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Reply to the reviewers

Below is our point-by-point response:

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

The manuscript of Lalanne and coworkers address the cellular responses to varied translation termination factor expression in Bacillus subtilis. The authors set-up a system to fine-tune the expression of release factor RF1, RF2 as well as PrmC that post-translationally modifies RF1/RF2 to maximize their catalytic hydrolysis activity. They then monitor the fitness costs associated with overexpression or depletion of the factor by following the changes in growth rate. The set-up is nicely illustrated in Figure 1. The results in Figure 2 show that overexpression of RF1 and RF2 has relatively modest effect on the growth rate compared to overexpression of PrmC that leads to dramatic growth rate reduction. By contrast, depletion of RF1 has a strong negative influence on fitness, whereas a similar level of depletion of RF2 had little influence on fitness. PrmC overexpression appears to be correlated with the induction of the sigmaB regulon, however, the authors do not manage to ascertain why this is. By contrast, RF2 depletion also results in the induction of the sigmaB regulon and the authors demonstrate convincingly that this is due to a termination defect within the rsbQ-rsbV operon that contains an overlapping start-stop AUGA

A few points that the authors might consider discussing

1. The natural abundance of each RF in bacteria in relation to the usage of different stop codons in different organisms.

Response: We thank the reviewer for their suggestion. A correlation between RF abundance and stop codon usage across bacterial species has been previously reported (Korkmaz et al., 2014; Wei et al., 2016), which is corroborated by our quantification (see below). This correlation provides further evidence that the RF expression may be optimized to meet their demands in translation termination. We now include a new discussion in the main text (p. 9, lines 410-415): "Our data thus corroborate several previous lines of evidence suggesting that RF expression might be precisely tuned. First, it was found that the relative expression between RF1 and RF2 correlates with stop codon usage between different species (Korkmaz et al., 2014; Wei et al., 2016). For instance, B. subtilis has a higher abundance of RF1 and more frequent UAG usage compared to E. coli, suggesting that RF1’s expression setpoint meets translational demand (Methods).”

Below we include additional analyses that may be of interests to the Reviewer.

From our ribosome profiling quantification in E. coli, B. subtilis, C. crescentus, and V. natriegens (Lalanne et al., 2018), we can compare the relative usage of the three stop codons (frequency of stop codons weighted by expression) with abundances of RF1 and RF2:

Despite the limited sample size, we find reasonable agreement with the expected correlation between codon usage and cognate RF abundance. In species with substantial differences between RF1 and RF2 abundances (E. coli and B. subtilis), the most heavily used non-UAA stop corresponds to the most highly expressed RF. This argues in favor of expression tuning of these important enzymes and is consistent with the growth optimization we directly observe.

As a word of caution, although the low usage of UAG in E. coli and low expression of RF1 (reported long ago, e.g., (Adamski et al., 1994)) is well established, it should be noted that strain MG1655’s RF2 factor harbors a debilitating missense A246T mutation near its active site (Dinçbas-Renqvist et al., 2000), which potentially complicates interpretation of the expression of E. coli’s release factors [interestingly, we do not see any difference in RF1 and RF2 expression from ribosome profiling data in strain NCM3722, which contains the RF2 variant without the A246T mutation (JBL, unpublished data)].

The role of the frameshifting mechanism in RF2 and how then RF1 levels are regulated.

Response: We thank the reviewer to raising the interesting topic of release factor expression regulation. We have added a section in our discussion to comment on RF2 regulation (p. 9, lines 415-420).

“Second, the gene encoding RF2 has a broadly conserved UGA-based frameshift event that autoregulates the expression based on its own activity (Baranov et al., 2002; Craigen and Caskey, 1986; Craigen et al., 1985). Interestingly, there are no reports of RF1 autoregulation to our knowledge, and we found that ectopic over- or under-expression does not affect its own promoter activity (Fig. S7). Therefore, a lack of autoregulation does not necessarily indicate that cells are less sensitive to small perturbations on its expression.”

The statement above includes an additional analysis on RF1 regulation that was motivated by the Reviewer’s comment. In contrast to RF2, no definitive evidence exists on autoregulatory mechanisms for RF1. Following the Reviewer’s comment, we realized that our dataset allowed us to search for evidence of endogenous regulation in B. subtilis: our RF1 expression strain has a markerless deletion of prfA and prmC genes, leaving the surrounding regions, and notably the promoter, intact. As such, possible unbeknownst regulatory mechanisms at the promoter level could be identified in our RNA-seq data under steady-state perturbation of RF1 levels. Quantifying the expression of the 5’ untranslated region and operonic gene ywkF at the ablated prfAlocus (presented in Fig. S7, reproduced below), we find no significant changes in expression across over 30-fold range in RF1 expression, arguing against such transcriptional regulatory mechanisms. Although this does not rule out other regulatory mechanisms at the post-transcriptional level, no such mechanisms have been documented for RF1 to our knowledge.

The authors observe queuing in front of the relevant stop codons upon RF depletion, however, do not discuss about readthrough events, which are usually competing with termination. Surprisingly, in this context the authors don't discuss the work from Mankin and coworkers showing sequestration of RFs from termination by peptides such as apideacin leads to translational readthrough.

Response: We concur with the Reviewer about the importance of the recent work from Mankin et al. This paper was referenced in our original submission, but our literature management software improperly formatted its citation. The corrected reference to (Mangano et al., 2020) is now included in the revised manuscript.

Translational readthrough is indeed clearly visible in our ribosome profiling data from acute CRISPRi knockdown of RF1/PrmC and RF2. Using an approach analogous to Mangano and Florin et al, we quantified readthrough as the ribosome footprint density downstream of the stop codon (+5 to +45 bp) to the density in the gene body for isolated genes (no codirectional genes within 55 bp). We find five-fold increase in the median readthrough for genes that are terminated by the RF under perturbation (shown in a new panel in the main text, Fig. 4b, reproduced below). This new analysis is included in the section regarding translational phenotypes identified from ribosome profiling under RF depletion, p. 7, lines 309-312.

“The stop-codon-specific queuing is associated with translational readthrough downstream (Fig. 4b), consistent with a recent observation based on inhibition of peptide release by the antimicrobial apidaecin in E. coli (Mangano et al., 2020).”

This additional analysis, in conjunction with (Mangano et al., 2020), also allows us to calibrate the depletion of RFs in our non steady-state CRISPRi perturbation. Given that apidaecin treatment (shown to lead to a nearly complete depletion of free RF in the cell) causes a >100-fold increase in readthrough, this suggests that our CRISPRi perturbation experiments only led to partial RF depletion at the moment of cell harvesting.

The efficiency of translation termination is well-known to be dependent on the context of the stop codon. Do the authors also observe such a trend. Especially, UGAC for RF2, one would expect to observe high levels of readthrough upon RF2 depletion.

Response: Further assessment of the sequence determinants that dictate susceptibility of certain genes and regulatory elements to RF perturbation is of great interest. We now include additional analyses for the effect of stop codon context on readthrough.

In our RF2 CRISPRi knockdown data, stratifying the translational readthrough (data from Fig. 4b) by stop codon and its next nucleotide, we observe only a modest (≈2×, p“We also observed a trend of tetranucleotide-dependent (UGAN) readthrough for RF2 knockdowns (Methods, Appendix Fig. 2) consistent with previous characterizations (Poole et al., 1995).”

As an additional point of interest, the importance of the 4th nucleotide in termination has not been studied outside of E. coli. Although indirect, one way to assess the influence of the 4th nucleotide is to determine the aggregated usage of each tetranucleotide stop signal by ribosome profiling. Interestingly, and as pointed out by the Reviewer, whereas E. coli (MG1655) displays a 16× increase in usage between the maximum UGAU (tetranucleotide usage 0.064) and minimum UGAC (tetranucleotide usage 0.004), no such difference is observed in B. subtilis (usage for UGAU and UGAC both at 0.015), suggesting that the immediate sequence context surrounding stop codons could have different consequences in different species.

Reviewer #2 (Significance (Required)):

Overall, the experiments are clearly performed and beautifully illustrated. Clearly, a lot of work has gone into this study but the end message that the cell regulates carefully RF concentrations is not surprising. Especially given that RF2 carefully regulates its own levels using an autoregulatory frameshifting mechanism. The major finding that the rsbQ-rsbV operon with the RF2 dependence leading to induction of the sigmaB regulon is in the end rather trivial since these regulators depend on RF2 for termination. Therefore, this manuscript is unlikely to have general interest to people in the translation field (such as myself) but rather those working in the field of synthetic biology.

Response: We thank the Reviewer for their positive assessment of our presentation and experimental methods, and for their judgment that our work will be of interest to synthetic biologists.

In our study, we used translation as a well-characterized system to interrogate the cellular response when enzyme concentrations are perturbed. Because the system is so well characterized, it allowed to ask whether the fitness effects are due to perturbations to the translation flux itself, or rather driven by spurious distal connections in the regulatory network. The end message we wish to convey is that enzyme expression is entrenched by spurious regulatory connections, suggesting that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts.

Although our focus is on the cellular response, there are several interesting findings related to translation. First, we show that even though RF1 and PrmC are not subject to the strict autoregulation as RF2 is, cell growth is similarly or even more sensitive to RF1 and PrmC abundance. Second, among the numerous regulators that depend on RF2 for termination, RbsV/RbsW is exceptionally sensitive to RF2 depletion (Fig. 4e). This result not only points to our incomplete understanding of translation regarding what makes this pair particularly susceptible, and further underscores the spurious nature of the cellular response to perturbations. We have expanded the discussions on the implication of these findings in the revised manuscript.

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

In this paper, the authors use a combination of RNA sequencing, ribosome profiling and measurements of cellular composition and growth rate to gain insight into the multi-scale affects that perturbations to translation termination factors have on general physiological states and reproductive fitness using Bacillus subtilis as their model organism. Specifically, they find that perturbing the expression levels of peptide chain release factors in any direction has a negative effect on growth-rate. This negative effect was not due to a direct impact of the gene on the cell, but instead due to a chain of regulatory interactions that cause the activation of the general stress regulon. This leads to upregulation of a large chunk of the genome and an indirect impact on the expression of all other genes. Critically, the knock-on effects observed for the specific perturbations studied suggest that it may be difficult to predict expression-fitness landscapes of a cell, without carrying out a detailed mapping of all genes and the cell's physiological state.

Overall, the core findings in the paper are well justified by the data presented and the experiments appear to have been rigorously carried out.

Response: We thank the reviewer for their positive assessment.

My only concern is that it is unclear if biological replicates of the ribosome profiling were performed. Also, biological replicates are mentioned for the RNA-seq data, but no data is shown. Even a simple graph demonstrating the expression levels across these would be useful to be assured of no issues in reproducibility given the complex processing of the data involved.

Response: We now include additional analyses for biological replicates of RNA-seq and ribosome profiling experiments, which show the same high degree of reproducibility as we have demonstrated in previous studies (Johnson et al., 2020; Lalanne et al., 2018; Li et al., 2014).

With respect to RNA-seq quantification, we compared our 6 wild-type datasets (biological replicates except for different inert inducer concentrations, using the same batch of conditioned MCC medium) against each other in all possible pairs. The data is now included as Appendix Fig. 1a (referred to in the main text, p. 4, line 138), and is reproduced below. Across pairs, the mRNA level quantification shows a median FC1090 (10th and 90th percentile in fold-change) between 0.86 to 1.16, and median R2 of log-transformed data at 0.99. These statistics showcasing reproducibility of our RNA-seq methodology are now included in our description of our RNA-seq approach in the Methods, p. S8, lines 313-320.

Regarding ribosome profiling quantification, we now include comparisons between pairs of two replicates for wild type cells, and pairs of replicates wild-type with inert fluorescent protein expression, each pair of samples with their own batch of conditioned MCC medium. These samples were taken under different inducer concentrations, which are expected to affect the expression of two genes and not others. As indicated in Appendix Fig. 1b and reproduced below, the Pearson correlation of log-transformed footprint density is respectively of R2=0.98 and 0.99 (genes with >100 reads mapped), with a 10th to 90th percentile of fold-changes between 0.83 to 1.17, and 0.91 to 1.12. These results are described in the Methods, p. S9, lines 339-345.

Related to this, I see no mention of data availability in the paper. For this study to be useful to others, providing the raw data (unprocessed) would be essential (ideally in a public repository).

Response: We are sorry that the statement on data availability was buried in the original Methods section that was not a part of the merged PDF file. The raw sequencing data were submitted to Gene Expression Omnibus under the accession number GSE162169. The processed data, including fitness scores, mRNA levels, protein synthesis rates, were included as Supplementary Data Tables 1-9. We now moved the data availability statement to the main document at p. 12, lines 512-516.

The presentation of the work is excellent, with very clear figures and text that helped guide the reader through the results. There were a few minor comments:

1. Abstract: "in bacterium Bacillus subtilis" should read "in the bacterium Bacillus subtilis".

Response: This typo is now corrected.

Page 4: "found that under numerous ways" should read "found that under the numerous ways".

Response: This typo is now corrected.

The authors mention that changes in the expression level of RF1 impacted motility and biofilm genes, but not how this impacts fitness. Would they be able to experimentally identify origin of RF1 growth defects in the same way they did for PrmC? This is not essential for the main findings but would help strengthen the work.

Response: The cause of the growth defect under RF1 knockdown is indeed interesting. We now present evidence ruling out the hypothesis that the growth defect is caused by the expression decrease for motility and biofilm genes.

This hypothesis is driven by our result that ablation of SigB regulon rescues the fitness defect during PrmC overexpression (Fig. 3g) and by the observed downregulation of motility and lyt operons and upregulation of the eps operon during RF1 knockdown. To test this hypothesis, we used a strain without sigD (the motility sigma factor), which displays similar expression changes to what we observed in RF1 knockdown (Chai et al., 2009). Comparing the growth rates of wild-type to DsigD, we found only a slight difference (30% growth defect measured upon RF1 knockdown, it appears that transcriptional changes to the motility regulon can only partially explain of the RF1 growth defect. These results are discussed on p. 10, lines 459-463. Further assessment will constitute interesting future research avenues.

It is difficult to know how generalisable the findings of this work are due to the very limited scope. It could be helpful for the authors in the discussion to consider and comment on how such approaches might be scaled-up to enable broader and more general studies of expression-fitness landscapes and where they will find most use.

Response: Indeed, the spurious nature of the expression-fitness landscape makes it difficult to generalize the exact mechanisms that we described here to other proteins. However, what is generalizable is our conclusion that such spurious connections limit the feasibility of bottom-up models for predicting fitness landscapes unless one has near-exhaustive characterization of all parts.

Our approach of mechanistic profiling of cell states under perturbations therefore provides a path forward that can be scaled up by recent developments in multiscale measurements. We now include a discussion for broader and more general studies on p. 11, lines 473-480.

“Various strategies can now generate expression-fitness landscapes for a large number of genes in parallel, for example using suites of promoters (Keren et al., 2016), genome-scale library of inducible gene expression (Arita et al., 2021), or tunable CRISPR perturbations (Hawkins et al., 2020; Jost et al., 2020; Mathis et al., 2021). Together with the advent of single-cell transcriptomics in bacteria (Blattman et al., 2020; Imdahl et al., 2020; Kuchina et al., 2020), these methods open the possibility of dissecting the molecular underpinnings of expression-fitness landscapes genome-wide, and to comprehensively identify instances of regulatory entrenchment.”

Reviewer #3 (Significance (Required)):

This work has a number of contributions. Firstly, it demonstrates how to combine several complementary sequencing approaches to characterize in detail the transcriptional and translational state of a cell, as well as its overall growth rate to generate comprehensive expression-fitness maps. Secondly, it shows how the interwoven nature of cellular regulatory networks and the molecular interactions encoded within the genome can lead to cryptic responses in cellular behavior and fitness at a system-level that can only be understood by taking a detailed "bottom-up" approach. Finally, it suggests that some of these regulatory interactions may in fact "entrench" an organism's evolutionary path, by causing small genetic perturbations to propagate and potentially amplify their negative effect. While the results are compelling and well supported by experiments, the limited scope of the work makes it difficult to know whether this is in fact a rare or common occurrence. However, I do believe there is significance to these findings and that it will likely spur further studies to assess the generality of these findings.

Overall, I believe the work will have a wide appeal covering areas such as Systems Biology, Gene Regulation, Evolution, Quantitative Biology, Sequencing, High-throughput Technologies.

Response: We thank the reviewer for their assessment that our work will be of appeal to a broad audience.

My field of expertise is in the quantitative measurement of core cellular processes (e.g. transcription and translation) using novel sequencing techniques and the application of this knowledge to biological design. As such, I believe I have sufficient expertise to review this paper in detail.

Response references

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Arita, Y., Kim, G., Li, Z., Friesen, H., Turco, G., Wang, R.Y., Climie, D., Usaj, M., Hotz, M., Stoops, E., et al. (2021). A genome-scale yeast library with inducible expression of individual genes. BioRxiv 2020.12.30.424776.

Baranov, P. V, Gesteland, R.F., and Atkins, J.F. (2002). Release factor 2 frameshifting sites in different bacteria. 3, 373–377.

Blattman, S.B., Jiang, W., Oikonomou, P., and Tavazoie, S. (2020). Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201.

Chai, Y., Normam, T., Kolter, R., and Losick, R. (2009). An epigenetic switch governing daughter cell separation in Bacillus subtilis. Genes Dev. 7824, 754–765.

Craigen, W.J., and Caskey, C.T. (1986). Expression of peptide chain release factor 2 requires high-efficiency frameshift. Nature 322, 273–275.

Craigen, W.J., Cook, R.G., Tate, W.P., and Caskey, C.T. (1985). Bacterial peptide chain release factors: Conserved primary structure and possible frameshift regulation of release factor 2. Proc. Natl. Acad. Sci. U. S. A. 82, 3616–3620.

Dinçbas-Renqvist, V., Engström, Å., Mora, L., Heurgué-Hamard, V., Buckingham, R., and Ehrenberg, M. (2000). A post-translational modification in the GGQ motif of RF2 from Escherichia coli stimulates termination of translation. EMBO J. 19, 6900–6907.

Hawkins, J.S., Silvis, M.R., Koo, B.M., Peters, J.M., Osadnik, H., Jost, M., Hearne, C.C., Weissman, J.S., Todor, H., and Gross, C.A. (2020). Mismatch-CRISPRi Reveals the Co-varying Expression-Fitness Relationships of Essential Genes in Escherichia coli and Bacillus subtilis. Cell Syst. 11, 523-535.e9.

Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A.E., and Vogel, J. (2020). Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat. Microbiol. 5, 1202–1206.

Johnson, G.E., Lalanne, J.B., Peters, M.L., and Li, G.W. (2020). Functionally uncoupled transcription–translation in Bacillus subtilis. Nature 585, 124–128.

Jost, M., Santos, D.A., Saunders, R.A., Horlbeck, M.A., Hawkins, J.S., Scaria, S.M., Norman, T.M., Hussmann, J.A., Liem, C.R., Gross, C.A., et al. (2020). Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat. Biotechnol. 38, 355–364.

Keren, L., Hausser, J., Lotan-Pompan, M., Vainberg Slutskin, I., Alisar, H., Kaminski, S., Weinberger, A., Alon, U., Milo, R., and Segal, E. (2016). Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness. Cell 166, 1282-1294.e18.

Korkmaz, G., Holm, M., Wiens, T., and Sanyal, S. (2014). Comprehensive analysis of stop codon usage in bacteria and its correlation with release factor abundance. J. Biol. Chem. 289, 30334–30342.

Kuchina, A., Brettner, L.M., Paleologu, L., Roco, C.M., Rosenberg, A.B., Carignano, A., Kibler, R., Hirano, M., William DePaolo, R., and Seelig, G. (2020). Microbial single-cell RNA sequencing by split-pool barcoding. Science (80-. ).

Lalanne, J.B., Taggart, J.C., Guo, M.S., Herzel, L., Schieler, A., and Li, G.W. (2018). Evolutionary Convergence of Pathway-Specific Enzyme Expression Stoichiometry. Cell 749–761.

Li, G.W., Burkhardt, D., Gross, C., and Weissman, J.S. (2014). Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635.

Mangano, K., Florin, T., Shao, X., Klepacki, D., Chelysheva, I., Ignatova, Z., Gao, Y., Mankin, A.S., and Vázquez-Laslop, N. (2020). Genome-wide effects of the antimicrobial peptide apidaecin on translation termination in bacteria. Elife 9, 1–24.

Mathis, A.D., Otto, R.M., and Reynolds, K.A. (2021). A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth. Nucleic Acids Res. 49, e6.

Poole, E.S., Brown, C.M., and Tate, W.P. (1995). The identity of the base following the stop codon determines the efficiency of in vivo translational termination in Escherichia coli. EMBO J. 14, 151–158.

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Referee #3

Evidence, reproducibility and clarity

In this paper, the authors use a combination of RNA sequencing, ribosome profiling and measurements of cellular composition and growth rate to gain insight into the multi-scale affects that perturbations to translation termination factors have on general physiological states and reproductive fitness using Bacillus subtilis as their model organism. Specifically, they find that perturbing the expression levels of peptide chain release factors in any direction has a negative effect on growth-rate. This negative effect was not due to a direct impact of the gene on the cell, but instead due to a chain of regulatory interactions that cause the activation of the general stress regulon. This leads to upregulation of a large chunk of the genome and an indirect impact on the expression of all other genes. Critically, the knock-on effects observed for the specific perturbations studied suggest that it may be difficult to predict expression-fitness landscapes of a cell, without carrying out a detailed mapping of all genes and the cell's physiological state.

Overall, the core findings in the paper are well justified by the data presented and the experiments appear to have been rigorously carried out. My only concern is that it is unclear if biological replicates of the ribosome profiling were performed. Also, biological replicates are mentioned for the RNA-seq data, but no data is shown. Even a simple graph demonstrating the expression levels across these would be useful to be assured of no issues in reproducibility given the complex processing of the data involved. Related to this, I see no mention of data availability in the paper. For this study to be useful to others, providing the raw data (unprocessed) would be essential (ideally in a public repository).

The presentation of the work is excellent, with very clear figures and text that helped guide the reader through the results. There were a few minor comments:

1. Abstract: "in bacterium Bacillus subtilis" should read "in the bacterium Bacillus subtilis".
2. Page 4: "found that under numerous ways" should read "found that under the numerous ways".
3. The authors mention that changes in the expression level of RF1 impacted motility and biofilm genes, but not how this impacts fitness. Would they be able to experimentally identify origin of RF1 growth defects in the same way they did for PrmC? This is not essential for the main findings but would help strengthen the work.
4. It is difficult to know how generalisable the findings of this work are due to the very limited scope. It could be helpful for the authors in the discussion to consider and comment on how such approaches might be scaled-up to enable broader and more general studies of expression-fitness landscapes and where they will find most use.

Significance

This work has a number of contributions. Firstly, it demonstrates how to combine several complementary sequencing approaches to characterize in detail the transcriptional and translational state of a cell, as well as its overall growth rate to generate comprehensive expression-fitness maps. Secondly, it shows how the interwoven nature of cellular regulatory networks and the molecular interactions encoded within the genome can lead to cryptic responses in cellular behavior and fitness at a system-level that can only be understood by taking a detailed "bottom-up" approach. Finally, it suggests that some of these regulatory interactions may in fact "entrench" an organism's evolutionary path, by causing small genetic perturbations to propagate and potentially amplify their negative effect. While the results are compelling and well supported by experiments, the limited scope of the work makes it difficult to know whether this is in fact a rare or common occurrence. However, I do believe there is significance to these findings and that it will likely spur further studies to assess the generality of these findings.

Overall, I believe the work will have a wide appeal covering areas such as Systems Biology, Gene Regulation, Evolution, Quantitative Biology, Sequencing, High-throughput Technologies.

My field of expertise is in the quantitative measurement of core cellular processes (e.g. transcription and translation) using novel sequencing techniques and the application of this knowledge to biological design. As such, I believe I have sufficient expertise to review this paper in detail.

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Referee #2

Evidence, reproducibility and clarity

The manuscript of Lalanne and coworkers address the cellular responses to varied translation termination factor expression in Bacillus subtilis. The authors set-up a system to fine-tune the expression of release factor RF1, RF2 as well as PrmC that post-translationally modifies RF1/RF2 to maximize their catalytic hydrolysis activity. They then monitor the fitness costs associated with overexpression or depletion of the factor by following the changes in growth rate. The set-up is nicely illustrated in Figure 1. The results in Figure 2 show that overexpression of RF1 and RF2 has relatively modest effect on the growth rate compared to overexpression of PrmC that leads to dramatic growth rate reduction. By contrast, depletion of RF1 has a strong negative influence on fitness, whereas a similar level of depletion of RF2 had little influence on fitness. PrmC overexpression appears to be correlated with the induction of the sigmaB regulon, however, the authors do not manage to ascertain why this is. By contrast, RF2 depletion also results in the induction of the sigmaB regulon and the authors demonstrate convincingly that this is due to a termination defect within the rsbQ-rsbV operon that contains an overlapping start-stop AUGA

A few points that the authors might consider discussing

1. The natural abundance of each RF in bacteria in relation to the usage of different stop codons in different organisms.
2. The role of the frameshifting mechanism in RF2 and how then RF1 levels are regulated.
3. The authors observe queuing in front of the relevant stop codons upon RF depletion, however, do not discuss about readthrough events, which are usually competing with termination. Surprisingly, in this context the authors don't discuss the work from Mankin and coworkers showing sequestration of RFs from termination by peptides such as apideacin leads to translational readthrough.
4. The efficiency of translation termination is well-known to be dependent on the context of the stop codon. Do the authors also observe such a trend. Especially, UGAC for RF2, one would expect to observe high levels of readthrough upon RF2 depletion.

Significance

Overall, the experiments are clearly performed and beautifully illustrated. Clearly, a lot of work has gone into this study but the end message that the cell regulates carefully RF concentrations is not surprising. Especially given that RF2 carefully regulates its own levels using an autoregulatory frameshifting mechanism. The major finding that the rsbQ-rsbV operon with the RF2 dependence leading to induction of the sigmaB regulon is in the end rather trivial since these regulators depend on RF2 for termination. Therefore, this manuscript is unlikely to have general interest to people in the translation field (such as myself) but rather those working in the field of synthetic biology.

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Referee #1

This reviewer did not leave any comments

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Reply to the reviewers

Reviewer response

We thank the reviews for the careful reviews, and were delighted to see that they assessed both the quality and significance of the work so highlty.

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

The authors investigated the cross-neutralization capacity of serum antibodies to past and future 229E coronaviruses using 229E spikes isolated from five time points and sera from two different periods (1985-1995 and 2020). They demonstrated a general pattern of asymmetric cross-neutralization, with sera cross-reactive to historical but not future strains. Using chimeras, the authors showed this pattern was mostly driven by antibodies to the evolving RBD. The rate of change in the neutralization titer, a possible measure of antigenic evolution, was estimated to be on par with that of flu B viruses. Interesting differences in individuals' cross-neutralization capacity were observed. The main take-away is that reinfection with 229E is enabled by antigenic escape, not "weak" immunity after infection (as proposed by others).

Thanks for the excellent summary of the paper. We agree with it, although we would note that our work does not exclude “weak immunity” as a possible compounding explanation for re-infection in addition to the antigenic evolution we demonstrate.

**Major comments:**

The key conclusions are convincing and justified by the data. The work is clearly presented and presented with sufficient detail for reproducibility. Characteristically and laudably, the authors have made all the code and data publicly available on GitHub.

Thanks for the favorable summary.

**Minor comments:**

p 3: Perhaps it is clearer to write that 229E has been identified/isolated in humans for >50 y? Or do you really mean to imply (by contrast with "circulated") that NL63 emerged very recently?

This is a good suggestion. We really do not know how long either CoV-229E or CoV-NL63 have been circulating humans, only that CoV-229E was first isolated >50 years ago whereas CoV-NL63 was first identified only in 2003. It is possible both viruses have been circulating for longer than that. We have made the suggested change to clarify.

p 3: An important citation for the antigenic implications of the ladder-like phylogeny AND phylogenetic clustering by date is the classic paper introducing phylodynamics by Grenfell et al. (2004, Science).

Thanks for pointing out this citation; we have added it.

p 4: I might not be like all readers, but I prefer to see a bit in the main text about the source of sera for this kind of study. (I wonder about age, if donors are healthy, etc.)

This is a good question, and we have expanded on it in both the main text and the methods. Briefly, the sera were all from apparently healthy individuals, and no information about recent respiratory virus infections were available. We have provided the age of the serum donor (at the time of serum collection) above the title of each plot showing person-specific neutralization data.

p 4: "Our reason for focusing..." stops short. Is the idea that these are probably people who were recently infected?

This is a good question, and we have elaborated in the revised text. We don’t have any direct information on whether the individuals had recent infections, although that seems plausible. More pragmatically, we reasoned that sera that had reasonably high initial titers would provide better dynamic range to see how titers changed as the virus evolved given our assay has a lower limit of detection.

p 5: Probably my biggest suggestion for the paper is that it mention another relevant study. In 1980, Anne Underwood demonstrated similar asymmetric cross-immunity among early strains of H3N2 (but using rabbits, not human sera), finding that antibodies raised to one strain reacted more strongly by HAI to past strains than to later strains (doi: 10.1128/IAI.27.2.397-404.1980). This relates to the significance of the paper (next section).

Thanks, this is a good and relevant citation, and we have added it when we discuss the possible asymmetry of antigenic change with respect to time.

Obviously, there are citations to update throughout due to the booming SARS-CoV-2 literature.

We have updated the other citations to keep pace with the fast-changing literature!

Reviewer #1 (Significance (Required)):

This study, if anything, undersells itself. Obviously it is a huge contribution to our understanding of how a seasonal coronavirus that bears important phenotypic resemblance to SARS-CoV-2 evolves, but I think it is also providing a foundational piece of evidence--a mechanism--of how rapid viral turnover (by antigenic evolution) occurs. There is no reason to think this should be limited to the coronaviruses, and I suspect the evidence here will go a long way to unifying the evolutionary and epidemiological dynamics of fast-evolving viruses.

Thanks for the praise of the manuscript. Indeed, we were surprised to find that no similarly designed studies have been done even for influenza virus, and so are now interested in expanding our future work to do that as we fully agree it could provide insight more broadly.

Asymmetric competition is nearly an ecological requirement for one strain to successfully invade and displace another. It is thought (unsure how widely?) that flu evolves antigenically, with new strains eventually displacing old ones, by mutating at key epitopes in ways that the immune system does not immediately pick up. That is, immune memory is biased to recall responses to conserved epitopes, which on average are probably less neutralizing. This will induce competition between mutant and resident viruses, but it would be symmetric, since infection with either would induce responses to conserved epitopes on the other. But if on infection with the mutant, immune memory sometimes reuses (boosts) antibody responses to target the mutated epitopes, those recycled antibodies might be less effective against the mutant, making the competition asymmetric.

What this paper and Underwood (1980) suggest is that we can get this asymmetric, antibody-mediated competition fairly easily and without extensive memory. Underwood showed this more powerfully in rabbits, but in this paper too we see an indirect suggestion of asymmetry in relatively inexperienced children (Fig. 3). Mutants (future strains) successfully invade when they can trigger presumably recalled antibodies that are more harmful to the resident (soon historical) strain than the mutant. If this is so easy to do, as judged by the extensive data here, then it could be common.

I've gone off on a theoretical limb here, but the paper is still important without these considerations. This work will be of interest to evolutionary biologists, epidemiologists, vaccinologists, and everyone else wondering what SARS-CoV-2 will do next and how immunity to antigenically variable pathogens works.

We completely agree with the ideas mentioned above, and appreciate having it put in this nice context, particularly alongside the Underwood paper (with which we were not previously familiar). That said, we believe that the small number of recent children sera samples in the current study preclude us from drawing strong conclusions about the asymmetry--as the reviewer says, our data provides an indirect suggestion too. So overall we have not tried to expand this angle here because as the reviewer says, the paper is still important without these considerations. However, we are actively working to see if we can design a similar study with more children sera in the future to separately address the questions about asymmetry.

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

An important question in coronavirology is what governs their ability to seemingly reinfect people regularly (within 2 or 3 years). While waning protective immunity has been proposed and is of current concern for SARS CoV-2, the role of antigenic drift driven by escape from neutralizing antibodies has not been well characterized. The authors have attempted to look at this through examining historical Spike proteins from HCoV-229E over a period of 30-odd years. The authors show that 229E evolves along a linear trajectory consistent with yearly selection by pre-existing immunity. Taking representative spike proteins from different time points into pseudovirus neut assays, they find that older spike proteins are less sensitive to neut by more recent sera. Conversely, spike proteins from prior to the birth of an individual display markedly less sensitivity to neut that those prevalent during the persons lifetime. Sequence analysis of the spike shows variation accruing in both N-termina regions and the RBD, parts of spike predominantly targeted by nABs. Lastly producing early spikes with chimeric RBDs from late viruses enhances the sensitivity to more recent sera.

This is a potentially important MS that addresses a pertinent question that is of wide interest for the CoV2 pandemic. While it is limited in addressing the relative contribution of antigenic escape vs waning Ab titers because of the nature of the sample, the MS makes a strong case for Spike evolution being driven by antigenic escape.

Thanks for the summary. We agree that our paper does not really address waning immunity because we don’t have sequential serum samples from the same individual. However, it does clearly show that antigenic evolution is important independent of waning immunity, because all of the experiments (e.g., Figure 2 and 3) show the same serum sample tested against newer spikes, and neutralization titers definitely decrease as the spike evolves. The reviewer is correct that this doesn’t rule out the possibility of waning immunity as a separate phenomenon, and we have been sure to emphasize that in the revised text.

Reviewer #2 (Significance (Required)):

While the Figs 1-3 are clear, the data in Fig 4 is somewhat preliminary. In all likelihood many people are making neutralizing antibodies both against RBD and the N-terminal region and the relative proportion probably underlies the variability in the data in Fig 4B. I think the MS would benefit from the following:

A comparison of NTD vs RBD vs NTD/RBD chimeras in Fig 4B to give a fuller picture of antigenic escape with statistical support.

The reviewer is correct that our manuscript does not provide a decisive answer on the relative role of NTD versus RBD targeting antibodies, although the data in Fig. 4B clearly show that RBD antibodies are important for many individuals as simply changing the RBD to that of newer viruses recapitulates the full spike antigenic evolution without any changes in the NTD or elsewhere (e.g., subject SD87_2 or SD85_3 in Fig 4B). However, for some other individuals NTD antibodies may play a role.

In general, full dissection of the role of RBD versus NTD antibodies is beyond the scope of our study (and in some cases not even possible with the available volumes of the older serum). In any case, the major point of our study—the first experimental demonstration that seasonal coronaviruses undergo antigenic evolution—does not depend on dissecting the relative roles of RBD and NTD antibodies. We have therefore added new text explaining that we cannot fully parse the relative role of antibodies to these domains beyond knowing that RBD antibodies play n important role. We have added text to emphasize that antibodies to other regions including the NTD could also be important.

A figure to map the polymorphic residues in Fig 4A onto the 229E spike structure to visualise their position and special relatedness, with perhaps a comparison with the latest knowledge of SASR CoV-2 epitopes.

We agree that visualizing the variable sites on the structure is useful and have added such a visualization as a new panel in Figure 4. This allows us to more clearly show the clustering of variability in the RBD and NTD. This clustering of mutations in those regions is consistent with what is currently being seen with the emergence of SARS-CoV-2 variants with mutations in those regions of spike. However, given the divergence between SARS-CoV-2 and CoV-229E, we are not able to do a more fine-grained comparison of epitope sites as many important sites in the RBD and NTD do not have a clear one-to-one alignment (for instance, the RBD’s don’t even bind the same receptor).

Additional discussion to reflect the new SARS CoV-2 variants and their potential selection by escape in the light of the authors data.

We have updated the manuscript to describe the new SARS-CoV-2 variants (which mostly emerged after submission of our original manuscript) and how this emerging antigenic evolution of SARS-CoV-2 is consistent with what we saw in CoV-229E.

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Referee #2

Evidence, reproducibility and clarity

An important question in coronavirology is what governs their ability to seemingly reinfect people regularly (within 2 or 3 years). While waning protective immunity has been proposed and is of current concern for SARS CoV-2, the role of antigenic drift driven by escape from neutralizing antibodies has not been well characterized. The authors have attempted to look at this through examining historical Spike proteins from HCoV-229E over a period of 30-odd years. The authors show that 229E evolves along a linear trajectory consistent with yearly selection by pre-existing immunity. Taking representative spike proteins from different time points into pseudovirus neut assays, they find that older spike proteins are less sensitive to neut by more recent sera. Conversely, spike proteins from prior to the birth of an individual display markedly less sensitivity to neut that those prevalent during the persons lifetime. Sequence analysis of the spike shows variation accruing in both N-termina regions and the RBD, parts of spike predominantly targeted by nABs. Lastly producing early spikes with chimeric RBDs from late viruses enhances the sensitivity to more recent sera.

This is a potentially important MS that addresses a pertinent question that is of wide interest for the CoV2 pandemic. While it is limited in addressing the relative contribution of antigenic escape vs waning Ab titers because of the nature of the sample, the MS makes a strong case for Spike evolution being driven by antigenic escape.

Significance

While the Figs 1-3 are clear, the data in Fig 4 is somewhat preliminary. In all likelihood many people are making neutralizing antibodies both against RBD and the N-terminal region and the relative proportion probably underlies the variability in the data in Fig 4B. I think the MS would benefit from the following:

• A comparison of NTD vs RBD vs NTD/RBD chimeras in Fig 4B to give a fuller picture of antigenic escape with statistical support.

• A figure to map the polymorphic residues in Fig 4A onto the 229E spike structure to visualise their position and special relatedness, with perhaps a comparison with the latest knowledge of SASR CoV-2 epitopes.

• Additional discussion to reflect the new SARS CoV-2 variants and their potential selection by escape in the light of the authors data.

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Referee #1

Evidence, reproducibility and clarity

The authors investigated the cross-neutralization capacity of serum antibodies to past and future 229E coronaviruses using 229E spikes isolated from five time points and sera from two different periods (1985-1995 and 2020). They demonstrated a general pattern of asymmetric cross-neutralization, with sera cross-reactive to historical but not future strains. Using chimeras, the authors showed this pattern was mostly driven by antibodies to the evolving RBD. The rate of change in the neutralization titer, a possible measure of antigenic evolution, was estimated to be on par with that of flu B viruses. Interesting differences in individuals' cross-neutralization capacity were observed. The main take-away is that reinfection with 229E is enabled by antigenic escape, not "weak" immunity after infection (as proposed by others).

Major comments:

The key conclusions are convincing and justified by the data. The work is clearly presented and presented with sufficient detail for reproducibility. Characteristically and laudably, the authors have made all the code and data publicly available on GitHub.

Minor comments:

p. 3: Perhaps it is clearer to write that 229E has been identified/isolated in humans for >50 y? Or do you really mean to imply (by contrast with "circulated") that NL63 emerged very recently?

p. 3: An important citation for the antigenic implications of the ladder-like phylogeny AND phylogenetic clustering by date is the classic paper introducing phylodynamics by Grenfell et al. (2004, Science).

p. 4: I might not be like all readers, but I prefer to see a bit in the main text about the source of sera for this kind of study. (I wonder about age, if donors are healthy, etc.)

p. 4: "Our reason for focusing..." stops short. Is the idea that these are probably people who were recently infected?

p. 5: Probably my biggest suggestion for the paper is that it mention another relevant study. In 1980, Anne Underwood demonstrated similar asymmetric cross-immunity among early strains of H3N2 (but using rabbits, not human sera), finding that antibodies raised to one strain reacted more strongly by HAI to past strains than to later strains (doi: 10.1128/IAI.27.2.397-404.1980). This relates to the significance of the paper (next section).

Obviously, there are citations to update throughout due to the booming SARS-CoV-2 literature.

Significance

This study, if anything, undersells itself. Obviously it is a huge contribution to our understanding of how a seasonal coronavirus that bears important phenotypic resemblance to SARS-CoV-2 evolves, but I think it is also providing a foundational piece of evidence--a mechanism--of how rapid viral turnover (by antigenic evolution) occurs. There is no reason to think this should be limited to the coronaviruses, and I suspect the evidence here will go a long way to unifying the evolutionary and epidemiological dynamics of fast-evolving viruses.

Asymmetric competition is nearly an ecological requirement for one strain to successfully invade and displace another. It is thought (unsure how widely?) that flu evolves antigenically, with new strains eventually displacing old ones, by mutating at key epitopes in ways that the immune system does not immediately pick up. That is, immune memory is biased to recall responses to conserved epitopes, which on average are probably less neutralizing. This will induce competition between mutant and resident viruses, but it would be symmetric, since infection with either would induce responses to conserved epitopes on the other. But if on infection with the mutant, immune memory sometimes reuses (boosts) antibody responses to target the mutated epitopes, those recycled antibodies might be less effective against the mutant, making the competition asymmetric.

What this paper and Underwood (1980) suggest is that we can get this asymmetric, antibody-mediated competition fairly easily and without extensive memory. Underwood showed this more powerfully in rabbits, but in this paper too we see an indirect suggestion of asymmetry in relatively inexperienced children (Fig. 3). Mutants (future strains) successfully invade when they can trigger presumably recalled antibodies that are more harmful to the resident (soon historical) strain than the mutant. If this is so easy to do, as judged by the extensive data here, then it could be common.

I've gone off on a theoretical limb here, but the paper is still important without these considerations. This work will be of interest to evolutionary biologists, epidemiologists, vaccinologists, and everyone else wondering what SARS-CoV-2 will do next and how immunity to antigenically variable pathogens works.

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Reply to the reviewers

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

**Summary:**

This study of Nils Halberg and colleagues aims to characterize tumor-associated immune cell

infiltrates in a mouse model of diet-induced obesity. Authors compared different syngeneic

tumor cell lines for mammary adenocarcinoma and pancreatic ductal adenocarcinoma. Tumor

infiltrating leukocytes were analyzed by a 36-parametric mass cytometry protocol. The authors

put a lot of efforts in the generation of high-quality data by applying state-of-the-art methods for

sample barcoding and batch analyses, removal of batch-specific variations and in the

subsequent pipeline of data analysis. The clinical relevance of the topic addressed is well

documented in several studies, showing a clear association between obesity and the

development of several tumors, including those tumors investigated in this study.

Main findings of this study can be summarized that in the model system used tumor-dependent

differences in the qualitative and quantitative composition of immune cell infiltrates were

observed. Unfortunately, the mouse model system used obviously did not reveal convincing

data whether obesity may modulate the process of tumor infiltration.

The manuscript is well written, quantity of figures is appropriately and of excellent quality and

prior studies were referenced appropriately.

In conclusion, authors made tremendous methodological and technical efforts to generate

robust and high-quality mass cytometry data, but the overall outcome of the study remains

limited in respect to shedding some new light how obesity is possibly involved in the qualitative

and quantitative modulation of tumor-related immune cell infiltration.

Authors’ Response: We thank the reviewer for their constructive and positive feedback as well

as appreciation of our rigorous approach. We would however argue that our data significantly

contributes to the understanding of how obesity affects tumor immunity. We believe that our

systemic approach across multiple tumor systems highlights that i) it matters what model you

choose, as each model have a separate response to the obesity challenge ii) for one model, the

E0771 model, our data reflect obesity-dependent alterations to the CD8+ T-cells population.

This was corroborated by a parallel publication by Rigel et al., 2020 as highlighted by the 2nd

reviewer. That being said, we too, were surprised that the pro-inflammatory obese environment

did not have more pronounced effects on the tumor immune infiltrates across the five models.

**Major comments:**

Due to the limited data really showing an association between obesity and immune cell

infiltration of tumors investigated I would suggest that authors should change emphasis of their

results more closely related to the findings of tumor-dependent immune cell infiltrations than

obesity-related associations. So, the title of the study should be appropriately changed since

"High dimensional immunotyping of the obese tumor micro-environment" rather implies

analyses of spatial relationships of immune, tumor and fat cells by immunohistological analyses,

which would indeed help to strengthen the outcome of this mass cytometry study.

Authors’ Response: We appreciate the constructive suggestion. We did not intend the title to

infer immunohistochemical analysis and apologize that was the case. We have therefore

changed the title to “High dimensional immunotyping of tumors grown in obese and non-obese

mice” in the revised version (line 1).

Although all the efforts made in mass cytometry data generation are quite commendable in this

study, basic statistical issues are not clearly addressed regarding the number of biological

replicates. How many mice were treated per tumor cell line? According to figure 1B nine chow

and eight HFD animals were used: does this mean that only one or two mice were analyzed per

cell line, respectively? Please explain how many animals belong to each of the seven mouse

cohorts.

Authors’ Response: We agree that this was not clearly defined in the manuscript. We have

updated Figure 1A and the corresponding legend to make it clearer. The mouse numbers,

referred to as tumors, are also located in Table 3. In total 69 mice were used, distributed as:

E0771_1 consists of 4 chow fed mice and 4 HFD mice (N=4/4, where N=chow/HFD, for a total

of 8 mice)

E0771_2 is N=5/4. Wnt1 is N=6/6. TeLi is N=5/5. C11_1 is N=5/4. C11_2 is N=5/5. UN-KC is N=5/6.

Figure 1B shows representative mouse weights only. The female mice are from breast cancer

cohort E0771_1 and the male mice are from pancreatic cancer cohort C11_1. We chose to only

show representative data since diet-induced obesity is well established in the C57Bl/6 strain.

Obviously, cell lines E07771 and C11 were analyzed as duplicates only. Regarding E0771,

tumor growth was 31 and 23 days, respectively. So, large inter-individual differences in tumor

growth were obvious and how this is reflected at the level of tumor infiltration? Therefore, please

explain which criteria were used to decide when the tumors had to be removed. Furthermore,

please indicate weight, viability and absolute cell number of each tumor sample in a

supplementary table to get an impression about variability in tumor growth.

Authors’ Response: The reviewer brings up an important point. The E0771 and C11 cohorts are

included in the paper as combined cohorts. The individual C11 cohorts had too few tumors

remaining after removal of samples with too low viability (as discussed below) to analyze

separately. The E0771 cohorts are presented together as a representation of that tumor model.

Data analysis for the E0771 cohorts separately shows comparable population abundance

differences and obesity-dependent changes between chow and HFD tumors. The metacluster

fold change for non-obese and obese tumors between E0771_1 and E0771_2 correlated with a

R2 = 0.8586. Presenting the data combined provides a more concise view of the model.

Removal of E0771 and C11 tumors in each individual cohort were time matched. E0771 tumors

were continuously measured by caliper and removed before they reached 1 cm3 according to the

local ethical guidelines. The E0771_2 cohort tumors had to be removed sooner as one tumor

reached 1 cm3 earlier. We have reported the tumor mass in Figure 1C as that is a more accurate

measurement of final tumor burden. Pancreatic tumors were removed based on optical imaging

of luciferase expressing cancer cells and careful monitoring of mouse distress based on the

grimace scale. The material and methods section has been updated to reflect this (line 556-558).

Only pancreatic tumors had viability poor enough that they had to be excluded from analysis. A

cutoff of CD45+ 5000 cells was set and applied to cells remaining following the gating strategy

shown in Figure 1D. Therefore, CyTOF data for tumors with fewer than 5000 CD45+/Cisplatin

negative cells were excluded from analysis as indicated with an X in Figure1C. As requested, we

have included tumor weights and available viability measurements in new Table 5.

**Minor comments:**

The generation of orthotopic pancreatic cancer mouse models is technically challenging, and

needs more complex imaging methods to monitor the growth of the implanted tumor cells.

Furthermore, orthotopic implantation of tumor cells into the pancreas by surgery can also inflict

significant physical trauma to the recipient animals. How authors have monitored tumor cell

implantation?

Authors’ Response: We agree that tracking tumor growth in the orthotopic pancreas cancer

model is challenging. As mentioned above, these cells were engineered to express luciferase

and optical imaging was used to monitor growth of the implanted cells. We did not report these

numbers as we were unable to convincingly correct for possible light absorption by the

enhanced adipose tissue mass in the high fat group. As such, these scans were used to

estimate the end point.

The number of CD45-positive cells per tumor sample is not given in the manuscript, but this

information would be important to know, because it can be expected that most of the samples

showed less than 20.000 cells. This relatively low number of total leukocytes would not allow a

statistically significant profiling of rare cell subsets, such as DC's or MDSC's. This limitation

should at least clearly addressed in the discussion section.

Authors’ Response: The reviewer raises a great point. Since the cells were live cryopreserved

and thawed before measuring CD45, we did not determine the total immune cell infiltrate. After

thawing, the CD45+ cells accounted for roughly 1-12% of the total events collected across all

batches leading to a total number of CD45+ cells ranging between 54,317 and 1,102,767 per

batch. Numbers for each batch can be found in Table 3. After gating and exclusion of tumors

with less than 5000 CD45+ cells, the remaining tumor data were equally sampled and 5206

CD45+ cells were included in further analysis from each tumor. Overall, we were focused on

broad phenotyping of the immune infiltrate and not on rare subsets. Some subsets had low

abundance in some tumors and high abundance in others. Because the analysis was performed

altogether, the overall phenotyping and clustering did not find any truly rare subsets. DCs and

MDSC were not rare when assessed across the datasets. While we cannot characterize the

subsets that are small in a specific tumor type, we can be confident in the characterization

provided by the streamlined analysis of the data as a whole.

According to table 2 authors have used 36 immune cell-related antigens including casp3, which

was only used to exclude apoptotic cells from downstream analyses. But as written in the

results section only 26 phenotyping markers were used to generate the viSNE map shown in

Figure 3. In Figure 3C-F 30 markers were shown. Please explain this obvious inconsistency of

markers used.

Authors’ Response: Thank you for this question. Our goal here was to generate a viSNE map

that best separated out immune cells by phenotype. Lineage markers and well-established

phenotyping markers were therefore included to create the well separated viSNE map. It follows

that some markers were not included: i) Markers that were used to gate the population of

interest (CD45 and c-Cas3) were excluded from the viSNE input parameters.; ii) Markers that

had relatively low signal were also excluded such as MHC-1 and CD117. Including negative

markers is computationally costly, provides limited biological insight, and can produce a worse

viSNE map by reducing cell separation due to shared lack of signal (Diggins et al., 2015); iii)

Activation/ exhaustion markers were excluded from the viSNE analysis because the focus of the

phenotyping was on major cell subsets and not on activation states. The hope was to observe

differences in exhaustion marker expression between chow and HFD; and iv) CD5 was

excluded because having two bright T cell markers skewed the map towards a more T cell

dominant view. Markers with meaningful expression were reintroduced in the MEM analysis

after the viSNE map was made. Exclusion of markers from viSNE analysis is a generally

accepted practice and has been applied previously (Wogsland et al., 2017, Cheng et al., 2016,

Huse et al., 2019, Leelatian et al., 2020, Doxie et al., 2018, Okamato et al., 2021, Henderson et

al., 2020). The reasoning behind using the 26 phenotyping markers have been included in the

revised manuscript (line 754 – 757)

How viability of tumor samples was determined?

Authors’ Response: Viability was measured at three points using membrane exclusion assays.

Viability was first measured upon tumor dissociation using trypan blue and a Countess cell

counter on the single cell suspension before freezing. Values were used to guide cell aliquoting

for cryopreservation. Viability was again measured with trypan blue upon thaw in order to

barcode and stain 3 million live cells per sample. Before fixation, cells were again stained for

viability, this time with cisplatin, to exclude dead cells after data collection with gating. This has

been added to the methods section (line 559-562)

Cells were additionally stained for cleaved-Cas3 as an indicator of cells undergoing apoptosis.

Only pancreatic tumors had viability poor enough that they had to be excluded form analysis.

Tumors with fewer than 5000 CD45+ Cisplatin negative cells were excluded from analysis as

indicated with an open X in Figure1C. The tumor count in parentheses in Table 3 indicates the

tumors that were not excluded.

Please indicate cell loss caused by cryopreservation of dispersed tumor tissue samples.

Authors state that mainly neutrophilic granulocytes will be lost during cryopreservation, and that

this would help to the "definitive identification and characterization of G-MDSC". But there are

several reports showing that MDSC-subsets also behave very sensitive during cryopreservation

and that it is recommended to analyze fresh samples if MDSC's are of particular interest (DOI:

10.1177/1753425912463618; DOI: 10.1177/1753425912463618). This possible limitation

should be discussed in the manuscript and not only highlighted as advantage on the way to

identify MDSC-subsets.

Authors’ Response: We thank the reviewer for this insightful comment. We agree that we likely

lost some MDSC during the cryopreservation process as shown in the reference above. But

since no neutrophils survive standard cryopreservation (Graham-Pole et al., 1977), the Ly6G

positive cells in our analysis are G-MDSC and not neutrophils. We assume that any cell death

related to cryopreservation would be consistent across samples, so although cell totals may be

lower than in the tumor, abundance differences and phenotype can still be evaluated. We have

included a discussion of this in the revised manuscript

(line 408 – 410).

In the Figure 1D X-axis named by "193Ir-NA" should be replaced by "193Ir-DNA".

Authors’ Response: NA is shorthand for nucleic acid since the iridium intercalates into DNA

and RNA. The figure legend has been updated to make this clear.

Furthermore, please explain "(T)" in the figure legend. Percentages in the last two dot plots

related to "all previous gates" are confusing: 20,44% of all DNA-containing single cells were

finally intact, living CD45+ cells, i.e. almost 80% of cells were excluded because they were dead

or apoptotic and this corresponds to 57,06% of intact, living CD45-positive cells related to all

CD45-positive cells? How these percentages are related to the "Percent of CD45/total raw

events" in the last column of Table 3?

Authors’ Response: These are great points. Thank you for bringing them to our attention. This

confusing notation has been removed since Figure 1D is a representative gating strategy. “All

previous gates” means that the previous gates were all applied to the population showing in that

plot. CyTOF data requires thorough gating to remove the events that are not representative of

actual cells so yes, many events were removed before analysis. Even more cells were excluded

here since our focus was on the CD45+ cells and not the cancer cells. The CD45+ cells

indicated in Table 3 and visualized in Figure 1E can be calculated by summing the total gated

CD45+ cells per Figure 1D for each batch and dividing that by the total number of events

collected per batch. The summed CD45+ values and the total collected events are also in Table

3.

Authors claimed that "155Gd_IRF4" was changed to "155Gd", but it is not clear why to mention

that IRF4 has been NOT used throughout the study? Please provide only those technical

details, which are necessary to understand what has been done.

Authors’ Response: We apologize for any confusion. This change was mentioned because

most cohorts included the IRF4 channel while a two (C11_2 and Wnt_1) did not. The FCS files

were changed to allow for simultaneous analysis. The IRF4 antibody did not work so there

shouldn’t be any bleed into other channels in the samples that were stained with IRF4.

According to general practice, we believe that it is important to make note of any manipulation to

the FCS files.

Re Figure 6: please explain the abbreviation "TNBC".

Authors’ Response: We apologize for not explaining this abbreviation. TNBC is short for triple

negative breast cancer. This has been corrected in the resubmitted version.

Experiments done with TKO mice are not described in the Materials and Methods section. In

particular, it would be important to know the number of replicates and the number of tumors

grown in this model. It should be also discussed that the growth kinetics of tumors in chow and

HFD TKO mice seem to be much faster as compared to wild type mice. Principally, the TKO

model used here is only of limited value to clarify especially the role of CD8 cells since all other

T- and B- cell subsets including NK cells are also absent in this knockout model and indirect

effects caused by these cells cannot be excluded.

Authors’ Response: We deeply apologize that the TKO experiments were not included in the

Triple knockout (Rag2-/-::CD47-/-::Il2rg-/-; TKO) mice were purchased from Jackson Laboratories (Stock No: 025730).

