells constitutively lack epsilon-tubulin. In these systems, the parental centrioles were diluted from the population over many cell divisions, and phenotypic analysis would only include the centrioles that formed in the absence of epsilon-tubulin. To make their major claim, the authors need to demonstrate that the basal bodies of sperm flagella with normal ultrastructure were formed in the absence of epsilon-tubulin, and were not normal parental centrioles. Given the difficulty of this experiment, the authors may instead choose to remove their claim that epsilon-tubulin plays a different role within mammalian germ cells.

Minor comments:

1. The authors claim that because the TUBE1 knockout mouse have abnormal centrosome numbers during meiosis, there is a role for TUBE1 in suppressing supernumerary centriole formation. While this is one possibility, it's also possible that abnormal centrosome numbers arose as a result of cell division defects, especially because binucleate cells are present in mutants. The authors should edit the text to state that abnormal centrosome numbers may arise from either supernumerary centriole formation (by the templated or de novo pathways) or from failure to complete cell division.

OPTIONAL: to test these possibilities, the authors may choose to 1) count the number of centrioles in meiosis with two different centriole markers 2) stain for markers of mature centrioles, such as Cep164, to determine the number of parental centrioles. 2. In figure 5, based on quantification of fluorescence intensity, the authors conclude that loss of epsilon-tubulin results in an increase in the levels of KATNAL1, KATNAL2, and KATNB1. Given the inherent variability in immunofluorescence staining, the authors should at a minimum normalize their intensity measurements to those of an unrelated control protein stained in the same cell (ex: alpha-tubulin). It would be more convincing to quantify the levels of these proteins by Western blot (again, normalized to a control protein or to total cellular protein), which should be feasible given that the authors can isolate elongating spermatids.

Significance

The strengths of this study lie in the careful phenotypic analysis of loss of epsilon-tubulin, which is well-done and very thorough. The limitations of the study are in interpretation of the results, specifically as relates to centriole formation, but can be addressed as indicated above. This work will be of interest to cell and developmental biologists, especially those interested in centrosomes, cilia, and spermatogenesis.

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Reply to the reviewers

Reviewer 1 major comments:

The authors show one configuration of the E1-E2 heterodimer in Figure 4d. As shown, the E1 protein is exterior to the E2 protein and would suggest E1 is on the surface on the spike complex and virus surface. However, another configuration of the glycoproteins has E2 on the exterior of E1 and also on the exterior of the virus. The latter conformation is what has been observed in cryoEM studies of alphaviruses. The first configuration represents the E1-E2 between the three heterodimers which are important for spike assembly. The reason the orientation of the E2-E1 dimer is important is the authors speculate on the importance of the 6 CHIK residues not found in ONNV based on the structure, but the structural interpretation is, in my opinion, not correct.

We thank reviewer 1 for pointing out the correct E2-E1 heterodimer configuration. To address this, we corrected the position of E2 and E1 in Figure 4 based on previous cryoEM study1, keeping E2 always on the exterior in the E2-E1 heterodimer. We also replaced the Indian Ocean Lineage (IOL) E2-E1 structure1 in the original Figure 4 with the CHIKV 181/clone 25 structure which was recently analyzed by Katherine Basore et al.2. In a single E2-E1 heterodimer, all six unique CHIKV positive selection sites are located on the outside of the structure after correcting the configuration. In addition, we investigated two of the unique CHIKV positively selected sites that are important for virion production, E2-V135 (V460 in the original manuscript version) and E1-V220 (V1029 in the original manuscript version), in trimerized structure of E2-E1 heterodimers. We found that the E2-V135 and E1-V220 residues in one heterodimer are facing E2 of the neighboring heterodimer on either side. Interestingly, while V135 is embedded between the E2 proteins of two different heterodimers, E1-V220 is partially embedded by E1 and the neighboring E2 and partially exposed to the outside. This suggests that even though both E2-V135 and E1-V220 might be crucial for CHIKV E2-E1 trimerization, E1-V220 provides an additional docking site for host factor interactions. We thank review 1 again for this important comment leading to these new findings. We have updated Figure 4F-4G and the corresponding result section (lines 201-209) in this partially revised manuscript.

1. Validation of E1 interaction with SPSC3 and eIF3k needs to be stronger. Some concerns/questions are listed below. A myc tag was inserted between E3 and E2. How efficiently does furin cleave E3 from E2 in this virus and how are viral titers of the myc-tagged virus compared to the non-tagged virus? I ask because is the IP looking at what is being pulled down by E2 or E3-myc-E2 that could be part of the spike polyprotein? The authors found E2 interacts with E3, E1 and a list of other host proteins. These results suggest several interactions including E2-host factor, E2-E1, E2-E3, E2-E1-host factor, E2-E3-E1, E2-E3-host factor. In figure 6d, and the subsequent conclusions, the authors suggest E1 is interacting with the host factor and do not see E2 alone and very low amounts of E3-E2-6K-E1. based on how the IP was performed I am not sure how an interaction between E1 and SPCS3 alone, without E2, would be detected. I would also like to see a reciprocal pull down using E1 and also E2 to see if these host factors are pulled down.

We thank the reviewer for these concerns. Given the low viral protein expression in macrophages (Figure 1A), we need an efficient system to enrich for large amounts of CHIKV glycoproteins for identifying host interactors through mass spectrometry. Adding tag/reporter proteins, such as mCherry, between E3 and E2 have been used to label alphavirus glycoproteins in previous study2, which is why we chose to use this myc tag labeling strategy coupled with myc Ab-conjugated agarose beads for AP-MS. However, like reviewer 1 speculated, inserting myc tag between E3 and E2 does attenuate CHIKV infectivity according to the reduced supernatant viral titers of 293T cells transfected with CHIKV/myc-E2 genomic RNA in comparison to those of cells transfected with unmodified CHIKV vaccine strain 181/clone 25 genomic RNA (shown in revision plan). Despite the attenuation, CHIKV/myc-E2 harvested from transfected 293T cells still reaches a titer over 108 pfu/ml, which allowed us to identify interactors by AP-MS.

We further analyzed the cleavage efficiency of glycoproteins by comparing the expression levels of E3-E2-6K -E1, E3-E2 (p62), E2, and E3 in 293T cells transfected with unmodified CHIKV or CHIKV/myc-E2 genomic RNA (result shown in revision plan). We didn’t detect any uncleaved forms of glycoproteins in cells transfected with either unmodified CHIKV or CHIKV/myc-E2 RNA when we probed with E2 antibody. However, probing with E3 antibody prior to longer exposure of the immunoblot showed higher E3-E2-6k-E1 and E3-E2 (p62) levels in cells transfected with CHIKV/myc-E2 RNA, suggesting that both mature E2 and E2-containing precursor polyproteins are available to be pulled down. Overall, the expression levels of mature E2 detected by E2 antibody are similar.

We thank reviewer 1 for providing a thorough dissection of all the possible interactions between the identified host factors and cleaved/uncleaved glycoproteins. This is a very interesting question. As reviewer 1 mentioned that E1 usually appears with E2 or E3-E2 in heterodimer forms, we were also surprised to find that E2 does not interact with either of the two host factors. To address this, we plan to conjugate E2 and E1 to protein A/G beads, respectively, for a reciprocal pulldown to validate CHIKV glycoprotein interactions with SPCS3 and eIF3k. Results from this experiment will be included in the fully revised manuscript.

1. If CHIK E1 is interacting with the host factors and that is antagonizing the antiviral response of SPSC3 (as one example), then what do pull downs using ONNV structural proteins look like? One would expect reduced interactions because the different amino acid causes a different E2-E1 dimer or attenuates the E1-host factor binding site.

We thank Reviewer 1 for this insightful suggestion. We agree that it would be informative to examine the interactions between ONNV glycoproteins and identified host factors (SPCS3 and eIF3k). Unfortunately, there is no commercial ONNV glycoprotein antibody available making this experiment unfeasible. Interestingly, we did observe reduced interactions between the host factors SPCS3 and eIF3k and the CHIKV E1-V220I mutant (V1029I in original manuscript version) where the positively selected site in E1 was mutated to the homologous ONNV residue (please refer to our response to Reviewer 3’s major comment #1). This result suggests that the ONNV glycoproteins likely have an attenuated E1-host factor binding site as the reviewer speculated.We have included this as Figure 7A in partially revised manuscript.

1. E1 and E2 are thought to interact during polyprotein translation and the initial dimer forms in the ER. If E1 is interacting with SPSC3 in the ER, is E2 also present? Or is a population of E1 not interacting with E2 in order to inhibit SPSC3? I would love a model of how the authors see all these factors coming together for this new role of E1.

We thank Reviewer 1 for proposing this interesting hypothesis. Given the unexpected absence of E2 in our validation of host factor-E1 pulldown, we speculate that a group of free E1 proteins with distinct function is interfering with host factors in the ER, which is a model worth further investigation and discussion. A great example of this is the alphavirus nonstructural protein 3 (nsP3) that plays essential roles in RNA replication, although depending on the alphavirus not all of the nsP3 in the cell colocalizes with dsRNA, suggesting there is a separate distinct pool of nsP3 outside of active viral replication complex that interacts with host factors in these observed larger cytoplasmic aggregates3. To address this, we plan to use laser confocal microscopy to observe the interactions between host factors (SPCS3, eIF3k), and CHIKV E2 and E1. We will include this result as well as our proposed model in the fully revised manuscript.

Reviewer 1 minor comments:

1. In Figure 1c, (-) RNA is shown but in the rest of the figures (+) RNA is shown. Show both or select one. I do find it interesting the (-) RNA levels are similar over time, even at 4 hours post transfection (early time). Related to this, ONNV has higher levels of (-) RNA but what is known about structural protein levels in ONNV and CHIK in macrophages? Are there comparable levels of CP and GP being produced?

We thank Reviewer 1 for this comment. The (-) RNA is synthesized before the synthesis of subgenomic mRNA and therefore can reflect more accurately early viral replication and nonstructural protein functions. This is the reason why we consider the (-) RNA levels evaluated by specific nsP1 TaqMan probes to be more appropriate for determining early stage differences between ONNV and CHIKV replication in Figure 1 as the goal of that figure is to define the steps in CHIKV life cycle that are more efficient than those of ONNV in THP-1 derived macrophages. On the other hand, the (+) RNA evaluated by E1 primers that we used in the later figures monitors viral RNA synthesis over time in the reflection of genomic (+) RNA and subgenomic mRNA transcribed from (-) RNA templates. Similar levels of (+) RNA and contrasting virion titers really point the difference to the later stages of subgenomic mRNA translation, viral glycoprotein secretion, and assembly.

We have generated ONNV/myc-E2 reporter virus and assessed viral glycoprotein expression through flow cytometry using a FITC -conjugated anti-myc antibody in the THP-1 derived macrophages transfected with CHIKV/myc-E2 and ONNV/myc-E2 (shown in revision plan). The results show that the expression of ONNV glycoproteins is more inhibited than that of CHIKV glycoproteins, though both of their expression levels in macrophages seem to be suppressed. Since there is no commercial ONNV antibody available, we were unable to compare capsid expression levels between the two viruses. Overall, differences in the myc-tagged glycoprotein expression levels of the two viruses reveals ONNV defect in either structural protein translation or glycoprotein maturation .

1. Figure 2e and figure 3 have ONNV has the first bar followed by CHIK. In figure 1 and 2b, CHIK is first and then ONNV. helps the reader to have the controls in the same order.

We thank Reviewer 1 for this suggestion. We have changed the order of ONNV and CHIKV bars in figure 2E and figure3 so the CHIKV bar consistently comes first in all the figures.

1. Line 143-145 the authors discuss that when ONNV is the backbone and CHIK proteins are inserted the infection is more attenuated because of the E2 and E1 are from CHIK and ONNV, not the same virus (could also be E2-CP interactions are disrupted). However the chimeras made with the CHIK backbone (in Figure 2) have a mismatch between E2 and E1 as well.

We thank Reviewer 1 for this informative comment. We agree that the incompatible E2-E1 heterodimer formation may not be the only reason that causes attenuation of ONNV/CHIKV E1 and ONNV/CHIKV E2. There may be multiple factors contributing to the fitness of the chimeras, which requires more in-depth mechanistic investigations and is out of the scope of this study. We have now removed the explanation “potentially due to incompatible heterodimer formation between ONNV E2 and CHIKV E1” in line 144.

1. When discussing the residues that were found in the FEL and MEME analysis, the authors start the amino acid numbering from CP and continue along the polyprotein. Usually when discussing amino acids in the structural proteins, each protein starts at amino acid 1. So V460 would be E2-V135. It would also be useful to know what the residues in ONNV were at these positions to see if amino acids changed in charge, size, bond forming potential, etc. Showing these residues in the E2-E1 conformation found in the virion would also allow one to find adjacent residues that could explain differences in spike assembly and potentially where/how E1 is binding to a host protein.

We thank Reviewer 1 for this comment. We revised the amino acid numbers in the manuscript to start from the beginning of each structural protein. To look more into these residues in ONNV, we aligned CHIKV and ONNV from different lineages and compared the 6 positively selected sites (refer to our response to Reviewer 1’s minor comment #5). We found that E2-135 and E1-220 which are essential for CHIKV production are valines in all the aligned CHIKV strains. For the aligned ONNV strains, E2-135 are all leucines and E1-220 are all isoleucines. While valine, leucine and isoleucine are all amino acids with hydrophobic side chains, valine has the shortest side chain. The length of the side chains may lead to different hydrophobic properties that affect protein folding, which warrants further structural analysis.

1. How effective is a non-attenuated CHIK strain in infecting macrophages? Could you make a SINV-La Reunion chimeric virus (which is BSL2) to see if a higher percentage of macrophages are infected and is this potentially contributing to the increased pathogenesis of La Reunion? Also how different is 181/25 with a pathogenic strain in the E2 and E1 residues? and compared to ONNV?

We thank Reviewer 1 for this question, which is also raised by Reviewer 2. In order to address this question, we plan to use the virulent CHIKV La Reunion strain to study the infection of THP-1 derived macrophages with non-attenuated CHIKV in BSL-3. We are getting trained in the BSL-3 facility and will soon be certified.

We thank Reviewer 1 for this insightful suggestion on investigating the conservation of these positively selected sites in different strains. We have aligned the sequences of ONNV and CHIKV strains from different lineages, including CHIKV vaccine strain 181/clone 25 and Thai strain AF15561 (the parental strain of CHIKV 181/clone 25) (alignment shown in revision plan). We found that the two positively selected sites with negative effects on virion production, E2-135 and E1-220 (sites 460 and 1029 in original manuscript version), are very conserved in either CHIKV or ONNV strains. CHIKV E2-135 is always valine (V) regardless of the lineages, while ONNV E2-135 is always leucine (L). CHIKV E1-220 is always V, while ONNV E1-220 is always isoleucine (I).

We also analyzed the amino acid heterogeneity of E2-135 and E1-220 in 397 CHIKV patient sequences from NCBI Virus database. Most of the amino acids at these 2 sites are V. The counts of each amino acid at E2-135 and E1-220 is summarized in table below. This result suggests that valine residues at E2-135 and E1-220 are crucial for CHIKV fitness and strongly selected during viral evolution. The sequence alignment and table will be included and discussed in the fully revised manuscript .

E2-135

E1-220

Valine (V)

394

392

Alanine (A)

1

3

Methionine (M)

1

0

Glutamic acid (E)

0

1

Glycine (G)

1

0

Isoleucine (I)

0

1

1. When describing the last results section, "CHIKV E1 binding proteins exhibit potent anit-CHIV activities" the authors use macrophages. In the rest of the text they consistently use THP-1 macrophages or human primary monocyte derived macrophages. The details of the cell type are extremely useful to the reader and having those in the last results section would be great.

We thank Reviewer 1 for pointing out the importance of cell type clarification in the last results section. We now consistently use “THP-1 derived macrophages” instead of “macrophages” in this section.

1. The paper is well-written. There is a slight disconnect as the authors go from discussing results in Figure 4 to Figure 5.

We thank Reviewer 1 for the comment regarding the disconnection of the last two figures in this paper which is also shared by the other reviewers. We have taken 3 approaches to address this comment: 1) We performed a pulldown of the host factors (SPCS3, eIF3k) identified in Figure 5 with CHIKV positively selected mutants examined in Figure 4 with deficient virion production. The result is presented in our response to Reviewer 3’ s major comment #1, suggesting that the positively selected site in E1 is essential for CHIKV glycoprotein interaction with host factors. 2) To complement our first experiment, we will also determine structural protein expression and processing of parental and E1 mutant CHIKV in eIF3k CRISPR knockout 293T cells. 3) Finally, we plan to perform CORUM analysis to identify high confidence functional protein complexes using our 14 hits found in both mass spec experiments, which will provide mechanistic insights into how these identified cellular complexes and processes might modulate CHIKV infection.

Reviewer 2’s major comments

The authors elegantly demonstrate that CHIKV structural proteins confer an advantage over ONNV structural proteins in a step in the replication cycle downstream of virus RNA synthesis, possibly virion assembly. This point would be strengthened determining the particle-to-PFU ratio of the parental viruses and the chimeras . Presumably, the ratio would increase in the chimeras containing CHIKV structural proteins.

We thank Reviewer 2 for this comment. We agree that determining particle-to-PFU ratios of parental and chimeric viruses will strengthen this study. To obtain the particle-to-PFU ratio, we infected THP-1 derived macrophages with CHIKV, ONNV and chimeras containing CHIKV glycoproteins (Chimera I, and ONNV/CHIKV E2+E1) for 24 h. To quantify the secreted viral particles, we extracted viral RNA in the supernatant and detected (+) viral RNA through TaqMan assay with specific nsp1 probes. The released infectious virions were evaluated through plaque assay. The particle-to-PFU ratios are summarized in the table below. The results show that ONNV has the highest particle-to-PFU ratio (41398), suggesting defective ONNV genome encapsidated in particles leading to defective virion production. On the other hand, the particle-to-PFU ratio of CHIKV (747) is 55-fold lower than that of ONNV. Replacing E3-E2-6K-E1 of ONNV with CHIKV homologous proteins reduces the particle-to-PFU ratio by 8 fold to 4875. Replacing E2 and E1 of ONNV with the ones from CHIKV (ONNV/CHIKV E2+E1) reduces the particle-to-pfu ratio by 20 fold to 2017, suggesting that CHIKV glycoproteins enhance the infectivity of viral progenies produced by THP-1 derived macrophages. We have included the results in Figure 3D-3E in our partially revised manuscript and described in lines 149-158.

1. Additionally, the authors should consider performing virion assembly blocking assays with a small molecule inhibitor to determine if this abrogates the virus production advantage of CHIKV structural proteins within the ONNV backbone.

We thank Reviewer 2 for this insightful comment. As the secretory pathway is commonly important for alphavirus glycoprotein maturation and assembly, it will be informative to interrogate CHIKV glycoprotein trafficking and assembly through this pathway using specific inhibitors, such as dihydropyridine FLI-06 and golgicide A . Golgicide A is a reversible inhibitor of the cis-Golgi GBF1, which leads to rapid disassembly of the Golgi and trans-Golgi network (TGN)4. FLI-06 is a new inhibitor that interferes with cargo recruitment to ER-exit sites and disrupts Golgi without depolymerizing microtubules or interfering GBF15. We pretreated THP-1 derived macrophages with 10 uM FLI-06 or golgicide A for 30 mins prior to infection with CHIKV, ONNV, Chimera I, or ONNV/ CHIKV E2+E1. After 1 hour of virus adsorption in PBS with 1% FBS in the absence of the inhibitors, the cells were treated with the inhibitors at the same concentration (10uM) in complete medium for 24 h. The plaque assay result shows that all the viruses are sensitive to secretory pathway inhibition, however, the production of viruses containing CHIKV glycoproteins is significantly more attenuated by FLI-06 and golgicide A. This suggests that CHIKV glycoproteins-mediated trafficking and assembly is more heavily dependent on the host secretory pathway . We will include this result in the fully revised manuscript.

1. Finally, the authors should perform competition experiments with the chimeric viruses and ONNV in macrophages to determine if the chimeras can outcompete the parental ONNV strain. Based on their data, the chimeric viruses should outcompete.

We thank Reviewer 2 for this inspiring suggestion. The competition experiment is an innovative and informative way to evaluate whether CHIKV glycoproteins confer a selective advantage on virion production in THP-1 derived macrophages. We plan to infect THP-1 derived macrophages with ONNV and ONNV/CHIKV E2+E1 and detect the viral glycoproteins secreted in the supernatant by western blot, although there is a possibility that this experiment might not work due to superinfection exclusion. Given that there is no commercial antibody of ONNV available, we need to use tagged viruses for this competition experiment. We constructed ONNV/CHIKV myc-E2+E1 that has a myc tag at the N-terminus of CHIKV E2, and ONNV/HA-E2 that has a HA tag at the N-terminus of ONNV E2. Our first attempt at concentrating the viral progenies released by THP-1 derived macrophages infected with the two tagged viruses has not been successful. We performed sucrose gradient ultracentrifugation of the supernatant viral particles but the myc and HA tags were not detected in the expected sucrose layer. Next, we plan to use myc-Ab and HA-Ab conjugated beads to pull down the supernatant viral particles to detect the ratio of ONNV/CHIKV myc-E2+E1 and ONNV/HA-E2 secreted by THP-1 derived macrophages. This will determine whether ONNV containing CHIKV glycoproteins can outcompete ONNV in co-infected cells due to increased viral fitness.

1. The authors use both primary macrophages and macrophage cell lines as their in vitro model system and make one of their major points (listed in the title) that the determinants they identified in the CHIKV structural proteins convert macrophages into dissemination vessels; however, they do not show: 1) an in vivo model that the CHIKV-ONNV chimeras disseminate more efficiently than the parental ONNV; and 2) that these chimeras generate virus more efficiently specifically in macrophages. It would be useful to show that ONNV and CHIKV have equivalent virion production in other cell lines and that the advantage conferred by CHIKV structural proteins in the ONNV backbone is specific to macrophages. The authors should also change their title to reflect that dissemination is not directly being addressed in their study; the implications of their in vitro experimentation in a mammalian host would be more appropriate for the discussion.

We acknowledge the limitations of the study, which include a lack of direct demonstration of in vivo dissemination. To address these concerns, we will include further discussion of our in vitro findings in the context of viral dissemination in mammalian hosts in the fully revised manuscript. We are also testing ONNV, CHIKV, Chimera I and ONNV/CHIKV E2+E1 infections in 293T cells to investigate whether the advantage conferred by CHIKV glycoproteins are macrophage specific.

We have also updated the title to accurately reflect the significance of this research: “Chikungunya virus glycoprotein targeting of host factors increases viral fitness in human macrophage”.

Reviewer 2’s optional comments

1. The authors use CHIKV-ONNV chimeras but it would be interesting to test other chimeras to determine if CHIKV structural proteins confer the same advantage in the backbone of other arthritogenic alphaviruses. The study would also be strengthened by using a pathogenic strain of CHIKV instead of the vaccine strain, as this is significantly attenuated in vivo.

We thank Reviewer 2 for this suggestion which is also suggested by Reviewer 1 in their minor comment #5. We plan to use virulent CHIKV La reunion strain and carry out infection experiments in BSL-3 to strengthen this study. We are getting trained in the BSL-3 facility and will be certified soon.

1. In Figure 4, the authors identify residues in the CHIKV structural proteins that appear to be under positive selection in human subjects and generate point mutants in these residues with the corresponding ONNV residues. They find that one mutation, V1029I located in E1, completely abolishes virion production in THP-1 macrophage cell lines. However, in their previous chimeric experiments, they find that neither CHIKV E1 or E2 was sufficient to increase virus production in the ONNV backbone. The authors should address this discrepancy, otherwise they should consider moving the data in their point mutation experiments to a supplementary figure. While worthy of reporting, especially given the patient data, these experiments do not buttress the points made in the previous figures.

We thank Reviewer 2 for this insightful comment. According to previous studies, E2 and E1 always interact with each other from the step of the formation of single heterodimer in the ER to heterodimer trimerization before viral particle assembly. Although the E1-V220 site (previously called V1029) on the exterior of a single E2-E1 heterodimer appears to not be engaged in the E2-E1 interaction E1-V220 is partially exposed and protruding into the groove formed by E1 and the E2 of neighboring heterodimer, accessible to host factors. As such, mutating CHIKV E1-V220 to the ONNV residue (E1-V220I) may not only disrupt E2-E1 trimerization but also interfere viral glycoprotein interaction with host factors(presented in our response to Reviewer 1’s major comment #1). Similarly, solely swapping E2 or E1 with CHIKV substitute in the ONNV backbone would also affect the interaction between neighboring E2 and E1 in trimerized spike, which may explain why neither ONNV/CHIKV E2 or ONNV/CHIKV E1 rescues virion production in THP-1 derived macrophages . We have included this in the partially revised discussion section lines __ __296-313.

1. The authors conclude their manuscript with an assessment of several host proteins, namely SPCS3 and eIF3k, that were identified by mass spectrometry and whose knockdown results in increased virion production. The authors speculate about the role of these proteins but do not provide any mechanistic detail on how they might be playing a role. It is unclear that the putative antiviral role of these proteins involves steps downstream of virus replication, especially given that the authors speculate translation might be affected by eIF3k which, if the case, RNA synthesis should also be expected to be affected.

We thank Reviewer 2 for this comment. We acknowledge that we have yet a full mechanistic understanding of how SPCS3 and eIF3k impact virion production. We plan to investigate their antiviral roles in our follow-up studies. For our partial revision, we have constructed several single eIF3k knockout (KO) clones of 293T cells. The eIF3k sgRNA we designed targets exon 3 which would eliminate expression of all 3 splice isoforms of eIF3k (KO schematic and sequence verification of CRISPR KO shown in revision plan). Unfortunately, we failed to obtain single clones of 293T cells with SPCS3 complete KO, consistent with a previous study by Rong Zhang et al6 that were unable to recover SPCS3 KO clones likely due to the importance of SPCS3 in cell survival. We infected an eIF3k KO clone (clone 9) with CHIKV vaccine strain 181/clone 25, ONNV SG650, and SINV Toto1101. Interestingly, we found that the antiviral activity of eIF3k is specific to CHIKV as CRISPR KO of eIF3k increases CHIKV production by 2.5 fold but not ONNV or SINV production (shown in revision plan). We have included this in the partially revised manuscript in__ line 272-282 (Figure 7B-7D).__

We presume that Reviewer 2’s inference of eIF3k’s potential effects on viral RNA synthesis is based on our speculation of its antiviral role in viral translation, which may affect viral nonstructural gene expression. We would like to clarify that eIF3k is not an initiation factor traditionally needed for cap-dependent translation. It is also not clear what translation process (nonstructural polyprotein translation from viral genomic RNA or structural polyprotein translation from viral subgenomic mRNA) involves eIF3k if it indeed affects viral protein expression. Notably, previous SINV studies imply that alphavirus structural polyprotein translation may employ unique mechanisms without the requirement of several crucial initiation factors4,5. It will be interesting to see whether eIF3k participates in viral subgenomic mRNA translation as that would affect viral glycoprotein expression leading to reduced virion production. We have now included additional discussion on eIF3k antiviral mechanisms in the partially revised manuscript in lines 345-353.

1. Overall, while the initial chimeric virus and domain swap approach is strong, the manuscript would benefit with a more thorough examination of virion assembly steps and a mechanistic link to virion production. Otherwise, the authors should revise the structure of their manuscript by de-emphasizing points about virion assembly and leave room for other mechanistic explanations of their chimeric data that more clearly link the host antiviral factor/E1 binding studies.

We thank the reviewer for these positive comments and suggestions. We have addressed this by further interrogating the production kinetics of CHIKV, ONNV, and the chimeras containing CHIKV glycoproteins through determining their particle-to-PFU ratios as well as treating infected cells with secretory pathway inhibitors (refer to our responses to Reviewer 2 major comments #1 and #2). We have also included additional discussion on eIF3k antiviral mechanisms specifically on how it may affect other steps of the viral life cycle in the partially revised manuscript in lines 345-353 (refer to our response to Reviewer 2 optional comment #3).

Reviewer 3’s critique comments

1. Overall, the manuscript is well written but in its current state it is more like two different stories because the effects of envelope proteins and list of interactors are not brought together in one story. A possible fix to this problem would be inclusion of ONNV and CHIKV containing env mutations that do and do not restore viral release from macrophages into the pulldown/association experiments shown in Figure 6.

We thank Reviewer 3 for the insightful suggestions to better connect the first (CHIKV determinants) and second (CHIKV glycoprotein interactors) parts of the manuscript. In response to the Reviewer’s comment, we tested the binding of SPCS3 and eIF3k to CHIKV E1 with E1-V220I (V1029I in original manuscript version) mutation (shown in revision plan) which was shown to abrogate virion production in THP-1 derived macrophages in Figure 4E. We transfected plasmids expressing 3XFLAG-tagged SPCS3/eIF3k or empty vector for 24 h followed by transfection with plasmids expressing either the parental CHIKV vaccine strain 181/clone 25 poly-glycoproteins (E3-myc-E2-6K-E1) or poly-glycoproteins with the E1-V220I mutation. Interestingly, we found that mutating CHIKV E1-V220 to the homologous ONNV residue reduces the binding to either SPCS3 or eIF3k. This result strongly suggests that the positively selected E1-V220 is located in the interaction interface between E1 and SPCS3/eIF3k, confirming the genetic conflict between E1 and these host factors to be one of the major drivers of CHIKV evolution observed at site E1-V220. We have included this result in partially revised manuscript in Figure 7A and in lines 265-271.

1. The other major issue is the lack of protein data for the viral mutants relative to WT ONNV and CHIKV and assessment of viral RNA in the supernatants to determine whether the block is release or an earlier event since viral RNA levels in the cell seems to be the same or at least normalized.

We thank Reviewer 3 for pointing out the insufficient clarification of the block leading to defective CHIKV mutant virion production. We previously detected E2 expression from 293T cells transfected with poly-glycoproteins (E3-myc-E2-6K-E1) containing E2-V135L (V460L in original manuscript version), E2-A164T (A489T in original manuscript version), E2-A246S (A571S in original manuscript version) and E1-V220I (V1029I in original manuscript version). We found that only E2-V135L mutation can lead to unexpected E2 cleavage (shown in revision plan) as we mentioned but not shown in the original manuscript. This explains why E2-V135L mutation attenuates infectious CHIKV production.

The E2 expression of E1-V220I appears to be not affected in 293T cells transfected with poly-glycoproteins with E1-V220I (shown in revision plan ). In addition, the E1-host factor binding result in our response to Reviewer 3’s major comment #1 showed that E1 with the positively selected site mutation V220I can also be successfully expressed in 293T cells after transfection with poly-glycoprotein. Based on these current data, E1-V220I mutation likely abrogates virion production without affecting glycoprotein expression.

Our previous result of the ONNV particle-to-PFU ratio reveals that ONNV RNA is released but encapsidated in defective particles causing its attenuation in infected macrophages. Thus, even though the glycoproteins of E1-V220I can be expressed, the diminished virion production of CHIKV E1-V220I can still be ascribed to 1) blocked viral particle release and 2) production of defective particles like ONNV. Given that it is not feasible to obtain particle-to-PFU ratio of E1-V220I mutant which fails to form plaques, Reviewer 3’s suggestion to assess the supernatant viral RNA will be a nice approach to address this question. To further address this concern, we plan to transfect THP-1 derived macrophages with CHIKV E1-V220I mutant RNA to detect the intracellular viral glycoprotein expression and supernatant viral RNA levels through western blot and TaqMan assay, respectively.

1. Lastly, knockdown experiments indicate an effect of things like OAS3 or other innate immune modulators. There are no controls to demonstrate that these are specific to CHIKV infection or if knockdown would assist growth of ONNV as well.