We agree with the reviewer it is an important point that the E0771 tumors overall grew faster in the TKO model. Ringel et al. 2020 saw similar results when depleting CD8 T cells in their MC38 model. Comparably, the most striking difference observed was that the tumor growth between obese and non-obese mice disappeared in the TKO mice.

We have modified the results section to include these points (line 309-310). Reviewer #1 (Significance (Required)):

material and methods section.

experiment was performed with N=5/5. The description of the TKO model has been added to

Orthotopic implantation and

monitoring of E0771 and C11 cells were performed as with the wild type C57BL/6 mice. Each

the methods section (line 520- 533) and number of mice used has been added to the figure

legend.

did not observe any major growth changes (overall growth rate and growth differences between

obese and non-obese mice) in the TKO mice compared to the wild type mice.

In the C11 model, interestingly, we

We agree that the combined lack of B- and NK- cells in combination to the lack of T-cells

exclude a direct conclusion on the effect of obesity-dependent alteration in T-cell phenotypes.

Altogether, this study is a paragon that a single technology-based study alone, even when well-

designed, is not sufficient to explore complex tumor microenvironment-immune cell interactions

and that additional information on spatial relationships of cells and possibly single cell-based

RNAseq techniques are necessary to shed new light on this ambitious topic. But there is no

doubt that the potential of mass cytometry has been not fully exploited in this study and that a

more focused view on particular cell types identified so far, such as macrophages or CD8 cells,

by using as many immunophenotypic and functionally-related parameters as necessary would

allow a more in depth-phenotyping of particular immune cell compartments.

The significance of this subject would have been tremendously increased if human samples will

be analyzed in a future confirmative study.

Authors’ Response: Again, these are important insights. To what extend we have taken full

advantage of the suspension mass cytometry technology is of course debatable. When we set

out to perform these studies, we were compelled to take a broad approach rather than focusing

on a single cell type for the following reasons: i) we had noticed extreme variability in immune

targeted analysis through FACS of murine cancer models. Since we set out to demonstrate

systemic effects of the obese environment rather than model-specific effects, the broad antibody

panel made the most sense and ii) tumor immune infiltrates are known to be composed of

multiple cell types and the effect of the obese state would likely affect multiple of these. To not

bias ourselves this prompted us to design a rather broad immune panel. With the knowledge

derived from this study and others (For example Rigel et al, Cell 2020 and Chung et all, Cell

2020), new and more focused panels could be developed and implemented for future studies.

We agree that the inclusion of human data would be of great value. We were, however, unable

to obtain suitable human material that could be used for this suspension mass cytometry

analysis. This was largely due to large inconsistencies in reported patient BMI and inadequate

tumor freezing conditions.

Even when I'm not a specialist in tumor biology, based on my expertise in the fields of chronic

inflammation and cytometry, I'm convinced that the outlined way of generating

immunophentypic data by single cell-based mass cytometry is of major interest not only for

tumor biologists, but will be for sure recognized by a broad scientific community interested in the

generation of single cell-based immunophenotypic data.

Authors’ Response: Thank you for your helpful and supportive feedback. It is indeed our hope

and motivation that the immunophenotyping platform presented herein will be broadly applicable

to other cancer immunologists and fields.

Reviewer #2

(Evidence, reproducibility and clarity (Required)):

Wogsland et al. apply herein mass cytometry (CyTOF) to investigate how obesity affects tumor

immune infiltrates. They use several models of murine breast and pancreatic cancers and

analyse their immune landscape thanks to an extended panel of 36 markers. They notably

describe a decrease in CD8 T cells in one breast cancer model fed with high fat diet inducing

obesity which favors tumor development.

Overall, the report is clearly written and follows a very logical plan. Figures are also clear and

nicely support the text. The mass cytometry approach appears quite original and could be

relevant for many readers.

Authors’ Response: We thank the referee for their constructive and positive comments on our work.

The referee raises the general criticism that our study is descriptive.

Nevertheless, some concerns have to be made and would need to be acknowledged by

authors:

-First of all, the paper appears very descriptive. Except at the end of the last figures, authors

only establish of catalog of immune cells in different tumors. Even if the trueness of such

observations is undisputable, their relevance to improve our understanding of tumor biology is

clearly questionable.

Authors’ Response:

While we agree that the majority of the manuscript is focused on establishing a robust immune

atlas in multiple tumor models grown in obese and non-obese mice, we believe that such work

has important merit: i) our immune cell atlas of 5 transplant models will be a valuable resource

for other cancer researchers interested in the immune-oncology field (as also highlighted by the

first referee); ii) our findings clearly underscore the critical need to apply multiple cell lines in

experimental setups when studying the interaction between tumors and immune cells –

particularly in the obese setting; iii) we have implemented an analysis pipeline that is broadly

applicable for high dimensional mass cytometry data that will be useful for future high-

dimensional immunotyping efforts, iv) through our unbiased analysis pipeline we did identify

obesity-dependent alterations to the CD8 cell population in the E0771 model. This finding was

corroborated by the studies by Ringel et al., 2020. Collectively, we strongly believe that our

studies will contribute to the advancement of our understanding of tumor biology.

-Moreover, the major finding claimed in this study (CD8 T cells decrease in tumors from HFD

mice) has been very recently published paper also providing mechanistic insights (Ringel et al.,

Cell 2020). Authors could legitimately be disappointed but the interest of their study is sadly

severely impacted by this prior publication. This key paper should be at least discussed and

included in references.

Authors’ Response: The paper by Ringel et al., was published after we originally submitted our

manuscript for review and was therefore not referenced or discussed (Ringel et al., 2020). In the

resubmitted version of the manuscript, we have included a thorough discussion of the paper’s

findings in terms of consistencies and inconsistencies with our conclusions (line 545-463).

-Finally, even if the initial strategy of integration of breast and pancreatic cancers was

indubitably a good one, results reported in figures 5 & 6 clearly show a quite specific

observation in the E0771 model. So in this context, integrating all these datasets do not improve

the understanding of this phenotype

Authors’ Response: We thank the referee for bringing up this point. Regardless of the outcome, we would strongly argue that the integrated approach to be advantageous to individual analysis. The integrated approach did not hinder new discoveries in any of the datasets – if anything, the integrated analysis pipeline developed herein would facilitate new discoveries that would be missed by repeating individual analysis. By integrating the datasets, we enabled the robust identification of more cell subsets. In particular cell types that displayed low abundance in some models. Those cells would have likely been hidden or even missed in a larger subset had the models been analyzed separately. As such, we maintain that the integrated approach is the correct and most biological meaningful to follow when given the possibility.

Besides these quite general comments, few more specific points:

-In Fig 2, the F4/80 signal appears very weak in all datasets except one (TeLi) with an almost

flat curve for all the other ones. It asks the question of the reproducibility of the staining that

could be only partially corrected with batch correction algorithms.

Authors’ Response: The F4/80 peak in the TeLi cohort is indicative of a large F4/80

population rather than a sign of signal intensity differences. TeLi tumors had much higher

abundance of F4/80+ cells than did the other tumor types as can be seen in Figure 4B. For

each mass cytometry run, we included a control sample to ensure equal staining patterns

between the antibodies in each run.

-Obesity is clearly known to be sex/hormone dependent as confirmed by authors themselves in

their Fig 1B so again the global integration (both sex and 2 organs) strategy is disputable here.

It is hard to know if there is no effect in the pancreas because of tissue or sex specificities.

Authors’ Response: Thank you for the feedback. We specifically tried to show the different

tumors side by side without making too many comparisons across tumor types because of the

sex and tissue differences (as was noted in the results section of the manuscript, line 267). Both

breast and pancreatic tumor models are relevant for studying the obesity cancer connection

which is why we have worked to develop these models with different cancer types. Even with

the sex and tissue differences, male and female mice became obese on a high fat diet, and

tumors from both tissues grew larger on a high fat diet

. It is our hope that this work will pave the

way for future studies to interrogate these differences.

-On Fig 1C, red dots are closed or open but explanation of this is lacking.

Authors’ Response: Thank you for pointing this out. The figure legend has been updated. The

X indicates a tumor that had too few live CD45+ cells to be included in the data CyTOF

analysis. We apologize this was not clear.

-Authors use 36 markers in their CyTOF panel but use only 26 for the dimension reduction

without clearly explaining this choice. Should be amended. For example, why excluding CD5?

Authors’ Response: Thank you for bringing this up. We have addressed these concerns in

response to Reviewer #1 above.

Reviewer #2 (Significance (Required)):

Severly impaired by Ringel et al., Cell 2020

Authors’ Response:

It is clear that the study by Ringel et al., demonstrate new and important mechanistic insights

into the connection between obesity, T-cell biology and tumor behavior. Our studies share many

of the same conclusions on tumor immune cell infiltrate in obesity – particularly the T-cell finding

in our E0771 model. However, we stipulate that our experimental approach and scientific

questions differ. Our approach was to generate high-dimensional immune phenotyping atlas

across multiple models to identify overarching obesity-dependent effects. The manuscript by

Ringel et al., has a more mechanistic focus. The field would benefit from the additive insights

from the two papers combined.

Rebuttal References:

CHENG, Y., WONG, M. T., VAN DER MAATEN, L. & NEWELL, E. W. 2016. Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding. The Journal of Immunology, 196, 924-932.

DIGGINS, K. E., FERRELL, P. B., JR. & IRISH, J. M. 2015. Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods, 82, 55-63.

DOXIE, D. B., GREENPLATE, A. R., GANDELMAN, J. S., DIGGINS, K. E., ROE, C. E., DAHLMAN, K. B., SOSMAN, J. A., KELLEY, M. C. & IRISH, J. M. 2018. BRAF and MEK inhibitor therapy eliminates Nestin-expressing melanoma cells in human tumors. Pigment Cell & Melanoma Research, 31, 708-719.

GRAHAM-POLE, J., DAVIE, M. & WILLOUGHBY, M. L. 1977. Cryopreservation of human granulocytes in liquid nitrogen. Journal of Clinical Pathology, 30, 758.

HENDERSON, L. A., HOYT, K. J., LEE, P. Y., RAO, D. A., JONSSON, A. H., NGUYEN, J. P., RUTHERFORD, K., JULÉ, A. M., CHARBONNIER, L.-M., CASE, S., CHANG, M. H., COHEN, E. M., DEDEOGLU, F., FUHLBRIGGE, R. C., HALYABAR, O., HAZEN, M. M., JANSSEN, E., KIM, S., LO, J., LO, M. S., MEIDAN, E., SON, M. B. F., SUNDEL, R. P., STOLL, M. L., NUSBAUM, C., LEDERER, J. A., CHATILA, T. A. & NIGROVIC, P. A. 2020. Th17 reprogramming of T cells in systemic juvenile idiopathic arthritis. JCI Insight, 5.

HUSE, K., WOGSLAND, C. E., POLIKOWSKY, H. G., DIGGINS, K. E., SMELAND, E. B., MYKLEBUST, J. H. & IRISH, J. M. 2019. Human Germinal Center B Cells Differ from Naïve and Memory B Cells in CD40 Expression and CD40L-Induced Signaling Response. Cytometry Part A, 95, 442-449.

LEELATIAN, N., SINNAEVE, J., MISTRY, A. M., BARONE, S. M., BROCKMAN, A. A., DIGGINS, K. E., GREENPLATE, A. R., WEAVER, K. D., THOMPSON, R. C., CHAMBLESS, L. B., MOBLEY, B. C., IHRIE, R. A. & IRISH, J. M. 2020. Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells. eLife, 9.

OKAMATO, Y., GHOSH, T., OKAMOTO, T., SCHUYLER, R. P., SEIFERT, J., CHARRY, L. L., VISSER, A., FESER, M., FLEISCHER, C., PEDRICK, C., AUGUST, J., MOSS, L., BEMIS, E. A., NORRIS, J. M., KUHN, K. A., DEMORUELLE, M. K., DEANE, K. D., GHOSH, D., HOLERS, V. M. & HSIEH, E. W. Y. 2021. Subjects at-risk for future development of rheumatoid arthritis demonstrate a PAD4-and TLR-dependent enhanced histone H3 citrullination and proinflammatory cytokine production in CD14hi monocytes. Journal of Autoimmunity, 117, 102581.

RINGEL, A. E., DRIJVERS, J. M., BAKER, G. J., CATOZZI, A., GARCÍA-CAÑAVERAS, J. C., GASSAWAY, B. M., MILLER, B. C., JUNEJA, V. R., NGUYEN, T. H., JOSHI, S., YAO, C.-H., YOON, H., SAGE, P. T., LAFLEUR, M. W., TROMBLEY, J. D., JACOBSON, C. A., MALIGA, Z., GYGI, S. P., SORGER, P. K., RABINOWITZ, J. D., SHARPE, A. H. & HAIGIS, M. C. 2020. Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity. Cell, 183, 1848-1866.e26.

WOGSLAND, C. E., GREENPLATE, A. R., KOLSTAD, A., MYKLEBUST, J. H., IRISH, J. M. & HUSE, K. 2017. Mass Cytometry of Follicular Lymphoma Tumors Reveals Intrinsic Heterogeneity in Proteins Including HLA-DR and a Deficit in Nonmalignant Plasmablast and Germinal Center B-Cell Populations. Cytometry B Clin Cytom, 92, 79-87.

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Referee #2

Evidence, reproducibility and clarity

Wogsland et al. apply herein mass cytometry (CyTOF) to investigate how obesity affects tumor immune infiltrates. They use several models of murine breast and pancreatic cancers and analyse their immune landscape thanks to an extended panel of 36 markers. They notably describe a decrease in CD8 T cells in one breast cancer model fed with high fat diet inducing obesity which favors tumor development.

Overall, the report is clearly written and follows a very logical plan. Figures are also clear and nicely support the text. The mass cytometry approach appears quite original and could be relevant for many readers.

Nevertheless, some concerns have to be made and would need to be acknowledged by authors:

-First of all, the paper appears very descriptive. Except at the end of the last figures, authors only establish of catalog of immune cells in different tumors. Even if the trueness of such observations is undisputable, their relevance to improve our understanding of tumor biology is clearly questionable.

-Moreover, the major finding claimed in this study (CD8 T cells decrease in tumors from HFD mice) has been very recently published paper also providing mechanistic insights (Ringel et al., Cell 2020). Authors could legitimately be disappointed but the interest of their study is sadly severely impacted by this prior publication. This key paper should be at least discussed and included in references.

-Finally, even if the initial strategy of integration of breast and pancreatic cancers was indubitably a good one, results reported in figures 5 & 6 clearly show a quite specific observation in the E0771 model. So in this context, integrating all these datasets do not improve the understanding of this phenotype

Besides these quite general comments, few more specific points: -In Fig 2, the F4/80 signal appears very weak in all datasets except one (TeLi) with an almost flat curve for all the other ones. It asks the question of the reproducibility of the staining that could be only partially corrected with batch correction algorithms.

-Obesity is clearly known to be sex/hormone dependent as confirmed by authors themselves in their Fig 1B so again the global integration (both sex and 2 organs) strategy is disputable here. It is hard to know if there is no effect in the pancreas because of tissue or sex specificities.

-On Fig 1C, red dots are closed or open but explanation of this is lacking.

-Authors use 36 markers in their CyTOF panel but use only 26 for the dimension reduction without clearly explaining this choice. Should be amended. For example, why excluding CD5?

Significance

Severly impaired by Ringel et al., Cell 2020

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Learn more at Review Commons

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Referee #1

Evidence, reproducibility and clarity

Summary:

This study of Nils Halberg and colleagues aims to characterize tumor-associated immune cell infiltrates in a mouse model of diet-induced obesity. Authors compared different syngeneic tumor cell lines for mammary adenocarcinoma and pancreatic ductal adenocarcinoma. Tumor infiltrating leukocytes were analyzed by a 36-parametric mass cytometry protocol. The authors put a lot of efforts in the generation of high-quality data by applying state-of-the-art methods for sample barcoding and batch analyses, removal of batch‐specific variations and in the subsequent pipeline of data analysis. The clinical relevance of the topic addressed is well documented in several studies, showing a clear association between obesity and the development of several tumors, including those tumors investigated in this study.

Main findings of this study can be summarized that in the model system used tumor-dependent differences in the qualitative and quantitative composition of immune cell infiltrates were observed. Unfortunately, the mouse model system used obviously did not reveal convincing data whether obesity may modulate the process of tumor infiltration. The manuscript is well written, quantity of figures is appropriately and of excellent quality and prior studies were referenced appropriately. In conclusion, authors made tremendous methodological and technical efforts to generate robust and high-quality mass cytometry data, but the overall outcome of the study remains limited in respect to shedding some new light how obesity is possibly involved in the qualitative and quantitative modulation of tumor-related immune cell infiltration.

Major comments:

Due to the limited data really showing an association between obesity and immune cell infiltration of tumors investigated I would suggest that authors should change emphasis of their results more closely related to the findings of tumor-dependent immune cell infiltrations than obesity-related associations. So, the title of the study should be appropriately changed since "High dimensional immunotyping of the obese tumor micro-environment" rather implies analyses of spatial relationships of immune, tumor and fat cells by immunohistological analyses, which would indeed help to strengthen the outcome of this mass cytometry study. Although all the efforts made in mass cytometry data generation are quite commendable in this study, basic statistical issues are not clearly addressed regarding the number of biological replicates. How many mice were treated per tumor cell line? According to figure 1B nine chow and eight HFD animals were used: does this mean that only one or two mice were analyzed per cell line, respectively? Please explain how many animals belong to each of the seven mouse cohorts. Obviously, cell lines E07771 and C11 were analyzed as duplicates only. Regarding E0771, tumor growth was 31 and 23 days, respectively. So, large inter-individual differences in tumor growth were obvious and how this is reflected at the level of tumor infiltration? Therefore, please explain which criteria were used to decide when the tumors had to be removed. Furthermore, please indicate weight, viability and absolute cell number of each tumor sample in a supplementary table to get an impression about variability in tumor growth.

Minor comments:

The generation of orthotopic pancreatic cancer mouse models is technically challenging, and needs more complex imaging methods to monitor the growth of the implanted tumor cells. Furthermore, orthotopic implantation of tumor cells into the pancreas by surgery can also inflict significant physical trauma to the recipient animals. How authors have monitored tumor cell implantation? The number of CD45-positive cells per tumor sample is not given in the manuscript, but this information would be important to know, because it can be expected that most of the samples showed less than 20.000 cells. This relatively low number of total leukocytes would not allow a statistically significant profiling of rare cell subsets, such as DC's or MDSC's. This limitation should at least clearly addressed in the discussion section. According to table 2 authors have used 36 immune cell-related antigens including casp3, which was only used to exclude apoptotic cells from downstream analyses. But as written in the results section only 26 phenotyping markers were used to generate the viSNE map shown in Figure 3. In Figure 3C-F 30 markers were shown. Please explain this obvious inconsistency of markers used. How viability of tumor samples was determined? Please indicate cell loss caused by cryopreservation of dispersed tumor tissue samples. Authors state that mainly neutrophilic granulocytes will be lost during cryopreservation, and that this would help to the "definitive identification and characterization of G-MDSC". But there are several reports showing that MDSC-subsets also behave very sensitive during cryopreservation and that it is recommended to analyze fresh samples if MDSC's are of particular interest (DOI: 10.1177/1753425912463618; DOI: 10.1177/1753425912463618). This possible limitation should be discussed in the manuscript and not only highlighted as advantage on the way to identify MDSC-subsets. In the Figure 1D X-axis named by "193Ir-NA" should be replaced by "193Ir-DNA". Furthermore, please explain "(T)" in the figure legend. Percentages in the last two dotplots related to "all previous gates" are confusing: 20,44% of all DNA-containing single cells were finally intact, living CD45+ cells, i.e. almost 80% of cells were excluded because they were dead or apoptotic and this corresponds to 57,06% of intact, living CD45-positive cells related to all CD45-positive cells? How these percentages are related to the "Percent of CD45/total raw events" in the last column of Table 3 ?

Authors claimed that "155Gd_IRF4" was changed to "155Gd", but it is not clear why to mention that IRF4 has been NOT used throughout the study? Please provide only those technical details, which are necessary to understand what has been done.

Re Figure 6: please explain the abbreviation "TNBC". Experiments done with TKO mice are not described in the Materials and Methods section. In particular, it would be important to know the number of replicates and the number of tumors grown in this model. It should be also discussed that the growth kinetics of tumors in chow and HFD TKO mice seem to be much faster as compared to wild type mice. Principally, the TKO model used here is only of limited value to clarify especially the role of CD8 cells since all other T- and B- cell subsets including NK cells are also absent in this knockout model and indirect effects caused by these cells cannot be excluded.

Significance

Altogether, this study is a paragon that a single technology-based study alone, even when well-designed, is not sufficient to explore complex tumor microenvironment-immune cell interactions and that additional information on spatial relationships of cells and possibly single cell-based RNAseq techniques are necessary to shed new light on this ambitious topic. But there is no doubt that the potential of mass cytometry has been not fully exploited in this study and that a more focused view on particular cell types identified so far, such as macrophages or CD8 cells, by using as many immunophenotypic and functionally-related parameters as necessary would allow a more in depth-phenotyping of particular immune cell compartments. The significance of this subject would have been tremendously increased if human samples will be analyzed in a future confirmative study.

Even when I'm not a specialist in tumor biology, based on my expertise in the fields of chronic inflammation and cytometry, I'm convinced that the outlined way of generating immunophentypic data by single cell-based mass cytometry is of major interest not only for tumor biologists, but will be for sure recognized by a broad scientific community interested in the generation of single cell-based immunophenotypic data.

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Reply to the reviewers

We thank all three reviewers for their very useful and constructive comments. Below is our point-by-point response.

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

The manuscript by Viais R et al describes a novel role for augmin complex in apoptosis prevention during brain development. Augmin complex recruits g TuRC to microtubule lattices to nucleate microtubule branches. The authors show how -in its absence- neural progenitors have elevated p53 activity and apoptotic rate, with severe consequences on overall brain development. In particular, augmin-deleted neural progenitors display spindle abnormalities and mitotic delay, which induce DNA damage accountable for p53-induced apoptosis.

One point that I personally found very interesting is the role of augmin-dependent MT nucleation depletion in interphase. The authors mention (line 152) that at stage E13.5, besides the number of neurons being reduced, a few neurons were misplaced in the apical region, indicating a role for augmin-driven MT nucleation in cell migration. Moreover, the authors showed that p53 genetic deletion in the Haus6 cKO rescues the apoptosis phenotype but not the tissue disorganisation, suggesting that augmin-dependent microtubule might play a role in tissue polarity. While this is well presented in the discussion, the title in line 268 narrowly refers to mitotic augmin roles. I would like here to see the authors referring to putative roles for augmin-mediated MT nucleation in interphase, by toning down the title in line 268.

We note that severe loss of tissue integrity is evident in the p53 KO background. In this background cells are allowed to repeatedly undergo defective cell divisions with aberrant chromosome segregation, producing increasingly abnormal daughter cells that may eventually fail to support epithelial integrity. Regarding possible neuronal migration defects, this has been previously observed in a study by the Hoogenraad group (Cunha-Ferreira et al., Cell Reports, 2018, 24, 791–800) and this is mentioned in our discussion. To account for the possibility that augmin may have roles beyond mitosis, we have changed the heading to a more neutral statement, not specifically referring to proliferation/mitosis: “Loss of augmin in p53 KO brains disrupts neuroepithelium integrity”.

Overall, the text is well written and flows easily. Figures are clear and legends provide sufficient information on experimental conditions, number of replicates and scale bars. I noticed that, although the number of repeats is specified, the number of cells scored per experiment is not always included. In my comments below I highlight cases where this missing information should be added.

**Specific points:**

1. In the Cep63 KO (Marjanovic et al, 2015) and the CenpJ KO mice (Insolera et al, 2014), as well as other recently published papers (e.g. Phan TP et al, EMBO Journal, 2020) part of the phenotypical characterisation of the KO mice displays pictures of the overall brain dissected from the mice. Could the author show these images?

The main difference between the cited studies (including our own on the role of CEP63 in brain development) and our current study is that in the previous studies brains are microcephalic but essentially intact, whereas in our current study brain development was aborted and accompanied by cell death and severe tissue disruption. As a result, these brains are very fragile and difficult/impossible to isolate. An additional challenge is the fact that brain disruption occurs at a very early developmental stage (before E13.5), where dissection is more difficult than at later stages. Indeed, we note that all the brains presented in the above cited studies were from later embryonic stages or newborn/adult mice. Therefore, instead of dissecting brains, we decided to present encephalic coronal and sagittal sections as shown in Fig. 1c, d, e, Fig. S1c, and Fig. 3b, e to show the overall impact of Haus6 cKO and Haus6 cKO p53 KO on embryonic brain morphology at E13.5 and E17.5.

Fig2d: do the insets correspond to higher magnification images? What is the zoom factor? I could not find it in the legend.

The zoom factor is 1.4 - we have added this information to the figure legend.

Fig2E,I and K graphs: how many cells were quantified here over how many experiments? I could not find information in the figure legend.

We have added the information regarding the number of embryos and counted cells to the figure legends.

The impact of Haus6 on mitotic spindle needs further clarification:

o Fig2F: here, the authors show quantification for abnormal and multipolar spindle together. Later on, the abnormal spindle phenotype is no longer discussed (Fig4). I was wondering what is the individual contribution of abnormal and multipolar spindle, separately. Which one of the two is more frequent? Could the authors explain in the text how they define an abnormal spindle? Is it the lack of MT with the condensed chromosome area?

We agree that our previous classification was somewhat confusing. The spindle defects in Haus6 cKO cells are directly linked to the spindle pole fragmentation phenotype shown in Fig. 2d, e. Association of spindle microtubules with these scattered PCM fragments causes spindles to appear overall disorganized. In some cases, multiple smaller asters are present, which is what we had termed “multipolar”. However, this does not always involve multipolar DNA configurations, which we separately quantify in Fig. 4. To avoid confusion, we now classify spindle morphologies based on tubulin staining simply as “normal” (bipolar configuration, two robust and focused asters) or “disorganized” (lack of bipolar configuration, in some cases multiple smaller asters). We have included a better description of this classification (lines 202-205).

o Could it be that augmin deletion induce an instability in MTs within the mitotic spindle, leading to the "empty" or with very few MTs spindles? Or could it be that more cold-sensitive MTs are affected by fixation? What is the percentage of the spindle with no MT in control?

It is possible that augmin-deficient spindles are less well-preserved during fixation due to compromised spindle microtubule stability. Indeed, in tissue culture cells augmin deficient spindle microtubules are more cold-sensitive than controls (Zhu et al., 2008, JCB, 183, 835-848). To address this we will determine the percentage of mitotic control and Haus6 cKO cells lacking microtubule staining.

o Did the authors quantify anaphase/telophase phenotypes as they did in Fig4f?

Yes, this quantification was already included in Fig. 4j, where we compared abnormal chromosome configurations between Haus6 cKO and Haus6 cKO p53 KO.

o How do authors explain PCM fragmentation here? Could this phenotype be due to an initial cytokinesis defect which led the cells to accumulate extra centrosomes? Or could this maybe be a product of aberrant PCM maturation/centrosome duplication? Could the authors add here a line to discuss the possible origin of pole fragmentation?

The PCM fragmentation phenotype has previously been described after augmin RNAi in cultured cells (Lawo et al., 2009, Curr Biol, 19, 816-826). We refer to this result in the discussion and we have added the above reference, to emphasize this point. The authors showed that this phenotype does not involve amplification of centriole number, but is caused by an imbalance in microtubule-dependent forces acting on the PCM and leading to its fragmentation. Thus, the extra poles were formed by acentriolar PCM fragments. We will clarify this issue by quantifying centriole numbers in mitotic cells (when centriole duplication is complete) in control and Haus6 cKO brains. We expect that this will confirm the data previously obtained in cell lines showing that in most cells the fragmented poles are not due to extra centrioles (see also below).

Apart from the PCM fragmentation phenotype that does not alter centriole number, previous work in cultured cells also described cytokinesis defects. Failed cytokinesis would indeed lead to increased centriole number. However, it would also increase DNA content, which would be visible by an increase in the size of interphase nuclei (which we observed in Haus6 cKO p53 KO cells and quantified in Fig. 4J) and a larger size of mitotic figures. We now refer to the possibility of cytokinesis defects and cite previous work in lines 272-274. In case we observe cells with increased centriole number, which we will quantify for the revised version of the manuscript (see above), we will also determine if this corelates with an increased size of the corresponding mitotic figures. If so, this would be consistent with failed cytokinesis as cause of extra centrosomes.

Fig 4 Did the authors quantify centrosome fragmentation and abnormal spindle here? As they characterised them for the Haus6 cKO mouse, it would be preferable to maintain the same characterisation for the Haus6 cKO p53KO.

We will quantify pole fragmentation and spindle defects also in Haus6 cKO p53 KO as shown for Haus6 cKO in Fig. 2.

Fig4c and d: how many replicates were done to obtain these graphs? I think the authors forgot to add this information in the figure legend.

This information has been included in the figure legend.

Fig4f,g, I and J: how many cells were counted per experiment? I appreciate the authors writing the n of experiments performed.

We have added this information to the figure legend.

Fig5d: how many cells were counted per experiment?

We have added this information to the figure legend.

Reviewer #1 (Significance (Required)):

While it was already known that mitotic delay affects the neuronal progenitor pool through activation of p53-dependent apoptosis (Pilaz L-J, Neuron 2016; Mitchell-Dick A, Dev Neurosci 2020), and that this can be triggered by depletion of centrosomal proteins as Cenpj and Cep63, the role of surface-dependent microtubule nucleation was not identified so far. Some insights come from a Haus6-KO mouse model which dies during blastocyst stage after several aberrant mitosis (Watanabe S, Cell Reports, 2016). In parallel, McKinley KL et al showed that Haus8 depletion in human cells (RPE1cells) triggered p53-dependent G1 arrest following mitotic defects (McKinley KL, Developmental Cell, 2017). Building on the Hause6 KO mouse and human cell line data, here Viais R et al discover a novel role for the augmin-mediated MT nucleation in neural progenitor growth and brain development in vivo, through prevention of p53-induced apoptosis.

Specifically, Viais R et al show that:

1. Surface-dependent microtubule nucleation depletion severely impacts brain development, disrupting partly or completely forebrain domains and cerebellum;
2. Surface-dependent microtubule nucleation depletion induce spindle abnormalities, resulting in mitotic delay in apical progenitors;

3. Mitotic delay results in DNA breaks, p53 activation and p53-induced apoptosis.

   This is a tidy, well-executed study with good quality data. These findings propose a novel mechanism that results essential for neural progenitor and overall brain development.

   In my opinion, a large audience will benefit from these discoveries: from developmental biologists to cell biologists focused on microtubule dynamics, cell cycle, differentiation, stem cells and cell polarity.

Key works describing my area of expertise: microtubule dynamics, centrosome function, cell cycle regulation and cell polarity.

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

Viais, Lüders and colleagues here present an analysis of augmin's roles in neural stem cell development. They describe a dramatic impact of the conditional ablation of Haus6 on embryonic brain development in the mouse, with mitotic problems that lead to greatly-increased levels of apoptosis. The rescue of this apoptosis by mutation of the gene that encodes p53 did not restore brain development, which was still aberrant, due to mitotic errors.

The paper is clearly written, with well-designed and controlled experiments. Its conclusions are well supported by the data presented. I have few comments on the technical aspects of the work- it appears very solid to me.

**Specific comments**

1. Clearer explanation of the mouse strains used should be provided. The section describing the generation of the Haus6 conditional on p.5 should specify that this is the same as was already published in the 2016 Watanabe paper (this is in the Materials and Methods, but this should be more clearly specified. More specific details of the p53 knockout mice from Jackson should be included in the Materials and Methods.

We have included additional information describing the generation of the Haus6 cKO mice in the main text (line 137-140). It is not exactly the same as described in the Watanabe et al. paper. The previously published strain (Watanabe et al., 2016, Cell Reports, 15, 54-60) contained a floxed Haus6 cKO allele with a flanking neomycin cassette. For the current study the neomycin cassette was removed. Details are described in the method section and also shown in Fig. S1a. Specific information regarding the p53 KO strain has been added to the method section.

Figure 1a contains minimal information on the Haus6 locus. More detail should be included for information, if this Figure is to remain (although reference to the targeting details in the original description would be sufficient). It is unclear what the timeline diagram is to convey and it should be improved or deleted. A similar comment applies for the details in Figure 3a, although the colour scheme for the different genotypes is useful.

More detailed information on the Haus6 locus is shown in the schematic of Fig. S1a and in the referenced study (Watanabe et al., 2016, Cell Reports, 15, 54-60). Since the targeting of Haus6 exon1 was previously described, we feel that including this information as a supplementary figure and referring to the previous study is appropriate.

Regarding the schematics in Fig. 1a and Fig. 3a, we have improved these. The timeline shows the time points of Cre expression and of obtaining embryos for analysis.

The important PCR controls in Figure S1b have an unexplained 1000 bp band that appears only in the floxed heterozygote. It would be helpful if the authors explained this in the relevant Figure legend.

This band is an artifact and represents heteroduplexes of floxed (1080 bp) and wild type (530 bp) DNA strands due to extended regions of complementary. We have explained this in the figure legend.

Assuming the putative centrosome 'clusters' in Figure 6c are similar to the fragmented structures seen in thalamus in Figure 2d, a different description should be used to avoid confusion with multiple centrosomes, which is not a phenotype here. It is not clear how the loss of centrosomes from the ventricular surface was scored, whether it was based on total gamma-tubulin signal or individual centrosomes; how fragmented poles would affect that is unclear, so the legend and relevant details should clarify this point.

The fragmented spindle poles shown in Fig. 2d are different from the centrosome clusters in Fig. 6c. The fragmented poles are fragments of PCM rather than extra centrosomes. Fragmentation is specific to mitosis, involving forces exerted by spindle microtubules (Lawo et al., 2009, Curr Biol, 19, 816-826). In contrast, the centrosome clusters that we observed in Haus6 cKO p53 KO apical progenitors represent centrosomes from multiple cells in interphase, most likely as part of apical membrane patches that have delaminated form the ventricular surface. In the intact epithelium of controls these centrosomes line the ventricular surface. To avoid confusion, we now indicate in the text and legend that these centrosome clusters involve interphase cells.

Phospho-histone H2AX should be referred to as a marker of activation of the DNA damage response, rather than DNA repair.

We have changed the text accordingly.

**Minor points**

i. Figure 1b should include a scale bar.

We have added the scale bar.

ii. The labelling of Figure 1f should be revised.

The labels have been fixed.

iii. Figure 2k is not labelled in this Figure.

This has been fixed.

iv. Scale bars should be included in the blow-ups in Figure 6c.

We have added the scale bars.

Reviewer #2 (Significance (Required)):

While it is striking that they see complete disruption of brain development, rather than microcephaly, arguably the mechanistic novelty of the findings is moderate, in that the impacts of Haus6 deficiency on mitotic spindle assembly are well established. The authors only allude to potential additional and novel activities of augmin (in neural progenitors, potentially) that might explain this possibly-unexpected outcome of this study. The topic is likely to be of interest to people in the field of mitosis, genome stability and brain development.

My expertise is cell biology/ mitosis, less so on murine brain development.

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

Jens Lüders &Co demonstrates the essential role of Augmin-mediated MT is critical for proper brain development in mice. The most striking point is that even p53 is eliminated, the microcephaly phenotypes of Haus6 KOs were not rescued. This could mean that the Augmin-mediated MT process is critical to cellular functions that are independent of p53. The authors claim that there are increased DNA damage and excessive mitotic errors. In these aspects, the current work is fascinating. Nevertheless, what causes massive damage to the neural epithelial tissues in the double mutant is not well explained or examined. Few questions appear in mind before I go into the detail. Are these animals still harbor functional centrosomes and their numerical status?

This is an important point that was also raised by the other reviewers. Based on previous work in cells lines (Lawo et al., 2009, Curr Biol, 19, 816-826), we do not expect that loss of augmin directly impairs centrosomes. Indeed, the authors showed that centriole number was unaffected. The only centrosome defect that the authors observed was fragmentation of the PCM during mitosis, but this was shown to be due to imbalanced forces exerted by spindle microtubules: fragmentation could be rescued by microtubule depolymerization or depletion of the cortical microtubule tethering factor NUMA (Lawo et al., 2009, Curr Biol, 19, 816-826). That being said, we will examine this issue also in our mouse model by staining and counting of centrioles in mitotic apical progenitors of control and Haus6 cKO embryos.

The microcephaly part of the introduction needs some more work. In particular, the authors need to explain apical progenitors' depletion, possibly the correct mechanisms in causing microcephaly. By saying cortical progenitors, it becomes vague. Indeed, there would also be cortical progenitors depleted. But, the fundamental mechanisms are the depletion of apical progenitors lined up at VZ's lumen. Two works in this connection generated brain tissues from microcephaly patients carrying mutations in CenpJ and CDK5RAP2 (Gabriel and Lancaster et al). Authors should cite their work and relate their findings to mouse brain data.

We have introduced text changes in the introduction to indicate the specific role of apical progenitor depletion in microcephaly and the differences in the underlying mechanism between mouse and human organoid models (line 63; lines 86-92). In this context we also cite the Gabriel et al. and Lancaster et al. studies.

-What makes me worry is, looking at figure 1E, there is pretty much no brain, and of course, authors have analyzed what is left over. How could one distinguish reduced PAX6 area and TUJ1 area is due to the gross defects in brain development. Clearly, Haus6 KO causes a severe defect in brain development. Thus, deriving a conclusion from the damaged brain can be misleading. One way to circumvent this problem is to perform 2D experiments with isolated cell types (let us say NPCs and testing if they can spontaneous differentiate).

We note that overall brain structures are only lost by E17.5, but brain structures (albeit defective) are still present at E13.5. Indeed, all of our quantifications were done at E13.5 or earlier stages. That being said, we understand the concern that quantifications in defective brain structures may be misleading. However, 2D cultures, for which cells are removed from their tissue context, may have similar issues. For this reason, we plan to provide two different type of analyses. We will measure PAX6 and TUJ1 layers in brains from embryos at E.11.5, since the relevant tissues will be less damaged at this earlier stage. In addition, we will use BrdU injection prior to fixation of embryos. Proliferating apical progenitors will incorporate the label during S phase and subsequently we will determine the relative amounts of BrdU-positive cell types (apical progenitors vs neurons) in control and Haus6 cKO brains. Tissue damage will have less impact in this short-term labelling experiment.

Figure 2: A nice illustration that Hau6 KO animals harbor many mitotic figures. The quantifications lack how many slices and how many cells were analyzed. Simply n=4 does not say much. 4 animals were considered but how many cells/slices would help identify mitotic cells/animals' distribution. A simple bar diagram does not tell a lot.

We have added this information to the figure legend.

As a minor point, how did the authors unambiguously scored prometaphase cells and other mitotic figures? Representative figures will help. Besides, what is the meaning of many prometaphase cells? At least a discussion would help.

This is a good suggestion and we will provide examples of the mitotic figures that we scored. We now explain the meaning of the increase in prometaphase cells in the description of this result (lines 178-180).

Can the authors probe centrosomes (not by using gamma-tubulin) and relate their presence or absence to p53 upregulation? This is an important point because a complete loss of centrosome is known to trigger p53 upregulation. This may be different in Haus6 KO. This could mean (i.e, centrosomes are normal in numbers or increase in numbers), p53 upregulation is regardless of centrosomes loss.

Indeed, we believe that p53 upregulation in Haus6-deficient brains is not caused by loss of centrosomes. Instead, our data suggest, as explained in the discussion, that mitotic delay caused by augmin deficiency is sufficient for p53 upregulation. We will further support this conclusion by counting centrioles in mitotic cells. At this point of the cell cycle centriole duplication is complete and we expect to observe largely normal centriole numbers. In some cells we may observe increased numbers due to cytokinesis failure (see response to reviewer #1).

I have a hard time to ascertain how the authors scored interphase cells that enriched with p53. Some representative images with identity markers will help.

Scoring p53-positive interphase cells is relatively straightforward since the p53 signal is nuclear and not observed in mitotic apical progenitors. We have included a magnified region of the tissue shown in Fig. 2j, displaying PAX6/p53-positive nuclei of individual cells.

Looking at the p53 status in Haus6 KO animals, it is intriguing that p53 upregulation is not unique to centrosome loss. At this point, it becomes essential to thoroughly analyze the centrosome status to cross-check if Haus6 loss abrogates centrosomes; if so, how much.

Since centrosome number is linked to centriole number, we will address this point by quantifying centriole numbers in mitotic apical progenitors (see above).

Double KO could subside the cell death, but not tissue growth is impressive. So what is going on there? Is there a premature differentiation that leads to NPCs depletion? I believe the authors should generate 2D experiments with cells derived from these double KO animals compared to Haus6 KO and test if there is a premature differentiation that can lead to malformation of the forebrain. Here staining for the forebrain progenitor markers will additionally help (Perhaps FOXG1).

As explained in response to reviewer #1, we prefer to analyse this issue in vivo rather than in cells that are removed from their native tissue context, which may affect cell fate decisions. To address whether cells prematurely differentiate, we will use injection of BrdU (incorporated by proliferating apical progenitors) prior to fixation, followed by staining for cell type-specific markers. If there is premature differentiation, this should be visible as an increase in BrdU-positive post-mitotic cells.

Looking at Figure 6, it becomes clear that the double KOs have severe issues in maintaining the apical progenitors suggesting that they undergo premature differentiation before attaining a sufficient pool of NPCs. Testing this will bridge the paper between descriptive findings to mechanisms.

This point relates to the reviewer’s previous point: do Haus6 cKO p53 KO apical progenitors prematurely differentiate? We believe that cell loss, tissue disruption, and aborted development may also be explained without premature differentiation. In the absence of p53, repeated abnormal mitoses and the resulting increasingly severe chromosomal aberrations including DNA damage (Fig. 5) may produce cells that eventually won’t be able to proliferate and function properly. However, we will test premature differentiation by BrdU injection and staining with appropriate markers as explained above.

The discussion section is excellent, but it should add some human relevance. In particular, are there p53 dependent cell deaths that have been described in human tissues. In my opinion, it seems specific in the mouse brain. The discussion can also have statements about why the human brain is so sensitive even for mild mutations. I am not sure if those human mutations can cause similar defects in the mouse brain. Most of the mice based studies have been focusing on eliminating complete genes of interest.

We have included a section in the discussion to relate our findings to human brain development and the differences with results obtained in mouse models regarding the role of apoptosis (lines 386-391).

Reviewer #3 (Significance (Required)):

Overall, this is a very well done work but requires some more experiments for mechanisms understanding. Addressing those will make the paper fit to get published.

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

Jens Lüders &Co demonstrates the essential role of Augmin-mediated MT is critical for proper brain development in mice. The most striking point is that even p53 is eliminated, the microcephaly phenotypes of Haus6 KOs were not rescued. This could mean that the Augmin-mediated MT process is critical to cellular functions that are independent of p53. The authors claim that there are increased DNA damage and excessive mitotic errors. In these aspects, the current work is fascinating. Nevertheless, what causes massive damage to the neural epithelial tissues in the double mutant is not well explained or examined. Few questions appear in mind before I go into the detail. Are these animals still harbor functional centrosomes and their numerical status? The microcephaly part of the introduction needs some more work. In particular, the authors need to explain apical progenitors' depletion, possibly the correct mechanisms in causing microcephaly. By saying cortical progenitors, it becomes vague. Indeed, there would also be cortical progenitors depleted. But, the fundamental mechanisms are the depletion of apical progenitors lined up at VZ's lumen. Two works in this connection generated brain tissues from microcephaly patients carrying mutations in CenpJ and CDK5RAP2 (Gabriel and Lancaster et al). Authors should cite their work and relate their findings to mouse brain data.

-What makes me worry is, looking at figure 1E, there is pretty much no brain, and of course, authors have analyzed what is left over. How could one distinguish reduced PAX6 area and TUJ1 area is due to the gross defects in brain development. Clearly, Haus6 KO causes a severe defect in brain development. Thus, deriving a conclusion from the damaged brain can be misleading. One way to circumvent this problem is to perform 2D experiments with isolated cell types (let us say NPCs and testing if they can spontaneous differentiate)

Figure 2: A nice illustration that Hau6 KO animals harbor many mitotic figures. The quantifications lack how many slices and how many cells were analyzed. Simply n=4 does not say much. 4 animals were considered but how many cells/slices would help identify mitotic cells/animals' distribution. A simple bar diagram does not tell a lot.

As a minor point, how did the authors unambiguously scored prometaphase cells and other mitotic figures? Representative figures will help. Besides, what is the meaning of many prometaphase cells? At least a discussion would help.

Can the authors probe centrosomes (not by using gamma-tubulin) and relate their presence or absence to p53 upregulation? This is an important point because a complete loss of centrosome is known to trigger p53 upregulation. This may be different in Haus6 KO. This could mean (i.e, centrosomes are normal in numbers or increase in numbers), p53 upregulation is regardless of centrosomes loss.

I have a hard time to ascertain how the authors scored interphase cells that enriched with p53. Some representative images with identity markers will help.

Looking at the p53 status in Haus6 KO animals, it is intriguing that p53 upregulation is not unique to centrosome loss. At this point, it becomes essential to thoroughly analyze the centrosome status to cross-check if Haus6 loss abrogates centrosomes; if so, how much.

Double KO could subside the cell death, but not tissue growth is impressive. So what is going on there? Is there a premature differentiation that leads to NPCs depletion? I believe the authors should generate 2D experiments with cells derived from these double KO animals compared to Haus6 KO and test if there is a premature differentiation that can lead to malformation of the forebrain. Here staining for the forebrain progenitor markers will additionally help (Perhaps FOXG1).

Looking at Figure 6, it becomes clear that the double KOs have severe issues in maintaining the apical progenitors suggesting that they undergo premature differentiation before attaining a sufficient pool of NPCs. Testing this will bridge the paper between descriptive findings to mechanisms.

The discussion section is excellent, but it should add some human relevance. In particular, are there p53 dependent cell deaths that have been described in human tissues. In my opinion, it seems specific in the mouse brain. The discussion can also have statements about why the human brain is so sensitive even for mild mutations. I am not sure if those human mutations can cause similar defects in the mouse brain. Most of the mice based studies have been focusing on eliminating complete genes of interest.

Significance

Overall, this is a very well done work but requires some more experiments for mechanisms understanding. Addressing those will make the paper fit to get published.

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

Viais, Lüders and colleagues here present an analysis of augmin's roles in neural stem cell development. They describe a dramatic impact of the conditional ablation of Haus6 on embryonic brain development in the mouse, with mitotic problems that lead to greatly-increased levels of apoptosis. The rescue of this apoptosis by mutation of the gene that encodes p53 did not restore brain development, which was still aberrant, due to mitotic errors.

The paper is clearly written, with well-designed and controlled experiments. Its conclusions are well supported by the data presented. I have few comments on the technical aspects of the work- it appears very solid to me.

Specific comments

1. Clearer explanation of the mouse strains used should be provided. The section describing the generation of the Haus6 conditional on p.5 should specify that this is the same as was already published in the 2016 Watanabe paper (this is in the Materials and Methods, but this should be more clearly specified. More specific details of the p53 knockout mice from Jackson should be included in the Materials and Methods.
2. Figure 1a contains minimal information on the Haus6 locus. More detail should be included for information, if this Figure is to remain (although reference to the targeting details in the original description would be sufficient). It is unclear what the timeline diagram is to convey and it should be improved or deleted. A similar comment applies for the details in Figure 3a, although the colour scheme for the different genotypes is useful.
3. The important PCR controls in Figure S1b have an unexplained 1000 bp band that appears only in the floxed heterozygote. It would be helpful if the authors explained this in the relevant Figure legend.
4. Assuming the putative centrosome 'clusters' in Figure 6c are similar to the fragmented structures seen in thalamus in Figure 2d, a different description should be used to avoid confusion with multiple centrosomes, which is not a phenotype here. It is not clear how the loss of centrosomes from the ventricular surface was scored, whether it was based on total gamma-tubulin signal or individual centrosomes; how fragmented poles would affect that is unclear, so the legend and relevant details should clarify this point.
5. Phospho-histone H2AX should be referred to as a marker of activation of the DNA damage response, rather than DNA repair.

Minor points

i. Figure 1b should include a scale bar.<br> ii. The labelling of Figure 1f should be revised. iii. Figure 2k is not labelled in this Figure. iv. Scale bars should be included in the blow-ups in Figure 6c.

Significance

While it is striking that they see complete disruption of brain development, rather than microcephaly, arguably the mechanistic novelty of the findings is moderate, in that the impacts of Haus6 deficiency on mitotic spindle assembly are well established. The authors only allude to potential additional and novel activities of augmin (in neural progenitors, potentially) that might explain this possibly-unexpected outcome of this study.

The topic is likely to be of interest to people in the field of mitosis, genome stability and brain development.

My expertise is cell biology/ mitosis, less so on murine brain development.

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

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---

Referee #1

Evidence, reproducibility and clarity

The manuscript by Viais R et al describes a novel role for augmin complex in apoptosis prevention during brain development. Augmin complex recruits g TuRC to microtubule lattices to nucleate microtubule branches. The authors show how -in its absence- neural progenitors have elevated p53 activity and apoptotic rate, with severe consequences on overall brain development. In particular, augmin-deleted neural progenitors display spindle abnormalities and mitotic delay, which induce DNA damage accountable for p53-induced apoptosis.

One point that I personally found very interesting is the role of augmin-dependent MT nucleation depletion in interphase. The authors mention (line 152) that at stage E13.5, besides the number of neurons being reduced, a few neurons were misplaced in the apical region, indicating a role for augmin-driven MT nucleation in cell migration. Moreover, the authors showed that p53 genetic deletion in the Haus6 cKO rescues the apoptosis phenotype but not the tissue disorganisation, suggesting that augmin-dependent microtubule might play a role in tissue polarity. While this is well presented in the discussion, the title in line 268 narrowly refers to mitotic augmin roles. I would like here to see the authors referring to putative roles for augmin-mediated MT nucleation in interphase, by toning down the title in line 268.

Overall, the text is well written and flows easily. Figures are clear and legends provide sufficient information on experimental conditions, number of replicates and scale bars. I noticed that, although the number of repeats is specified, the number of cells scored per experiment is not always included. In my comments below I highlight cases where this missing information should be added.

Specific points:

1. In the Cep63 KO (Marjanovic et al, 2015) and the CenpJ KO mice (Insolera et al, 2014), as well as other recently published papers (e.g. Phan TP et al, EMBO Journal, 2020) part of the phenotypical characterisation of the KO mice displays pictures of the overall brain dissected from the mice. Could the author show these images?
2. Fig2d: do the insets correspond to higher magnification images? What is the zoom factor? I could not find it in the legend.
3. Fig2E,I and K graphs: how many cells were quantified here over how many experiments? I could not find information in the figure legend.
4. The impact of Haus6 on mitotic spindle needs further clarification:

o Fig2F: here, the authors show quantification for abnormal and multipolar spindle together. Later on, the abnormal spindle phenotype is no longer discussed (Fig4). I was wondering what is the individual contribution of abnormal and multipolar spindle, separately. Which one of the two is more frequent? Could the authors explain in the text how they define an abnormal spindle? Is it the lack of MT with the condensed chromosome area?

o Could it be that augmin deletion induce an instability in MTs within the mitotic spindle, leading to the "empty" or with very few MTs spindles? Or could it be that more cold-sensitive MTs are affected by fixation? What is the percentage of the spindle with no MT in control?

o Did the authors quantify anaphase/telophase phenotypes as they did in Fig4f?

o How do authors explain PCM fragmentation here? Could this phenotype be due to an initial cytokinesis defect which led the cells to accumulate extra centrosomes? Or could this maybe be a product of aberrant PCM maturation/centrosome duplication? Could the authors add here a line to discuss the possible origin of pole fragmentation?

1. Fig 4 Did the authors quantify centrosome fragmentation and abnormal spindle here? As they characterised them for the Haus6 cKO mouse, it would be preferable to maintain the same characterisation for the Haus6 cKO p53KO.
2. Fig4c and d: how many replicates were done to obtain these graphs? I think the authors forgot to add this information in the figure legend.
3. Fig4f,g, I and J: how many cells were counted per experiment? I appreciate the authors writing the n of experiments performed.
4. Fig5d: how many cells were counted per experiment?

Significance

While it was already known that mitotic delay affects the neuronal progenitor pool through activation of p53-dependent apoptosis (Pilaz L-J, Neuron 2016; Mitchell-Dick A, Dev Neurosci 2020), and that this can be triggered by depletion of centrosomal proteins as Cenpj and Cep63, the role of surface-dependent microtubule nucleation was not identified so far. Some insights come from a Haus6-KO mouse model which dies during blastocyst stage after several aberrant mitosis (Watanabe S, Cell Reports, 2016). In parallel, McKinley KL et al showed that Haus8 depletion in human cells (RPE1cells) triggered p53-dependent G1 arrest following mitotic defects (McKinley KL, Developmental Cell, 2017). Building on the Hause6 KO mouse and human cell line data, here Viais R et al discover a novel role for the augmin-mediated MT nucleation in neural progenitor growth and brain development in vivo, through prevention of p53-induced apoptosis.

Specifically, Viais R et al show that:

1. Surface-dependent microtubule nucleation depletion severely impacts brain development, disrupting partly or completely forebrain domains and cerebellum;
2. Surface-dependent microtubule nucleation depletion induce spindle abnormalities, resulting in mitotic delay in apical progenitors;
3. Mitotic delay results in DNA breaks, p53 activation and p53-induced apoptosis.

This is a tidy, well-executed study with good quality data. These findings propose a novel mechanism that results essential for neural progenitor and overall brain development.

In my opinion, a large audience will benefit from these discoveries: from developmental biologists to cell biologists focused on microtubule dynamics, cell cycle, differentiation, stem cells and cell polarity.

Key works describing my area of expertise: microtubule dynamics, centrosome function, cell cycle regulation and cell polarity.

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Reply to the reviewers

Response to reviewers: Woodcock et al. 2021

Reviewer 1 (Evidence, reproducibility, and clarity):

Summary The authors resolved the biosynthesis of trehalose and alpha-glucan in Pseudomonas aeruginosa and the role of these two compounds in osmotic and desiccation stress.

We thank the reviewer for their positive review of our manuscript. Our responses to their specific queries are interspersed below.

Major comments:

• Are the key conclusions convincing? Yes

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Not necessary, comprehensive coverage of research topic.

• • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. * Not applicable

• Are the data and the methods presented in such a way that they can be reproduced? Yes

• • Are the experiments adequately replicated and statistical analysis adequate? * Yes, everything is adequate but just one subtle concern: check the significance of the number of digits in the entries listed in Table S3. Revise Table S3.

Table S3 has been revised as requested. The data in this table is now presented correct to one decimal place.

Minor comments:

• Specific experimental issues that are easily addressable. Not applicable (Table S3: see above)

• • Are prior studies referenced appropriately? No. Refs. 18- 32: The subjects of 'trehalose' and 'osmotic stress' have already been addressed in the Pseudomonas field and should be referenced. The authors cite work carried out on trehalose and osmotic stress on phylogenetically distant microorganisms, but do not cite related work from the Pseudomonas field which I consider to be inappropriate. Similarly, trehalose biosynthesis in Pseudomonas* has not only been covered by refs. 47 and 48.

This is a fair comment. The focus of our introduction came from a desire to concentrate specifically on the metabolism and intracellular function of trehalose/α-glucan in Pseudomonas. In hindsight, we acknowledge that our introduction is a little too narrowly focussed. We have expanded the introduction and discussion sections to include additional discussion of trehalose in Pseudomonas and its regulation in the CF lung.

• Are the text and figures clear and accurate? Extremely well written manuscript and prepared figures

• • Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Revise the list of references and discuss more thoroughly your novel findings in the light of existing knowledge in the Pseudomonas* field.

    Please see previous comment relating to the literature.

Reviewer 1 (Significance):

Significance

• • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. Conceptual advance: The authors identified and characterized the enzymatic pathway of trehalose and alpha-glucan biosynthesis in Pseudomonas aeruginosa and its role to cope with osmotic and desiccation stress. The authors' conclusions do not correspond with recently published peers' work; hence they should discuss in more detail why they consider their data to be more accurate to discern the role of trehalose to contain desiccation and osmotic stress in P. aeruginosa*.

    Please see previous comment relating to the literature. In general, the published work to date on trehalose in Pseudomonas spp. does not consider GlgE pathway-mediated link to α-glucan that we characterise in this paper. Our work demonstrates that synthesis and metabolism of the two molecules are implicitly linked in species where the GlgE pathway is present, and they cannot be considered in isolation. For this reason we are very confident that our study represents the most accurate model to date for trehalose and α-glucan metabolism and their associated phenotypes in P. aeruginosa. We have therefore emphasised that the role of trehalose in Pseudomonas spp. should be re-evaluated in light of our findings.

• • Place the work in the context of the existing literature (provide references, where appropriate). Existing literature focusing on trehalose, osmotic stress, desiccation stress in the Pseudomonas *field not cited by the authors:

• These papers are of variable scientific quality, but the conceptual work by Hallsworth and the work by Behrens on the PA metabolome in CF lungs are worth discussing. All other work provides pieces of information on function and biosynthesis of trehalose up to now known by the Pseudomonas community. The authors resolved the function of the GlgA operon which will be definitely appreciated.

  We thank the reviewer for these helpful suggestions. We have reviewed these papers carefully and have incorporated several, including the papers from Hallsworth and Behrens into the revised manuscript.

Strengths of the manuscript:

• Meticulously planned and carefully executed experiments, not a single experimental flaw
• Very high technical quality of experiments and primary data
• Comprehensive coverage of the research topic

• Excellent presentation in text and illustrations Only weakness:

• Insufficient consideration of peers' published work on trehalose and its role in stress response in P. aeruginosa

  Please see previous comment relating to the literature.

• • State what audience might be interested in and influenced by the reported findings. Scientists working in the fields of glycoconjugate and carbohydrate research, biochemists, microbiologists with interest in metabolic pathways, stress response and/or Pseudomonas. __Reviewer #2 (Evidence, reproducibility, and clarity):*__

It will be difficult for me to write a review of this paper and for the authors to make sense of my review because the manuscript's pages / lines are not numbered…

We apologise to the reviewer for this oversight.

Summary The authors carried out a comprehensive characterization of the metabolism of trehalose in Pseudomonas aeruginosa PA01, using techniques of biochemistry, reverse genetics, and bioinformatics. The main findings include that the disaccharide trehalose is synthesized in this organism from branched chain α-glucans and that the catabolism of trehalose proceeds via another disaccharide, maltose and is fed back into the synthesis of α-glucans. Trehalose and α-glucans have been implicated in conferring resistance to abiotic stresses in other organisms. The authors show that mutants that are blocked in the synthesis of trehalose are sensitive to high salinity but are normal with respect to their sensitivity to desiccation, whereas mutants impaired in the accumulation of α-glucans are sensitive to desiccation without being unduly sensitive to osmotic stress. These results indicate that trehalose and α-glucans have different roles in abiotic stress-tolerance.

Major points

This manuscript describes an impressive amount of careful work and presents new insights into the metabolism of trehalose, maltose, and α-glucans. However, the authors should address the following major comments before the paper is accepted.

We thank the reviewer for their thorough and positive assessment of the manuscript. We address their specific points below.

• Discussion: the authors state that "trehalose protects Pseudomonas ssp. against osmotic stress, most likely due to its role as a compatible solute." According to Table 2, P. aeruginosa grown in the medium of low osmolarity accumulated 0.13% trehalose per gram dry weight, i.e. ~4 μmol / g dry weight. Assuming that the dry weight / wet weight ratio of P. aeruginosa is the same as that of P. putida, which is ~1/3 (PMID: 6508285), the concentration of trehalose in the cells calculates to be ~2 mM. It is not plausible that trehalose could be significant as compatible solute at this low concentration.

• One way out could be if the accumulation of this disaccharide were increased by osmotic stress. The authors should also measure the trehalose content of cells grown in medium containing 0.85 M NaCl. In case of positive results in this experiment, it would be interesting to determine the effects of osmotic stress on the levels of trehalose biosynthetic and catabolic enzymes, but this would not be necessary for the acceptance of the paper.

  This is a fair point. To address this, we measured the trehalose and maltose-1-phosphate levels for PA01 grown in the presence of 0.85 M NaCl. We saw a highly significant increase in the abundance of trehalose, compared to growth on standard M9 media. This strongly suggests that trehalose accumulates under conditions of osmotic stress as suggested by the reviewer. These new results have been added to the relevant sections of the manuscript (M&M, results, table 2 and discussion). The student (Danny Ward) who conducted these new experiments has been added to the author list.

• However, there is also an extensive literature suggesting that trehalose has antioxidant functions e.g. PMID: 29241092 (the first paper that came up in Google search for "trehalose as antioxidant"). The authors should discuss this possible alternate role of trehalose.

  The reviewer is correct that trehalose has well-documented antioxidant functions in various species. We have modified the introduction to address this. To maintain the focus of our manuscript on bacteria we have used a different example to that suggested by the reviewer.

• It is not described adequately in the Materials and Methods how the cellular contents of trehalose and maltose-1-phosphate (M1P) were determined.

  The Materials and Methods section has been revised to include more details of this method.

• I found the growth curves in Figure 8, especially in panel B, to be uninterpretable. The authors should spread these data into more panels or use some other method to make them clearer.

  We have expanded the legend for Figure 8 to describe more fully what is going on in this figure. The results in Figure 8 are grouped according to the operons in which each set of genes is located. As such, the graphs contain unequal numbers of curves, with 8B containing the most and 8C only showing data for WT and ΔglgP.

• The statement "The GlgA and GlgE proteins . . . enable two alternate mechanisms for linear α-glucan biosynthesis", which is echoed a number of times in the manuscript, seems to create the impression that there are two de novo pathways of synthesis of these polysaccharides. However, as shown in Figure 1, the GlgA pathway is the only route to the net synthesis of α-glucans, and GlgE is only part of a recycling pathway. Therefore, it cannot be true that "the vast majority of α-glucan accumulated by P. aeruginosa will be produced by GlgE".

  We have revised this section to further clarify what we mean when we state that the majority of α-glucan accumulated by P. aeruginosa will be produced by GlgE. Our data suggest that there is a big difference between the generation of α-glucan (conducted by both GlgA and GlgE) and its accumulation (flux through GlgA generated α-glucan is high, so only GlgE generated α-glucan can accumulate to generate large polymers).

• The authors state that "MalQ disproportionates (sic) α-glucan with glucose to produce maltose." Figure 1 shows that GlgE uses an "acceptor", which I assume could be glucose. How is free glucose synthesized? Could cells grown on a non-carbohydrate as sole carbon source make free glucose?

  P. aeruginosa is able to carry out gluconeogenesis, so it can produce glucose from non-carbohydrate carbon sources if necessary.

Our data show that GlgE acceptor preference gets lower as the acceptor molecule gets shorter. It is possible to detect GlgE activity without an acceptor. In this case we see a lag, implying M1P hydrolyses slowly at first and priming with glucose is also slow. Eventually however, the products get long enough for the reaction to take off. MalQ will work with DP2 or longer as the donor and DP1 or longer as the acceptor, moving one glucose unit at a time.

• Pedantic point, but "disproportionation" means an oxidation-reduction reaction in which two identical molecules are used to produce two different molecules (https://en.wikipedia.org/wiki/Disproportionation). The reaction catalysed by MalQ does not involve electron transfer. Don't the authors mean that this enzyme is a glycosyl transferase?

  We have checked this, and our use of disproportionation in the manuscript is correct. The definition of disproportionation is any desymmetrizing reaction of the following type: 2 A → A' + A", and is not limited to redox reactions. MalQ carries out a reaction of this type when presented with a maltooligosaccharide.

• The authors state that TreS had "a very high Km for trehalose (>100 mM)". In view of the low concentration of trehalose (Point 1, above), the physiological relevance of this suggested activity is questionable.

  See response to question 1 above. As trehalose levels are elevated under osmostress conditions this concern becomes less critical. It is of course true that conditions in vitro may not fully reflect cellular conditions and that this activity may be higher in vivo, but this is a general limitation of all protein biochemistry studies. The important point here is that trehalose synthase activity is detected for PA01 TreS.

• Explain better what "predicted mean log10(CFU) means.

  The predicted mean refers to the value of log10(CFU) predicted by the statistical model we use. We have clarified this in the relevant sections of the manuscript.

• Can the authors suggest how "α-glucan protects PA01 against desiccation"?

  Without further investigation we can only speculate as to how α-glucan confers desiccation tolerance in PA01. One possibility is that α-glucan functions as a hydrogel, like the exopolysaccharide alginate, trapping water molecules and slowing their evaporation. Alternatively, it may confer a structural role akin to that of trehalose, preventing the loss of cell integrity as water levels decrease. We now address these possibilities in the discussion.

• Can P. aeruginosa metabolize exogenous trehalose or maltose? If the authors know either way, they should mention it. If they don't know, I am not suggesting that they should test this for this paper, but it would be interesting to know whether these compounds would induce the expression of the trehalose or maltose catabolic enzymes or repress the relevant biosynthetic enzymes. >P. aeruginosa is able to metabolise exogenous maltose and trehalose. While the experiments that the reviewer suggests are certainly interesting, in our view tre/glg gene regulation is beyond the scope of the current manuscript. This field is certainly worth investigating in the future, however.

Minor points

• First page under "Results": "phosphomutase" should be "phosphoglucomutase"?

  Changed as requested.

• Discussion: insert "P. syringae" before "Pto".

  Changed as requested.

• Materials and Methods: describe how ADP was quantified in the maltokinase assay.

  The materials and methods section has been updated as requested.

Reviewer 2 (Significance):

Significance

Until this work, the biosynthesis of trehalose has been most extensively characterized in Escherichia coli, in which it has been shown that this disaccharide is made by the reaction of glucose-6-phosphate and UDP-glucose to give trehalose-6-phosphate and dephosphorylation to trehalose, catalysed by OtsA and OtsB. The authors discovered a very different pathway in P. aeruginosa in which the synthesis of trehalose goes through α-glucans as intermediates.

Because trehalose and α-glucans are needed for osmotic stress- and desiccation-tolerance, respectively, this work is of significance to researchers studying abiotic stress resistance.

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Referee #2

Evidence, reproducibility and clarity

Review of manuscript "Trehalose and α-glucan mediate distinct abiotic responses in Pseudomonas aeruginosa" by S. D. Woodcock et al.

It will be difficult for me to write a review of this paper and for the authors to make sense of my review because the manuscript's pages / lines are not numbered. I will do my best write a review, but for the future, I urge this Journal to print the text on pages in which the lines are numbered or require this of the authors.

Summary.

The authors carried out a comprehensive characterization of the metabolism of trehalose in Pseudomonas aeruginosa PA01, using techniques of biochemistry, reverse genetics, and bioinformatics. The main findings include that the disaccharide trehalose is synthesized in this organism from branched chain α-glucans and that the catabolism of trehalose proceeds via another disaccharide, maltose and is fed back into the synthesis of α-glucans. Trehalose and α-glucans have been implicated in conferring resistance to abiotic stresses in other organisms. The authors show that mutants that are blocked in the synthesis of trehalose are sensitive to high salinity but are normal with respect to their sensitivity to desiccation, whereas mutants impaired in the accumulation of α-glucans are sensitive to desiccation without being unduly sensitive to osmotic stress. These results indicate that trehalose and α-glucans have different roles in abiotic stress-tolerance.

Major points.

This manuscript describes an impressive amount of careful work and presents new insights into the metabolism of trehalose, maltose, and α-glucans. However, the authors should address the following major comments before the paper is accepted.

1. Discussion: the authors state that "trehalose protects Pseudomonas ssp. against osmotic stress, most likely due to its role as a compatible solute." According to Table 2, P. aeruginosa grown in the medium of low osmolarity accumulated 0.13% trehalose per gram dry weight, i.e. ~4 μmol / g dry weight. Assuming that the dry weight / wet weight ratio of P. aeruginosa is the same as that of P. putida, which is ~1/3 (PMID: 6508285), the concentration of trehalose in the cells calculates to be ~2 mM. It is not plausible that trehalose could be significant as compatible solute at this low concentration.<br> One way out could be if the accumulation of this disaccharide were increased by osmotic stress. The authors should also measure the trehalose content of cells grown in medium containing 0.85 M NaCl. In case of positive results in this experiment, it would be interesting to determine the effects of osmotic stress on the levels of trehalose biosynthetic and catabolic enzymes, but this would not be necessary for the acceptance of the paper.<br> However, there is also an extensive literature suggesting that trehalose has antioxidant functions e.g. PMID: 29241092 (the first paper that came up in Google search for "trehalose as antioxidant"). The authors should discuss this possible alternate role of trehalose.<br> It is not described adequately in the Materials and Methods how the cellular contents of trehalose and maltose-1-phosphate (M1P) were determined.
2. I found the growth curves in Figure 8, especially in panel B, to be uniterpretable. The authors should spread these data into more panels or use some other method to make them clearer.
3. The statement "The GlgA and GlgE proteins . . . enable two alternate mechanisms for linear α-glucan biosynthesis", which is echoed a number of times in the manuscript, seems to create the impression that there are two de novo pathways of synthesis of these polysaccharides. However, as shown in Figure 1, the GlgA pathway is the only route to the net synthesis of α-glucans, and GlgE is only part of a recycling pathway. Therefore, it cannot be true that "the vast majority of α-glucan accumulated by P. aeruginosa will be produced by GlgE".
4. The authors state that "MalQ disproportionates (sic) α-glucan with glucose to produce maltose." Figure 1 shows that GlgE uses an "acceptor", which I assume could be glucose.<br> How is free glucose synthesized? Could cells grown on a non-carbohydrate as sole carbon source make free glucose? Pedantic point, but "disproportionation" means an oxidation-reduction reaction in which two identical molecules are used to produce two different molecules (https://en.wikipedia.org/wiki/Disproportionation). The reaction catalyzed by MalQ does not involve electron transfer. Don't the authors mean that this enzyme is a glycosyl transferase?
5. The authors state that TreS had "a very high Km for trehalose (>100 mM)". In view of the low concentration of trehalose (Point 1, above), the physiological relevance of this suggested activity is questionable.
6. Explain better what "predicted mean log10(CFU) means.
7. Can the authors suggest how "α-glucan protects PA01 against desiccation"?
8. Can P. aeruginosa metabolize exogenous trehalose or maltose? If the authors know either way, they should mention it. If they don't know, I am not suggesting that they should test this for this paper, but it would be interesting to know whether these compounds would induce the expression of the trehalose or maltose catabolic enzymes or repress the relevant biosynthetic enzymes.

Minor points.

1. First page under "Results": "phosphomutase" should be "phosphoglucomutase"?
2. Discussion: insert "P. syringae" before "Pto".
3. Materials and Methods: describe how ADP was quantified in the maltokinase assay.

Significance

Significance.

Until this work, the biosynthesis of trehalose has been most extensively characterized in Escherichia coli, in which it has been shown that this disaccharide is made by the reaction of glucose-6-phosphate and UDP-glucose to give trehalose-6-phosphate and dephosphorylation to trehalose, catalyzed by OtsA and OtsB. The authors discovered a very different pathway in P. aeruginosa in which the synthesis of trehalose goes through α-glucans as intermediates.<br> Because trehalose and α-glucans are needed for osmotic stress- and desiccation-tolerance, respectively, this work is of significance to researchers studying abiotic stress resistance.

The Reviewers' guidelines stipulate that Reviewers should define their fields of expertise.

My credentials are: a) I have been solicited to review this paper, and b) I have publications in osmotic stress adaptation and trehalose biosynthesis in Enterobacteriaceae.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

The authors resolved the biosynthesis of trehalose and alpha-glucan in Pseudomonas aeruginosa and the role of these two compounds in osmotic and desiccation stress.

Major comments:

• Are the key conclusions convincing?

yes

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

no

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Not necessary, comprehensive coverage of research Topic

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

Not applicable

• Are the data and the methods presented in such a way that they can be reproduced?

yes

• Are the experiments adequately replicated and statistical analysis adequate?

Yes, everything is adequate but just one subtle concern: check the significance of the number of digits in the entries listed in Table S3. Revise Table S3.

Minor comments:

• Specific experimental issues that are easily addressable.

not applicable (Table S3: see above)

• Are prior studies referenced appropriately?

No. Refs. 18- 32: The subjects of 'trehalose' and 'osmotic stress' have already been addressed in the Pseudomonas field and should be referenced. The authors cite work carried out on trehalose and osmotic stress on phylogenetically distant microorganisms, but do not cite related work from the Pseudomonas field which I consider to be inappropriate. Similarly, trehalose biosynthesis in Pseudomonas has not only been covered by refs. 47 and 48.

• Are the text and figures clear and accurate?

Extremely well written manuscript and prepared figures

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

Revise the list of references and discuss more thoroughly your novel findings in the light of existing knowledge in the Pseudomonas field

Significance

2. Significance

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

Conceptual advance: The authors identified and characterized the enzymatic pathway of trehalose and alpha-glucan biosynthesis in Pseudomonas aeruginosa and its role to cope with osmotic and desiccation stress. The authors' conclusions do not correspond with recently published peers' work, hence they should discuss in more detail why they consider their data to be more accurate to discern the role of trehalose to contain desiccation and osmotic strass in P. aeruginosa.

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

Existing literature focusing on trehalose, osmotic stress, desiccation stress in the Pseudomonas field not cited by the authors

Pazos-Rojas LA, Muñoz-Arenas LC, Rodríguez-Andrade O, López-Cruz LE, López- Ortega O, Lopes-Olivares F, Luna-Suarez S, Baez A, Morales-García YE, Quintero- Hernández V, Villalobos-López MA, De la Torre J, Muñoz-Rojas J. Desiccation- induced viable but nonculturable state in Pseudomonas putida KT2440, a survival strategy. PLoS One. 2019 Jul 19;14(7):e0219554. doi:10.1371/journal.pone.0219554.

Wang T, Jia S, Dai K, Liu H, Wang R. Cloning and expression of a trehalose synthase from Pseudomonas putida KT2440 for the scale-up production of trehalose from maltose. Can J Microbiol. 2014 Sep;60(9):599-604. doi: 10.1139/cjm-2014-0330.

Harty CE, Martins D, Doing G, Mould DL, Clay ME, Occhipinti P, Nguyen D, Hogan DA. Ethanol Stimulates Trehalose Production through a SpoT-DksA-AlgU-Dependent Pathway in Pseudomonas aeruginosa. J Bacteriol. 2019 May 22;201(12):e00794-18. doi: 10.1128/JB.00794-18.

Cross M, Biberacher S, Park SY, Rajan S, Korhonen P, Gasser RB, Kim JS, Coster MJ, Hofmann A. Trehalose 6-phosphate phosphatases of Pseudomonas aeruginosa. FASEB J. 2018 Oct;32(10):5470-5482. doi: 10.1096/fj.201800500R.

Wang T, Jia S, Dai K, Liu H, Wang R. Cloning and expression of a trehalose synthase from Pseudomonas putida KT2440 for the scale-up production of trehalose from maltose. Can J Microbiol. 2014 Sep;60(9):599-604. doi: 10.1139/cjm-2014-0330.

Behrends V, Ryall B, Zlosnik JE, Speert DP, Bundy JG, Williams HD. Metabolic adaptations of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections. Environ Microbiol. 2013 Feb;15(2):398-408. doi: 10.1111/j.1462-2920.2012.02840.x

Behrends V, Ryall B, Wang X, Bundy JG, Williams HD. Metabolic profiling of Pseudomonas aeruginosa demonstrates that the anti-sigma factor MucA modulates osmotic stress tolerance. Mol Biosyst. 2010 Mar;6(3):562-9. doi: 10.1039/b918710c.

Matthijs S, Koedam N, Cornelis P, De Greve H. The trehalose operon of Pseudomonas fluorescens ATCC 17400. Res Microbiol. 2000 Dec;151(10):845-51. doi: 10.1016/s0923-2508(00)01151-7.

van der Werf MJ, Overkamp KM, Muilwijk B, Koek MM, van der Werff-van der Vat BJ, Jellema RH, Coulier L, Hankemeier T. Comprehensive analysis of the metabolome of Pseudomonas putida S12 grown on different carbon sources. Mol Biosyst. 2008 Apr;4(4):315-27. doi: 10.1039/b717340g.

Hallsworth JE, Heim S, Timmis KN. Chaotropic solutes cause water stress in Pseudomonas putida. Environ Microbiol. 2003 Dec;5(12):1270-80. doi: 10.1111/j.1462-2920.2003.00478.x.

Ball P, Hallsworth JE. Water structure and chaotropicity: their uses, abuses and biological implications. Phys Chem Chem Phys. 2015 Apr 7;17(13):8297-305. doi: 10.1039/c4cp04564e

Cray JA, Russell JT, Timson DJ, Singhal RS, Hallsworth JE. A universal measure of chaotropicity and kosmotropicity. Environ Microbiol. 2013 Jan;15(1):287-96. doi: 10.1111/1462-2920.12018.

Chin JP, Megaw J, Magill CL, Nowotarski K, Williams JP, Bhaganna P, Linton M, Patterson MF, Underwood GJ, Mswaka AY, Hallsworth JE. Solutes determine the temperature windows for microbial survival and growth. Proc Natl Acad Sci U S A. 2010 Apr 27;107(17):7835-40. doi: 10.1073/pnas.1000557107.

These papers are of variable scientific quality, but the conceptual work by Hallsworth and the work by Behrens on the PA metabolome in CF lungs are worth discussing. All other work provides pieces of information on function and biosynthesis of trehalose up to now known by the Pseudomonas community. The authors resolved the function of the GlgA operon which will be definitely appreciated.

Strengths of the manuscript:

• Meticulously planned and carefully executed experiments, not a single experimental flaw • very high technical quality of experiments and primary data • comprehensive coverage of the research topic • excellent presentation in text and illustrations

only weakness: • insufficient consideration of peers' published work on trehalose and its role in stress response in P. aeruginosa

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

Scientists working in the fields of glycoconjugate and carbohydrate research, biochemists, microbiologists with interest in metabolic pathways, stress response and/or Pseudomonas

• 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.

Reviewer's expertise: Pseudomonas genomics and physiology, respiratory tract infections, solid background in biochemistry and molecular biology

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Reviewer #4

Evidence, reproducibility and clarity

We have reviewed "A specific regulator of neuronal V-ATPase in Drosophila melanogaster." by Dulac et al. The authors have identified VhaAC45L as a regulator of neuronal V-ATPase in Drosophila melanogaster. The authors have utilized multiple techniques to determine the localization of VhaAC45L in neurons and specifically in the synapse. The use of multiple approaches including determining RNA levels in different regions of the fly, and using CRISPR-Cas9 technique to insert V5 tag, makes a very convincing argument about the synapse-specific expression of VhaAC45L.

The combined use of co-immunoprecipitation technique and LC/MS to show that VhaAC45L co-precipitated with V-ATPase complex subunits is convincing that VhaAC45L is a subunit of V-ATPase. To determine the role of VhaAC45L in acidification of synaptic vesicles the authors have utilized pHluorins in combination with multiple RNAi lines. The authors have used a well-designed experiment to prove that VhaAC45L regulates acidification of the synaptic vesicles. Further, larval locomotion and quantal size determination using VhaAC45LRNAi which is known to be altered due to pH gradient of synaptic vesicles shows the functional role of VhaAC45L in synaptic vesicle acidification.

Minor comments:

1. For all graphs, please remove gridlines to make data points more visible.
2. Line 120-123: Authors indicate the VhaAC45LRNAi induced lethal phenotype when expressed in glutamatergic and cholinergic drivers but the figure is missing. Please indicate as "data not shown" if not included in Figure.
3. A diagram summarizing the role of VhaAC45L in V-ATPase enzymatic complex and specific role is recommended.

Significance

V-ATPase play a crucial role at the synapse by being responsible for acidification of the synaptic vesicles and identification of a synaptic vesicle specific regulator of V-APTase is important to understand the complex regulation of synapse function. The authors have used well-designed experiments to convince the localization and function of VhaAC45L in synaptic vesicle acidification.

Referees cross commenting

The summary of Reviewer#2 looks good!

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Reviewer #3

Evidence, reproducibility and clarity

In this study, Dulac and colleagues investigated roles of VhaAC45-like gene, which codes one of the V-ATPase accessory proteins in Drosophila, in synaptic transmission. First, they demonstrated that VhaZC45L transcripts are expressed selectively in neurons and that the gene products are addressed to synaptic areas. Second, they showed that VhaAC45L is co-immunoprecipitated with some subunits of V-ATPases, which is consistent with bio-informatics predictions. They further demonstrated that VhaAC45L-knockdown (KD) resulted in defects in synaptic vesicle acidification as well as a reduction in quantal size of glutamate, indicating that VhaAC45L play a key role in regulating neurotransmitter release by modulating the driving force for transmitter uptake. Last, not least, they demonstrated that VhaAC45L-KD in motoneurons attenuated larvae locomotor performance, indicating its physiological relevance. Overall, this study is rigorously executed and nicely presented, and adds one more component of the V-ATPase that is responsible for neurotransmitter uptake into synaptic vesicles. However, since this study simply confirmed an established notion from other species such as yeast and mammals that AC45 is one of the accessory proteins of the V-ATPase complex, a conceptual novelty beyond the previous knowledge is relatively poor in its present form. Thus, this reviewer would suggest several issues as following to improve the comprehensiveness as well as novelty of the current manuscript.

1. The reason why the authors focused on VhaAC45-'like' is somewhat obscure, and therefore should be explained. How different VhaAC45 and VhaAC45L are in terms of amino acid sequences, tissue distributions, and KO phenotypes. It seems more comprehensive if the authors provide some experimental evidence on VhaAC45; e.g. whether it is also expressed in neurons or not (Fig. 1), and, if VhaAC45 is neuronal, whether it can rescue the phenotypes of VhaAC45L-KD to certain degree (Figs 4 & 5).
2. What is the mechanism of Ac45 in regulating V-ATPase activity? In mammals, it has been suggested that Ac45 is essential for proper sorting of the V-ATPase to the destined organelles (e.g. Jansen et al., Mol. Biol. Cell., 2010; Jansen et al., BBA, 2008). In this context, it should be examined whether VhaAC45L-KD would affect the synaptic localization of other V-ATPase subunits.
3. Given that a rodent brain SV contains a few copies of the V-ATPase on average (Takamori et al., 2006, and some newer papers by others), it is interesting that >80% reduction of Ac45 showed moderate effects on quantal size. If SVs under study also contains 1 or 2 V-ATPase per SV, there must be some SVs lacking VhAC45L upon KD. In this context, it is interesting to see how VhaAC-KD (RNAi1~3) affect the frequencies of minis.
4. In general, decrease in mini amplitudes is accounted for by changes in postsynaptic sensitivity for neurotransmitters. Although acidification deficits would support that decrease in quantal size is due to the decrease in the driving force for glutamate uptake, it should be examined whether the postsynaptic receptor fields are not affected by VhaAC45L-KD by recording postsynaptic response upon application of non-saturable concentrations of glutamate.
5. Related to 4, it is also interesting to see if evoked responses are also attenuated as a result of VhaAC45L-KD, which is more physiologically relevant for locomotor activity phenotype than minis.

Minor points

1. Quantal size of glutamate is not affected by reduced expression of DVGLUT (Daniels et al., Neuron, 2006), which highly contrasts with VhaAC45L, expression of which defines quantal size. Distinct regulation of quantal size by the transporter and the V-ATPase subunit should be discussed.
2. For electrophysiological experiments, respective sample traces should be shown in Figure 5.
3. Only RNAi1 and RNAi2 lines were examined for SV pH estimation and mini analysis. The results from RNAi3 should be presented, or at least mentioned in the text.

Significance

As mentioned above, as it stands, the authors merely confirmed the pre-existing bioinformatic knowledge on one of the AC45 homologues in Drosophila. The audience of The EMBO Journal might be interested in how different/similar VhaAC45 and VhaAC45-like are, and their functional relevance. In particular, is VhaAC45 also mandatory for the V-ATPase functioning in neurons? Adding some basic information of VhaAC45, e.g. tissue distribution, KO phenotypes, and ability to rescue the VhaAC45-like-KD phenotypes, will certainly improve the comprehensiveness of this study, and capture audience's attention.

Referees cross commenting

I am fine with the summary of Reviewer#2

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Reviewer #2

Evidence, reproducibility and clarity

In this study and using Drosophila melanogaster as a model system, Dulac et al report the very interesting discovery of a previously characterized neuronal specific regulator of the V-ATPase called VhaAC45L. They combine genetics, IHC, Mass spec and ephys to unravel the expression pattern and function of this protein. They find that it is required to acidify synaptic vesicles in glutamatergic neurons of the Drosophila larval neuromuscular junction, for appropriate synaptic transmission and for larval locomotion. The experiments are very well performed, the data presented very convincing and the paper is well written. Nonetheless, a few additional pieces of evidence and some level of expanded analysis would strengthen the conclusions and increase the depth of the work.

Major comments:

1. Figure 1F: the while the localization to the presynaptic terminal is convincing, where exactly the protein is localized to is not studied. The imaging in these experiments could use increased resolution and concomitantly colocalization studies with more specific synaptic vesicle markers.
2. Figure 3B-G: these experiments should be complemented by a rescue experiment, ideally of the null mutant using a UAS construct and a pan neuronal driver, or - if such animals are viable to the third larval instar stage - a glutamatergic driver. If possible, it would also be good to study the NMJ phenotype of the null mutant rescued to viability using a neuronal driver that does not express in motor neurons (e.g. Chat-G4).
3. Figure 5: the authors focus on quantal size which measures the postsynaptic response to spontaneous release from the presynaptic terminal. However, it is unclear how this directly relates to the locomotor deficit beyond signaling potential deficits in vesicle loading or fusion. It would be more convincing to also study evoked release, and expand the analysis of presynaptic properties (number of events, amplitude, frequency).
4. General: showing some level of genetic interaction with V-ATPase subunits in at least some of the assays would be welcome.

Minor comments:

Some of the images, especially those in Figure 3, should be larger for ease of visualization.

Significance

The discovery of a neuronal specific regulator of the V-ATPase is very interesting. To my knowledge it is the first description of a neuronal specific V-ATPase related protein since the description of Vha100-1 by Hiesinger and colleagues in 2005. The work is therefore of great interest to researchers working on synaptic function in general and on synaptic vesicle biology in particular.

I note that I do not have in depth expertise in electrophysiology, although I am sufficiently familiar with basic NMJ physiology experiments to render the opinions stated above.

Referees cross commenting

There seems to be overall consensus among the reviewers on 3 issues:

1. A somewhat more precise understanding of the role of vhaAC45L in the synaptic vesicle cycle through better localization studies and some classic assays (like FM dye uptake).
2. A little more characterization of the transmission defects (e.g. studying evoked responses) would be welcome.
3. Ascertaining the validity of the alleles with rescue experiments, perhaps in the V5 mutant background to allow localization analysis in a rescued background.

I think further biochemical analysis is interesting but probably beyond the scope of this initial description and would take too much time.

The minor issues are easy to address

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Reviewer #1

Evidence, reproducibility and clarity

Dulac et al. present a first in vivo characterization of the 'accessory' v-ATPase subunit vhaAC45L in Drosophila. The key findings are localization and association of the protein with v-ATPase complexes at synapses and a functional requirement based on lethality and reduced synaptic function. This is certainly a useful contribution to our understanding of neuronal v-ATPase functions in vivo. The main weakness of the study is a lack of depth. The study focuses on localization, co-IP of associated proteins, an analysis of acidification and reduced synaptic function in fly larvae, thus providing a baseline for mechanistic study. However, the mechanism of vhaAC45L is not addressed in this short report. How does is vhaAC45L function different from its homolog vhaAC45? Is it required for v-ATPase assembly? Is it required to localize the full v-ATPase complex (or just V0) to the synapse? Is the defect really due to partial loading of synaptic vesicles or does loss of vhaSC45L also affect endosomal and lysosomal function at synapses? The work as is certainly represents a publishable contribution without answering any of these questions - more as an invite for the community to study the role of vhaAC45L; however, I feel this is a bit of a missed opportunity to put the function of a new potential regulator of specific synaptic v-ATPase functions in the context of the most basic functions obvious in this field.

My main concerns are:

1. clearly, vhaAC45L is required for SOME function of v-ATPase in neurons - but it remains entirely unclear which one. It is not even clear what compartments are affected. Reduced quantal size of single vesicle exocytosis events can be a direct or indirect consequence of problems in SV biogenesis and recycling. Is exo- /endocytosis unaffected? (FM1-43 uptake!). What compartments are affects? (markers for synaptic vesicles versus lysosomal compartments!).
2. molecular function: is vhaAC45L required for v-ATPase assembly? (IP/Pull-downs of v-ATPase complexes in the presence or absence of vhaAC45L with other subunits!).
3. vha100 was proposed in Drosophila to function on synaptic vesicles and the lysosomal pathway, but, if I remember correctly, here quantal size was normal. I am missing a comparison between the two.
4. The V5 knock-in is used both as a mutant as well as a tool to analyze protein localization. This is likely okay, but a little concern of course has to be that by creating a mutant protein through stop codon deletion its subcellular localization, turnover, etc. are not normal. Similarly, anti-V5 co-IPs will isolate proteins bound to the mutant variant of vhaAC45L. Minimally, IPs or pull-downs using other members of the V0 complex should be done to understand the role of vhaAC45L in direct comparison with vhaAC45 on complex assembly and possibly targeting to the synapse (or ideally targeting to specific compartments).

Significance

There is significance to the reporting of an accessory v-ATPase subunit required for SOME function of the v-ATPase in neurons. There is some lack of significance in the absence of basic mechanistic insight as to what vhaAC45L does to the v-ATPase in neurons.

Referees cross commenting

I'm fine with this summary!

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Reply to the reviewers

We would like to thank the editors and the four reviewers for their careful consideration of our manuscript. We are very grateful for their positive appreciation of our work and we believe that their suggestions, which have been included in the preliminary revised version of the manuscript whenever possible, have greatly improved the quality of the paper and have helped us deepen our understanding of the results.

We were happy to note that all the reviewers found value in our work, as stated in their general comments: “This is certainly a useful contribution to our understanding of neuronal V-ATPase functions in vivo” (…)” (Reviewer 1) – “Dulac et al report the very interesting discovery of a previously uncharacterized neuronal specific regulator of the V-ATPase. (…) The experiments are very well performed, the data presented very convincing and the paper is well written.” (Reviewer 2) – “The discovery of a neuronal specific regulator of the V-ATPase is very interesting (…) The work is therefore of great interest to researchers working on synaptic function in general and on synaptic vesicle biology in particular.” (Reviewer 3) – “The authors have used well-designed experiments to convince the localization and function of VhaAC45L in synaptic vesicle acidification.” (Reviewer 4).

In their remarks, the reviewers suggested additional experiments that could be done to improve our understanding of the role of this new V-ATPase regulator, as well as several minor issues. We have addressed all their comments in our answers below, in which the full text of the reviews is included in blue type, and the responses in black. The line numbers refer to the revised version of the manuscript.

Reviewer #1

Dulac et al. present a first in vivo characterization of the 'accessory' v-ATPase subunit vhaAC45L in Drosophila. The key findings are localization and association of the protein with v-ATPase complexes at synapses and a functional requirement based on lethality and reduced synaptic function. This is certainly a useful contribution to our understanding of neuronal v-ATPase functions in vivo. The main weakness of the study is a lack of depth. The study focuses on localization, co-IP of associated proteins, an analysis of acidification and reduced synaptic function in fly larvae, thus providing a baseline for mechanistic study. However, the mechanism of vhaAC45L is not addressed in this short report. How does is vhaAC45L function different from its homolog vhaAC45? Is it required for v-ATPase assembly? Is it required to localize the full v-ATPase complex (or just V0) to the synapse? Is the defect really due to partial loading of synaptic vesicles or does loss of vhaAC45L also affect endosomal and lysosomal function at synapses? The work as is certainly represents a publishable contribution without answering any of these questions - more as an invite for the community to study the role of vhaAC45L; however, I feel this is a bit of a missed opportunity to put the function of a new potential regulator of specific synaptic v-ATPase functions in the context of the most basic functions obvious in this field.

My main concerns are:

1. clearly, vhaAC45L is required for SOME function of v-ATPase in neurons - but it remains entirely unclear which one. It is not even clear what compartments are affected. Reduced quantal size of single vesicle exocytosis events can be a direct or indirect consequence of problems in SV biogenesis and recycling.

Is exo- /endocytosis unaffected? (FM1-43 uptake!).

We agree that alterations in the synaptic vesicle release/recycling cycle could indeed contribute to the locomotion defect, in addition to the acidification impairment observed in VhaAC45L knockdown larvae. As suggested by the reviewer, we plan to carry out FM-dye assays to measure endocytosis and exocytosis at the neuromuscular junction of control versus VhaAC45L-KD animals. If successful, a new figure will be added to the final version of the paper.

What compartments are affected? (markers for synaptic vesicles versus lysosomal compartments!).

Finding out whether VhaAC45L is specifically involved in the acidification of synaptic vesicles, or if it also plays a similar role in other synaptic organelles, in particular lysosomes, would be very interesting indeed. However, we found that it was technically difficult to address this issue in the Drosophila nervous system. A good way would be to check whether the lysosomal pH is affected by VhaAC45L knockdown, as it is the case for synaptic vesicles.

Unfortunately, because lysosomes are not abundant in neurons, lysosome-specific pH-sensitive probes such as Lysotracker do not yield detectable signals at Drosophila larval synapses. So, whether VhaAC45L is specific for synaptic vesicles or involved in the regulation of V-ATPase activity in all neuronal compartments reminas an open question for now.

1. molecular function: is vhaAC45L required for v-ATPase assembly? (IP/Pull-downs of v- ATPase complexes in the presence or absence of vhaAC45L with other subunits!).

In accordance with the reviewer, we are also very much eager to learn more about the precise molecular function of VhaAC45L, and in particular whether it is required or not for assembly of the V-ATPase complex. Pull-downs of V-ATPase proteins in controls versus VhaAC45L-KD could be used to address this question, but this would require a large quantity of antibodies directed against subunits of the V0 and V1 domains, respectively. Unfortunately, there are no such antibodies commercially available against Drosophila V-ATPase proteins. We have tried several antibodies that recognize V-ATPase subunits from other species and were predicted to react against Drosophila homologs, but with no success. The only V-ATPase antibodies currently at our disposal were samples generously sent to us by other laboratories in insufficient quantities for carrying out such experiments. To our regret, therefore, we were not able to answer this question until now because of the lack of appropriate tools.

1. vha100 was proposed in Drosophila to function on synaptic vesicles and the lysosomal pathway, but, if I remember correctly, here quantal size was normal. I am missing a comparison between the two.

We thank the reviewer for this comment. A comparison with previously published results on subunit Vha100-1 has now been added (lines 458-469) in the discussion related to this topic in the revised manuscript.

1. The V5 knock-in is used both as a mutant as well as a tool to analyze protein localization. This is likely okay, but a little concern of course has to be that by creating a mutant protein through stop codon deletion its subcellular localization, turnover, etc. are not normal. Similarly, anti-V5 co-IPs will isolate proteins bound to the mutant variant of vhaAC45L. Minimally, IPs or pull- downs using other members of the V0 complex should be done to understand the role of vhaAC45L in direct comparison with vhaAC45 on complex assembly and possibly targeting to the synapse (or ideally targeting to specific compartments).

It is indeed a legitimate concern to question the physiological relevance of results obtained by studying V5-tagged VhaAC45L. However, the V5 tag is very small (14 amino acids) and we fused it in place of the stop codon to keep intact the whole sequence of the protein. In addition, we found that the V5 knock-in flies are viable and fertile as homozygous. Given that the null mutants, as well as strong RNAi knockdowns, are lethal at early developmental stage, this suggests that the V5 knock-in has limited negative effects, if any, on VhaAC45L function. This led us to believe that at least a good portion of the V5-tagged protein might be targeted to the right subcellular compartment, and associate to its physiological partners.

Significance:

There is significance to the reporting of an accessory v-ATPase subunit required for SOME function of the v-ATPase in neurons. There is some lack of significance in the absence of basic mechanistic insight as to what vhaAC45L does to the v-ATPase in neurons.

We agree that we did not elucidate here the precise molecular mechanisms by which VhaAC45L contributes to synaptic vesicle acidification. It is rather an initial description of a novel neuronal protein that appears to be essential for proper synaptic functioning, and we provide consistent evidence that its function requires specific interaction with the V-ATPase complex, and in particular with three subunits that reproducibly co-immunoprecipitated with VhaAC45L (namely Vha1C39-1, Vha100-1 and ATP6AP2). Please note that it took many years and many papers before the molecular mechanisms of action of comparable accessory subunits, such as ATP6AP1/AC45 or ATP6AP2, was better understood, and it is still nowadays a matter of investigation. It is therefore very demanding to expect that we describe the exact function of the previously uncharacterized VhaAC45L at all levels in a single first paper.

Reviewer #2

In this study and using Drosophila melanogaster as a model system, Dulac et al report the very interesting discovery of a previously uncharacterized neuronal specific regulator of the V-ATPase called VhaAC45L. They combine genetics, IHC, Mass spec and ephys to unravel the expression pattern and function of this protein. They find that it is required to acidify synaptic vesicles in glutamatergic neurons of the Drosophila larval neuromuscular junction, for appropriate synaptic transmission and for larval locomotion. The experiments are very well performed, the data presented very convincing and the paper is well written. Nonetheless, a few additional pieces of evidence and some level of expanded analysis would strengthen the conclusions and increase the depth of the work.

Major comments:

1. Figure 1F: the while the localization to the presynaptic terminal is convincing, where exactly the protein is localized to is not studied. The imaging in these experiments could use increased resolution and concomitantly colocalization studies with more specific synaptic vesicle markers.

We agree that it would be very good to show this additional result. However, confocal microscopy does not provide sufficient resolution to localize the protein at the membrane of individual synaptic vesicles. Another way would be to see if VhaAC45L immunostaining co- localizes with domains enriched in synaptic vesicle markers, but these organelles are rather ubiquitously distributed in the synaptic boutons at the Drosophila neuromuscular junction. To correctly perform this experiment, we would have to do immuno-electron microscopy, a technique we do not master in our laboratory and that we did not plan to implement for the present work.

1. Figure 3B-G: these experiments should be complemented by a rescue experiment, ideally of the null mutant using a UAS construct and a pan neuronal driver, or - if such animals are viable to the third larval instar stage - a glutamatergic driver. If possible, it would also be good to study the NMJ phenotype of the null mutant rescued to viability using a neuronal driver that does not express in motor neurons (e.g. Chat-G4).

Although a rescue experiment could potentially add a further evidence that Vha45ACL deficiency is responsible for the synaptic vesicle acidification defect described in Figure 3, we don’t think that it is a requisite here because we obtained similar results by knocking down the gene using two different RNAis. As described in the manuscript, the pan-neuronal expression of Vha45ACL could rescue the embryonic lethality of the null mutant, so it would be theoretically possible to check the acidity level of synaptic vesicles at the neuromuscular junction of the recued larvae. However, this would involve making rather complex genetic constructions to express VMAT-pHluorin in motor neurons in rescued mutant background. In addition, the conclusions we could draw from such experiment would be limited by the lack of comparison. Indeed, in Figure 3 the defect was observed in knockdown context, and the same experiment could not be performed in knockout larvae due to the early lethality. If we could measure the acidity level of rescued null mutants, we would not have any comparison point besides the knockdown experiments. As knockout and knockdown are not likely to produce identical phenotype (especially in terms of magnitude of effect), the ideal would be to compare the rescued phenotype to the null mutant expressing VhaAC45L in all neurons except motoneurons, as suggested by the reviewer. However, such genotype would certainly not be viable, since we observed that expression of VhaAC45L RNAis with a stronger motoneurons driver (D42-Gal4) was sufficient to induce lethality at early developmental stage.

1. Figure 5: the authors focus on quantal size which measures the postsynaptic response to spontaneous release from the presynaptic terminal. However, it is unclear how this directly relates to the locomotor deficit beyond signaling potential deficits in vesicle loading or fusion. It would be more convincing to also study evoked release, and expand the analysis of presynaptic properties (number of events, amplitude, frequency).

We fully agree with this comment shared by Reviewers 2 and 3 related to the electrophysiology experiments. Note that these experiments have been carried out in collaboration with another laboratory located in another city. The Covid-19 situation during the past year has prevented, and is still complicating, movements between labs, preventing us from going further with the electrophysiology analyses of VhaAC45L KD. If the situation in the near future allows it, we would very much like to add a more extensive electrophysiological analysis, including in particular the study of evoked release. In the revised manuscript, we have nevertheless completed Figure 5 by adding representative distributions of spontaneous mEPSP amplitudes in control and VhaAC45L knockdown larvae, as well as the results of new analyses showing lack of effects the KD on the mean EPSP frequency.

1. General: showing some level of genetic interaction with V-ATPase subunits in at least some of the assays would be welcome.

We are definitely in accordance with the reviewer on that point, but we think that this would involve a lot of work and be beyond the scope of the present initial description. Here we show by proteomic analyses that at least 12 proteins co-precipitate and so potentially interact with VhaAC45L, three of them being previously identified constitutive or accessory V-ATPase subunits. In our opinion, studying the interactions between VhaAC45L and these proteins through genetic and molecular studies will be the subject of future works. As stated by Reviewer 2 in the Referees cross commenting below: “further biochemical analysis is interesting but probably beyond the scope of this initial description and would take too much time”. We fully agree with this statement.

Minor comments:

Some of the images, especially those in Figure 3, should be larger for ease of visualization.

As requested, the images of Figure 3 have been enlarged.

Significance

The discovery of a neuronal specific regulator of the V-ATPase is very interesting. To my knowledge it is the first description of a neuronal specific V-ATPase related protein since the description of Vha100-1 by Hiesinger and colleagues in 2005. The work is therefore of great interest to researchers working on synaptic function in general and on synaptic vesicle biology in particular.

We are grateful to the reviewer for his very positive assessment of our work.

I note that I do not have in depth expertise in electrophysiology, although I am sufficiently familiar with basic NMJ physiology experiments to render the opinions stated above.

Reviewer #3

In this study, Dulac and colleagues investigated roles of VhaAC45-like gene, which codes one of the V-ATPase accessory proteins in Drosophila, in synaptic transmission. First, they demonstrated that VhaZC45L transcripts are expressed selectively in neurons and that the gene products are addressed to synaptic areas. Second, they showed that VhaAC45L is co- immunoprecipitated with some subunits of V-ATPases, which is consistent with bio-informatics predictions. They further demonstrated that VhaAC45L-knockdown (KD) resulted in defects in synaptic vesicle acidification as well as a reduction in quantal size of glutamate, indicating that VhaAC45L play a key role in regulating neurotransmitter release by modulating the driving force for transmitter uptake. Last, not least, they demonstrated that VhaAC45L-KD in motoneurons attenuated larvae locomotor performance, indicating its physiological relevance. Overall, this study is rigorously executed and nicely presented, and adds one more component of the V- ATPase that is responsible for neurotransmitter uptake into synaptic vesicles. However, since this study simply confirmed an established notion from other species such as yeast and mammals that AC45 is one of the accessory proteins of the V-ATPase complex, a conceptual novelty beyond the previous knowledge is relatively poor in its present form. Thus, this reviewer would suggest several issues as following to improve the comprehensiveness as well as novelty of the current manuscript.

1. The reason why the authors focused on VhaAC45-'like' is somewhat obscure, and therefore should be explained. How different VhaAC45 and VhaAC45L are in terms of amino acid sequences, tissue distributions, and KO phenotypes. It seems more comprehensive if the authors provide some experimental evidence on VhaAC45; e.g. whether it is also expressed in neurons or not (Fig. 1), and, if VhaAC45 is neuronal, whether it can rescue the phenotypes of VhaAC45L- KD to certain degree (Figs 4 & 5).

Following the reviewer’s request, we have added a sequence alignment of VhaAC45 and VhaAC45L, as well as a graph showing tissue distributions of both genes in Supplementary Figure 1 of the revised manuscript. To our knowledge, there is no published functional study of VhaAC45 in Drosophila, so we can only make assumptions derived from studies on predicted homologs in evolutionarily distant species. For that reason, it is difficult to compare VhaAC45 to VhaAC45L, as it would first require an entire new study of VhaAC45 function in flies. Since our interest is to study neuronal physiology, we focused on VhaAC45L because compelling evidence indicates that this subunit is specific to the nervous system, as described in our manuscript, rather than on VhaAC45 which seems to be expressed in all tissues. In addition, homologs of VhaAC45L have never been functionally characterized to date in any species, making it very interesting to study this new protein in a genetically tractable organism.

1. What is the mechanism of Ac45 in regulating V-ATPase activity? In mammals, it has been suggested that Ac45 is essential for proper sorting of the V-ATPase to the destined organelles (e.g. Jansen et al., Mol. Biol. Cell., 2010; Jansen et al., BBA, 2008). In this context, it should be examined whether VhaAC45L-KD would affect the synaptic localization of other V-ATPase subunits.

We thank the reviewer for pointing out these very interesting references. We have indeed tried to determine the relative abundance of two other V-ATPase subunits at the larval neuromuscular junction in control and VhaAC45L knockdown contexts. However, because the tested subunits are not specific to neurons, and are expressed at relatively low levels in synapses, it was not possible for us to properly separate the synaptic signal from the background immunostaining in surrounding muscles. This unfortunately prevented us from performing an accurate and reliable quantification.

1. Given that a rodent brain SV contains a few copies of the V-ATPase on average (Takamori et al., 2006, and some newer papers by others), it is interesting that >80% reduction of Ac45 showed moderate effects on quantal size. If SVs under study also contains 1 or 2 V-ATPase per SV, there must be some SVs lacking VhAC45L upon KD. In this context, it is interesting to see how VhaAC-KD (RNAi1~3) affect the frequencies of minis.

The reviewer’s valuable comment prompted us to undertake new analyses on our electrophysiological recordings. We have now added in Figure 5E graphs showing the mean EPSP frequency for larvae expressing VhAC45L RNAi1 and RNAi2, which are the ones that were used in the quantal analysis. Both of these RNAi apparently decreased the frequency compared to controls, but this difference was not statistically significant. As detailed in the Discussion (line 458-469), this may suggest that VhaAC45L does not influence the abundance of the V-ATPase complex at nerve terminals, but rather its efficiency.

1. In general, decrease in mini amplitudes is accounted for by changes in postsynaptic sensitivity for neurotransmitters. Although acidification deficits would support that decrease in quantal size is due to the decrease in the driving force for glutamate uptake, it should be examined whether the postsynaptic receptor fields are not affected by VhaAC45L-KD by recording postsynaptic response upon application of non-saturable concentrations of glutamate.

Testing for potential postsynaptic receptor field alteration by glutamate application would be an interesting experiment indeed, but, as we believe, not a critical control for the present manuscript. Because we expressed RNAis presynaptically, any modification in the postsynaptic receptor field would have to be an indirect consequence of VhaAC45L downregulation in motoneurons, and so, likely to be related to the synaptic vesicle acidification defect. It would not change, therefore, our conclusion that VhaAC45L deficiency in motoneurons induces a decrease in quantal size. Because electrophysiology experiments were carried out in collaboration with another laboratory located in another city, the current sanitary context has so far prevented us from performing this test (please refer to our answer to comment 3 of Reviewer 2 for more details).

1. Related to 4, it is also interesting to see if evoked responses are also attenuated as a result of VhaAC45L-KD, which is more physiologically relevant for locomotor activity phenotype than minis.

We also agree with this comment, shared by Reviewer 2, to which we already responded above in our answer to comment 3 of Reviewer 2.

Minor points:

1. Quantal size of glutamate is not affected by reduced expression of DVGLUT (Daniels et al., Neuron, 2006), which highly contrasts with VhaAC45L, expression of which defines quantal size. Distinct regulation of quantal size by the transporter and the V-ATPase subunit should be discussed.

As suggested by the reviewer, a discussion of this point has been added (lines 458-469). and Daniels et al. 2006 is now cited in the revised manuscript.

1. For electrophysiological experiments, respective sample traces should be shown in Figure 5.

Quantal size is not directly visible in sample traces, so we added instead representative distributions of spontaneous mEPSP amplitudes in control and VhaAC45L knockdown larvae in the new Figure 5C.

1. <![endif]>Only RNAi1 and RNAi2 lines were examined for SV pH estimation and mini analysis. The results from RNAi3 should be presented, or at least mentioned in the text.

These experiments were performed using two different RNAi constructs to ensure that similar effects were observed and to exclude the possibility of potential off-targets. Knocking down VhaAC45L in neurons with RNAi1 and 2 was lethal at pupal stages, suggesting that they give similar levels of inactivation. RNAi3 systematically induced lighter phenotypes, producing viable adults, which led us to believe it had a lower efficiency. Because the results on synaptic vesicle acidification and electrophysiology were very consistent with RNAi1 and RNAi2, we considered that it was not necessary to repeat the experiment with RNAi3.

Significance

As mentioned above, as it stands, the authors merely confirmed the pre-existing bioinformatic knowledge on one of the AC45 homologues in Drosophila. The audience of The EMBO Journal might be interested in how different/similar VhaAC45 and VhaAC45-like are, and their functional relevance. In particular, is VhaAC45 also mandatory for the V-ATPase functioning in neurons? Adding some basic information of VhaAC45, e.g. tissue distribution, KO phenotypes, and ability to rescue the VhaAC45-like-KD phenotypes, will certainly improve the comprehensiveness of this study, and capture audience's attention.

As mentioned in our response to point 1 of the reviewer above, we have added more data comparing the structure and distribution of VhaAC45 and VhaAC45L in the revised manuscript. VhaAC45 appears to be ubiquitously expressed whereas VhaAC45L is neuron-specific.

Comparing VhaAC45 to VhaAC45L would require a completely new study of VhaAC45 function, because it has never been done before in Drosophila to our knowledge. This would require repeating all the experiments with this other gene, probably involving two more years of work, and would make for a much longer and very different manuscript. It is understandable that this cannot be envisaged. Because homologs of VhaAC45L have never been functionally characterized to date in any species, we considered that it was worth studying this new protein on its own.

Reviewer #4

We have reviewed "A specific regulator of neuronal V-ATPase in Drosophila melanogaster." by Dulac et al. The authors have identified VhaAC45L as a regulator of neuronal V-ATPase in Drosophila melanogaster. The authors have utilized multiple techniques to determine the localization of VhaAC45L in neurons and specifically in the synapse. The use of multiple approaches including determining RNA levels in different regions of the fly, and using CRISPR- Cas9 technique to insert V5 tag, makes a very convincing argument about the synapse-specific expression of VhaAC45L.

The combined use of co-immunoprecipitation technique and LC/MS to show that VhaAC45L co- precipitated with V-ATPase complex subunits is convincing that VhaAC45L is a subunit of V- ATPase. To determine the role of VhaAC45L in acidification of synaptic vesicles the authors have utilized pHluorins in combination with multiple RNAi lines. The authors have used a well- designed experiment to prove that VhaAC45L regulates acidification of the synaptic vesicles.

Further, larval locomotion and quantal size determination using VhaAC45LRNAi which is known to be altered due to pH gradient of synaptic vesicles shows the functional role of VhaAC45L in synaptic vesicle acidification.