We also thank Reviewer 3 for the suggestion to check whether the identified host factors specifically target CHIKV or inhibit the infection of ONNV as well. We previously tried but were facing some issues. Since only a small fraction of macrophages can be infected with CHIKV and even a smaller fraction can be infected with ONNV (Figure 1A), it is hard to elucidate the roles of these identified host factors in ONNV infection by siRNA knockdown. We decided to take a more rigorous approach to investigate the antiviral specificity of identified host factors, especially understudied SPCS3 and eIF3k, to different alphaviruses by generating complete knockout 293T single cell clones. Despite the fact that we did not successfully generate SPCS3 complete KO, we obtained an eIF3k KO single cell clone and infected it with CHIKV, ONNV and SINV (refer to our response to Reviewer 2 optional comment #3). We found that eIF3k only has antiviral activity against CHIKV with almost no effects on ONNV or SINV infection. We have included this in our partially revised manuscript in line 272-282 (Figure 7B-7D).

Reviewer 3's minor comments:

Other points to consider:

1. The title does not fit the manuscript findings and should be modified.

We thank Reviewer 3 for this important comment, which was also brought up by Reviewer 2. We have now changed our title to “Chikungunya virus glycoprotein targeting of host factors increases viral fitness in human macrophage”, which more accurately reflects the significance of our research.

1. It is unclear why the authors show results for SINV and RRV in Figure 1. Either these should be removed or the viruses should be carried throughout the experiments described in the Figure. Better yet would be to add additional alphaviruses to this analysis to determine if there are additional viruses that act similarly to CHIKV.

We apologize for the confusion caused by including SINV and RRV results in Figure 1. We intended to show the superiority of CHIKV in infecting primary monocyte derived macrophages among arthritogenic alphaviruses, which we speculate may provide the molecular basis for macrophage-mediated CHIKV dissemination and disease. We would like to keep the SINV and RRV infection results in Figure 1 to highlight the relative susceptibility of macrophages to CHIKV. To echo the additional alphaviruses tested in Figure 1 and bring the story full circle, we included the result of SINV infection of eIF3k CRISPR KO 293T cells in Figure 7B-7D. These results uncover inhibitory activities of eIF3k that are specific to CHIKV.

1. Is the data presented in Figure 1A significant?

We thank Reviewer 3 for this question. We infected both THP-1 derived macrophages and primary monocyte derived macrophages with EGFP-expressing alphaviruses each in duplicates for two independent times. The general low expression of EGFP in all virus-infected groups refrains us from drawing conclusions based on statistically significant differences observed with MFI, hence we chose to show representative scatter plots in the original manuscript. To address Reviewers 3's question, we plotted the infected cell (EGFP+) based on the percentages of the experimental duplicates (shown in revision plan), and found CHIKV infection to be the most significantly different from that of the other alphaviruses in primary monocyte derived macrophage . The numbers above the bar charts are the mean percentages of EGFP+ cells.

1. The justification for inclusion of Figure 4A is lacking. It is unclear what this panel is supposed to be demonstrating.

This is an excellent suggestion as the host factors identified by AP-MS not only contain interactors of CHIKV mature E2 but also those of uncleaved E2-containing precursor polyproteins. We modified Figure 4A to reflect all E2/E2-containing poly-glycoproteins present in CHIKV-infected cells (shown in revision plan).

1. There is little justification for the candidates assessed in

We understand Reviewer 3’s concern. Due to the nature of mass spectrometry studies which predict protein-protein interactions rather than direct functional validation, we acknowledge that we may miss some host candidates that have anti- or pro-CHIKV activities. Although justification of hit selection from mass spectrometry datasets is more difficult than that from CRISPR KO screen datasets, we set up specific criteria to identify host protein candidates with the greatest potential to functionally interact with CHIKV glycoproteins. Most of the proteins we chose to validate (Figure 6a) were identified in both of our independent AP-MS experiments, which both pass through a P-value threshold of 0.05 and log2 fold change of 0.

1. Extended data Figure 3 is very difficult to read due to the small font size.

We apologize for the small font in Extended data Figure 3. We plan to replace Figure EV3 ( Extended data 3 in unrevised version) with a CORUM protein-protein interaction network that centers on the significant hits identified by both AP-MS experiments, but includes hits from either one of the two experiments in these functional protein complexes. The figure will be more concise and centralized, and the font will be bigger.

1. Just to be clear, the blots shown in Figure 6D are different from those depicted in Extended data Figure 4b, because some of them look very similar.

We thank Reviewer 3 for this question. In Figure 6D, we expressed CHIKV glycoproteins through transfecting CHIKV genomic RNA into 293T cells, while, in Figure 4B, we expressed CHIKV glycoproteins through transfecting poly-glycoprotein plasmid (pcDNA3.1-E3-myc-E2-6K-E1) into 293T cells, which are complementary approaches to express CHIKV glycoproteins to validate their interactions with identified host factors. We have now added schematics to illustrate the different experimental strategies above the figures in this partially revised manuscript (shown in revision plan).

References:

Voss, J. E. et al. Glycoprotein organization of Chikungunya virus particles revealed by X-ray crystallography. Nature 468, 709–712 (2010). Jose, J., Tang, J., Taylor, A. B., Baker, T. S. & Kuhn, R. J. Fluorescent Protein-Tagged Sindbis Virus E2 Glycoprotein Allows Single Particle Analysis of Virus Budding from Live Cells. Viruses 7, 6182–6199 (2015). Götte, B., Liu, L. & McInerney, G. M. The Enigmatic Alphavirus Non-Structural Protein 3 (nsP3) Revealing Its Secrets at Last. Viruses 10, 105 (2018). Saenz, J. B. et al. Golgicide A reveals essential roles for GBF1 in Golgi assembly and function. Nat. Chem. Biol. 5, 157–165 (2009). Krämer, A. et al. Small molecules intercept Notch signaling and the early secretory pathway. Nat. Chem. Biol.9, 731–738 (2013). Zhang, R. et al. A CRISPR screen defines a signal peptide processing pathway required by flaviviruses. Nature 535, 164–168 (2016).

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Referee #3

Evidence, reproducibility and clarity

Review: In this manuscript the authors generated macrophages derived from the THP-1 cell line or human peripheral blood mononuclear cells stimulated with MCSF and infected them with alphaviruses some containing GFP expression cassettes. In Figure 1, they demonstrate that CHIKV infected these cells more robustly than RRV, SINV or the related ONNV. The authors generated an extensive array of CHIKV/ONNV chimeras to identify the viral proteins that dictate release from infected macrophages and narrowed it down to the envelop proteins E1 and E2. Fine mapping identified a couple of single mutations that affected macrophage infection outcomes. The authors then shifted their approach to identifying env protein interactors using a myc-tag pulldown methods followed by mass spectrometry. The assay identified a number of proteins including those involved in vesicular transport and interferon pathways. siRNA knockdown experiments were performed to identify interactors and many of them were shown to improve virus output.

Critique: Overall, the manuscript is well written but in its current state it is more like two different stories because the effects of envelop proteins and list of interactors are not brought together in on one story. A possible fix to this problem would be inclusion of ONNV and CHIKV containing env mutations that do and do not restore viral release from macrophages into the pulldown/association experiments shown in Figure 6. The other major issue is the lack of protein data for the viral mutants relative to WT ONNV and CHIKV and assessment of viral RNA in the supernatants to determine whether the block is release or an earlier event since viral RNA levels in the cell seems to be the same or at least normalized. Lastly, knockdown experiments indicate an effect of things like OAS3 or other innate immune modulators. There are no controls to demonstrate that these are specific to CHIKV infection or if knockdown would assist growth of ONNV as well.

Other points to consider:

1. The title does not fit the manuscript findings and should be modified.
2. It is unclear why the authors show results for SINV and RRV in Figure 1. Either these should be removed or the viruses should be carried throughout the experiments described in the Figure. Better yet would be to add additional alphaviruses to this analysis to determine if there are additional viruses that act similarly to CHIKV.
3. Is the data presented in Figure 1A significant?
4. The justification for inclusion of Figure 4A is lacking. It is unclear what this panel is supposed to be demonstrating.
5. There is little justification for the candiates assessed in
6. Extended data Figure 3 is very difficult to read due to the small font size.
7. Just to be clear, the blots shown in Figure 6D are different from those depicted in Extended data Figure 4b, because some of them look very similar.

Significance

The study provides a fresh look at Alphavirus replication in macrophages. There are a number of issues that should be worked out that would enhance impact and interpretation of this study.

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Referee #2

Evidence, reproducibility and clarity

Summary: The authors utilize: 1) chimeric arthritogenic alphaviruses; evolution selection analyses with virus sequences isolated from human patients; and 3) mass spectrometry and proteomics to interrogate determinants of chikungunya virus (CHIKV) permissiveness in primary human macrophages and the human macrophage cell line, THP-1. The authors find that the vaccine strain, CHIKV 181/clone 25 replicates the most efficiently in primary monocyte-derived macrophages compared to other arthritogenic alphaviruses. Using o'nyong o'nyong (ONNV) as a comparison, the authors generate several chimeric viruses with CHIKV structural proteins and ONNV non-structural proteins (and vice versa) and perform a series of E1 and E2 domain swap experiments. They determine that both CHIKV structural proteins, E2 and E1, are necessary to confer efficient virus production over ONNV in the absence of a difference in viral RNA production. The authors also identify a specific residue in E1 that appears to be important for efficient virus production in THP-1 macrophage cell lines. Finally, using mass spectrometry, the authors identify two host proteins, SPCS3 and eIF3k, that bind to CHIKV E1 structural protein and appear to act as antiviral host factors.

Major comments: The authors elegantly demonstrate that CHIKV structural proteins confer an advantage over ONNV structural proteins in a step in the replication cycle downstream of virus RNA synthesis, possibly virion assembly. This point would be strengthened determining the particle-to-PFU ratio of the parental viruses and the chimeras. Presumably, the ratio would increase in the chimeras containing CHIKV structural proteins. Additionally, the authors should consider performing virion assembly blocking assays with a small molecule inhibitor to determine if this abrogates the virus production advantage of CHIKV structural proteins within the ONNV backbone. Finally, the authors should perform competition experiments with the chimeric viruses and ONNV in macrophages to determine if the chimeras can outcompete the parental ONNV strain. Based on their data, the chimeric viruses should outcompete. These experiments would likely take 3-4 weeks to complete.

The authors use both primary macrophages and macrophage cell lines as their in vitro model system and make one of their major points (listed in the title) that the determinants they identified in the CHIKV structural proteins convert macrophages into dissemination vessels; however, they do not show: 1) an in vivo model that the CHIKV-ONNV chimeras disseminate more efficiently than the parental ONNV; and 2) that these chimeras generate virus more efficiently specifically in macrophages. It would be useful to show that ONNV and CHIKV have equivalent virion production in other cell lines and that the advantage conferred by CHIKV structural proteins in the ONNV backbone is specific to macrophages. The authors should also change their title to reflect that dissemination is not directly being addressed in their study; the implications of their in vitro experimentation in a mammalian host would be more appropriate for the discussion.

OPTIONAL: The authors use CHIKV-ONNV chimeras but it would be interesting to test other chimeras to determine if CHIKV structural proteins confer the same advantage in the backbone of other arthritogenic alphaviruses. The study would also be strengthened by using a pathogenic strain of CHIKV instead of the vaccine strain, as this is significantly attenuated in vivo. In Figure 4, the authors identify residues in the CHIKV structural proteins that appear to be under positive selection in human subjects and generate point mutants in these residues with the corresponding ONNV residues. They find that one mutation, V1029I located in E1, completely abolishes virion production in THP-1 macrophage cell lines. However, in their previous chimeric experiments, they find that neither CHIKV E1 or E2 was sufficient to increase virus production in the ONNV backbone. The authors should address this discrepancy, otherwise they should consider moving the data in their point mutation experiments to a supplementary figure. While worthy of reporting, especially given the patient data, these experiments do not buttress the points made in the previous figures.

The authors conclude their manuscript with an assessment of several host proteins, namely SPCS3 and eIF3k, that were identified by mass spectrometry and whose knockdown results in increased virion production. The authors speculate about the role of these proteins but do not provide any mechanistic detail on how they might be playing a role. It is unclear that the putative antiviral role of these proteins involves steps downstream of virus replication, especially given that the authors speculate translation might be affected by eIF3k which, if the case, RNA synthesis should also be expected to be affected.

Overall, while the initial chimeric virus and domain swap approach is strong, the manuscript would benefit with a more thorough examination of virion assembly steps and a mechanistic link to virion production. Otherwise, the authors should revise the structure of their manuscript by de-emphasizing points about virion assembly and leave room for other mechanistic explanations of their chimeric data that more clearly link the host antiviral factor/E1 binding studies.

Minor comments: In Figure 3e, the line under "with CHIKV E1" should be moved over to include the E2-II+E1 virus.

Figure 5a, 5b, and 6a should be replaced with higher resolution images.

Significance

Strengths of the study include the initial chimeric virus and domain swap approach to determine factors that allow for the productive replication of chikungunya virus in macrophages compared to other arthritogenic alphaviruses. This approach yielded useful insights and could be adapted to other viruses. The study is limited, however, by the lack of mechanistic detail linking the antiviral host factors identified which bind to the E1 structural protein, and the advantage conferred by CHIKV structural proteins in the ONNV backbone. The study would be greatly improved by structural studies of the chimeric viruses that directly demonstrate more efficient virion production and that knockdown of the identified factors specifically affects virion production. This point could be addressed either through additional experimentation or tempering of the authors' conclusions about the mechanism by which CHIKV structural proteins provide an advantage over those of ONNV.

The study advances knowledge in the field on what might advantage different pathogenic alphaviruses and explain differences in disease pathology. Additionally, the authors devise a simple and clever strategy that could be applied across different alphaviruses and would be useful to test in vivo in future studies. This study would be useful to a virology-specific audience.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this work Yao et al. show CHIK is able to infect macrophages in contrast to other arthritogenic alphaviruses RRV, ONNV, and SINV. They use a series to chimeric viruses made with ONNV, the closest species to CHIK, and determine the E2-E1 proteins are important viral determinants which allow CHIK to replicate in machophages compared to ONNV. By comparing 397 CHIK sequences from infected patients, they identified 14 residues under pervasive and positive selection. Of these, 3 residues in E2 and 3 residues in E1 (amino acids) were different between CHIK and ONNV suggesting these residues contributed to the difference in macrophage tropism of CHIK compared to ONNV. The authors go on to determine what host factors the CHIK E2 protein is interacting with to presumably connect the viral and host determinants for CHIK infection in macrophages.

Major concerns:

1. The authors show one configuration of the E1-E2 heterodimer in Figure 4d. As shown, the E1 protein is exterior to the E2 protein and would suggest E1 is on the surface on the spike complex and virus surface. However, another configuration of the glycoproteins has E2 on the exterior of E1 and also on the exterior of the virus. The latter conformation is what has been observed in cryoEM studies of alphaviruses. The first configuation represents the E1-E2 between the three heterodimers which are important for spike assembly. The reason the orientation of the E2-E1 dimer is important is the authors speculate on the importance of the 6 CHIK residues not found in ONNV based on the structure, but the structural interpretation is, in my opinion, not correct.
2. Validation of E1 interaction with SPSC3 and eIF3k needs to be stronger. Some concerns/questions are listed below. A myc tag was inserted between E3 and E2. How efficeintly does furin cleave E3 from E2 in this virus and how are viral titers of the myc-tagged virus compared to the non-tagged virus? I ask because is the IP looking at what is being pulled down by E2 or E3-myc-E2 that could be part of the spike polyprotein? The authors found E2 interacts with E3, E1 and a list of other host proteins. These results suggest several interactions including E2-host factor, E2-E1, E2-E3, E2-E1-host factor, E2-E3-E1, E2-E3-host factor. In figure 6d, and the subsequent conclusions, the authors suggest E1 is interacting with the host facor and do not see E2 alone and very low amounts of E3-E2-6K-E1. based on how the IP was performed I am not sure how an interaction between E1 and SPCS3 alone, without E2, would be detected. I would also like to see a reciprocal pull down using E1 and also E2 to see if these host factors are pulled down.
3. If CHIK E1 is interacting with the host factors and that is antagonizing the antiviral response of SPSC3 (as one example), then what do pull downs using ONNV structural proteins look like? One would expect reduced interactions because the different amino acid causes a different E2-E1 dimer or attenuates the E1-host factor binding site.
4. E1 and E2 are thought to interact during polyprotein translation and the initial dimer forms in the ER. If E1 is interacting wht SPSC3 in the ER, is E2 also present? Or is a population of E1 not interacting with E2 in order to inhibit SPSC3? I would love a model of how the authors see all these factors coming together for this new role of E1.

Minor concerns:

1. In Figure 1c, (-) RNA is shown but in the rest of the figures (+) RNA is shown. Show both or select one. I do find it interesting the (-) RNA levels are similar over time, even at 4 hours post transfection (early time). Related to this, ONNV has higher levels of (-) RNA but what is known about structural protein levels in ONNV and CHIK in macrophages? Are there comparable levels of CP and GP being produced?
2. Figure 2e and figure 3 have ONNV has the first bar followed by CHIK. In figure 1 and 2b, CHIK is first and then ONNV. helps the reader to have the controls in the same order.
3. Line 143-145 the authors discuss that when ONNV is the backbone and CHIK proteins are inserted the infection is more attenuated because of the E2 and E1 are from CHIK and ONNV, not the same virus (could also be E2-CP interactions are disrupted). However the chimeras made witht he CHIK backbone (in Figure 2) have a mismatch between E2 and E1 as well.
4. When discussing the residues that were found in the FEL and MEME analysis, the authors start the amino acid numbering from CP and continue along the polyprotein. Usually when discussing amino acids in the structural proteins, each protein starts at amino acid 1. So V460 would be E2-V135. It would also be useful to know what the residues in ONNV were at these positions to see if amino acids changed in charge, size, bond forming potential, etc. Showing these residues in the E2-E1 conformation found in the virion would also allow one to find adjeacent residues that could explain differences in spike assembly and potentially where/how E1 is binding to a host protein.
5. How effective is a non-attenuated CHIK strain in infecting macrophages? Could you make a SINV-La Reunion chimeric virus (which is BSL2) to see if a higher percentage of macrophages are infected and is this potentially contributing to the increased pathogenesis of La Reunion? Also how different is 181/25 with a pathogenic strain in the E2 and E1 resdiues? and compared to ONNV?
6. When describing the last results section, "CHIK E1 binding proteins exhibit potent anit-CHIV activities" the authors use macrophages. In the rest of the text they consistently use THP-1 macrophages or human primary monocyte derived macrophages. The details of the cell type are extremely useful to the reader and having those in the last results section would be great.
7. The paper is well-written. There is a slight disconnect as the authors go from discussing results in Figure 4 to Figure 5.

Referees cross-commenting

I agree with R#2 that having some Particle:PFU data would add some data to determine why such differences in titers/infectivity.

I also see how this m/s could be split into two different m/s. One that focuses on the chimeric viruses and another that identifies the host factors important and goes in more depth with mechanism

Significance

Strengths:

The authors have tackeled an intriguing question: why do some alphaviruses infect macrophages and others do not. They have used a chimeric approached to very systematically identify the viral determinants E2 and E1 as being important in macrophage infection. Using AP-MS they identify host factors that interact with E2 (possibly E2 and E1, see comments above) but if their findings that E1 has a role in attenuating a host antiviral factor, this would be fantastic.

More and more examples of viral proteins having multiple roles during infection are in the literature. The idea that structural proteins also attenutate host antivirals is a developing field and vastly understudied. By fleshing out the results some more the authors might be onto something ery important in alphavirus virology.

Limitations:

The study has it is presented is limited in the validation of host factors and their interacting partners. I have many questions about the methodology, validation, and model from this last section.

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Reply to the reviewers

We thank the reviewers for their careful reading of the document and feedback which will help us to improve our manuscript. We will go through their comments one by one.

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

This study would be much convincing if additional line of eukaryotic cells can be used to demonstrate the GEF-GAP synergy tis important for cell physiology. In addition, it would be best to demonstrate the spatiotemporal interaction of GEF-GAP using high-resolution live cell imaging.

Response from the authors:

The reviewer requests additional in vivo data to support our in vitro findings:

(1) The reviewer requests in vivo data showing that GEF-GAP synergy is important for cell physiology. We believe that in order to show GEF-GAP synergy in vivo, Cdc42 cycling rates would need to be measured in vivo. For that single-molecule resolution is required – to track a single Cdc42 molecule and measure its GTPase cycling. We agree that such data would indeed be interesting, but are unaware of established techniques that would facilitate measurements of Cdc42 cycling rates in vivo.

(2) The reviewer requests in vivo data showing the spatiotemporal interaction of GEF-GAP. Cdc24 and Rga2 are shown to interact (direct or mediated by another protein) (McCusker et al. 2007, Breitkreutz et al. 2010, Chollet et al. 2020). Cdc24 and Rga2 share 11 binding partners (https://thebiogrid.org/31724/table/saccharomyces-cerevisiae-s288c/cdc24.html, https://thebiogrid.org/32438/table/saccharomyces-cerevisiae-s288c/rga2.html) and have been found at the polarity spot (Gao et al. 2011). Live cell imaging of fluorescently tagged Cdc24 and Rga2 will show that they exhibit some interaction, but not specify the role of the interaction nor if the interaction is direct or mediated by one of the shared binding partners. In order to show a direct interaction between Cdc24 and Rga2, one could consider (A) super-resolution imaging or (B) FRET experiments: For both fluorescently tagged Cdc24 and Rga2 cell lines would need to be constructed.

(A) Super-resolution imaging could show direct interaction between Cdc24 and Rga2, but even with the techniques available this would be on the limit. Further, it is usually done in fixed cells, and not in live cells (as requested from the reviewer).

(B) To show a direct interaction of Cdc24 and Rga2 using FRET, suitable protein constructs would need to be engineered. We believe that the main obstacle in showing direct binding of Cdc24 and Rga2 using FRET is to design the fluorophore linker. The linker would need to be designed in such a way that it is flexible enough to give a FRET signal even if the two large proteins bind on the opposite sites of the fluorophore, but also is stiff/short enough to not show binding if both proteins are in close proximity through binding to a common binding partner.

__We believe that an investigation of GEF GAP binding in vivo is beyond the scope of this study. Instead, we will further explore one possible mechanism underlying GEF GAP synergy - Cdc24 Rga2 binding - through conducting Size-Exclusion Chromatography Multi-Angle Light Scattering experiments with purified Cdc24 and Rga2 (alone and in combination). __

Reviewer #1 (Significance (Required)):

The revised study would provide first line evidence that GEF-GAP synergy to be general regulatory property in eukaryotic kingdom.

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

The study entitled, "The GEF Cdc24 and GAP Rga2 synergistically regulate Cdc42 GTPase cycling" by Tschirpke et al., uses an in vitro GTPase assay to examine the GTPase cycle of Cdc42 in combination with its GEF and GAP effectors. The authors find that the Cdc24 GEF activity scales non-linearly with its concentration and the GAP Rga2 has substantially weaker effect on stimulating Cdc42 GTPase activity. Not surprisingly, the combined addition of Cdc24 and Rga2 lead to a substantial increase in Cdc42 GTPase activity.

**Referees cross-commenting**

In Zheng, Y., Cerione, R., and Bender, A. (1994) J. Biol. Chem. 269: 2369-2372 (Fig. 3C), the authors show that Cdc24 combined with the GAP Bem3 lead to a large synergy in boosting Cdc42 GTPase activity.

Reviewer #2 (Significance (Required)):

There is very little new information in this manuscript. Previous studies (Rapali et al. 2017) have shown that the scaffold protein Bem1 enhances the GEF activity of Cdc24. It is expected that the reconstitution of a GEF and GAP protein promote the GTPase cycle and indeed Zheng et al. (1994) showed that that Cdc24 combined with the GAP Bem3 lead to a large synergy in boosting Cdc42 GTPase activity. Hence the only potentially interesting finding in this work is that, in solution Cdc24 activity scales non-linearly with its concentration. However as this GEF and Cdc42 are associated with the membrane, the relevance of solution studies are less clear and furthermore the mechanistic basis for the non-linearity is not explored in detail. Given the limited new information from this work, the findings are, in their current form, too preliminary.

Response from the authors:

__We appreciate the reviewer recognizing our work on the non-linear concentration-dependence of Cdc24’s activity. We disagree that this is the only new finding in our study: __

We explore the effect of Cdc24 and Rga2 on Cdc42’s entire GTPase cycle and show that Cdc24 and Rga2 synergistically upregulate Cdc42 cycling. So-far Cdc42 effectors were only characterized in isolation (with the exception of Cdc24-Bem1 (Rapali et al. 2017)) and through how they affect a specific GTPase cycle step. The regulation of single GTPase cycle steps through an effector yields mechanistic insight into this specific GTPase cycle step. However, it does not show how the effector affects overall GTPase cycling of Cdc42 – a process Cdc42 constantly undergoes in vivo. Our approach allows us to study synergistic effects between proteins affecting different GTPase cycle steps. Synergies are another regulatory layer of the polarity system, adding further complexity: Which polarity proteins exhibit synergy, to which extend? The assay employed here, which studies the entire GTPase cycle, enables studying the effect of any GTPase cycle regulator, alone and in combination with another regulator.

The reviewer states that the GEF GAP synergy is to be expected, as it was already shown in Zheng et al. 1994. In Fig. 3C Zheng et al. shows the time course of the GTPase activity of Cdc42 in presence of Cdc24, Bem3, and Cdc24 plus Bem3. Fig. 3C is the only data in which the combined effect of a GEF (Cdc24) and a GAP (Bem3) is investigated. The data indicates synergy, but is neither discussed as such in the text of the publication, nor analyzed quantitatively. Further, only one concentration of each effector (GEF/GAP) is used and the study uses a Bem3 peptide containing codons 751-1128 (30%) of the full-length BEM3 gene. Zheng et al. 1994 gives an early indication of GEF GAP synergy, but does not claim, discuss, or further investigate the synergy as such. In contrast, we use full-length Rga2 (not Bem3) as GAP, conduct several concentration-dependent assays, and analyze them quantitatively. We thank the reviewer for pointing out the pioneering character of Zheng et al.‘s study and will mention it more prominently in our report. However, we disagree that Zheng et al. sufficiently studied the GEF GAP interaction. To our awareness no theoretical studies include a GEF GAP synergy term, which we would expect if GEF GAP synergy is well-established in the field.

The reviewer criticizes the relevance of bulk in vitro studies (lacking membranes) of proteins that bind to membranes in vivo. We agree that the presence of a membrane can affect the protein’s property, and we can not exclude that membrane-binding could alter the magnitude of a GEF GAP synergy. However, we believe that membrane-binding does not impede the GEF GAP synergy altogether. If membrane binding would influence GTPase properties that strongly, other studies on Cdc42’s GTPase activity and GEF and GAP activity, that do not include a membrane, would be inconclusive as well (e.g. Zheng et al. 1993, Zheng et al. 1994, Zheng et al. 1995, Zhang et al. 1997, Zhang et al. 1998, Zhang et al. 1999, Zhang et al. 2000, Zhang et al. 2001, Smith et al. 2002, Rapali et al. 2017). Both studies mentioned by the reviewer (Zheng et al. 1994, Rapali et al. 2017) were also conducted without membranes present.

We believe that an inclusion of membrane-binding into reconstituted Cdc42 systems will enhance our understanding of Cdc42 and recognize it as a next step, which could be enabled by the assay used in our study.

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

This work reports a biochemical analysis of the effects of a recombinant yeast GEF (Cdc24) and GAP (Rga2) on Cdc42 GTPase cycling in vitro. The central conclusion is that the GEF and GAP act "synergistically", which occurs "due to proteins enhancing each other's effects". By this they appear to mean that the GEF enhances the GAP's activity and vice versa. I was not persuaded that this is correct, and was confused by many aspects of the approach and interpretation, as outlined below.

1. GEF and GAP are expected to accelerate GTPase cycle synergistically even with no effect on each other's activity:

The Cdc42 GTPase cycle is understood to occur via distinct steps (GDP release, GTP binding, and GTP hydrolysis): GDP release and GTP hydrolysis are intrinsically slow steps that are accelerated by GEFs (GDP release) and GAPs (GTP hydrolysis). This fundamental biochemistry was established in the 1990s using biochemical assays that measure each step independently. Here instead the authors use an assay that measures [GTP] decline in a mix with 5 uM starting GTP, 1 uM Cdc42, plus or minus some amount of GEF or GAP. They assume exponential decline of [GTP] with time, yielding a cycling "rate". If that is so, then one would expect that added GEF would accelerate only the first step, leaving a slow GTP hydrolysis step that limits the overall cycling rate, while added GAP would accelerate only the last step, leaving a slow GDP release step that limits the overall cycling rate. Adding both together would speed up both steps, and should therefore "synergistically" accelerate cycling. This would be expected based on previous work and does not imply that GEF or GAP are affecting each other's action (except trivially by providing substrate for the next reaction). If the authors wish to demonstrate that something more complex is indeed happening, they need to use assays that directly measure the sub-reaction of interest, as done by prior investigators.

Response from the authors:

The reviewer raises the point that we do not consider a simpler, rate-limiting model and that this rate-limiting model could explain our synergy between GAP and GEF in accelerating the GTPase cycle.

We very much welcome this consideration of the reviewer! We will add a clarification to our manuscript to explain why a rate-limiting model/interpretation does not match our data.

Intuitively, the rate-limiting model is appealing, as it permits interpretation of cycle rate increases in terms of individual biochemical steps. So, a consideration of this model is indeed relevant. However, as also noted by the reviewer in the next points, data from e.g., figure 3e are not compatible with a simple rate-limiting model with two steps (hydrolysis and nucleotide exchange). We will explain how the acceleration of the total rate by both GAP and GEF individually does not match the rate-limiting model, even if we assume maximal effects of adding GAPs and GEF to the cycle. For this purpose, we consider the rate-limiting model scenario where the sensitivity of the GTPase cycle to adding GAP/GEF is maximized, so the best case-scenario for the rate limiting step-model.

In the rate-limiting step model, we assume that we have a GTPase cycle in which at least one of the three GTPase cycle steps is rate-limiting: (A) GTP binding, (B) GTP hydrolysis, and (C) GDP release.

We assume that the addition of a GEF and GAP only accelerates GDP release and GTP hydrolysis respectively. Biochemically, all three steps in the GTPase cycle are expected to be relevant. However, here we will consider only the final two steps, as sensitivity to rate limitation by GAP/GEF is maximized when time spent in the GAP/GEF-independent step in the cycle (step A: GTP) is negligible (i.e. never rate-limiting). The two-step model thus consists of (1) a nucleotide exchange step (step C+A) which is dominated by GDP release (step C) and assumed to be accelerated exclusively by the GEF, and (2) a GTP hydrolysis step (step B) exclusively enhanced by the GAP.

In the rate limiting step model GEF-GAP synergy can appear if one of the conditions applies:

1. the addition of a GAP speeds up the GTP hydrolysis step so much that the hydrolysis step stops (or almost stops) being the rate-limiting step, or
2. the addition of a GEF speeds up the GDP release step so much that the release step stops (or almost stops) being the rate-limiting step. In these conditions, the acceleration of the GTPase cycle, accomplished by adding only a GAP or adding only a GEF, is interdependent. Therefore, we consider the possible acceleration of the GTPase cycle by GAP and GEF individually, and compare these to our observations to determine whether the rate-limiting step model can explain our data.

The GTPase cycle time Tc is thus composed of hydrolysis Th and nucleotide exchange time Te, and the rates r are connected through:

1/rc=1/rh + 1/re

If we compare the ratio of the rates with protein (GAP/GEF) added in the assay (index 1) with the basal rate without protein added (index 0), we obtain the cycle acceleration factor alpha:

alpha=rc1/rc0=(1/rh0 + 1/re0)/(1/rh1 + 1/re1)=(re0 + rh0)/(re0*rh0/rh1 + rh0*re0/re1)

Here, rc1 and rc0 are the total GTPase cycle rate with and without effector respectively, rh1 and rh0 are the GTP hydrolysis rate with and without effector respectively, and re1 and re0 are the nucleotide exchange rate with and without effectors respectively.