Minor comments:

1. For all graphs, please remove gridlines to make data points more visible.

We found that gridlines can be helpful for the readers to assess approximate values on the graphs. So, we have not removed them but rather changed the colour to a light grey so it does not affect any more visibility. We have also placed the points over the error bars in all the graphs, so they become more apparent.

1. Line 120-123: Authors indicate the VhaAC45LRNAi induced lethal phenotype when expressed in glutamatergic and cholinergic drivers but the figure is missing. Please indicate as "data not shown" if not included in Figure.

This mention has been added in the manuscript (line 125).

1. A diagram summarizing the role of VhaAC45L in V-ATPase enzymatic complex and specific role is recommended.

We believe that it is too early in this first report to draw an accurate diagram summarizing the role of this new protein in the V-ATPase complex.

Significance

V-ATPase play a crucial role at the synapse by being responsible for acidification of the synaptic vesicles and identification of a synaptic vesicle specific regulator of V-ATPase is important to understand the complex regulation of synapse function. The authors have used well-designed experiments to convince the localization and function of VhaAC45L in synaptic vesicle acidification.

We thank the reviewer for his very positive appreciation of our work.

Referees cross commenting

(Written by Reviewer 2)

There seems to be overall consensus among the reviewers on 3 issues:

1. A somewhat more precise understanding of the role of vhaAC45L in the synaptic vesicle cycle through better localization studies and some classic assays (like FM dye uptake).

—See our answers to comments 1 of Reviewer 1 and Reviewer 2.

1. A little more characterization of the transmission defects (e.g. studying evoked responses) would be welcome.

—See our answers comment 3 of Reviewer 2.

1. Ascertaining the validity of the alleles with rescue experiments, perhaps in the V5 mutant background to allow localization analysis in a rescued background.

—See our answers to comment 2 of Reviewer 2.

I think further biochemical analysis is interesting but probably beyond the scope of this initial description and would take too much time.

We fully agree with this statement.

The minor issues are easy to address

We have addressed all of them in the preliminary revised version of the manuscript.

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Reply to the reviewers

Reviewer 1

We would like to thank you for the comments concerning our manuscript. We responded to each question, as described below. All the authors feel that our manuscript has been much improved by your comments.

Minor comments:

Q1. Fig 1 any male vs female mice differences in ATF6b expression?

Response. We performed qPCR using several tissues from male and felame WT mice, and confirmed no significant differences in Atf6b mRNA levels between male and female mice. We put this result in Figure S1 C.

Q2. Fig 2C. Please show molecular weight markers on blots

Response. We put molecular weight markers in Fig.2C, as you suggested.

Q3. Fig 2C. what are the doublet bands on calnexin?

Response. Calnexin is sometimes shown as double bands in tissues such as kidney, liver and heart by western blotting (Zeng et al., PLoS One. 2009 Aug 26;4(8):e6787). Although the mechanism is unknown, it could be due to the post-translational modification such as phosphorylation (Wong et al, J Biol Chem. 1998 Jul 3;273(27):17227-35) or partial degradation although proteinase inhibitors are added in the lysis buffer. To my knowledge, alternative splicing is not likely to be the case.

Q4. Fig 3. what are the ERSE sequences? several different binding sites are reported in literature.

Response. We put the ERSE sequence in Materials and Methods and in the Figure legends for Figure 3 as “CCAATN9CCACG (Yoshida et al., 1998)”.,

Q5. p8. What is meant by 5' Atf6b lacks 10 and 11?

Response We corrected to “Atf6b transcript, which lacks exon 10 and 11, in these mice”.

Discussion: Please clarify if anti-ATF6-beta antibodies were available for these studies.

Response. We tried different anti-ATF6β antibodies to detect endogenous ATF6β in culture neurons by western blot. We successfully observed both full-length and N-terminal fragment (the active form) using the one from Biolegend (#853202) (Figure 1E in the new version). We replaced the result with FLAG antibody in HEK293T cells in the old version.

Discussion: It is puzzling that ATF6a induces calreticulin more potently than ATF6b, but the calreticulin defect is selectively dependent on ATF6b. Could authors speculate on this paradox? It would be interesting to expand on differences between ATF6a and ATF6b function and phenotypes in Discussion in mouse and in people.

Response. In Discussion, we added sentences regarding a bit puzzling role for ATF6β in CRT expression in the CNS, as below. “All the data from RNA-sequence to the promoter analysis suggested that CRT expression was ATF6β-dependent in primary hippocampal neurons. However, overexpression of ATF6α and ATF6β both enhanced CRT promoter activity…”

And we proposed a new scenario as below,

“These results may raise a scenario that, in the CNS, expression of molecular chaperones in the ER is generally governed by ATF6α as previously described (Yamamoto et al., 2007) and that ATF6β functions as a booster if their levels are too low. However, expression of CRT is somewhat governed by ATF6β, and ATF6α functions as a booster. The underlying mechanism for this scenario is not clear yet, but neurons may require a high level of CRT expression even under normal condition, as described in Table S2, which may lead to the development of a unique biological system to constitutively produce CRT in neurons. Further studies are required to clarify the molecular basis how this unique system is constructed and regulated.”

Reviewer 2

We would like to thank you for the comments concerning our manuscript. We responded to each question, as described below. All the authors feel that our manuscript has been much improved by your comments.

Major comments:

Q1. The post-translational processing of ATF6beta must be demonstrated in hippocampal neurons and not in HEK293T cells in Figure 1E. The authors conclude on Page 6, line 18 that "these results suggest that ATF6beta functions in neurons" but it is not obvious how expression in HEK293T cells contributes to this conclusion in any way.

Response. We performed western blot with different anti-ATF6β antibodies to detect endogenous ATF6β in culture neurons. We successfully observed both full-length and N-terminal fragment (the active form) from the one from Biolegend (#853202). We therefore replaced the result in HEK293T cells with the one in the hippocampal neurons (Figure 1E in new version).

Q2. The hippocampal neurons are affected by the loss of ATF6β, even though the mice are not exposed to tunicamycin. Could the authors present evidence that there is physiological ER stress in hippocampal neurons? If not, why is ATF6beta required.

Response Evidence suggests that neuronal activities including excitatory signals can cause physiological ER stress and induce the UPR at the distal dendrites in the hippocampal neurons (Murakami et al., Neuroscience. 2007 Apr 25;146(1):1-8, Saito et al., J Neurochem. 2018 Jan;144(1):35-49). Among the UPR branches, Ire1-XBP1 pathway has been reported to play an important role in this dendritic UPR and expression of BDNF in cell soma (Saito et al., 2018). Although the present study focuses on the role of ATF6β in the pathological ER stress which causes neuronal death, we believe that it will be intriguing to analyze its role of ATF6β in the physiological ER stress and in the local UPR machinery in neurons.

Q3. In Figure 3, is there a specific reason why the authors do not mutate the ERSEs in the mouse CRT reporter, pCC1 and instead opt to analyze the huCRT reporter? Given that all the other observations in the manuscript are in mouse calreticulin, it is important to show that the ERSEs in the mouse calreticulin promoter are also regulated in an ATF6beta-dependent manner. Similar to the huCRT reporter, it is also crucial to examine if ATF6beta can regulate the mouse CRT promoter. This would provide an explanation for why calreticulin expression is not completely abolished in ATF6beta mutants.

Response We added the data of the deletion mutant of mouse CRT promoter, pCC3, which has only 415bp, but still keeps both ERSE1 and 2 in it. pCC3 showed similar promoter activity to pCC1 (Figure 3 B) and huCRT (wt) (Figure 3 C) in both of WT and Atf6b-/- neurons. Because pCC5, which has 260bp but does not have ERSEs in it, lost completely CRT promoter activity (Waser et al., 1997), it is most likely that mouse and human CRT promoters are regulated in a similar manner via ERSEs.

Q4. In Figure 5A and B, the density of Tubulin staining varies from panel to panel, and is much lower in ATF6beta mutants treated with Tg/Tm. Presumably this is because of cell death but this should be clarified in the main text. Additionally, it is unclear if the EthD-1 staining is nuclear localized. It would help if single channel images for Hoechst and EthD-1 were provided to visualize this.

Response In Figure 5A and B, we added the statement for the reduction of Calcein-AM (A) and βIII tubulin (B) in the main text. We also added single channel images for Hoechst and EthD-1 in Figure S4 to confirm the nuclear localization of EthD-1.

Q5. The literature reports that BAPTA-AM treatment itself could cause ER stress (e.g. PMID: 12531184). Here, the authors report the opposite effect. How could the authors reconcile the difference? The effects of BAPTA-AM and 2-APB must individually be examined in Figure 6C and not just in combination with Tm.

Response. We added the data that BAPTA-AM and 2-APB alone did not cause neuronal death at the concentrations used in this study in Figure S6 B and in the main text.

Q6. The authors allude to "impairment of Ca2+ homeostasis in ATF6beta mutants" in Page 13 Line 2, but do not show any direct evidence in support of it. While treatment with BAPTA-AM and 2-APB is a start in that direction, it certainly does not demonstrate that under homeostatic conditions in vivo or in vitro there is any change in calcium flux in ATF6beta hippocampal neurons. To make the case that there is indeed perturbation of Ca2+ in ATF6beta mutant hippocampal neurons, the authors need to examine calcium flux and measure calcium indicators and how they are affected when ER stress is induced in these mutant cells.

Response We added the data that the Ca2+ store in the ER was reduced and Ca2+ concentration in the cytosol increased in Atf6b-/- neurons both under normal and ER stress conditions in Figure 4C.

Q7. The effect of 2-APB and salubrinal alone on hippocampal neurons need to be examined in Figure 9B-D to eliminate the possibility that these drugs are not enhancing cell survival under normal conditions in a parallel manner.

Response We added the data that 2-APB and salubrinal alone did not cause neuronal death in the hippocampus in our model in Figure S8 C.

Q8. The rationale for the examination of Fos, Fosb and Bdnf is poorly described (page 14, line 13) and the conclusions from this line of experimentation are rather weak. The results from Figure 9 to some extent serve to confirm in vivo the data seen in Figure 6C but by no means provide a mechanism for why ATF6beta mutants have perturbed calcium homeostasis (page 14, line 22).

Response We agreed with your comments that the examination of Fos, Fosb and Bdnf is relatively weak. We, therefore, moved these data to supplementary information (Figure S8 A and B).

Minor comments:

Q1. Page 8, line 3: Their rationale for why ATF6beta 5'UTR sequences are seen in their RNA seq data is not clearly explained. This must be rewritten for clarity.

Response In Atf6b-/- mice, exon 10 and 11 were deleted by homologous recombination. Therefore, 5’ part of Atf6b gene including exon 1-9 can be transcribed. We added the statement in Results, as below.

“this may be due to the presence of the 5’ Atf6b transcript with exon 1-9 in these mice, in which exon 10 and 11 were deleted by homologous recombination.”

Q2. Page 8, line 5, the authors write that besides Atf6β , CRT was the only UPR-regulated gene downregulated in Atf6β mutant mice. The authors need to state how they defined "UPR-regulated genes". There must be a list, which the authors do not cite.**

Response. To avoid the possible confusion, we changed the term “UPR-regulated genes” to “ER stress-responsive genes”.

Q3. Page 9, line 10: A reference is required for ERSEs.

Response We added the reference for ERSEs, as you suggested.

Q4. Page 10, line 6: The authors say "ATF6beta specifically induces CRT promoter activity". This is a confusing statement because "induction" is in response to stress, but the context here is homeostatic regulation since there is ostensibly no stress being induced. This distinction should be made and corrected here and throughout the manuscript.

Response To avoid the confusion, we changed the sentence to “ATF6β specifically enhances CRT promoter activity”.

Q5. Page 10, line 16: The use of "latter" here is confusing and it would help to restructure this sentence for clarity.

Response To avoid the confusion, we changed the phrase to “under control condition and after stimulation with Tg (Figure 4A upper row) or Tm (Figure 4A lower row)

Q6. Figure 9A is missing Y-axis labels.

Response We changed Figure 9A (Figure S8 A in new version) and Figure Legends to clarify what each axis indicates.

Reviewer 3

We would like to thank you for the comments concerning our manuscript. We responded to each question, as described below. All the authors feel that our manuscript has been much improved by your comments.

Major comments

Comment #1. The authors show that overexpression of either Atf6a or Atf6b both increase Crt expression in Atf6b knockout cells. While it is clear that deletion of Atf6a does not basally reduce Crt levels, the overexpression experiment does lead to a question as to how Atf6b can specifically be involved in regulating Crt expression. In the discussion, the authors seem to propose that homo- and hetero-dimerization of ATf6a and Atf6b are required for the basal expression of Crt and that Atf6b serves as a 'booster' of ER chaperone expression. They explicitly state that "Atf6a and Atf6b are required to induce CRT expression". However, it remains unclear to me why in this case would Atf6a deletion not impair Crt expression? The authors address this by invoking a mechanism whereby hippocampal neurons are more reliant on Atf6b for Crt expression, but this doesn't really make sense to me. Ultimately, this point underscores the lack of clear mechanistic basis to explain how Atf6b selectively regulates Crt in the hippocampus. This needs to be better resolved through more experimentation. For example, a ChIP experiment monitoring the binding of ATF6b and ATF6a to the Crt promoter in hippocampal and control cells would go a long way towards addressing this issue.**

Response. In Discussion, we first made the point clearer that CRT expression is ATF6β-dependent, while those of other molecular chaperones in the ER are ATF6α-dependent. Then, we raised a scenario that, in the CNS, expression of molecular chaperones in the ER is generally governed by ATF6α as previously described (Yamamoto et al., 2007) and ATF6β functions as a booster if their levels are too low. However, expression of CRT is somewhat governed by ATF6β, and ATF6α functions as a booster. We also wrote the limitation of the current study and requirement of the further study to clarify the molecular basis of the unique system to ensure CRT expression in neurons.

Comment #2. The importance of ATF6b for protecting against insults needs to be better described. For example, the authors should show that overexpression of ATF6b protects against ER stress induced neuronal toxicity in cell culture and in vivo kainate induced neuronal toxicity. Similarly, the authors should evaluate how overexpression of ATF6a protects against these insults to better define the specific dependence of hippocampal neurons on ATF6b. The authors do show that overexpression of ATF6b can rescue the reduced Crt observed in Atf6b-deleted neurons, but the protection should similarly be demonstrated.**

Response. We performed rescuing experiments to see both of ATF6β and ATF6α overexpression improve cell viability of Atf6b-/- neurons under ER stress. Interesting. ATF6β, but not ATF6α, rescued Atf6b-/- neurons. In Discussion, we raised the possible reasons as below.

“The lack of rescuing effect of ATF6α may be due to the fact that this molecule enhances the expression of different genes including cell death-related molecule CHOP in addition to molecular chaperons in the ER (Yoshida et al., 2000).”

Comment #3. Similar to #2, the authors should show that the potential for ATF6b (and ATF6a) overexpression to protect against different insults is impaired in Crt+/- neurons. The authors demonstrate that Crt-depletion increases sensitivity to toxic insults. This would go a long way to demonstrate the importance of the proposed ATF6b-CRT signaling axis in regulating neuronal survival in response to pathologic insults.**

Response. Unfortunately, right now, the breeding of Calr+/- mice is not in good condition. Although we are increasing the number of mice used for breeding, we have to wait pregnancies to get embryos for isolating neurons from hippocampus. Once we get enough number of mice, we would try the rescuing experiment of Calr+/- hippocampal neurons with ATF6β and ATF6α. However, we also think rescuing experiments of Atf6b-/- neurons by ATF6β, ATF6α, and CRT may be enough in this paper.

Comment #4. When reporting the RNAseq data, the authors should use the q-value (i.e., FDR) instead of the p-value. This will likely affect the number of genes reported in Table 1, but it is the appropriate statistical test for this type of data.**

Response. As you suggested, we replace Table1 with a new list which was filtered with the q-value. However, some important and consistent information were obtained from the list filtered with the p-value, we keep it as Table S1 in the supplementary information.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, the authors define the functional importance of ATF6b in the hippocampus. They show that ATF6b is highly expressed in the hippocampus relative to other tissues. They demonstrate that deletion or depletion of ATF6b in cultured hippocampal neurons enhances ER stress induced death. Similarly, Atf6b-/- mice show increased sensitivity to kainate induced neuronal death. These results reveal an important role for ATF6b in regulating hippocampal survival in response to pathologic insults. To define a molecular basis for this protection, the authors utilized RNAseq to identify the lectin chaperone calreticulin (Crt) as a gene whose expression is basally reduced in cultured hippocampal neurons where Atf6b is deleted. They show the re-overexpression of Atf6b (or Atf6a) both restore Crt levels in these neurons, underscoring the importance of Atf6 in regulating basal Crt levels. They go on to demonstrate that loss of Atf6b impairs ER stress-dependent increases in Crt, while minimally impacting other Atf6 target genes, again highlighting the importance of Atf6b for Crtregulation. Importantly, overexpression of Crt rescues the increased ER stress-induced toxicity observed in Atf6b knockout neurons, indicating that a primary mechanism by which Atf6b regulates neuronal survival in response to ER stress is through increased Crt expression. Consistent with this, mimicking the 50% reduction in Crt observed in Atf6b knockout neurons using Crt+/- mice showed similar sensitivity to kainate induced neuronal death. Collectively, these results describe an Atf6-Crt axis that is important for regulating neuronal survival in response to pathologic insults.

Overall the experiments are interesting and provide new insights into the importance of Atf6b in neuronal survival. Notably, the evidence showing that loss of Atf6b increases hippocampal neuron sensitivity to ER stress and kainate induced toxicity are compelling. Any results describing the biological function of Atf6b are interesting, considering how little we know about this ER stress sensing protein. That being said, I have some concerns about the work described that require addressing before publication. Notably, I think more work needs to be done to define the molecular basis for the specific dependence of Crt expression on ATF6b in hippocampal neurons. Further, the authors need to do more experiments to demonstrate the specific importance of ATF6b signaling in the context of ER stress and in vivo neuronal death. I outline these various concerns below:

Comment #1. The authors show that overexpression of either Atf6a or Atf6b both increase Crt expression in Atf6b knockout cells. While it is clear that deletion of Atf6a does not basally reduce Crt levels, the overexpression experiment does lead to a question as to how Atf6b can specifically be involved in regulating Crt expression. In the discussion, the authors seem to propose that homo- and hetero-dimerization of ATf6a and Atf6b are required for the basal expression of Crt and that Atf6b serves as a 'booster' of ER chaperone expression. They explicitly state that "Atf6a and Atf6b are required to induce CRT expression". However, it remains unclear to me why in this case would Atf6a deletion not impair Crt expression? The authors address this by invoking a mechanism whereby hippocampal neurons are more reliant on Atf6b for Crt expression, but this doesn't really make sense to me. Ultimately, this point underscores the lack of clear mechanistic basis to explain how Atf6b selectively regulates Crt in the hippocampus. This needs to be better resolved through more experimentation. For example, a ChIP experiment monitoring the binding of ATF6b and ATF6a to the Crt promoter in hippocampal and control cells would go a long way towards addressing this issue.

Comment #2. The importance of ATF6b for protecting against insults needs to be better described. For example, the authors should show that overexpression of ATF6b protects against ER stress induced neuronal toxicity in cell culture and in vivo kainate induced neuronal toxicity. Similarly, the authors should evaluate how overexpression of ATF6a protects against these insults to better define the specific dependence of hippocampal neurons on ATF6b. The authors do show that overexpression of ATF6b can rescue the reduced Crt observed in Atf6b-deleted neurons, but the protection should similarly be demonstrated.

Comment #3. Similar to #2, the authors should show that the potential for ATF6b (and ATF6a) overexpression to protect against different insults is impaired in Crt+/- neurons. The authors demonstrate that Crt-depletion increases sensitivity to toxic insults. This would go a long way to demonstrate the importance of the proposed ATF6b-CRT signaling axis in regulating neuronal survival in response to pathologic insults.

Comment #4. When reporting the RNAseq data, the authors should use the q-value (i.e., FDR) instead of the p-value. This will likely affect the number of genes reported in Table 1, but it is the appropriate statistical test for this type of data.

Significance

This manuscript provides new context for understanding the functional relationship between Atf6a and the less-studied Atf6b in regulating neuronal survival. As with other studies focused on the relationship between these two ATF6 isoforms, this study demonstrates that these transcriptional programs integrate to coordinate a tissue-specific response to ER stress. Intriguingly, these studies indicate that ATF6b has a specific role in regulating the ER lectin chaperone CRT and that this ATF6b-CRT axis uniquely regulates neuronal survival in response to ER stress. While additional experiments are required to support this claim, the work described herein is a nice addition to our evolving understanding of the importance of ATF6b in regulating ER and cellular physiology during pathologic insults.

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Referee #1

Evidence, reproducibility and clarity

Summary

Unfolded Protein Response (UPR) refers to homeostatic signaling pathways that play protective roles in various cell types. This work by Nguyen et al focuses on the UPR-mediator ATF6. In mammals, there are two isoforms of ATF6, alpha and beta. Nguyen et al show that the expression of the ATF6beta isoform is higher in hippocampal neurons whereas the ATF6alpha isoform is more evenly distributed across various neuronal subtypes. By performing gene expression profiling in mouse brain samples, they identify the ER chaperone calreticulin (CRT) as being significantly downregulated in ATF6beta null mutants. They further validate this observation by comparing hippocampi from ATF6alpha and ATF6beta null mice, where CRT is lowered in the latter but not the former. They identify and mutate putative ER stress response elements (ERSE) in the CRT promoter region to show that expression of CRT can be regulated by both ATF6alpha and beta. They demonstrate that treatment of hippocampal neurons with ER stress inducing chemicals leads to induction of CRT, which is suppressed in ATF6beta mutants. Such treatment also leads to cell death, which is exacerbated in ATF6beta mutants but rescued by ectopic expression of CRT. They also extend these observations to cell death induced by treatment with the glutamate receptor agonist, kainate, which was also exacerbated in ATF6beta mutants, but was rescued by counter treatment with ER stress inhibitors. Together, their data suggest a protective role for ATF6beta in hippocampal neurons in the context of ER stress.

Major comments

The primary advantage of this work is that much of it was done in vivo in mice, providing immediate context for the role of ATF6β under physiological conditions. They identify a specific region of the brain that requires ATF6beta. On the other hand, the ATF6-CRT signaling axis reported here had been established previously, and therefore, this study brings limited conceptual advances regarding the signaling mechanism itself (see Significance section below).

Overall, the authors' data support their primary claim that ATF6β has a neuroprotective role in the context of ER stress. The data presented are clear and convincing, and their methods appear rigorous. The manuscript could be further improved if the authors could provide sufficient rationale for some of their experiments, which are discussed below.

1. The post-translational processing of ATF6beta must be demonstrated in hippocampal neurons and not in HEK293T cells in Figure 1E. The authors conclude on Page 6, line 18 that "these results suggest that ATF6beta functions in neurons" but it is not obvious how expression in HEK293T cells contributes to this conclusion in any way.
2. The hippocampal neurons are affected by the loss of ATF6β, even though the mice are not exposed to tunicamycin. Could the authors present evidence that there is physiological ER stress in hippocampal neurons? If not, why is ATF6beta required.
3. In Figure 3, is there a specific reason why the authors do not mutate the ERSEs in the mouse CRT reporter, pCC1 and instead opt to analyze the huCRT reporter? Given that all the other observations in the manuscript are in mouse calreticulin, it is important to show that the ERSEs in the mouse calreticulin promoter are also regulated in an ATF6beta-dependent manner. Similar to the huCRT reporter, it is also crucial to examine if ATF6beta can regulate the mouse CRT promoter. This would provide an explanation for why calreticulin expression is not completely abolished in ATF6beta mutants.
4. In Figure 5A and B, the density of Tubulin staining varies from panel to panel, and is much lower in ATF6beta mutants treated with Tg/Tm. Presumably this is because of cell death but this should be clarified in the main text. Additionally, it is unclear if the EthD-1 staining is nuclear localized. It would help if single channel images for Hoechst and EthD-1 were provided to visualize this.
5. The literature reports that BAPTA-AM treatment itself could cause ER stress (e.g. PMID: 12531184). Here, the authors report the opposite effect. How could the authors reconcile the difference? The effects of BAPTA-AM and 2-APB must individually be examined in Figure 6C and not just in combination with Tm.
6. The authors allude to "impairment of Ca2+ homeostasis in ATF6beta mutants" in Page 13 Line 2, but do not show any direct evidence in support of it. While treatment with BAPTA-AM and 2-APB is a start in that direction, it certainly does not demonstrate that under homeostatic conditions in vivo or in vitro there is any change in calcium flux in ATF6beta hippocampal neurons. To make the case that there is indeed perturbation of Ca2+ in ATF6beta mutant hippocampal neurons, the authors need to examine calcium flux and measure calcium indicators and how they are affected when ER stress is induced in these mutant cells.
7. The effect of 2-APB and salubrinal alone on hippocampal neurons need to be examined in Figure 9B-D to eliminate the possibility that these drugs are not enhancing cell survival under normal conditions in a parallel manner.
8. The rationale for the examination of Fos, Fosb and Bdnf is poorly described (page 14, line 13) and the conclusions from this line of experimentation are rather weak. The results from Figure 9 to some extent serve to confirm in vivo the data seen in Figure 6C but by no means provide a mechanism for why ATF6beta mutants have perturbed calcium homeostasis (page 14, line 22).

Minor comments

1. Page 8, line 3: Their rationale for why ATF6beta 5'UTR sequences are seen in their RNA seq data is not clearly explained. This must be rewritten for clarity.
2. Page 8, line 5, the authors write that besides Atf6β , CRT was the only UPR-regulated gene downregulated in Atf6β mutant mice. The authors need to state how they defined "UPR-regulated genes". There must be a list, which the authors do not cite.
3. Page 9, line 10: A reference is required for ERSEs.
4. Page 10, line 6: The authors say "ATF6beta specifically induces CRT promoter activity". This is a confusing statement because "induction" is in response to stress, but the context here is homeostatic regulation since there is ostensibly no stress being induced. This distinction should be made and corrected here and throughout the manuscript.
5. Page 10, line 16: The use of "latter" here is confusing and it would help to restructure this sentence for clarity.
6. Figure 9A is missing Y-axis labels.

Significance

The authors summarize their major findings of the study (at the beginning of the Discussion) as ATF6β being required for CRT induction in the hippocampus, and that this ATF6β -CRT axis is important for the survival of hippocampal neurons. The idea that ATF6 induces CRT had been previously shown by others (PMID 9837962), and therefore, this is not the major new discovery of this study. In addition, the ATF6-calreticulin axis having a cell protective role had been reported in other biological contexts (e.g. PMID: 32905769), so that concept is also not a novel concept presented in this work. Similarly, the role of UPR in glutamate receptor agonist-induced neuronal cell death had been shown previously (the authors cite Kitao e tal., 2001; Sokka et al., 2007; Kezuka et al., 2016), so this link is not the major novel discovery revealed by this study. Instead, this study reports that ATF6β KO mice have specific phenotypes in hippocampal neurons, which had not been reported previously. Furthermore, this manuscript reports detailed information regarding Atf6β's downstream target genes in this tissue. In summary, this study's finding that ATF6 regulates CRT is confirmatory, rather than bringing new conceptual advances. The merit of this study is in the identification of the hippocampus as the organ that specifically requires ATF6beta. While the findings here may not appeal to a broader audience interested in UPR signaling mechanisms, it may draw interest from those who study hippocampal neuron physiology.

For the editor's reference, this reviewer's field of expertise is in UPR signaling mechanisms in animal models

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Referee #2

Evidence, reproducibility and clarity

Nguyen and colleagues provide evidence that ATF6-beta selectively induces calreticulin expression in mouse hippocampal neurons to protect these neurons from ER stress-inducing toxins. This is a well-written and well-organized report that provides functional information about ATF6-beta, a poorly studied homolog of the ATF6-alpha Unfolded Protein Response regulator. The report suggests that ATF6-beta has a previously unknown and important function in helping brain neurons survive ER stress by regulating calreticulin.

The study shows that addition of BAPTA, 2-APB, or salubrinal significantly improves neuronal survival in ATF6-/- explants and mice brains in response to ER toxins. But, prior study (PMID: 15705855) used salubrinal at much higher concentration 75uM with little effect at the 5uM dose used in the current study. Evidence should be provided that these drugs are specifically inhibiting ER stress or off-target mechanisms should be discussed in their experimental models.

Minor comments:

Fig 1 any male vs female mice differences in ATF6b expression?

Fig 2C. Please show molecular weight markers on blots

Fig 2C. what are the doublet bands on calnexin?

Fig 3. what are the ERSE sequences? several different binding sites are reported in literature.

p8. What is meant by 5' Atf6b lacks 10 and 11?

Discussion: Please clarify if anti-ATF6-beta antibodies were available for these studies.

Discussion: It is puzzling that ATF6a induces calreticulin more potently than ATF6b, but the calreticulin defect is selectively dependent on ATF6b. Could authors speculate on this paradox? It would be interesting to expand on differences between ATF6a and ATF6b function and phenotypes in Discussion in mouse and in people.

Significance

ATF6-beta is homolog of ATF6-alpha and assumed to function like ATF6-alpha. This report describes a selective function of ATF6-beta in inducing calreticulin in mouse neurons during ER stress. This suggests ATF6-beta has some different functions than ATF6-alpha in the mouse hippocampal neurons.

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Reply to the reviewers

RESPONSE TO REVIEWERS

We thank Review Commons and its three reviewers. Reviewers 2 and 3 provide detailed comments, which we address individually. Reviewer 1, however, gives a general critique of how we have approached asking how genome architecture affects the extent of evolution and the details of evolutionary trajectories. Our interpretation of their comments is that our approach and the one that they advocate represent two philosophically different, but complementary, views about how to study evolution in the laboratory. We begin by discussing this difference and then proceed to a point by point response to the three reviews.

Reviewer 1

Philosophical differences with Reviewer 1

We interpret Reviewer 1’s comments as endorsing a formal, quantitative study of evolution that aims to explain the factors that control the rate at which fitness increases during experimental evolution. This approach derives from classical population genetics and aims to use a mixture of theory and experiment to uncover general principles that would allow rates of evolution and evolutionary trajectories, expressed as population fitness over time, to be predicted from quantitative parameters, such population sizes, mutation rates, distributions of the fitness effects of mutations (including their degree of dominance in diploids), and global descriptions of either general (e.g. diminishing returns) or allele-specific epistasis.

This approach aims to predict how the average fitness trajectory should be affected by variations in these parameters and describe the variation, at the level of fitness, in the outcomes in a set of parallel experiments. This is an important approach and have previously used it to investigate how the strength of selection influences the advantage of mutators (Thompson, Desai, & Murray, 2006) and to produce and test theory that predicts how mutation rate and population size control the rate of evolution (Desai, Fisher, & Murray, 2007). Like every approach to evolution, this one has limitations: 1) if it doesn’t identify mutations or investigate phenotype other than fitness, it cannot reveal the biological and biochemical basis of adaptation or report on how variations in population genetic parameters (population size, haploids versus diploids, etc.) influence which genes acquire adaptive mutations, and 2) if the details of experiments (e.g. whether populations are clonal or contain standing variation, or which phenotypes are being selected for) have strong effects on the population genetic parameters, these must be measured before theoretical or empirical relationships could be used to predict the mean and variance of fitness trajectories produced by a given selection. A variety of evidence suggests that the second limitation is real. Examples include the absence of a universal finding that diploid populations evolve more slowly than haploids (discussed on Lines 437-442), even within the same experimental organism, and the finding that diminishing returns epistasis applies well to domesticated yeast evolving in a variety of laboratory environments (e. g. papers from the Desai lab, starting with (Kryazhimskiy, Rice, Jerison, & Desai, 2014) but not to the evolutionary repair experiments that we have conducted (Fumasoni & Murray, 2020; Hsieh, Makrantoni, Robertson, Marston, & Murray, 2020; Laan, Koschwanez, & Murray, 2015).

The second approach to experimental evolution, which we, as molecular geneticists and cell biologists, predominantly take, is to follow the molecular and cell biological details of how organisms adapt to selective pressure. We subject organisms to defined selective forces, identify candidate causative mutations, test them by reconstructing the evolved mutations, individually and in combination, and perform additional experiments to ask how these mutations are increasing fitness. Because these experiments are performed on model organisms and often address phenotypes that have been studied by classical and molecular genetics, we can often say a good deal about the cell biological and biochemical mechanisms that increase fitness and this work can complement and extend what we know from classical and molecular genetics.

The current manuscript and its predecessor are examples of finding causative mutations and asking how they improve fitness, with the first paper (Fumasoni & Murray, 2020) demonstrating how mutations in three functional modules could overcome most of the fitness cost of removing an important but non-essential protein and the current paper asking how alterations in genome architecture and dynamics (diploidy and eliminating double-strand break-dependent recombination) affect the extent to which populations increase in fitness and which genes and functional modules acquire mutations as they do so.

By definition, such experiments are anecdotal: they report on how particular genotypes and genome architectures respond to particular selection pressures. Any individual set of experiments can produce conclusions about the effects of variables, such a population size, mutation rate, and genome architecture, on the mutations that increased fitness in response to the specific selection, but they can do more than lead to speculation and inference about what would happen in other experiments: speculation from the results of a single project and inferences from the combined results of multiple projects. Our interpretation is that the evolutionary repair experiments that we have performed, which have perturbed budding, DNA replication, and the linkage between sister chromatids do indeed lead to a common set of inferences: most of the selected mutations reduce or eliminate the function of genes, the interactions between the selected mutations are primarily additive, and the mutations cluster in a few functional modules.

We believe that the population and molecular genetic approaches to experimental evolution are complementary and that a full understanding of evolution will require combining both of them. We think this will be especially true as we try to use the findings from laboratory studies to improve our understanding of evolution outside the lab, which takes place over longer periods, in more temporally and spatially variable environments, and is subject to variation in multiple population genetic and biological parameters.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): In their previous work the authors examined adaptation in response to replication stress in haploid yeast, via experimental evolution of batch cultures followed by sequencing. Here they extend this approach to include diploid and recombination-deficient strains to explore the role of genome architecture in evolution under replication stress. On the whole, a common set of functional modules are found to evolve under all genetic architectures. The authors discuss the molecular details of adaptation and use their findings to speculate on the determinants of adaptation rate.

**SECTION A - Evidence, reproducibility and clarity** Experimental evolution can reveal adaptive pathways, but there are some challenges when applying this approach to compare genetic backgrounds or environments. They key challenge is that adaptation potentially depends on both the rate of mutation and the nature of selection. Distinct adaptation patterns between groups could therefore reflect differential mutation, selection, or both. The authors allude to this dichotomy but have very limited data to address it. The closest effort is engineering putatively-adaptive variants into all genetic background including those where they did not arise; the fact that such variants remain beneficial suggests they did not arise in certain backgrounds because of a lower mutation rate, but this is a difficult issue to tackle quantitatively.

We agree, wholeheartedly, that adaptation depends on the combination of mutation rates and the nature of selection and our goal was to ask how the molecular nature of adaptation depends on genome architecture when three different architectures are subjected to the same selection: constitutive replication stress caused by removing an important component of replisome. We used a haploid strain as a baseline and compared it to two other strains chosen to influence either the effect of mutations (a diploid, where fully recessive mutations that were beneficial in the haploid would become neutral) or the rate of mutations (a recombination-defective strain that would be unable to use ectopic recombination to amplify segments of the genome). In both cases, we expected to see effects that are closer to qualitative than quantitative: the absence of fully recessive mutations in evolved diploids and absence of segmental amplification in the recombination-deficient haploid. We see both effects and they then allow us to ask two other questions: 1) does influencing the effect of a class of mutation (diploids) or preventing a class of mutation (recombination defect) have a major effect on the rate of evolution, and 2) do these differences affect which modules adaptive mutations occur in. As far as we can tell, the answer is no to both questions. We use “as far as we can tell” because our experiments do have limitations. First, the recombination-defective strain has a higher point mutation rate making it impossible to tell how much this elevation, rather than any other factor, accounts for it showing a greater fitness increase than the recombination-proficient haploid. Unfortunately, to our knowledge, it’s impossible to abolish recombination without affecting mutation rates. Second, we only experimentally tested a subset of the inferred causative mutations meaning that for many genes, our assertion that they are adaptive is a statistical inference and their assignment to a particular functional module is based on prior literature rather than our own experiments. In response to this criticism, we have now rephrased some of our sentences (see below).

From mutation accumulation experiments, where the influence of selection is minimized, there is evidence that genetic architecture affects the rate and spectrum of spontaneous mutations. In this experiment, the allele used to eliminate recombination, rad52, will also increase the mutation rate generally. The diploid strain is also likely to have a distinct mutational profile--as a null expectation diploids should have twice the mutation rate of haploids. Recent evidence indicates the mutation rate difference between haploid and diploid yeast might be less than two-fold, but that there are additional differences in the mutation spectrum, including rates of structural change. The context for this study is therefore three genetic architectures likely to differ in multiple dimensions of their mutation profiles, but mutation rates are not measured directly.

The reviewer is correct that we did not explicitly measure mutation rates, although the frequency of synonymous mutations (Figure 3-S1B) is a proxy for the point mutation rate as long as the majority of these mutations are assumed to be neutral. By this measure, the mutation rates for ctf4∆ haploids and ct4∆/ctf4∆ diploids, expressed per haploid genome, are close to each other (1.94 for haploids and 1.37 for diploids) but different enough to return p = 0.044 by Welch’s test, whereas the mutation rate for the recombination-deficient, ctf4∆ rad52∆ haploid is 4 to 5-fold higher (7.03). In contrast, we can infer that the ctf4∆ rad52∆ strain has much lower rates of segmental aneuploidy produced by recombination: we see only one such event in this strain in contrast to 16 in the ctf4∆ haploid and 44 in the ctf4∆/ctf4∆ diploid (Supplementary table 4), even though the amplification of the cohesin loader gene, SCC2, confers similar benefits in all three strains.

The nature of selection on haploids and diploids is expected to differ because of dominance, but ploidy-specific selection is also possible. The authors discuss how recessive beneficial alleles may be less available to diploids, though this can be offset by relatively rapid loss of heterozygosity. However, diploids should also incur more mutations, all else being equal. The rate of beneficial mutation, as opposed to the rate of mutation generally, will depend on the mutational "target size" of fitness, and the authors findings recapitulate other literature (particularly regarding "compensatory" adaptation) that points to faster adaptation in genotypes with lower starting fitness.

We agree with the reviewer and tried to make the point that which mutations are fixed is primarily determined by the product of the rate at which they occur and the benefit which they confer (lines 193-196). Evidence in budding yeast suggests that in diploid cells, removing one copy of most genes fails to produce a measurable fitness benefit (Deutschbauer et al., 2005), suggesting that losing one copy of many genesis purely recessive. If this was always the case, it would be very hard for such heterozygous, loss-of-function mutations to contribute to evolution in diploids: a mutation that inactivates one copy of a gene would have to rise to high enough frequency by genetic drift that homozygosis of this mutation mitotic recombination would have a significant probability. Instead we find that heterozygous mutations in some genes (inactivation of RAD9, what are likely to be hypomorphic mutations in SLD5) but not others (inactivation of IXR1) confer benefits in diploids that allow their frequency to rise much more rapidly by selection than they would by drift, allowing them to reach frequencies at which mitotic recombination becomes probable.

There is ample literature on the above topics, particularly discussions of the evolutionary advantages of haploidy versus diploidy. While adaptation to replication stress provides a novel starting point for this investigation, much of the manuscript is devoted to long-standing questions that are not specific to replication stress. Unfortunately, the data the authors collected is not sufficient to shed light on these questions, because mutation and selection cannot be effectively distinguished. The Discussion states that "We find that the genes that acquire adaptive mutations, the frequency at which they are mutated, and the frequency at which these mutations are selected all differ between architectures but that mutations that confer strong benefits always lie in the same three modules" (line 379), but it is not clear that these statements are all supported by the data.

The reviewer makes two points: we fail to make a significant contribution to long-standing questions about the evolutionary genetics of adaptation and the we make statements that are not supported by our data. On the first we disagree: unlike much of the previous work which compares the effects of mutation rates and population sizes on the rates of evolution, we sequence genomes, identify putative causative mutations, verify that they increase fitness, and test, by reconstruction, how their contribution to fitness is affected by fully characterized genome architectures. We know of no comparable work and we believe that this is a useful contribution to understanding evolution. In addition, some of the literature, for example the discussion of haploidy versus diploidy, has failed to reach a universal conclusion. On the second point, we realized that the statement that the reviewer quotes is stronger than it should be since we do not show “that mutations that confer strong benefits always lie in the same three modules”. What we do show is that mutations in all three modules are found in all three genome architectures (Figure 5), and that combining one mutation from each module (using mutations in genes that are found in that architecture) can reproduce the observed fitness increase in each architecture (Figure 6 B), but the reviewer is correct that we have not demonstrated that every clone from every population has an adaptive mutation in all three modules. We have therefore modified the quoted sentence as follows (altered wording underlined)

"We find that the genes that acquire adaptive mutations, the frequency at which they are mutated, and the frequency at which these mutations are selected all differ between architectures but that mutations conferring strong benefits can occur in all three modules in each architecture" (Lines 405-408)

Focusing on the more novel aspect of their experiment-the presence of replication stress-would arguably be a better approach. On this topic the authors have some interesting observations and speculation, but clear predictions are lacking. The introduction section could be redesigned to explicitly state why genome architecture might affect adaptation in response to replication stress in particular, rather than (or in addition to) adaptation generally. If there were no differences in mutation, does the nature of Ctf4 lead to predictions that the molecular basis of compensatory adaptation should differ among genome architectures? Without such predictions it will be difficult for readers to know whether the observation that different genome architectures follow similar adaptive paths is surprising or not.

We believe that following this suggestion would diminish the paper. We set out to ask how genome architecture affected adaptation to the strong fitness defect produced by removing an important component of an essential process, DNA replication. We chose replication stress as an example of cell biological damage that cells would have to repair with the hope that the results would give general clues about evolutionary repair, rather than hoping that the experiment would inform us about how replication stress altered the types of mutation (e. g. point mutations versus segmental amplification) that were selected As we point out at the beginning of our response, we recognize that the result of any one such experiment must be anecdotal and any attempt to generalize must be described as speculation if it refers only to this one experiment, or inference if it refers to this experiment and other published work. In those cases where we discuss the effect of genome architecture on evolutionary trajectories, we can draw conclusions that apply to our own experiments, but can only speculate on adaptation to different selections. In others, where we see commonalities between our experiments and previous work on evolutionary repair (cite Review), we can make inferences about evolution to adapt to removing important proteins and speculate about other forms of selection. We have revised the discussion to make it clear where we conclude, where we speculate, and where we infer. We suspect that our finding that genome architecture has a larger effect on which genes acquire adaptive mutations than it does on which modules these mutations alter will generalize to other evolutionary repair experiments and may be true even more broadly.

We deliberately did not make predictions about the effect of genome architecture on the rate at which population fitness increased or the mechanism of adaptation to replication stress because we believed that our ignorance and the diverging results of previous experiments was sufficient to make both exercises worthless. After the fact, we interpret our results to suggest that mutations that reduce the activity of components, such as Sld5, that are stably associated with replication forks should be semi-dominant, but we were not nearly smart enough to make such a specific prediction before the experiment began!

**Minor comments:** Shifts in ploidy from diploid to haploid are less common than the reverse change, so the observation of such a shift (Fig. 1) should be discussed in more detail.

We now mention that haploids becoming diploids is more common than the reverse transformation and point out that genome sequencing reveals that these strains are true haploids rather than aneuploids.

“One diploid population (EVO14) gave rise to a population with a haploid genome content, suggesting a possible haploidization event during evolution. Sequencing revealed no aneuploidies as a potential explanation of this phenomenon. While diploidization has been recurrently observed during experimental evolution with budding yeast (Aleeza C. Gerstein & Otto, 2011; Aleeza C Gerstein, Chun, Grant, & Otto, 2006; Harari, Ram, Rappoport, Hadany, & Kupiec, 2018; Venkataram et al., 2016), reports of spontaneous haploidization events have been instead scarce. Given the difficulties introduced by the change of ploidy over the 1000 generations, we have excluded EVO14 from all our analyses.” (Lines 122-128)

We believe that the most likely mechanism is that the strain sporulated to produce haploids that were fitter than their diploid parent, but because this event occurred in only one out of eight populations and the proposed explanation is pure speculation we have not included in the revised manuscript.

Line 88 typo 'stains'.

Fixed. Thank you.

Reviewer #1 (Significance (Required)): **SECTION B - Significance** The novel aspect of this study is the combination of replication stress and genome architecture, but here the significance is limited by a lack of clear predictions on how these factors might interact. On the other hand, much of the manuscript is devoted to why adaptation might vary among genome architectures in general, but this long-standing and important question is not particularly well resolved by this experimental approach, which can't disentangle mutation and selection.

Our belief is that quantitatively predicting how selection will change fitness is nearly impossible because we lack the detailed knowledge of population genetic parameters that apply to our experiments. Prediction is even harder if the goal is to identify which genes will fix adaptive mutations and understand how these mutations alter cellular phenotypes to increase fitness. Thus our approach is almost entirely empirical: we do experiments that alter interesting variables, collect data, and do our best to interpret them and suggest how the conclusions of individual experiments might generalize.

The authors highlight the dichotomy when discussing the evolution of ploidy: "We suggest that... genome architecture affects two aspects of the mutations that produce adaptation: the frequency at which they occur and the selective advantage they confer" (line 399), but presenting this as a novel inference does not appropriately acknowledge prior research and discussion of these ideas; several relevant papers are cited by the authors in other contexts. It may be possible to recast these findings as a test of the role of genome architecture in adaptation generally, but the authors should clarify the limitations of experimental evolution and more fully consider the theory and data outlined in previous research. In particular, few studies can claim to directly compare mutation rates between genome architectures, and it is not obvious that the present study is an example of such.

We have the disadvantage that the reviewer doesn’t identify the literature we fail to cite. To us the argument the reviewer quotes is self-evident. As we mention above, our goal was not to test either general or detailed predictions and the level at which we analyzed our experiment, especially demonstrating that mutations were causal and reconstructing them individually and in combination, is missing from previous work. Finally measuring mutation rates is supremely difficult: you either need good ways of following all possible forms of mutation, quantitatively and without selection, or you resort to selecting mutations with a particular phenotype and molecularly characterizing them, knowing that these assays may well give different ratios of the rates of different types of mutation at different loci. We do make and report one measure of mutation rate, the rate of synonymous mutation in protein coding genes, which we discuss above.

Reviewer expertise: Evoutionary genetics; experimental evolution; mutation.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary** This manuscript investigates the effect of an organism's genotype (or, as the authors call it, an organism's 'genome architecture') on evolutionary trajectories. For this, the authors use Saccharomyces cerevisiae strains that experience some form of replication stress due to specific gene deletions, and that further differ in ploidy and/or the type of gene(s) deleted. They find the same three functional modules (DNA replication, DNA damage checkpoint, sister chromatid cohesion) are affected across the 3 different genotypes tested; although the specific genes that are mutated varies. **Major comments** This is a solid and exceptionally eloquent paper, comprising a large body of work that is in general well presented. That said, I do have some suggestions and questions. At several points in the manuscript, the authors should perhaps be more careful in their wording and avoid to overgeneralize data without providing additional evidence for these claims.

We thank the reviewer for their constructive review and address their request for more careful wording below.

• Some key points of the study are not entirely clear to me; possibly because the study builds upon a previous study that was recently published in eLife. Anyhow, I think it would be useful to clarify the following points a bit more:

  • Why exactly was ctf4∆ chosen as a model for replication stress? What is the evidence that ctf4∆ is a good model for replication stress? Without including some evidence for this, it is unclear how well the findings in this study really can be generalized to replication stress (which is what the authors do now).

We described the reasons for choosing CTF4 deletion to mimic DNA replication stress in our previous eLife paper, to which we refer at. Nevertheless, the reviewer is right in asking us not to assume that the reader will have read our previous work. Briefly: DNA replication stress is a term that is loosely defined as the combination of the defects in DNA metabolism and the cellular response to these defects in cells whose replication has been substantially perturbed (Macheret & Halazonetis, 2015). Established methods in the field to induce DNA replication stress consist of either pharmacological treatments or genetic perturbation. Pharmacological treatments include hydroxyurea, which target the ribonucleotide reductase and hence stalls forks as a result of dNTP depletion (Crabbé et al., 2010), or aphidicolin, which directly inhibits polymerases α, ε and δ (Vesela, Chroma, Turi, & Mistrik, 2017b; Wilhelm et al., 2019). For genetic perturbation, the conditional depletion of replicative polymerases (Zheng, Zhang, Wu, Mieczkowski, & Petes, 2016) is frequently used. These methods are incompatible with experimental evolution, as cells can mutate the targets of replication inhibitors or alter the expression of genes that have been reduced in expression or activity. Removing an important but non-essential component of the replication machinery avoids these problems. We chose CTF4 deletion as a manipulation that affected the coordination of events at the replication fork: in the absence of Ctf4, the polα-primase complex is no longer physically bound to the replicative helicase, and thus the polymerase’s abundance at the replisome decreases (Tanaka et al., 2009). This manipulation achieves the same effects as polymerase depletion and replisome stalling, producing a constitutive DNA replication stress that can only be overcome by mutations in other genes. Multiple studies have shown that ctf4**D cells display replication intermediates commonly associated to DNA replication stress, such as the accumulation of ssDNA gaps and reversed forks (Abe et al., 2018; Fumasoni, Zwicky, Vanoli, Lopes, & Branzei, 2015), fork stalling (Fumasoni & Murray, 2020), checkpoint activation (Poli et al., 2012; Tanaka et al., 2009) and altered chromosome metabolism (Kouprina et al., 1992).

We now justify our choice of deleting CTF4 at line 74:

“DNA replication stress is often induced with drugs or by reducing the level of DNA polymerases (Crabbé et al., 2010; Vesela, Chroma, Turi, & Mistrik, 2017a; Wilhelm et al., 2019; Zheng et al., 2016). To avoid evolving drug resistance or increased polymerase expression, which would rapidly overcome DNA replication stress,** we deleted the CTF4 gene, which encodes a non-essential subunit of the DNA replication machinery (the replisome) (Kouprina NYu, Pashina, Nikolaishwili, Tsouladze, & Larionov, 1988). Ctf4 is a homo-trimer that functions as a structural hub within the replisome (Villa et al., 2016; Yuan et al., 2019) by binding to the replicative DNA helicase, primase (the enzyme that makes the RNA primers that initiate DNA replication), and other accessory factors (Gambus et al., 2009; Samora et al., 2016; Simon et al., 2014; Villa et al., 2016). In the absence of Ctf4, the Pol**a-primase and other lagging strand processing factors are poorly recruited to the replisome (Samora et al., 2016; Tanaka et al., 2009; Villa et al., 2016), causing several characteristic features of DNA replication stress, such as accumulation of single strand DNA (ssDNA) gaps (Abe et al., 2018; Fumasoni et al., 2015), reversed and stalled forks (Fumasoni & Murray, 2020; Fumasoni et al., 2015), cell cycle checkpoint activation (Poli et al., 2012; Tanaka et al., 2009) and altered chromosome metabolism (Hanna, Kroll, Lundblad, & Spencer, 2001; Kouprina et al., 1992). As a consequence of these defects, ctf4**D cells have substantially reduced reproductive fitness (Fumasoni & Murray, 2020).**”

Would the authors expect to see similar routes of adaptation if a 'genomic architecture' with a less severe/other replication defect would have been used? I realize the last question is perhaps difficult to address without actually doing the experiment (which I am not suggesting the authors should do); I just want to point out that perhaps some data should not be over-generalized.

We share the reviewer’s interest in asking whether different forms of DNA replication stress would lead to the same results described, and we plan to rigorously investigate this question in a separate paper. We note that the careful comparison between different forms of DNA replication stress has never been made and that authors studying this phenomenon often rely on a single perturbation to induce DNA replication stress (Crabbé et al., 2010; Wilhelm et al., 2019; Zheng et al., 2016). We agree that such a comparison will be useful, but we believe (as indicated by the reviewer) it will require an amount of work that goes beyond the scope of our study. To avoid over-generalization, we are using now using “a form of DNA replication stress” in lines 33, 244, 401, 414 and 461, to make it clear that our conclusions (as opposed to inferences and speculations) are restricted to the response to a single example of replication stress.

Likewise, why was RAD52 selected as the gene to delete to affect homologous recombination? I understand that it is a key gene, but on the flipside, absence of RAD52 affects multiple cellular pathways and (as the authors also observe in their populations) also results in increased mutation rates which might confound some of the results.

We aimed to observe the largest deficiency in DNA recombination possible and therefore chose to delete RAD52 because of its many roles in different forms of homologous recombination (Pâques & Haber, 1999) . The choice of other genes, such as RAD51, would have inhibited canonical double strand break (DSB) repair, but allowed other mechanisms that can rescue stalled replication forks (Ait Saada, Lambert, & Carr, 2018), such as break induced replication (BIR) or single strand annealing (SSA) (Ira & Haber, 2002).

Our position regarding the inevitable increase in mutations rates obtained while working with genome maintenance process has been instead elaborated in response to reviewer #1 above.

A sentence describing our choice to delete RAD52 has now been included at line 86:

“…as well as from haploids impaired in homologous recombination due to the deletion of RAD52 (Figure 1A), which encodes a conserved enzyme required for pairing homologous DNA sequences during recombination (Pâques & Haber, 1999). Because Rad52 is involved in different forms of homologous recombination, it’s absence produces the most severe recombination defects and thus allows us to achieve the largest recombination defect achievable with a single gene deletion (Symington, 2002)..”

Related to the first comment, it is also unclear to me how well the system chosen by the authors is representative of the replication stress experienced by tumor cells (as briefly touched upon in the final section of the discussion). Are some of the homologs key oncogenes that drive carcinogenesis?

We should have been clearer. Our goal was to argue that the lesions and responses produced by replication stress in tumor cells, such as stalled replication forks and checkpoint activation, were similar to those seen in yeast cells lacking Ctf4. We did not mean to imply removing Ctf4 from yeast cells had the same effects on cell proliferation and survival as inactivating tumor suppressors and activating proto-oncogenes have in mammalian cells. Despite the difference between direct (removing Ctf4) and indirect effects on DNA replication (tumor cells), the replication intermediates (ssDNA, stalled and reversed forks), the cell cycle defects (G2/M delay), the genetic instability (increased mutagenesis and chromosome loss) and chromosome dynamics (late replication zones and chromosome bridges) generated by the absence of Ctf4 are similar to those observed in oncogene-induced DNA replication stress in mammalian cells (Kotsantis, Petermann, & Boulton, 2018). We therefore believe our experiments reveal evolutionary responses to a constitutive DNA replication stress that resembles the replication stress seen in cancer cells. Nevertheless, we agree that the comparison with cancer evolution remains speculative and we therefore avoided mentioning cancer in the title our paper or our conclusions, and only discuss it in a speculative section of the discussion.

We have modified this section of the discussion as follows (line 554):

“While generated through a different mechanism (unrestrained proliferation, rather than replisome perturbation), oncogene induced DNA replication stress produces cellular consequences (Kotsantis et al., 2018) which are remarkably similar to those seen in the absence of Ctf4, such as the accumulation of ssDNA, stalled and reversed forks (Abe et al., 2018; Fumasoni & Murray, 2020; Fumasoni et al., 2015), genetic instability (Fumasoni et al., 2015; Hanna et al., 2001; Kouprina et al., 1992) and DNA damage response activation (Poli et al., 2012; Tanaka et al., 2009). Based on these similarities we speculate that evolutionary adaptation to DNA replication stress could reduce its negative effects on cellular fitness and thus assist tumor evolution.”