There is indeed an interdependence created between how much the GAP and GEF can both accelerate the total cycle, if the GAP and GEF are assumed to only accelerate GTP hydrolysis and nucleotide exchange respectively. E.g., how much the total GTPase cycle rate rc is accelerated by an increase in GTP hydrolysis rate rh depends on and can be limited by the current nucleotide exchange rate re. However, this interdependence is too strict to match the data in Figure 3e, as we will explain in the next paragraphs:

When we only add a GAP and the GAP accelerates only the GTP hydrolysis rate (re1=re0), then the maximal total GTPase cycle rate acceleration alphaGAP that the GAP can accomplish is when rh1>>rh0,re0:

alphaGAP=rc1/rc0=(1/rh0 +1/re0)/(1/rh1+1/re0)=(re0+rh0)/(re0*rh0/rh1+rh0)

~(re0+rh0)/rh0=1+ re0/rh0

We thus assume the GAP accelerates the cycle so much that the hydrolysis step is much faster than the exchange step, at which point the effect of adding more GAP would saturate. We note that we do not consider the GAP concentration regime where we see saturation, thus in reality the acceleration by the GAP is more restricted than predicted here.

Analogously, if the GEF accelerates only the nucleotide exchange rate (rh1=rh0), then the maximum GTPase cycle rate ratio will be when re1>>re0,rh0 , yielding acceleration factor alphaGEF :

alphaGEF= rc1/rc0=1+ rh0/re0

Again, note we assume the GEF accelerates the cycle so much that the exchange step is much faster than the hydrolysis step, at which point the effect of adding more GEF would saturate. We note that we do not observe the GEF concentration regime where we see saturation, thus in reality the acceleration by the GEF is more restricted than predicted here.

We see that the maximum gain in rates for GAP-only and GEF-only assays is limited by the same basal GTP hydrolysis and nucleotide exchange rates (rh0 and re0), leading to the following interdependence:

alphaGAP=1+ 1/(alphaGEF -1)=alphaGEF/(AlphaGEF -1)

In our GAP-only and GEF-only assays (Fig. 3e, Tab. 2), we see both a 2-fold and 100-fold increase in the total rate respectively. A 100-fold acceleration factor of the GEF would maximize the GAP acceleration factor to 1.01 (or alternatively, the 2-fold GAP acceleration would maximize the GEF acceleration to 2), which are both significantly lower than what we observe. So even though we made favorable assumptions for the rate-limiting model to maximize rate sensitivity to GAP/GEF, namely neglecting nucleotide binding and assuming GAP/GEF concentrations that saturate in their effects, we still cannot reproduce the acceleration factors in our GAP-only and GEF-only assays.

Moreover, a rate-limiting step model would also imply saturation effects as stated in the next point of the reviewer. While we observe saturation in total rate acceleration for certain GAP concentrations, we use GEF and GAP concentrations in the combined protein assays for which no saturation effects were observed. Absence of saturation in both cycle steps simultaneously is also not reconcilable with the rate-limiting step model, as will be further discussed in the next point of the reviewer.

In summary, this means that the rate-limiting model is not sufficient to explain our results: the GAP/GEF synergy we observe is not simply resulting from GEF and GAP independently lifting two different rate-limiting steps.

Model-based interpretation of the GTPase assay is poorly supported:

The assay employed measures overall GTP concentration with time. It is assumed (but not well documented-see below) that [GTP] declines exponentially, and that the rate constant for a particular condition can be fit by the sum of a series of terms that are linear or quadratic in the concentrations of Cdc42, GEF, and GAP. There is no theoretical derivation of this model from the elementary reactions, and the assumptions involved are not well articulated.

As discussed in point 1 above, one would expect that a GEF or GAP alone could only accelerate the cycle to a certain point, where the other (slow) reaction becomes rate limiting. But that does not appear to be true for their phenomenological model, where slow steps (small terms in the sum) will always be overwhelmed by fast steps. This is not the traditional understanding of how GTPases operate.

Response from the authors:

The reviewer expresses the concern that because we do not derive our coarse-grained model from elementary reactions, we miss important effects that can occur when adding GAP and GEFs, particularly saturation.

We understand the concern of the reviewer that if a rate-limiting step model is considered, saturation effects of GAP/GEF will limit the amount with which these effectors can speed up the total cycle. Our coarse-grained model indeed does not account for this saturation. However, as discussed in the previous point of the reviewer, we do not opt for the rate-limiting model interpretation, as the GAP and GEF effects are not compatible with the rate-limiting step model.

Secondly, we agree that for high enough concentrations of GEF and GAPs, we would experience a saturation in the effect of adding the effectors. We are aware of this possibility, and we verify that we are not in saturation regimes with our added proteins by checking the plots of the individual protein titrations (see Figure 3a-d). If we enter the saturation regime, we expect a negative second derivative in the rate as function of protein concentration (the curve shallows off). We do not see this for any protein except for Rga2 at some point, as discussed in our main text of the manuscript. However, for this protein we only use the data in the linear regime for further analysis. In short, we understand the concern of the author but we empirically check that we are not in the saturation regime.

Data that do not conform to expectation are not explained: Strangely, the data (as interpreted by the model assumptions) also appear inconsistent with the expectation of rate-limiting steps. GEF addition (alone) is said to accelerate cycling 100-fold, while GAP addition (alone) accelerates it 2-fold. But that would seem to imply that GDP release takes up >99% of the basal cycle (so accelerating that step alone reduces cycling time 100-fold), while GTP hydrolysis takes up >50% of the basal cycle (so accelerating that step alone reduces cycling time 2-fold). In the conventional understanding of GTPase cycles, these cannot both be be true (as the steps would then add to >100% of the basal cycle). There is no attempt to reconcile these findings with previous work.

Response from the authors:

The reviewer raises the point that our findings do not match the expectations of the rate-limiting model perspective.

We fully agree with the reviewer that our data is not compatible with the rate-limiting step model. The 100-fold and 2-fold gain of the total cycle rates for GEF-only and GAP-only assays are one of our arguments against the rate-limiting model view, as described in the first point of the reviewer. Also, our lack of saturation as described in the previous point of the reviewer provides another argument against using expectations based on rate-limiting steps to interpret our findings.

Lack of detailed timecourse data:

The decline in [GTP] with time is stated to be exponential, allowing extraction of an overall cycling "rate". But this claim is supported only weakly (S3 Fig. 1 uses only 3 timepoints, is not plotted on semi-log axis, and does not report fit to exponential vs other models) and only for the Cdc42-alone scenario: no data at all are presented to support exponential decline in reactions with GEF or GAP. Most assays seem to measure only a single timepoint, so extraction of a "rate" is very heavily influenced by the unsupported assumption of exponential decline. And if the decline is not exponential, it becomes extremely difficult to interpret what a single timepoint means.

Response from the authors:

The reviewer requests additional timeseries data with GEF and GAP to support the assumption of an exponential decline of GTP in the assay and requests to plot it on a semi-log axis.

We will add data for Cdc42 + Cdc24 and for Cdc42 + Rga2 with two to three time points, and plot it as requested on a semi-log axis.

Other issues with interpretation of the data:

(i) It is unclear why the authors chose to employ an assay that is much harder to interpret than the biochemical assays used by others. In biochemical studies, assays that report an output of multiple reactions are always harder to interpret than assays targeting a single reaction. As well-established assays are available for each individual step in GTPase cycles, any conclusions must be supported using such assays.

Response from the authors:

The reviewer wonders why an assay that investigates several GTPase steps at once was chosen over assays that investigate sub-steps of the GTPase cycle, given that these give more mechanistic insights.

We agree that assays investigating GTPase cycle substeps can give more mechanistic insights into these specific steps. However, they do not allow to study how proteins affecting different steps act together. We were interested in investigating the overall GTPase cycle of Cdc42 and a possible interplay of GEFs and GAPs. Cdc42 GTPase cycling was found to be a requirement for polarity establishment (Wedlich-Soldner et al. 2004) and Cdc42 GTPase cycling is physiologically relevant. Ultimately, we hope that in vitro results provide stepping stones towards understanding the complex and less controlled in vivo environment. The in vivo environment often entails the output of many reactions combined, so there is every incentive to study aggregated effects of a full cycle which are not necessarily the sum of individual outputs.

__We believe that both assay types – assays that investigate sub-steps and yield mechanistic details, and assays that investigate the entire cycle – are important and disagree that one assay type is superior to the other. Instead, we believe they complement each other. __

(ii) The reported basal (and GEF/GAP-accelerated) rates are very slow, perhaps due to poor folding of recombinant proteins. This raises the possibility that much of the Cdc42 is inactive. If so, then accelerated GTP hydrolysis could come from increasing the active fraction of Cdc42, rather than catalyzing a specific step.

Response from the authors:

The reviewer wonders whether the reported rates are slow due to poor folding of recombinant Cdc42. We used S. cerevisae Cdc42, for which it has been shown that it has a significantly lower basal GTPase activity than Cdc42 of other organisms (see Zhang et al. 1999). Many other studies on Cdc42 were conducted with human Cdc42, which has a significantly higher basal GTPase activity (Zhang et al. 1999). We assessed the activity of several recombinantly expressed Cdc42 constructs previously (Tschirpke et al. 2023). We there observed that most constructs had a similar GTPase activity, only some purification batches and constructs had a significantly reduced GTPase activity (which might be linked to poor folding). The Cdc42 construct used here shows a similar activity as the active Cdc42 constructs in Tschirpke et al. 2023, and we therefore believe that it exhibits proper folding. If recombinant Cdc42 folds poorly, we would expect greater variations between Cdc42 constructs and purification batches (caused by different levels of folding/ a different fraction of active Cdc42) than what we observed previously (see Tschirpke et al. 2023).

Tschirpke et al. 2023:

Tschirpke et al. A guide to the in vitro reconstitution of Cdc42 activity and its regulation (2023) BioRxiv. (https://doi.org/10.1101/2023.04.24.538075) (in submission at Current Protocols)

(iii) The GEF and GAP preparations include multiple partial degradation products and it is unclear whether the measured activities come from full-length proteins or more active fragments.

Response from the authors:

We agree with the reviewer that the Cdc24 and Rga2 preparations contain degradation products.

It would be more ideal if the protein purifications were entirely pure, but this is experimentally very difficult to achieve for the used proteins (which are large and partially unstructured, making them prone to partial degradation). Further, it is not uncommon to use protein preparations where some degradation products were present (e.g. Zheng et al. 1993, Zheng et al. 1994). Other studies did not show their purified preparations.

The vast majority of the Cdc24 preparation is the full-length protein. We therefore expect that the degradation fragments only contribute in a small extend to the overall protein behavior.

The Rga2 preparation contains a higher amount of degradation product, but only larger size protein fragments (> 60kDa), suggesting that the fragments contain at least and more than 1/3 of the full-length protein (the protein fragments are thus the size or larger than of the GAP peptides used previously). The fragments could in principle have a higher or lower activity. We account for fragments of no/lower activity by comparing our cycling rates to those of BSA/Casein, which has no specific effect on Cdc42. The cycling rate Rga2 is almost an order of magnitude greater than that of BSA/Casein, suggesting that the effect of the full-length protein dominates. We could only imagine that a Rga2 fragment has a higher GAP activity if the fragment consists mainly of the GAP domain and if in Rga2 the activity of the GAP domain is downregulated. Nevertheless, we will do an additional experiment using a purified GAP domain peptide to assess that if a GAP domain by itself has a higher GAP activity than our Rga2 preparation. Using that data, we will discuss possible implication of the GAP fragments in our manuscript.

(iv) Cdc42 cycling is also accelerated by BSA and casein, suggesting that there are poorly understood aspects of the assay and that GEF and GAP actions may (like BSA and casein) involve non-canonical effects on Cdc42. As GEF and GAP are expected to interact better with Cdc42 than BSA or casein, these effects could dominate the observed changes in GTP levels.

Response from the authors:

The reviewer raises the concern that the effects of the added effector proteins on the rates could be caused by non-canonical effects. We do not believe non-canonical effects play a relevant role in our assays. While BSA and casein accelerate the GTPase cycle in our assays, the GAP effect and GEF effect are orders of magnitude stronger.

(v) Cdc42-alone cycling assays are said to be reproducible. However, assays with added GEF/GAP/BSA/Casein yield rates that vary almost an order of magnitude between replicates. This poor reproducibility further reduces confidence in the findings.

Response from the authors:

The reviewer is concerned about the variations in Cdc42 effector rates.

__We disagree that the variations are concerning and believe to have accounted for them in our analysis: __The Cdc42 (Cdc42 alone) data is very reproducible (see Tschirpke et al. 2023). The GTPase assay is generally sensitive to small concentration changes and errors introduced through pipetting small volumes (as required for the assay). We believe that the small variation observed for Cdc42 alone is because Cdc42 has such a low basal rate and therefore the small concentration changes due to pipetting have a smaller effect. Once other effectors are added, especially highly GTPase stimulating ones as Cdc24, small concentration changes due to pipetting can lead to larger variations between assays (small variations in Cdc24 concentration lead to larger changes in remaining GTP due to Cdc24’s strong and non-linear effect on Cdc42). We conduct the assays multiple times to account for these variations. In our analysis we do not compare single rate numbers but the orders of magnitude of the rate, and report the variations present. Even given the present variations, the differences in effect sizes are still significant. We map and discuss assay variation in (Tschirpke et al. 2023), to which we refer to several times throughout the manuscript.

Tschirpke et al. 2023:

Tschirpke et al. A guide to the in vitro reconstitution of Cdc42 activity and its regulation (2023) BioRxiv. (https://doi.org/10.1101/2023.04.24.538075) (in submission at Current Protocols)

(vi) It is unclear what timepoint was used for the different assays. 1.5 h at 30 degrees seems to be the standard here for the Cdc42-alone assays, but I assume that cannot be what was measured to assess GTP decline for GEF-containing assays as there would be very little GTP left at 1.5 h.

Response from the authors:

We used 60-100 min as incubation times for all assays. The assay data will be published on a data server, where all these numbers can be checked. We further added a clarification to the materials and methods section. In order to still have remaining GTP for the Cdc42 GEF mixtures after 60-100 min, we lowered the used protein concentrations.

(vii) The graph reporting GEF activity is plotted only for [GEF]Response from the authors:

The graphs show the full range of protein concentrations used.

In order to calculate K1, K2, K3,Cdc24, K3,Rga2, K3,Cdc24,Rga2 from k1, k2, k3,Cdc24, k3,Rga2, k3,Cdc24,Rga2, …, a protein concentration has to be included in the term (as K1 = k1 [Cdc42], ….). In order to make K comparable, we chose to use 1uM for all protein concentrations. This was done to compare the cycling rate values of different proteins. 1uM was a choice, in the same fashion 0.2uM could have been chosen.

__We will further discuss in the manuscript how the choices in protein concentration affect the effector strength on Cdc42. __

(viii) S8 Data with casein seems very noisy and it is no longer at all clear that the quadratic fit for [Cdc24] is justified. Also, the symbol colors are very similar so it is hard to tell what data corresponds to what condition. The synergy between Cdc24 and Rga2 is also very noisy and the fits seem arbitrary.

Response from the authors:

The reviewer is concerned with (1) the noise in the S8 data, and (2) the Cdc42-Cdc24-Rga2 fits.

(1) We acknowledge in the manuscript that the S8 data is noisy and should be viewed with caution. We do not put much emphasis on these data sets and their interpretation and show them only in the supplement.

(2) We disagree that the Cdc42-Cdc24-Rga2 fits are arbitrary. The fits contain several data points per protein, and reproduce the rate values from Cdc42-Cdc24 and Cdc42-Rga2 assays well.

The reviewer is concerned with the color scheme choice in the fits.

__We will adapt the color scheme of the fits to make the colors more distinguishable. __

(ix) It is disturbing that different Cdc42 constructs behave quite differently (S4). This suggests that protein behavior is influenced by the various added epitope tags and protease cleavage sites (they also leave the C-terminal CAAX box rather than removing the AAX as would happen in vivo). These features raise the concern that these findings may not be directly relevant to the situation with endogenous yeast Cdc42. Of course, it is also the case that relevant Cdc42 biochemistry occurs with prenylated Cdc42 on membranes.

Response from the authors:

The reviewer is concerned that the behavior of the Cdc42 constructs is influenced by their tags. In a previous manuscript (Tschirpke et al. 2023) we explored the effect of various N- and C-terminal tags on Cdc42, by comparing it to Cdc42 that is not tagged in that position. We found that most tags, including the tags present in the Cdc42 construct used here, do not affect Cdc42’s properties.

Instead, we found a general, tag independent, heterogeneity in Cdc42 behavior (which can occur between purification batches and between constructs (but not between different assays)): in some batches GTPase activity depended quadratically on its concentration, others showed a linear relationship. Most batches exhibited a mixed behavior. The differences between the batches are generally small, and only visible in the activity to concentration plots and because of the assay’s high accuracy. We use a two-parameter fit (k1 [Cdc42] + k2 [Cdc42]2) to phenomenologically account for this heterogeneity, and to estimate the basal Cdc42 GTPase activity. We do not interpret this heterogeneity, as more research is needed. We believe that Cdc42 still has unexplored properties, of which this heterogeneous behavior can be one. We speculate in Tschirpke et al. 2023 that it is linked to Cdc42 dimerization mediated by its polybasic region, a relationship that is far from being fully understood yet. __We believe that it is of scientific interest to point out heterogeneous behaviors to encourage more research. __

Tschirpke et al. 2023:

Tschirpke et al. A guide to the in vitro reconstitution of Cdc42 activity and its regulation (2023) BioRxiv. (https://doi.org/10.1101/2023.04.24.538075) (in submission at Current Protocols)

The reviewer is concerned that our findings are biologically not relevant, as our experiments (1) included Cdc42 that was not prenylated and (2) did not include membranes.

(1) We here used recombinantly purified proteins, which do not contain posttranslational modifications, such as prenylations. So-far Cdc42’s prenyl group, which is responsible for binding it to membranes, has not been linked to its GTPase properties. We therefore believe that unprenylated Cdc42 is an equal choice to prenylated Cdc42 when studying Cdc42’s GTPase cycle. Further, the use of recombinantly purified proteins can be of advantage: when proteins are purified from their native host, the post-translationally modified protein is purified. However, many proteins contain a multitude of post-translational modifications (PTMs). Thus, the purified protein is a mixture of protein with different PTMs. For example, S. cerevisae Cdc42 undergoes ubiquitinylation (Swaney et al. 2013, Back, Gorman, Vogel, & Silva 2019), phosphorylation (Lanz et al. 2021), farnesylation and geranyl-geranylation (Caplin, Hettich, & Marshall 1994). We here used protein preparations that do not contain PTMs, and show how they behave. Natively purified proteins would be mixtures of various PTMs, and the observed protein behavior would be that of the mixture. If Cdc42’s PTMs affect it’s GTPase behavior, the observed behavior of natively purified Cdc42 would represent the average behavior of the mixture. It then would require additional work to disentangle which PTMs affect the GTPase cycling in which way. The use of recombinantly expressed Cdc42 does not require this work, and can set the baseline for how Cdc42 without PTMs behaves. If in the future a link between Cdc42’s GTPase behavior and PTMs are found, the work here could be used as a baseline for Cdc42’s behavior when it is without PTMs.

(2) The concern about missing membranes was also raised by reviewer 2 (significance), and we like to refer to our response there.

Reviewer #3 (Significance (Required)):

The basic biochemistry of Cdc42 cycles was figured out about 30 years ago. However, those studies did not examine how combinations of Cdc42 regulators (as opposed to individual regulators) might interact to produce effects not expected from combining their individual actions. Recently, this combination approach did lead to interesting findings by Rapali et al. This approach is worthwhile and addresses a major question of interest to the broader field of GTPase biochemistry.

One main limitation of this study is technical: the main assay is less informative (though perhaps easier) than traditional assays, and it is unclear whether the recombinant proteins employed retain their normal activities. Another limitation is the model-based interpretation of the assay that does not include the potential for rate-limiting steps.

Response from the authors:

We thank the reviewer for the detailed comments.

One important point of confusion originated from our lack of discussion concerning a rate-limiting step model, which is an obvious starting point for modelling the GTPase cycle. We thank the reviewer for pointing this out, and we will include an explanation in our manuscript why we reject this model and instead opt for a coarse-grained model.

Firstly, a rate-limiting model would generate saturation effects that we would observe when adding GEF and/or GAPs. In assays exploring GEF GAP synergy we use GEF and GAP concentrations for which no saturation effects were observed.

Secondly, in our data we observed a two-fold increase of the total GTPase cycling rate when adding a GAP and a 100-fold rate increase when a GEF is added. These increases are not compatible with a model where either hydrolysis or nucleotide exchange limits the GTPase cycle. While a synergy could arise from the rate-limiting model perspective, the incompatibility of the rate-limiting model with the GAP-only and GEF-only assay data excludes this synergy explanation. Finally, through coarse-graining our model we avoid using single step parameters from literature which are incompatible in terms of proteins/buffers used. (For example; the mayor studies that kinetically characterized the individual GTPase steps of Cdc42 used human Cdc42 (Zhang et al. 1997, Zhang et al. 2000). Because human Cdc42 exhibits a higher basal GTPase activity (Zhang et al. 1999) we are skeptical how useful it is to transfer these parameters to S. cerevisae Cdc42.)

At the same time, coarse-graining our model permits absorbing unidentified molecular details which is essential when we wish to incorporate BSA and casein rate contributions.

The reviewer finds our assay, which investigates the GTPase cycle as a whole, less informative. Assays investigating single GTPase cycle sub-steps give more mechanistic insights into these steps. We opted for an assay that studies GTPase cycling as a whole instead, as we were interested in studying how proteins effecting different steps act together. We believe that both assay types are important as they complement each other.

The reviewer is concerned about our use of recombinant proteins, and whether they retain their normal activities. We assessed Cdc42’s GTPase activity and the influence of added purification tags extensively (Tschirpke et al. 2023), and found that added tags do not affect Cdc42’s GTPase properties. We checked Cdc24’s GEF activity using the GTPase assay and found that it bound strongly to Bem1, as expected (Tschirpke et al. 2023). The Cdc24 concentrations needed to affect Cdc42’s GTPase activity were similar to those used previously (Rapali et al. 2017), suggesting that it is fully active. A similar comparison for Rga2 was not possible, as so-far only domains of Rga2 were used (Smith et al. 2002). We here used recombinantly purified proteins, which do not contain posttranslational modifications (PTMs). To our knowledge the PTMs of the herein used proteins are not linked to their GTPase/GEF/GAP properties. Thus, a lack of PTMs does not diminish our findings. Further, when proteins are purified from their native host, the post-translationally modified protein is purified. However, many proteins contain a multitude of post-translational modifications in vivo. Natively purified proteins would be mixtures of various PTMs, and the observed protein behavior would be that of the mixture. We here used protein preparations that do not contain PTMs, and show how they behave, setting the baseline for proteins without PTMs behaves. If in the future a link between GTPase behavior and PTMs are found, the work here could be used as a baseline for the proteins behavior when it is without PTMs.

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

Summary

The GTPase cdc42 is a key determinant of yeast polarization. Its activity is amplified at the site of polarization through a poorly defined positive feedback mechanism, and depends on numerous GAPs regulating GTP hydrolysis and the GEF cdc24 that regulates GDP release. These components have previously been evaluated for their quantitative effects on the individual steps in the GTPase cycle that they modulate, but potential interactions between the cdc24 GEF and any GAP could not be examined based on these assays. The authors validate and employ a bulk assay of the total GTPase cycle based on GTP consumption to study the activities of and potential interactions between cdc24 and the GAP Rga2. Fitting their data to a mathematical model, they come to three central conclusions: (1) the activating activity of cdc24 to activate cdc42 GTPase activity is nonlinear, showing a quadratic relationship, (2) Rga2 shows a much lower activating activity that is linear at low levels before saturating, and (3) there is a strongly synergistic interaction between the activating activities of cdc24 and Rga2. Some hypotheses for the mechanistic bases of these findings are hypothesized, but not further investigated. Their conclusions are well supported by the data which appears to be of sufficient rigor.

Major comments

The three main conclusions of the manuscript are well supported by the data and associated modeling.

One unresolved issue is the discrepancy between the authors' conclusion that the non-linear activation by cdc24 is likely a result of oligomerization, whereas Mionnet et al 2008 reach the opposite conclusion. It seems that the authors wish to discount the Mionnet results because they used truncated constructs to test deficient oligomerization and an engineered construct to test induced oligomerization. If the authors are correct, then a relatively easy test would be to introduce the oligomerization deficient mutants defined by Mionnet into their fuill length construct and compare to wild type protein. While the authors' measured results don't depend on the offered mechanism and this experiment is therefore optional, their explanation is quite unsatisfying, especially since an experiment to resolve the difference is entirely feasible and not very strenuous.

Response from the authors:

__The reviewer suggests to conduct experiments with oligomerization deficient Cdc24 mutants to test our hypothesis that the non-linear concentration dependence of Cdc24’s activity is due to Cdc24 oligomerization. __

We agree that this is an insightful experiment, and will conduct it. In order to observe the effect in our GTPase assays, we require a mutant that is oligomerizes substantially less than wild-type protein. Mionnet et al. constructed several Cdc24 mutants, but none were entirely oligomerization deficient. However, the DH5 (L339A/E340A) mutant showed a 10-fold reduction in oligomerization and the DH3 (F322A) mutant exhibited 2.5-fold reduction in oligomerization. We will therefore use the DH5 and DH3 mutant for two additional experiments.

Minor comments

The results in Fig S4 serve as assay validation, and this should be pointed out early in the Results section. I was initially concerned when the assay was described as based on consumption of GTP that a significantly diminished pool would alter the rate and thereby distort results, and being made aware of the S4 result would have alleviated that concern as I read further.

Response from the authors:

We believe that the reviewer refers to S3 (not S4). We appreciate this suggestion and now mention it earlier.

On page 4 and Fig S4 the authors mention several cdc42 constructs, some of which show linear activity curves and others slightly non-linear curves. I was unable to find where these constructs or their differences are discussed. The authors should also tell us if the construct used for the remaining experiments was one of the two shown in S4, or a different one.

Response from the authors:

We added the requested information and explanations to the manuscript.

It seems that in Fig 4 and Fig S8, some points are missing from the graphs. Were all concentrations for each condition not always assayed, or is some data omitted for some reason? For example, for the 0.125 microM Rga2 condition, only two points are shown vs 4 for some other conditions, and the two missing ones are expected to not be excluded by the >5% GTP remaining criterion.

Response from the authors:

The reviewer wonders whether Fig.4 and Fig. S8 miss data points. This is not the case, and __we added clarifying information to the manuscript. __

In detail: Not all assays contain the same amount of data points/ concentrations for each protein. We first assessed Cdc42 alone using several Cdc42 concentration. We then examined the individual Cdc42 – effector mixtures, using a larger number of effector concentrations. We included a reduced number of effector concentrations in the assays containing two effectors and Cdc42. It would be ideal to include more concentrations, but this is not always feasible: The assay involves a multitude of pipetting steps and is sensitive to any pipetting errors. Further, assays can vary slights from each other, therefore all samples that ought to be compared need to be included in each assay.

Each three-protein assay contains samples shown (Cdc42, Cdc42 + effector 1, Cdc42 + effector 2, Cdc42 + effector 1 + effector 2) and additional ‘buffer’ wells used for normalization. Each data point shown corresponds to the average of 3-4 replica samples per assay. We therefore did not include all concentrations in all conditions. As pointed out, Fig. 4a only shows two data points for the 0.125uM Rga2 axis (Rga2 + Cdc42 and Rga2 + Cdc24 + Cdc42). The rational was the following: We included three Cdc24 concentrations (for proper fitting for K3,Cdc24), three Rga2 concentrations (for proper fitting for K3,Rga2), and 5 mixtures of the used Cdc24 and Rga2 concentrations (for proper fitting for K3,Cdc24,Rga2).

The Cdc42-Rga2-BSA and Cdc42-Rga2-Casein data is rather sparse and would benefit from additional data points. However, we only use those as control experiments and are cautious in their interpretation.

In these graphs, a diamond symbol of slightly varying color is used for the different conditions. The different colors are hard to distinguish. Please use different shape symbols for the different conditions, and choose colors that are more distinct.

Response from the authors:

We will adapt the color scheme of the fits to make the colors more distinguishable.

There are a few sentences that are of unclear meaning, for example on page 10, "It was suggested that each GAP plays a distinct role in Cdc42 regulation, of which the level of GAP activity could be a part of [Smith et al., 2002]." There are also typos and grammatical errors that should be fixed.

Response from the authors:

__We will further check the document for potentially unclear sentences and will try to clarify them, as well as further check for grammatical and spelling errors. __

Reviewer #4 (Significance (Required)):

Significance

The most novel and important finding is the strong synergy observed between cdc24 and Rga2 in activating cdc42 GTPase activity. This is undoubtedly an important mechanism underlying positive feedback in polarization. The measured non-linear activity of cdc24 alone is also quite important given that availability of cdc24 is thought to be a critical in vivo stimulus for polarization. However, the unexplained discrepancy between this result and that of Mionnet leaves one to wonder which result is more reliable. Only Mionnet attempts to directly test whether oligomerization is important in cdc24 activity.

The conclusions are of importance to a broad audience of cell biologists, though the lack of any mechanism for the synergy or the non-linearity of cdc24 activity somewhat diminishes significance.

Note that my expertise and that of my co-reviewer is in the biology, and while we are able to follow the contributions of the modeling, we do not have the expertise to critically evaluate for potential errors or weaknesses in the modeling itself.

The reviewer wonders whether our data or the data of Mionnet et al. on the link between Cdc24 oligomerization and its GEF activity is more reliable and suggests to conduct experiments with oligomerization deficient Cdc24 mutants.

We thank the reviewer for this recommendation and we will do the suggested experiments to resolve the seemingly contradicting observations by us and Mionnet et al..

The reviewer would find mechanistic insights into (2) the non-linear concentration dependence of Cdc24’s activity and (2) the Cdc24-Rga2 synergy useful.

(1) We will conduct experiments with partially oligomerization deficient Cdc24 mutants, as suggested by the reviewer.

(2) We speculate that Cdc24-Rga2 binding could lead to the synergy. ____We will add data on Cdc24 – Rga2 binding (in vitro: Size-Exclusion Chromatography Multi-Angle Light Scattering) to this study.

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Referee #4

Evidence, reproducibility and clarity

Summary

The GTPase cdc42 is a key determinant of yeast polarization. Its activity is amplified at the site of polarization through a poorly defined positive feedback mechanism, and depends on numerous GAPs regulating GTP hydrolysis and the GEF cdc24 that regulates GDP release. These components have previously been evaluated for their quantitative effects on the individual steps in the GTPase cycle that they modulate, but potential interactions between the cdc24 GEF and any GAP could not be examined based on these assays. The authors validate and employ a bulk assay of the total GTPase cycle based on GTP consumption to study the activities of and potential interactions between cdc24 and the GAP Rga2. Fitting their data to a mathematical model, they come to three central conclusions: (1) the activating activity of cdc24 to activate cdc42 GTPase activity is nonlinear, showing a quadratic relationship, (2) Rga2 shows a much lower activating activity that is linear at low levels before saturating, and (3) there is a strongly synergistic interaction between the activating activities of cdc24 and Rga2. Some hypotheses for the mechanistic bases of these findings are hypothesized, but not further investigated. Their conclusions are well supported by the data which appears to be of sufficient rigor.

Major comments

The three main conclusions of the manuscript are well supported by the data and associated modeling.

One unresolved issue is the discrepancy between the authors' conclusion that the non-linear activation by cdc24 is likely a result of oligomerization, whereas Mionnet et al 2008 reach the opposite conclusion. It seems that the authors wish to discount the Mionnet results because they used truncated constructs to test deficient oligomerization and an engineered construct to test induced oligomerization. If the authors are correct, then a relatively easy test would be to introduce the oligomerization deficient mutants defined by Mionnet into their fuill length construct and compare to wild type protein. While the authors' measured results don't depend on the offered mechanism and this experiment is therefore optional, their explanation is quite unsatisfying, especially since an experiment to resolve the difference is entirely feasible and not very strenuous.