The authors should consider rephrasing some sentences regarding the occurrence of adaptive mutations. Sentences such as 'which genes are mutated depends on the selective advantage' (p1; lines 15-16); 'genome architecture controls the frequency at which mutations occur' (p15), "genome architecture controls which genes are mutated" (p1, line 20) makes it sound like the initial occurrence of mutations is not random, whereas in reality, the mutational landscape is the result of the combined effect of occurrence and fitness effect of the mutations, with the later rather than the former likely being the main driver behind the observed patterns.

We thank the reviewer for asking for more precision in the above sentences, whose proposed changes we now list:

“Mutations in individual genes are selected at different frequencies in different architectures, but the benefits these mutations confer are similar in all three architectures, and combinations of these mutations reproduce the fitness gains of evolved populations.” (Lines 13-15)

“Genome architecture influences the distribution of adaptive mutants” (Line 277)

"genome architecture influences the frequency at which mutations occur, the fitness benefit they confer, and the extent of overall adaptation." (Lines 462-463)

Some important methodological information is missing or unclear in the manuscript:

The authors should provide more details on how they decided which clones to select for sequencing. Did they select the biggest colonies; were colonies picked randomly, ...

This following sentence is now reported in the materials and methods section (Line 603)

“To capture the within-population genetic variability we selected the clones displaying the largest divergence of phenotypes in terms of resistance to genotoxic agents (methyl-methanesulfonate, hydroxyurea and camptothecin).”

What is the population size during the evolution experiment?

We now added the following sentence at line 599:

“In this regime, the effective population size is calculated as N0 x g where N0 is the size of the population bottleneck at transfer and g is the number of generations achieved during a batch growth cycle and corresponds to approximately to 107 cells.”

Sequencing of populations and clones: coverage should be mentioned

The following sentence has now been added at line 616:

“Clones and populations were sequenced at approximately the following depths: 25-30X for haploid clones, 50-60X for diploid clones, 50-60X for haploid populations and 120-130X for diploid populations.”

Identification of mutations (p19, line 573): Is this really how the authors defined whether a variant is a mutation? Based on the definition given here, DNA mutations that lead to a synonymous mutation in the protein are not considered as mutations?

We apologize for this typo. We do identify and consider synonymous mutations as evidenced by Figure 3-S1B. Now the sentence at line 626 correctly reports:

“A variant that occurs between the ancestor and an evolved strain is labeled as a mutation if it either (1) causes a substitution in a coding sequence or (2) occurs in a regulatory region, defined as the 500 bp upstream and downstream of the coding sequence.”

Perhaps the information can be found elsewhere, but the source data excel files for mutations is incomplete and should at the very least contain information on the type of mutation (eg. T->A), as well as the location of this mutation in the respective gene.

Perhaps the reviewer is referring to Supplementary table 2, where we list the number of times a gene has been mutated in different populations (and thus summaries different types of mutations affecting the same gene). The information they request is reported in Supplementary table 1 for all the variants detected in populations and clones sequencing.

**Minor comments** • While the author already cite several significant papers relevant for their manuscript, some other studies could also be included:

• On how genome architecture influences evolution: https://doi.org/10.1093/molbev/msaa172
• Ploidy and fitness/adaptation: DOI:https://doi.org/10.1016/j.cub.2018.01.062

We thank the reviewer for highlighting these references, which are now cited at line 28

From the text in the abstract, it is unclear what the three genomic architectures (line 13) exactly are, the authors should consider spelling this out.

In repose o the reviewer request for clarity we now propose the following change in line 13:

“We asked how these trajectories depend on a population’s genome architecture by comparing the adaptation of haploids to that diploids and recombination deficient haploids.” (Lines 9-11)

Can the authors speculate on why a homozygous ctf4D/ctf4D rad52D/rad52D would be lethal, and a haploid not?

See below

The authors note that a diploid ctf4D/ctf4D strain is less fit than its haploid counterpart. Why do the authors think this is the case?

In response to the two previous questions, we now propose the following speculations that we include in the text (Line 97):

“Diploid cells require twice as many forks as haploids and Ctf4-deficient diploids are thus more likely to have forks that cause severe cell-cycle delays or cell lethality. We speculate that this increased probability explains the more prominent fitness defect displayed by diploid cells. Interestingly, homologs of Ctf4 are absent in prokaryotes, where the primase is physically linked to the replicative helicase (Lu, Ratnakar, Mohanty, & Bastia, 1996) and Ctf4 is essential in the cells of eukaryotes with larger genomes such as chickens (Abe et al., 2018) and humans (Yoshizawa-Sugata & Masai, 2009). Rad52 is likely involved in rescuing stalled replication forks by recombination-dependent mechanisms (Fumasoni et al., 2015; Yeeles, Poli, Marians, & Pasero, 2013). We speculate that the absence of Rad52 increases the duration of these stalls and leads some of them to become double-stranded breaks resulting in cell lethality and explaining the decreased fitness of ctf4D rad52D haploid double mutants. In diploids ctf4D rad52D cells, which have twice as many chromosomes, the number of irreparably stalled fork may be sufficient to kill most of the cells in a population, thus explaining the unviability of the strain.”

The authors passage their cells for 100 cycles and assume that this corresponds to around 1000 generations for each population. However, the fitness differences between the different starting strains (see also Figure 1B) are likely to cause considerable differences in number of generations between the different strains. Do the authors have more precise measurements of number of generations per population? If not, perhaps it should be noted that some lineages may have undergone more doublings than others, and perhaps also discuss if and how this could influence the results?

In a batch culture regime, where populations are allowed to reach saturation after each dilution, the number of generations at each passage are dictated by the dilution factor (Van den Bergh, Swings, Fauvart, & Michiels, 2018). A dilution of 1:1000 from a saturated culture will allow for approximately 10 generations before populations reach a new saturated phase. As long as saturation is allowed to occur, this number is independent of the fitness of the cultured strains: Slower-dividing strains will simply employ more time to reach saturation after each dilution. At the beginning of the experiment, we had to dilute the ctf4D rad52**D strains being passaged every 48hrs instead of 24hrs. After generation 50, ctf4D rad52**D strains reached saturation within 24hrs and were then diluted daily. The total count considers the number of passages a culture has undergone, and not the number of days of culture, and thus should guarantee approximately the same number of generations in all three genome architectures.

Panel A of figure 1A is somewhat confusing; as this seems to indicate that the ctf4∆ was introduced after strains were made, for example, haploid recombination deficient (which is not how these strains were constructed). Perhaps a better way of representing would be to have the indication of DNA replication stress pictured inside the yeast cells.

We have modified Figure 1A to better represent the way the strains were constructed. For space reasons we have not represented a perturbed fork within each cell, but rather above all of them.

Legend to Figure 1: is fitness expressed relative to haploid or diploid WT cells for the diploid strains?

We apologize for having missed this detail in the figure legends. Throughout the figures, haploid and diploid cells were competed against reference strains with the same ploidy. We now add this sentence in Figure 1 and in the materials and methods (line 686).

Figure 3: to improve readability of this figure, the authors could consider placing the legend of the different symbols (#, *,..) in the figure as well and not just in the figure legend.

We now include the symbols legend in Figure 3.

Figure 5 shows Indels, but if I am correct, these mutations are not discussed in the text; nor is it mentioned what the authors used as a cut-off to determine indels (the authors use the term 'small indels' without defining it)? For example, the data shown in Figure 3 and Figure 4 only includes SNPs and not indels (correct?) - but the indels should also be taken into account when investigating which modules are hit.

Gapped alignments of the relatively long 150 paired-end reads in our data set permits the identification of small indels ranging in size from 1–55 bp using VarScan pileup2indels tool (Koboldt et al., 2012). All small indels (and the respective sequence affected) are listed together with SNPs in Supplementary table 1. Figure 3A, Figure 4 and Figure 5B are representation of ‘gene mutations’ which include both SNPs and small InDels. Large chromosomal Insertion and deletions, not detectable by short read gap alignment are instead identified using the VarScan pileup2copynumber tool (Koboldt et al., 2012), and are represented as amplifications or deletions in Figure 3B and 5C.

The following sentence has been added to the material and methods at line 629:

“Gapped alignments of the 150 paired-end reads in our data set permits the identification of small indels ranging in size from 1–55 bp using VarScan pileup2indels tool (Koboldt et al., 2012). All small indels (and the respective sequence affected) are listed together with SNPs in Supplementary table 1.”

The following definition has been added in Figure legends 3A, 4 and 5A and B.

“Gene mutations (SNPs and small InDels 1-55bp)”

Figure 5 mentions: # gene mutations. So these are only the mutations in genes, and not in their up- or downstream regulatory regions?

We use a broader definition of a gene, not restricted to the open reading frame, and including its regulatory regions. The following definition has been added to figure 5’s legend.

“Frequency of SNPs and small InDels (1-55bp) affecting genes (Open reading frames and associated regulatory regions).”

Figure 3-S1: labels of C panels are missing.

Labels are now included in Figure 3-S1

Figure 3-S1, panel B: why did the authors focus on synonymous mutations?

The panel B is commented upon in line 186 and contrasted with panel A to argue that the increased number of mutations detected in ctf4∆ rad52∆ strains is due to a higher mutation rate(which is expected to increase synonymous mutations) instead of an higher number of adaptive mutations (which are less likely to be synonymous) being selected.

Reviewer #2 (Significance (Required)): This is a solid and clearly written study, comprising a large body of work that is generally well presented and that will be of interest to scientists active in the field of (experimental) evolution and replication. However, many aspects studied in this manuscript have already been studied and reported before; including the recent eLife paper by the same group, as well as studies by other labs that have investigated how genome architecture / genotype affects evolutionary trajectories, the effect of ploidy on evolution, .... Because of this, I do feel that the authors should put their findings more in the context of existing literature context, including a general description of which results are truly novel, which confirm previous findings and which results seem to go against previous reports. This is already so at some points in the text, but I feel this could be done even more.

We now rephrase the following paragraphs in our discussion to better highlight the main conclusions in contrast to the existing literature:

“Engineering one mutation in each module into an ancestral strain lacking Ctf4 is enough to produce the evolved fitness increase in all three genomic architectures. Furthermore, engineering mutations in individual genes confer benefits in all three architectures (Fig. 6A) ,even in those where the mutations in these genes was rare, and combining these mutations recapitulated the evolved fitness increase in all three architectures (Fig. 6B). Altogether our results demonstrate the existence of a common pathway for yeast cells to adapt to a form of constitutive DNA replication stress.” (Lines 409-414)

“Our results thus go against the trend of slower adaptation in diploids as compared to haploids reported by the majority other studies (A. C. Gerstein, Cleathero, Mandegar, & Otto, 2011; Marad, Buskirk, & Lang, 2018; Zeyl, Vanderford, & Carter, 2003). This effect is not limited to populations experiencing DNA replication stress (Figure 2A) but is also present in control wild-type populations (Figure 2B). Our results support the idea that the details of genotypes, selections, and experimental protocols can determine the effect of ploidy on adaptation.” (Lines 437-442)

“Our results therefore agree with previous reports observing declining adaptability across strains with different initial fitness but largely fail to observe diminishing return epistasis as a potential justification of this phenomenon. Our experiments and two previous evolutionary repair experiments (Hsieh et al., 2020; Laan et al., 2015) both show interactions that are approximately additive between different selected mutations. The reasons for this difference are currently unknown.” (Lines 450-455)

Additionally, I think the authors should be more careful not to over-generalize their findings, which come from only a few specific genetic manipulations that might not be representative for general replication stress. For example (p15), can the authors really claim that they have unraveled general principles of adaptation to constitutive DNA replication stress? Perhaps a better motivation of the choice of ctf4 as a model mutation for DNA replication stress could also help (see also my earlier comments). A similar comment applies to the molecular mechanisms affecting adaptation in diploid cells - what evidence do the authors have that their findings are not specific to the one specific type of diploid strain they used in their study? Adding a bit more background information or nuance for some of the claims would help tackle this issue.

We now followed the suggestions made previously by the reviewer to justify our experimental choices better and to use a language that avoids over-generalizations.

Field of expertise of this reviewer: genetics, evolution, genomics

Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary:** Here the authors carry out an evolution experiment, propagating replicate populations of the budding yeast with the CTF 4 gene deleted in three different genetic backgrounds: haploid , diploid and recombination deficient (RAD52 deletion). The authors find that the rate of evolution depends on the initial fitness of the different genetic backgrounds which is consistent with a repeated finding of evolution experiments: that beneficial mutations tend to have a smaller fitness effect in high fitness genetic backgrounds. Curiously even though the targets of selection tended to be specific to each of the three different genetic backgrounds, genetic reconstruction experiments showed beneficial mutations convert a fitness increase in all genetics backgrounds. The authors go on to provide a plausible explanation for why each of the three genetic backgrounds are predisposed to certain types of beneficial mutations. Overall, these results provide important context and caveats for an emerging consensus that genetic background determines the rate of evolution, a comprehensive molecular breakdown of adaptation to DNA replication stress and a mechanistic explanation for why different beneficial mutations are favoured in diploids, haploids and recombination deficient strains. This is a well-executed study that is beautifully presented and easy to follow. This will be of great interest to those in the experimental evolution community and the data an excellent resource.

We thank reviewer #3 for emphasizing that reconstructed mutations are beneficial even in architectures where they were not ultimately detected at the end of the experiment. We have now highlighted this point in our conclusions as a response to the reviewer’s #1 and #2 request for more clarity regarding our novel findings.

“We find that the genes that acquire adaptive mutations, the frequency at which they are mutated, and the frequency at which these mutations are selected all differ between architectures but that mutations that confer strong benefits can occur in all three modules in each architecture. Engineering one mutation in each module into an ancestral strain lacking Ctf4 is enough to produce the evolved fitness increase in all three genomic architectures. Furthermore, reconstruction of a panel of mutations into all three architectures proved they are adaptive even in architectures where the affected genes were not found significantly mutated by the end of the experiment. Altogether our results demonstrate the existence of a common pathway for yeast cells to adapt to a form of constitutive DNA replication stress.” (Lines 405-414)

**Major comments:**

• Are the key conclusions convincing? Yes, the convergent evolution analysis, fitness assays, and genetic reconstructions are sufficient to characterise the genetic causes of adaptation in this experiment, and are of the highest standard. The authors do particularly well to fully recover the fitness increases that evolved with their genetic reconstructions, which imparts a completeness to their understanding of what happened in their evolution experiment.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No, in nearly all cases the authors make reasonable claims. One exception is on L419 in the discussion, where the authors speculate why some mutations do not follow diminishing returns epistasis, but this idea does not really have any basis (no citation or reasons to suggest that DNA repair genes are less connected with other genes in the genome). If the authors cannot support this statement, it should be removed, and instead write that is currently unknown why some individual mutations do not follow the pattern of diminishing returns.

On reflection, we agree with the reviewer and now state,

“Our results confirm previous reports observing declining adaptability across strains with different initial fitness but largely fail to observe diminishing return epistasis as a potential justification of this phenomenon. Our experiments and two previous evolutionary repair experiments (Hsieh et al., 2020; Laan et al., 2015) both show interactions that are approximately additive between different selected mutations. The reasons for this difference are currently unknown.”

A hypothesis, which would need experimental validation, could be that the different mutations have different degrees of epistatic interactions with the rest of the genome. Ixr1, whose mutation follows diminishing return epistasis, is a transcription factor that could in principle affect the expression of many other genes implicated in different cellular modules. Sld5, Scc2 and Rad9 instead, whose mutations have the same effect across different genome architectures, having more mechanistic roles in genome maintenance may have strong epistatic interactions only with a restricted number of cellular modules implicated with DNA metabolism.

• Would additional experiments be essential to support the claims of the paper? No.
  • Are the data and the methods presented in such a way that they can be reproduced? Yes, but some more details are needed for the convergent evolution analysis, see minor comments.
  • Are the experiments adequately replicated and statistical analysis adequate? Yes, but some more statistic reporting in the main text or figure legends would be helpful, for example. L159: Please report the statistical test, test statistic and p value in the text or in the figure legend. Currently significance is indicated, but the methods do not specify the test.

We apologize for the lack of clarity in the main text. The test used for all fitness analysis was only reported in the materials and methods as follow:

“The P-values reported in figures are the result of t-tests assuming unequal variances (Welch’s test)”

We now include the test and the associated p-value in line 184, and write the above sentence in all the relevant figures.

This should also be done for the GO analysis shown in figure 3A.

We thank reviewer #3 pointing out this omission. We now include the following section:

“Gene ontology (GO) enrichment analysis:

The list of genes with putatively selected mutations (Figure 3A) or homozygous mutations in diploids (Figure 4) were input as ‘multiple proteins’ in the STRING database, which reports on the network of interactions between the input genes (https://string-db.org). The GO term enrichment analysis provided by STRING are reported in Supplementary Table 3 and Supplementary Table 6 respectively. Briefly, the strength of the enrichment is calculated as Log10(O/E), where O is the number of ‘observed’ genes in the provided list (of length N) which belong to the GO-term, and E is the number of ‘expected’ genes we would expect to find matching the GO-term providing a list of the same length N made of randomly picked genes. P-values are computed using a Hypergeometric test and corrected for multiple testing using the Benjamini-Hochberg procedure. The resulting P-values are represented as ‘False discovery rate’ in the supplementary tables and describe the significance of the GO terms enrichment (Franceschini et al., 2013).”

**Minor comments:**

• Specific experimental issues that are easily addressable. Not a new experiment, but extra details are required. The authors carried out both clone and whole population sequencing. For their convergent evolution analysis, what is the criteria for a mutation to be included- ie, does it need to be fixed, have attained a certain frequency? This is important- if the criteria were low (say 5%), it would be important to know whether gene A had fixed in 4 populations, while gene B had attained a frequency of 10% in 5 populations. As it stands both would be described as examples of convergent evolution. This can be handled by providing these details in the methods.

For the population sequencing we disregarded variants found at less than 25% and 35% of the reads in haploid and diploid populations respectively as we observed they were largely the product of alignment errors. All the variants found at frequencies higher than the thresholds indicated were used for the parallel evolution analysis. The frequency at which each individual variant was detected in each population is reported in Supplementary table 1, while the average frequency at which a gene has been found mutated across different populations is reported in Supplementary table 2. The reason why we didn’t solely focus on fixed mutations for our convergent evolution analysis was that from previous work we knew of the existence of clonal interference which kept the frequency of verified adaptive mutations that coexisted in the same population (e.g. ixr1 and sld5) well below 90% (Fumasoni & Murray, 2020).

For clarity we now add the following sentence in the material and methods:

“Variants found in less than 25% and 35% of the reads in haploid and diploid populations respectively were discarded, since many of these corresponded to misalignment of repeated regions. For clone sequencing, only variants found in more than 75% of the reads in haploids and 35% of the reads in diploids (to account for heterozygosity) were considered mutations. The frequency of the reads associated with all the variants detected are reported in Supplementary table 1”

• Are prior studies referenced appropriately? I note that the authors use the term declining adaptability where as other papers use the term diminishing returns epistasis- I am sure the authors have good reasons for their choice of nomenclature but I think it would be helpful for their readers to connect this work to other work by mentioning that declining adaptability is also referred to as diminishing returns.

We use both terms (for instance in line 446 and line 448) with a different meaning : By ‘declining adaptability’ we refer the phenomenon where more fit strains display lower adaptation rates than less fit ones. By ‘diminishing returns epistasis’ we refer to a possible explanation of such a phenomenon, where adaptive mutations have different fitness effects due to their ‘global’ epistatic interactions with other alleles. It has to be noted that ‘diminishing returns epistasis’ is not the only proposed explanation of the phenomenon of declining adaptability (Couce & Tenaillon, 2015). In our case, we do find evidence of declining adaptability but very limited evidence for diminishing return epistasis (only 1 mutation in 5 has a different fitness effect in different architectures).

A reference the authors have missed: L419, as well as citing the Desai Lab bioxive paper, they should cite another theory paper that obtained similar conclusions. Lyons, D.M., et al. https://doi.org/10.1038/s41559-020-01286-y.

We thank the reviewer for the suggested reference, which is now cited at line 450.

• Are the text and figures clear and accurate? This paper is beautifully written and easy to follow, a lot of thought has gone into the figures which are aesthetically pleasing and easy to navigate.

  • Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No.

  **Typos**

  L32 "do" should be "to" L95 analyzed L219 are the authors referring to ref 15 here? I think so, but please specify

We thank the reviewer for carefully finding the typos, which are now all corrected.

Reviewer #3 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This paper is an important conceptual result and an immediate advance for basic research. The authors have done an outstanding job of showing the potential for the clinical translation of this research, especially regarding cancer biology.
• Place the work in the context of the existing literature (provide references, where appropriate). This study follows up on and builds upon an earlier paper by these same authors published in E-life in 2020. Conceptually this work is most closely related to work in Michael Desai's, Sergey Kryazhimskiy's, Tim Coopers and Chris Marx's labs work looking at diminishing returns epistasis in yeast, and studies contrasting evolution of haploids and diploids led by Greg Lang's and Sarah Otto's labs.
• State what audience might be interested in and influenced by the reported findings. This work will be of great interest to the Experimental evolution and molecular evolution communities and also of interest to those who study cancer genomics and DNA replication and repair.
• 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. Microbial experimental evolution.

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Yoshizawa-Sugata, N., & Masai, H. (2009). Roles of human AND-1 in chromosome transactions in S phase. J Biol Chem., 284(31), 20718–20728. https://doi.org/10.1074/jbc.M806711200

Yuan, Z., Georgescu, R., Santos, R. de L. A., Zhang, D., Bai, L., Yao, N. Y., … Li, H. (2019). Ctf4 organizes sister replisomes and Pol α into a replication factory. ELife, 8, e47405. https://doi.org/10.7554/eLife.47405

Zeyl, C., Vanderford, T., & Carter, M. (2003). An Evolutionary Advantage of Haploidy in Large Yeast Populations. Science, 299(5606), 555–558. https://doi.org/10.1126/SCIENCE.1078417

Zheng, D.-Q., Zhang, K., Wu, X.-C., Mieczkowski, P. A., & Petes, T. D. (2016). Global analysis of genomic instability caused by DNA replication stress in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A., 113(50), E8114–E8121. https://doi.org/10.1073/pnas.1618129113

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Referee #4

This reviewer did not leave any comments

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Referee #3

Evidence, reproducibility and clarity

Summary:

Here the authors carry out an evolution experiment, propagating replicate populations of the budding yeast with the CTF 4 gene deleted in three different genetic backgrounds: haploid , diploid and recombination deficient (RAD52 deletion). The authors find that the rate of evolution depends on the initial fitness of the different genetic backgrounds which is consistent with a repeated finding of evolution experiments: that beneficial mutations tend to have a smaller fitness effect in high fitness genetic backgrounds. Curiously even though the targets of selection tended to be specific to each of the three different genetic backgrounds, genetic reconstruction experiments showed beneficial mutations convert a fitness increase in all genetics backgrounds. The authors go on to provide a plausible explanation for why each of the three genetic backgrounds are predisposed to certain types of beneficial mutations. Overall, these results provide important context and caveats for an emerging consensus that genetic background determines the rate of evolution, a comprehensive molecular breakdown of adaptation to DNA replication stress and a mechanistic explanation for why different beneficial mutations are favoured in diploids, haploids and recombination deficient strains. This is a well-executed study that is beautifully presented and easy to follow. This will be of great interest to those in the experimental evolution community and the data an excellent resource.

Major comments:

• Are the key conclusions convincing? Yes, the convergent evolution analysis, fitness assays, and genetic reconstructions are sufficient to characterise the genetic causes of adaptation in this experiment, and are of the highest standard. The authors do particularly well to fully recover the fitness increases that evolved with their genetic reconstructions, which imparts a completeness to their understanding of what happened in their evolution experiment.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No, in nearly all cases the authors make reasonable claims. One exception is on L419 in the discussion, where the authors speculate why some mutations do not follow diminishing returns epistasis, but this idea does not really have any basis (no citation or reasons to suggest that DNA repair genes are less connected with other genes in the genome). If the authors cannot support this statement, it should be removed, and instead write that is currently unknown why some individual mutations do not follow the pattern of diminishing returns.
• Would additional experiments be essential to support the claims of the paper? No.
• Are the data and the methods presented in such a way that they can be reproduced? Yes, but some more details are needed for the convergent evolution analysis, see minor comments.
• Are the experiments adequately replicated and statistical analysis adequate? Yes, but some more statistic reporting in the main text or figure legends would be helpful, for example. L159: Please report the statistical test, test statistic and p value in the text or in the figure legend. Currently significance is indicated, but the methods do not specify the test. This should also be done for the GO analysis shown in figure 3A.

Minor comments:

• Specific experimental issues that are easily addressable. Not a new experiment, but extra details are required. The authors carried out both clone and whole population sequencing. For their convergent evolution analysis, what is the criteria for a mutation to be included- ie, does it need to be fixed, have attained a certain frequency? This is important- if the criteria were low (say 5%), it would be important to know whether gene A had fixed in 4 populations, while gene B had attained a frequency of 10% in 5 populations. As it stands both would be described as examples of convergent evolution. This can be handled by providing these details in the methods.
• Are prior studies referenced appropriately? I note that the authors use the term declining adaptability where as other papers use the term diminishing returns epistasis- I am sure the authors have good reasons for their choice of nomenclature but I think it would be helpful for their readers to connect this work to other work by mentioning that declining adaptability is also referred to as diminishing returns.

A reference the authors have missed: L419, as well as citing the Desai Lab bioxive paper, they should cite another theory paper that obtained similar conclusions. Lyons, D.M., et al. https://doi.org/10.1038/s41559-020-01286-y. .

• Are the text and figures clear and accurate? This paper is beautifully written and easy to follow, a lot of thought has gone into the figures which are aesthetically pleasing and easy to navigate.
• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No.

Typos

L32 "do" should be "to" L95 analyzed<br> L219 are the authors referring to ref 15 here? I think so, but please specify

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This paper is an important conceptual result and an immediate advance for basic research. The authors have done an outstanding job of showing the potential for the clinical translation of this research, especially regarding cancer biology.
  • Place the work in the context of the existing literature (provide references, where appropriate). This study follows up on and builds upon an earlier paper by these same authors published in E-life in 2020. Conceptually this work is most closely related to work in Michael Desai's, Sergey Kryazhimskiy's, Tim Coopers and Chris Marx's labs work looking at diminishing returns epistasis in yeast, and studies contrasting evolution of haploids and diploids led by Greg Lang's and Sarah Otto's labs.
  • State what audience might be interested in and influenced by the reported findings. This work will be of great interest to the Experimental evolution and molecular evolution communities and also of interest to those who study cancer genomics and DNA replication and repair.
  • 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. Microbial experimental evolution.

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Referee #2

Evidence, reproducibility and clarity

Summary

This manuscript investigates the effect of an organism's genotype (or, as the authors call it, an organism's 'genome architecture') on evolutionary trajectories. For this, the authors use Saccharomyces cerevisiae strains that experience some form of replication stress due to specific gene deletions, and that further differ in ploidy and/or the type of gene(s) deleted. They find the same three functional modules (DNA replication, DNA damage checkpoint, sister chromatid cohesion) are affected across the 3 different genotypes tested; although the specific genes that are mutated varies.

Major comments

This is a solid and exceptionally eloquent paper, comprising a large body of work that is in general well presented. That said, I do have some suggestions and questions. At several points in the manuscript, the authors should perhaps be more careful in their wording and avoid to overgeneralize data without providing additional evidence for these claims.

• Some key points of the study are not entirely clear to me; possibly because the study builds upon a previous study that was recently published in eLife. Anyhow, I think it would be useful to clarify the following points a bit more:

• Why exactly was ctf4∆ chosen as a model for replication stress? What is the evidence that ctf4∆ is a good model for replication stress? Without including some evidence for this, it is unclear how well the findings in this study really can be generalized to replication stress (which is what the authors do now). Would the authors expect to see similar routes of adaptation if a 'genomic architecture' with a less severe/other replication defect would have been used? I realize the last question is perhaps difficult to address without actually doing the experiment (which I am not suggesting the authors should do); I just want to point out that perhaps some data should not be over-generalized.

• Likewise, why was RAD52 selected as the gene to delete to affect homologous recombination? I understand that it is a key gene, but on the flipside, absence of RAD52 affects multiple cellular pathways and (as the authors also observe in their populations) also results in increased mutation rates which might confound some of the results.

• Related to the first comment, it is also unclear to me how well the system chosen by the authors is representative of the replication stress experienced by tumor cells (as briefly touched upon in the final section of the discussion). Are some of the homologs key oncogenes that drive carcinogenesis?

• The authors should consider rephrasing some sentences regarding the occurrence of adaptive mutations. Sentences such as 'which genes are mutated depends on the selective advantage' (p1; lines 15-16); 'genome architecture controls the frequency at which mutations occur' (p15), "genome architecture controls which genes are mutated" (p1, line 20) makes it sound like the initial occurrence of mutations is not random, whereas in reality, the mutational landscape is the result of the combined effect of occurrence and fitness effect of the mutations, with the later rather than the former likely being the main driver behind the observed patterns.
• Some important methodological information is missing or unclear in the manuscript:

• The authors should provide more details on how they decided which clones to select for sequencing. Did they select the biggest colonies; were colonies picked randomly, ...

• What is the population size during the evolution experiment?

• Sequencing of populations and clones: coverage should be mentioned

• Identification of mutations (p19, line 573): Is this really how the authors defined whether a variant is a mutation? Based on the definition given here, DNA mutations that lead to a synonymous mutation in the protein are not considered as mutations?

• Perhaps the information can be found elsewhere, but the source data excel files for mutations is incomplete and should at the very least contain information on the type of mutation (eg. T->A), as well as the location of this mutation in the respective gene.

Minor comments

• While the author already cite several significant papers relevant for their manuscript, some other studies could also be included:

• On how genome architecture influences evolution: https://doi.org/10.1093/molbev/msaa172
• Ploidy and fitness/adaptation: DOI:https://doi.org/10.1016/j.cub.2018.01.062

• From the text in the abstract, it is unclear what the three genomic architectures (line 13) exactly are, the authors should consider spelling this out.

• Can the authors speculate on why a homozygous ctf4/ctf4 rad52/rad52 would be lethal, and a haploid not?

• The authors note that a diploid ctf4/ctf4 strain is less fit than its haploid counterpart. Why do the authors think this is the case?

• The authors passage their cells for 100 cycles and assume that this corresponds to around 1000 generations for each population. However, the fitness differences between the different starting strains (see also Figure 1B) are likely to cause considerable differences in number of generations between the different strains. Do the authors have more precise measurements of number of generations per population? If not, perhaps it should be noted that some lineages may have undergone more doublings than others, and perhaps also discuss if and how this could influence the results?

• Panel A of figure 1A is somewhat confusing; as this seems to indicate that the ctf4∆ was introduced after strains were made, for example, haploid recombination deficient (which is not how these strains were constructed). Perhaps a better way of representing would be to have the indication of DNA replication stress pictured inside the yeast cells.

• Legend to Figure 1: is fitness expressed relative to haploid or diploid WT cells for the diploid strains?

• Figure 3: to improve readability of this figure, the authors could consider placing the legend of the different symbols (#, *,..) in the figure as well and not just in the figure legend.

• Figure 5 shows Indels, but if I am correct, these mutations are not discussed in the text; nor is it mentioned what the authors used as a cut-off to determine indels (the authors use the term 'small indels' without defining it)? For example, the data shown in Figure 3 and Figure 4 only includes SNPs and not indels (correct?) - but the indels should also be taken into account when investigating which modules are hit.

• Figure 5 mentions: # gene mutations. So these are only the mutations in genes, and not in their up- or downstream regulatory regions?

• Figure 3-S1: labels of C panels are missing.

• Figure 3-S1, panel B: why did the authors focus on synonymous mutations?

Significance

This is a solid and clearly written study, comprising a large body of work that is generally well presented and that will be of interest to scientists active in the field of (experimental) evolution and replication.

However, many aspects studied in this manuscript have already been studied and reported before; including the recent eLife paper by the same group, as well as studies by other labs that have investigated how genome architecture / genotype affects evolutionary trajectories, the effect of ploidy on evolution, .... Because of this, I do feel that the authors should put their findings more in the context of existing literature context, including a general description of which results are truly novel, which confirm previous findings and which results seem to go against previous reports. This is already so at some points in the text, but I feel this could be done even more.

Additionally, I think the authors should be more careful not to over-generalize their findings, which come from only a few specific genetic manipulations that might not be representative for general replication stress. For example (p15), can the authors really claim that they have unraveled general principles of adaptation to constitutive DNA replication stress? Perhaps a better motivation of the choice of ctf4 as a model mutation for DNA replication stress could also help (see also my earlier comments). A similar comment applies to the molecular mechanisms affecting adaptation in diploid cells - what evidence do the authors have that their findings are not specific to the one specific type of diploid strain they used in their study? Adding a bit more background information or nuance for some of the claims would help tackle this issue.

Field of expertise of this reviewer: genetics, evolution, genomics

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Referee #1

Evidence, reproducibility and clarity

In their previous work the authors examined adaptation in response to replication stress in haploid yeast, via experimental evolution of batch cultures followed by sequencing. Here they extend this approach to include diploid and recombination-deficient strains to explore the role of genome architecture in evolution under replication stress. On the whole, a common set of functional modules are found to evolve under all genetic architectures. The authors discuss the molecular details of adaptation and use their findings to speculate on the determinants of adaptation rate.

SECTION A - Evidence, reproducibility and clarity

Experimental evolution can reveal adaptive pathways, but there are some challenges when applying this approach to compare genetic backgrounds or environments. They key challenge is that adaptation potentially depends on both the rate of mutation and the nature of selection. Distinct adaptation patterns between groups could therefore reflect differential mutation, selection, or both. The authors allude to this dichotomy but have very limited data to address it. The closest effort is engineering putatively-adaptive variants into all genetic background including those where they did not arise; the fact that such variants remain beneficial suggests they did not arise in certain backgrounds because of a lower mutation rate, but this is a difficult issue to tackle quantitatively.

From mutation accumulation experiments, where the influence of selection is minimized, there is evidence that genetic architecture affects the rate and spectrum of spontaneous mutations. In this experiment, the allele used to eliminate recombination, rad52, will also increase the mutation rate generally. The diploid strain is also likely to have a distinct mutational profile--as a null expectation diploids should have twice the mutation rate of haploids. Recent evidence indicates the mutation rate difference between haploid and diploid yeast might be less than two-fold, but that there are additional differences in the mutation spectrum, including rates of structural change. The context for this study is therefore three genetic architectures likely to differ in multiple dimensions of their mutation profiles, but mutation rates are not measured directly.

The nature of selection on haploids and diploids is expected to differ because of dominance, but ploidy-specific selection is also possible. The authors discuss how recessive beneficial alleles may be less available to diploids, though this can be offset by relatively rapid loss of heterozygosity. However, diploids should also incur more mutations, all else being equal. The rate of beneficial mutation, as opposed to the rate of mutation generally, will depend on the mutational "target size" of fitness, and the authors findings recapitulate other literature (particularly regarding "compensatory" adaptation) that points to faster adaptation in genotypes with lower starting fitness.

There is ample literature on the above topics, particularly discussions of the evolutionary advantages of haploidy versus diploidy. While adaptation to replication stress provides a novel starting point for this investigation, much of the manuscript is devoted to long-standing questions that are not specific to replication stress. Unfortunately, the data the authors collected is not sufficient to shed light on these questions, because mutation and selection cannot be effectively distinguished. The Discussion states that "We find that the genes that acquire adaptive mutations, the frequency at which they are mutated, and the frequency at which these mutations are selected all differ between architectures but that mutations that confer strong benefits always lie in the same three modules" (line 379), but it is not clear that these statements are all supported by the data.

Focusing on the more novel aspect of their experiment-the presence of replication stress-would arguably be a better approach. On this topic the authors have some interesting observations and speculation, but clear predictions are lacking. The introduction section could be redesigned to explicitly state why genome architecture might affect adaptation in response to replication stress in particular, rather than (or in addition to) adaptation generally. If there were no differences in mutation, does the nature of Ctf4 lead to predictions that the molecular basis of compensatory adaptation should differ among genome architectures? Without such predictions it will be difficult for readers to know whether the observation that different genome architectures follow similar adaptive paths is surprising or not.

Minor comments:

Shifts in ploidy from diploid to haploid are less common than the reverse change, so the observation of such a shift (Fig. 1) should be discussed in more detail.

Line 88 typo 'stains'.

Significance

SECTION B - Significance

The novel aspect of this study is the combination of replication stress and genome architecture, but here the significance is limited by a lack of clear predictions on how these factors might interact. On the other hand, much of the manuscript is devoted to why adaptation might vary among genome architectures in general, but this long-standing and important question is not particularly well resolved by this experimental approach, which can't disentangle mutation and selection.

The authors highlight the dichotomy when discussing the evolution of ploidy: "We suggest that... genome architecture affects two aspects of the mutations that produce adaptation: the frequency at which they occur and the selective advantage they confer" (line 399), but presenting this as a novel inference does not appropriately acknowledge prior research and discussion of these ideas; several relevant papers are cited by the authors in other contexts. It may be possible to recast these findings as a test of the role of genome architecture in adaptation generally, but the authors should clarify the limitations of experimental evolution and more fully consider the theory and data outlined in previous research. In particular, few studies can claim to directly compare mutation rates between genome architectures, and it is not obvious that the present study is an example of such.

Reviewer expertise: Evoutionary genetics; experimental evolution; mutation.

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Reply to the reviewers

Response to Reviewers

We thank the reviewers for their careful reading of our manuscript and their valuable suggestions and comments. To address the reviewers’ concerns and improve our manuscript, we will complete the additional experiments and further revise the text as described below.

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

**Summary:**

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2. The authors present an in vivo analysis of pdzd8 (CG10362) and a synthetic ER-mitochondria tether in the regulation of locomotor activity, lifespan, and mitochondrial turnover of Drosophila melanogaster, using basic bioinformatics, RNAi, SPLICS, imaging and microscopies observations (i. e. TEM, SIM), fly lines, and a representative AD fly disease model, etc. The research methodologies were detailed in good order. The model system employed was suitable to address the research topic. The manuscript was written in a clear language and statistical analysis were correctly applied.

**Major comments:**

*-Are the key conclusions convincing?*

Yes. The results/conclusions are logical and provide an overview of Pdzd8 in the regulation of mitochondrial quality control and neuronal homeostasis.

*-Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.*

No. Experiments were generally well performed, and all the data support the conclusions.

*-Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.*

No suggested experiments needed.

*-Are the data and the methods presented in such a way that they can be reproduced?*

Yes. The authors have followed proper experimental design and methods have been described in sufficient detail.

*-Are the experiments adequately replicated and statistical analysis adequate?*

Yes, they are.

**Minor comments:**

*-Specific experimental issues that are easily addressable.*

No comment.

*-Are prior studies referenced appropriately?*

Yes. The relevant literatures have been cited appropriately.

*-Are the text and figures clear and accurate?*

1.Please pay attention to the correct spelling of the described protein name (Pdzd8) and gene name (should be in 'italic') throughout the manuscript, i. e. line 36, 98, and 556, etc.

As this is the first published characterization of the fly homolog of the mammalian Pdzd8 We have decided to name the fly protein pdzd8, using the lower case “p” to distinguish it from the mammalian protein. We have checked and corrected our use of italics for the gene name as noted in track changes.

2.In figure 1C and its figure legend, please state what the numbers "201" and "195" stand for.

We have added the text “numbers on bars indicate number of mitochondria analysed” to the figure legend.

3.Your data needs to be converted the lowercase letter "x" to math symbol "×" when representing times sign, i. e. line 523, 5x, etc.

Corrected

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

No comment.

Reviewer #1 (Significance (Required)):

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

Discoveries from this study include 1) characterization of the tethering protein Pdzd8 in Drosophila melanogaster, and 2) shed light on a possible way on how to enhance mitochondrial quality control and to help promote healthy aging of neurons by manipulating MERCs.

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

With this manuscript, the authors present a straightforward but sound piece of scientific research, with the intent to illustrate the consequences of neuronal depletion of pdzd8 in Drosophila melanogaster. Since Pdzd8 plays specific functions in ER-mitochondrial tethering complexes and dysregulations of MERCs are damaging to neurons, this protein represents a good potential target. In this context the characterization of Pdzd8 should represent an interesting starting point. To this purpose, the gene was knockdown and the tether construct was recombinantly produced. The fly lines were then subjected to analysis both at the organismal and at the cellular level.

*-State what audience might be interested in and influenced by the reported findings.* Audience might include those who are in the field of neuroscience and pharmaceutical, and benefit from an awareness of this research.

*-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.*

Key words in my field of expertise: Ageing, neurodegenerative diseases, Alzheimer's disease, mitophagy, NAD+, neuroprotection. My group is investigating the molecular mechanisms of ageing and age-related neurodegeneration (especially AD) using cross-species model systems, ranging from human brain samples, iPSCs, C. elegans, Drosophila melanogaster, and mice, therefore I have sufficient expertise to evaluate this paper.

**Referees Cross-commenting**

To this reviewer the key novelty of this paper was the study of the regulation of the mitochondrial-ER contact sites (MERCs) in life and health. The data indicate that MERCs mediated by the tethering protein pdzd8 play a critical role in the regulation of mitochondrial homeostasis, neuronal function, and lifespan. In a transitional perspective, this reviewer would ask to check whether this mechanism conserves in rodents or not (e.g. to to memory in the AD mice and to run lifespan in mitochondrial toxin condition). This may be to much. But will depend on the standard of the journal. We thank the reviewer for their input, evaluation and interest. We too are keen to know whether this mechanism is conserved and hope to investigate this in our ongoing work including characterizing a mouse mutant, but the current work already represents a substantial investment of resources and a worthy study in its own right as the first description of the in vivo role of pdzd8, so we feel it is beyond the scope of the current work.

Reviewer #2

(Evidence, reproducibility and clarity (Required)):

Hewitt et al. describe and characterize for the first time the ortholog of pdzd8 in Drosophila melanogaster. In accordance with pdzd8's previously described function as a member of mitochondrial-ER contact sites (MERCs) the authors show reduced MERCs upon RNAi mediated depletion of pdzd8 via TEM, SIM and a split-GFP based contact site sensor. Pdzd8 depletion results in the increased life span as well as improved locomotor activity in aging flies while increase of MERCs with a synthetic tether accelerates the age-related declines in survival and locomotion. Moreover, pdzd8 depleted flies are more resistant against mitochondrial toxins. The authors correlate these protective effects of pdzd8 knockdown with an increase in mitophagy using a mitophagy sensor and describe a rescue of locomotor defects in an Alzheimer disease fly model by pdzd8 depletion.

**Major comments:**

1.The authors quantify the number of MERCs in thin sections of TEM (Fig 1B and C). It would add to the paper if the authors would show a representative reconstruction of the quantified somata, as a 3D reconstruction would visualize ER-Mito contacts more reliable than thin sections.

We agree that the 3D reconstruction of TEM images would provide a satisfying addition to the current analyses, however such advanced techniques are not readily available. The current samples used to collect these data cannot be used to generate 3D reconstructions. To counter this, we have used three independent methods to analyse the changes in MERCs, all of which show a decrease in MERCs in the flies with less pdzd8 supporting that these observations are reproducible and robust.

2.The authors quantify MERCs in pdzd8 KD also by SIM (Fig1F, G). However, they quantify the number of MERCs in epidermal cells while they also show SIM images of larval neurons (Fig S1D). For consistency and to support their claim of MERC reduction in neurons, we ask the authors to include the quantification based on larval neurons especially as the authors show that pdzd8 is predominantly expressed in the CNS.

Unfortunately, the soma of larval neurons have extremely limited cytosol (see fig. S1D) which creates very challenging conditions to discern the spatial separation of ER and mitochondria by light microscopy. While co-localisation of organelle markers in such cells has been reported in the literature, we are extremely concerned that the lack of space within the cytosol renders such analysis unreliable. However, we will attempt to quantify the extent of co-localisation of the ER and mitochondria in these cells. In contrast, epidermal cells are much larger providing greater spatial separation of ER and mitochondria. Notably, we complement the co-localisation analysis of epidermal cells with two additional approaches, TEM analysis and the SPLICS reporter construct, to demonstrate pdzd8-RNAi results in decreased MERCs specifically in neurons.

3.The authors describe a decreased NMJ volume in Fig 4G. It would improve and complete the functional characterization of pdzd8 in flies if the authors can provide further data whether pdzd8 KD causes a general synaptic defect. Can the authors show morphological synaptic defects in the existing TEM data of the adult brain or provide additional ERG recordings, which would elucidate the functional consequences of pdzd8 depletion in the CNS?

Our TEM data are not suitable for us to properly analyse defects in synaptic morphology as our images centered around the cell bodies where the organelle morphology was easiest to distinguish and there are very few synapses. While it is not surprising that the knockdown of pdzd8 has some detrimental effects, we chose to focus our efforts on trying to determine the cause of the protective effect on locomotor activity in aged flies rather than to exhaustively characterise the myriad phenomena which may be impacted as a knock-on effect of the disrupted cell biology that we have demonstrated. We hope to further explore the detrimental functional consequences of pdzd8 depletion on such phenomena as neurotransmission in future work.

1. Hewitt et al. suggest a beneficial effect of increased turnover of mitochondria for healthy aging. To convince readers we would like to ask the following:

a) This claim is based on their observation of increased mitophagy in pdzd8 depleted flies using one reporter (Fig 5). Can the authors support their data with an alternative method as this is one of the key claims of the manuscript?

The mitoQC tool is well established in the field and we have found it to perform better but consistent with mito-Keima (Lee et al. 2018 JCB doi: 10.1083/jcb.201801044). We would be happy to consider other assays if the reviewer can suggest an unbiased and established alternative.

b) An increased turnover of Mitochondria would also suggest that there are more "young" mitochondria present in the pdzd8 KD neurons. Can the authors experimentally address that?

We understand the reviewer’s point here but due to the continual fission and fusion, as well as piecemeal turnover of mitochondria (see Vincow et al. 2019 Autophagy doi: 10.1080/15548627.2019.1586258), the concept of ‘young’ versus ‘old’ mitochondria is misplaced. The mitochondrial network essentially exists as a milieu of components which are produced and degraded at different rates.

c)Furthermore, we would like to ask the authors to use also the MERC tether as control in the mitophagy assay. This would allow further conclusions about the role of the mitophagy, its protective effect during aging and the role of MERCs in this process.

We remind the reviewer that this MERC tether is constructed from an RFP with N- and C-terminal tethering peptides. The presence of this RFP prevents the proper analysis of the mitoQC mCherry signal. However, given the dramatic phenotypes we think that it is unlikely that a decrease in mitophagy alone can explain the detrimental effects of increased tethering.

1. In Fig6 A,B the authors should include also the pdzd8 KD to support their claim that the rescue of climbing defects correlates with an reduction of MERCs.

We thank the reviewer for this suggestion and we will perform this experiment.

Moreover, it would be beneficial for their final conclusion, if the authors could show that increases mitophagy in the background of Ab42 expressing flies.

We thank the reviewer for this suggestion and we will perform this experiment.

**Minor comments:**

1.Can the authors add to the figure legend of Fig 1F how the ER and Mitochondria were labeled?

We have added the constructs to the figure legend (full genotypes for all figures are given in Table S2).

2.Error bars should be added in the quantification of MERCs in Fig1C.

The MERCs are quantified in three brains per genotype but as there were variable numbers of sections suitable for imaging from each brain the total values are combined to give a single percentage.

3.A reference to Supplementary Fig S1D is missing in the main text.

This figure is referenced in line 135

4.Can the authors label the individual genotypes in Fig S3C and 4F?

Figure labels and legends have been modified to clarify this.

5.Can the author specify which brain region they imaged in Fig 5C?

The regions imaged and quantified were chosen for their clear organelle morphology rather than targeting a specific brain region. All images were from the protocerebrum and the methods and figure legends have been updated to note this.

6.Are the ATP levels normalized to ADP in Fig S3D? Can the authors specify in the figure and figure legend to what ATP was normalized?

Figure labels and legends have been modified to clarify the ATP levels are normalised to total protein quantification of the samples.

7.Please sort the supplementary figures in accordance to their reference order in the text.

We thank the reviewer for checking this. This figure order will be rechecked in the final version as addressing reviewer comments is likely to lead to further changes.

Reviewer #2 (Significance (Required)):

The authors present here novel insights about the functional role of a new member of the MERCs, pdzd8, using RNAi mediated depletion and Drosophila melanogaster as a model system. As MERCs receive more attention especially in the context of their potential role in neurological diseases, the author's manuscript will be of high interest to the scientific community. The in vivo model combined with multiple different technical approaches add to the significance of the paper. There are some controls and additional experiments that are required to support the author's main claims and complete the functional characterization of pdzd8 (see major comments).

Field of expertise: neuroscience, fly genetics, neurodegeneration.

Reviewer #3

(Evidence, reproducibility and clarity (Required)):

This manuscript entitled "Decreasing pdzd8-mediated mitochondrial-ER contacts in neurons improves fitness by increasing mitophagy" by Hewitt and collaborators describes the role of the Drosophila ortholog of PDZD8 in ER-mitochondria contacts in neurons and the physiological consequence of pdzd8 loss. The authors show that ER-mitochondria contacts are reduced in fly neurons expressing a pdzd8-RNAi construct. Decreasing pdzd8 expression in neurons was accompanied by a slowed age-associated decline in locomotor activity, and an increased lifespan. In presence of mitochondrial toxins, neurons deficient for pdzd8 were protected. Finally, the authors showed that pdzd8 silencing increased mitophagy in aged neurons, and protected against neurodegeneration in a model of Alzheimer's disease.

**Major points:**

1)There are important controls that are missing. RNAi expression often affects off-target genes which could unfortunately modify the observed phenotypes. The authors should verify that a) the phenotypes observed by RNAi-mediated pdzd8 silencing can be rescued by the expression of an RNAi-insensitive pdzd8 construct (the authors should verify the rescue of the most crucial phenotypes described in the manuscript); b) the RNAi-LacZ-line that they use as control in the paper does not behave differently from a WT line, which could be induced by an off-target effect of the RNAi-LacZ (again with the most crucial phenotypes).

While the Drosophila community is fortunate to have a plethora of readily available tools for interrogating the function of nearly all genes in the genome – tools which form the foundation of most work in Drosophila labs worldwide – the availability is not limitless. In this instance, the transgenic RNAi line generated as a resource for the community comprises a 500 bp hairpin, computed to be the most selective target for that gene. Being a 500 bp sequence it is unrealistic to be able to establish an RNAi-resistant variant that still faithfully functions as normal. Nevertheless, although imperfect we show in Figure S3B that pdzd8-RNAi rescues the climbing defect produced by overexpressing pdzd8, providing evidence the construct is specifically acting on this sequence.

Similarly, the availability of ‘control’ RNAi reagents is generous but still limited. This LacZ-RNAi line is one of a few well-established controls that has provided a cornerstone reference for a wealth of studies. Nevertheless, we will provide experimental data that aged climbing of nSyb>LacZ-RNAi is highly comparable to several other well-established control genotypes.

2) Did the author analyzed their EM data in a blinded-way to minimize subjective bias? This type of analysis is complicated by the manual annotation of ultrastructures, which is by nature subjective. For instance, this reviewer would have annotated the two mitochondria in the middle of Fig 1B, right as "Mitochondria with ER contact", as there is a membrane tube present at the interface of these two organelles.

The EM data were analysed blinded to the genotypes. This is noted in the methods section.

3) There is a controversy in the field on the role of PDZD8: some papers show its involvement in ER-mitochondria contacts, others in ER-lysosome contacts. The authors should discuss this point in more details. Moreover, the authors should localize the protein in Drosophila neurons; is the protein associated with mitochondria or endo/lysosomes?

We recognize that there is some debate in the field over the localization and role of PDZD8. However, since there is currently no antibody against the Drosophila protein and the sequence is sufficiently divergent such that antibodies against the mammalian protein will not recognize the fly protein, we are not well-positioned to determine the localization of Drosophila pdzd8. Consequently, we will expand our discussion to reflect the differing views.

We can offer instead to quantify the localization of mouse PDZD8 in our newly generated NIH-3T3 Pdzd8-Halo knock in line to help resolve the controversy regarding the location(s) and function(s) of mammalian Pdzd8.

4) The authors should specify in more details how the different quantifications were performed. For instance Fig 1G: how many samples were quantified (i.e. how many flies, and how many neurons); what is compared? Fields-of-view, neurons, flies...?

Further details have been added to the figure legends 1G (now H), 4I, 5 and Fig S2.

**Minor point:**

1)Could the authors show the SIM images Fig1F together with the binarized images.

These images have been added to Figure 1 and the legend and text updated accordingly.

2) It is surprising to see that data otherwise similar are represented with so many different types of graph (For instance Fig 5, bar graph, box-plot, violin plot). Why individual data points are not always present on the graphs?

The graphs will be redrawn using more consistent representations once the data for the revisions has been gathered.

3) The way that data are presented is sometimes odd: for instance, line 101, the authors wrote "To establish that MERCs were decreased...". This would imply that they knew the result before performing the experiment. And later, line 103 "Accordingly...".

These sentences have been rephrased “To determine whether MERCs were decreased..” and “These results showed the…”

Reviewer #3 (Significance (Required)):

This study about the role of pdzd8 is timely. The functional description of inter-organelle contacts is a hot topic in cell biology. There are several recent reports describing the identification of pdzd8 role in inter-organelle contact formation. This manuscript provides data on the role of pdzd8 in a whole organism and expands our understanding of this protein.

My expertise: inter-organelle contacts (human cells)

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Referee #3

Evidence, reproducibility and clarity

This manuscript entitled "Decreasing pdzd8-mediated mitochondrial-ER contacts in neurons improves fitness by increasing mitophagy" by Hewitt and collaborators describes the role of the Drosophila ortholog of PDZD8 in ER-mitochondria contacts in neurons and the physiological consequence of pdzd8 loss. The authors show that ER-mitochondria contacts are reduced in fly neurons expressing a pdzd8-RNAi construct. Decreasing pdzd8 expression in neurons was accompanied by a slowed age-associated decline in locomotor activity, and an increased lifespan. In presence of mitochondrial toxins, neurons deficient for pdzd8 were protected. Finally, the authors showed that pdzd8 silencing increased mitophagy in aged neurons, and protected against neurodegeneration in a model of Alzheimer's disease.

Major points:

1)There are important controls that are missing. RNAi expression often affects off-target genes which could unfortunately modify the observed phenotypes. The authors should verify that a) the phenotypes observed by RNAi-mediated pdzd8 silencing can be rescued by the expression of an RNAi-insensitive pdzd8 construct (the authors should verify the rescue of the most crucial phenotypes described in the manuscript); b) the RNAi-LacZ-line that they use as control in the paper does not behave differently from a WT line, which could be induced by an off-target effect of the RNAi-LacZ (again with the most crucial phenotypes).

2)Did the author analyzed their EM data in a blinded-way to minimize subjective bias? This type of analysis is complicated by the manual annotation of ultrastructures, which is by nature subjective. For instance, this reviewer would have annotated the two mitochondria in the middle of Fig 1B, right as "Mitochondria with ER contact", as there is a membrane tube present at the interface of these two organelles.

3)There is a controversy in the field on the role of PDZD8: some papers show its involvement in ER-mitochondria contacts, others in ER-lysosome contacts. The authors should discuss this point in more details. Moreover, the authors should localize the protein in Drosophila neurons; is the protein associated with mitochondria or endo/lysosomes?

4)The authors should specify in more details how the different quantifications were performed. For instance Fig 1G: how many samples were quantified (i.e. how many flies, and how many neurons); what is compared? Fields-of-view, neurons, flies...?

Minor point:

1)Could the authors show the SIM images Fig1F together with the binarized images.

2)It is surprising to see that data otherwise similar are represented with so many different types of graph (For instance Fig 5, bar graph, box-plot, violin plot). Why individual data points are not always present on the graphs?