Minor comments

The results in Fig S4 serve as assay validation, and this should be pointed out early in the Results section. I was initially concerned when the assay was described as based on consumption of GTP that a significantly diminished pool would alter the rate and thereby distort results, and being made aware of the S4 result would have alleviated that concern as I read further.

On page 4 and Fig S4 the authors mention several cdc42 constructs, some of which show linear activity curves and others slightly non-linear curves. I was unable to find where these constructs or their differences are discussed. The authors should also tell us if the construct used for the remaining experiments was one of the two shown in S4, or a different one.

It seems that in Fig 4 and Fig S8, some points are missing from the graphs. Were all concentrations for each condition not always assayed, or is some data omitted for some reason? For example, for the 0.125 microM Rga2 condition, only two points are shown vs 4 for some other conditions, and the two missing ones are expected to not be excluded by the >5% GTP remaining criterion.

In these graphs, a diamond symbol of slightly varying color is used for the different conditions. The different colors are hard to distinguish. Please use different shape symbols for the different conditions, and choose colors that are more distinct.

There are a few sentences that are of unclear meaning, for example on page 10, "It was suggested that each GAP plays a distinct role in Cdc42 regulation, of which the level of GAP activity could be a part of [Smith et al., 2002]." There are also typos and grammatical errors that should be fixed.

Significance

The most novel and important finding is the strong synergy observed between cdc24 and Rga2 in activating cdc42 GTPase activity. This is undoubtedly an important mechanism underlying positive feedback in polarization. The measured non-linear activity of cdc24 alone is also quite important given that availability of cdc24 is thought to be a critical in vivo stimulus for polarization. However, the unexplained discrepancy between this result and that of Mionnet leaves one to wonder which result is more reliable. Only Mionnet attempts to directly test whether oligomerization is important in cdc24 activity.

The conclusions are of importance to a broad audience of cell biologists, though the lack of any mechanism for the synergy or the non-linearity of cdc24 activity somewhat diminishes significance.

Note that my expertise and that of my co-reviewer is in the biology, and while we are able to follow the contributions of the modeling, we do not have the expertise to critically evaluate for potential errors or weaknesses in the modeling itself.

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 work reports a biochemical analysis of the effects of a recombinant yeast GEF (Cdc24) and GAP (Rga2) on Cdc42 GTPase cycling in vitro. The central conclusion is that the GEF and GAP act "synergistically", which occurs "due to proteins enhancing each other's effects". By this they appear to mean that the GEF enhances the GAP's activity and vice versa. I was not persuaded that this is correct, and was confused by many aspects of the approach and interpretation, as outlined below.

1. GEF and GAP are expected to accelerate GTPase cycle synergistically even with no effect on each other's activity:

The Cdc42 GTPase cycle is understood to occur via distinct steps (GDP release, GTP binding, and GTP hydrolysis): GDP release and GTP hydrolysis are intrinsically slow steps that are accelerated by GEFs (GDP release) and GAPs (GTP hydrolysis). This fundamental biochemistry was established in the 1990s using biochemical assays that measure each step independently. Here instead the authors use an assay that measures [GTP] decline in a mix with 5 uM starting GTP, 1 uM Cdc42, plus or minus some amount of GEF or GAP. They assume exponential decline of [GTP] with time, yielding a cycling "rate". If that is so, then one would expect that added GEF would accelerate only the first step, leaving a slow GTP hydrolysis step that limits the overall cycling rate, while added GAP would accelerate only the last step, leaving a slow GDP release step that limits the overall cycling rate. Adding both together would speed up both steps, and should therefore "synergistically" accelerate cycling. This would be expected based on previous work and does not imply that GEF or GAP are affecting each other's action (except trivially by providing substrate for the next reaction). If the authors wish to demonstrate that something more complex is indeed happening, they need to use assays that directly measure the sub-reaction of interest, as done by prior investigators. 2. Model-based interpretation of the GTPase assay is poorly supported:

The assay employed measures overall GTP concentration with time. It is assumed (but not well documented-see below) that [GTP] declines exponentially, and that the rate constant for a particular condition can be fit by the sum of a series of terms that are linear or quadratic in the concentrations of Cdc42, GEF, and GAP. There is no theoretical derivation of this model from the elementary reactions, and the assumptions involved are not well articulated.

As discussed in point 1 above, one would expect that a GEF or GAP alone could only accelerate the cycle to a certain point, where the other (slow) reaction becomes rate limiting. But that does not appear to be true for their phenomenological model, where slow steps (small terms in the sum) will always be overwhelmed by fast steps. This is not the traditional understanding of how GTPases operate. 3. Data that do not conform to expectation are not explained: Strangely, the data (as interpreted by the model assumptions) also appear inconsistent with the expectation of rate-limiting steps. GEF addition (alone) is said to accelerate cycling 100-fold, while GAP addition (alone) accelerates it 2-fold. But that would seem to imply that GDP release takes up >99% of the basal cycle (so accelerating that step alone reduces cycling time 100-fold), while GTP hydrolysis takes up >50% of the basal cycle (so accelerating that step alone reduces cycling time 2-fold). In the conventional understanding of GTPase cycles, these cannot both be be true (as the steps would then add to >100% of the basal cycle). There is no attempt to reconcile these findings with previous work. 4. Lack of detailed timecourse data:

The decline in [GTP] with time is stated to be exponential, allowing extraction of an overall cycling "rate". But this claim is supported only weakly (S3 Fig. 1 uses only 3 timepoints, is not plotted on semi-log axis, and does not report fit to exponential vs other models) and only for the Cdc42-alone scenario: no data at all are presented to support exponential decline in reactions with GEF or GAP. Most assays seem to measure only a single timepoint, so extraction of a "rate" is very heavily influenced by the unsupported assumption of exponential decline. And if the decline is not exponential, it becomes extremely difficult to interpret what a single timepoint means. 5. Other issues with interpretation of the data:

(i) It is unclear why the authors chose to employ an assay that is much harder to interpret than the biochemical assays used by others. In biochemical studies, assays that report an output of multiple reactions are always harder to interpret than assays targeting a single reaction. As well-established assays are available for each individual step in GTPase cycles, any conclusions must be supported using such assays.

(ii) The reported basal (and GEF/GAP-accelerated) rates are very slow, perhaps due to poor folding of recombinant proteins. This raises the possibility that much of the Cdc42 is inactive. If so, then accelerated GTP hydrolysis could come from increasing the active fraction of Cdc42, rather than catalyzing a specific step.

(iii) The GEF and GAP preparations include multiple partial degradation products and it is unclear whether the measured activities come from full-length proteins or more active fragments.

(iv) Cdc42 cycling is also accelerated by BSA and casein, suggesting that there are poorly understood aspects of the assay and that GEF and GAP actions may (like BSA and casein) involve non-canonical effects on Cdc42. As GEF and GAP are expected to interact better with Cdc42 than BSA or casein, these effects could dominate the observed changes in GTP levels.

(v) Cdc42-alone cycling assays are said to be reproducible. However, assays with added GEF/GAP/BSA/Casein yield rates that vary almost an order of magnitude between replicates. This poor reproducibility further reduces confidence in the findings.

(vi) It is unclear what timepoint was used for the different assays. 1.5 h at 30 degrees seems to be the standard here for the Cdc42-alone assays, but I assume that cannot be what was measured to assess GTP decline for GEF-containing assays as there would be very little GTP left at 1.5 h.

(vii) The graph reporting GEF activity is plotted only for [GEF]<0.2 uM, but the rates used in the subsequent experiments are reported for mixtures with 1 uM GEF. The full range of GEF data should be plotted.

(viii) S8 Data with casein seems very noisy and it is no longer at all clear that the quadratic fit for [Cdc24] is justified. Also, the symbol colors are very similar so it is hard to tell what data corresponds to what condition. The synergy between Cdc24 and Rga2 is also very noisy and the fits seem arbitrary.

(ix) It is disturbing that different Cdc42 constructs behave quite differently (S4). This suggests that protein behavior is influenced by the various added epitope tags and protease cleavage sites (they also leave the C-terminal CAAX box rather than removing the AAX as would happen in vivo). These features raise the concern that these findings may not be directly relevant to the situation with endogenous yeast Cdc42. Of course, it is also the case that relevant Cdc42 biochemistry occurs with prenylated Cdc42 on membranes.

Significance

The basic biochemistry of Cdc42 cycles was figured out about 30 years ago. However, those studies did not examine how combinations of Cdc42 regulators (as opposed to individual regulators) might interact to produce effects not expected from combining their individual actions. Recently, this combination approach did lead to interesting findings by Rapali et al. This approach is worthwhile and addresses a major question of interest to the broader field of GTPase biochemistry.

One main limitation of this study is technical: the main assay is less informative (though perhaps easier) than traditional assays, and it is unclear whether the recombinant proteins employed retain their normal activities. Another limitation is the model-based interpretation of the assay that does not include the potential for rate-limiting steps.

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Referee #2

Evidence, reproducibility and clarity

The study entitled, "The GEF Cdc24 and GAP Rga2 synergistically regulate Cdc42 GTPase cycling" by Tschirpke et al., uses an in vitro GTPase assay to examine the GTPase cycle of Cdc42 in combination with its GEF and GAP effectors. The authors find that the Cdc24 GEF activity scales non-linearly with its concentration and the GAP Rga2 has substantially weaker effect on stimulating Cdc42 GTPase activity. Not surprisingly, the combined addition of Cdc24 and Rga2 lead to a substantial increase in Cdc42 GTPase activity.

Referees cross-commenting

In Zheng, Y., Cerione, R., and Bender, A. (1994) J. Biol. Chem. 269: 2369-2372 (Fig. 3C), the authors show that Cdc24 combined with the GAP Bem3 lead to a large synergy in boosting Cdc42 GTPase activity.

Significance

There is very little new information in this manuscript. Previous studies (Rapali et al. 2017) have shown that the scaffold protein Bem1 enhances the GEF activity of Cdc24. It is expected that the reconstitution of a GEF and GAP protein promote the GTPase cycle and indeed Zheng et al. (1994) showed that that Cdc24 combined with the GAP Bem3 lead to a large synergy in boosting Cdc42 GTPase activity. Hence the only potentially interesting finding in this work is that, in solution Cdc24 activity scales non-linearly with its concentration. However as this GEF and Cdc42 are associated with the membrane, the relevance of solution studies are less clear and furthermore the mechanistic basis for the non-linearity is not explored in detail. Given the limited new information from this work, the findings are, in their current form, too preliminary.

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Referee #1

Evidence, reproducibility and clarity

This study would be much convincing if additional line of eukaryotic cells can be used to demonstrate the GEF-GAP synergy tis important for cell physiology. In addition, it would be best to demonstrate the spatiotemporal interaction of GEF-GAP using high-resolution live cell imaging.

Significance

The revised study would provide first line evidence that GEF-GAP synergy to be general regulatory property in eukaryotic kingdom.

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Reply to the reviewers

Manuscript number: RC-2023-01901

Corresponding author(s): Gavin, Sherlock

We thank the reviewers for their comments and their generally positive reviews – the reviews were constructive, and we have revised the manuscript to deal with all the requested changes and suggestions. We believe the manuscript is improved as a result, and hope that the reviewers agree that it is now suitable for publication. Below with provide a point-by-point reply that explains what revisions we have made. Reviewers’ comments are italicized, while our responses are highlighted.

Reviewer #1:

*It would be interesting have an idea of the global mutation rates and spectra in the diploid and haploid lineages across the conditions as well. The S. cerevisiae mutational spectrum has been shown to be dependent on the environment and genetic background to an extent but not ploidy. Ploidies differ in terms of just the frequency. How similar/ dissimilar are the overall mutational spectra here? Were there any homozygous mutations in the diploids? *

• We have plotted the mutation types in the diploid and haploid lineages across the conditions to compare the frequencies of each type of mutation between ploidies, which is now presented as Supplemental Figure 3. The mutation types between ploidies for each of the conditions look similar. Homozygous diploids are indicated in Table 2.

  *Fitness gains and losses can happen without trade-offs if neutral home mutations are non-neutral in non-home conditions. Can the authors comment on that in this context. Physico-chemically, how different are the home/ non-home environments? How do the fitness effects correlate across the environments in the absence of these adaptive mutations? It would also be useful to know the extent of fitness variance of the populations in the home and away environments, this would aid the reader better grasp the significance of fitness gains/loss. *

• We agree that trade-offs could occur as a result of mutations that are neutral in the home condition showing trade-offs in the non-home conditions. However, in the newly added Supplemental Table 1, it is clear that most lineages have several passenger mutations, yet for lineages carrying the mutations in the same candidate beneficial mutation, they have largely similar pleiotropic profiles, suggesting that the influence of neutral mutations that arise in the home environment do not play a large role in determining fitness in other environments, at least for those tested. We have not generated strains that only contain the passenger mutations – while that would empirically test the fitness effects of the passenger mutations, it would be extremely time consuming to generate such strains, and the results would be unlikely to change our claims in the paper.

  *A summary table of the pleiotropic effects would be very useful as in Bakerlee et al. 2021. *

• We have added a summary table (Supplemental Table 3) of the pleiotropic effects as suggested. Reviewer #2

*The major conclusion of the manuscript state that "mutations in the same genes tend to produce similar pleiotropic effects", suggesting that a number of times this does not occur. For instance, the authors comment on the case of PDR3, which does not always produce 'cost-free' adaptation across environments. I believe that, to strengthen and better define their conclusion, the authors should develop a quantitative analysis of the reproducibility of pleiotropic profiles (that considers how many times genes have been found mutated). The heatmaps provided are compelling, but make it hard to generalize on how often, and to what extent, the gene mutated can predict pleiotropy across various environments. *

• We have calculated pairwise correlations between pleiotropic profiles for mutations that arose in the same environment either in the same gene, or in different genes, and added this Supplemental Figure 10. These data show that by and large, correlations between mutations in the same gene are higher than those for different genes.

  *In the concluding sentence of the discussion, it is unclear whether the authors are speculating about a role of the strength of selection in determining pleiotropy based on their results, or if that only represents a suggested hypothesis to test in future studies. *

• We have modified the concluding sentence to clarify its meaning (it was a suggested hypothesis)

  *The method used to identify putative adaptive mutations should be described in more detail. For instance, I seem to understand that only one mutation per lineage is considered 'adaptive'. However, many lineages seem to have more than one mutation. Based on what reported in the method section, the adaptive mutations have been hand-picked based on previous knowledge of selection in the environments of choice ("the list of genes was curated based on those genes' interactions with other identified genes or pathways known to be involved in the adaptation of that specific condition from previous work"). If this assumption is correct, the criteria for such a curation should be specified in more detail. *

• We have further clarified our criteria in the text; note, there was not a requirement for there to be only a single beneficial mutation per lineage, though very few lineages had two candidate beneficial mutations.

  *The term 'Pareto front' is technical and left undefined. *

• We have clarified the meaning of Pareto front

  *The section ' adaptation can be cost-free' only refer to figure 4, (with adaptive mutant lineages from populations evolved in fluconazole), while it comments extensively on mutation isolated in clotrimazole (reported in Sup. Fig10, not mentioned in the section). *

• We thank the reviewer for noting our oversight – we have also now referenced the supplementary figure too (now Supplementary Figure 11). Reviewer #3

*It would be helpful if the authors could clearly provide information on the zygosity of the evolved mutations, as the presence of mutations in homozygous or heterozygous states can impact the results of the study. *

• We have added zygosity information to the genotypes in the text and in Table 2, Summary of Adaptive Mutations

  *Do any of the evolved lineages have multiple adaptive mutations or other potentially adaptive mutations? If so, it would be great if the authors could provide a table listing these lineages and mutations. *

• We have added Supplemental File 1, which enumerates the adaptive and passenger mutations found in each lineage. Candidate adaptive mutations are in highlighted in red. Of the ~200 adaptive lineages, 4 have two candidate adaptive mutations, while the rest have only one.

  *In the Pooling of the Isolated Clones section of the Methods, the ancestor and subject pools were mixed in different ratios for different types of pools. While not strictly necessary, it would be helpful to provide a brief explanation for this. *

• We have added a brief explanation

  *The conditions listed in Table 1 and Supplemental Figure 2 do not seem to match perfectly. *

• We have corrected Supplemental Figure 2 such that it matches Table 1

  Supplementary Figure 6 demonstrates reproducible fitness estimates across lineages with the same mutations but distinct barcodes, supporting the authors' inference of adaptive mutations. However, it also appears to show no evidence of interactions among these mutations. Can the authors clarify if this is due to the absence of lineages with multiple mutations or if no observable interactions were found?

• See response above – there are very few lineages with more than one candidate beneficial mutation. The remaining passenger mutations are thus likely neutral.

  *In the Pleiotropy is common, strong and variable section of the results, all three conditions were noted to have their evolved lineages tested in other conditions and presented in Supplementary Figure 5. However, due to the rapid dominance of lineages evolved in clotrimazole, there is no comparison data for them in Supplementary Figure 5. *

• Unfortunately, we were not able to generate robust fitness remeasurements in the clotrimazole condition, due to the rapid takeover by lineages that were evolved in that condition

  *In the Results section on cost-free adaptation, it would be beneficial to include any compositional differences, such as pH, between the two drugs used that could have contributed to the fitness effects of the evolved lineages in pH 7.3. *

• We are not aware of any such differences – we did not pH any of the media other than the media with a specific pH.

  *Results - Adaptation can be cost-free: While the authors did state "at least across the conditions in which we remeasured fitness" at the end of the paragraph, it may be prudent to exercise caution when stating "cost-free adaptation" as only a few conditions were tested. For instance, an all-beneficial or all-deleterious result can sometimes be obtained solely based on the chosen conditions. *

• We have added additional caution in the text based on the reviewer’s suggestion.

  *Colormaps in Figure 4, Supplemental Figure 6, 10, and 11: The colors for values below -0.2 are uniform, whereas the heatmaps exhibit darker blues. *

• We have edited the color scales on Figure 4 and Supplemental Figures 6, 10, and 11 (now Supplemental Figures 6, 11, and 12) such that the scales are uniform.

  *Results - Pleiotropy varies according to the mutated gene: "For example, haploid lineages adapted in glycerol/ethanol with mutations in IRA1 show the same pattern of fitness effects across conditions (Supplemental Figure 6)." I believe the authors are referring to Supplemental Figure 11. *

• The reviewer is correct – we have fixed this reference to what is now Supplemental Figure 12

  *On the topic of IRA1, IRA2, and GPB2 in the section "Pleiotropy varies according to mutated gene" in the Results: Although IRA1 mutants exhibit highly similar patterns, it is challenging to ascertain which of the two genes, GPB2 or IRA2, has a more similar pattern. *

• We have create a new supplemental figure showing the correlation between mutations in the same gene and mutations in different genes for lineages evolved in the same condition – see response to Reviewer #1 above.

  Results - Pleiotropy varies according to mutated gene: From "If lineages isolated from the same home environment have similar pleiotropic profiles..." to the end of that paragraph. While it is true that "pleiotropy varies according to target genes and not environment alone," it may be premature to suggest that the environment is the "main driving force" of pleiotropy without some form of statistical analysis.

• We did not intend to suggest that environment is the main driving force - that section was somewhat poorly worded. We have modified the wording to make that clearer.

  *Discussion - line 5, paragraph 2: "For example, in glycerol/ethanol, the haploid adapted lineages have a trade off at 37{degree sign}C but the diploid adapted lineages do not (Supplemental Figure 11)." I believe the authors are referring to Supplemental Figure 6. *

• We thank the reviewer for spotting this and have fixed the figure reference.

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors present an intriguing study on the pleiotropic effects of adaptive mutations in yeast populations evolving in different environments. The study used haploid and diploid barcoded budding yeast populations to understand the pleiotropic effects of adaptive mutations in "non-home" environments where they were not selected. The findings indicate that pleiotropy is common, and most adaptive evolved lineages show fitness effects in non-home environments, which can be beneficial or deleterious. The results also highlight how ploidy influences the observed adaptive mutational spectra in different conditions. The methodology involved whole-genome sequencing and pooled fitness remeasurement assays in 12 environments with various perturbations. The study concludes that pleiotropic effects are unpredictable, but lineages with adaptive mutations in the same genes tend to show similar effects. Overall, the study provides insights into the dynamics of adaptation and the impact of pleiotropy in different environments.

Major comments

1. It would be helpful if the authors could clearly provide information on the zygosity of the evolved mutations, as the presence of mutations in homozygous or heterozygous states can impact the results of the study.
2. Does any of the evolved lineages have multiple adaptive mutations or other potentially adaptive mutations? If so, it would be great if the authors could provide a table listing these lineages and mutations.

Minor comments

1. In the Pooling of the Isolated Clones section of the Methods, the ancestor and subject pools were mixed in different ratios for different types of pools. While not strictly necessary, it would be helpful to provide a brief explanation for this.
2. The conditions listed in Table 1 and Supplemental Figure 2 do not seem to match perfectly.
3. Supplementary Figure 6 demonstrates reproducible fitness estimates across lineages with the same mutations but distinct barcodes, supporting the authors' inference of adaptive mutations. However, it also appears to show no evidence of interactions among these mutations. Can the authors clarify if this is due to the absence of lineages with multiple mutations or if no observable interactions were found?
4. In the Pleiotropy is common, strong and variable section of the results, all three conditions were noted to have their evolved lineages tested in other conditions and presented in Supplementary Figure 5. However, due to the rapid dominance of lineages evolved in clotrimazole, there is no comparison data for them in Supplementary Figure 5.
5. In the Results section on cost-free adaptation, it would be beneficial to include any compositional differences, such as pH, between the two drugs used that could have contributed to the fitness effects of the evolved lineages in pH 7.3.
6. Results - Adaptation can be cost-free: While the authors did state "at least across the conditions in which we remeasured fitness" at the end of the paragraph, it may be prudent to exercise caution when stating "cost-free adaptation" as only a few conditions were tested. For instance, an all-beneficial or all-deleterious result can sometimes be obtained solely based on the chosen conditions.
7. Colormaps in Figure 4, Supplemental Figure 6, 10, and 11: The colors for values below -0.2 are uniform, whereas the heatmaps exhibit darker blues.
8. Results - Pleiotropy varies according to the mutated gene: "For example, haploid lineages adapted in glycerol/ethanol with mutations in IRA1 show the same pattern of fitness effects across conditions (Supplemental Figure 6)." I believe the authors are referring to Supplemental Figure 11.
9. On the topic of IRA1, IRA2, and GPB2 in the section "Pleiotropy varies according to mutated gene" in the Results: Although IRA1 mutants exhibit highly similar patterns, it is challenging to ascertain which of the two genes, GPB2 or IRA2, has a more similar pattern.
10. Results - Pleiotropy varies according to mutated gene: From "If lineages isolated from the same home environment have similar pleiotropic profiles..." to the end of that paragraph. While it is true that "pleiotropy varies according to target genes and not environment alone," it may be premature to suggest that the environment is the "main driving force" of pleiotropy without some form of statistical analysis.
11. Discussion - line 5, paragraph 2: "For example, in glycerol/ethanol, the haploid adapted lineages have a trade off at 37{degree sign}C but the diploid adapted lineages do not (Supplemental Figure 11)." I believe the authors are referring to Supplemental Figure 6.

Significance

General assessment:

The research is well-conducted, utilizing both haploid and diploid barcoded yeast populations, and isolating adaptive clones to determine fitness effects in non-home environments. The double-barcoding system allowed the authors to perform pooled fitness measurements of a large number of lineages coming from different home-environments in a plethora of conditions accurately and efficiently. The inclusion of a multiple evolution conditions followed by fitness measurements in a broader range of conditions allowed the authors to study the effect of environment to pleiotropy.

The low number of generations used in this study, however, could hamper the discovery of more adaptive mutations, particularly those with smaller effects, and also make the study underpowered for studying epistasis among the evolved mutations. Furthermore, while the definition of pleiotropy in this study is reasonable and practical, it also makes most, if not all, generalist mutations pleiotropic and hence it's not surprising to see pleiotropy to be so common in this study.

Advance:

This study is an extension of a few recent publications. Jerison et al. (2020) evolved 20 haploid founder replicates in 11 environments for about 700 generations and measured the fitness of evolved clones - one clone from each replicate - across these conditions. This study provides in-depth analyses of how environments affect pleiotropy and a certain level of analyses for the underlying mutations. Bakerlee et al. (2021) evolved several hundred barcoded haploid and diploid populations in a few environments for 1000 generations and traced not only the fitness changes but also the dynamics of pleiotropy longitudinally. The environments used in this study are similar to each other, and so were the results, with the exception of environments with high (37˚C) and low (21˚C) temperatures. The current study utilized their double-barcoding system to allow for testing both haploids and diploids in a broader range of conditions. Although only three of the starting environments were chosen for further analyses, these environments are more dissimilar, and the putative underlying adaptive mutations in the evolved clones were identified and more thoroughly analyzed.

The study's findings offer valuable insights into the intricate relationship between adaptation, pleiotropy, and environmental dynamics. While the complexity of pleiotropy and the multitude of factors that influence it make it challenging to comprehensively address all aspects in a single study, the results presented here contribute significantly to our understanding of this phenomenon. Nevertheless, further research of this nature is crucial to deepen our knowledge of the underlying mechanisms and to identify overarching patterns that can be applied across diverse systems. Overall, this study represents a promising step towards advancing our understanding of pleiotropy and its role in adaptive evolution.

Audience:

While the current study focuses on yeast as a model organism for evolutionary experiments, the implications of pleiotropy extend far beyond basic research. An understanding of the pleiotropic effects of mutations is crucial for comprehending the mechanisms of evolution and developing effective clinical interventions. As pleiotropy can affect disease outcomes and drug responses, the insights gained from this study can have far-reaching implications in the fields of biology and medicine. Thus, this study contributes not only to our understanding of yeast genetics but also to broader areas of research and application.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors address the question of pleiotropy (the multiple effects of a single mutation on different traits) of adaptive mutations occurred during evolutionary processes. To do this, they evolve populations of S.cerevisiae strains in 12 environments, they identify the major adaptive mutation occurring in a subset of them, and they use a barcoding system to address their effect on fitness in environments where they were not evolved. The study confirm a number of conclusions from previous studies, such as the frequency of positive and negative pleiotropy of evolved lines when tested in other environments. The major novelty of this work is represented by the focus on single adaptive alleles and the conclusion that mutations in the same genes tend to produce similar pleiotropic effects.

Major comments:

1. The major conclusion of the manuscript state that "mutations in the same genes tend to produce similar pleiotropic effects", suggesting that a number of times this does not occur. For instance, the authors comment on the case of PDR3, which does not always produce 'cost-free' adaptation across environments. I believe that, to strengthen and better define their conclusion, the authors should develop a quantitative analysis of the reproducibility of pleiotropic profiles (that considers how many times genes have been found mutated). The heatmaps provided are compelling, but make it hard to generalize on how often, and to what extent, the gene mutated can predict pleiotropy across various environments.
2. In the concluding sentence of the discussion, it is unclear whether the authors are speculating about a role of the strength of selection in determining pleiotropy based on their results, or if that only represents a suggested hypothesis to test in future studies.
3. The method used to identify putative adaptive mutations should be described in more detail. For instance, I seem to understand that only one mutation per lineage is considered 'adaptive'. However, many lineages seem to have more than one mutation. Based on what reported in the method section, the adaptive mutations have been hand-picked based on previous knowledge of selection in the environments of choice ("the list of genes was curated based on those genes' interactions with other identified genes or pathways known to be involved in the adaptation of that specific condition from previous work"). If this assumption is correct, the criteria for such a curation should be specified in more detail.

Minor comments:

1. The term 'Pareto front' is technical and left undefined.
2. The section ' adaptation can be cost-free' only refer to figure 4, (with adaptive mutant lineages from populations evolved in fluconazole), while it comments extensively on mutation isolated in clotrimazonle (reported in Sup. Fig10, not mentioned in the section).

Referees cross-commenting

I tend to agree with reviewers 1 and 3 that, given the focus on individual mutations of this manuscript, more information about their nature is important. On top of the zygosity, I would be curious to know whether mutations predicted to inactivate the gene (frameshifts, stop codon), have different pleiotropic profiles than AA substitutions.

To answer the reviewer's 2 second major point, my understanding is that most of the lines have accumulated other mutations (marked with a '+' sign in Fig4 and FigS6,S10,S11). I suspect, however, that none of these mutations have been considered adaptive given the criteria described in the 'identifying adaptive mutations' session (e.g. mutations in coding regions appearing in more than one clone in a given condition, and with median fitness in the original home greater than 0).

Significance

The manuscript address a question (the emerge of pleiotropy during evolutionary adaptation) which has been extensively studied. However, it does it in a more comprehensive way that previously achieved, including many environments, both haploid and diploid organisms, and by focusing on single adaptive mutations. Most of the conclusions match the ones of previous studies. Perhaps the only exception is represented by the conclusion that individual genes, more than home environments, are proposed to dictate the pleiotropy profiles. However, the fact that mutations affecting the same genes often produce similar pleiotropy profiles is not necessarily unexpected. Overall, the paper is clearly written and can represent a valuable resource for a rather specialized community interested in the origin of pleiotropy during evolutionary adaptation.

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Referee #1

Evidence, reproducibility and clarity

Summary

The objective of the study was to investigate the impact of specific adaptive mutations on trade-offs and pleiotropic effects in haploid and diploid Saccharomyces cerevisiae populations. The authors identified adaptive mutations from strains evolved in three specific home conditions and then conducted fitness assays in up to 12 environments (home/non-home). They highlight that ploidy level plays a condition-specific role in shaping the adaptive mutation spectra. Adaptive mutations showed fitness effects in both home and non-home environments, which were beneficial in some cases and detrimental in others.

Major comments

It would be interesting have an idea of the global mutation rates and spectra in the diploid and haploid lineages across the conditions as well. The S. cerevisiae mutational spectra has been shown to be dependent on the environment and genetic background to an extent but not ploidy. Ploidies differ in terms of just the frequency. How similar/ dissimilar are the overall mutational spectra here? Were there any homozygous mutations in the diploids?

Fitness gains and losses can happen without trade-offs if neutral home mutations are non-neutral in non-home conditions. Can the authors comment on that in this context. Physico-chemically, how different are the home/ non-home environments? How do the fitness effects correlate across the environments in the absence of these adaptive mutations? It would also be useful to know the extent of fitness variance of the populations in the home and away environments, this would aid the reader better grasp the significance of fitness gains/loss.

Minor comments

A summary table of the pleiotropic effects would be very useful as in Bakerlee et al. 2021. Citation errors e.g. Consistent with prior work (Jerison, et al., 2021)... is either incorrect or not referenced.

Significance

The paper is well written highlighting an interesting question, the take home message is incremental at best in the context of the overall literature. In general, my suggestion would be to tone down the conclusions a bit, as the evidence isn't very clear cut .

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Reply to the reviewers

We would like to thank Review Commons for their innovative approach to scientific peer-review and publishing. We thank all the Reviewers for their positive, highly complementary assessment of the manuscript and for highlighting the high quality and reproducibility of the work and the novelty and significance of the results: “The experiments are well-designed and perfectly executed and presented”; “I felt that this is a strong manuscript for peer-review as it serves diversified interests in modern cell biology.”; “The manuscript would be of interest to basic researchers working on epithelial development. Also potentially to basic researchers working on cancer, due to the mitotic errors described.”. We are grateful for the Reviewers’ comments and suggestions that have contributed to improving the revised manuscript. We have addressed all the Reviewers’ concerns, as detailed below in the point-by-point response to the Reviewers. Textual changes in the revised manuscript are marked in Blue.

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

*The manuscript "Crosstalk between the plasma membrane and cell-cell adhesion maintains epithelial identity for correct polarised cell divisions" by Dr. Hosawi and colleagues reports the characterisation of the mitotic connection between plasma membrane dynamics and division orientation in polarised mammalian epithelial cells in culture. The authors start from the comparison of mitotic events of human mammary MCF10A cells grown at optimal density or at low density. They observed that only at optimal density MCF10A cells polarise by E-cadherin mediated cell-cell contacts, and display uniform membrane enrichment at the cortex, whereas cells grown at low density do not show cortical E-Cadherin enrichment, and distribute aberrantly the plasma membrane at one side and in cytoplasmic vesicles, generating daughter cells with unequal size. Consistently, further analyses revealed that low-density MCF10A cells undergo misoriented mitosis, with chromosome congression and misegregetion defects. Mechanistically, low density MCF10A cells fail to organise a symmetric mitotic spindle and center it in metaphase. This is due to an increased cortical actomyosin thickness coupled to abnormal astral microtubule stability. Building on previous data from the Elias lab, the authors uncover a role of the membrane-associated S100A11 protein in maintaining correct plasma membrane dynamics and E-cadherin localisation in mitosis. Further dissection of the molecular mechanism underlying this mitotic function od S10011A revealed that it enriches at the cortex only in optimal-density MCF10A cells, and promotes spindle orientation by association with LGN and E-cadherin, upstream of E-cadherin. This evidence depicts the plasma membrane and S100A11 proteins as a key mechanical sensors of cell-cell adhesion orchestrating the recruitment of E-cadherin and LGN-dependent force generators to ensure correct division orientation. *

*Major points: *

*- Important information is presented in Supplementary Figure S3. I suggest to move these panels in the main figures. Specifically, I would replace figure 4A with S3A showing the distribution of endogenous S100A11 in MCF10A cells, rather than the one of the GFP-tagged version which is over-expressed. *

__Authors response: __We thank the Reviewer for this suggestion. We have now moved Figure S3A to Figure 4, to replace Figure 4A and show the localisation of endogenous S100A11 during mitosis and included new quantifications in new Figure 4B. We have moved Figure 3A to supplementary figures (new Figure S4A). We have amended the text of the results section and the Source Data file accordingly.

*- The mechanisms of division orientation governed by S100A11 seems to impinge on the control of cortical F-actin and astral microtubule dynamics. This is illustrated in figure S3C, which in my opinion should be shown in the main figures with some more explanation / experiments. The authors mention the " tight actin F-actin bundles at the cell-cell contacts" that are lost in S100A11-depleted cells, and that interact with astral microtubules. However this is not fully clear in figure S3C. I think the authors should find a way to present better these evidence which is key in supporting their molecular model. *

__Authors response: __As requested by the Reviewer we have now moved Figure S3C to the main manuscript, as new Figure 5. To clarify further the effect of S100A11 depletion on the tight actin bundle formation at the cell-cell contacts, we have now included a new illustration in new Figure 5C. Additionally, we have clarified further these findings in the results section (page 11). While we agree with the Reviewer that additional experiments, for example using live imaging of MCF-10A cells co-labelled for F-actin and tubulin, would help assess further the crosstalk between cortical actin and astral microtubules, based on our experience these live imaging experiments are challenging and can take up to several months to optimise and may not warrant successful outcome.

*- I think the discussion would benefit from the addition of a graphical cartoon model illustrating the role of S100A11 in controlling plasma membrane dynamics in mitosis and spindle orientation. *

__Authors response: __We thank the Reviewer for this suggestion. We have now added a graphical cartoon (new Figure 7), summarising the role of S100A11-mediated regulation of plasma membrane dynamics in polarised cell division orientation, progression and outcome. We hope this new illustration clarifies further the mechanisms described in this study.

*- Finally, to understand the relevance of S100A11 in the context of 3D polarised mammary epithelia, it would be very interesting to analyse the effect of S100A11 knock-downn in mouse mammary epithelial acini grown in matrigel. This is not essential for the proposed studies, but would add biological relevance to the mechanisms characterised in 2D colture. *

__Authors response: __We agree with the Reviewer that validating our findings in 3D cultures of mammary epithelial cells will be important to determine the influence of S100A11-mediated regulation of plasma membrane dynamics during mitosis on lumen formation and tissue morphogenesis. This is exactly the direction where the follow-up of these findings will go. While the first author who led this work has graduated and left our lab, we have recently recruited a new PhD student to address this important question, which will need a few years of investigation to provide important insights, similarly to what we did in our previous work (Fankhaenel et al., 2023 Nat Commun).

*Minor comments: *

*- It would be preferable to mention the known functions of S100A11 in the introduction rather than at the beginning of the paragraph at pg. 9. *

__Authors response: __In response to the Reviewer’s suggestion, we have now moved the paragraph describing known functions of S100A11 to the introduction of the revised manuscript (see page 5).

*- at pg 10, beginning of paragraph, I find it a weird phrasing that "LGN interacts with F-actin". As reported in the reference cited here, this is through Afadin, which binds simultaneously LGN and cortical F-actin. I would rephrase it. *

__Authors response: __We thank the Reviewer for clarifying this point, which we have now rectified in the revised manuscript (see page 11).

__Reviewer #1 (Significance (Required)): __

*The description of cell adhesion as key factor instructing correct mitotic progression and execution of oriented division of vertebrate epithelial cells by controlling plasma membrane dynamics is novel and interesting for scientist in the spindle orientation/polarity field. The experiments are well-designed and perfectly executed and presented. I am in favour of publication of the manuscript, providing that a few points are addressed. *

Authors response: We thank the Reviewer for their very positive evaluation of our work.

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

*Establishment and maintenance of cell polarity are fundamental processes for physiology in multi-cellular organism given the fact that more than 380 million epithelial cell renewal for every second in human adults. However, the precise mechanisms linking plasma membrane polarity and cortical cytoskeleton dynamics of epithelial cells during mitotic exit and interphase remain ill-illustrated. Salah Elias and her colleagues experimentally manipulated the density of mammary epithelial cells in culture, which led to several mitotic defects. Specifically, they found that perturbation of cell-cell adhesion integrity impairs the dynamics of the plasma membrane during mitosis, affecting the shape and size of mitotic cells and resulting in defects in mitosis progression and generating daughter cells with aberrant cytoarchitecture. In these conditions, F-actin-astral microtubule crosstalk is impaired leading to mitotic spindle misassembly and misorientation, which in turn contributes to chromosome mis-segregation. Mechanistically, they identified the S100 Ca2+-binding protein A11 as a key membrane-associated regulator that forms a complex with E-cadherin and LGN to coordinate plasma membrane remodelling with E-cadherin-mediated cell adhesion and LGN-dependent mitotic spindle machinery. I felt that this is a strong manuscript for peer-review as it serves diversified interests in modern cell biology. *

Authors response: We thank the Reviewer for their overall very positive feedback on our manuscript.

__Reviewer #2 (Significance (Required)): __

Several key cellular experiments should be repeated using a second line of epithelial cells such as RPE1.

__Authors response: __We agree with the Reviewer it will be interesting to test our findings in other epithelial cells, including RPE1 cells, a widely used epithelial cell model to study the mechanisms controlling cell division. Nonetheless, we would like to emphasise that while our work demonstrates the importance of the interplay between plasma membrane dynamics and cell-cell adhesion for correct execution of polarised cell divisions in mammary epithelial cells, our aim is not to generalise the role of these S100A11-mediated mechanisms. An elegant study has shown that the mechanisms controlling plasma membrane remodelling and elongation during mitosis to ensure correct positioning of the mitotic spindle and symmetric division differ between HeLa cells and RPE1 cells (Kiyomitsu and Cheeseman, 2013 Cell). Additional experiments in a second cell line will require a thorough characterisation of the expression and localisation of S100A11 during the cell cycle, as well as the use of extensive and time-consuming knockdown and imaging experiments over several months and may lead to different observations requiring further mechanistic investigation, which is beyond the initial scope of this study. Additionally, the PhD student who led this study has graduated and left the lab and presently we don’t have capacity or resources to conduct these suggested experiments. Finally, to precisely address the Reviewer’s concern, we have now amended the revised manuscript to make our statements more specific to mammary epithelial cells throughout the text.

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

*Summary: your understanding of the study and its conclusions. *

*The scope of the study is to understand the links between cell-cell adhesion integrity, plasma membrane dynamics and mitotic spindle in mammalian epithelial tissues. To test this, the authors cultured mammary epithelial cells at optimal or low density as a way of perturbing cell-cell adhesion. The authors conclude that perturbing cell-cell adhesion alters plasma membrane dynamics, causing mitotic defects and that S100A11 coordinates this link via E-cadherin. Whilst this is an interesting manuscript, illustrating the differences of mitotic success in optimal density vs. low density cell cultures, I do not think that the conclusions are supported by the evidence presented for the reasons stated below. *

*Major comments: major issues affecting the conclusions. *

*- The manuscript clearly shows that culturing cells at a lower density results in a higher incidence of asymmetric division (figure 1) and mitosis defects (figure 2). Cells round more and faster and there is more actin at the cortex during rounding (figure 3). However, whilst differences in cell-cell adhesion are likely to play a role in mediating these effects, I don't think that it is possible to claim from the data presented that these defects are specifically due to cell-cell adhesion differences. This is because the morphology of cells at low density is also very different - cells appear more mesenchymal, with migratory front-rear polarity instead of apical-basal polarity. These cells will therefore have many differences between them, cell-adhesion being just one. The data is also not showing a 'loss' of cell-cell adhesion integrity but are rather illustrating the differences between cells that have formed cell-cell adhesions and those that have not. To really test the specific role of cell-cell adhesions, the authors would need to inhibit adhesions directly but without altering the cell density - for example via chemical or genetic perturbation within a confined environment. I suggest that the authors either need to do these experiments or to requalify what their data is telling us. *

__Authors response: __We thank the Reviewer for their insightful discussion of the proposed mechanisms described in our manuscript. Several of the Reviewer’s comments pinpoint and exactly match the messages that we would like to convey to the scientific community. Therefore, to address the Reviewer’s comments, we have carefully requalified our statements in several places in the revised manuscript, to ensure they are more clear and more precise.

We agree with the Reviewer’s comment that our experiments using sub-optimal density of mammary epithelial cells rather prevents the formation of cell-cell adhesions than disturbing them. The Reviewer is right, our experiments in low-density cultures suggest that perturbation of cell-cell adhesion formation impairs mammary epithelial identity, where cells lose their polarity and adopt a more mesenchymal phenotype, associated with plasma membrane remodelling defects. This affects the dynamics and progression of cell division. Nonetheless, our observations suggest an interplay between cell-cell adhesion and the plasma membrane to maintains correct cell shape during mitosis. To test this hypothesis, we explored the function of S100A11 which we have identified in the LGN interactome in mitotic mammary epithelial cells (Fankhaenel et al., 2023 Nat Commun), and which has been shown to interact with E-cadherin at adherens junctions in MDCK cells (Guo et al., 2014 Sci Signal). This, together with the fact that S100A11 controls plasma membrane repair (Jaiswal et al., 2014 Nat Commun), suggested S100A11 as an interesting candidate to investigate the interplay between cell-cell adhesion and membrane remodelling during mitosis. The data presented here suggest that we were right and the perturbation of our membrane-bound target, S100A11, indeed leads to the same mitotic phenotypes. S100A11 RNAi-mediated knockdown (48h) affects E-cadherin localisation at the plasma membrane and impairs cell-cell adhesion formation with effects on plasma membrane dynamics that phenocopy the defects observed in our low-density culture experiments. Remarkably, perturbation of cell-cell adhesions persisted in cell treated with si-S100A11 for 72h (see Figure S3). Of note, all our siRNA experiments have been carried out in cells cultured at optimal density to establish cell-cell adhesions. Thus, S100A11 knockdown allows genetic perturbation of E-cadherin-mediated cell-cell adhesion and recapitulates the plasma membrane and mitotic defects observed in sub-optimal cultures of mammary epithelial cells. Future experiments will be key to dissect these S100A11-mediated mechanisms to further understand how plasma membrane remodelling and cell-cell adhesions are coordinated during mitosis. Finally, as suggested by the Reviewer, we have now requalified our conclusions as appropriate in the revised manuscript.

*- The current manuscript also demonstrates that cell adhesion is affected when S100A11 is knocked down (figure 4). It shows binding between and colocalization of S100A11 and E-cadherin, and shows that LGN cortical distribution is affected when S100A11 is knocked down (Figure 5). The results presented are suggestive of S100A11 being upstream of E-cadherin. However, I don't understand how the data shows "crosstalk between the plasma membrane, cell-cell adhesion, and the cell cortex during mitosis". For example, on P9: "We observed unequal distribution of CellMaskTM in a vast majority of S100A11-depleted cells (si-S100A11#1: ~79% versus si-Control: ~26%), indicating defects in plasma membrane remodelling (Figures 4B and 4C)." I don't agree that this demonstrates a defect in PM remodelling. Rather the cells in the representative images are less adherent and have adopted a more migratory cell state similar to that seen in figure 1 when seeded at low density. The fluidity of the much larger cells shown in knock down cells in panel F also appears higher, again suggesting an adhesion defect. *

• *

__Authors response: __The Reviewer has raised very important points here, which we would like to clarify.

We agree with the Reviewer that our results in S100A11-depleted cells indicate impaired cell adhesions which generates cells displaying an invasive/migratory behaviour. However, our analysis of S100A11-depleted mitotic cells labelled with CellMaskTM reveals abnormal plasma membrane elongation generating two daughter cells displaying defective geometry as compared to control cells. These defects in the plasma membrane and cell shape were not noticeable upon E-cadherin knockdown (see previous Figures 5K and 5L; now new Figures 6K and 6L). Thus, our results strongly suggest that S100A11 acts as an upstream cue that coordinates plasma membrane dynamics with E-cadherin-mediated cell adhesions, and that additional mechanisms may be regulated by S100A11 to coordinate cell-cell adhesion with plasma membrane remodelling. How S100A11 ensures such a dynamic interplay between the plasma membrane and E-cadherin during mitosis remains a key question that we have not fully addressed in this initial study. An attractive mechanism could be mediated by the function of S100A11 in regulating the dynamic interaction between F-actin and the plasma membrane, as previously reported (Jaiswal et al., 2014 Nat Commun). Increasing evidence shows the importance of the crosstalk between the plasma membrane, the cortex and cell shape for correct execution of mitosis (Rizzelli et al., 2020 Open Biol). In our experiments, we show that impaired plasma membrane remodelling and cell shape are associated with defects in F-actin and astral microtubule organisation. Thus, our findings reinforce a model whereby S100A11 is a key membrane-associated protein that coordinates the crosstalk between the plasma membrane, cell-cell adhesion, and the cell cortex during mitosis. It will be key to characterise the interactome of S100A11 during mitosis to provide important mechanistic insights into this new role of S100A11; it is our intention to investigate this in the future.

To address the points raised by the Reviewer, we have changed and clarified the statements they highlighted above, in the revised manuscript (pages 10 and 11).

*- An earlier paper from the same lab this year identified Annexin A1 as directing mitotic spindle orientation via localising LGN at lateral cortex. During this earlier paper they also identified S100A11, which is a partner for Annexin A1. The authors could more clearly explain what S100A11 is in the current manuscript and how the current study builds on this earlier study. *

__Authors response: __We thank the Reviewer for highlighting our previous work characterising the interactome of LGN in mitotic mammary epithelial cells (Fankhaenel et al., 2023 Nat Comms), and identifying Annexin A1 (ANXA1) as a polarity cue regulating the localisation and function of the evolutionarily conserved mitotic spindle orientation LGN complex. We also showed that ANXA1 direct partner S100A11 co-purifies with LGN and that perturbation of the ANXA1-S100A11 complex impairs the localisation of the LGN complex at the cell cortex during mitosis. Thus, as rightly pointed out by the Reviewer, this work builds on our previous work discussed above, but also on previous studies establishing S100A11 as a key regulator of plasma membrane repair by regulating the dynamic interplay between F-actin and the plasma membrane (Jaiswal et al., 2014 Nat Commun), and studies showing that S100A11 interacts with E-cadherin at adherens junctions (Guo et al., 2014 Sci Signal). To address the Reviewer’s point (also raised by Reviewer 1), we have now included a paragraph in the introduction (page 5) and results (page 10) of the revised manuscript describing these and other functions of S100A11 to provide a strong rational to our decision to investigate this protein.

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*- Based on the data presented, I suggest that the authors should requalify their data. I suggest that the conclusions that can be drawn from the data are that cellular state is important for regulating mitosis orientation and fidelity (i.e. adherent epithelia cells vs. less adherent more migratory cells). S100A11 is important for promoting cell-cell adhesions and might be upstream of the known role of E-cadherin in regulating spindle orientation. Whilst I suggest that more quantified experiments would need to be included in order to assess possible effects on plasma membrane remodelling, the manuscript could be generally improved by a clearer explanation of the open question that they are addressing and what specific advance this manuscript has made in relation to the current literature, including their own. I do not currently feel that the title of the manuscript is appropriate since I don't think that a crosstalk between the plasma membrane and cell-cell adhesion has been shown here. *

__Authors response: __We would like to reiterate our agreement with the Reviewer’s suggestion about the conclusions drawn from our data. In the initial submission we proposed that perturbation of S100A11-mediated regulation of cell adhesion and plasma membrane impairs the identity of mammary epithelial cells, which affects their shape during mitosis leading to aberrant mitotic progression and outcome. While we have not checked the migratory behaviour of cells not forming cell-cell adhesions, we suggested that the cells adopted a mesenchymal phenotype. Furthermore, we discussed the implication of epithelial-to-mesenchymal transition on chromosome segregation fidelity and execution of mitosis, and how precisely they link with our study (see initial submission’s pages 14 and 19). As suggested by the Reviewer, we have now clarified further these observations in the results (pages 7 and 11) and discussion (pages 15 and 19) of the revised manuscript.

We have quantified several aspects of the changes in plasma membrane dynamics and remodelling throughout, in the initial manuscript (Figure 1D-H; Figure 4C). To address the Reviewer’s point, we have now added quantifications of membrane blebbing (new Figure 1I).

We would like to emphasise that the introduction of the initial manuscript has included the open questions that led to this study. These questions have been addressed further in the discussion, where we have also formulated new hypotheses and discussed what we think are the important outstanding questions for future investigations, in light of our findings. In this study we demonstrate that maintaining epithelial identity is essential for correct execution of polarised cell divisions. Our findings indicate that mammary epithelial cells grown at sub-optimal density lose their epithelial identity, which results in several mitotic defects. We propose a novel mechanism in which S100A11 acts as a molecular sensor of external cues coordinating the interplay between plasma membrane dynamics and cell-cell adhesion to maintain epithelial identity and integrity, thereby ensuring correct progression, orientation, and outcome of cell division. As suggested by the Reviewer, we have clarified further the advances made in this study, in the revised Results and Discussion sections.

To address the Reviewer’s final point, we would like to suggest the following revised title “Interplay between the plasma membrane and cell-cell adhesion maintains epithelial identity for correct polarised cell divisions”, which we hope reflects better the results described in our studies.

*Minor comments: important issues that can confidently be addressed. *

- P3: I wouldn't describe the junctional proteins listed as polarity proteins.

__Authors response: __We have now made this rectification in page 3, as suggested by the Reviewer.

*- Figure 1 - can the membrane blebbing phenotype by quantified? At the moment this part is observational so can't really be used to determine the role of plasma membrane remodelling. *

• *

__Authors response: __We have now included quantifications of blebbing in the revised manuscript, as suggested by the Reviewer (new Figure 1I).

*- Figure 3. I'm not sure what the 'subcortical actin cloud' measurement is. Figure 3G suggests it may be the distance from the cortex to the spindle pole but how does this relate to actin? *

__Authors response: __The Reviewer is right, the subcortical actin cloud includes a pool of dynamic subcortical actin that extends from the cortex (excluding the stiff cortical actin) to the cytoplasm, interacting with the centrosomes and concentrating near the retraction fibres. The subcortical actin cloud has been shown to mediate cortical forces and to concentrate force-generating proteins at the retraction fibres acting on centrosome dynamics and pulling on astral microtubules to orient the mitotic spindle (for example, please see Kwon et al., 2015 Dev Cell). We have now included this clarification in the revised manuscript (page 10).

*- Figure 4A. I can't see GFP-S100A11 accumulating at the cell surface. To me these images suggest that it is relatively ubiquitously expressed throughout the cytoplasm and surface, which is different to the later antibody stains, that show localisation at the cell surface. *

__Authors response: __A similar point has been raised by Reviewer 1. Although our retroviral-mediated transduction allows to avoid excessive expression of GFP-S100A11, the ectopic S100A11 is expressed at higher levels as compared to its endogenous counterpart. Our live images show an accumulation of the protein at the cell surface, but relatively high levels are also visible in the cytoplasm (previous Figure 4A, new Figure S4A). By contrast immunolabelling for endogenous S100A11 shows an obvious accumulation of the protein at the plasma membrane. This difference could also be due to a dynamic behaviour of the protein translocation of GFP-S100A11 between the cell surface and cytoplasm that is captured in our live imaging. Similar slight differences between immunofluorescence and live imaging of cortical proteins involved in mitosis, such as Dynein, NuMA, LGN and CAPZB, have been reported in several studies (to cite a few: di Pietro et al., 2017 Curr Biol; Elias et al., 2014 Stem Cell Rep; Fankhaenel et al., 2023). To address this point, we have now moved the panel showing S100A11 immunofluorescence in Figure S3A to new Figure 4A (also see response to Reviewer 1 Major Point 1).

*- Fig 4H doesn't show an active process of translocation of E-Cadherin to the cytoplasm. It shows representative images with slightly higher levels of E-Cadherin in the cytoplasm. This could be due to translocation or it could be to do with lack of E-Cadherin assembly. *

• *

__Authors response: __We thank the Reviewer for pointing this out. We have rectified this statement accordingly (page 11).

*- 4I I don't understand where the line profile is derived from - where is apical and where is basal in the images? Could a diagram be included? *

__Authors response: __We have now included an illustration of this quantification, in the revised manuscript (new Figure 4J).

- The discussion could be shortened and more clearly written - perhaps with subheadings of the main findings.

__Authors response: __We have clarified several ideas and statements, based on the specific points addressed above. While it is challenging to reduce the size of this section, given that the study addresses several mechanisms of mitosis, we have now shortened the discussion in the revised manuscript.

*- Methods: Why is cholera toxin used in the cell culture medium? *

• *

__Authors response: __Cholera toxin is a key component of MCF-10A medium, which has been shown to stimulate cAMP activation promoting cell proliferation in culture. This culture protocol is a gold standard in the field (Debnath et al., 2023 Methods). Given that cholera toxin is a highly regulated chemical and takes several months to purchase, we have tried culturing MCF-10A without the toxin, but this negatively affected proliferation and passage of this cells. Therefore, we concluded that adding it to the culture medium is important.

__Reviewer #3 (Significance (Required)): __

*In general, this is an interesting paper about the fidelity of mitosis in cells in adherent monolayers vs. in more migratory, non-adherent states. There is existing literature on this topic (some cited in the manuscript, alongside reviews of the topic). *

• *

*The main conceptual advance, as far as I can see, is that S100A11 is important for promoting cell-cell adhesions and might be upstream of the known role of E-cadherin in regulating spindle orientation via LGN. The main limitation is that plating cells at different densities is not a direct 'perturbation' of cell-cell adhesion. This means that the phenotypes seen could be due to many factors, not just cell adhesion. Assessment of plasma membrane and cytoskeletal dynamics are also often observational and not conclusive. *

• *

*The manuscript would be of interest to basic researchers working on epithelial development. Also potentially to basic researchers working on cancer, due to the mitotic errors described. *

• *

*I have expertise in epithelial cell biology. *

I estimate the authors would need between 3 and 6 months for revisions if they decide to do further experiments and between 1 and 3 months if they decide to re-qualify their claims.

• *

__Authors response: __We thanks the Reviewer for their overall positive feedback on our work and its broader importance for researchers in epithelial development and cancer biology.

We would like to reiterate our agreement with the Reviewer’s assessment of the conceptual advances of our work. We show that S100A11 complexes with E-cadherin and LGN during mitosis to control cell-cell adhesion assembly and the mitotic spindle machinery, respectively, which in turn ensures faithful chromosome segregation. Our results also suggest that S100A11 lies upstream of E-cadherin in the regulation of the LGN-mediated mitotic spindle machinery. We also agree with the Reviewer that plating epithelial cells at low density does not directly affect cell-cell adhesion, because, in these culture conditions, cells are not dense enough to establish cell-cell contacts necessary to assemble stable adherens junctions. Rather, and as rightly pointed out by the Reviewer, cells grown at low density fail to maintain their epithelial identity and adopt a more mesenchymal and elongated behaviour, which is accompanied by dramatic changes in plasma membrane remodelling throughout mitosis. Interestingly, our results show that both S100A11 and E-cadherin do not localise at the plasma membrane in these sub-optimal culture conditions. This along with our results showing that depletion of S100A11 phenocopies the effect of low-density culture conditions on plasma membrane remodelling and E-cadherin mediated cell-cell adhesion assembly, allow us to propose a mechanism whereby the membrane-associated S100A11 protein acts as a molecular sensor of external cues bridging plasma membrane remodelling to E-cadherin-dependent cell adhesion to coordinate correct progression and outcome of mammary epithelial cell divisions.

We are grateful for the Reviewer’s insightful discussion of our findings. As we discussed above in our responses to their specific points, we have requalified many of our statements to clarify further our main findings and conclusions throughout the revised manuscript. We have also added new quantifications in response to the Reviewer’s suggestions. We believe, that together, these revisions have advanced further the initial manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary: your understanding of the study and its conclusions.

The scope of the study is to understand the links between cell-cell adhesion integrity, plasma membrane dynamics and mitotic spindle in mammalian epithelial tissues. To test this, the authors cultured mammary epithelial cells at optimal or low density as a way of perturbing cell-cell adhesion. The authors conclude that perturbing cell-cell adhesion alters plasma membrane dynamics, causing mitotic defects and that S100A11 coordinates this link via E-cadherin. Whilst this is an interesting manuscript, illustrating the differences of mitotic success in optimal density vs. low density cell cultures, I do not think that the conclusions are supported by the evidence presented for the reasons stated below.

Major comments: major issues affecting the conclusions.

The manuscript clearly shows that culturing cells at a lower density results in a higher incidence of asymmetric division (figure 1) and mitosis defects (figure 2). Cells round more and faster and there is more actin at the cortex during rounding (figure 3). However, whilst differences in cell-cell adhesion are likely to play a role in mediating these effects, I don't think that it is possible to claim from the data presented that these defects are specifically due to cell-cell adhesion differences. This is because the morphology of cells at low density is also very different - cells appear more mesenchymal, with migratory front-rear polarity instead of apical-basal polarity. These cells will therefore have many differences between them, cell-adhesion being just one. The data is also not showing a 'loss' of cell-cell adhesion integrity but are rather illustrating the differences between cells that have formed cell-cell adhesions and those that have not. To really test the specific role of cell-cell adhesions, the authors would need to inhibit adhesions directly but without altering the cell density - for example via chemical or genetic perturbation within a confined environment. I suggest that the authors either need to do these experiments or to requalify what their data is telling us. The current manuscript also demonstrates that cell adhesion is affected when S100A11 is knocked down (figure 4). It shows binding between and colocalization of S100A11 and E-cadherin, and shows that LGN cortical distribution is affected when S100A11 is knocked down (Figure 5). The results presented are suggestive of S100A11 being upstream of E-cadherin. However, I don't understand how the data shows "crosstalk between the plasma membrane, cell-cell adhesion, and the cell cortex during mitosis". For example, on P9: "We observed unequal distribution of CellMaskTM in a vast majority of S100A11-depleted cells (si-S100A11#1: ~79% versus si-Control: ~26%), indicating defects in plasma membrane remodelling (Figures 4B and 4C)." I don't agree that this demonstrates a defect in PM remodelling. Rather the cells in the representative images are less adherent and have adopted a more migratory cell state similar to that seen in figure 1 when seeded at low density. The fluidity of the much larger cells shown in knock down cells in panel F also appears higher, again suggesting an adhesion defect. An earlier paper from the same lab this year identified Annexin A1 as directing mitotic spindle orientation via localising LGN at lateral cortex. During this earlier paper they also identified S100A11, which is a partner for Annexin A1. The authors could more clearly explain what S100A11 is in the current manuscript and how the current study builds on this earlier study.

Based on the data presented, I suggest that the authors should requalify their data. I suggest that the conclusions that can be drawn from the data are that cellular state is important for regulating mitosis orientation and fidelity (i.e. adherent epithelia cells vs. less adherent more migratory cells). S100A11 is important for promoting cell-cell adhesions and might be upstream of the known role of E-cadherin in regulating spindle orientation. Whilst I suggest that more quantified experiments would need to be included in order to assess possible effects on plasma membrane remodelling, the manuscript could be generally improved by a clearer explanation of the open question that they are addressing and what specific advance this manuscript has made in relation to the current literature, including their own. I do not currently feel that the title of the manuscript is appropriate since I don't think that a crosstalk between the plasma membrane and cell-cell adhesion has been shown here.

Minor comments: important issues that can confidently be addressed.

P3: I wouldn't describe the junctional proteins listed as polarity proteins. Figure 1 - can the membrane blebbing phenotype by quantified? At the moment this part is observational so can't really be used to determine the role of plasma membrane remodelling.

Figure 3. I'm not sure what the 'subcortical actin cloud' measurement is. Figure 3G suggests it may be the distance from the cortex to the spindle pole but how does this relate to actin?

Figure 4A. I can't see GFP-S100A11 accumulating at the cell surface. To me these images suggest that it is relatively ubiquitously expressed throughout the cytoplasm and surface, which is different to the later antibody stains, that show localisation at the cell surface.

Fig 4H doesn't show an active process of translocation of E-Cadherin to the cytoplasm. It shows representative images with slightly higher levels of E-Cadherin in the cytoplasm. This could be due to translocation or it could be to do with lack of E-Cadherin assembly.

4I I don't understand where the line profile is derived from - where is apical and where is basal in the images? Could a diagram be included?

The discussion could be shortened and more clearly written - perhaps with subheadings of the main findings.

Methods: Why is cholera toxin used in the cell culture medium?

Significance

In general, this is an interesting paper about the fidelity of mitosis in cells in adherent monolayers vs. in more migratory, non-adherent states. There is existing literature on this topic (some cited in the manuscript, alongside reviews of the topic).

The main conceptual advance, as far as I can see, is that S100A11 is important for promoting cell-cell adhesions and might be upstream of the known role of E-cadherin in regulating spindle orientation via LGN. The main limitation is that plating cells at different densities is not a direct 'perturbation' of cell-cell adhesion. This means that the phenotypes seen could be due to many factors, not just cell adhesion. Assessment of plasma membrane and cytoskeletal dynamics are also often observational and not conclusive.

The manuscript would be of interest to basic researchers working on epithelial development. Also potentially to basic researchers working on cancer, due to the mitotic errors described.

I have expertise in epithelial cell biology.

I estimate the authors would need between 3 and 6 months for revisions if they decide to do further experiments and between 1 and 3 months if they decide to re-qualify their claims.

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Referee #2

Evidence, reproducibility and clarity

Establishment and maintenance of cell polarity are fundamental processes for physiology in multi-cellular organism given the fact that more than 380 million epithelial cell renewal for every second in human adults. However, the precise mechanisms linking plasma membrane polarity and cortical cytoskeleton dynamics of epithelial cells during mitotic exit and interphase remain ill-illustrated. Salah Elias and her colleagues experimentally manipulated the density of mammary epithelial cells in culture, which led to several mitotic defects. Specifically, they found that perturbation of cell-cell adhesion integrity impairs the dynamics of the plasma membrane during mitosis, affecting the shape and size of mitotic cells and resulting in defects in mitosis progression and generating daughter cells with aberrant cytoarchitecture. In these conditions, F-actin-astral microtubule crosstalk is impaired leading to mitotic spindle misassembly and misorientation, which in turn contributes to chromosome mis-segregation. Mechanistically, they identified the S100 Ca2+-binding protein A11 as a key membrane-associated regulator that forms a complex with E-cadherin and LGN to coordinate plasma membrane remodelling with E-cadherin-mediated cell adhesion and LGN-dependent mitotic spindle machinery. I felt that this is a strong manuscript for peer-review as it serves diversified interests in modern cell biology.