3)The way that data are presented is sometimes odd: for instance, line 101, the authors wrote "To establish that MERCs were decreased...". This would imply that they knew the result before performing the experiment. And later, line 103 "Accordingly...".

Significance

This study about the role of pdzd8 is timely. The functional description of inter-organelle contacts is a hot topic in cell biology. There are several recent reports describing the identification of pdzd8 role in inter-organelle contact formation. This manuscript provides data on the role of pdzd8 in a whole organism and expands our understanding of this protein.

My expertise: inter-organelle contacts (human cells)

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Referee #2

Evidence, reproducibility and clarity

Hewitt et al. describe and characterize for the first time the ortholog of pdzd8 in Drosophila melanogaster. In accordance with pdzd8's previously described function as a member of mitochondrial-ER contact sites (MERCs) the authors show reduced MERCs upon RNAi mediated depletion of pdzd8 via TEM, SIM and a split-GFP based contact site sensor. Pdzd8 depletion results in the increased life span as well as improved locomotor activity in aging flies while increase of MERCs with a synthetic tether accelerates the age-related declines in survival and locomotion. Moreover, pdzd8 depleted flies are more resistant against mitochondrial toxins. The authors correlate these protective effects of pdzd8 knockdown with an increase in mitophagy using a mitophagy sensor and describe a rescue of locomotor defects in an Alzheimer disease fly model by pdzd8 depletion.

Major comments:

1.The authors quantify the number of MERCs in thin sections of TEM (Fig 1B and C). It would add to the paper if the authors would show a representative reconstruction of the quantified somata, as a 3D reconstruction would visualize ER-Mito contacts more reliable than thin sections.

2.The authors quantify MERCs in pdzd8 KD also by SIM (Fig1F, G). However, they quantify the number of MERCs in epidermal cells while they also show SIM images of larval neurons (Fig S1D). For consistency and to support their claim of MERC reduction in neurons, we ask the authors to include the quantification based on larval neurons especially as the authors show that pdzd8 is predominantly expressed in the CNS.

3.The authors describe a decreased NMJ volume in Fig 4G. It would improve and complete the functional characterization of pdzd8 in flies if the authors can provide further data whether pdzd8 KD causes a general synaptic defect. Can the authors show morphological synaptic defects in the existing TEM data of the adult brain or provide additional ERG recordings, which would elucidate the functional consequences of pdzd8 depletion in the CNS?

4.Hewitt et al. suggest a beneficial effect of increased turnover of mitochondria for healthy aging. To convince readers we would like to ask the following:

a)This claim is based on their observation of increased mitophagy in pdzd8 depleted flies using one reporter (Fig 5). Can the authors support their data with an alternative method as this is one of the key claims of the manuscript?

b)An increased turnover of Mitochondria would also suggest that there are more "young" mitochondria present in the pdzd8 KD neurons. Can the authors experimentally address that?

c)Furthermore, we would like to ask the authors to use also the MERC tether as control in the mitophagy assay. This would allow further conclusions about the role of the mitophagy, its protective effect during aging and the role of MERCs in this process.

5.In Fig6 A,B the authors should include also the pdzd8 KD to support their claim that the rescue of climbing defects correlates with an reduction of MERCs. Moreover, it would be beneficial for their final conclusion, if the authors could show that increases mitophagy in the background of Ab42 expressing flies.

Minor comments:

1.Can the authors add to the figure legend of Fig 1F how the ER and Mitochondria were labeled?

2.Error bars should be added in the quantification of MERCs in Fig1C.

3.A reference to Supplementary Fig S1D is missing in the main text.

4.Can the authors label the individual genotypes in Fig S3C and 4F?

5.Can the author specify which brain region they imaged in Fig 5C?

6.Are the ATP levels normalized to ADP in Fig S3D? Can the authors specify in the figure and figure legend to what ATP was normalized?

7.Please sort the supplementary figures in accordance to their reference order in the text.

Significance

The authors present here novel insights about the functional role of a new member of the MERCs, pdzd8, using RNAi mediated depletion and Drosophila melanogaster as a model system. As MERCs receive more attention especially in the context of their potential role in neurological diseases, the author's manuscript will be of high interest to the scientific community. The in vivo model combined with multiple different technical approaches add to the significance of the paper. There are some controls and additional experiments that are required to support the author's main claims and complete the functional characterization of pdzd8 (see major comments).

Field of expertise: neuroscience, fly genetics, neurodegeneration.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2. The authors present an in vivo analysis of pdzd8 (CG10362) and a synthetic ER-mitochondria tether in the regulation of locomotor activity, lifespan, and mitochondrial turnover of Drosophila melanogaster, using basic bioinformatics, RNAi, SPLICS, imaging and microscopies observations (i. e. TEM, SIM), fly lines, and a representative AD fly disease model, etc. The research methodologies were detailed in good order. The model system employed was suitable to address the research topic. The manuscript was written in a clear language and statistical analysis were correctly applied.

Major comments:

-Are the key conclusions convincing?

Yes. The results/conclusions are logical and provide an overview of Pdzd8 in the regulation of mitochondrial quality control and neuronal homeostasis.

-Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

No. Experiments were generally well performed, and all the data support the conclusions.

-Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

No suggested experiments needed.

-Are the data and the methods presented in such a way that they can be reproduced?

Yes. The authors have followed proper experimental design and methods have been described in sufficient detail.

-Are the experiments adequately replicated and statistical analysis adequate?

Yes, they are.

Minor comments:

-Specific experimental issues that are easily addressable.

No comment.

-Are prior studies referenced appropriately?

Yes. The relevant literatures have been cited appropriately.

-Are the text and figures clear and accurate?

1.Please pay attention to the correct spelling of the described protein name (Pdzd8) and gene name (should be in 'italic') throughout the manuscript, i. e. line 36, 98, and 556, etc.

2.In figure 1C and its figure legend, please state what the numbers "201" and "195" stand for.

3.Your data needs to be converted the lowercase letter "x" to math symbol "×" when representing times sign, i. e. line 523, 5x, etc.

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

No comment.

Significance

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

Discoveries from this study include 1) characterization of the tethering protein Pdzd8 in Drosophila melanogaster, and 2) shed light on a possible way on how to enhance mitochondrial quality control and to help promote healthy aging of neurons by manipulating MERCs.

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

With this manuscript, the authors present a straightforward but sound piece of scientific research, with the intent to illustrate the consequences of neuronal depletion of pdzd8 in Drosophila melanogaster. Since Pdzd8 plays specific functions in ER-mitochondrial tethering complexes and dysregulations of MERCs are damaging to neurons, this protein represents a good potential target. In this context the characterization of Pdzd8 should represent an interesting starting point. To this purpose, the gene was knockdown and the tether construct was recombinantly produced. The fly lines were then subjected to analysis both at the organismal and at the cellular level.

-State what audience might be interested in and influenced by the reported findings. Audience might include those who are in the field of neuroscience and pharmaceutical, and benefit from an awareness of this research.

-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.

Key words in my field of expertise: Ageing, neurodegenerative diseases, Alzheimer's disease, mitophagy, NAD+, neuroprotection. My group is investigating the molecular mechanisms of ageing and age-related neurodegeneration (especially AD) using cross-species model systems, ranging from human brain samples, iPSCs, C. elegans, Drosophila melanogaster, and mice, therefore I have sufficient expertise to evaluate this paper.

Referees Cross-commenting

To this reviewer the key novelty of this paper was the study of the regulation of the mitochondrial-ER contact sites (MERCs) in life and health. The data indicate that MERCs mediated by the tethering protein pdzd8 play a critical role in the regulation of mitochondrial homeostasis, neuronal function, and lifespan. In a transitional perspective, this reviewer would ask to check whether this mechanism conserves in rodents or not (e.g. to to memory in the AD mice and to run lifespan in mitochondrial toxin condition). This may be to much. But will depend on the standard of the journal.

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Reply to the reviewers

Overall:

We thank the reviewers for their thoughtful comments and suggestions on how to improve the manuscript. We also thank the reviewers for describing the study as “highly significant,” “rigorous and reliable as described and can be reproduced by others,” and as “relevant to investigators working in the field of rickettsial diseases and to a broader audience studying mechanisms of intracellular parasitism and host responses.”

In this revised manuscript we have addressed all the minor points raised by the reviewers. In regard to additional experiments, all three reviewers suggested that we perform histology of skin lesions, and in a revised manuscript we propose to thoroughly address this by performing histology at multiple time points in infected wild type and in interferon receptor-deficient mice. We will also attempt to use immunohistochemistry to identify the infected cell types in the skin and in internal organs. We will compare these findings to histology of human eschars. We feel that the reviewer comments support our contention that a manuscript containing these proposed additional experiments will be of strong significance in the field.

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

“Rickettsial eschars are hallmarks of less severe spotted fever diseases. The underlying mechanisms involved in the formation of the eschar caused by pathogenic rickettsiae remains unknown. The authors of this manuscript studied this interesting research question by using Ifnar-/-Ifngr-/- mice and Sca2 or OmpB mutant of R. parkeri. R. parkeri probably is the best rickettsial species to study rickettsial eschar due to the clinical features of R. parkeri rickettsioses and the biosafety level required to work with it. The data presented in the manuscript are very promising. The conclusions are supported by the presented results. For the first time, this study recapitulated human eschar-like skin lesion observed in patients with R. parkeri rickettsioses in the mouse models. More interestingly, mice inoculated with Sca2 mutant of R. parkeri i.d. had less disseminated rickettsiae in tissues, which helps us to understand the mechanisms by which pathogenic rickettsiae cause systemic infection after the arthropod bite.”

**Minor comments: **

“1) Figure 2D, it looks likely the lethality of mice i.d. infection with R. parkeri is not dose dependent. For example, mice inoculated with 10^4 showed greater lethality compared to 10^7. The authors might want to explain it in the Discussion.”

The reviewer is correct in observing that the lethality between different doses of R. parkeri in Ifnar-/-Ifngr-/-mice after intradermal infection is not dose dependent with the current number of mice used per group. We do not understand the reason for this, and more broadly we don’t understand the mechanism of lethality. We speculate that there could be a bottleneck; however, answering this question will require future investigations into the mechanisms of lethality that are beyond the scope of this study. To address the reviewer’s point, we now include this statement: “Degrees of lethality between different doses in Ifnar-/-Ifngr-/- mice were not significantly different from one another, and the cause of lethality in this model remains unclear.”

“2) Line 202, innate immunity in vitro might need to be revised.”

We agree that the previous description was vague. We changed the description to be more specific and it now reads: “…Sca2 does not significantly enhance the ability of R. parkeri to evade interferon-stimulated genes or inflammasomes in vitro.”

“3) It is unclear what is the unit of the inoculum in animal experiments, PFU?”

Yes, it is PFU. We have now indicated this in the figure legends.

“4) Line 36, in the study of "Reference 16", C3H/HeN mice, not B6 mice, were used.”

We thank the reviewer for noticing this error and we have changed the text to C3H/HeN.

“5) The conclusion on eschar will be greatly strengthened if histological analysis is included, particularly whether dermis necrosis is present or not.”

In the revised manuscript we will perform histology on eschars in wild type and Ifnar-/-Ifngr-/- mice over time. We will also use immunohistochemistry to analyze the infected cell types and will compare this to data on human eschars. We agree that this will greatly strengthen our conclusions regarding the similarities between the mouse and human eschars.

“6) Line 357, it is not clear what "spinfection" means.”

We have changed this to “infection” for clarity.

“Reviewer #1 (Significance (Required)): Several approaches employed in the study are new to the field of animal models of the rickettsioses. For example, fluorescent dextran was used to investigating the vascular damage in skin at the inoculation site; body temperature for mice infected with R. parkeri. Overall, the study is highly significant since it has answered the important questions in the research area of spotted fever rickettsioses and employed appropriate approaches. No major concerns were noticed.”

We thank the reviewer for appreciating the significance of this work.

**Referees cross commenting** I agree with other reviewers' comments. Thanks for the invite.”

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

“The manuscript utilizes a new model of spotted fever rickettsiosis. Using this model, the authors have determined that knockout of the sca2 or ompB gene attenuates Rickettsia parkeri, and vaccination with the attenuated rickettsiae provides protection against virulent challenge. However, the model is far less than ideal as it has eliminated important effectors of immunity.”

We thank the reviewer for their comments and we hope to thoroughly address their concerns. In regard to the effects of interferons on long-lasting immunity to R. parkeri, we note to the reviewer that we observed that immunized Ifnar-/-Ifngr-/- mice were completely and robustly protected from rechallenge. No lethality and no loss of body weight or temperature was observed after a rechallenge dose of 10x the LD-50. These data reveal that interferons are dispensable for long-lasting immunity to R. parkeri in inbred mice and are not important effectors of adaptive immunity to R. parkeri. This is thus the first model that can be used to investigate the factors required for adaptive immunity to R. parkeri in mice.

If the reviewer’s comment is not referring to long-lasting adaptive immunity to R. parkeri but is instead referring to the general concept of using immunocompromised mice as models, we note that immunocompromised mice are used as models for a variety of pathogens, including many Rickettsia species (reviewed in Osterloh, Med Microbiol Immunol 2017), and Ifnar-/-Ifngr-/- mice specifically are used as models for Zika and Dengue virus infections. Unlike many other immunocompromised mice, Ifnar-/-Ifngr-/- mice do not require maintenance on antibiotics and they have no noticeable differences to wild type mice in regard to breeding or growth.

“Manuscript also fails to recognize that there is a Amblyomma maculatum tick transmitted model of Rickettsia parkeri infection that causes an eschar and disseminated pathology”

In the previous version of the manuscript in lines 266-269 we cited and acknowledged the reported tick transmission model in non-human primates (Banajee et al., 2015). As also noted by Reviewer 3, our model with needle inoculation is significantly less time consuming and expensive than a tick transmission model. Moreover, needle inoculation makes it feasible to precisely measure the number of bacteria that are administered, which is not true with ticks. Lastly, the tick model was described in non-human primates, which are significantly more expensive than inbred mice and are not amenable to genetic manipulation. Thus, our model provides many significant advantages over the tick model in non-human primates, including cost, time, availability of genetic mutants, and reproducibility.

“The model that they have used is inadequately characterized. The cutaneous lesion was not evaluated histologically to determine if it features the actual characteristics of an eschar.”

We thank the reviewer for the suggestion and as a part of our revision plan, we propose to thoroughly analyze the lesion histologically.

“Although bacteria were found in the liver and spleen, in which macrophages are significant target, there was no evaluation of the vital organs including lung and brain nor demonstration of the target cells or pathologic lesions.”

In previous work from our lab (Engström et al., 2019), we found that lungs of wild type mice contained similar number of infectious R. parkeri as the spleen and liver after intravenous infection. Thus, in order to be able to process more samples quickly, we did not include lungs in the experiments described here. In unreported data, we also found that organs including the brain, kidneys, and heart had no/little recoverable PFUs. As a part of our revision plan, we propose to perform immunohistochemistry in the spleen, liver, lung, and skin to identify the infected cell types. Identifying the infected cell types will reveal if the same cell types are infected in our mouse model as in humans.

“Unfortunately, the assay of vascular permeability was applied only to the inoculation site and not to the disseminated visceral organs such as lung and brain.”

We have performed the vascular permeability assay using internal organs alongside the skin; however, little/no fluorescence was observed in any sample. We were unable to distinguish differences between control groups or between control and experimental groups in organs from mice that were treated and untreated with the fluorescent dextran. Thus, we were unfortunately not able to apply the described vascular damage assay to organs other than the skin. We now indicate this in the revised text.

Reviewer #2 (Significance (Required)):

“The authors all have misrepresented the eschar as a critically important lesion whereas the patients usually do not even know i's presence until they began to develop systemic symptoms and it is a detected by a physician examining the patient.”

We did not intend to suggest that the eschar is either more or less critically important than other features of rickettsial disease. We simply described the eschar as a “hallmark feature” of eschar-associated rickettsiosis. Additionally, as the reviewer notes, patients report systemic symptoms, and our model elicits systemic disease by R. parkeri in mice. Thus, the model we describe recapitulates both an eschar and disseminated disease and is the first mouse model for R. parkeri that exhibits both of the disease manifestations mentioned by the reviewer.

“On line 30 the authors state that mice are the natural reservoir of Rickettsia parkeri. The references cited describe the failure of acquisition by feeding ticks, meaning that it is not a true reservoir. The reference describing animals with antibodies merely indicates exposure to a spotted fever group Rickettsia not sufficient evidence of a role as a reservoir.”

We thank the reviewer for making this important distinction and we have altered the text to read: “…small rodents including mice have been found as seropositive for R. parkeri in the wild.”

“In response to the request for my expertise, I have contributed a large amount of data to understanding mechanisms of immunity to rickettsiae and have developed several useful animal models of Rickettsial diseases. I also have expertise on clinical aspects of spotted fever group rickettsioses, including the eschar.”

**Referees cross commenting**

“This is not the first Mouse model of rickettsiosis to contain an eschar. There is a model of Rickettsia parkeri transmitted by Amblyomma maculatum ticks in which eschars occur.”

As noted above by us and also by Reviewer 3, we cited and discussed the tick transmission model in non-human primates (Banajee et al., 2015) in the Discussion. We also note to the reviewer the many advantages of our i.d. infection model, including how it will make these experiments more widely accessible, more reproducible, less expensive, faster, and enable the infection of mice with various genetic modifications.

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

“This manuscript reports novel observations pertinent to development in inbred mice of an eschar lesion and generalized lethal infection following intradermal infection with Rickettsia parkeri, the mice are deficient in two types of interferon receptors. This is a new observation for the murine system and expands the existing repertoire of model infections for tick-borne rickettsiae. This study also reports that Sca2-mediated actin-based motility is required for R. parkeri dissemination and provides indirect evidence that OmpB protein is involved in eschar formation, thus corroborating previous knowledge about these major surface exposed antigens of rickettsiae and host cell interactions and host responses to these organisms.”

“The study is rigorous and reliable as described and can be reproduced by others given availability of adequate funding, access to similar facilities, strains of mice and rickettsial mutants, and technical personnel with similar skills and training. There are no ethical or technical concerns.”

We thank the reviewer for their thoughtful comments and for appreciating the advantages of this model.

“The main limitation of the manuscript is due to the fact that histological and immunohistochemical analysis of the eschar was not performed; therefore, it is not clear if pathological processes and features of this lesion formation are the same or related to the human pathology.”

We thank the reviewer for this suggestion. As a part of the revision plan, we propose to perform histological and immunohistochemical analysis of the eschar and will compare these findings to reported data from humans. We will also identify the cell types infected in the skin and internal organs in wild type and Ifnar-/-Ifngr-/- mice.

“Similarly, in an attempt to generalize (as the authors try very hard), it is not clear how these observations will be relevant to rickettsial pathogens which are responsible for more severe forms of rickettsioses (such as R. rickettsii and R. prowazekii) but are not known to cause eschar formation as a part of their clinical manifestations.”

Our findings with Sca2 are in agreement with findings on R. rickettsii Sca2 in guinea pigs (Kleba et al., 2010), which showed that Sca2 was required for eliciting fever and an antibody response. Our work also expands on these findings by showing that sca2 mutants immunize against rechallenge and by finding reduced bacterial burdens in internal organs after intradermal infection with sca2 mutant bacteria. Thus, we believe that studying R. parkeri genes in Ifnar-/-Ifngr-/- mice can serve as a model to better understand conserved virulence genes in diverse rickettsial pathogens.

Beyond virulence genes, we note that our model also recapitulates systemic disease including dissemination to internal organs. Thus, it provides a platform to study disease manifestations beyond the eschar that may be relevant to other rickettsial pathogens including R. rickettsii and R. prowazekii.

Some other virulent rickettsial pathogens cause limited/no disease in WT C57Bl/6 mice, including R. akari, R. conorii, R. typhi, and O. tsutsugamushi (reviewed in Osterloh, Med Microbiol Immunol 2017). Thus, Ifnar-/-Ifngr-/- mice may potentially serve as models for these pathogens. We now include this point in the Discussion.

“The other deficiency is due to a limited description of the Sca2 and OmpB mutants used in this study. It was necessary to locate and review previous publications by this group in order to understand the experiments conducted here and their interpretation. It would be useful to the readers if this information (a better more complete description of the mutants and their properties) is summarized in this manuscript.”

We have now provided a more complete description of the mutants in the Introduction and Results.

Reviewer #3 (Significance (Required)):

*“The study is relevant to investigators working in the field of rickettsial diseases and to a broader audience studying mechanisms of intracellular parasitism and host responses.

The study argues that difference(s) in dermal IFN signaling mechanism(s) distinguish human and murine susceptibility to R. parkeri infection. This is a very useful speculation; however, a better and deeper discussion would be helpful to demonstrate the relevance of these observations and their connection(s) to events occurring during the course of human infections. Regrettably, there are almost no citations of classic or current literature addressing these aspects of rickettsial pathogenesis and the role of IFN-dependent mechanisms beyond self-citations. Overall, the discussion includes four relatively short paragraphs, each addressing different directions of possible research, which indicates ample possible utility of this murine model; however, a more coherent and convincing discussion is desirable.”*

We thank the reviewer for the suggestion, and we have now expanded the Discussion to address the role for IFN-dependent mechanisms in humans and mice during rickettsial infections, including classic and current literature citations.

**Referees cross commenting**

“I agree with the Reviewer #2 that per se this is not the first murine model reproducing eschar upon A. maculatum transmission; however, this is the first model that allows to monitor eschar formation using needle inoculation. This model can be widely used; while many labs maybe limited by their facility setup and can't afford/conduct tick transmission experiments. The authors acknowledged existing of the tick transmission model and discuss inclusion of this option in their future experiments.”

We thank the reviewer for recognizing the many advantages of this model.

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Referee #3

Evidence, reproducibility and clarity

This manuscript reports novel observations pertinent to development in inbred mice of an eschar lesion and generalized lethal infection following intradermal infection with Rickettsia parkeri, the mice are deficient in two types of interferon receptors. This is a new observation for the murine system and expands the existing repertoire of model infections for tick-borne rickettsiae. This study also reports that Sca2-mediated actin-based motility is required for R. parkeri dissemination, and provides indirect evidence that OmpB protein is involved in eschar formation, thus corroborating previous knowledge about these major surface exposed antigens of rickettsiae and host cell interactions and host responses to these organisms.

The study is rigorous and reliable as described, and can be reproduced by others given availability of adequate funding, access to similar facilities, strains of mice and rickettsial mutants, and technical personnel with similar skills and training. There are no ethical or technical concerns.

The main limitation of the manuscript is due to the fact that histological and immunohistochemical analysis of the eschar was not performed; therefore, it is not clear if pathological processes and features of this lesion formation are the same or related to the human pathology. Similarly, in an attempt to generalize (as the authors try very hard), it is not clear how these observations will be relevant to rickettsial pathogens which are responsible for more severe forms of rickettsioses (such as R. rickettsii and R. prowazekii) but are not known to cause eschar formation as a part of their clinical manifestations.

The other deficiency is due to a limited description of the Sca2 and OmpB mutants used in this study. It was necessary to locate and review previous publications by this group in order to understand the experiments conducted here and their interpretation. It would be useful to the readers if this information (a better more complete description of the mutants and their properties) is summarized in this manuscript.

Significance

The study is relevant to investigators working in the field of rickettsial diseases, and to a broader audience studying mechanisms of intracellular parasitism and host responses.

The study argues that difference(s) in dermal IFN signaling mechanism(s) distinguish human and murine susceptibility to R. parkeri infection. This is a very useful speculation; however, a better and deeper discussion would be helpful to demonstrate the relevance of these observations and their connection(s) to events occurring during the course of human infections. Regrettably, there are almost no citations of classic or current literature addressing these aspects of rickettsial pathogenesis and the role of IFN-dependent mechanisms beyond self-citations. Overall the discussion includes four relatively short paragraphs, each addressing different directions of possible research, which indicates ample possible utility of this murine model; however, a more coherent and convincing discussion is desirable.

Referees cross commenting

I agree with the Reviewer #2 that per se this is not the first murine model reproducing eschar upon A. maculatum transmission; however, this is the first model that allows to monitor eschar formation using needle inoculation. This model can be widely used; while many labs maybe limited by their facility setup and can't afford/conduct tick transmission experiments. The authors acknowledged existing of the tick transmission model and discuss inclusion of this option in their future experiments.

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Referee #2

Evidence, reproducibility and clarity

The manuscript utilizes a new model of spotted fever rickettsiosis. Using this model, the authors have determined that knockout of the sca2 or ompB gene attenuates Rickettsia parkeri, and vaccination with the attenuated rickettsiae provides protection against virulent challenge. However, the model is far less than ideal as it has eliminated important effectors of immunity. Manuscript also fails to recognize that there is a Amblyomma maculatum tick transmitted model of Rickettsia parkeri infection that causes an eschar and disseminated pathology. The model that they have used is inadequately characterized. The cutaneous lesion was not evaluated histologically to determine if it features the actual characteristics of an eschar. Although bacteria were found in the liver and spleen, in which macrophages are significant target, there was no evaluation of the vital organs including lung and brain nor demonstration of the target cells or pathologic lesions. Unfortunately the assay of vascular permeability was applied only to the inoculation site and not to the disseminated visceral organs such as lung and brain.

Significance

The authors all have misrepresented the eschar as a critically important lesion whereas the patients usually do not even know i's presence until they began to develop systemic symptoms and it is a detected by a physician examining the patient.

On line 30 the authors state that mice are the natural reservoir of Rickettsia parkeri. The references cited describe the failure of acquisition by feeding ticks, meaning that it is not a true reservoir. The reference describing animals with antibodies merely indicates exposure to a spotted fever group Rickettsia not sufficient evidence of a role as a reservoir.

In response to the request for my expertise, I have contributed a large amount of data to understanding mechanisms of immunity to rickettsiae and have developed several useful animal models of Rickettsial diseases. I also have expertise on clinical aspects of spotted fever group rickettsioses, including the eschar.

Referees cross commenting

This is not the first Mouse model of rickettsiosis to contain an eschar.There is a model of Rickettsia parkeri transmitted by Amblyomma maculatum ticks in which eschars occur.

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Referee #1

Evidence, reproducibility and clarity

Rickettsial eschars are hallmarks of less severe spotted fever diseases. The underlying mechanisms involved in the formation of the eschar caused by pathogenic rickettsiae remains unknown. The authors of this manuscript studied this interesting research question by using Ifnar-/-Ifngr-/- mice and Sca 2 or OmpB mutant of R. parkeri. R. parkeri probably is the best rickettsial species to study rickettsial eschar due to the clinical features of R. parkeri rickettsioses and the biosafety level required to work with it. The data presented in the manuscript are very promising. The conclusions are supported by the presented results. For the first time, this study recapitulated human eschar-like skin lesion observed in patients with R. parkeri rickettsioses in the mouse models. More interestingly, mice inoculated with Sca2 mutant of R. parkeri i.d. had less disseminated rickettsiae in tissues, which helps us to understand the mechanisms by which pathogenic rickettsiae cause systemic infection after the arthropod bite.

Minor comments:

1)Figure 2D, it looks likely the lethality of mice i.d. infection with R. parkeri is not dose-dependent. For example, mice inoculated with 10^4 showed greater lethality compared to 10^7. The authors might want to explain it in the "Discussion".

2)Line 202, innate immunity in vitro might need to be revised.

3)It is unclear what is the unit of the inoculum in animal experiments, PFU?

4)Line 36, in the study of "Reference 16", C3H/HeN mice, not B6 mice, were used.

5)The conclusion on eschar will be greatly strengthened if histological analysis is included, particularly whether dermis necrosis is present or not.

6)Line 357, it is not clear what "spinfection" means.

Significance

Several approaches employed in the study are new to the field of animal models of the rickettsioses. For example, fluorescent dextran was used to investigating the vascular damage in skin at the inoculation site; body temperature for mice infected with R. parkeri. Overall, the study is highly significant since it has answered the important questions in the research area of spotted fever rickettsioses and employed appropriate approaches. No major concerns was noticed.

Referees cross commenting

I agree with other reviewers' comments. Thanks for the invite.

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Reply to the reviewers

We would like to thank the reviewers for taking the time to carefully evaluate our manuscript. The paper will be significantly improved by their suggestions, and we are grateful for their perspectives.

To address the reviewers’ concerns, we will complete additional control experiments and revise the manuscript as detailed below.

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

In the present work Stumpff, Reinholdt and co-workers investigate the mechanism by which micronuclei contribute to tumorigenesis. Micronuclei are classic markers of genomic instability widely used in the diagnosis of cancer, but whether they work as drivers of the process has recently attracted significant attention due to their link with chromothripsis. Here, the Stumpff/Reinhold labs have explored an interesting model to test some ideas about the role of micronuclei as drivers of tumorigenesis, based on Kif18A/p53 double KO mice. They confirm the formation of micronuclei in these animals, but find no substantial increase in survival and tumor incidence relative to p53 KO animals, despite higher incidence of micronuclei in Kif18A/p53 KO tumors. They conclude that, per se, micronuclei do not have the capacity to form tumors, regardless of p53 status. This was surprising, given the well-established role of p53 in preventing the proliferation of micronucleated cells. To shed light into this apparent paradox, they compared micronuclei from Kif18A KO cells with micronuclei generated by a number of other experimental conditions that promote formation of anaphase lagging chromosomes or generates acentric fragments. They found that micronuclei derived from Kif18A are intrinsically different from micronuclei generated by those other means and essentially showed increased accumulation of lamin B, were more resistant to rupture and preserved the capacity to expand as cells exited mitosis. Of note, they find a correlation between chromosome proximity to the poles/main chromosome mass and the different features that characterize micronuclei from Kif18A KO cells, compared with the other experimental conditions in which late lagging chromosomes are more frequent. Overall, I find this study extremely interesting, well designed and executed in a rigorous way that characterizes the consistent solid work from these laboratories over the years. I have just few minor points that I recommend to be addressed prior to publication. 1-Abstract and main text lines 70 and 100: the authors indicate that Kif18A mutant mice produce micronuclei due to unaligned chromosomes. This is correct, but at the same time misleading. The authors should clarify that although micronuclei derive from compromised congression, I was convinced from previous works (Fonseca et al., JCB, 2019) that it was their asynchronous segregation in anaphase that led to micronuclei formation. As is, a less familiar reader may conceive that misaligned chromosomes directly result in micronuclei, for example by being detached from the main chromosome mass.

We thank the reviewer for raising this point. We agree that micronuclei form in the absence of KIF18A due to chromosome alignment defects, which reduces interchromosomal compaction and leads to asynchronous arrival of chromosomes at spindle poles during anaphase. As the reviewer suggests, micronuclei form around chromosomes that travel longer distances and arrive late to the poles. We have revised the manuscript to clarify this (Lines 12-13, 72-73, 102).

2-Page 2, line 59: "cells entering cell division...become fragmented". It is not the cells, but the chromosomes that fragment. Please correct.

We have revised this wording to indicate it is the chromosomes within micronuclei which fragment (Line 60-63).

3-Page 4, line 149: "reduced survival in the Kif18A null, p53 mice". P53 what? KO, WT? Please clarify.

We have revised this wording as suggested, to read: “reduced survival in the Kif18agcd2/gcd2, p53-/- mice,” (Line 158).

4-Page 5, line 212: the authors refer that micronuclei were scored for absence of lamin A/C, but previously they scored it as "continuous/discontinuous". Please clarify.

Thank you for raising this question. When we scored lamin A/C, we noted cases where lamin A/C signal was incompletely present (not fully co-localizing with the micronuclear area, as indicated by DAPI). In these infrequent cases, micronuclei were identified as having “discontinuous” lamin A/C signal and were binned with those micronuclei lacking lamin A/C, for purposes of creating a binary readout of the micronuclear envelope: either 1) “intact” (having full, completely continuous lamin A/C signatures) or 2) “ruptured” (lacking a complete micronuclear signal of lamin A/C). We will update the text and the methods to more clearly reflect this categorization (Lines 221-225; 603-607).

5-Page 6, line 243: "Kif18A is not required for micronuclear envelope rupture". Shouldn't it be micronuclear envelope "integrity"?

We apologize for the confusion here. The experiment performed was designed to distinguish whether micronuclear envelopes are more stable in KIF18A KO cells or if KIF18A itself is somehow required for the rupture of all micronuclear envelopes to occur. Since nocodazole-induced micronuclei were able to rupture in KIF18A KO cells at similar frequencies to those seen in control cells, the data indicate that KIF18A is not required for the process of micronuclear envelope rupture. We modified the text to improve clarity (lines 252-253).

6-One of the most interesting results of the paper is the correlation between envelope formation in micronuclei with their respective position relative to the poles/midzone. Could the authors try to investigate causality? For instance, the authors refer to works from other labs in which MT bundles and a midzone Aurora B activity gradient might play a role in the different features associated with micronuclei envelope formation, depending on their origin. Could the authors manipulate this gradient and investigate whether it changes the outcome in terms of nuclear envelope assembly properties on micronuclei? Are there any detectable features in midzone MT organization in Kif18A KO cells that would justify the observed differences?

We agree that this result is very interesting. However, we feel the proposed experiments would repeat previous work and are somewhat outside the purview of the present study. Elegant experiments to address Aurora’s role in preventing micronucleus formation have already been performed using genetic approaches in Drosophila neuroblasts and small molecule inhibitors in mammalian cells and Drosophila S2 cells (PMIDs: 24925910, 25877868, and 29986897). Interpreting effects of Aurora B inhibition are complicated by the many critical roles Aurora B plays in ensuring proper and faithful chromosome segregation. Thus, experiments to precisely test Aurora’s effect on micronuclear envelope stability require addition of Aurora B inhibitors on a cell-by-cell basis, administered within a narrow window of minutes during anaphase. It would require significant effort to obtain enough cells from different experimental conditions to make a meaningful comparison.

The suggestion to investigate detectable differences or features in midzone MT organization in KIF18A KO cells is also appreciated. We have not observed gross differences in midzone microtubules in KIF18A KO cells, but we will quantitatively evaluate this and add these results to the revised manuscript.

Reviewer #1 (Significance (Required)):

Kif18A plays a key role in chromosome alignment, without apparently affecting kinetochore-microtubule attachments in non-transformed cells. Because they cannot establish a proper metaphase plate Kif18A KO cells enter anaphase with highly asynchronous segregation due to non-uniform chromosome distribution along the spindle axis. Consequently, some "delayed" chromosomes form micronuclei, in cell culture and in vivo. Interestingly, prior art has failed to detect any increased signs of genomic instability in Kif18A KO cells and mice, and, contrary to what would be expected based on current trends, these mice do now show any signs of increased incidence of tumors, in fact they even show some protective effect to induced colitis-associated colorectal cancer. Noteworthy, all previous experimental works pointing to a role of micronuclei as key intermediates of genomic instability in cancer relied on models in which the tumor suppressor protein p53 had been inactivated. In the present work, the authors explore the relationship between micronuclei formation and p53 inactivation by investigating tumor formation in Kif18A/p53 double KO animals (1 or 2 alleles of p53 inactivated).The reported results are timely and will attract the interest of a broad readership, while decisively contributing to shed light into an ongoing debate. I am therefore all in favor for the publication of this work in any journal affiliated with review commons, pending some minor revisions.

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

Sepaniac and colleagues use in vivo and in vitro approaches to examine why micronuclei generated by lack of KIF18A activity do not promote tumorigenesis. The authors conclude that micronuclei in KIF18A depleted cells form stable micronuclear envelopes, which may be a result from lagging chromosomes being closer to the spindle pole when the micronuclear envelope forms. The authors further conclude that the stability of the micronuclei arising from lack of KIF18A can explain why Kif18a mutant mice do not develop tumors. These results also suggest that the consequences of micronuclei and their possible contribution to tumorigenesis depend on the context of their genesis. While the mouse model data and characterization of the stability of micronuclei generated by different insults support the conclusions, the lagging chromosome positioning data could be improved. Moreover, a number of other issues should be addressed prior to publication.

**Major issues:**

1.Line 153-155. The authors conclude that the slight reduction in overall survival is "due to a reduced ability of Kif18a mutants to cope with rapid tumorigenesis," but it is unclear why this would be the case. There is also an increase in micronucleated cells in thymic lymphomas from Kif18a/p53 homozygous mice (Fig. 2B)-could this not contribute? In Fig. 3C, the authors show that micronuclear rupture is similar in both Kif18a WT and mutant mice, so it seems possible that the increase in the frequency of micronuclei (Fig. 2B) coupled with a similar frequency of micronuclear rupture (Fig. 3C) could lead to the reduced survival. Then, in the discussion, the authors downplay this finding by saying (line 371) "loss of Kif18a had modest or no effect on survival of Trp53 homozygotes and heterozygotes." Why then speculate earlier in the text that loss of Kif18a reduces the ability to cope with tumorigenesis?

We thank the reviewer for pointing out this issue. Our goal here was to try and explain why the Kif18a/p53 mutant homozygotes display a small but significant reduction in survival compared to p53 mutants, while the Kif18a mutation does not impact survival of p53 heterozygotes, which could be considered a more sensitive model for detecting decreased survival. Kif18a homozygous mutants do display a small reduction in survival shortly after birth compared to heterozygote and wild type littermates (PMID: 25824710). Thus, we can’t exclude the possibility that incompletely penetrant, postnatal lethality might be coincident with reduced fitness in surviving mutants, thus naming them more sensitive to loss of p53 loss of function. We have removed this statement form the revised text.

However, the reviewer’s point that the combination of increased micronuclei in Kif18a/p53 homozygous mutants combined with a similar rupture rate seen in p53 mutants could also underlie or at least contribute to reduced survival is a good one. We have softened our conclusion in the Results section regarding the reduced survival of double homozygous mice (lines 158-164). We also agree that the way in which this point is addressed in the results and discussion sound contradictory. Thus, we have edited the language in the Discussion to improve consistency (lines 393-399).

2.Related to the point above, the authors show in figure 3 that the micronuclei found in healthy tissues display infrequent membrane rupture (panel B). However, micronuclear membrane rupture in tumor tissues is much more frequent (panel C). How do the authors explain this? Do they hypothesize that the micronuclei in the tumors originate by mechanisms other than the misalignment caused by lack of KIF18A? Does KIF18A depletion cause aneuploidy due to segregation of two sisters to the same pole? If so, one could expect the tumors to be aneuploid (is this the case?) and aneuploidy has been shown by numerous groups to cause genomic instability. Such genomic instability could then explain the difference in membrane rupture.

We agree that this is an interesting question. We plan to investigate several possible contributors to increased rupture in tumor cells in a separate study. As outlined in the Discussion (lines 443-458), we hypothesize that rupture could increase in tumor tissue due to changes in lamin expression or cytoskeletal forces in these cells. However, as the reviewer notes, differences in aneuploidy could also potentially explain the differences in membrane rupture observed in healthy (non-tumorous) and thymic lymphoma tissues. For example, an increase in chromosome number could lead to lagging chromosomes being positioned closer to the midzone in Kif18a mutant cells or, as the reviewer suggests, the micronuclei could occur in aneuploid tumors due mitotic defects other than misalignment. This may be difficult to determine unequivocally in primary cell or tissue samples. However, we do have a limited quantity of primary thymic lymphoma-derived cells and we will use these to initially investigate aneuploidy in the two genotypes. The results of these studies will be added to the final revised manuscript. In addition, we will incorporate a discussion of how aneuploidy may increase rupture frequency in tumors into the revised manuscript.

3.The authors conclude that lagging chromosomes in KIF18A KO cells are found closer to the main chromatin mass. The Stumpff lab showed in a 2019 JCB paper that KIF18 KO cells have a chromosome alignment defect and as a result during anaphase the chromosomes can be scattered rather than forming the tight, uniform mass that is observed in WT cells. The scattering of kinetochores resulting from this phenotype could affect the value of "Avg Chromosomes Distances" in Fig 7B and the normalized distance in the KIF18A KO cells. Therefore, live-cell imaging experiments would be helpful to resolve this and possibly strengthen this conclusion. RPE1 cells with fluorescently tagged CENP-A and centrin could be used to ensure that the lagging chromosomes will not rejoin the main nucleus. Moreover, these cells could be used for correlative live-fixed cell experiments in which fixed cell analysis following micronucleus formation could be used to show that chromosomes that lag farther away from the spindle pole are more likely to have defective micronuclear envelopes.

The reviewer’s concern that the unalignment phenotype, characteristic of KIF18A KO cells, may impact the value of average chromosome distances used to set a threshold for chromosomes meeting our definition of lagging is valid. To address this, we analyzed the standard deviations for chromosome-to-pole distances within half spindles of KIF18A KO and nocodazole-washout treated anaphase cells as a way to compare chromosome scattering in these two conditions. This analysis revealed no significant difference between the standard deviations of chromosome positions in the two groups, suggesting that scattering is similar in nocodazole treated and KIF18A KO cells. We have included these data in the manuscript (Line 351-356, and additional data added to Figure S2C).

In order to further strengthen this conclusion, we are certainly willing to attempt the live cell imaging experiments suggested by the reviewer. We would like to point out that the frequency of micronucleus formation in the KIF18A KO cells is relatively low compared to the frequency seen after other experimental treatments (~7% of divisions result in a micronucleus). Thus, a large number of individual cells would need to be imaged with relatively high temporal resolution to make conclusions about the effects of chromosome position on micronuclear envelope formation (such analyses are not possible with the live data sets we currently have, where cells were imaged every 2 minutes). This difficulty led us to perform these measurements in synchronized and fixed cells to begin with.

4.Based on the Fonseca et al. 2019 JCB paper (video 2), micronuclei from KIF18A KO do not exclusively arise from lagging chromosomes. Instead, chromosomes can also escape the main chromatin mass after segregation and subsequently be excluded from the main nucleus. It would be important to know what fraction of the micronuclei in KIF18A KO cells arise via lagging chromosomes. Since Aurora B and/or bundled microtubules at the spindle midzone are believed to prevent proper nuclear envelope formation, chromosomes that properly segregate but later become separated from the main nucleus would be more likely to form proper micronuclear envelopes than those arising from lagging chromosomes. The correlative microscopy experiment suggested in the previous point could allow differentiation between these two routes to micronucleus formation.

The reviewer is correct that we did occasionally see chromosomes escape the main chromatin mass after segregation in the Fonseca et al., 2019 study referenced. We did not quantify the frequency of these events in that study, but they were rare. To address this quantitatively, we have measured the incidence of micronuclear formation around lagging chromosomes and chromosomes that escape the main chromatin mass after segregation in videos of KIF18A KO cells. We find that when micronuclei form in these cells, they form around lagging chromosomes 98% (46 out of 47 events) of the time. These data were derived from 4 live cell imaging experiments. This information has been added to the Results section (line 328-330).

**Minor issues:**

1.Some parts of the manuscript are excessively wordy and some sentences are unclear or convoluted (e.g., lines 148-153 and 238-239).

Thank you for this feedback. We have revised the text in these two locations to improve clarity (lines 159-162 and 247-248 in the revised manuscript).

2.Lines 59-61. This sentence is formulated incorrectly. First of all, the subject of the sentence is "cells" and the verb is "can become fragmented." However, the authors mean that the DNA in the micronucleus can become fragmented (not the cells). Moreover, the way the sentence is currently formulated seems to suggested that the fragmentation occurs during cell division. However, this is not the case. Please, revise the text to make it more accurate.

We appreciate this point and have revised this text to reflect more precise language to describe this model. It is certainly the micronucleated chromatin which may become fragmented, and this fragmentation occurs as a result of replication stress, including replication fork collapse, after an existing micronucleated cell enters a subsequent round of S or G2 phase (PMIDs: 22258507, 26017310).

3.Lines 114-115. Please, provide references in support of this statement.

The statement in question: “This arrest was at least partially dependent on p53, consistent with other reports of cell cycle arrest following micronucleation,” shares the same references as the sentence that follows it (Sablina 1998, Thompson and Compton, 2010; Fonseca et al., 2019). We have updated the references to appear after the first statement to make this clear.

4.Line 153. The authors refer to Fig. 1C, but I think they mean Fig. 1B.

Thank you, we have updated the text to read Fig 1B.

5.Line 324. the authors find that RPE1 KIF18A KO cells have lagging chromosomes in ana/telophase 9% of the time, then say that this shows that lagging chromosomes are rare in KIF18A KO cells. However, this is a large increase compared to normal RPE1 cells, which only have 1-2% frequency of lagging chromosomes. So, they should revise the text here to say that the rates of lagging chromosomes from KIF18A KO are lower compared to the rates induced by nocodazole washout.

This is an important distinction. We have removed this confusing statement from the revised text (lines 336-338).

6.Line 383. The references listed here should be moved earlier and specifically after the statement summarizing the results of the studies instead of being listed after the authors' conclusion/interpretation of the data. The same issue was noted in other parts of the manuscript.

We have corrected this error (Lines 402-408). Before final submission, we will further amend the style of the manuscript throughout to cite relevant papers after the statement summarizing the results of those studies, rather than after our interpretation of the studies.

7.Figure 1A. In the text, the authors say they cross a Kif18a heterozygous mutant mouse with a p53 heterozygous mutant mouse, but the two mice in this figure are already heterozygous for both. Please, revise the text or depict the previous additional cross necessary to obtain the double heterozygous.

We thank the reviewer for catching this discrepancy. We have revised the text to describe the crosses necessary to obtain the double heterozygous mice shown in the figure (lines 121-123). The gcd2 mutation in Kif18a was named due to the “germ cell depleted” phenotype it causes. These homozygous mice are therefore infertile (Czechanski et al., 2015). For this reason, heterozygous mice for each gene were crossed to achieve the necessary homozygous progeny.

8.Figure 3A. Arrows or dotted circles outlining the micronuclei in the insets of the middle and bottom rows would be helpful since the DAPI signal in the micronuclei is low and somewhat difficult to see.

We have updated these figures as suggested to more clearly indicate the micronuclear area.

9.Figure 3B. Error bars should be added to the graph. Moreover, the authors noted that the differences are not significant. However, this seems surprising, given that in some cases there is a three- to five-fold difference between certain pairs. Indeed, a chi-square test using the numbers from table S1 indicated p values We appreciate this feedback on the statistical tests and comparisons among these data. The main point of these analyses is to demonstrate that tissues other than blood form micronuclei in vivo in the absence of Kif18a function and that the majority of these micronuclear envelopes are completely surrounded by Lamin A/C. The data presented in Figure 3B were obtained by counting several tissue types from a single mouse of each genotype. Thus, we do not believe that error bars are appropriate in this context. To avoid confusion, we have also removed the statistical bars which had indicated no significant differences in rupture frequency among the genotypes in each sampled tissue, as these are also probably inappropriate.

We understand the reviewer’s point that some pairwise comparisons of the data in Table S1 indicate that they are significantly different. We originally used a Chi-square test to compare the data from all three genotypes for each tissue. Because these data did not rise to the threshold of significance necessary to reject the null hypothesis across all three genotypes within each individual tissue type, we did not think performing pairwise comparisons between only two of those genotypes was appropriate (Whitlock and Schluter, The Analysis of Biological Data, 2009). Specifically, analyses of rupture frequency for spleen, liver, and thymus tissue gave p-values above 0.05 (spleen, p = 0.35; liver, p = 0.056; thymus, p = 0.052). Thus, we did not proceed with pairwise comparisons. In contrast, the analyses of p53 effects on micronucleus levels in peripheral blood in Fig 1D utilized samples from 8 individual mice for each genotype, and are therefore more amenable to statistical comparisons. If the reviewer believes any of the details of this approach are incorrect, we are happy to revise the analyses.

10.Figure 5G. When referring to this figure (lines 292-294), the authors talk about correlation. However, the points in this graph seem to be scattered a bit randomly.

To address this concern, we performed a Pearson’s correlation test on the data in Figure 5G. As suspected by the reviewer, this analysis did not indicate a significant correlation, and we have removed this plot from the manuscript.

11.Figure 6B-D. The Y-axis titles of the three graphs are a bit confusing. Please, consider revising.

We have updated the Y-axis titles for these graphs to more accurately represent what is displayed on each plot.

12.In Figure 7 and the text, the authors use the terms "late-lagging" and "lagging" chromosomes interchangeably, which is somewhat confusing in this context because lagging chromosome distance from the main chromosome mass is thought to contribute to defective assembly of micronuclear envelopes. It is not clear whether the authors intend to indicate, with this term, that the lagging chromosome is farther away from the main chromosome mass or that the lagging chromosome is in a "late" anaphase cell. Because this is confusing, I suggest just using the term "lagging chromosome" consistently. It could be useful to include representative images of lagging chromosomes located at different distances from the main chromosome mass. And certainly, the authors should include an example of a lagging chromosome in the KIF18A KO cells.

We agree with the reviewer’s concern regarding confusion of these terms. We have updated the text to use the term “lagging chromosome” consistently, as the reviewer suggests. We have also updated Figure 7A to include a representative image of a lagging chromosome in a KIF18A KO cell.

13.Figure S2A. The example in the bottom right image looks more like a chromosome bridge than a lagging chromosomes. Kinetochore staining is necessary to unequivocally identify lagging chromosomes.

We agree with the reviewer that kinetochore staining is necessary to precisely identify lagging chromosomes. We had used these images to quickly and crudely assess the presence and frequency of potentially lagging chromosomes, observed in late-anaphase cells by eye, and for subsequent experiments where lagging chromosomes were measured, repeated these experiments with proper staining of poles and kinetochores to make precise, quantifiable assessments. Reviewer #2 (Significance (Required)):

Based on the previous knowledge on the factors that cause abnormal assembly of the micronuclear membrane, the results presented in this study were somewhat predictable. However, these findings will add to the knowledge of how micronuclei form and the potential factors that lead to micronuclear membrane rupture. Previous studies investigating micronucleus behavior have focused on micronuclei arising via merotelic kinetochore mis-attachments. These mis-attachments lead to formation of micronuclei close to the spindle midzone. In the present study, instead, the micronuclei arising from lack of KIF18A activity form farther away from the spindle midzone. The results presented here suggest that the positioning of these micronuclei farther away from the midzone enables assembly of a more stable micronuclear membrane that will be less likely to rupture during the following cell cycle. A recent study showed that the microtubule bundles in the spindle midzone interfere with micronuclear membrane assembly. Based on this, it is not surprising that micronuclei forming away from the spindle midzone (like those resulting from lack of KIF18A activity) assemble more normal membranes. Although somewhat expected, this study provides the actual data in support of this phenomenon. This study will be of interest to cell biologists interested in cell division and genomic instability. My research has focused on cell division, aneuploidy, and chromosomal instability for nearly thirty years. Therefore, I believe I am fully qualified to evaluate this manuscript.

**Referees cross-commenting**

My areas of expertise do not include nuclear membrane structure and function. Therefore, I encourage the authors to consider the comments of reviewer #3 for issues related to reliable quantification of micronuclear membrane rupture.

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

**Summary** Sepaniac et al demonstrate that loss of KIF18a, a motor protein required for proper chromosome congression and chromatin compaction during mitosis, is insufficient to drive tumor development in mice although it does increase the frequency of micronuclei (MN), nuclear compartments that form around broken or missegregated chromosomes, in both normal and tumor tissue. MN are thought to increase genome instability and metastasis by undergoing DNA damage and activating innate immune signaling after irreparable nuclear membrane rupture. The authors use a non-transformed human cell line, hTERT-RPE-1, with KIF18a knocked out to demonstrate that MN formed as a result of KIF18a loss have more stable nuclear membranes than MN generated by other methods. They go on to correlate this increased stability with increased chromosome proximity to the main chromatin mass during nuclear envelope assembly and increased chromatin decompaction by a combination of fixed and live cell imaging.

**Major Comments**

1.This study relies heavily on the use of lamin A loss or discontinuity to identify ruptured micronuclei. Although the authors validate this marker against "leakage" of the soluble nuclear protein mCherry-NLS, there are several lines of evidence suggesting that lamin A loss or disruption is not a reliable reporter. In figure S3C, the top two panels of intact MN in the KIF18KO appear ruptured based on the gH2AX labeling, yet have significant levels of lamin A and are labeled as intact. In figure 4D, the rate of MN rupture after nocodazole release (60% ruptured in 2 hours) is much faster than that reported in other papers (40-60% in 16-18 hours, Liu et al; 60% in 16 hours; Hatch et al). In addition, images in Hatch et al, 2013 show lamin A localizing to both intact and ruptured MN and anecdotal information in the field suggests that lamin A localization is not a reliable reporter.

These discrepancies may be due to how the authors' define "mCherry-NLS leakage", which needs to be defined in the methods as previous studies have demonstrated that MN frequently have delayed or reduced nuclear import even though the membrane is intact. Regardless, the authors need to provide compelling independent evidence that lamin A loss and disruption faithfully recognize ruptured MN by either validating this marker against additional rupture reporters, such as Lap2, LBR, or emerin accumulation, or by repeating key experiments in cells expressing mCherry-NLS.

Our decision to use lamin A/C as a reporter was based on its use as a marker for micronuclear envelope presence in prior studies (Hatch, 2013; Liu, 2018). We were unaware of anecdotal information in the field that suggests that lamin A localization may not be a reliable reporter.

However, we think we understand the reviewer’s point to be that although it is clear from prior studies that gaps in the nuclear lamina are a known predictor of micronuclear rupture, these gaps can persist for some time before rupture has actually occurred. We agree that this is an important distinction and thank the reviewer for raising these questions.

As the reviewer notes, we performed control experiments to address this issue and validate the use of lamin A/C as a marker of micronuclear envelope rupture. Our approach involved correlating lamin staining with the localization of mCherry-NLS signal to the micronucleus (Figure S1). We found that these signals correlated well. As the reviewer points out, this analysis in fixed cells could be misleading in cases where nuclear import is reduced, but the micronuclear envelope is intact. If this were a significant contributor, we may have expected to see greater instances of micronuclei that exhibit continuous lamin A/C signal but lack nuclear localization of mCherry-NLS. However, we found this combination was rare among the KIF18A and RPE1 nocodazole washout treated cells (2%, or 1 of 46 micronuclei had continuous lamin A/C while lacking mCherry-NLS). We admit that this assumption may be oversimplified though.

The reviewer’s point about the timing of nocodazole treatment and washout something we have definitely considered. We note that prior studies have used differing time points after nocodazole treatment and release. For Hatch et al., 2013: U2OS cells were treated for 6 hours with nocodazole and then subjected to mitotic shakeoff, 48% of micronuclei were ruptured after 6 hours and ~60% were ruptured after 16 hours. Similarly, in Liu et al., 2018 60% of micronuclei were ruptured 16 hours post mitotic shake off and nocodazole release. While these results suggest that rupture increases with time after mitosis, it isn’t clear how early rupture may occur. In other words, does it take several hours in G2 before nearly half of micronuclei rupture or do many of these rupture shortly after cell division?

We note that other explanations could also potentially contribute to the differences in rupture rates reported in our study compared to those in previous publications. For example, we used a short nocodazole treatment (2 hrs) compared to the longer treatments (6 hrs) used in previous studies. We did this originally in order to produce a similar percentage of micronucleated cells as is seen in KIF18A KO cell populations. However, the difference in nocadozole treatment length could potentially influence the types and frequencies of kinetochore microtubule attachments formed. For example, if centrosomes stay closer together in mitotic cells after short nocodazole treatments, this could increase the number of abnormal attachments (e.g. PMID: 22130796). Such an effect would be expected to increase the frequency of lagging chromosomes and/or potentially produce more lagging chromosomes within the anaphase midzone.

The best way to address this issue would be to repeat our analyses of mcherry-NLS in live cells to track the formation and rupture of micronuclei. We did attempt these live imaging experiments previously and have found this experiment challenging due to: 1) the low frequency of micronuclear formation in KIF18A KO cell population; 2) a low transfection/expression efficiency for the mCherry-NLS plasmid in RPE1 cells, and 3) photobleaching of the mCherry-NLS plasmid. For these reasons, we transitioned into fixed cell experiments for the mCherry-NLS reporter. However, we propose to troubleshoot this assay and attempt to obtain the data necessary to determine when rupture is occurring. In addition, we will use additional markers to investigate micronuclear envelope stability, as the reviewer has suggested.