Significance

Several key cellular experiments should be repeated using a second line of epithelial cells such as RPE1.

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Referee #1

Evidence, reproducibility and clarity

The manuscript "Crosstalk between the plasma membrane and cell-cell adhesion maintains epithelial identity for correct polarised cell divisions" by Dr. Hosawi and colleagues reports the characterisation of the mitotic connection between plasma membrane dynamics and division orientation in polarised mammalian epithelial cells in culture. The authors start from the comparison of mitotic events of human mammary MCF10A cells grown at optimal density or at low density. They observed that only at optimal density MCF10A cells polarise by E-cadherin mediated cell-cell contacts, and display uniform membrane enrichment at the cortex, whereas cells grown at low density do not show cortical E-Cadherin enrichment, and distribute aberrantly the plasma membrane at one side and in cytoplasmic vesicles, generating daughter cells with unequal size. Consistently, further analyses revealed that low-density MCF10A cells undergo misoriented mitosis, with chromosome congression and misegregetion defects. Mechanistically, low density MCF10A cells fail to organise a symmetric mitotic spindle and center it in metaphase. This is due to an increased cortical actomyosin thickness coupled to abnormal astral microtubule stability. Building on previous data from the Elias lab, the authors uncover a role of the membrane-associated S100A11 protein in maintaining correct plasma membrane dynamics and E-cadherin localisation in mitosis. Further dissection of the molecular mechanism underlying this mitotic function od S10011A revealed that it enriches at the cortex only in optimal-density MCF10A cells, and promotes spindle orientation by association with LGN and E-cadherin, upstream of E-cadherin. This evidence depicts the plasma membrane and S100A11 proteins as a key mechanical sensors of cell-cell adhesion orchestrating the recruitment of E-cadherin and LGN-dependent force generators to ensure correct division orientation.

Major points:

• Important information is presented in Supplementary Figure S3. I suggest to move these panels in the main figures. Specifically, I would replace figure 4A with S3A showing the distribution of endogenous S100A11 in MCF10A cells, rather than the one of the GFP-tagged version which is over-expressed.
• The mechanisms of division orientation governed by S100A11 seems to impinge on the control of cortical F-actin and astral microtubule dynamics. This is illustrated in figure S3C, which in my opinion should be shown in the main figures with some more explanation / experiments. The authors mention the " tight actin F-actin bundles at the cell-cell contacts" that are lost in S100A11-depleted cells, and that interact with astral microtubules. However this is not fully clear in figure S3C. I think the authors should find a way to present better these evidence which is key in supporting their molecular model.
• I think the discussion would benefit from the addition of a graphical cartoon model illustrating the role of S100A11 in controlling plasma membrane dynamics in mitosis and spindle orientation.
• Finally, to understand the relevance of S100A11 in the context of 3D polarised mammary epithelia, it would be very interesting to analyse the effect of S100A11 knock-downn in mouse mammary epithelial acini grown in matrigel. This is not essential for the proposed studies, but would add biological relevance to the mechanisms characterised in 2D colture.

Minor comments:

• It would be preferable to mention the known functions of S100A11 in the introduction rather than at the beginning of the paragraph at pg. 9.
• at pg 10, beginning of paragraph, I find it a weird phrasing that "LGN interacts with F-actin". As reported in the reference cited here, this is through Afadin, which binds simultaneously LGN and cortical F-actin. I would rephrase it.

Significance

The description of cell adhesion as key factor instructing correct mitotic progression and execution of oriented division of vertebrate epithelial cells by controlling plasma membrane dynamics is novel and interesting for scientist in the spindle orientation/polarity field. The experiments are well-designed and perfectly executed and presented. I am in favour of publication of the manuscript, providing that a few points are addressed.

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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

This MS contains carefully carried out and well controlled experiments describing a new pFFAT in ELYS. There is a similarly convincing demonstration of functionally relevant colocalisation by proximity ligation assay (PLA), particularly that both ELYS and VAP are nuclear envelope proteins in interphase without interacting (neg control in Fig 4D).

Major Issue: Functional significance

A key conclusion is that experiments prove that "ELYS serves as the crucial initiation factor for post-mitotic NPC-assembly" (p5). However, evidence for this is lacking as this would require reconstitution of NPC assembly with a mutant form of ELYS carefully changing the FFAT motif (e.g. 1321A 1324E) and exclusion of other probable VAP targets in experiments with mutant VAP. VAPs are among the proteins with the highest number of documented interactors (see Huttlin 2015/7 etc, e.g. PMID 26186194), so knocking down VAP may have pleiotropic effects and quite indirect read-outs in many aspects of cell function. In addition, for this work specifically there are other NE proteins that are known interactors of VAP: Emerin (EMD) and LBR both interact with VAP (high-throughput data, VAPA and VAPB). EMD has a motif similar to the canonical phospho-FFAT: 98 SYFTTRT 104. LBR has no motif. These findings should not be overlooked in this work. For example, was the interaction with emerin (page 4) sensitive to mutating VAP or ELYS? Could the effect seen in Figure 5 result from interactions with proteins other them ELYS?

Further experiments should be carried out to justify all statements in the current MS of functional significance. Instead of doing more experiments, an alternative for the authors would be to describe the current set of results more cautiously. However, that would require changing much of the impact of the current MS, from the title onwards.

Moderate Issue: VAPA

From the start of the Introduction and some elements of the Discussion, include VAPA in equal measure with VAPB. When describing interactions of ELYS with VAP note that Huttlin et al., reported interactions twice for each of VAPA and VAPB. When describing own results (James et al. 2019) and those of others (Saiz-Ros et al., 2019) that focused on VAPB, clarify if the authors' view is that VAPA would (or would not) have the same interaction.

Is there any evidence that only VAPB is on NE? Note that some refs in the Introduction relate to VAPA: Mesmin (not VAPB); ACBD5: although article titles refer to VAPB, early work (10.1083/jcb.201607055) showed almost identical involvement of VAPA. Also, this redundancy likely explains "function of VAPB in mitosis is not essential," (in Discussion). The lack of effect of VAPA knock-down may indicate that in these cells VAPB is dominant, but does not exclude a role for VAPA when VAPB is reduced. That might be tested by depleting both. Even following that, there is MOSPD2 to consider

Other aspects of the writing

"two amino acid residues are crucial for the interaction (VAPB K87 and M89)." This is wrong. Many residues are critical, these are merely 2 of possibly >10 that were chosen by Kaiser et al (2005) to create their non-binder.. Others have used different mutations to block FFAT binding.

"They may exhibit a certain binding preference to specific members of the VAP ... family...". I cannot think of any example. I note no citation is given.

When listing many or all MSP proteins, the text should state that MOSPD2 is uniquely close to VAPA/B. CFAP65 is typically not mentioned in the VAP-like lists as it does not have any of the conserved sequence that binds FFAT. If however the authors wish to include all human MSP domain protein, they should also include Hydin.

Slightly wrong to cite De Vos et al., 2012 about PTPIP51's FFAT as that paper makes no mention of the motif. Better pick Di Mattia (again)

On VAPB (and also A) on INM: there are references to be cited esp. relating to intranuclear Scs2 in yeast (Brickner et al 2004, Ptak et al 2021)

Citations for VAP at ER-mito contacts "De Vos et al., 2012; Gómez-Suaga et al., 2019; Stoica et al., 2014)". These all refer to the same bridging protein, PTPIP51. Reduce to one citation. Then mention other proteins at the same site VPS13A, mitoguardin(MIGA)-2 ...

"The domain interacts with characteristic peptide sequences ..." add citation to this sentence

"Several variants of such motifs have been described: (i)" ... "(ii)": (i) and (ii) are entirely unlinked. Delete these and also "Several variants of such motifs have been described." Which is repeated later

"FFAT-like motifs come in different flavors and may even lack the two phenylalanine residues (Murphy and Levine, 2016)": while motifs can tolerate variation at both positions, this text is misleading as it implies much more variation than is known. The 1st F can only be conservatively substituted (Y).

Minor aspects in Results:

ORP1L peptide as positive control: cite Kaiser 2005

Was phosphoproteomics done in such a way as to find peptides that have both S1314 and S1326?

Figure 4D, row 2: Comment on intranuclear staining in Prophase (at approx 4 o'clock) of both ELYS & VAP that is PLA positive

Referees cross-commenting

I agree with this point from Reviewer #1. We all agree that the main issue can be resolved experimentally to determine the effect of subtle point mutations in ELYS. Both other reviewers have done a good job in finding issues with the experiments that can also be addressed.

Significance

This work documents an interaction between the protein ELYS, that is involved in the reformation of nuclear pore complexes after mitosis, and the ER membrane protein VAPB. The interactions was previously known through high-throughput studies, along with many 100's of others for VAP, but here it is studied in detail and with care, identifying how the motif is induced by phosphorylation of ELYS. The two proteins are co-localised using convincing proximity ligation assays. This biochemistry and cell biological localisation is well done.

Functional experiments then show that VAP (in this case VAPB) knock-down affects mitosis and chromosome segregation. While the result is incontrovertible, it has many possible interpretations, mainly because VAP has hundreds of interactions, including with multiple proteins involved in mitosis beyond just ELYS. This means that there are major limitations on how the interaction and co-localisation should be interpreted, reducing the advance associated with the current manuscript to incremental, and the limiting the audience to specialized.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, James et al. follow up on their prior discovery that the ER contact site protein VAPB localizes to the nuclear envelope and is a putative binding partner of the nucleoporin ELYS, which coordinates nuclear envelope reformation (NER) with nuclear pore complex (NPC) biogenesis at mitotic exit and is also a constituent of the nuclear facing Y-complexes of mature NPCs. Using a series of complementary biochemical approaches the authors 1) demonstrate that VAPB and ELYS directly interact, 2) map the binding sites on ELYS that are sufficient to bind VAPB, 3) show that mutations that disrupt VAPB-FFAT motif binding also abrogate binding to ELYS including of the full-length protein, 4) define mitotic phosphorylation sties on VAPB-bound ELYS, 5) demonstrate that phosphorylation of ELYS, specifically at the FFAT2 motif, is required for binding to VAPB, and 6) demonstrate that the phosphorylation of ELYS that regulates VAPB binding occurs in mitosis. Turning to cell biology, the authors find that VAPB, which is an established ER protein, has some preference for non-core regions during NER (like ELYS). In addition, PLA analysis suggests that the interaction of VAPB and ELYS is most robust during anaphase and is somewhat disrupted when the binding of VAPB to FFAT motifs is lost due to targeted mutation. Last, the authors demonstrate that depletion of VAPB leads to metaphase delay and lagging chromosomes.

Major comments:

The data supporting direct binding of peptides encoding the FFAT 1 and FFAT2 motif derived from ELYS to VAPB in a manner similar to other FFAT sequences is strong, as is the effect of phosphorylation of FFAT1 on the strength of this interaction.

The evidence supporting the mitotic-specific nature of the ELYS-VAPB interaction is strong, and that this interaction is direct, is also strong, and was rigorously tested using a combination of endogenous expression, heterologous expression, and recombinant protein approaches. Moreover, the sensitivity of this interaction to established mutations in VAPB abrogating FFAT interactions reinforces the outlined underlying biochemical interaction mechanism. The essentiality of ELYS phosphorylation (and therefore the mechanism underlying the mitotic specificity of the interaction) is also strongly supported by the data using phosphatase treatment. Although it is an intuitive model, whether the cell biological evidence support the simplest view that the ELYS-VAPB complex bridges the nuclear envelope to chromatin during NER in late anaphase / at mitotic exit is far less solid and, at a minimum, alternative models should be considered/discussed. For example, how a delay in metaphase in the siVAPB condition is consistent with a role in NER, which occurs exclusively post-metaphase, is unclear. Is it not possible that the VAPB-ELYS complex is regulated by phosphorylation during mitotic progression such that VAPB and/or ELYS can only exert their biological effects when released from the complex? In other words, might ELYS be licensed to act in NER only when it is released from VAPB, which could prevent premature NER/NPC biogenesis? Subtleties of when during mitosis the phosphorylation occurs is challenging, and it could be that the anaphase A to anaphase B transition, when many mitotic entry phosphorylation events begin to be reversed, could be relevant here. Along these lines, in Fig. 5 how the VAPB knock-down does or does not recapitulate the phenotype of an ELYS knock-down in this cell type (and the effect of the combination, to address epistasis) is needed for context, as is whether VAPB knock-down affects ELYS distribution in mitosis. ELYS knock-down would also be very beneficial for the PLA analysis to establish the "floor" of measurable signal. Last, it is also possible that VAPB has other roles in mitosis that should be acknowledged - for example although it is in yeast, it is relevant that a VAPB orthologue Scs2 is required for normal nuclear envelope expansion in mitosis by regulating SUMOylation (Ptak, Saik et al., JCB, 2021 and Saik et al., JCB, 2023) - this work should be referenced as well. Of course, the ideal experiment would be one in which an ELYS knock-down is complemented with a resistant form that encodes the S to A mutations in the FFAT2 region to assess its localization and to see if it can complement the knock-out function of ELYS in post-mitotic NPC assembly or, as suggested by a sequestration model, it can drive the same metaphase delay seen upon VAPB knock-down. This is technically challenging for sure, particularly given the size of the ELYS gene, but it would address the cell biological function of this interaction in the most direct manner. Several other observations that could warrant further comment or study include 1) is there a VAPB signal at the metaphase poles as suggested by Fig. 4A and, if so, could this represent aa distinct mitotic function?; 2) Does the HA-VAPB KD/MD mutant localize differently in mitosis compared to the WT - it appears that it might be less enriched in non-core regions (Fig. 4E)?; 3) does VAPB alter post-mitotic NPC biogenesis/number?

Minor comments:

I would suggest avoiding the use of "novel" when describing newly assembled NPCs or post-mitotic nuclear envelope reformation, as its other meaning of "non-standard" makes this wording confusing. It is unclear whether when the authors state that ELYS localizes "to the nuclear side of the nuclear envelope" they are referring to the nuclear aspect of the NPC and/or a separate pool at the INM - please edit to clarify. More descriptive y-axes for the plots in Fig. 4F and 5F and related legends would be useful; although the details are in the methods section, it would be nice not to have to hunt them down. Also, please clarify the meaning of blue and orange points in Fig. 4F.

Significance

General assessment: The biochemical analysis is rigorous and compelling and establishes the mitotic-specific interaction of VAPB and ELYS including detailed information about the binding interface and its regulation by phosphorylation. The new insight provided into the function of this VAPB-ELYS interaction is somewhat less well developed as the current manuscript, in its current form, does not yet mechanistically define the function of the VAPB-ELYS interaction in mitosis.

Advance: Conceptually, to the best of my knowledge, the idea that VAPB contributes to mitosis in mammalian cells is novel and is therefore impactful and will motivate further work. As the authors connect VAPB biochemically to ELYS, an established factor that promotes the coordination of NER and NPC biogenesis, this interaction is likely to be mechanistically important, although the specific details by which this interaction facilitate normal mitotic progression is not yet clear.

Audience: This work will be of interest to a broad swath of cell biologists including those interested in NPCs, the nuclear envelope, the ER, membrane remodeling, and chromosome segregation.

My expertise is in nuclear envelope dynamics, nuclear pore complexes, and chromatin organization.

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Referee #1

Evidence, reproducibility and clarity

Summary

The VAP proteins are well established as tail anchored proteins of the ER membrane. VAPs mediates co-operation between the ER and other organelles by creating a transient molecular tether with binding partners on opposing organelles to form a membrane contact site over which lipids and metabolites are exchanged. Proteins which bind VAPs generally contain a short FFAT motif, of varying sequence which binds the MSP domain of VAP. More recently the FFAT motif has been more extensively analysed in multiple different proteins and differential phosphorylation of the FFAT motif has been shown to either enhance or block VAP binding depending on the position of the phosphosite.

Recent work conducted by the authors demonstrated that a small population of VAPB is not exclusively localised to the ER and can also reach the inner nuclear membrane. They also identified ELYS as a potential interaction partner of VAPB in a screening approach. ELYS is a nucleoporin that can be found at the nuclear side of the nuclear envelope where it forms part of nuclear pore complexes. During mitosis, ELYS serves as an assembly platform that bridges an interaction between decondensing chromosomes and recruited nucleoporin subcomplexes to generate new nuclear pore complexes for post-mitotic daughter cells. In this manuscript, James et al seek to explore this enigmatic potential interaction between ELYS and VAPB to address why VAPB may be found at the inner nuclear membrane.

Peptide binding assays and some co-immunoprecipitation experiments are used to demonstrate that interactions occur via the MSP-domain of VAPB and FFAT-like motifs within ELYS. In addition, it is demonstrated that, for the ELYS FFAT peptides, the interaction is dependent on the phosphorylation status of serine residues of a particular FFAT-motif that can either promote or reduce its affinity to VAPB. Of most relevance is a serine in the acidic tract (1314) which, when phosphorylated increases VAPB binding. This is completely in line with what is already known about the FFAT motif and so is not surprising, in particular when using a peptide in an in vitro assay.

The authors then utilise cell synchronisation techniques to provide evidence that both phosphorylation of ELYS and its binding to VAPB are heightened during mitosis. Immunofluorescence and proximity ligation assays are used to demonstrate that the proteins co-localise specifically during anaphase and at the non-core regions of segregating chromosomes.

The manuscript is concluded by investigating the effect of VAPB depletion on mitosis with some evidence to suggest that transition from meta-anaphase is delayed and defects such as lagging chromosomes are observed.

Major comments

Overall, this manuscript is well written and the data presented in Figures 1-3 convincingly show the nature of the interaction between ELYS and VAPB. Clearly the proteins interact via FFAT motifs and this interaction appears to be enhanced during mitosis. However, the work as is, relies heavily on peptide binding assays and would benefit from additional experiments to further support the results. The authors need to more clearly show that this specific phosphorylation happens during mitosis, they may have this data but it is not clearly explained. In addition, the data that VAPB-ELYS interaction contributes to temporal progression of mitosis (as per the title) is not sufficiently clear. VAPB silencing appears to have some impact on mitosis but this is not the same thing. So this section needs to be strengthened before this statement can be made.

The authors claim that the study "suggests an active role of VAPB in recruiting membrane fragments to chromatin and in the biogenesis of a novel nuclear envelope during mitosis". Given the data presented in Figures 4 and 5, this appears to be rather speculative with little evidence to support it, so data should be provided or this statement toned down. Currently, without additional supporting data the authors may wish to revise the overarching conclusions of the study and change the title.

Specific points.

Peptide pull down assays clearly show which FFAT-like motifs are important in facilitating binding. The co-immunoprecipitation systems used in Figure 2 also provide useful information on the interaction in a cell context. The authors should combine these findings by introducing full length ELYS mutants with altered FFAT-like motifs into their stably expressing GFP-VAPB HeLa cell line and then performing Co-IPs to help identify which FFAT motif/s drive the mitotic interaction. Other mutants of ELYS harbouring either phosphomimetic or phospho-resistant residues may also be introduced to further investigate mechanisms of the molecular switch in a cellular environment to support the work currently done with peptides alone. This is an obvious gap in the work which, based on the other data the authors have shown, should presumably be straightforward and would also lead directly into the next major point.

• Whilst silencing VAPB does appear to delay mitosis, no reference is made to ELYS throughout Figure 5 nor as part of its associated discussion. Given that VAPB has more than 250 proposed binding partners, the observed aberration of mitotic progression could result from a huge number of indirect processes. Further work is needed to link the experiment specifically to the VAPB-ELYS interaction and not just loss of VAPB. We would suggest generating a complementation system where ELYS is either knocked out or silenced and then wild-type ELYS and an ELYS FFAT mutant (which cannot interact with VAPB),and/or a phospho mutant (whose interaction cannot be regulated during mitosis) are introduced. Then the observed effects can be better attributed to the VAPB-ELYS interaction and not just loss of VAPB.
• The immunofluorescence and PLA results in Figure 4 could be strengthened by including other ER markers. This would show that co-localisation of ELYS at the non-core region is specific to VAPB protein, not any ER protein or rather than an artefact of the ER being pushed out of the organelle exclusion zone during mitosis and therefore 'bunching' at the periphery of the nuclear envelope. It would be worthwhile repeating these experiments with candidates such as VAPA, other ER membrane proteins or at least GFP-KDEL, to make this phenomenon more convincing. As part of this the authors should ideally generate a complemented ELYS KO (see point above) to avoid the residual activity attributed to endogenous background in the PLA Figure 4E.
• Authors should clarify if the phosphorylation events (in particular S1314) only occur or are increased during mitosis. This may be data they have from the MS experiment in Figure 3 or it could also be shown using a phospho-antibody (although this can be challenging if a suitable antibody cannot be made).
• The authors should clarify why they need to do these semi in-vitro assays with purified GST-VAPB-MSP on beads and then lysates added and not just a standard co-IP. If this is simply signal intensity due to a very small proportion of VAPB binding to ELYS then this is fine but this should be stated and it should be made clear that ELYS is not a major binding partner - most of VAPB is on the ER. Otherwise, this is misleading.

I estimate that the suggested alterations above would incur approximately 3-6 months of additional experimental work, depending on if KO cell lines were required.

Minor comments

• To show that the observed interactions and potential role of VAPB-ELYS interaction is universal it would be useful to have at least a subset of experiments also shown in another cell line or system - this is now also a requirement for some journals.
• Consider re-wording the title of the manuscript to better reflect the data presented within the study. Alternatively, provide further evidence that VAPB-ELYS interactions directly affect temporal progression of mitosis to validate this claim, as discussed above.
• Quantification of blots in Figure 2A could allow measurement of relative binding affinities between VAPB-ELYS throughout the cell cycle. The same could be applied to the effect of phosphorylation on binding affinity in Figure 2D.
• The cells used are never clearly mentioned in the text - I assume this is always in HeLa but this should be added in all cases for clarity
• Page 8: "As shown in Fig. 2A,a large proportion of GFP-VAPB was precipitated under our experimental conditions." - I don't understand how this is shown in this figure as the non-bound fraction is not shown?
• Please provide some controls to demonstrate the extent to which the samples used are asyn, G1/M or M.
• Page 9 - why are Phos-tag gels not shown as this would make this result more convincing?
• Figure 3A - I find the SDS-PAGE gel confusing. Why not show the whole gel and why is the band size apparently reduced in the mitotic fraction when previously it was increased (by phosphorylation)? It would also be useful to see if there were any other band shifts.
• "FFAT-2 of ELYS is regulated by phosphorylation" The way you have setup the experiment leads the reader to think you are going to show which sites are differentially phosphorylated in mitosis, but then this is not the case - so there seems no purpose to doing the experiment this way. If you used TMT MS approach you would be able to potentially quantify the change in phosphorylation at the FFAT motif sites in mitosis. Otherwise what is the purpose of using these 2 samples, mitotic and AS?
• For all of the antibodies used, in particular for the PLA, please provide evidence of validation of the antibodies.
• Just a minor point to consider - In the methods for your lysis buffer you use 400mM NaCl - might this slightly reduce the VAPB-FFAT interaction? Worth considering reducing this?
• "The rather small difference observed between the wild-type and the mutant protein observed in this experiment probably results from the presence of endogenous VAPB in the stable cell lines, which could form dimers with the exogeneous HA-tagged versions." If this is the case then please demonstrate that this is happening, or use the KO approach in the major points above.
• "we now show that the proteins can indeed interact with each other, without the need for additional bridging factors (Figs. 1 and 3)." You show that the peptides can bind - but this is not the same thing as the peptide in the full context of the protein - so this should be toned down or removed.
• "Remarkably, this region is highly conserved between species, suggesting that it is important for protein functions (data not shown)". Please show the alignments so the reader can judge for themselves. It is conserved in ALL species and the phosphosites are also conserved??
• "In our experiments, knockdown of VAPA alone did not lead to a delay in mitosis (data not shown). " Why not show this data - as this is a very interesting and potentially important observation? Also add the validation of knockdown of VAPA.
• I find the end to the discussion to the paper rather abrupt. It would be interesting to discuss further how VAPB, but not apparently VAPA reaches the INM and if so why this function is required of an ER adaptor and not another more obvious adaptor protein. In short - why would VAPB be performing this role?

Referees cross-commenting

I agree with the comments of the other reviewers, and they are very much in line with my own review. We all seem convinced that VAPB binds ELYS via a pFFAT, and that this interaction is enhanced during mitosois. However the role of this interaction in mitotic progression remains unclear and based on this data should not be claimed in the title or discussion of the paper.

Significance

Overall, if the manuscript could be improved with the suggested changes, then this could be a considerable conceptual advance in how we understand the VAP proteins, showing functions beyond those as an ER adaptor. This would be significant for the field.

In the context of the existing literature the work does not advance our knowledge of FFAT-VAP interactions, this has already been shown, but it would give a nice example of how this can be regulated during mitosis and how VAP can contribute beyond just as an ER adaptor at membrane contact sites.

There would be a wide audience in the cell biology field and more widely as mutations in VAPB cause a form of ALS, and many people are working in this area.

My field of expertise is in organelle cell biology and membrane contact sites.

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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

This work uses state-of-the art cell imaging and careful image quantification to study early secretory pathway dynamics during budding yeast gametogenesis. The work builds on previous findings of roles for Sec16 in ER exit site formation, Sec-body formation during specific developmental stages, and for Vps13 in lipid homeostasis. The work appears to be carefully conducted and is nicely presented.

Much of the early work - here relating to meiosis in budding yeast, reflects that from studies of mitosis in other systems. This work therefore adds nicely to our understanding of membrane dynamics during cell division. Figures 1 and 2 are useful additions to the literature in this regard. I would have preferred images to be presented in magenta/green rather than red/green for wider accessibility.

The advance is therefore not conceptual but functional. It would, in my view, be unfair to dismiss this as incremental.

Major

My major comment here relates to the FRAP data - the difference in half-life of recovery is clear but there are also substantial differences in the immobile fraction. It is vital that this is expanded on and discussed - it has direct relevance to the conclusions relating to the ongoing functional activity of ERES and the comparison to Sec bodies. Is it not possible that the immobile element here is a functional "reserve" like with Sec bodies? This might be consistent with multiple pools of COPII proteins acting at different stages to maintain then promote secretory activity. Some consideration needs to be given to expanding this and possibly including these data in the main figure. Further analysis and controls should also be included here, other COPII proteins and other markers that one might predict would not alter dynamics in these conditions.

The key mechanistic advance in the manuscript relates to the role of Gip1 with clearly defined outcomes showing its role in ERES remodelling in nascent spores, regeneration of the Golgi and PSM elongation. The context of this part of the work is most important. Specifically, the comparison to VPS13 mutant needs to be expanded on and better explained. The analysis in Fig.4 needs a clearer explanation within the figure of how localization to the PSM is defined. The detail in the methods is also insufficient and the "custom R script" should be published with the work (or on a publicly accessible repository such as Git/Zenodo etc).

The development of this work with the delta-sep mutant gives useful insight and the analysis of Sec16 does indeed support a model where this is an early marker for the process. Despite the link to septins no direct analysis of YSW1 is included (which suppresses the sporulation defect in gip1 ts alleles.

Minor:

Figure 3 introduces new data on reticulons and their impact on ER membrane shape. Again, this reflects findings in other systems but does not add much to the specific narrative of this story but is useful for those in the field. Similar to this, the data on Sec4 are of interest to the specialist but add little to the overall story.

The discussion is quite lengthy and speculative dealing with themes and ideas that are not addressed directly by this work. My comments on the FRAP data relate directly to the models in Figure 8 and this discussion. Given the emphasis on nutrient starvation in the final discussion more detail is needed on the relative experimental conditions used here and in flies/mammals.

Some relevant prior work should also be cited e.g. on the role of Sec16 on exit from mitosis PMID: 21045114, other work relating to gip1 mutants (PMID: 19465564).

Consider presenting images as magenta/green.

Referees cross-commenting

I agree broadly with the other reviewers comments.

While there are elements that could be developed much further. I am not familiar with the role of GIP1 in transcriptional regulation - is this from work in yeast or solely Arabidopsis (is GIP1 here - GBF1 interacting protein, a true equivalent?).

I agree with the comments on the need for further - and well explained - statistical analyses.

Significance

Overall, the work is solid and adds nicely to our understanding. It is likely to be of most interest to a quite specialist audience. The work on PSM formation and spore formation is a clear advance with significant sections of the work being of interest to a wider audience working on early secretory pathway (notably COPII dynamics). Deeper mechanistic insight is missing but non-trivial. More depth would be added by studying further deletion mutants but I am not entirely convinced that this will rapidly advance the field further than this current presentation.

My expertise is in early secretory pathway function.

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Referee #2

Evidence, reproducibility and clarity

Suda et al. conducted an in-depth investigation of gametogenesis in budding yeast, focusing on the formation of the prospore membrane (PSM) through membrane trafficking rearrangement. They made an interesting observation that the number of endoplasmic reticulum exit sites (ERES) fluctuates during PSM formation, transitioning from decreasing to increasing. The study proposed that ERES regeneration, facilitated by protein phosphatase-1 and its specific subunit, Gip1, plays a crucial role in this process. However, the mechanism by which Gip1 regulates ERES numbers remains unclear, and the authors primarily used Gip1 mutants that may affect transcriptional regulation through Glc7, raising concerns about potential indirect effects. It is essential for the authors to experimentally validate the key mechanisms underlying their findings to strengthen their conclusions.

Major Points:

1. The conclusion that the loss of ERES causes a transient stall in membrane trafficking and leads to Golgi loss is based on the phenotype of GIP1 KO and SED4 KO cells. However, how Gip1 regulates ERES numbers remains unclear. The authors need to define whether Gip1 mediates this regulation through Glc7 dephosphorylation or via transcriptional regulation.
2. The claim of ERES fluctuation during gametogenesis lacks statistical validation (Figure 1D). Since the difference is very small, the authors should perform a statistical analysis to determine if there is a significant difference in ERES numbers during different stages of gametogenesis.
3. The conclusion regarding the loss and regeneration of the Golgi apparatus is based on qualitative observations of Mnn9, Sys1, and Sec7 signals. A quantitative analysis is necessary to strengthen these findings, as some cells may retain these signals despite their disappearance in representative images.
4. Based on phenotypic similarity between GIP1 KO and SED4 KO cells, they concluded that Gip1 regulates the ERES number required for PSM expansion. They demonstrated that the number of ERES and Golgi dramatically decreased in GIP1 KO cells. The authors also need to do this experiment in SED4 KO cells? Since Sed4 affects ER function in general, the authors should demonstrate that SED4 KO cells are appropriate to make a conclusion about ERES regulation and PSM expansion.

Referees cross-commenting

Consistent with the other two reviewers, we feel our comments should be addressed prior to publication of this manuscript.

Significance

Overall, the study presents a high-quality imaging analysis of gametogenesis in budding yeast. However, the authors should experimentally validate the mechanisms underlying ERES regulation by Gip1 and conduct rigorous statistical analyses to support their observations. Additionally, since gametogenesis and Gip1 are yeast specific, the significance of this study might be limited.

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Referee #1

Evidence, reproducibility and clarity

Summary: Yeast gametogenesis requires major membrane reorganization to ensure proper spore formation for survival during starvation, but many questions remain for how this process occurs. This current manuscript by Suda et al. uses fluorescence live imaging to visualize the dynamics of secretory pathway components which are critical for contributing lipids to the prospore membrane (PSM) in the developing spore. The authors find that ER exit sites (ERES) initially decrease and then gradually increase, coinciding with their appearance inside the PSM, suggesting that new lipids for PSM growth are trafficked through the secretory pathway from within the PSM. By screening through known genetic mutants that cause meiosis defects, the authors identify Gip1p, an adaptor for protein phosphatase 1, as a master upstream factor for prospore-associated ERES formation. Interestingly, the authors additionally identify a non-essential component of ERES in vegetative cells, sed4, to be important for sporulation and ERES PSM localization.