Regardless of the outcome of these experiments, we have measured a clear difference between the lamin deposition within micronuclear envelopes of KIF18A KO cells compared to those formed following other insults. Lamin recruitment is well established as a predictor of nuclear envelope stability. If necessary, we could alter the text to indicate that the presence of lamin A/C and B within micronuclear envelopes of KIF18A KO cells are indicative of nuclear envelope stability, and that this is distinct from the lamin profiles of micronuclei in cells subjected to nocodazole-washout.

2.Micronuclei in tumor sections and other dense tissues can appear very similar to other types of chromatin, including blebs from adjacent nuclei and dead cells. To verify that the quantified structures are bona fide micronuclei, the authors need to include a marker for the cell boundary. This is especially critical in the lamin a stained tumor sections with heterogenous lamin A protein expression.

We appreciate the point this reviewer raises and we carefully considered accurate identification of micronuclei in tissues. Three optical sections were collected from each sample. During analyses, we scrolled through the ~2-micron thick sections to exclude chromatin bodies connected to an out-of-plane nucleus or nuclear bleb. We have a limited number of sectioned and preserved thymic lymphoma tissues remaining. We will use these samples to reassess micronuclear frequency in the presence of a cell boundary marker.

3.Figure 4 compares MN rupture frequency between cells treated with different inducers of micronuclei - KIF18A KO, nocodazole release, and irradiation. These treatments have different effects on the cell cycle: KIF18A causes minor delays, nocodazole arrests cells in mitosis, and g-IR likely causes delays in S and G2. Since MN rupture frequency increases with the duration of interphase, the authors need to assess rupture frequency at similar time points after mitosis for all three conditions. One way to accomplish this would be to repeat this experiment and analyze cells collected by mitotic cells by shake-off prior to fixation and labeling.

We appreciate this point regarding differences in mitotic timing. Since micronuclear rupture frequency increases with time in interphase, we would expect the MN in KIF18A KO cells to exhibit the highest level of rupture if cell cycle timing were the primary variable affecting stability in our experiments. KIF18A KO cells are asynchronously dividing, and the micronuclei examined in populations of those cells could have been generated at any time. We do not have the same type of temporal control of these events as we do with drug treatment. In contrast, the vast majority of the MN in nocodazole washout cells would not have been in interphase for more than 1.5 hours in our experiments, yet showed increased lamin A/C defects. RPE1 cells treated with MAD2 siRNA knockdown, which do not experience mitotic delays (PMID: 9606211; 15239953), also showed greater frequencies of micronuclear envelopes which lacked lamin A/C compared to those arising in KIF18A KO cells.

To further address this question, we could attempt a mitotic shake-off assay, however, we believe that the formation of micronuclei, as a percentage in the population of KIF18A KO cells, will be limiting in these experiments.

As an alternative, we propose to use live cell imaging to follow micronuclear formation and rupture, as described above in reference to point 1.

**Minor Comments**

1.In figure 6A, it is unclear when the videos start and how micronuclei are selected for analysis. Do the micronuclei have to be continuously visible from the time they missegregate? Do the videos all start at the same time point during mitosis or is it contingent on when the MN appears separated from the main nucleus? One concern is that a consistent delay in micronucleus appearance in the nocodazole treated cells could artificially decrease the amount of MN expansion observed.

We thank the reviewer for these questions. The individual micronuclei did not need to be continuously visible from the time that they missegregated, though the majority were. When a micronucleus was not sufficiently in the plane of focus for an accurate area measurement, the individual measurement at that time point was not collected. In cases where one or more frames which were not measurable, a micronucleus was only included in the final data set if it was 1) the only micronucleus present in the daughter cell or 2) easily identifiable to be the same micronucleus. Measurements were taken until the micronuclear area reached an equilibrium for several frames. Final fold change in area was established by dividing final area measurements by initial measurements.

The initial measurement for each micronucleus taken from the videos all start at the same relative point during mitosis, which is just after chromosome segregation has occurred.

2.In figure 7A, it is difficult to identify the "lagging" chromosome in the top panel. It would be helpful to label the chromosome that becomes the MN, or ideally, to include a video or still images to demonstrate how micronuclei form in the KIF18A KO cells.

We have updated the images in Figure 7A to include an example of a lagging chromosome in a KIF18A KO cell. We will also include a more explicit reference to our previous study (Fonseca et al., 2019), which described how micronuclei form around lagging chromosomes in KIF18A KO RPE1 cells.

3.The two image panels in figure 7A are imaged at significantly different times during anaphase (early anaphase on bottom versus late anaphase/telophase on top). A better comparison would be between two cells at the same time point in anaphase.

We have updated the images in Figure 7A to compare cells at similar stages of anaphase. In our quantification of lagging chromosomes, we also accounted for anaphase-timing differences by normalizing all measurements within each half-spindle.

Reviewer #3 (Significance (Required)):

In this study, the authors identify chromatin decondensation in micronuclei as a new predictor of membrane stability. Although these results are correlative, if their micronucleus rupture results can be validated as described in major comment 1, this study would advance our understanding of the micronucleus rupture mechanism by linking mitotic spindle location, chromatin decondensation, and lamin B1 protein recruitment. This would provide needed support to a current model in the field that micronucleus stability is largely determined during nuclear envelope assembly. In addition, if KIF18a loss generates stable micronuclei at high frequency, it will become a critical system for testing MN rupture hypotheses in the field. Thus, this work would be of significant interest to cell biologists working on nuclear envelope structure and function, chromosome organization, and mitosis. I include myself in this group as a cell biologist studying nuclear envelope structure and function with an expertise in membrane dynamics. The authors also find that mice mutant for KIF18a have increased micronucleation in normal tissues but not increased tumor initiation. They hypothesize that this is due to the low rupture frequency of KIF18a-induced MN, however their data cannot reject the null hypothesis that the small increase in MN they see in KIF18a mutant mice would be insufficient to induce tumorigenesis even if rupture frequency was high. Thus the significance of their finding that micronucleation is not sufficient for cancer progression is unclear. However, the thorough analysis of micronucleation and rupture in several healthy tissues as well as a tumor model in KIF18 mutant mice would be of interest to both pathologists and cancer researchers focused on mechanisms of genome instability. These types of experiments are critical to determine how micronuclei contribute to cancer progression and the quantifications presented in this paper are truly impressive.

We appreciate this reviewer’s enthusiasm for our work and acknowledge that we cannot definitively conclude that micronuclear envelope stability explains why Kif18a mutant mice do not form tumors. However, it is interesting to note that the micronuclear loads measured using a peripheral erythrocyte assay are similar in Kif18agcd2/gcd2 mutant mice (0.6% micronucleated erythrocytes, of total erythrocytes) and ATMtm1 Awb/tm1 Awb mutant mice (0.6% of micronucleated erythrocytes, of total) (Fonseca et al., 2019). Yet, the tumor frequency in these two models is dramatically different: Kif18agcd2/gcd2 mutant mice do not spontaneously form tumors – while the majority of ATMtm1 Awb/tm1 Awb mutant mice do develop thymic lymphoma tumors between 2 and 4 months (Barlow, 1996). It is not clear how much micronuclei contribute to tumorigenesis in the ATM mutant model, but this comparison does suggest that the increase in MN seen in Kif18a mutants may be physiologically relevant. We have added this information to the revised text (lines 125-130).

**Referees cross-commenting**

I agree with the concerns raised by the other 2 reviewers, especially their comments about the need to clarify the mechanism of chromosome lagging versus chromosome congression and compaction. I think that all of these suggestions, though, are contingent on them being able to reproduce their micronucleus rupture results with a better marker of nucleus integrity. I strongly believe that additional validation of lamin A as a micronucleus rupture marker will demonstrate that it is unreliable, based both on our own observations in RPE-1 cells and the images they show

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Referee #3

Evidence, reproducibility and clarity

Summary

Sepaniac et al demonstrate that loss of KIF18a, a motor protein required for proper chromosome congression and chromatin compaction during mitosis, is insufficient to drive tumor development in mice although it does increase the frequency of micronuclei (MN), nuclear compartments that form around broken or missegregated chromosomes, in both normal and tumor tissue. MN are thought to increase genome instability and metastasis by undergoing DNA damage and activating innate immune signaling after irreparable nuclear membrane rupture. The authors use a non-transformed human cell line, hTERT-RPE-1, with KIF18a knocked out to demonstrate that MN formed as a result of KIF18a loss have more stable nuclear membranes than MN generated by other methods. They go on to correlate this increased stability with increased chromosome proximity to the main chromatin mass during nuclear envelope assembly and increased chromatin decompaction by a combination of fixed and live cell imaging.

Major Comments

1.This study relies heavily on the use of lamin A loss or discontinuity to identify ruptured micronuclei. Although the authors validate this marker against "leakage" of the soluble nuclear protein mCherry-NLS, there are several lines of evidence suggesting that lamin A loss or disruption is not a reliable reporter. In figure S3C, the top two panels of intact MN in the KIF18KO appear ruptured based on the gH2AX labeling, yet have significant levels of lamin A and are labeled as intact. In figure 4D, the rate of MN rupture after nocodazole release (60% ruptured in 2 hours) is much faster than that reported in other papers (40-60% in 16-18 hours, Liu et al; 60% in 16 hours; Hatch et al). In addition, images in Hatch et al, 2013 show lamin A localizing to both intact and ruptured MN and anecdotal information in the field suggests that lamin A localization is not a reliable reporter.

These discrepancies may be due to how the authors' define "mCherry-NLS leakage", which needs to be defined in the methods as previous studies have demonstrated that MN frequently have delayed or reduced nuclear import even though the membrane is intact. Regardless, the authors need to provide compelling independent evidence that lamin A loss and disruption faithfully recognize ruptured MN by either validating this marker against additional rupture reporters, such as Lap2, LBR, or emerin accumulation, or by repeating key experiments in cells expressing mCherry-NLS.

2.Micronuclei in tumor sections and other dense tissues can appear very similar to other types of chromatin, including blebs from adjacent nuclei and dead cells. To verify that the quantified structures are bona fide micronuclei, the authors need to include a marker for the cell boundary. This is especially critical in the lamin a stained tumor sections with heterogenous lamin A protein expression.

3.Figure 4 compares MN rupture frequency between cells treated with different inducers of micronuclei - KIF18A KO, nocodazole release, and irradiation. These treatments have different effects on the cell cycle: KIF18A causes minor delays, nocodazole arrests cells in mitosis, and g-IR likely causes delays in S and G2. Since MN rupture frequency increases with the duration of interphase, the authors need to assess rupture frequency at similar time points after mitosis for all three conditions. One way to accomplish this would be to repeat this experiment and analyze cells collected by mitotic cells by shake-off prior to fixation and labeling.

Minor Comments

1.In figure 6A, it is unclear when the videos start and how micronuclei are selected for analysis. Do the micronuclei have to be continuously visible from the time they missegregate? Do the videos all start at the same time point during mitosis or is it contingent on when the MN appears separated from the main nucleus? One concern is that a consistent delay in micronucleus appearance in the nocodazole treated cells could artificially decrease the amount of MN expansion observed.

2.In figure 7A, it is difficult to identify the "lagging" chromosome in the top panel. It would be helpful to label the chromosome that becomes the MN, or ideally, to include a video or still images to demonstrate how micronuclei form in the KIF18A KO cells.

3.The two image panels in figure 7A are imaged at significantly different times during anaphase (early anaphase on bottom versus late anaphase/telophase on top). A better comparison would be between two cells at the same time point in anaphase.

Significance

In this study, the authors identify chromatin decondensation in micronuclei as a new predictor of membrane stability. Although these results are correlative, if their micronucleus rupture results can be validated as described in major comment 1, this study would advance our understanding of the micronucleus rupture mechanism by linking mitotic spindle location, chromatin decondensation, and lamin B1 protein recruitment. This would provide needed support to a current model in the field that micronucleus stability is largely determined during nuclear envelope assembly. In addition, if KIF18a loss generates stable micronuclei at high frequency, it will become a critical system for testing MN rupture hypotheses in the field. Thus, this work would be of significant interest to cell biologists working on nuclear envelope structure and function, chromosome organization, and mitosis. I include myself in this group as a cell biologist studying nuclear envelope structure and function with an expertise in membrane dynamics.

The authors also find that mice mutant for KIF18a have increased micronucleation in normal tissues but not increased tumor initiation. They hypothesize that this is due to the low rupture frequency of KIF18a-induced MN, however their data cannot reject the null hypothesis that the small increase in MN they see in KIF18a mutant mice would be insufficient to induce tumorigenesis even if rupture frequency was high. Thus the significance of their finding that micronucleation is not sufficient for cancer progression is unclear. However, the thorough analysis of micronucleation and rupture in several healthy tissues as well as a tumor model in KIF18 mutant mice would be of interest to both pathologists and cancer researchers focused on mechanisms of genome instability. These types of experiments are critical to determine how micronuclei contribute to cancer progression and the quantifications presented in this paper are truly impressive.

Referees cross-commenting

I agree with the concerns raised by the other 2 reviewers, especially their comments about the need to clarify the mechanism of chromosome lagging versus chromosome congression and compaction.

I think that all of these suggestions, though, are contingent on them being able to reproduce their micronucleus rupture results with a better marker of nucleus integrity. I strongly believe that additional validation of lamin A as a micronucleus rupture marker will demonstrate that it is unreliable, based both on our own observations in RPE-1 cells and the images they show

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Referee #2

Evidence, reproducibility and clarity

Sepaniac and colleagues use in vivo and in vitro approaches to examine why micronuclei generated by lack of KIF18A activity do not promote tumorigenesis. The authors conclude that micronuclei in KIF18A depleted cells form stable micronuclear envelopes, which may be a result from lagging chromosomes being closer to the spindle pole when the micronuclear envelope forms. The authors further conclude that the stability of the micronuclei arising from lack of KIF18A can explain why Kif18a mutant mice do not develop tumors. These results also suggest that the consequences of micronuclei and their possible contribution to tumorigenesis depend on the context of their genesis. While the mouse model data and characterization of the stability of micronuclei generated by different insults support the conclusions, the lagging chromosome positioning data could be improved. Moreover, a number of other issues should be addressed prior to publication.

Major issues:

1.Line 153-155. The authors conclude that the slight reduction in overall survival is "due to a reduced ability of Kif18a mutants to cope with rapid tumorigenesis," but it is unclear why this would be the case. There is also an increase in micronucleated cells in thymic lymphomas from Kif18a/p53 homozygous mice (Fig. 2B)-could this not contribute? In Fig. 3C, the authors show that micronuclear rupture is similar in both Kif18a WT and mutant mice, so it seems possible that the increase in the frequency of micronuclei (Fig. 2B) coupled with a similar frequency of micronuclear rupture (Fig. 3C) could lead to the reduced survival. Then, in the discussion, the authors downplay this finding by saying (line 371) "loss of Kif18a had modest or no effect on survival of Trp53 homozygotes and heterozygotes." Why then speculate earlier in the text that loss of Kif18a reduces the ability to cope with tumorigenesis?

2.Related to the point above, the authors show in figure 3 that the micronuclei found in healthy tissues display infrequent membrane rupture (panel B). However, micronuclear membrane rupture in tumor tissues is much more frequent (panel C). How do the authors explain this? Do they hypothesize that the micronuclei in the tumors originate by mechanisms other than the misalignment caused by lack of KIF18A? Does KIF18A depletion cause aneuploidy due to segregation of two sisters to the same pole? If so, one could expect the tumors to be aneuploid (is this the case?) and aneuploidy has been shown by numerous groups to cause genomic instability. Such genomic instability could then explain the difference in membrane rupture.

3.The authors conclude that lagging chromosomes in KIF18A KO cells are found closer to the main chromatin mass. The Stumpff lab showed in a 2019 JCB paper that KIF18 KO cells have a chromosome alignment defect and as a result during anaphase the chromosomes can be scattered rather than forming the tight, uniform mass that is observed in WT cells. The scattering of kinetochores resulting from this phenotype could affect the value of "Avg Chromosomes Distances" in Fig 7B and the normalized distance in the KIF18A KO cells. Therefore, live-cell imaging experiments would be helpful to resolve this and possibly strengthen this conclusion. RPE1 cells with fluorescently tagged CENP-A and centrin could be used to ensure that the lagging chromosomes will not rejoin the main nucleus. Moreover, these cells could be used for correlative live-fixed cell experiments in which fixed cell analysis following micronucleus formation could be used to show that chromosomes that lag farther away from the spindle pole are more likely to have defective micronuclear envelopes.

4.Based on the Fonseca et al. 2019 JCB paper (video 2), micronuclei from KIF18A KO do not exclusively arise from lagging chromosomes. Instead, chromosomes can also escape the main chromatin mass after segregation and subsequently be excluded from the main nucleus. It would be important to know what fraction of the micronuclei in KIF18A KO cells arise via lagging chromosomes. Since Aurora B and/or bundled microtubules at the spindle midzone are believed to prevent proper nuclear envelope formation, chromosomes that properly segregate but later become separated from the main nucleus would be more likely to form proper micronuclear envelopes than those arising from lagging chromosomes. The correlative microscopy experiment suggested in the previous point could allow differentiation between these two routes to micronucleus formation.

Minor issues:

1.Some parts of the manuscript are excessively wordy and some sentences are unclear or convoluted (e.g., lines 148-153 and 238-239).

2.Lines 59-61. This sentence is formulated incorrectly. First of all, the subject of the sentence is "cells" and the verb is "can become fragmented." However, the authors mean that the DNA in the micronucleus can become fragmented (not the cells). Moreover, the way the sentence is currently formulated seems to suggested that the fragmentation occurs during cell division. However, this is not the case. Please, revise the text to make it more accurate.

3.Lines 114-115. Please, provide references in support of this statement.

4.Line 153. The authors refer to Fig. 1C, but I think they mean Fig. 1B.

5.Line 324. the authors find that RPE1 KIF18A KO cells have lagging chromosomes in ana/telophase 9% of the time, then say that this shows that lagging chromosomes are rare in KIF18A KO cells. However, this is a large increase compared to normal RPE1 cells, which only have 1-2% frequency of lagging chromosomes. So, they should revise the text here to say that the rates of lagging chromosomes from KIF18A KO are lower compared to the rates induced by nocodazole washout.

6.Line 383. The references listed here should be moved earlier and specifically after the statement summarizing the results of the studies instead of being listed after the authors' conclusion/interpretation of the data. The same issue was noted in other parts of the manuscript.

7.Figure 1A. In the text, the authors say they cross a Kif18a heterozygous mutant mouse with a p53 heterozygous mutant mouse, but the two mice in this figure are already heterozygous for both. Please, revise the text or depict the previous additional cross necessary to obtain the double heterozygous.

8.Figure 3A. Arrows or dotted circles outlining the micronuclei in the insets of the middle and bottom rows would be helpful since the DAPI signal in the micronuclei is low and somewhat difficult to see.

9.Figure 3B. Error bars should be added to the graph. Moreover, the authors noted that the differences are not significant. However, this seems surprising, given that in some cases there is a three- to five-fold difference between certain pairs. Indeed, a chi-square test using the numbers from table S1 indicated p values <0.05 for several pairwise comparisons.

10.Figure 5G. When referring to this figure (lines 292-294), the authors talk about correlation. However, the points in this graph seem to be scattered a bit randomly.

11.Figure 6B-D. The Y-axis titles of the three graphs are a bit confusing. Please, consider revising.

12.In Figure 7 and the text, the authors use the terms "late-lagging" and "lagging" chromosomes interchangeably, which is somewhat confusing in this context because lagging chromosome distance from the main chromosome mass is thought to contribute to defective assembly of micronuclear envelopes. It is not clear whether the authors intend to indicate, with this term, that the lagging chromosome is farther away from the main chromosome mass or that the lagging chromosome is in a "late" anaphase cell. Because this is confusing, I suggest just using the term "lagging chromosome" consistently. It could be useful to include representative images of lagging chromosomes located at different distances from the main chromosome mass. And certainly, the authors should include an example of a lagging chromosome in the KIF18A KO cells.

13.Figure S2A. The example in the bottom right image looks more like a chromosome bridge than a lagging chromosomes. Kinetochore staining is necessary to unequivocally identify lagging chromosomes.

Significance

Based on the previous knowledge on the factors that cause abnormal assembly of the micronuclear membrane, the results presented in this study were somewhat predictable. However, these findings will add to the knowledge of how micronuclei form and the potential factors that lead to micronuclear membrane rupture. Previous studies investigating micronucleus behavior have focused on micronuclei arising via merotelic kinetochore mis-attachments. These mis-attachments lead to formation of micronuclei close to the spindle midzone. In the present study, instead, the micronuclei arising from lack of KIF18A activity form farther away from the spindle midzone. The results presented here suggest that the positioning of these micronuclei farther away from the midzone enables assembly of a more stable micronuclear membrane that will be less likely to rupture during the following cell cycle. A recent study showed that the microtubule bundles in the spindle midzone interfere with micronuclear membrane assembly. Based on this, it is not surprising that micronuclei forming away from the spindle midzone (like those resulting from lack of KIF18A activity) assemble more normal membranes. Although somewhat expected, this study provides the actual data in support of this phenomenon. This study will be of interest to cell biologists interested in cell division and genomic instability. My research has focused on cell division, aneuploidy, and chromosomal instability for nearly thirty years. Therefore, I believe I am fully qualified to evaluate this manuscript.

Referees cross-commenting

My areas of expertise do not include nuclear membrane structure and function. Therefore, I encourage the authors to consider the comments of reviewer #3 for issues related to reliable quantification of micronuclear membrane rupture.

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Referee #1

Evidence, reproducibility and clarity

In the present work Stumpff, Reinholdt and co-workers investigate the mechanism by which micronuclei contribute to tumorigenesis. Micronuclei are classic markers of genomic instability widely used in the diagnosis of cancer, but whether they work as drivers of the process has recently attracted significant attention due to their link with chromothripsis. Here, the Stumpff/Reinhold labs have explored an interesting model to test some ideas about the role of micronuclei as drivers of tumorigenesis, based on Kif18A/p53 double KO mice. They confirm the formation of micronuclei in these animals, but find no substantial increase in survival and tumor incidence relative to p53 KO animals, despite higher incidence of micronuclei in Kif18A/p53 KO tumors. They conclude that, per se, micronuclei do not have the capacity to form tumors, regardless of p53 status. This was surprising, given the well-established role of p53 in preventing the proliferation of micronucleated cells. To shed light into this apparent paradox, they compared micronuclei from Kif18A KO cells with micronuclei generated by a number of other experimental conditions that promote formation of anaphase lagging chromosomes or generates acentric fragments. They found that micronuclei derived from Kif18A are intrinsically different from micronuclei generated by those other means and essentially showed increased accumulation of lamin B, were more resistant to rupture and preserved the capacity to expand as cells exited mitosis. Of note, they find a correlation between chromosome proximity to the poles/main chromosome mass and the different features that characterize micronuclei from Kif18A KO cells, compared with the other experimental conditions in which late lagging chromosomes are more frequent. Overall, I find this study extremely interesting, well designed and executed in a rigorous way that characterizes the consistent solid work from these laboratories over the years. I have just few minor points that I recommend to be addressed prior to publication.

1-Abstract and main text lines 70 and 100: the authors indicate that Kif18A mutant mice produce micronuclei due to unaligned chromosomes. This is correct, but at the same time misleading. The authors should clarify that although micronuclei derive from compromised congression, I was convinced from previous works (Fonseca et al., JCB, 2019) that it was their asynchronous segregation in anaphase that led to micronuclei formation. As is, a less familiar reader may conceive that misaligned chromosomes directly result in micronuclei, for example by being detached from the main chromosome mass.

2-Page 2, line 59: "cells entering cell division...become fragmented". It is not the cells, but the chromosomes that fragment. Please correct.

3-Page 4, line 149: "reduced survival in the Kif18A null, p53 mice". P53 what? KO, WT? Please clarify.

4-Page 5, line 212: the authors refer that micronuclei were scored for absence of lamin A/C, but previously they scored it as "continuous/discontinuous". Please clarify.

5-Page 6, line 243: "Kif18A is not required for micronuclear envelope rupture". Shouldn't it be micronuclear envelope "integrity"?

6-One of the most interesting results of the paper is the correlation between envelope formation in micronuclei with their respective position relative to the poles/midzone. Could the authors try to investigate causality? For instance, the authors refer to works from other labs in which MT bundles and a midzone Aurora B activity gradient might play a role in the different features associated with micronuclei envelope formation, depending on their origin. Could the authors manipulate this gradient and investigate whether it changes the outcome in terms of nuclear envelope assembly properties on micronuclei? Are there any detectable features in midzone MT organization in Kif18A KO cells that would justify the observed differences?

Significance

Kif18A plays a key role in chromosome alignment, without apparently affecting kinetochore-microtubule attachments in non-transformed cells. Because they cannot establish a proper metaphase plate Kif18A KO cells enter anaphase with highly asynchronous segregation due to non-uniform chromosome distribution along the spindle axis. Consequently, some "delayed" chromosomes form micronuclei, in cell culture and in vivo. Interestingly, prior art has failed to detect any increased signs of genomic instability in Kif18A KO cells and mice, and, contrary to what would be expected based on current trends, these mice do now show any signs of increased incidence of tumors, in fact they even show some protective effect to induced colitis-associated colorectal cancer. Noteworthy, all previous experimental works pointing to a role of micronuclei as key intermediates of genomic instability in cancer relied on models in which the tumor suppressor protein p53 had been inactivated. In the present work, the authors explore the relationship between micronuclei formation and p53 inactivation by investigating tumor formation in Kif18A/p53 double KO animals (1 or 2 alleles of p53 inactivated).The reported results are timely and will attract the interest of a broad readership, while decisively contributing to shed light into an ongoing debate. I am therefore all in favor for the publication of this work in any journal affiliated with review commons, pending some minor revisions.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

We thank the reviewers for their constructive suggestions, which have substantially improved this work. We have comprehensively revised the manuscript, and detail individual responses below:

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

The study by Forbes et al describes and characterizes a 2nd generation peptide-based inhibitor of the MYB:CBP interaction, termed CRYBMIM, which they use to study MYB:cofactor interactions in leukemia cells. The CRYBMIM has improved properties relative to the MYBMIM peptide, and display more potency in biochemical and cell-based assays. Using a combination of epigenomics and biochemical screens, the authors define a list of candidate MYB cofactors whose functional significance as AML dependencies is supported by analysis of the DepMap database. Using genomewide profiling of TF and CBP occupancy, the authors provide evidence that CRYBMIM treatment reprograms the interactome of MYB in a manner that disproportionately changes specific cis-elements over others. Stated differently, the overall occupancy pattern of many TFs/cofactors shows gains and losses at specific cis elements, resulting in a complex modulation of MYB function and changes in transcription in leukemia cells. Overall, this is a strong, well-written study, with clear experimental results and relatively straightforward conclusions. The therapeutic potential of modulating MYB in cancer is enormous, and hence I believe this study will attract a broad interest in the cancer field and will likely be highly cited. I list below a few control experiments that would clarify the specificity of CRYBMIM. 1) Does CRYBMIM bind to other KIX domains, such as of MED15. It would be important to evaluate the specificity of this peptide for whether it binds to other KIX domains.

Response: We analyzed all known human KIX domain sequences, and found that the most similar one to CBP/P300 is MED15 (38% identity), as shown in revised Supp. Fig. 2D. The sequence similarity of the remaining human KIX domains is substantially lower. To determine the specificity of CRYBMIM in binding the CBP/P300 versus MED15, we exposed human AML cell extracts to biotinylated CRYBMIM immobilized on streptavidin beads versus beads alone. Whereas CRYBMIM binds efficiently to CBP/P300, it does not exhibit any measurable binding to MED15 (even though MED15 is highly expressed), as shown in revised Supp. Fig. 2E, and reproduced for convenience below. While this does not exclude the possibility that CRYBMIM binds to other proteins, the biochemical specificity observed here, combined with the genetic requirement of CBP for cellular effects of CRYBMIM as shown by a genome-wide CRISPR screen (Fig. 1B and below), indicate that CRYBMIM is a specific ligand of CBP/P300. The manuscript has been revised on page 6 and 4-5 accordingly.

2) Similarly, it would be useful to perform a mass spec analysis to all nuclear factors that associate with streptavidin-immobilized CRYBMIM. This again would be help the reader to understand the specificity of this peptide.

Response: We agree with the reviewer that macromolecular ligands like CRYBMIM may interact with cellular proteins in complex ways. To define specific effects, we utilized four orthogonal strategies, explained below.

First, we purified the CBP-containing nuclear complex using immunoprecipitation and determined its composition by mass spectrometry proteomics. This analysis revealed 833 proteins that are specifically associated with CBP (revised Table S6). Although technically feasible, the fact that CBP is associated with hundreds of proteins would make the experiment suggested by the reviewer difficult to interpret, because it would be a major challenge to distinguish proteins bound directly by the peptide versus proteins purified indirectly by virtue of the fact that CRYBMIM binds to CBP/P300, which in turn binds to many other proteins. While we recently developed improved methods for cross-linking mass spectrometry proteomics that permit the identification of direct protein-protein interactions (Ser, Cifani, Kentsis 2019, https://doi.org/10.1021/acs.jproteome.9b00085), we believe that these experiments are beyond the scope of the current manuscript, which already includes 40 new figure panels as part of this revision.

In lieu of this experiment, we purified the CBP-containing nuclear complex after treatment with CRYBMIM or control using immunoprecipitation and determined its composition by targeting Western blotting. This analysis revealed RUNX1, LYL1 and SATB1 are specifically associated with CBP (revised Fig. 14B), among which RUNX1 is specifically remodeled in the MYB:CBP/P300 complex upon CRYBMIM binding. This transcriptional factor recruitment and remodeling support the idea of CRYBMIM’s specificity for the MYB:CBP/P300 complex.

Second, to define the specificity of CRYBMIM, we used glycine mutants of CRYBMIM and its parent MYBMIM, CG3 and TG3, respectively, in which residues that form key salt bridge and hydrophobic interactions with KIX are replaced with glycines, but otherwise retain all other features of the active probes. Both CG3 and TG3 exhibit significantly reduced effects on the viability of AML cell lines, consistent with the specific effects of CRYBMIM (Fig. 3D).

To confirm that this is due to CBP binding, we purified cellular CBP/P300 by binding to biotinylated CRYBMIM, and observed that it can be efficiently competed by excess of free CRYBMIM, but not TAT (Fig. 2E).

Finally, to establish definitively that cellular CBP is responsible for CRYBMIM effects, we generated isogenic cell lines that are either deficient or proficient for CBP using CRISPR genome editing. This experiment demonstrated that CBP deficiency confers significant resistance to CRYBMIM, indicating that CBP is required for CRYBMIM-mediated effects (revised Figure 4), and reproduced below. We revised the manuscript on pages 21, 8, 6 and 9 accordingly.

3) The major limitation of this study which modestly lessens my enthusiasm of this work is that the mechanistic model of MYB-sequestered TFs proposed here is based on a face-value interpretation of IP-MS data coupled with ChIP-seq data. Normally, I would expect such a mechanism to be supported with some additional focused biochemical experiments of specific interactions, to complement all of the omics approaches. For example, can the authors evaluate and/or validate further how MYB physically interacts with LYL1, CEBPA, SPI1, or RUNX1. Are these interactions direct or indirect? Which domains of these proteins are involved? Does CRYBMIM treatment modulate the ability of these proteins to associate with one another in a co-IP? Do these interactions occur in normal hematopoietic cells? A claim is made throughout this study that these are aberrant TF complexes, but I believe more evidence is required to support this claim.

Response: We appreciate the reviewer’s comment and totally agree with this point. To examine how MYB aberrantly assembles transcription factors in AML, we performed MYB co-immunoprecipitation (co-IP) in a panel of seven genetically diverse AML cell lines with varying susceptibility to CRYBMIM, chosen to represent the common and refractory forms of human AML. Here, we confirmed co-assembly of CBP/P300, LYL1, E2A, LMO2 in all AML cell lines tested, and cell type-specific co-assembly of SATB1 and CEBPA, as shown in revised Fig. 8A, which are in agreement with the IP-MS and ChIP-seq results. We further corroborated these findings by co-IP studies of CBP/P300, as shown in the revised Fig. 8B. We performed similar co-IP experiments in normal hematopoietic progenitor cells, and found most of the co-assembled factors in AML cells were not observed in normal cells except for CBP/P300 and LYL1, as shown in the revised Figure 9E. Combined with the apparently aberrant expression of E2A and SATB1 in AML cells but not normal blood cells, this leads us to conclude that MYB assembles aberrant transcription factor complexes in AML cells. These complexes can be remodeled by peptidomimetic inhibitors, leading to their redistribution on chromatin, suppression of oncogenic gene expression and induction of cellular differentiation. We confirmed this mechanism by direct biochemical experiments in AML cells, demonstrating disassembly and remodeling of CBP/P300 complexes, as shown in the revised Figure 14. At least some of these interactions are direct, given the known direct binding between MYB and CEBPA (Oelgeschläger, Nuchprayoon, Lüscher, Friedman 1996, https://doi.org/10.1128/mcb.16.9.4717). We revised the manuscript text on pages 13, 15 and 21 accordingly.

Reviewer #1 (Significance (Required)):

Overall, this is a strong, well-written study, with clear experimental results and relatively straightforward conclusions. The therapeutic potential of modulating MYB in cancer is enormous, and hence I believe this study will attract a broad interest in the cancer field and will likely be highly cited.

Response: We appreciate this sentiment and completely agree with the reviewer. The phenomenon reported in this work represents the first of its kind demonstration of the aberrant organization of transcription factor control complexes in cancer, and its pharmacologic modulation. We believe that this concept will serve as a transformative paradigm for understanding oncogenic gene control and the development of effective therapies for its definitive treatment.

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

This manuscript reports the generation of a new and improved peptide mimetic inhibitor of the interaction between MYB and CBP/P300. The original MYBMIM inhibitor of this interaction, reported recently by the same laboratory, was modified by addition and substitution of peptide sequences from CREB, thus improving the affinity of the resulting CRYBMIM peptide to CBP/P300. The improved inhibitor profile results in increased anti-AML efficacy of CRYBMIM over MYBMIM. The authors go on to examine the mechanism underlying the anti-AML activity of CRYBMIM by integrating gene expression analysis, chromatin immunoprecipitation sequencing and mass spectrometric protein complex identification in human AML cells. I have some minor questions the authors may wish to comment on:

1) The relocation of MYB, along with CBP/P300, to genes controlling myeloid differentiation (clusters 4 and 9) upon CRYBMIM treatment is reminiscent of the increased binding of MYB to myeloid pro-differentiation genes in AML cells following RUVBL2 silencing, recently reported in Armenteros-Monterroso et al. 2019 Leukemia 33:2817. Do the authors know if there is any overlap between genes in either of the clusters and the list reported in the latter study?

Response: We thank the reviewer for making this suggestion. We also observe both RUVBL2 and RUVBL1 in the protein complex specifically associated with MYB (Fig. 7A and B). We compared the gene expression changes induced by CRYBMIM with those reported by Armenteros-Monterroso et al in 2019 (https://doi.org/10.1038/s41375-019-0495-8), and found that 37% of upregulated genes by RUVBL2 silencing were shared with genes induced by CRYBMIM treatment. In addition, upregulated genes in cluster 4 and 9 included myeloid differentiation-related genes, such as JUN, FOS and FOSB, which were also induced RUVBL2 silencing. We revised the manuscript to reflect this association on page 12.

2) Could the authors comment on a possible mechanism to explain the co-localization of MYB and CBP/P300 to the loci in clusters 4 and 9 following CRYBMIM treatment? Is it possible that CBP/P300 is recruited by other transcription factors to these loci, independently of binding to MYB? Or is the binding of CBP/P300 to MYB at these loci somehow more resistant to disruption by CRYBMIM?

Response: The reviewer has focused on an interesting point. At least for cluster 9, these genes exhibit gain of CBP/P300 in association with RUNX1 (Figure 12A), which we confirm by direct biochemical studies of MYB and CBP/P300 complexes immunoprecipitated from AML cells (revised Figure 14B-C). These experiments show that CRYBMIM treatment disrupts the MYB:CBP/P300 complexes, leading to the increased assembly of CBP/P300 with RUNX1. These findings are consistent with a dynamic competition mechanism that governs availability of CBP/P300 to transcriptional co-activation, in which distinct transcription factors compete for limiting amounts of CBP/P300. This possible mechanism is discussed in the revised manuscript (page 18-19 and 21).

3) In the first paragraph of page 9, the text states: "Previously, we found that MYBMIM can suppress MYB:CBP/P300-dependent gene expression, leading to AML cell apoptosis that required MYB-mediated suppression of BCL2 (Ramaswamy et al., 2018)." I think this is a typo, since in this study, MYBMIM treatment results in loss of MYB binding to the BCL2 gene and consequent reduction in BCL2 expression. Do the authors mean 'MYBMIM-mediated suppression of BCl2' or 'loss of MYB-mediated activation of BCL2'?

Response: We thank the reviewer and have corrected this typographic error in the text.

4) The authors explain the failure of excess CREBMIM to displace CBP/P300 from immobilised CREBMIM (Figure 1E-F) by the nature of the CREB:CBP/P300 interaction. Does this imply that CREBMIM is unable to disrupt the interaction between CREB and CBP/P300 in living cells and that the CBP/P300 purified from native MV4;11 lysates by immobilised CREBMIM was from a pool not associated with CREB?

Response: We thank the reviewer for making this point. Indeed, we reproducibly observe that CRYBMIM binding to CBP can be competed with excess free CRYBMIM, but CREBMIM binding cannot be competed by excess CREBMIM. This may be due to the different stabilities of the CBP complexes that are available for binding in cells. Alternatively, it is also possible that CREB binding to CBP, as reflected by CREBMIM, has a relatively slow dissociation rate, as compared to MYB, as reflected by CRYBMIM. We have begun to purify cellular CBP complexes (revised Fig 8. and response to comment 2 for Reviewer 1), and aim to define their determinants in future studies, as enabled by the introduction of CRYBMIM, CREBMIM and MLLMIM probes in the current work.

Reviewer #2 (Significance (Required)):

Based on this integrative analysis, the authors propose a convincing hypothesis, involving the assembly of aberrant transcription factor complexes and sequestration of P300/CBP from genes involved in normal myeloid development, for the oncogenic activity of MYB in AML. As well as the obvious therapeutic potential of the CRYBMIM inhibitor itself, the data reported here reveal multiple avenues for future investigation into novel anti-AML therapeutic strategies. This is an innovative and important study.

This study will be of interest to scientists and clinicians involved in leukaemia research as well as cancer biology in general.

My field of expertise: leukaemia biology, leukaemia models, aberrant transcription factor activity in leukaemia

Response: We appreciate and agree with this assessment.

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

This manuscript describes an improved MYB-mimetic peptide (cf the group's earlier work published in Nature Communications, 2018) and its effects on AML cell lines. It also describes - and this constitutes the majority of the paper - the dynamics of chromatin occupancy by MYB and other associated transcription factors upon disruption of the MYB-CBP/P300 interaction. The authors suggest this represents a shift from an oncogenic program to a myeloid differentiation program. \*Major comments:***

Regarding the improved affinity, and biological activity, of CRYBMIM:

1.Improved affinity of CRYBMIM cf MYBMIM: clearly, it is improved, but not by a lot. By MST the increased affinity is about 3x. In terms of effects on AML cell viability: there is no direct comparison, and this should be included. In the group's previous paper there is no direct estimate for MYBMIM but it looks like the IC50 is between 10 and 20 micromolar so the effect is again around 2.5 fold. Also, the effects of the amino acid substitutions in CG3 are also very small (2.4x) given that 3 critical residues are altered. This is quite concerning.

Response: As pointed out by the reviewer, CRYBMIM exhibits several fold increase in binding affinity, as measured using purified proteins in vitro. Similar increase in cellular potency is observed after short-term treatment of AML cells, as shown in revised Figure 3C, and reproduced below. However, increasing the duration of treatment to several days leads to substantial improvement in apparent cellular potency (Figure 3G). For example, while MYBMIM induces approximately 100-fold reduction in cell viability of MV411 cells, CRYBMIM induces more than 1,000-fold reduction. Similarly, whereas MYBMIM exhibited relatively modest effects on OCIAML3 and SKM1 cells, CRYBMIM induces more than 1,000-fold reduction in cell viability. As we show in the revised manuscript, this appears to be due to the combination of increased biochemical affinity and specific proteolysis of MYB, which cooperate to induce extensive remodeling of MYB transcriptional complexes and gene expression (revised Figure 11). In all, this exemplifies how pharmacologic modulators of protein interactions can achieve significantly improved biological potency from relatively modest affinity effects, a concept that recently has been successfully used to develop a variety of PROTACs that leverage this “event-driven” as opposed to occupancy-driven pharmacology. The manuscript has been revised on page 8 and 18 to clarify this point.

2.Does CRYBMIM really "spare" normal hematopoietic cells? Not according to Fig 2E, where there is only a 2-fold difference in IC50.

Response: To better define the relative toxicity of CRYBMIM and MYBMIM, we examined their effects on the growth and survival of normal hematopoietic progenitor cells as compared to AML cells using colony forming assays in methylcellulose under more physiologic conditions in the presence of human hematopoietic cytokines (revised Figure 3E, and reproduced below). While CRYBMIM significantly reduced the clonogenic capacity, growth and survival of MV411 AML cells, there were no significant effects on the total clonogenic activity of normal CD34+ human umbilical cord blood progenitor cells under these conditions. At the highest dose, CRYBMIM induced modest reduction in CFU-MG colony formation, and modest increase in BFU-E colony formation of normal hematopoietic progenitor cells. We revised the manuscript to indicate that CRYBMIM “relatively spares” normal blood progenitor cells on page 8.

Response: We appreciate the attention to this issue. In the original manuscript, we showed dose-response curves of cord blood progenitor cells cultured in suspension supplemented with fetal bovine serum, a system that is known to induce in appropriate hematopoietic cell differentiation (https://doi.org/10.1016/j.molmed.2017.07.003). In the revised manuscript, we show results of colony formation assays of hematopoietic progenitor cells cultured in serum-free, semi-solid conditions supplemented with human hematopoietic cytokines (revised Figure 3E and 3F). This is a more physiologic system which more faithfully maintains normal hematopoietic cell differentiation, as compared to the cellular differentiation induced by fetal bovine serum-containing media lacking hematopoietic growth factors, as used in the experiments in our original manuscript. To establish a positive control, in addition to treating AML cells under the same condition, we used doxorubicin, which is part of current treatment of patients with AML, and which in our experiments, exhibits significant and pronounced reduction in the clonogenic capacity, growth and survival of normal blood progenitor cells (revised Figure S3B). The manuscript has been revised on page 8 accordingly.

1. Fig 2F doesn't include any lines that express very low or undetectable levels of MYB. Some of these should be included to further examine specificity.

Response: We have now tested CRYBMIM against a large panel of non-hematopoietic tumor and non-tumor cell lines, with varying degrees of MYB expression. Some of those cells exhibit high level of MYB gene expression and MYB genetic dependency, which is at least in part correlated with susceptibility to CRYBMIM. (revised Figure S4, and reproduced below). The manuscript has been revised on page 8 accordingly.

Effects on gene expression and MYB binding:

Data on MYB target gene expression and apoptosis/differentiation, and the conclusions drawn per se are sound, but:

5.Fig S3 seems to show that MYB protein is lost on treatment with CRYBMIM. This isn't even mentioned in the text but raises a whole range of major questions eg why is this the case? Is this what is responsible for the loss of MYB-p300 interaction and/or biological effects on AML cells? Is this what is responsible for the effects on MYB target gene expression in Fig 3 and MYB binding to chromatin in Fig 4? This must be addressed.

Response: We have revised the manuscript to include this discussion, and performed additional experiments to define this phenomenon. We confirmed rapid reduction in MYB protein levels upon CRYBMIM treatment on the time-scale of one to four hours in diverse AML cell lines (revised Figure 11), with the rate of MYB protein loss correlating to the cellular susceptibility to CRYBMIM (revised Figure 11, and reproduced below). The manuscript has been revised on page 18 accordingly.

This is consistent with the specific proteolysis of MYB induced by the peptidomimetic remodeling of the MYB:CBP/P300 complex. We confirmed this by combined treatment with the proteosomal/protease inhibitor MG132 (revised Figure 11C, and reproduced below). This effect was specific because overexpression of BCL2, which blocks MYBMIM-induced apoptosis (Ramaswamy et al, Kentsis, https://doi.org/10.1038/s41467-017-02618-6), was unable to rescue CRYBMIM-induced proteolysis of MYB, arguing that MYB proteolysis is a specific effect of CRYBMIM rather than a non-specific consequence of apoptosis. The manuscript has been revised on page 18 accordingly.

6.Fig 4 and the accompanying text are a bit hard to follow, but if I understood them correctly, I am surprised that the "gained MYB peaks" don't include the MYB binding motif itself? This at least deserves some comment. Also, there doesn't seem to have been any attempt to integrate the ChIP-Seq data with the expression data of Fig 3. This would provide clearer insights into the identities and types of MYB-regulated genes that are directly affected by suppression of CBP/p300 binding to MYB.

Response: We thank the reviewer for this suggestion. The revised manuscript now includes a comprehensive and integrated analysis of chromatin and gene expression dynamics (revised Figures 13A and 13B). In contrast to the model in which blockade of MYB:CBP/P300 induces loss of gene expression and loss of transcription factor and CBP/P300 chromatin occupancy, we also observed a large number of genes with increased expression and gain of CBP/P300 occupancy (revised Figure 13A-B, and reproduced below). This includes numerous genes that control hematopoietic differentiation, such as FOS, JUN, and ATF3. As a representative example, in the case of FOS, we observed that CRYBMIM-induced accumulation of CBP/P300 was associated with increased binding of RUNX1, and eviction of CEBPA and LYL1 (revised Figure 13C). Thus, the absence of “gained MYB peaks” is due to the redistribution of CBP/P300 with alternative transcription factors, such as RUNX1. In all, these results support the model in which the core regulatory circuitry of AML cells is organized aberrantly by MYB and its associated co-factors including LYL1, CEBPA, E2A, SATB1 and LMO2, which co-operate in the induction and maintenance of oncogenic gene expression, as co-opted by distinct oncogenes in biologically diverse subtypes of AML (revised Figure 14). This involves apparent sequestration of CBP/P300 from genes controlling myeloid cell differentiation. Thus, oncogenic gene expression is associated with the assembly of aberrantly organized MYB transcriptional co-activator complexes, and their dynamic remodeling by selective blockade of protein interactions can induce AML cell differentiation. The manuscript has been revised on page 20-21 accordingly.

7.The MS studies on MYB-interacting proteins seem very interesting and novel. I am not an expert on MS, though, so I'd suggest this section be reviewed by someone who is. Moreover, I was unable to see the actual data from this study because the material I was provided with didn't include Table S4 and S5.

Response: We appreciate this point. For this reason, we have deposited all of our mass spectrometry data to be openly available via PRIDE (accession number PXD019708), and also openly provide all of the analyzed data via Zenodo (https://doi.org/10.5281/zenodo.4321824), as additionally provided in the Supplementary Material for this manuscript.

\Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?* 8.Claims regarding biological activity, specificity and improvements cf MYBMIM should be moderated given the small size of these effects as mentioned above (points 1 and 3).*

Response: As explained in detail in response to comments 1-3 above (page 12-14 of this response), we have substantially revised the manuscript to incorporate both new experimental results and additional explanations (pages 6-8).

9.I found the description of the studies related to Figs 5 and 6 somewhat difficult to follow and convoluted. While changes in MYB and CBP/p300 chromatin occupancy clearly occur on M CRYBMIM treatment, it is not clear that the complexes seen on genes prior to treatment represent "aberrant" complexes. These may just be characteristic of undifferentiated (myeloid) cells. The authors appear to argue that because some of the candidate co-factors show "apparently aberrant expression in AML cells" based on comparison of (presumably mRNA) expression data with normal cells, the presence of these factors in the complexes make them "aberrant" (moreover, the "aberrancy score" of Fig 5 C is not defined anywhere, as far as I can see). This inference is drawing a rather long bow, given that the AML-specific factors may not actually be absent from the complexes in normal cells. So this conclusion should be moderated if a more direct MS comparison cannot be provided (for which I understand the technical difficulties).

Response: We have now measured protein abundance levels of key transcription factors assembled with MYB in AML cells in various normal human hematopoietic cells (revised Figure 9, and reproduced below). We found that most transcription factors that are assembled with MYB in diverse AML cell lines could be detected in one or more normal human blood cells, albeit with variable abundance, with the exception of CEBPA and SATB1 that were measurably expressed exclusively in AML cells (revised Figure 9A). Using unsupervised clustering and principal component analysis, we defined the combinations of transcription factors that are associated with aberrant functions of MYB:CBP/P300, as defined by their susceptibility to peptidomimetic remodeling (revised Figure 9B-D). In addition, we directly examined the physical assembly of MYB with key transcription factors in normal hematopoietic cells using co-immunoprecipitation studies (revised Figure 9E). In agreement with the physical association of MYB seen in AML cell lines, we observed association with CBP/P300 and LYL1 in normal hematopoietic cells. However, we did not observe physical association with E2A and SATB1 in normal cells, which indicates aberrant association of these in AML cell lines. This leads us to propose that these complexes are aberrantly assembled, at least in part due to the inappropriate transcription factor co-expression. The manuscript has been revised on page 15 accordingly.

\Would additional experiments be essential to support the claims of the paper?*

Response: As explained in detail in response to comment 5 above (page 16 of this response), we have carried out extensive studies of the specific proteolysis of MYB. We conclude that MYB transcription complexes are regulated both by MYB:CBP/P300 binding and by specific factor proteolysis, and can be induced by its peptidomimetic blockade in AML cells. Such “event-driven” pharmacology is emerging as a powerful tool to modulate protein function in cells, and studies reported in our work should enable its translation into improved therapies for patients, and improved probes for basic science.

11.Provision of a positive control for the experiment of Fig S2.

Response: As explained in detail in response to comment 2 above (page 13-14 of this response), we precisely defined the effects of CRYBMIM and MYBMIM on the clonogenic capacity, growth and survival of normal hematopoietic progenitor cells in serum-free, methylcellulose media supplemented with human hematopoietic cytokines. These experiments showed relatively modest effects (9.3 ± 3.8% reduction) of CRYBMIM on normal cells (Figure 3E), as compared to substantial inhibition (54 ± 2.4 % reduction) of the growth and survival of AML cells (Figures 3E). For comparison, doxorubicin led to more than 98 % reduction in clonogenic capacity (revised Figure S3B).

12.\Are the data and the methods presented in such a way that they can be reproduced?**

-Mostly yes

Response: The revised manuscript includes a complete description of all methods, including a detailed supplement, listing technical details, with all analyzed data available openly via Zenodo (https://doi.org/10.5281/zenodo.4321824).

13.\Are the experiments adequately replicated and statistical analysis adequate?**

-Mostly yes

Response: All experiments were performed in at least three replicates, with all quantitative comparisons performed using appropriate statistical tests, as explained in the manuscript.

**Minor comments:**

*Specific experimental issues that are easily addressable.*

-These are mostly indicated above.

In addition:

14.Why is BCL2 expression down-regulated by MYBMIM but not CRYMYB?

Response: We made the same observation, and attribute this difference to the fact that BCL2 expression is regulated by several transcription factors, including CEBPA, which is affected by CRYBMIM but not MYBMIM. Similar to MYBMIM treatment, MYB occupancy at the BCL2 enhancer was reduced upon CRYBMIM treatment. However, new binding sites of other factors, such as CBP/P300 and RUNX1, appeared simultaneously, suggesting that redistribution of transcription factors following CRYBMIM treatment can affect transcriptional regulation of BCL2 expression (revised Figure S9 and shown below).

*Are prior studies referenced appropriately?

-Yes *Are the text and figures clear and accurate?*

15.Generally, although some details are missing eg what aberrancy score in Fig 5C means.

Response: Thank you for pointing this out. We have revised this figure to clarify this score, which is defined as the ratio of gene expression in AML cells relative to normal hematopoietic progenitor cells (revised Figure 7C).

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

-The title of this manuscript could and I think should be changed. The term "therapeutic", is not appropriate because no therapeutic agents are described in the m/s nor is any form of AML, even experimentally, treated. Also "CBP" should be replaced with CBP/P300, especially since most evidence suggests that P300 is the likely more important partner of MYB (eg Zhao et al 2011

Response: We agree and have revised the title to clarify the significance of this work: “Convergent organization of aberrant MYB complexes controls oncogenic gene expression in acute myeloid leukemia.” We have revised the manuscript to indicate CBP/P300.

17.-It would be worth discussing the core observation that disruption of the MYB-CBP/P300 interaction actually results in changes in MYB DNA binding. That this would occur is not at all obvious, because CBP/p300 doesn't interact with MYB's DNA binding domain nor does it have intrinsic DNA binding activity.

Response: We thank the reviewer for this comment, and agree that remodeling of the MYB complex must affect the binding of MYB and other cofactors to DNA, at least in part mediated by potential acetylation by CBP/P300 (page 24).

Reviewer #3 (Significance (Required)):

**The Nature and Significance of the Advance**

1) The major significance of this work lies in the chromatin occupancy and MYB complex studies. There are a number of very interesting findings including the apparent redistribution of MYB and/or CBP/P300 upon treatment with CRYBMIM. These suggest a series of changes in factors associated with particular gene sets involved in myeloid differentiation, although as mentioned above particular target genes are not specifically identified. However the pathways corresponding to these are listed in Table S6.

Response: We have revised the manuscript to include the target genes in revised Supplemental Table 4 as well as DESeq2 tables (deposited in Zenodo, https://doi.org/10.5281/zenodo.4321824).

2) The new peptide design (CRYBMIM) is interesting but its differences in binding and biological effects of MYBMIM are mostly incremental. See above.

Response: We respectfully disagree and would like to explain how this work is significant both for conceptual and technical reasons. First, while the biochemical affinity of CRYBMIM is quantitatively increased compared with MYBMIM, this quantitatively increased affinity translates into qualitatively improved biological potency, as a result of “event-driven” pharmacology that characterizes pharmacologic protein interaction modulators (please also see response to Reviewer 3, comment 1, page 6 of this response). MYBMIM suppresses the growth and survival mostly of MLL-rearranged leukemias, whereas CRYBMIM does so for the vast majority (10 out of 11) of studied subtypes of AML. This now enables its therapeutic translation, as we are currently pursuing in collaboration with Novartis. Second, its improved biological activity led to the discovery of the previously unknown and unanticipated CBP/P300 sequestration mechanism of oncogenic gene control. We use this discovery to develop a precise model of aberrant gene control in AML that for the first time unifies previously disparate observations into a general mechanism. This is highly significant because it provides shared molecular dependencies for most subtypes of AML, a long-standing conundrum in cancer biology.

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

-This m/s builds on and extends the report from the same group in Nature Communications (2018), which described the earlier peptide MYBMIM, some effects on MYB target genes and on AML cells. It and the previous paper also draw on the findings regarding the role of the MYB-CBP/P300 interaction in myeloid leukemogenesis (Pattabirman et al 2014) and on previous genome-wide studies of MYB target genes (Zhoa et al 2011; Zuber et al 2011).

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

-This m/s will likely be of interest to scientists interested in MYB per se, in AML, in cancer genomics and transcriptional regulation.

*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.* -My expertise: AML, experimental hematology, transcription, MYB, cancer genomics

3) As mentioned above, I feel that additional expertise is required to review the MS studies.

Response: We have deposited all raw data in PRIDE (accession number PXD019708) and all processed data in Zenodo (https://doi.org/10.5281/zenodo.4321824), making it available for the community for further analysis.

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Referee #3

Evidence, reproducibility and clarity

Summary

This manuscript describes an improved MYB-mimetic peptide (cf the group's earlier work published in Nature Communications, 2018) and its effects on AML cell lines. It also describes - and this constitutes the majority of the paper - the dynamics of chromatin occupancy by MYB and other associated transcription factors upon disruption of the MYB-CBP/P300 interaction. The authors suggest this represents a shift from an oncogenic program to a myeloid differentiation program.

Major comments:

Regarding the improved affinity, and biological activity, of CRYBMIM:

1.Improved affinity of CRYBMIM cf MYBMIM: clearly, it is improved, but not by a lot. By MST the increased affinity is about 3x. In terms of effects on AML cell viability: there is no direct comparison, and this should be included. In the group's previous paper there is no direct estimate for MYBMIM but it looks like the IC50 is between 10 and 20 micromolar so the fecct is again around 2.5 fold. Also, the effects of the amino acid substitutions in CG3 are also very small (2.4x) given that 3 critical residues are altered. This is quite concerning.

2.Does CRYBMIM really "spare" normal hematopoietic cells? Not according to Fig 2E, where there is only a 2-fold difference in IC50.

3.Fig 2E and Supp Fig S2 appear to be contradictory. The latter shows no effect of 20micromolar CRYBMIM on colony formation by normal CD34+ cells, in complete contrast to killing with IC50 of 12.8 micromolar in Fig 2E. There is no +ve control for Fig S2 ie does the peptide work under colony assay conditions? This MUST be addressed.

4.Fig 2F doesn't include any lines that express very low or undetectable levels of MYB. Some of these should be included to further examine specificity.2

Effects on gene expression and MYB binding:

Data on MYB target gene expression and apoptosis/differentiation, and the conclusions drawn per se are sound, but:

5.Fig S3 seems to show that MYB protein is lost on treatment with CRYBMIM. This isn't even mentioned in the text but raises a whole range of major questions eg why is this the case? Is this what is responsible for the loss of MYB-p300 interaction and/or biological effects on AML cells? Is this what is responsible for the effects on MYB target gene expression in Fig 3 and MYB binding to chromatin in Fig 4? This must be addressed.

6.Fig 4 and the accompanying text are a bit hard to follow, but if I understood them correctly, I am surprised that the "gained MYB peaks" don't include the MYB binding motif itself? This at least deserves some comment. Also, there doesn't seem to have been any attempt to integrate the ChIP-Seq data with the expression data of Fig 3. This would provide clearer insights into the identities and types of MYB-regulated genes that are directly affected by suppression of CBP/p300 binding to MYB.

7.The MS studies on MYB-interacting proteins seem very interesting and novel. I am not an expert on MS, though, so I'd suggest this section be reviewed by someone who is. Moreover, I was unable to see the actual data from this study because the material I was provided with didn't include Table S4 and S5.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

8.Claims regarding biological activity, specificity and improvements cf MYBMIM should be moderated given the small size of these effects as mentioned above (points 1 and 3).

9.I found the description of the studies related to Figs 5 and 6 somewhat difficult to follow and convoluted. While changes in MYB and CBP/p300 chromatin occupancy clearly occur on M CRYBMIM treatment, it is not clear that the complexes seen on genes prior to treatment represent "aberrant" complexes. These may just be characteristic of undifferentiated (myeloid) cells. The authors appear to argue that because some of the candidate co-factors show "apparently aberrant expression in AML cells" based on comparison of (presumably mRNA) expression data with normal cells, the presence of these factors in the complexes make them "aberrant" (moreover, the "aberrancy score" of Fig 5 C is not defined anywhere, as far as I can see). This inference is drawing a rather long bow, given that the AML-specific factors may not actually be absent from the complexes in normal cells. So this conclusion should be moderated if a more direct MS comparison cannot be provided (for which I understand the technical difficulties).

Would additional experiments be essential to support the claims of the paper?

1. Address the issue of the apparent loss of MYB protein upon CRYBMIM treatment. If this is occurring, the whole premise of the subsequent work is undermined.

12.Provision of a positive control for the experiment of Fig S2.

Are the data and the methods presented in such a way that they can be reproduced?

-Mostly yes

Are the experiments adequately replicated and statistical analysis adequate?

-Mostly yes

Minor comments:

Specific experimental issues that are easily addressable. -These are mostly indicated above.

In addition: oWhy is BCL2 expression down-regulated by MYBMIM but not CRYMYB?

*Are prior studies referenced appropriately?

-Yes

Are the text and figures clear and accurate?

-Generally, although some details are missing eg what aberrancy score in Fig 5C means.

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

-The title of this manuscript could and I think should be changed. The term "therapeutic", is not appropriate because no therapeutic agents are described in the m/s nor is any form of AML, even experimentally, treated. Also "CBP" should be replaced with CBP/P300, especially since most evidence suggests that P300 is the likely more important partner of MYB (eg Zhao et al 2011

-It would be worth discussing the core observation that disruption of the MYB-CBP/P300 interaction actually results in changes in MYB DNA binding. That this would occur is not at all obvious, because CBP/p300 doesn't interact with MYB's DNA binding domain nor does it have intrinsic DNA binding activity.

Significance

The Nature and Significance of the Advance

-The major significance of this work lies in the chromatin occupancy and MYB complex studies. There are a number of very interesting findings including the apparent redistribution of MYB and/or CBP/P300 upon treatment with CRYBMIM. These suggest a series of changes in factors associated with particular gene sets involved in myeloid differentiation, although as mentioned above particular target genes are not specifically identified. However the pathways corresponding to these are listed in Table S6.

-The new peptide design (CRYBMIM) is interesting but its differences in binding and biological effects cf MYBMIM are mostly incremental. See above.

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

-This m/s builds on and extends the report from the same group in Nature Communications (2018), which described the earlier peptide MYBMIM, some effects on MYB target genes and on AML cells. It and the previous paper also draw on the findings regarding the role of the MYB-CBP/P300 interaction in myeloid leukemogenesis (Pattabirman et al 2014) and on previous genome-wide studies of MYB target genes (Zhoa et al 2011; Zuber et al 2011).

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

-This m/s will likely be of interest to scientists interested in MYB per se, in AML, in cancer genomics and transcriptional regulation.

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.

-My expertise: AML, experimental hematology, transcription, MYB, cancer genomics

-As mentioned above, I feel that additional expertise is required to review the MS studies.

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Referee #2

Evidence, reproducibility and clarity

This manuscript reports the generation of a new and improved peptide mimetic inhibitor of the interaction between MYB and CBP/P300. The original MYBMIM inhibitor of this interaction, reported recently by the same laboratory, was modified by addition and substitution of peptide sequences from CREB, thus improving the affinity of the resulting CRYBMIM peptide to CBP/P300. The improved inhibitor profile results in increased anti-AML efficacy of CRYBMIM over MYBMIM. The authors go on to examine the mechanism underlying the anti-AML activity of CRYBMIM by integrating gene expression analysis, chromatin immunoprecipitation sequencing and mass spectrometric protein complex identification in human AML cells.

I have some minor questions the authors may wish to comment on:

1) The relocation of MYB, along with CBP/P300, to genes controlling myeloid differentiation (clusters 4 and 9) upon CRYBMIM treatment is reminiscent of the increased binding of MYB to myeloid pro-differentiation genes in AML cells following RUVBL2 silencing, recently reported in Armenteros-Monterroso et al. 2019 Leukemia 33:2817. Do the authors know if there is any overlap between genes in either of the clusters and the list reported in the latter study?

2) Could the authors comment on a possible mechanism to explain the co-localization of MYB and CBP/P300 to the loci in clusters 4 and 9 following CRYBMIM treatment? Is it possible that CBP/P300 is recruited by other transcription factors to these loci, independently of binding to MYB? Or is the binding of CBP/P300 to MYB at these loci somehow more resistant to disruption by CRYBMIM?

3) In the first paragraph of page 9, the text states: "Previously, we found that MYBMIM can suppress MYB:CBP/P300-dependent gene expression, leading to AML cell apoptosis that required MYB-mediated suppression of BCL2 (Ramaswamy et al., 2018)." I think this is a typo, since in this study, MYBMIM treatment results in loss of MYB binding to the BCL2 gene and consequent reduction in BCL2 expression. Do the authors mean 'MYBMIM-mediated suppression of BCl2' or 'loss of MYB-mediated activation of BCL2'?

4) The authors explain the failure of excess CREBMIM to displace CBP/P300 from immobilised CREBMIM (Figure 1E-F) by the nature of the CREB:CBP/P300 interaction. Does this imply that CREBMIM is unable to disrupt the interaction between CREB and CBP/P300 in living cells and that the CBP/P300 purified from native MV4;11 lysates by immobilised CREBMIM was from a pool not associated with CREB?

Significance

Based on this integrative analysis, the authors propose a convincing hypothesis, involving the assembly of aberrant transcription factor complexes and sequestration of P300/CBP from genes involved in normal myeloid development, for the oncogenic activity of MYB in AML. As well as the obvious therapeutic potential of the CRYBMIM inhibitor itself, the data reported here reveal multiple avenues for future investigation into novel anti-AML therapeutic strategies. This is an innovative and important study.

This study will be of interest to scientists and clinicians involved in leukaemia research as well as cancer biology in general.

My field of expertise: leukaemia biology, leukaemia models, aberrant transcription factor activity in leukaemia

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Referee #1

Evidence, reproducibility and clarity

The study by Forbes et al describes and characterizes a 2nd generation peptide-based inhibitor of the MYB:CBP interaction, termed CRYBMIM, which they use to study MYB:cofactor interactions in leukemia cells. The CRYBMIM has improved properties relative to the MYBMIM peptide, and display more potency in biochemical and cell-based assays. Using a combination of epigenomics and biochemical screens, the authors define a list of candidate MYB cofactors whose functional significance as AML dependencies is supported by analysis of the DepMap database. Using genomewide profiling of TF and CBP occupancy, the authors provide evidence that CRYBMIM treatment reprograms the interactome of MYB in a manner that disproportionately changes specific cis-elements over others. Stated differently, the overall occupancy pattern of many TFs/cofactors shows gains and losses at specific cis elements, resulting in a complex modulation of MYB function and changes in transcription in leukemia cells.

Overall, this is a strong, well-written study, with clear experimental results and relatively straightforward conclusions. The therapeutic potential of modulating MYB in cancer is enormous, and hence I believe this study will attract a broad interest in the cancer field and will likely be highly cited. I list below a few control experiments that would clarify the specificity of CRYBMIM.

1) Does CRYBMIM bind to other KIX domains, such as of MED15. It would be important to evaluate the specificity of this peptide for whether it binds to other KIX domains.

2) Similarly, it would be useful to perform a mass spec analysis to all nuclear factors that associate with streptavidin-immobilized CRYBMIM. This again would be help the reader to understand the specificity of this peptide.

The major limitation of this study which modestly lessens my enthusiasm of this work is that the mechanistic model of MYB-sequestered TFs proposed here is based on a face-value interpretation of IP-MS data coupled with ChIP-seq data. Normally, I would expect such a mechanism to be supported with some additional focused biochemical experiments of specific interactions, to complement all of the omics approaches. For example, can the authors evaluate and/or validate further how MYB physically interacts with LYL1, CEBPA, SPI1, or RUNX1. Are these interactions direct or indirect? Which domains of these proteins are involved? Does CRYBMIM treatment modulate the ability of these proteins to associate with one another in a co-IP? Do these interactions occur in normal hematopoietic cells? A claim is made throughout this study that these are aberrant TF complexes, but I believe more evidence is required to support this claim.

Significance

Overall, this is a strong, well-written study, with clear experimental results and relatively straightforward conclusions. The therapeutic potential of modulating MYB in cancer is enormous, and hence I believe this study will attract a broad interest in the cancer field and will likely be highly cited.

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Reply to the reviewers

Reviewer #1 The authors study allostery with a beautiful genotype-phenotype experiment to study the fitness landscape of an allosteric lac repressor protein. The authors make a mutational library using error prone pcr and measure the impact on antibiotic resistance protein expression at varying levels of ligand, IPTG, expression. After measuring the impact of mutations authors fill-in the missing data using a neural net model. This type of dose response is not standard in the field, but the richness of their data and the discovery of the "band pass" phenomena prove its worth here splendidly. Using this mixed experimental/predicted data the authors explore how each mutation alters the different parameters of a hill equation fit of a dose response curve. Using higher order mutational space the authors look at how mutations can qualitatively switch phenotypes to inverted or band-stop dose-response curves. To validate and further explore a band-stop novel phenotype, the authors focused on a triple mutant and made all combinations of the 3 mutations. The authors find that only one mutation alone alters the dose-response and only in combination does a band-stop behavior present itself. Overall this paper is a fantastic data heavy dive into the allosteric fitness landscape of protein. Overall, the data presented in this paper is thoroughly collected and analyzed making the conclusions well-based. We do not think additional experiments nor substantial changes are needed apart from including basic experimental details and more biophysical rationale/speculation as discussed in further detail below.

The authors do a genotype-phenotype experiment that requires extensive deep sequencing experiments. However, right now quite a bit of basic statistics on the sequencing is missing. Baseline library quality is somewhat shown in supplementary fig 2 but the figure is hard to interpret. It would be good to have a table that states how many of all possible mutations at different mutation depths (single, double, etc) there are. Similarly, sequencing statistics are missing- it would be useful to know how many reads were acquired and how much sequencing depth that corresponds to. This is particularly important for barcode assignment to phenotype in the long-read sequencing. In addition, a synonymous mutation comparison is mentioned but in my reading that data is not presented in the supplemental figures section.

We thank the reviewer for this succinct summary of the manuscript and the results. We appreciate the reviewer identifying data of interest that were not included in the original manuscript. We agree that this information is necessary to consider the results. Specific changes are summarized in the comments below.

The paper is very much written from an "old school" allostery perspective with static end point structures that are mutually exclusive - eg. p5l10 "relative ligand-binding affinity between the two conformations" - however, an ensemble of conformations is likely needed to explain their data. This is especially true for the bandpass and inverted phenotypes they observe. The work by Hilser et al is of particular importance in this area. We would invite the authors to speculate more freely about the molecular origins of their findings.

We agree with the suggestions to adopt a modern allosteric perspective. We have changed the language throughout the manuscript to align with the ensemble model of allostery. We continue to frame results using the Monad-Wyman-Changeaux model, which reliably predicts LacI activity from biophysical parameters and is not exclusive of more modern models of allostery.

**Minor** There are a number of small modifications. In general this paper is very technical and could use with some explanation and discussion for relevance to make the manuscript more approachable for a broader audience. P1L23: Ligand binding at one site causes a conformational change that affects the activity of another > not necessarily true - and related to using more "modern" statistical mechanical language for describing allostery.

We agree with the reviewer’s comment. We have addressed this comment by adopting language in line with more modern view of allostery, for example:

“With allosteric regulation, ligand binding at one site on a biomolecule changes the activity of another, often distal, site. Switching between active and inactive states provides a sense-and-response function that defines the allosteric phenotype.”

P2L20: The core experiment of this paper is a selection using a mutational library. In the main body the authors mention the library was created using mutagenic pcr but leave it at that. More details on what sort of mutagenic pcr was used in the main body would be useful. According to the methods error prone pcr was used. Why use er-pcr vs deep point mutational libraries? Presumably to sample higher order phenotype? Rationale should be included. Were there preliminary experiments that helped calibrate the mutation level?

We agree that justifying the decision to use error-prone PCR for library construction would be helpful. To explain this decision, we have added to the main text to explain this decision and to reflect on the consequences.

“We used error-prone PCR across the full lacI CDS to investigate the effects of higher-order substitutions spread across the entire LacI sequence and structure.”

And

“Novel phenotypes emerged at mutational distances greater than one amino acid substitution, highlighting the value in sampling a broader genotype space with higher-order mutations. Furthermore, the untargeted, random mutagenesis approach used here was critical for finding these novel phenotypes, as the genotypes required for these novel phenotypes were unpredictable.”

P2L20: Baseline library statistics would be great in a table for coverage, diversity, etc especially as this was done by error prone pcr vs a more saturated library generation method. This is present in sup fig2 but it's a bit complicated.

To more clearly convey the diversity within the library, we have included a heatmap of amino acid substitution counts found within the library (Supplementary Fig. 4). Additionally, we have added Supplementary Table 1, which lists the distribution of mutational distances of LacI variants found within the library, and the corresponding coverage of all possible mutations for each mutational distance.

P2L26: How were FACS gates drawn? This is in support fig17 - should be pointed to here.

We agree that a better description of the FACS process would be helpful. To address this we have included Supplementary Fig. 2, showing flow cytometry measurements of the library before and after FACS. Additionally, we have extended the description of the FACS process:

“The initial library had a bimodal distribution of G__­0, as indicated by flow cytometry results, with a mode at low fluorescence (near G__­0 of wildtype LacI), and mode at higher gene expression. To generate a library in which most of the LacI variants could function as allosteric repressors, we used fluorescence activated cell sorting (FACS) to select the portion of the library with low fluorescence in the absence of ligand, gating at the bifurcation of the two modes (Sony SH800S Cell Sorter, Supplementary Fig. 2).”

__

P3L4: Where is the figure/data for the synonymous SNP mutations? This should be in the supplement.

We agree this data is necessary to support the claim that LacI function was not impacted by synonymous mutations. We have included a new Supplementary Fig. 9, which shows the distribution of Hill equation parameters for LacI variants that code for the wild-type amino acid sequence, but with non-identical coding DNA sequences. Additionally, we included the results of a statistical analysis in the main text, this analysis compared all synonymous sequences in the library:

“__We compared the distributions of the resulting Hill equation parameters between two sets of variants: 39 variants with exactly the wild-type coding DNA sequence for LacI (but with different DNA barcodes) and 310 variants with synonymous nucleotide changes (i.e. the wild-type amino acid sequence, but a non-wild-type DNA coding sequence). Using the Kolmogorov-Smirnov test, we found no significant differences between the two sets (p-values of 0.71, 0.40, 0.28, and 0.17 for G0, G∞, EC50, and n respectively, Supplementary Fig. 9).” __

P3L20: The authors use a ML learning deep neural network to predict variant that were not covered in the screen. However, the library generation method is using error prone pcr meaning there could multiple mutations resulting in the same amino acid change. The models performance was determined by looking at withheld data however error prone pcr could result in multiple nonsynomymous mutations of the same amino acid. For testing were mutations truly withheld or was there overlap? Because several mutations are being represented by different codon combinations. Was the withheld data for the machine learning withholding specific substitutions?

We thank the reviewer for identifying the need to clarify this critical data analysis. Data was held-out at the amino acid level, and so no overlap between the training and testing datasets occurred. We have clarified the description of the method in the main text:

“We calculated RMSE using only held-out data not used in the model training, and the split between held-out data and training data was chosen so that all variants with a specific amino acid sequence appear in only one of the two sets.”

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In addition, higher order protein interactions are complicated and idiosyncratic. I am surprised how well the neural net performs on higher order substitutions. P4L4: Authors find mutations at the dimer/tetramer interfaces but don't mention whether polymerization is required. is dimerization required for dna binding? Tetramerization?

We agree with the reviewer that, overall, a description of LacI structure and function would improve messaging the reported results. As such, we have added Supplementary Table 2, which defines the structural features discussed throughout the manuscript. Additionally, we have strived to describe the relevant structural and functional role of specific amino acids that are discussed in the text. Finally, we have also added a paragraph to the main text that summarizes the structure and function of LacI.

“The LacI protein has 360 amino acids arranged into three structural domains__22–24__. The first 62 N-terminal amino acids form the DNA-binding domain, comprising a helix-turn-helix DNA-binding motif and a hinge that connects the DNA-binding motif and the core domain. The core domain, comprising amino acid positions 63-324, is divided into two structural subdomains: the N-terminal core and the C-terminal core. The full core domain forms the ligand-binding pocket, core-pivot region, and dimer interface. The tetramerization domain comprises the final 30 amino acids and includes a flexible linker and an 18 amino acid α-helix (Fig. 3, Supplementary Table 2). Naturally, LacI functions as a dimer of dimers: Two LacI monomers form a symmetric dimer that further assembles into a tetramer (a dimer of dimers).”

P4L8: Substitutions near the dimer interface both impact g0 and ec50, which authors say is consistent with a change in the allosteric constant. Can authors explain their thinking more in the paper to make it easier to follow? Are the any mutations in this area that only impact g0 or ec50 alone? Why may these specific residues modify dimerization?

We agree that a more in-depth discussion on the possible mechanisms behind these phenotypic changes would improve the manuscript. We have added discussion throughout the subsection “Effects of amino acid substitutions on LacI phenotype,” we believe this added discussion improve the manuscript and clarify the relationship between the observed allosteric phenotypes and the molecular mechanisms behind them. W

Overall, we have made a number of changes in the manuscript that we hope will address these concerns.

P4L8: The authors discuss the allosteric constant extensively within the paper but do not explain it. It would be helpful to have an explanation of this to improve readability. This explanation should include the statistical mechanical basis of it and some speculation about the ways it manifests biophysically.

The allosteric constant is a critical concept, and we agree that it must be defined and discussed clearly throughout the manuscript. We have greatly expanded the discussion of the effects of single amino acid substitutions, and in the process we give examples of biochemical changes in the protein, and how they may affect the allosteric constant. We think this added text improves the manuscript and helps clarify the allosteric constant and the biomolecular processes that affect it.

P4L1-16: Authors see mutations in the dimerization region that impact either G0 and Gsaturated in combination with Ec50 but not g0 and gsaturated together. Maybe we do not fully understand the hill equation but why are there no mutations that impact both g0 and gsaturated seen in support fig 13c? Why would mutations in the same region potentially impacting dimerization impact either g0 or gsaturated? What might be the mechanism behind divergent responses?

It is important to recognize that the dimer interface does not just support the formation of dimers. There are many points of contact along the dimer interface that change when LacI switches between the active and inactive states. So, the dimer interface also helps regulate the balance between the active and inactive states. Our results show that different substitutions near the dimer interface can push this balance either toward the active or inactive states to varying degrees. We’ve added text throughout the description of single-substitutions effects to give specific examples and added a new paragraph at the end of that section to provide additional discussion and context. With regard to the more specific question of changes to both G0 and Ginf, the models indicate that simultaneous changes to those Hill Equation parameters requires an unusual combination of biophysical changes. To clarify this point, we added a short paragraph to the text:

“None of the single amino substitutions measured in the library simultaneously decrease __G∞ and increase G0 (Supplementary Fig. 20c). This is not surprising, since substitutions that shift the biophysics to favor the active state tend to decrease G∞ while those that favor the inactive state tend to increase G0, and the biophysical models2,14,15 indicate that only a combination of parameter changes can cause both modifications to the dose-response. The library did, however, contain several multi-substitution variants with simultaneously decrease __G∞ and increase G0. These inverted variants, and their associated substitutions are discussed below.”

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P4L29: for interpretability it would be good to explain what log-additive effect means in the context of allostery.

We agree that this information would be useful to the reader and have added additional text to explain log-additivity. We thank the reviewer for pointing out this oversight.

“Combining multiple substitutions in a single protein almost always has a log-additive effect on EC50. That is, the proportional effects of two individual amino acid substitutions on the EC50 can be multiplied together. For example, if substitution A results in a 3-fold change, and substitution B results in a 2-fold change, the double substitution, AB, behaving log-additively, results in a 6-fold change__.”__

P4L34-P5L19: This section is wonderful. Really cool results and interesting structural overlap! P5L34 Helix 9 of the protein is mentioned but it's functional relevance is not. This is common throughout the paper - it would be useful for there to be an overview somewhere to help the reader contextualize the results with known structural role of these elements.

We agree with the reviewer that this information would help to contextualize the results. We have made a number of changes to address this. First, we have added Supplementary Table 2, which describes the structural features of LacI. Second, we have added a paragraph overviewing the structure and function of LacI. Third, we have expanded the section “the effects of individual amino acid substitutions on the function of LacI” to discuss the structural or biochemical impact of specific substitutions. We thank the reviewer for this suggestion.

P5L39: The authors identified a triple mutant with the band-stop phenotype then made all combination of the triple mutant. Of particular interest is R195H/G265D which is nearly the same as the triple mutant. It would be nice if the positions of each of these mutations and have some discussion to begin to rationalize this phenotype, even if to point out how far apart they are and that there is no easy structural rationale!

We appreciate the reviewer highlighting this area of interest. We have added structural information to Fig. 6, which indicates to position of the amino acid substitutions that result in the band-stop phenotype, as well as a small discussion in the main text:

“To further investigate the band-stop phenotype, we chose a strong band-stop LacI variant with only three amino acid substitutions (R195H/G265D/A337D). These three positions are distributed distally on the periphery of the C-terminal core domain, and the role that each of these substitutions plays in the emergence of the band-stop phenotype is unclear.”

P6L9: There should be more discussion of the significance of this work directly compared to what is known. For instance, negative cooperativity is mentioned as an explanation for bi-phasic dose response but this idea is not explained. Why would the relevant free energy changes be more entropic? Another example is the reverse-TetR phenotype observed by Hillen et al.

We agree that more discussion is necessary to frame the results reported in the manuscript. To address this, we have added additional discussion throughout the manuscript that relates the results to the current understanding of allostery. Also, in the Conclusion, we added specific examples that lead us to link the ideas of bi-phasic dose response, negative cooperativity, and entropy/disorder. We believe these additions have improved the manuscript and we thank the reviewer for this suggestion.

P6L28: The authors mention that phenotypes exist with genotypes that are discoverable with genotype-phenotype landscapes. This study due to the constraints of error prone pcr were somewhat limited. How big is the phenotypic landscape? Is it worth doing a more systematic study? What is the optimal experimental design: Single mutations, doubles, random - where is there the most information. How far can you drift before your machine learning model breaks down? How robust would it be to indels?

The reviewer raises some excellent questions here, some of which are appropriate subjects for future work. The optimal experimental design depends on the objective: If the goal is to understand every possible mutation, a systematic site-saturation approach would be more appropriate. However, the landscape of a natural protein is limited by its wild-type DNA coding sequence, and so some substitutions are inaccessible (due to the arrangement of the codon table). The approach we took allowed to us characterize most of the accessible amino acid substitutions, while also allowing us to identify novel functions that would not have been identified with other approaches. We have added a little to the main text to discuss this (below). With regard to the DNN model, in the manuscript (SI Fig. 14), we show how the predictive accuracy degrades with mutational distance from the wild-type. It is possible that the type of DNN that we used could handle indels, since it effectively encodes each variant as a set of step-wise changes from the wild-type. But as with all machine-learning methods, it would require training with a dataset that included indels.

“Novel phenotypes emerged at mutational distances greater than one amino acid substitution, highlighting the value in sampling a broader genotype space with higher-order mutations. Furthermore, the untargeted, random mutagenesis approach used here was critical for finding these novel phenotypes, as the genotypes required for these novel phenotypes were unpredictable.”

Figures: Sup figs 3-7: The comparison of library-based results and single mutants is a great example of how to validate genotype-phenotype experiments!

Thank you.

Supp fig 5.: Missing figure number.

We appreciate the reviewer catching this error and have attempted to properly label all figures and tables in this revision. Thank you.

Supp fig7: G0 appears to have very poor fit between library vs single mutant version. Why might this be? R^2 would likely be better to report here as opposed to RMSE as RMSE is sensitize to the magnitude of the data such that you cannot directly compare RMSE of say 'n' to G0.

We agree that these are important discussion points and have addressed this concern with an expanded discussion in the main text, as well as the addition of coefficient of correlation (R^2) in the caption for Figure 2 (previously supplementary figure 7). We believe these additions contribute meaningfully to the manuscript, and they address the concerns of the reviewer. The additional text reads:

“We compared the Hill equation parameters from the library-scale measurement to those same parameters determined from flow cytometry measurements for each of the chemically synthesized LacI variants (Fig. 2). This served as a check of the new library-scale method’s overall ability to measure dose-response curves with quantitative accuracy. The accuracy for each Hill equation parameter in the library-scale measurement was: 4-fold for G0, 1.5-fold for G∞, 1.8-fold for EC50, and ± 0.28 for n. For G0, G∞, and EC50, we calculated the accuracy as: __, where __ is the root-mean-square difference between the logarithm of each parameter from the library-scale and cytometry measurements. For n, we calculated the accuracy simply as the root-mean-square difference between the library-scale and cytometry results. The accuracy for the gene expression levels (G0 and G∞) was better at higher gene expression levels (typical for G∞) than at low gene expression levels (typical for G0), which is expected based on the non-linearity of the fitness impact of tetracycline (Supplementary Figs. 10-11). Measurements of the Hill coefficient, n, had high relative uncertainties for both barcode-sequencing and flow cytometry, and so the parameter n was not used in any quantitative analysis.”

Sup fig13c: it is somewhat surprising that mutations only appear to effect g0 and not gsaturated. This implies that basal and saturated activity are not coupled. Is this expected? Why or why not?

This comment is partially addressed with a response above (P4L1-16). Coupled gene expression increases do occur, especially with substitutions at the start codon that result in fewer copies of LacI in the cell. In this instance, both G0 and G∞ are increased. Otherwise, changes to multiple biophysical parameters are required to increase both G0 and G∞.

Reviewer #1 (Significance (Required)): Allostery is hard to comprehend because it involves many interacting residues propagating information across a protein. The Monod-Wyman-Changeux (MWC) and Koshland, Nemethy, and Filmer (KNF) models have been a long standing framework to explain much of allostery, however recent formulations have focused on the role of the conformational ensemble and a grounding in statistical mechanics. This manuscript focuses on the functional impact of mutations and therefore contribution of the amino acids to regulation. The authors unbiased approach of combining a dose-response curve and mutational library generation let them fit every mutant to a hill equation. This approach let the authors identify the allosteric phenotype of all measured mutations! The authors found inverted phenotypes which happen in homologs of this protein but most interesting is the strange and idiosyncratic 'Band-stop' phenotype. The band-stop phenotype is bi-phasic that will hopefully be followed up with further studies to explain the mechanism. This manuscript is a fascinating exploration of the adaptability of allosteric landscapes with just a handful of mutations. Genotype-phenotype experiments allow sampling immense mutational space to study complex phenotypes such as allostery. However, a challenge with these experiments is that allostery and other complicated phenomena come from immense fitness landscapes altering different parameters of the hill equation. The authors approach of using a simple error prone pcr library combined with many ligand concentrations allowed them to sample a very large space somewhat sparsely. However, they were able to predict this data by training and using a neural net model. I think this is a clever way to fill in the gaps that are inherent to somewhat sparse sampling from error prone pcr. The experimental design of the dose response is especially elegant and a great model for how to do these experiments. With some small improvements for readability, this manuscript will surely find broad interest to the genotype-phenotype, protein science, allostery, structural biology, and biophysics fields. We were prompted to do this by Review Commons and are posting our submitted review here: Willow Coyote-Maestas has relevant expertise in high throughput screening, protein engineering, genotype-phenotype experiments, protein allostery, dating mining, and machine learning. James Fraser has expertise in structural biology, genotype-phenotype experiments, protein allostery, protein dynamics, protein evolution, etc.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): The authors use deep mutational scanning to infer the dose-response curves of ~60,000 variants of the LacI repressor and so provide an unprecedently systematic dataset of how mutations affect an allosteric protein. Overall this is an interesting dataset that highlights the potential of mutational scanning for rapidly identifying diverse variants of proteins with desired or unexpected activities for synthetic biology/bioengineering. The relatively common inverted phenotypes and their sequence diversity is interesting, as is the identification of several hundred genotypes with non-sigmoidal band-stop dose-response curves and their enrichment in specific protein regions. A weakness of the study is that some of the parameter estimates seem to have high uncertainty and this is not clearly presented or the impact on the conclusions analysed. A second shortcoming is that there is little mechanistic insight beyond the enrichments of mutations with different effects in different regions of the protein. But as a first overview of the diversity of mutational effects on the dose-response curve of an allosteric protein, this is an important dataset and analysis. **Comments** **Data quality and reproducibility** "The flow cytometry results confirmed both the qualitative and quantitative accuracy of the new method (Supplementary Figs. 3-7)"

• There need to be quantitative measures of accuracy in the text here for the different parameters.

We believe this comment is addressed along with the following two comments.

• Sup fig 7 panels should be main text panels - they are vital for understanding the data quality In particular, the G0 parameter estimates from the library appear to have a lower bound ie they provide no information below a cytometry Go of ~10^4. This is an important caveat and needs to be highlighted in the main text. The Hill parameter (n) estimate for wt (dark gray) replicate barcodes is extremely variable - why is this?

• In general there is not a clear enough presentation of the uncertainty and biases in the parameter estimations which seem to be rather different for the 4 parameters. Only the EC50 parameter seems to correlate very well with the independent measurements.

We thank the reviewer for identifying a need for more information on the accuracy of this method. So, we have moved Supplementary Fig. 7 to the main text (Fig 2 in the revised manuscript) and have added coefficients of correlation to each Hill equation parameter in that figure caption. Furthermore, we have added new data (Supplementary Fig. 11), which shows the uncertainty associated with different gene expression levels. Finally, we have added a discussion on the accuracy of this method for each parameter of the Hill equation to the main text. Estimation of the Hill coefficient (n) from data is often highly uncertain and variable, because that parameter estimate can be highly sensitive to random measurement errors at a single point on the curve. The estimate for the wild type appears to be highly variable because the plot contains 53 replicate measurements. So, the plotted variability represents approximately 2 standard deviations. The spread of wild-type results in the plot is consistent with the stated RMSE for the Hill coefficient. Furthermore, the Hill coefficient is not used in any of the additional quantitative analysis in our manuscript, partially because of its relatively high measurement uncertainty, but also because, based on the biophysical models, it is not as informative of the underlying biophysical changes.

“We compared the Hill equation parameters from the library-scale measurement to those same parameters determined from flow cytometry measurements for each of the chemically synthesized LacI variants (Fig. 2). This served as a check of the new library-scale method’s overall ability to measure dose-response curves with quantitative accuracy. The accuracy for each Hill equation parameter in the library-scale measurement was: 4-fold for G0, 1.5-fold for G∞, 1.8-fold for EC50, and ± 0.28 for n. For G0, G∞, and EC50, we calculated the accuracy as: "exp" ["RMSE" ("ln" ("x" ))], where "RMSE" ("ln" ("x" )) is the root-mean-square difference between the logarithm of each parameter from the library-scale and cytometry measurements. For n, we calculated the accuracy simply as the root-mean-square difference between the library-scale and cytometry results. The accuracy for the gene expression levels (G0 and G∞) was better at higher gene expression levels (typical for G∞) than at low gene expression levels (typical for G0), which is expected based on the non-linearity of the fitness impact of tetracycline (Supplementary Figs. 10-11). Measurements of the Hill coefficient, n, had high relative uncertainties for both barcode-sequencing and flow cytometry, and so the parameter n was not used in any quantitative analysis.”

• The genotypes in the mutagenesis library contain a mean of 4.4 aa substitutions and the authors us a neural network to estimate 3 of the Hill equation parameters (with uncertainties) for the 1991/2110 of the single aa mutations. It would be useful to have an independent experimental evaluation of the reliability of these inferred single aa mutational effects by performing facs on a panel of single aa mutants (using single aa mutants in sup fig 3-7, if there are any, or newly constructed mutants).

We agree that the predictive performance of the DNN requires experimental validation. We evaluated the performance by withholding data from 20% of the library, including nearly 200 variants with single amino acid substitutions, and then compared the predicted effect of those substitutions to the measured effect. The results of this test are reported in Supplementary Fig. 14. Additionally, we have adjusted the main text to more clearly explain the evaluation process.

“To evaluate the accuracy of the model predictions, we used the root-mean-square error (RMSE) for the model predictions compared with the measurement results. We calculated RMSE using only held-out data not used in the model training, and the split between held-out data and training data was chosen so that all variants with a specific amino acid sequence appear in only one of the two sets.” __ __

• fig3/"Combining multiple substitutions in a single protein almost always has a log-additive effect on EC50." How additive are the other 2 parameters? this analysis should also be presented in fig 3. If they are not as additive is it simply because of lower accuracy of the measurements? If the mutational effects are largely additive, then a simple linear model (rather than the DNN) could be used to estimate the single mutant effects from the multiple mutant genotypes.

We agree with the reviewer that exploring the log-additivity of the Hill equation parameters is informative, and have included Supplementary Figure 21, which displays this information. Furthermore, we expanded the discussion of log-additivity on all three parameters in the main text:

“Combining multiple substitutions in a single protein almost always has a log-additive effect on EC50. That is, the proportional effects of two individual amino acid substitutions on the EC50 can be multiplied together. For example, if substitution A results in a 3-fold change, and substitution B results in a 2-fold change, the double substitution, AB, behaving log-additively, results in a 6-fold change. Only 0.57% (12 of 2101) of double amino acid substitutions in the measured data have EC50 values that differ from the log-additive effects of the single substitutions by more than 2.5-fold (Fig. 4). This result, combined with the wide distribution of residues that affect EC50, reinforces the view that allostery is a distributed biophysical phenomenon controlled by a free energy balance with additive contributions from many residues and interactions, a mechanism proposed previously1,39 and supported by other recent studies17, rather than a process driven by the propagation of local, contiguous structural rearrangements along a defined pathway.

A similar analysis of log-additivity for G0 and G∞ is complicated by the more limited range of measured values for those parameters, the smaller number of substitutions that cause large shifts in G0 or G∞, and the higher relative measurement uncertainty at low G(L). However, the effects of multiple substitutions on G0 and G∞ are also consistent with log-additivity for almost every measured double substitution variant (Supplementary Fig. 21).”

**Presentation/clarity of text and figures**

• The main text implies that the DNN is trained to predict 3 parameters of the Hill equation but not the Hill coefficient (n). This should be clarified / justified in the main text.

We agree that the decision to exclude the parameter ‘n’ requires explanation in the main text. To address this, we have added to the main text:

“Measurements of the Hill coefficient, n, had high relative uncertainties for both barcode-sequencing and flow cytometry, and so the parameter n was not used in any quantitative analysis.”

and

“We trained the model to predict the Hill equation parameters G0, G∞, and EC50 (Supplementary Fig. 13), the three Hill equation parameters that were determined with relatively low uncertainty by the library-scale measurement.”

• The DNN needs to be better explained and justified in the main text for a general audience. How do simpler additive models perform for phenotypic prediction / parameter inference?

We agree with the reviewer that the DNN needs to be justified in the main text. As part of the revision plan, we propose to compare the predictive performance of the DNN to an additive model.

• Ref 14. analyses a much smaller set of mutants in the same protein but using an explicit biophysical model. It would be helpful to have a more extensive comparison with the approach and conclusions to this previous study.

Throughout the manuscript, we frame the results and discussion in terms of the referenced biophysical model. Using the model, we describe the biophysical effects that a substitution may have on LacI, based on observed changes to function associated with that substitution. We also comment briefly on the limitations of this model when applied to the extensive dataset presented here.

“Most of the non-silent substitutions discussed above are more likely to affect the allosteric constant than either the ligand or operator affinities. Within the biophysical model, those affinities are specific to either the active or inactive state of LacI, i.e. they are defined conditionally, assuming that the protein is in the appropriate state. So, almost by definition, substitutions that affect the ligand-binding or operator-binding affinities (as defined in the models) must be at positions that are close to the ligand-binding site or within the DNA-binding domain. Substitutions that modify the ability of the LacI protein to access either the active state or inactive state, by definition, affect the allosteric constant. This includes, for example, substitutions that disrupt dimer formation (dissociated monomers are in the inactive state), substitutions that lock the dimer rigidly into either the active or inactive state, or substitutions that more subtly affect the balance between the active and inactive states. Thus, because there are many more positions far from the ligand- and DNA- binding regions than close to those regions, there are many more opportunities for substitutions to affect the allosteric constant than the other biophysical parameters. Note that this analysis assumes that substitutions don’t perturb the LacI structure too much, so that the active and inactive states remain somehow similar to the wild-type states. Our results suggest that this is not always the case: consider, for example, the substitutions at positions __K84 and M98 discussed above and the substitutions resulting in the inverted and band-stop phenotypes discussed below.”__

• Enrichments need statistical tests to know how unexpected that results are e.g. p5 line 12 "67% of strongly inverted variants have substitutions near the ligand-binding pocket"

We agree that this information is necessary to interpret the results. We have included p-values (previously reported only in the Methods section) throughout the main text of the manuscript.

• missing citation: Poelwijk et al 2011 https://www.cell.com/fulltext/S0092-8674(11)00710-0 previously reported an inverted dose-response curve for a lacI mutant.

The publication by Poelwijk et al. was considered extensively when planning this work, and failing to cite that manuscript would have been tremendously unjust. We have included it, as well as a few additional references that have identified and discussed inverted LacI variants. We sincerely thank the reviewer for identifying this oversight.

• What mechanisms do the authors envisage that could produce the band-stop dose response curves? There is likely previous theoretical work that could be cited here. In general there is little discussion of the biophysical mechanisms that could underlie the various mutational effects.

We agree with the reviewer, that discussing the biophysical mechanisms that underlie many of the reported mutations is important to understand the results. We have expanded the subsection “Effects of amino acid substitutions on LacI phenotype” to include discussion on several of the key substitutions (or groups of substitutions) and their potential biophysical effects. Additionally, we consider mechanism that may underlie the band-stop sensor, and propose one model that could explain the band-stop phenotype:

“In particular, the biphasic dose-response of the band-stop variants suggests negative cooperativity: that is, successive ligand binding steps have reduced ligand binding affinity. Negative cooperativity has been shown to be required for biphasic dose-response curves__42,43. The biphasic dose-response and apparent negative cooperativity are also reminiscent of systems where protein disorder and dynamics have been shown to play an important role in allosteric function1, including catabolite activator protein (CAP)44,45 and the Doc/Phd toxin-antitoxin system46. This suggests that entropic changes may also be important for the band-stop phenotype. A potential mechanism is that band-stop LacI variants have two distinct inactive states: an inactive monomeric state and an inactive dimeric state. In the absence of ligand, inactive monomers may dominate the population. Then, at intermediate ligand concentrations, ligand binding stabilizes dimerization of LacI into an active state which can bind to the DNA operator and repress transcription. When a second ligand binds to the dimer, it returns to an inactive dimeric state, similar to wildtype LacI. This mechanism, and other possible mechanisms, do not match the MWC model of allostery or its extensions2,13–15__ and require a more comprehensive study and understanding of the ensemble of states in which these band-stop LacI variants exist.”

• "This result, combined with the wide distribution of residues that affect EC50, suggests that LacI allostery is controlled by a free energy balance with additive contributions from many residues and interactions." 'additive contributions and interactions' covers all possible models of vastly different complexity i.e. this sentence is rather meaningless.

We have attempted to contextualize this statement by adding additional discussion and references. We hope these additions give more meaning to this section.

“__This result, combined with the wide distribution of residues that affect EC50, reinforces the view that allostery is a distributed biophysical phenomenon controlled by a free energy balance with additive contributions from many residues and interactions, a mechanism proposed previously1,39 and supported by other recent studies17, rather than a process driven by the propagation of local, contiguous structural rearrangements along a defined pathway.”__

• fig 4 c and d compress a lot of information into one figure and I found this figure confusing. It may be clearer to have multiple panels with each panel presenting one aspect. It is also not clear to me what the small circular nodes exactly represent, especially when you have one smaller node connected to two polygonal nodes, and why they don't have the same colour scale as the polygonal nodes.

We agree with the reviewer that figure 4 (or Figure 5 in the revised manuscript) contains a lot of information. The purpose of this figure is to convey the structural and genetic diversity among the sets of inverted variants and band-stop variants. We designed this figure to convey this point at two levels: a brief overview, where the diversity is apparent by quickly considering the figure, and at a more informative level, with some quantitative data and structurally relevant points highlighted. We have modified the caption slightly, in an effort to improve clarity.

• line 25 - 'causes a conformational change' -> 'energetic change' (allostery does not always involve conformational change

We thank the reviewer for this comment and have adopted a more modern language describe allostery throughout the manuscript.

• sup fig 5 legend misses '5'

We thank the reviewer for pointing this out, we have attempted to number all figures and tables more carefully.

• sup fig 7. pls add correlation coefficients to these plots (and move to main text figures).

We agree that this information is of interest and have included this data as main text Figure 2. In addition, we have included coefficients of correlation in the caption of this figure.

• Reference 21 is just a title and pubmed link

We thank the reviewer for identifying this error, we have corrected this in the references.

• "fitness per hour" -> growth rate

To ensure that this connection is clearly established, when we introduce fitness for the first time, we clarify that it relates to growth rate:

“Consequently, in the presence of tetracycline, the LacI dose-response modulates cellular fitness (i.e. growth rate) based on the concentration of the input ligand isopropyl-β-D-thiogalactoside (IPTG).”

Also, we define ‘fitness’ in the Methods section:

“The experimental approach for this work was designed to maintain bacterial cultures in exponential growth phase for the full duration of the measurements. So, in all analysis, the Malthusian definition of fitness was used, i.e. fitness is the exponential growth rate__58__.”

• page 6 line 28 - "discoverable only via large-scale landscape measurements" - directed evolution approaches can also discover such genotypes (see e.g. Poelwijk /Tans paper). Please re-phrase.

We agree with the reviewer and have adjusted the main text accordingly.

“__Overall, our findings suggest that a surprising diversity of useful and potentially novel allosteric phenotypes exist with genotypes that are readily discoverable via large-scale landscape measurements.”__

• pls define jargon the first time it is used e.g. band-stop and band-pass

We agree that all unconventional terms should be explicitly defined when used, and we have attempted to define the band-pass and band-stop dose-response curves more clearly in the main text:

“These include examples of LacI variants with band-stop dose-response curves (i.e. variants with high-low-high gene expression; e.g. Fig. 1e, Supplementary Fig. 7), and LacI variants with band-pass dose-response curves (i.e. variants with low-high-low gene expression; e.g. Supplementary Fig. 8).”

**Methods/data availability/ experimental and analysis reproducibility:** The way that growth rate is calculated on page 17 equation 1- This section is confusing. Please be explicit about how you accounted for the lag phase, what the lag phase was, and total population growth during this time. In addition, please report the growth curves from the wells of the four plates, the final OD600 of the pooled samples, and exact timings of when the samples were removed from 37 degree incubation in a table. These are critical for calculating growth rate in individual clones downstream.

We thank the reviewer for identifying the need to clarify this section of text. The ‘lag’ in this section referred to a delay before tetracycline began impacting the growth rate of cells. To address this, we have changed ‘lag’ in this context to ‘delay.’ Furthermore, we have attempted to clarify precisely the cause of this delay, and how we accounted for it in calculating growth rates:

For samples grown with tetracycline, the tetracycline was only added to the culture media for Growth Plates 2‑4. Because of the mode of action of tetracycline (inhibition of translation), there was a delay in its effect on cell fitness: Immediately after diluting cells into Growth Plate 2 (the first plate with tetracycline), the cells still had a normal level of proteins needed for growth and proliferation and they continued to grow at nearly the same rate as without tetracycline. Over time, as the level of proteins required for cell growth decreased due to tetracycline, the growth rate of the cells decreased. Accordingly, the analysis accounts for the variation in cell fitness (growth rate) as a function of time after the cells were exposed to tetracycline. With the assumption that the fitness is approximately proportional to the number of proteins needed for growth, the fitness as a function of time is taken to approach the new value with an exponential decay:

(3)

where μitet is the steady-state fitness with tetracycline, and α is a transition rate. The transition rate was kept fixed at α = log(5), determined from a small-scale calibration measurement. Note that at the tetracycline concentration used during the library-scale measurement (20 µg/mL), μitet was greater than zero even at the lowest G(L) levels (Supplementary Fig. 10). From Eq. (3), the number of cells in each Growth Plate for samples grown with tetracycline is:

• What were the upper and lower bounds of the measurements? (LacI deletion vs Tet deletion / autofluoresence phenotype - true 100% and true 0% activity). Knowing and reporting these bounds will also allow easier comparison between datasets in the future.

We agree that knowing the limitations of the measurement are important for contextualizing the results. To address this point, we have included Supplementary Fig. 11, which shows the uncertainty of the measurement across gene expression levels.

Please clarify whether there was only 1 biological replicate (because the plates were pooled before sequencing)? Or if there were replicates present an analysis of reproducibility.

We thank the reviewer for pointing out the ambiguity in the original manuscript. The library-scale measurement reported here was completed once, the 24 growth conditions were spread across 96 wells, so each condition occupied 4 wells. The 4 wells were combined prior to DNA extraction. We have clarified this process in the methods by removing ‘duplicate’:

“Growth Plate 2 contained the same IPTG gradient as Growth Plate 1 with the addition of tetracycline (20 µg/mL) to alternating rows in the plate, resulting in 24 chemical environments, with each environment spread across 4 wells.”

Despite there being only a single library-scale measurement, the accuracy and reliability of the results are supported by many distinct biological replicates within the library (i.e. LacI variants with the same amino acid sequence but with different barcodes, see new Supplementary Fig. 9), as well as over 100 orthogonal dose-response curve measurements completed with flow cytometry (Figure 2). We believe these support the reproducibility of the work and we have included statistical analysis on the accuracy of the library-scale measurement results.

“To test the accuracy of the new method for library-scale dose-response curve measurements, we independently verified the results for over 100 LacI variants from the library. For each verification measurement, we chemically synthesized the coding DNA sequence for a single variant and inserted it into a plasmid where LacI regulates the expression of a fluorescent protein. We transformed the plasmid into E. coli and measured the resulting dose-response curve with flow cytometry (e.g. Fig. 1e). We compared the Hill equation parameters from the library-scale measurement to those same parameters determined from flow cytometry measurements for each of the chemically synthesized LacI variants (Fig. 2). This served as a check of the new library-scale method’s overall ability to measure dose-response curves with quantitative accuracy. The accuracy for each Hill equation parameter in the library-scale measurement was: 4-fold for G0, 1.5-fold for G∞, 1.8-fold for EC50, and ± 0.28 for n. For G0, G∞, and EC50, we calculated the accuracy as: "exp" ["RMSE" ("ln" ("x" ))], where "RMSE" ("ln" ("x" )) is the root-mean-square difference between the logarithm of each parameter from the library-scale and cytometry measurements. For n, we calculated the accuracy simply as the root-mean-square difference between the library-scale and cytometry results (Supplementary Fig. 7).”

• Please provide supplementary tables of the data (in addition to the raw sequencing files). Both a table summarising the growth rates, inferred parameter values and uncertainties for genotypes and a second table with the barcode sequence counts across timepoints and associated experimental data.

We agree that access to this information is critical. Due to the size of the associated data, we have made this data available for download in a public repository. We direct readers to the repository information in the “Data Availability” statement:

“The raw sequence data for long-read and short-read DNA sequencing have been deposited in the NCBI Sequence Read Archive and are available under the project accession number PRJNA643436. Plasmid sequences have been deposited in the NCBI Genbank under accession codes MT702633, and MT702634, for pTY1 and pVER, respectively.

The processed data table containing comprehensive data and information for each LacI variant in the library is publicly available via the NIST Science Data Portal, with the identifier ark:/88434/mds2-2259 (https://data.nist.gov/od/id/mds2-2259 or https://doi.org/10.18434/M32259). The data table includes the DNA barcode sequences, the barcode read counts, the time points used for the libarary-scale measurement, fitness estimates for each barcoded variant across the 24 chemical environments, the results of both Bayesian inference models (including posterior medians, covariances, and 0.05, 0.25, 0.75, and 0.95 posterior quantiles), the LacI CDS and amino acid sequence for each barcoded variant (as determined by long-read sequencing), the number of LacI CDS reads in the long-read sequencing dataset for each barcoded variant, and the number of unintended mutations in other regions of the plasmid (from the long-read sequencing data).

Code Availability

All custom data analysis code is available at https://github.com/djross22/nist_lacI_landscape_analysis.”

Reviewer #2 (Significance (Required)): The authors present an unprecedently systematic dataset of how mutations affect an allosteric protein. This illustrates the potential of mutational scanning for rapidly identifying diverse variants of allosteric proteins / regulators with desired or unexpected activities for synthetic biology/bioengineering. Previous studies have identified inverted dose-response curve for a lacI phenotypes https://www.cell.com/fulltext/S0092-8674(11)00710-0 but using directed evolution i.e. they were not comprehensive in nature. The audience of this study would be protein engineers, the allostery field, synthetic biologists and the mutation scanning community and evolutionary biologists interested in fitness landscapes. My relevant expertise is in deep mutational scanning and genotype-phenotype landscapes, including work on allosteric proteins and computational methods. Reviewer #3 (Evidence, reproducibility and clarity (Required)): In this interesting manuscript the authors developed in ingenious high throughput screening approach which utilizes DNA barcoding to select variants of LacI proteins with different allosteric profiles for IPTG control using E. coli fitness (growth rate) in a range of antibiotic concentrations as a readout thus providing a genotype-phenotype map for this enzyme. The authors used library of 10^5-10^ variants of LacI expressed from a plasmid and screened for distinct IPTG activation profiles under different conditions including several antibiotic stressors. As a result they identified various patterns of activation including normal (sigmoidal increase), inverted (decrease) and unusual stop-band where the dependence of growth on [IPTG] is non-monotonic. The study is well-conceived, well executed and provides statistically significant results. The key advance provided by this work is that it allows to identify specific mutations in LacI connected with one of three allosteric profiles. The paper is clearly written all protocols are explained and it can be reproduced in a lab that possesses proper expertise in genetics. Reviewer #3 (Significance (Required)): The significance of this work is that it discovered libraries of LacI variants which give rise to distinct profiles of allosteric control of activation of specific genes (in this case antibiotic resistance) by the Lac mechanism. The barcoding technology allowed to identify specific mutations which are (presumably) causal of changes in the way how allosteric activation of LacI by IPTG works. As such it provides a rich highly resolved dataset of LacI variants for further exploration and analysis. Alongside with these strengths several weaknesses should also be noted:

1. First and foremost the paper does not provide any molecular-level biophysical insights into the impact of various types of mutations on molecular properties of LacI. Do the mutations change binding affinity to IPTG? Binding side? Communication dynamics? Stability? The diagrams of connectivity for the stop-band mutations (Fig.4) do not provide much help as they do not tell much which molecular properties of LacI are affected by mutations and why certain mutations have specific effect on allostery. A molecular level exploration would make this paper much stronger.

We address this comment with comment (2), below.

1. In the same vein a theoretical MD study would be quite illuminating in answering the key unanswered question of this work: Why do mutations have various and pronounced effects of allosteric regulation by LacI?. I think publication of this work should not be conditioned on such study but again adding would make the work much stronger.

We appreciate the reviewer’s comments and agree that investigating the molecular mechanisms driving the phenotypic changes identified in this work is a compelling proposition. Throughout the manuscript, we identify positions and specific amino acid substitutions that affect the measurable function of LacI, and occasionally discuss the biophysical effects that may underly these changes. We have expanded the discussion to include possible molecular-level effects.

The dataset reported here identifies many potential candidates for molecular-level study, either computationally or experimentally. However, this manuscript is scoped to report a large-scale method to measure the genotype-phenotype landscape of an allosteric protein, and a limited investigation into the emergence of novel phenotypes that are identified in the landscape.

1. Lastly a recent study PNAS v.116 pp.11265-74 (2019) explored a library of variants of E. coli Adenylate Kinase and showed the relationship between allosteric effects due to substrate inhibition and stability of the protein. Perhaps a similar relationship can explored in this case of LacI.

We thank the reviewer for highlighting this publication. We agree with the reviewer that similar effects may play a role in the activity of LacI. Establishing such a relationship would require additional experimentation, and, we think, is outside the scope of the submitted manuscript. Although, we hope follow-up studies using this dataset will investigate this phenomenon and other related mechanisms, that may underlie the band-stop phenotype and other observed effects.