Overall, the biological question is interesting and the imaging quality is appropriate. The general conclusion that ERES foci localize inside developing PSMs in a gip1- and sed4- dependent manner is supported by the data. However, the manuscript is purely descriptive; not much molecular insight is gleaned into how secretory pathway components localize to the inside of the PSM, nor is it clear how important this localization is in contributing new lipids to the PSM. Additionally, there are multiple points within the writing and presentation of the results, some specified below, that require clarification; more details in the quantifications also need to be included to ascertain whether the data robustly support the authors' current conclusions.

Major comments:

• The loss of gip1 affects multiple aspects of sporulation and leads to an early termination of spore formation, giving little insight into how ERES are established inside the PSM. The most intriguing result is that loss of sed4, a nonessential paralog of the membrane-bound Sar1 GEF, leads not only to sporulation defects, but also affects the localization of Sec13/ERES to the spores. Given that some spores still form in the sed4 cells, more experiments detailing ERES and golgi localization within the forming spores could be done. Does the golgi no longer localize within the PSM in sed4 cells? Is there are a PSM size difference between those that do and do not have ERES foci in this genetic background? Where does Sed4 localize in gip1 cells?
• While Vps13 is introduced as an additional pathway for supplying lipids, this manuscript does not address the relative contribution of vesicular trafficking versus vps13 lipid transport in PSM formation. Where does Vsp13 localize in the sed4 cells? Are they enriched around/within those spores that do form?
• The clarity of writing in the results and discussion section could be improved, some of which I point out below. The discussion could also be shortened.

Specific comments:

• For all quantifications, more information is necessary, including sample sizes, mean/median values, and number of biological replicates. It may be helpful to include these values in a separate supplemental table.
• Relatedly, for 1D, 3C, and 4C graphs: It is difficult to judge whether the changes of ERES # are significantly different across the various genetic backgrounds as displayed, and given the large spread and small changes, statistical analyses are required to make such conclusions. Could the authors comment on why there is a minor yet noticeable percent of cells with very high ERES numbers?
• To make specific conclusions that ERES 'regenerate' inside PSMs, more detailed quantifications of ERES foci # inside the developing prospores should be included, with appropriate statistical analysis.
• Figure 6A, B: The localization of Glc7 does not look different to me, as claimed. The septin-like cable localization presumably occurs during elongation, as seen in 6A, and gip1D cells do not enter this phase, then it should be expected that there would be no septin-like localization. In 6B, the lower panels seem to show mature, closed PSMs; can the authors label the phases and explain why this is?
• Figure 8, Top panels, indicate the purple coverage is PSM. It is unclear why the authors suddenly say that ERES are 'transiently inactivated' here and in the discussion to describe the lower # of ERES foci, whereas the appearance of PSM-associated ERES foci is considered 'regenerated' (which implies de novo assembly). In general, from the present data, one cannot conclude inactivation vs. formation/regeneration, so some caution in terminology is warranted.

Minor comments:

• A schematic showing the different stages of meiosis and of PSM formation would be useful.
• Scale bar dimensions are missing for most of the figures.
• It may be helpful to use an alternate color combination for merged images (i.e. cyan/yellow, red/cyan, or magenta/green), to accommodate colorblind readers.
• For Figure 1C, authors should show orthogonal views along the z plane at timepoint 8 to show that ERES foci are indeed inside the PSM.
• Figure 1E legend, define closed arrowhead; additionally, include an explanation in the main text of what the Spo20(51-91) marker is.
• For kymograph displays (Figures 1E, 5A, 7E), please include time points in each frame.
• Figure 4D, 4E legend, the multiple terms describing PSM circumference length is confusing: 'cell perimeter, 'PSM perimeter' and 'PSM length'. Please choose one term and describe this fully in the text.
• Fig 5: The images in Fig 5 are dim and the gain should be adjusted accordingly. Figure 5B, is this an intensity trace of one punctum, or punctae from multiple cells as implied in the text?
• More details, not just references, in methods for sporulation induction and image analysis should be included.

Referees cross-commenting

I also agree that our comments should be addressed prior to publication. Of the existing data, the need for further statistical analysis is a high priority.

Significance

The data and conclusions presented here are for a specialized, basic audience interested in yeast meiosis, especially focused on how membranes and the secretory pathway are remodeled during this process. The paper's results have some implications for the reproductive aging field, but this area is not directly investigated in this current manuscript. The paper uses mostly established organelle markers and gene mutants previously known to be involved. The finding of Sed4's involvement in sporulation is, to my knowledge, novel and intriguing.

Reviewer Expertise: organelle morphology, the secretory pathway, protein aggregation, stress responses, aging, fluorescence microscopy, yeast, C. elegans, mammalian cells, biochemistry

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Reply to the reviewers

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

1. General Statements

We were naturally pleased to read the enthusiasm coming from both reviewers. Both mentioned that an extension to experimentation in cells would increase the impact of the study, even though both recognize that the biophysical and biochemical experiments constitute a study that is significant and interesting to a broad readership.

2. Point-by-point description of the revisions

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

This manuscript by Bryan et al., describes the use of Hydrogen/Deuterium-exchange Mass Spectrometry (HXMS) as a powerful tool to identify key amino acid residues and associated interactions driving liquid-liquid demixing. They have particularly focused on the Chromosomal Passenger Complex (CPC), an important regulator of chromosome segregation, which has recently been shown to undergo liquid-liquid demixing in vitro. Their work presented here allowed them to identify a few key electrostatic interactions as molecular determinants driving the liquid-liquid demixing of the CPC. Their work also shows that crystal packing information of protein molecules, where available, can provide valuable insight into likely factors driving liquid-liquid demixing.

Major comments:

[#1] A previous study by Trivedi et al., NCB 2019 identified an unstructured region in Borealin (aa residues 139-160) as the main region driving the phase separation of CPC. Interestingly, this region only shows a moderate reduction in HX upon liquid-liquid demixing. But no experiments or discussions related to this observation are presented in the current version of the manuscript.

In the Trivedi et al. paper, the authors were careful to state that the region of borealin between 139-160 contributed to phase separation, but there was clearly a remaining propensity to phase separate in vitro in the mutant. Thus, it is fully expected that there should be other regions in the complex that contribute to phase separation. It was satisfying that this region was independently identified in the hydrogen-deuterium exchange experiments and we suggest that a “moderate” reduction is consistent with a protein condensate having liquid properties. Since this region was already characterized we have focused our work in this paper to the new region identified by the hydrogen-deuterium exchange experiments.

[#2] In the absence of cellular data on if and how these mutations (within the triple-helical bundle region) affect CPC's ability to phase separate in cells, the implication of this work is very limited - One can't say for sure these are interactions driving phase separation of CPC in a cellular environment. In the absence of any cellular data with the mutants described here, much of the discussion on the possible roles of CPC phase separation in cells does not appear relevant to this manuscript. I would suggest that the authors focus mainly on highlighting the power of using HXMS as a tool to characterise the molecular determinants of liquid-liquid demixing at a relatively high resolution.

We have now added cellular data in the form of one of the key experiments used to explore CPC liquid-liquid demixing utilizing the Cry2 optogenetic system for inducible dimerization. The results of testing WT Borealin versus the mutant we identified is defective in droplet formation are shown in the all new Fig. 6. Some relation of our overall findings, encompassing observations made with purified components and now in cells, to the cellular function of the CPC is pertinent. In light of the reviewer comments, we have also reduced this aspect in the discussion (see the substantial edits on pg. 12).

Minor comments:

[#3] The authors should ensure that the introduction cites relevant literature thoroughly. For example, where the potential role of Borealin residues 139-160 in conferring phase separation properties to the CPC is mentioned, the authors failed to cite Abad et al., 2019, which showed the contribution of the same Borealin region in conferring nucleosome binding ability to the CPC.

We have made this particular change on pg. 4 and also have gone through to ensure we are appropriately citing relevant literature.

Reviewer #1 (Significance (Required)):

This is a highly relevant and significant work, particularly considering the rapidly growing list of examples for Phase separation of proteins/protein assemblies and their potential biological roles (in spite of ongoing debates in the field about the cellular relevance of several phase separation claims). The data presented in this manuscript are solid and convincingly establish HXMS as a useful tool to characterise molecular interactions driving liquid-liquid demixing. Considering its applicability to characterise wide-ranging protein assemblies implicated in phase separation, this work will be of interest to a broad readership.

We thank the reviewer for the strong praise of the significance of our study.

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

In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC. Comments: 1) "The three non-catalytic subunits of the CPC (INCENP1-58, Borealin, and Survivin) form soluble homotrimers that have a propensity to undergo liquid-liquid phase separation.8 " Do the authors mean the hetero-trimeric CPC?

Yes, we meant heterotrimers. It is now corrected.

2) For better clarity, the authors can indicate the residue numbers of each of the components INCENP, Survivin, and Borealin in the CPC trimeric helix-bundle crystallographic structure in Fig 1.

These are included on the revised Figure 1A.

3) "In the condition we identified, 90% +/- 5% of the ISB protein was found within the rapidly sedimenting droplet population (Fig. 1C)." The authors should include the time-point corresponding to the gel shown in Fig 1C.

This information is now directly labeled in Fig. 1C.

4) Prior to the HXMS experiments on the phase-separated ISB protein complex, were the samples subjected to sedimentation to separate the dispersed from the condensed droplet phase? Since several time points after formation of phase-separated ISB complex have been characterized to compare and contrast between the dispersed and the droplet phase, the authors can consider performing a time-dependent sedimentation assay to ascertain the fraction of the ISB complex in the droplet phase.

The HXMS experiments were not performed on sedimented samples, so this complication in our HX workflow is not necessary. We note that the sedimentation that we include in our study (Figs. 1C, 5E, and S6), involves centrifugation for 10 minutes, and that length of time presents a substantial design challenge to our HX experimentation. We considered it at the outset of our study, but, in the end, our study was facilitated by our finding early on that this separation step was unnecessary. Further, we note that we report statistically significant differences at the earliest HX timepoints in the areas prominently protected from HX upon droplet formation (10 and 100 s; see Fig. 1C for an example). Indeed, we do not observe broadening of our HXMS spectra (examples shown for all timepoints, Fig. 2B,F) that would be expected if there were a large degree of mixed states (i.e. a large population of molecules in the free protein state and a large population of molecules in the droplet state) each having different HXMS rates. One can imagine that this sort of envelope broadening behavior (“EX1-like”) could be observed in other samples where there are multiple substantially populated states of a protein present at a particular timepoint, but this is not what we observe in the experiments we performed in this study.

5) "At the 100 s timepoint, the most prominent differences between the soluble and droplet state were located within the three-helix bundle of the ISB, with long stretches in two subunits (INCENP and Borealin) and a small region at the N-terminal portion of the impacted a-helix in Survivin (Fig. 1F)" According to Fig 1F, at the 100 s time-point, there is also another small region in Survivin (approximately residues 12-20) that exhibits slower exchange rates in the droplet state. Can the authors comment on whether this region undergoes any conformational change or if it exhibits homotypic interactions retarding the hydrogen/deuterium exchange rates in the droplet phase?

Our general approach in the Black lab over the past decade-plus of HXMS has been to restrict our conclusions whenever practical to do so to the consensus behavior. This permits multiple partially overlapping peptides to be used to generate confidence in the changes that drive our conclusions. The reviewer carefully recognizes the behavior of a single peptide (in 2 different charge states) that might have actual changes relative to some of the longer peptides that it partially overlaps with, and smaller changes can yield larger percentage changes on small peptides. We have chosen to not include this single peptide in the text describing our main conclusions from the work to be consistent with our longstanding strategy for rigorous interpretation of HXMS data. Our conclusion is that this region of not substantially changed upon droplet formation.

6) The authors mention that: "By the latest timepoint, 3000 s, there was some diminution in the number of droplets which may indicate the start of a transition of the droplets to a more solid state (i.e., gel-like)." As a result of this time points beyond 3000 s have not been used for comparing Hydrogen/Deuterium exchange rates in the condensed droplet phase with the soluble state. Can the authors comment on what happens to the nature of these specific interactions between the components of the CPC in the 'gel-like state'? A combination of both non-specific weak interactions as well as strong site-specific interactions between macromolecular components has been widely known to contribute towards the formation of several phase-separated compartments. It will be interesting to know the perspective of the authors on what sort of interactions get populated within these compartments to give rise to a more solid gel-like state. At this later time points, do the droplets exhibit reversibility under higher ionic strength conditions? Do the authors have some data to show how the material property of these droplets evolve as a function of time?

We offered the idea of a transition to a more solid state to the reader because it was a reasonable conclusion, although challenging to prove (something the Stukenberg lab is actively working on, though, see our response to point #9, below). The vast majority of our conclusions in the paper, and essentially all of what we emphasize are the important ones, are based on earlier timepoints where this is not an issue. Thus, we find an extended study of the late-developing features in our droplets something more appropriate for separate studies outside the scope of the current one.

7) "Examination of the entire time course shows that during intermediate levels of HX (i.e., between 100-1000 s), this region takes about three times as long to undergo the same amount of exchange when the ISB is in the droplet state relative to when it's in the free protein state (Figs. 2B, C and Supplemental Fig. 2). Upon droplet formation, HX protection within Borealin is primarily located in the interacting a-helix and is less pronounced at any given peptide when compared to INCENP peptides (Fig. 2E). Nonetheless, similar to INCENP peptides, it still takes about twice as long to achieve the same level of deuteration for this region of Borealin in the droplet state as compared to the free state." How do the hydrogen/deuterium exchange rates and extent of deuteration in the N-terminal part (residues 98-142) of the Survivin polypeptide chain, constituting the three-helix bundle core, evolve as a function of time? Also, how do the exchange rates for peptides in this region compare with those of the other protein subunits Borealin and INCENP and what inference can be drawn from these differences?

The peptides from a.a. 98-142 of Survivin exhibit HX protection through the timecourse (and before and after droplet formation) consistent with a folded a-helix (and comparable to the overall HX behavior of the other helices in the 3-helix bundle of the ISB)(Fig. S2). There is subtly slower HX in the droplet state for this region at later timepoints for this portion of Survivin (Fig. S4), and this is explicitly highlighted in the Results section on pg. 6.

8) The authors mention that mutating either all the glutamate residues or combinations of these residues on the acidic patch on the INCENP subunit, to positively charged residues, causes a decrease in the propensity of phase separation, as formation of salt bridges with Borealin subunit from adjacent hetero-trimeric complexes appears to be the major driving force for phase separation. Can the authors elaborate on how the reduction in the phase separation propensity of these salt-bridge inhibiting mutants might be directly affecting the subsequent localization of the CPC to the inner centromeres? Can the authors supplement their existing in vitro data with further in vivo characterization of CPC recruitment or localization to the centromeres, for each of the constructs exhibiting reduced propensity of phase separation?

As we state in the introduction, the recruitment to centromeres requires established ‘conventional’ targeting via the specific histone marks to which we refer. We also cite the correlations demonstrated between prior mutations in Borealin (impacting aa 139-160) that both disrupt phase separation in vitro and reduce CPC levels at the centromere. In our revision, we have added what we feel are the most critical cell-based experiments to relate to our HX studies in the new Fig. 6. We are preparing for future studies to study mutants arising from our HX studies, and our plans are to pursue gene replacement approaches that will rigorously test the impact on the mitotic function of the CPC. In the process of these future studies, the impact on localization will be measured, too. As others in the field are investigating the correlations between observations made with purified components and those made in the cell, and where there are nuances at play in how the actual experiments are conducted, we are certain our cell-based studies will extend far beyond the timeframe appropriate for our HX-focused study. Rigorous cell-based studies of mitotic functions are what is needed, however, and we have made our plans with that in mind.

9) It might be really interesting for the authors to look at the recent preprint from Hedtfeld et al. 2023 Molecular Cell, (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4472737). In this preprint they have recombinantly purified a stoichiometric trimer (referred to as CPC-TARGWT) comprising full length survivin, borealin, and a 1-350 residue fragment of INCENP (instead of 1-58 used in this study) and have tried to assess if any correlation exists between the in-vitro phase behaviour of CPC-TARGWT mutants and their corresponding recruitment to the inner centromere, to form a phase separated compartment. Targeting residues in the BIR domain of Survivin involved in interactions with the N-terminus of the Histone H3, Shugosin 1 or in the recognition of H3T3phos, and substituting them with Alanine or completely deleting C-terminal domain of Borealin (a region implicated in CPC dimerization and centromere recruitment), was found to result in poor centromere localization, although the in vitro phase separation properties of these constructs were found to be indistinguishable, suggesting no evident correlation between the two phenomena. Thus it might be a useful piece of data to correlate the phase separation propensities of the ISB complex variants used in this current study with the extents of their in vivo recruitment to the inner centromere. This maybe beyond the scope of the paper, but it would be good to comment on this.

For the correlation studies, please refer to our response to point #8, above. From our reading of the June 2023 preprint that the reviewer mentions, the main concern raised by the authors is questioning whether the region first identified in the Trivedi et al paper in Borealin (aa 139-160) has a role in phase separation. As the reviewer noted, Hedtfeld et al report using a complex that includes more of the INCENP protein than used in the Trivedi et al study, complicating the direct comparison between studies. Using the data in figure 5E of the Hedtfeld et al preprint, the authors suggest that the condensate formation of their version of the Borealin mutant D139-160 in vitro complex has similar phase separation properties as the wild type. However, we note that in our inspection of these data we see numerous differences. The mutant forms rounder, and larger condensates than WT and have reduced concentration of protein (less bright intensity). Finally only the WT protein has a “grape bunch” morphology. We note that unpublished data in the Stukenberg lab show these same differences can represent a defect in liquid demixing properties of a version of the purified CPC. While it is intuitive that larger condensates represent more phase separation, the unpublished data mentioned above suggests the opposite is true for the CPC. In particular, the data from the Stukenberg lab suggest the size of a droplet is mostly governed by the amount of droplet fusion in the first minutes after dilution and thus is limited by relatively rapid hardening of the complex. We note that in the course of discussions with the corresponding author of the preprint mentioned by the reviewer we did apprise them of the unpublished observations mentioned, above, in case they saw fit to include in their ongoing studies what would seem to be critical measurements (e.g. measuring circularity, droplet size, droplet intensity, and FRAP) to assess our suspicion that their construct contains a portion of INCENP that can accelerate condensate formation. If true, the Hedtfeld et al data are fully consistent with the Borealin mutant D139-160 having a significant condensate formation potential than the WT protein.

10[A]) "Our data also provide an important clue about the previously identified region on Borealin that is required for liquid-demixing in vitro and proper CPC assembly in cells 8. Specifically, our data (Fig. 1F, Supplementary Figs. 2, 4A) suggest this region of Borealin adopts secondary structure that undergoes additional HX protection in the liquid-liquid demixed state" This data fits perfectly with previous studies from Trivedi et al. (2019), which states that deletion of the Borealin 139-160 fragment obliterates its phase separation in vitro and also reduces the accumulation of CPC at the centromere. On the contrary, in the recent preprint from Hedtfeld et al. 2023 Molecular Cell, they have shown that the phase separation behaviour of their reconstituted CPC-TARGWT harboring the Borealin 139-160 deletion mutant was found to be indistinguishable from the WT. Can the authors comment on what might be the reason for this difference? Is it possible that this central Borealin region is involved in interactions with the additional fragment of INCENP subunit used in the helical bundle reconstitution, or with other centromere component proteins, whereby the deletion of region is causing inefficient recruitment to the inner centromere? This can be elaborated in the discussion section of the manuscript.

This is discussed in the response to #9, above. Through this format (the Review Commons procedure for public posting of author responses before submission of the study to a journal), our comments herein will be made public for those with the most interest in comparing our data to what is has been posted on preprint servers. We think that is the most appropriate for now, with more to surely come when the aforementioned results from the Stukenberg lab are posted/published and, hopefully when there is more information about the nature of the droplets reported in the Hedtfeld et al., study.

10 [B]) It is also well known that in addition to these electrostatic interactions, the core of the ISB helical bundle is formed by an extensive network of hydrophobic interactions. Have the authors ever looked into how perturbing any of these intra-trimeric complex hydrophobic interactions affect their ability to phase separate and perform their subsequent function?

We think there is some confusion, here. The electrostatics we focus on are between heterotrimers rather than within them. We certainly would predict that disrupting the hydrophobic surface that generates a stable heterotrimer would, in turn, disrupt individual heterotrimers. Our study assumes a stable heterotrimer as a starting point, so we view this type of perturbation as unrelated to our conclusions.

11) The phase separated CPC compartment is known to enrich several other inner centromere proteins such as the Histone H3, Sgo1, the histone H3T3phos, among others. Have the authors tried to increase the complexity of the reconstituted CPC scaffold by incorporating more components to look into whether that changes any of the interaction interfaces between the ISB trimeric complexes within the condensed phase? Can this CPC compartment be reconstituted using a bottom-up approach?

We are glad that our studies with a reductionist biochemical reconstitution approach have inspired the questions that require increased complexity. They are now warranted based on the advance we have made in the present study, and hopefully will form the basis for future, separate studies.

Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation.

Reviewer #2 (Significance (Required)):

In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC.

Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation

We thank the reviewer for the positive comments on the significance of our study.

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Referee #2

Evidence, reproducibility and clarity

Structural Basis for the Phase Separation of the Chromosome Passenger Complex Nikaela W. Bryan, Ewa Niedzialkowska, Leland Mayne, P. Todd Stukenberg, and Ben E. Black# Reviewer Comments Manuscript Number: RC-2023-02017

In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC.

Comments: 1. "The three non-catalytic subunits of the CPC (INCENP1-58, Borealin, and Survivin) form soluble homotrimers that have a propensity to undergo liquid-liquid phase separation.8 " Do the authors mean the hetero-trimeric CPC? 2. For better clarity, the authors can indicate the residue numbers of each of the components INCENP, Survivin, and Borealin in the CPC trimeric helix-bundle crystallographic structure in Fig 1. 3. "In the condition we identified, 90% +/- 5% of the ISB protein was found within the rapidly sedimenting droplet population (Fig. 1C)." The authors should include the time-point corresponding to the gel shown in Fig 1C. 4. Prior to the HXMS experiments on the phase-separated ISB protein complex, were the samples subjected to sedimentation to separate the dispersed from the condensed droplet phase? Since several time points after formation of phase-separated ISB complex have been characterized to compare and contrast between the dispersed and the droplet phase, the authors can consider performing a time-dependent sedimentation assay to ascertain the fraction of the ISB complex in the droplet phase. 5. "At the 100 s timepoint, the most prominent differences between the soluble and droplet state were located within the three-helix bundle of the ISB, with long stretches in two subunits (INCENP and Borealin) and a small region at the N-terminal portion of the impacted a-helix in Survivin (Fig. 1F)" According to Fig 1F, at the 100 s time-point, there is also another small region in Survivin (approximately residues 12-20) that exhibits slower exchange rates in the droplet state. Can the authors comment on whether this region undergoes any conformational change or if it exhibits homotypic interactions retarding the hydrogen/deuterium exchange rates in the droplet phase? 6. The authors mention that: "By the latest timepoint, 3000 s, there was some diminution in the number of droplets which may indicate the start of a transition of the droplets to a more solid state (i.e., gel-like)." As a result of this time points beyond 3000 s have not been used for comparing Hydrogen/Deuterium exchange rates in the condensed droplet phase with the soluble state. Can the authors comment on what happens to the nature of these specific interactions between the components of the CPC in the 'gel-like state'? A combination of both non-specific weak interactions as well as strong site-specific interactions between macromolecular components has been widely known to contribute towards the formation of several phase-separated compartments. It will be interesting to know the perspective of the authors on what sort of interactions get populated within these compartments to give rise to a more solid gel-like state. At this later time points, do the droplets exhibit reversibility under higher ionic strength conditions? Do the authors have some data to show how the material property of these droplets evolve as a function of time? 7. "Examination of the entire time course shows that during intermediate levels of HX (i.e., between 100-1000 s), this region takes about three times as long to undergo the same amount of exchange when the ISB is in the droplet state relative to when it's in the free protein state (Figs. 2B, C and Supplemental Fig. 2). Upon droplet formation, HX protection within Borealin is primarily located in the interacting a-helix and is less pronounced at any given peptide when compared to INCENP peptides (Fig. 2E). Nonetheless, similar to INCENP peptides, it still takes about twice as long to achieve the same level of deuteration for this region of Borealin in the droplet state as compared to the free state." How do the hydrogen/deuterium exchange rates and extent of deuteration in the N-terminal part (residues 98-142) of the Survivin polypeptide chain, constituting the three-helix bundle core, evolve as a function of time? Also, how do the exchange rates for peptides in this region compare with those of the other protein subunits Borealin and INCENP and what inference can be drawn from these differences? 8. The authors mention that mutating either all the glutamate residues or combinations of these residues on the acidic patch on the INCENP subunit, to positively charged residues, causes a decrease in the propensity of phase separation, as formation of salt bridges with Borealin subunit from adjacent hetero-trimeric complexes appears to be the major driving force for phase separation. Can the authors elaborate on how the reduction in the phase separation propensity of these salt-bridge inhibiting mutants might be directly affecting the subsequent localization of the CPC to the inner centromeres? Can the authors supplement their existing in vitro data with further in vivo characterization of CPC recruitment or localization to the centromeres, for each of the constructs exhibiting reduced propensity of phase separation? 9. It might be really interesting for the authors to look at the recent preprint from Hedtfeld et al. 2023 Molecular Cell, (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4472737). In this preprint they have recombinantly purified a stoichiometric trimer (referred to as CPC-TARGWT) comprising full length survivin, borealin, and a 1-350 residue fragment of INCENP (instead of 1-58 used in this study) and have tried to assess if any correlation exists between the in-vitro phase behaviour of CPC-TARGWT mutants and their corresponding recruitment to the inner centromere, to form a phase separated compartment. Targeting residues in the BIR domain of Survivin involved in interactions with the N-terminus of the Histone H3, Shugosin 1 or in the recognition of H3T3phos, and substituting them with Alanine or completely deleting C-terminal domain of Borealin (a region implicated in CPC dimerization and centromere recruitment), was found to result in poor centromere localization, although the in vitro phase separation properties of these constructs were found to be indistinguishable, suggesting no evident correlation between the two phenomena. Thus it might be a useful piece of data to correlate the phase separation propensities of the ISB complex variants used in this current study with the extents of their in vivo recruitment to the inner centromere. This maybe beyond the scope of the paper, but it would be good to comment on this. 10. "Our data also provide an important clue about the previously identified region on Borealin that is required for liquid-demixing in vitro and proper CPC assembly in cells 8. Specifically, our data (Fig. 1F, Supplementary Figs. 2, 4A) suggest this region of Borealin adopts secondary structure that undergoes additional HX protection in the liquid-liquid demixed state" This data fits perfectly with previous studies from Trivedi et al. (2019), which states that deletion of the Borealin 139-160 fragment obliterates its phase separation in vitro and also reduces the accumulation of CPC at the centromere. On the contrary, in the recent preprint from Hedtfeld et al. 2023 Molecular Cell, they have shown that the phase separation behaviour of their reconstituted CPC-TARGWT harboring the Borealin 139-160 deletion mutant was found to be indistinguishable from the WT. Can the authors comment on what might be the reason for this difference? Is it possible that this central Borealin region is involved in interactions with the additional fragment of INCENP subunit used in the helical bundle reconstitution, or with other centromere component proteins, whereby the deletion of region is causing inefficient recruitment to the inner centromere? This can be elaborated in the discussion section of the manuscript. 10. It is also well known that in addition to these electrostatic interactions, the core of the ISB helical bundle is formed by an extensive network of hydrophobic interactions. Have the authors ever looked into how perturbing any of these intra-trimeric complex hydrophobic interactions affect their ability to phase separate and perform their subsequent function? 11. The phase separated CPC compartment is known to enrich several other inner centromere proteins such as the Histone H3, Sgo1, the histone H3T3phos, among others. Have the authors tried to increase the complexity of the reconstituted CPC scaffold by incorporating more components to look into whether that changes any of the interaction interfaces between the ISB trimeric complexes within the condensed phase? Can this CPC compartment be reconstituted using a bottom-up approach?

Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation.

Significance

In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC.

Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation

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

This manuscript by Bryan et al., describes the use of Hydrogen/Deuterium-exchange Mass Spectrometry (HXMS) as a powerful tool to identify key amino acid residues and associated interactions driving liquid-liquid demixing. They have particularly focused on the Chromosomal Passenger Complex (CPC), an important regulator of chromosome segregation, which has recently been shown to undergo liquid-liquid demixing in vitro. Their work presented here allowed them to identify a few key electrostatic interactions as molecular determinants driving the liquid-liquid demixing of the CPC. Their work also shows that crystal packing information of protein molecules, where available, can provide valuable insight into likely factors driving liquid-liquid demixing.

Major comments:

A previous study by Trivedi et al., NCB 2019 identified an unstructured region in Borealin (aa residues 139-160) as the main region driving the phase separation of CPC. Interestingly, this region only shows a moderate reduction in HX upon liquid-liquid demixing. But no experiments or discussions related to this observation are presented in the current version of the manuscript.

In the absence of cellular data on if and how these mutations (within the triple-helical bundle region) affect CPC's ability to phase separate in cells, the implication of this work is very limited - One can't say for sure these are interactions driving phase separation of CPC in a cellular environment.

In the absence of any cellular data with the mutants described here, much of the discussion on the possible roles of CPC phase separation in cells does not appear relevant to this manuscript. I would suggest that the authors focus mainly on highlighting the power of using HXMS as a tool to characterise the molecular determinants of liquid-liquid demixing at a relatively high resolution.

Minor comments:

The authors should ensure that the introduction cites relevant literature thoroughly. For example, where the potential role of Borealin residues 139-160 in conferring phase separation properties to the CPC is mentioned, the authors failed to cite Abad et al., 2019, which showed the contribution of the same Borealin region in conferring nucleosome binding ability to the CPC.

Significance

This is a highly relevant and significant work, particularly considering the rapidly growing list of examples for Phase separation of proteins/protein assemblies and their potential biological roles (in spite of ongoing debates in the field about the cellular relevance of several phase separation claims). The data presented in this manuscript are solid and convincingly establish HXMS as a useful tool to characterise molecular interactions driving liquid-liquid demixing. Considering its applicability to characterise wide-ranging protein assemblies implicated in phase separation, this work will be of interest to a broad readership.

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Reply to the reviewers

I - General criticisms

Reviewer #1: My main criticism is unfortunately inherent to the approach: comparative studies are absolutely critical, but they can only provide a very sparse sampling of diversity. Fortunately, thanks to high-throughput sequencing, bioinformatic analyses can now be performed on a large number of species, but experimental validation is typically restricted to two or three species. The consequence of this for the present manuscript is that while the functional conservation of the Gwl site is convincingly shown, the exact mechanisms responsible for the reduced effect of PKA phosphorylation remain relatively vaguely defined. Indeed, in their Discussion the authors list a number of experimental approaches to address this - but I understand that these would all involve substantial efforts to address. In particular, testing chimeric constructs around the consensus PKA site and from multiple species could be very informative.

We completely agree with the reviewer that comparative approaches are critical to understanding biological mechanisms, and are excited by the increasing possibilities to perform not only sequence and descriptive comparisons but functional studies across a range of emerging model organisms. We hope that more and more researchers in cell and molecular biology will profit from experimental tools and techniques now available in such species, and to pioneer new ones. Of course, and he/she rightly points out, conclusions are currently limited by the number of species studied, but comparisons between two judiciously chosen species can already be very informative. Thus, in our study, the use of Xenopus and Clytia allowed us to make significant progress towards our main objective of understanding the cAMP-PKA paradox in the control of oocyte maturation; specifically by showing both that PKA phosphorylation of Clytia ARPP19 is lower in efficiency and that the phosphorylated protein has a lower effect on oocyte maturation than the Xenopus protein. As the reviewer points out, unravelling the exact mechanisms underlying these differences will require a large amount of additional work and is beyond the scope of the current study. Actually, we have embarked on several series of experiments to this end using some of the approaches listed in the Discussion. Specifically, we are testing the biochemical and functional properties of chimeric constructs containing the consensus PKA site from various species. This is a substantial undertaking which will require one to two years to complete, but is already giving some very interesting findings.

Reviewer #1: The figures and text could be slightly condensed down to about 6 figures.

We have reduced the number of figure panels but we prefer to maintain the number of figures, because the experimental data presented in them is essential to the interpretation of our results and the overall conclusions of the article. If the journal editor would like us to reduce the number of figures, we could do this by displacing Figure 4 and some panels of other figures (to then fuse some of them) to supplementary material, but this would be a pity.

____________II - Abstract

As recommended by Reviewer #2, we have reworked the Abstract to make it more accessible to new readers, attempting to bring out more clearly and simply the main results and conclusions of the study. We correspondingly simplified and shortened the title of the article. Changes: Page 2.

____________III- Introduction points

Reviewer #2: I believe that it would be interesting to include some time-references when introducing the prophase arrest of Clytia and Xenopus oocytes. How long is prophase arrest in Xenopus compared to Clytia or other organisms? How can this affect the prophase arrest mechanisms? It seems that the prophase arrest in Xenopus oocytes is found to be significantly more prolonged compared to Clytia and various other organisms, and also meiotic maturation proceeds much more rapidly in Clytia than in Xenopus. This should be indicated in the introduction with a short introduction of why, and not others, were these species chosen for this study.

Differences in timing of oocyte prophase arrest and in maturation kinetics across animals are indeed highly relevant in relation to the underlying biochemical mechanisms. Unfortunately, not enough information is currently available concerning the duration of the successive phases of oocyte prophase arrest across species to make any meaningful correlations with PKA regulation of maturation initiation. We have nevertheless expanded the Introduction to cover this issue as follows:

• We start the introduction by mentioning how the length of the prophase arrest varies across species. Changes: Page 3, lines 5-11.
• We have added examples of species which likely have similar durations of prophase arrest but show cAMP-stimulated vs cAMP-inhibited release. Changes: Page 4, lines 28-35.
• We have specified the temporal differences in meiotic maturation in Xenopus (3-7 hrs) and Clytia (10-15 min). Changes: Page 5, lines 32-33.

Reviewer #2: why, and not others, were these species [Xenopus, Clytia] chosen for this study. A brief justification is included in lines 1-page 5 "..a laboratory model hydrozoan species well suited to oogenesis studies", but it does not explain why this and not other hydrozoan species like Hydra, that has also been used for meiosis studies.

As requested by Reviewer #2, fuller details are now included about the advantages of Clytia compared to other hydrozoan species, citing several articles and recent reviews here and also in the Discussion. Changes: Page 5, lines 21-32 & 37-39.

Hydra is a classic cnidarian experimental species and has proved an extremely useful model for regeneration and body patterning, but is not suitable for experimental studies on oocyte maturation because spawning is hard to control and fully-grown oocytes cannot easily be obtained, manipulated or observed. In contrast many hydromedusae (including Clytia, Cytaeis, and Cladonema) have daily dark/light induced spawning and accessible gonads, so provide great material for studying oogenesis and maturation. Of these, Clytia has currently by far the most advanced molecular and experimental tools.

Reviewer #2: The proteins MAPK is not introduced properly, as it is first mentioned in the results section in line 12. Given the importance of the results provided with it, it should be presented in the introduction prior to the results section.

As requested by Reviewer #2, the involvement of MAPK activation during Xenopus oocyte meiotic maturation is now introduced, explaining how its phosphorylation serves as a marker of Cdk1 activation. Changes: Page 5, lines 1-5.

Reviewer #2: These sentences need a more elaborate explanation: Page 4 Lines 16-17 "... no role for cAMP has been detected in meiotic resumption, which is mediated by distinct signaling pathways" Which pathways?

We now give the example of the well-characterized pathway Gbg-PI3K pathway for oocyte maturation initiation in the starfish. Changes: Page 4, lines 1-15.

Reviewer #2: Page 4 line 34-39. Introduction indicates that the phosphorylation of ARPP19 on S67 by Gwl is a poorly understood molecular signaling cascade (line 34). However, the positive role of ARPP19 on Cdk1 activation, through the S67 phosphorylation by Gwl, appears to be widespread across all eukaryotic mitotic and meiotic divisions studied (lines 36-37). These two sentences seem a little contradictory. If the general pathway has been identified but the signaling cascade is still not well described, please indicate that in a clearer way.

We apologise that the wording we used was not clear and implied that the mechanisms of PP2A inhibition by Gwl-phosphorylated ARPP19 were poorly understood. On the contrary, they are very well studied. The part that remains mysterious concerns the upstream mechanisms. We have reworded the paragraph to make this point unambiguous. Changes: Page 5, lines 1-8.

____________IV - Results

Reviewer #2: The text of the results is generally well described; however, all the sections start with a long introductory paragraph. I believe this facilitates the contextualization of the experiments, but please try to summarize when possible. For example, in page 5 lines 12-25, or page 7 lines 30-37, are all introduction information.

As requested by Reviewer #2, we have shortened or removed the introductory passages of the Results section paragraphs, which were redundant with the information given in the introduction. We did not restrict to the two examples cited by the reviewer, but have shortened all the Results passages that repeat information already provided in the Introduction. Changes: Page 7, lines 3-4 & 14-16 & 36-37 - Page 8, lines 12-15 - Page 8, lines 37-40 & Page 9, lines 1-6.

Reviewer #2: Page 7, Lines 14-19 present a general conclusion of the findings explained in lines 20-27. I think these results are important and they should be explained better, in my opinion they are slightly poorly described.

We have followed the reviewer's recommendation. The explanation of the experiments and the results are more detailed and the paragraph ends with a general conclusion which came too early in the previous version. Changes: Page 8, lines 22-24 & 32-34.

Reviewer #2: Page 8, lines 16-17: "It was not possible to increase injection volumes or protein concentrations without inducing high levels of non-specific toxicity". What are the non-specific toxicity effects? How was this addressed? What fundaments this conclusion?

Clytia oocytes are relatively fragile. Sensitivity of oocytes to injection varies between batches, while in general increasing injection volumes or protein concentrations increases the levels of lysis observed. We do not know exactly what causes this but lysis can happen either immediately following injection or during the natural exaggerated cortical contraction waves that accompany meiotic maturation, suggesting that it relates to mechanical trauma. We have expanded this paragraph and the legend of Fig. 3C to explain these injection experiments more fully in the text and to clarify these issues. Changes: Page 9, lines 16-29 - Page 32, lines 34-41 & Page 33, lines 1-11 - Supplementary Table 1.

Same paragraph: Lines 25-27 of page 8. Text reads, "These results suggest that PP2A inhibition is not sufficient to induce oocyte maturation in Clytia, although we cannot rule out that the quantity of OA or Gwl thiophosphorylated ARPP proteins delivered was insufficient to trigger GVBD.". Please provide evidence if higher concentrations of OA or Gwl were tested to state this conclusion.

As explained above, we could not increase the concentrations of ARPP19 protein beyond 4mg/ml. It is important to note that at the same concentration, both Clytia and Xenopus proteins induce activation of Cdk1 and GVBD in the Xenopus oocyte.

Concerning OA, it is well documented in many systems including Xenopus, starfish and mouse oocytes as well as mammalian cell cultures, that high concentrations lead to cell lysis/apoptosis as a result of a massive deregulation of protein phosphorylation (Goris et al, 1989; Rime & Ozon, 1990; Alexandre et al, 1991; Boe et al, 1991; Gehringer, 2004; Maton el al, 2005; Kleppe et al, 2015). Specific tests in Xenopus oocytes, have shown that injecting 50 nl of 1 or 2 mM OA specifically inhibits PP2A, while injecting 5 mM also targets PP1 and higher OA concentrations inhibit all phosphatases. For these reasons, we did not increase OA concentrations over 2 mM. When injected in Xenopus oocyte at 1 or 2 mM, OA induces Cdk1 activation, GVBD but then the cell dies because PP2A has multiple substrates essential for cell life. When injected at 2 mM in Clytia oocytes, OA does not induce Cdk1 activation nor GVBD but promotes cell lysis. This supports the conclusion that 2 mM OA is sufficient to inhibit PP2A (and possibly other phosphatases) but that PP2A inhibition is not sufficient to induce oocyte maturation in Clytia.

We have reworded the relevant text to make these points clearer. The previous statement that “we cannot rule out that the quantity of OA or Gwl thiophosphorylated ARPP proteins delivered was insufficient to trigger GVBD” has been removed because it was unnecessarily cautious in the context of the literature cited above, as now fully explained_._ Changes: Page 9, lines 31-35 - Page 32, lines 34-41 & Page 33, lines 1-11 - Supplementary Table 1.

References: Alexandre et al, 1991, doi: 10.1242/dev.112.4.971; Boe et al, 1991, doi: 10.1016/0014-4827(91)90523-w; Gehringer, 2004, doi: 10.1016/s0014-5793(03)01447-9; Goris et al, 1989, doi: 10.1016/0014-5793(89)80198-x; Kleppe et al, 2015, doi: 10.3390/md13106505; Maton el al, 2005, doi: 10.1242/jcs.02370; Rime & Ozon, 1990, doi: 10.1016/0012-1606(90)90106-s

Reviewer #2: Lines 12-13: the sentence "This in vitro assay thus places S81 as the sole residue in ClyARPP19 for phosphorylation by PKA." is overstated. As not all residues had been tested, please indicate that "it is likely that" or "among the residues tested", in contrast to "the sole residue in ClyARPP19".

We realise that we had not explained clearly enough how the thiophosphorylation assay works. In this assay, γ-S-ATP will be incorporated into any amino acid of ClyARPP19 phosphorylatable by PKA. The observed thiophosphorylation of the wild-type protein, demonstrates that one or more residues are phosphorylated by PKA. This thiophosphorylation was completely prevented by mutation of a single residue, S81. This experiment thus shows that S81 is entirely responsible for phosphorylation by PKA in this assay. We have rewritten this section more clearly. Changes: Page 10, lines 18-28.

____________V - Figures and text related to the figures

Figure 1A

Reviewer #2: Why is mouse not included in Figure 1A? Although it might be very similar to human, given that mouse is the species that is most commonly use as a mammalian model, I believe it could be included. However, this is optional upon decision by the authors.

We have replaced the human sequence in Figure 1A with the mouse sequence as suggested. The sequences of each of the mouse and human ENSA/ARPP19 proteins are indeed virtually identical across mammals. Changes: Fig. 1A.

Figure 1C

Reviewer #2: There should be a better explanation in the text of the results sections for the image included in in Fig1 C. Note that Clytia is not a commonly used species, therefore images should be properly explained for general readers. Please indicate in the text that ClyARPP19 mRNA is expressed in previtellogenic oocytes and not in vitellogenic, plus any additional information needed to understand the image. In addition, the detection of ARPP19 in the nerve rings is intriguing. This is mentioned in the discussion section, any idea of its function there? Please include some additional information or additional references, if they exist.

We have expanded the explanations of Fig. 1C in the text and in the figure legend. We have also added cartoons to the figure to help readers understand the organisation of the Clytia jellyfish and gonad. As now explained, ClyARPP19 mRNA is detected in oocytes at all stages, but the signal is much stronger in pre-vitellogenic oocytes because all cytoplasmic components including mRNAs are significantly diluted by high quantity of yolk proteins as the oocytes grow to full size. Changes: page 7, line 40 & page 8, lines 1-9 - Fig. 1C - Legend page 31, lines 19-31.

Nothing is known about the function of ARPP19 in the Clytia nervous system. The only data linking ARPP19 and the nervous system concerns mammalian ARPP16, an alternatively spliced variant of ARPP19. ARPP16 is highly expressed in medium spiny neurons of the striatum and likely mediates effects of the neurotransmitter dopamine acting on these cells (Andrade et al, 2017; Musante et al, 2017). This point is included in the Discussion in relation to the hypothesis that PKA phosphorylation of ARPP19 proteins in animals first arose in the nervous system and only later was coopted into oocyte maturation initiation. Changes: page 16, lines 12-13 & 17-20 - page 19, lines 6-9.

Figure 2A

Reviewer #1: Fig. 2A (and similar plots in subsequent figures): is it really necessary to cut the x axis? Would it be possible to indicate the number of oocytes for each experiment (maybe in the legend in brackets)?

As requested by reviewer #1, the x-axis is no longer cut. The number of oocytes for each experiment is now provided in the legend of Fig. 2A and in similar plots of Fig. 5A and 5D. Changes: Fig. 2A - Legends page 31, lines 37-38 (Fig. 2A), page 33, line 25 (Fig. 5A) - page 33, line 34 (Fig. 5D).

Figure 2D-E (as well as Figure 6C-D and Figure 8B-C)

Reviewer #1: Fig. 2D (and all similar plots below): I am lacking the discrete data points that were measured. Without these it is impossible to evaluate the fits. The half-times shown in 2E are somewhat redundant, and the information could be combined on a single plot.

We added all the data points to the concerned plots: 2D, 6C and 8B. As recommended by reviewer #1, we combined on a single plot the phosphorylation levels and the half-times. 2D-E => 2D, 6C-D => 6C and 8B-C => 8B. Changes: Figs 2D, 6C and 8B - Legends page 32, lines 9-14 (Fig. 2D), page 34, lines 24-30 (Fig. 6C) - page 35, lines 13-18 (Fig. 8B).

Figure 3A and 3B

Reviewer #1: Fig. 3: why is the blot for PKA substrates cut into 3 pieces? It would be clearer to show the entire membrane.

In western blot experiments using Clytia oocytes, the amount of material was limited so the membranes were cut into three parts. The central part was incubated sequentially in distinct antibodies. We finally incubated all three parts of the membrane with the anti-phospho-PKA substrate antibody to reveal the full spectrum of proteins recognized by this antibody. The 3 pieces in Fig. 3A therefore together make up the same original membrane. We had separated them on the figure to make it clear that the membrane had been cut. In the new presentation, the 3 pieces are shown next to each other, making it clear that all the membrane is present, with dotted lines indicating the cut zone as explained in the legend. Changes: Fig. 3A and 3B - Legend page 32, lines 22-25 (Fig. 3A), lines 30-33 (Fig. 3B) - Page 24, lines 3-6 (Methods).

Figure 3C

Reviewer #2: Fig. 3C needs a better explanation in the text. The way these graphs are presented is somehow confusing. The meaning of the dots is not self-explanted in the graph, and it seems that each experiment was done independently but then the complete set of results is presented. Legend says that "each dot represents one experiment" but this is difficult to read as in every analysis the figure also indicates the average and the total number of oocytes. If authors wish so, they can keep the figure as it is, but then please explain this graph better in the text, and please include statistical analysis. These results are very robust, but a comparison between the number of oocytes that go through spontaneous GVBD of lysis in the different conditions will benefit their understanding.

This figure is intended to provide an overview of all the Clytia oocyte injection experiments that we performed, for which full details are given in Supplementary Table 1. Since these experiments were not equivalent in terms of exact timing and types of observation (or films) made and oocyte sensitivity to injection -as ascertained by buffer injections-, it is not justified to make statistical comparisons between groups. We apologise that the presentation was misleading in this respect and hope that the new version is easier to understand. We removed from the figure the average percentage of maturation for each condition between experiments to avoid any misunderstanding of the nature of the data, and rather represent the values of each experiment independently. We also now explain the data included in the figure fully in the text and figure legend. Changes: Page 9, lines 16-39 - Fig. 3C and Supplementary Table 1 - Legend page 32, lines 34-41 & page 33, lines 1-11.

Reviewer #2: Also, please provide in the text a plausible explanation for the cause of oocyte lysis for all experimental conditions (Fig 3C). Given that in the control experiments with buffer this effect is also observed in some oocytes, please explain if this is caused by a mechanical disruption of the oocyte during the injection. In contrast, okadaic acid induces the lysis in all the 14/14 oocytes analyzed, is this due also to the mechanical approach? Or is there other reason more related to the PP2A inhibition? Please explain.

These points are treated above in the response to this reviewer concerning the Results section.

Figure 5

Reviewer #2: In Figure 5 D-F, cited in page 9 lines 35-35. Can you provide an explanation of why the time course of meiosis resumption was delayed?

The binding partners/effectors of XeARPP19-S109D that are involved in maintaining the prophase arrest have not yet been identified. The most probable explanation of the delay in meiotic maturation induced by ClyARPP19-S109D is that Clytia protein recognizes less efficiently these unknown ARPP19 effectors that mediate the prophase arrest. As a result, maturation would be delayed, but not blocked. This explanation was provided in the Discussion (page 17, lines 14-17) and is now mentioned in the Results section. Changes: page 11, lines 16-19.

____________VI - Discussion

Reviewer #2: Although it presents highly interesting suggestions, discussion may border on being overly speculative, especially from line 37 of page 15 till the end.

We agree and have reduced the speculation in this part of the discussion, in particular regrouping and reformulating ideas about evolutionary scenarios in a single paragraph. Changes: page 17, lines 37-41 - page 18, lines 1-41 - page 19, lines 1-18.

SUMMARY - Point by point responses to individual reviewers’ comments in their order of appearance.

Reviewer 1

• The figures and text could be slightly condensed down to about 6 figures.

We have reduced the number of figure panels but we prefer to maintain the number of figures, because the experimental data presented in them is essential to the interpretation of our results and the overall conclusions of the article. If the journal editor would like us to reduce the number of figures, we could do this by displacing Figure 4 and some panels of other figures (to then fuse some of them) to supplementary material, but this would be a pity.

• The exact mechanisms responsible for the reduced effect of PKA phosphorylation remain relatively vaguely defined. Indeed, in their Discussion the authors list a number of experimental approaches to address this - but I understand that these would all involve substantial efforts to address. In particular, testing chimeric constructs around the consensus PKA site and from multiple species could be very informative.

As the reviewer points out, unravelling these exact mechanisms will require a large amount of additional work and is beyond the scope of the current study.

• 2A (and similar plots in subsequent figures): is it really necessary to cut the x axis? Would it be possible to indicate the number of oocytes for each experiment (maybe in the legend in brackets)?

Fig. 2A has been changed in line with the reviewer's request (as well as similar plots in Fig. 5A and 5D). Changes: Fig. 2A - Legends page 31, lines 37-38 (Fig. 2A), page 33, line 25 (Fig. 5A) - page 33, line 34 (Fig. 5D).

• 2D (and all similar plots below): I am lacking the discrete data points that were measured. Without these it is impossible to evaluate the fits. The half-times shown in 2E are somewhat redundant, and the information could be combined on a single plot.

Fig. 2D has been changed in line with the reviewer's request (as well as similar plots in Figs 6C-D and 8B-C). Changes: Fig. 2D, 6C and 8B - Legends page 32, lines 9-14 (Fig. 2D), page 34, lines 24-30 (Fig. 6C) - page 35, lines 13-18 (Fig. 8B).

• 3: why is the blot for PKA substrates cut into 3 pieces? It would be clearer to show the entire membrane.

In western blot experiments using Clytia oocytes, the amount of material was limited so the membranes were cut into three parts. The central part was incubated sequentially in distinct antibodies. We finally incubated all three parts of the membrane with the anti-phospho-PKA substrate antibody to reveal the full spectrum of proteins recognized by this antibody. The 3 pieces in Fig. 3A therefore together make up the same original membrane. In the new presentation, the 3 pieces are shown next to each other, making it clear that all the membrane is present, with dotted lines indicating the cut zone as explained in the legend. Changes: Fig. 3A and 3B - Legend page 32, lines 22-25 (Fig. 3A), lines 30-33 (Fig. 3B) - Page 24, lines 3-6 (Methods).

Reviewer 2

• Abstract needs to be simplified if wants to reach a broader range of readers.

We have reworked the Abstract to make it more accessible to new readers. Changes: Page 2.

• It would be interesting to include some time-references when introducing the prophase arrest of Clytia and Xenopus oocytes. This should be indicated in the introduction with a short introduction of why, and not others, were these species chosen for this study.

We have expanded the Introduction to cover the issue of time-references. Fuller details are now included about the advantages of Clytia compared to other hydrozoan species. Changes: Page 3, lines 5-11, page 4, lines 28-35, page 5, lines 32-33, page 5, lines 21-32 & 37-39.

• The proteins MAPK is not introduced properly, as it is first mentioned in the results section.

The involvement of MAPK activation during Xenopus oocyte meiotic maturation is now introduced. Changes: Page 5, lines 1-5.

• Page 4 Lines 16-17 "... no role for cAMP has been detected in meiotic resumption, which is mediated by distinct signaling pathways" Which pathways?

We now give the example of the well-characterized pathway Gbg-PI3K pathway for oocyte maturation in starfish, also mentioning that in many species the pathways are still unknown. Changes: Page 4, lines 1-15.

• Page 4 line 34-39. Introduction indicates that the phosphorylation of ARPP19 on S67 by Gwl is a poorly understood molecular signaling cascade (line 34). However, the positive role of ARPP19 on Cdk1 activation, through the S67 phosphorylation by Gwl, appears to be widespread across all eukaryotic mitotic and meiotic divisions studied (lines 36-37). These two sentences seem a little contradictory.

The mechanisms of PP2A inhibition by Gwl-phosphorylated ARPP19 are very well studied. The part that remains mysterious concerns the upstream mechanisms. We have reworded the paragraph to make this point unambiguous. Changes: Page 5, lines 1-8.

• Why is mouse not included in Figure 1A?

We have replaced the human sequence in Figure 1A with the mouse sequence. Changes: Fig. 1A.

• 1C: There should be a better explanation in the text of the results sections for the image included in in Fig1 C. Please indicate in the text that ClyARPP19 mRNA is expressed in previtellogenic oocytes and not in vitellogenic.

We have expanded the explanations of Fig. 1C in the text. We have also added cartoons to the figure to help readers understand the organisation of the Clytia jellyfish and gonad. As now explained, ClyARPP19 mRNA is detected in oocytes at all stages, but the signal is much stronger in pre-vitellogenic oocytes because all cytoplasmic components are significantly diluted by high quantity of yolk proteins. Changes: page 7, line 40 & page 8, lines 1-9 - Fig. 1C - Legend page 31, lines 19-31.

• In addition, the detection of ARPP19 in the nerve rings is intriguing. Any idea of its function there?

The only data linking ARPP19 and the nervous system concerns a mammalian variant of ARPP19 that is highly expressed in the striatum. This point is included in the Discussion_. Changes: page 16, lines 12-13 & 17-20 - page 19, lines 6-9._

• Figure 3C. The way these graphs are presented is somehow confusing. If authors wish so, they can keep the figure as it is, but then Also, please provide in the text a plausible explanation for the cause of oocyte lysis for all experimental conditions. please explain this graph better in the text, and please include statistical analysis.

This figure is intended to provide an overview of all the Clytia oocyte injection experiments, for which full details are given in Supplementary Table 1. We have modified the figure and now clarified this fully in the text and figure legend. Clytia oocytes are relatively fragile. Sensitivity of oocytes to injection varies between batches, while in general increasing injection volumes or protein concentrations increases the levels of lysis observed. We do not know exactly what causes this but it probably relates to mechanical trauma. We now explain these injection experiments more fully in the text. Changes: Page 9, lines 16-39 - Fig. 3C and Supplementary Table 1 - Legend page 32, lines 34-41 & page 33, lines 1-11.

• In Figure 5 D-F, cited in page 9 lines 35-35. Can you provide an explanation of why the time course of meiosis resumption was delayed?

The most probable explanation is that Clytia protein recognizes less efficiently the unknown ARPP19 effectors that mediate the prophase arrest in Xenopus. This explanation is provided in the Results section. Changes: page 11, line 16-19.

• All the sections start with a long introductory paragraph. I believe this facilitates the contextualization of the experiments, but please try to summarize when possible.

As requested, we have shortened or removed the introductory passages of the Results section paragraphs, which were redundant with the information given in the introduction. Changes: Page 7, lines 3-4 & 14-16 & 36-37 - Page 8, lines 12-15 - Page 8, lines 37-40 & Page 9, lines 1-6.

• Page 7, Lines 14-19 present a general conclusion of the findings explained in lines 20-27. I think these results are important and they should be explained better, in my opinion they are slightly poorly described.

The explanation of the experiments and the results are now more detailed and the paragraph ends with a general conclusion which came too early in the previous version. Changes: Page 8, lines 22-24 & 32-34.

• Page 8, lines 16-17: "It was not possible to increase injection volumes or protein concentrations without inducing high levels of non-specific toxicity". What are the non-specific toxicity effects? How was this addressed? What fundaments this conclusion?

As explained above, increasing injection volumes or protein concentrations increases the levels of lysis observed due probably to mechanical trauma. But it is important to note that at the same concentration, both Clytia and Xenopus proteins induce activation of Cdk1 and GVBD in the Xenopus oocyte. Changes: Page 9, lines 16-29 - Page 32, lines 34-41 & Page 33, lines 1-11 - Supplementary Table 1.

• Lines 25-27 of page 8. "These results suggest that PP2A inhibition is not sufficient to induce oocyte maturation in Clytia, although we cannot rule out that the quantity of OA or Gwl thiophosphorylated ARPP proteins delivered was insufficient to trigger GVBD." Please provide evidence if higher concentrations of OA or Gwl were tested to state this conclusion.

High OA concentrations lead to cell lysis/apoptosis as a result of a massive deregulation of protein phosphorylation. For these reasons, we cannot increase OA concentrations over 2 µM. When injected in Xenopus oocyte at 1 or 2 µM, OA induces Cdk1 activation, but then the cell dies because PP2A has multiple substrates essential for cell life. When injected at 2 µM in Clytia oocytes, OA does not induce Cdk1 activation but promotes cell lysis. This supports the conclusion that 2 µM OA is sufficient to inhibit PP2A but that PP2A inhibition is not sufficient to induce oocyte maturation in Clytia. We have reworded the relevant text. Changes: Page 9, lines 31-35 - Page 32, lines 34-41 & Page 33, lines 1-11 - Supplementary Table 1.

• Lines 12-13: the sentence "This in vitro assay thus places S81 as the sole residue in ClyARPP19 for phosphorylation by PKA." is overstated. As not all residues had been tested, please indicate that "it is likely that" or "among the residues tested", in contrast to "the sole residue in ClyARPP19".

The observed thiophosphorylation of the wild-type protein demonstrates that one or more residues are phosphorylated by PKA. This thiophosphorylation was completely prevented by mutation of a single residue, S81. This experiment thus shows that S81 is entirely responsible for phosphorylation by PKA in this assay. We have rewritten this section more clearly. Changes: Page 10, lines 18-28.

• Some parts of the discussion are a bit speculative.

We have reduced the speculation in this part of the discussion, in particular regrouping and reformulating ideas about evolutionary scenarios into a single paragraph. Changes: page 17, lines 37-41 - page 18, lines 1-41 - page 19, lines 1-18.

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Referee #2

Evidence, reproducibility and clarity

Summary of the main findings of the study.

This work presents very interesting data about the maintenance and release of the prophase arrest of oocytes during sexual reproduction. Authors approach some of the remaining questions about oocyte maturation in animals by taking a comparative approach between two species (Clytia and Xenopus) that use opposing cAMP/PKA signaling pathways to trigger oocyte maturation. To do it they focused on phosphorylation characteristics and function of the regulatory protein ARPP19 from the amphibian Xenopus and its orthologue in the hydrozoan Clytia. Results suggest that the low capacity of Clytia ARPP19 to be phosphorylated by PKA. Moreover, Clytia ARPP19 is inherently a poorer PKA substrate than Xenopus ARPP109 both in vivo and in vitro, despite the presence of a functional PKA site. In addition, the absence of functional interactors mediating its negative effects on Cdk1 activation may provide a double security allowing induction of meiosis resumption in Clytia by elevated PKA activity despite the presence of ARPP19, while additional and yet unidentified mechanisms ensure the Clytia oocyte prophase arrest.

Minor comments: read detailed review below. Figure 1 and Figure 3 need a better explanation of the results. Abstract needs to be simplified if wants to reach a broader range of readers. Some parts of the discussion are a bit speculative.

Overall, this work used a robust set of molecular experiments that strongly support the conclusions of the study.

Significance

Strengths and limitations of this work:

The primary strength of this work lies in its innovative use of two distinct species and the integration of molecular experiments to extract conclusions from their different signaling pathways. The well-designed and executed experiments, particularly those of figures 5-9, contribute to an elaborated exploration of the topic, elucidating the underlying mechanisms with clarity. The explanation of each experiment in the results section further adds to the clarity and depth of the study.

The abstract requires improvement, particularly from lines 10 to 21, as it becomes fully understood only after reading the entire manuscript. To make the work more accessible to new readers, it would be good to present the abstract in a more approachable manner. Figures 1C and 3C need a better explanation in the text. Additionally, some sentences would benefit from citations or further clarification in the results or discussion section. Although is presents highly interesting suggestions, discussion may border on being overly speculative, especially from line 37 of page 15 till the end.

Detailed review

Introduction:<br /> I believe that it would be interesting to include some time-references when introducing the prophase arrest of Clytia and Xenopus oocytes. How long is prophase arrest in Xenopus compared to Clytia or other organisms? How can this affect the prophase arrest mechanisms? It seems that the prophase arrest in Xenopus oocytes is found to be significantly more prolonged compared to Clytia and various other organisms, and also meiotic maturation proceeds much more rapidly in Clytia than in Xenopus. This should be indicated in the introduction with a short introduction of why, and not others, were these species chosen for this study. A brief justification is included in lines 1-page 5 "..a laboratory model hydrozoan species well suited to oogenesis studies", but it does not explain why this and not other hydrozoan species like Hydra, that has also been used for meiosis studies.<br /> The proteins MAPK is not introduced properly, as it is first mentioned in the results section in line 12. Given the importance of the results provided with it, it should be presented in the introduction prior to the results section.

These sentences need a more elaborate explanation:<br /> Page 4 Lines 16-17 "... no role for cAMP has been detected in meiotic resumption, which is mediated by distinct signaling pathways" Which pathways?

Page 4 line 34-39. Introduction indicates that the phosphorylation of ARPP19 on S67 by Gwl is a poorly understood molecular signaling cascade (line 34). However, the positive role of ARPP19 on Cdk1 activation, through the S67 phosphorylation by Gwl, appears to be widespread across all eukaryotic mitotic and meiotic divisions studied (lines 36-37). These two sentences seem a little contradictory. If the general pathway has been identified but the signaling cascade is still not well described, please indicate that in a clearer way.

Results section: this review will first comment the figures, and then the text.<br /> Figure 1<br /> Why is mouse not included in Figure 1A? Although it might be very similar to human, given that mouse is the species that is most commonly use as a mammalian model, I believe it could be included. However, this is optional upon decision by the authors.<br /> There should be a better explanation in the text of the results sections for the image included in in Fig1 C. Note that Clytia is not a commonly used species, therefore images should be properly explained for general readers. Please indicate in the text that ClyARPP19 mRNA is expressed in previtellogenic oocytes and not in vitellogenic, plus any additional information needed to understand the image. In addition, the detection of ARPP19 in the nerve rings is intriguing. This is mentioned in the discussion section, any idea of its function there? Please include some additional information or additional references, if they exist.

Figure 3<br /> The way these graphs are presented is somehow confusing. The meaning of the dots is not self-explanted in the graph, and it seems that each experiment was done independently but then the complete set of results is presented. Legend says that "each dot represents one experiment" but this is difficult to read as in every analysis the figure also indicates the average and the total number of oocytes. If authors wish so, they can keep the figure as it is, but then please explain this graph better in the text, and please include statistical analysis. These results are very robust, but a comparison between the number of oocytes that go through spontaneous GVBD of lysis in the different conditions will benefit their understanding.

Also