A modified fluctuation assay reveals a natural mutator phenotype that drives mutation spectrum variation within Saccharomyces cerevisiae

  1. Pengyao Jiang
  2. Anja R Ollodart
  3. Vidha Sudhesh
  4. Alan J Herr
  5. Maitreya J Dunham
  6. Kelley Harris

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Abstract

Although studies of Saccharomyces cerevisiae have provided many insights into mutagenesis and DNA repair, most of this work has focused on a few laboratory strains. Much less is known about the phenotypic effects of natural variation within S. cerevisiae ’s DNA repair pathways. Here, we use natural polymorphisms to detect historical mutation spectrum differences among several wild and domesticated S. cerevisiae strains. To determine whether these differences are likely caused by genetic mutation rate modifiers, we use a modified fluctuation assay with a CAN1 reporter to measure de novo mutation rates and spectra in 16 of the analyzed strains. We measure a 10-fold range of mutation rates and identify two strains with distinctive mutation spectra. These strains, known as AEQ and AAR, come from the panel’s ‘Mosaic beer’ clade and share an enrichment for C > A mutations that is also observed in rare variation segregating throughout the genomes of several Mosaic beer and Mixed origin strains. Both AEQ and AAR are haploid derivatives of the diploid natural isolate CBS 1782, whose rare polymorphisms are enriched for C > A as well, suggesting that the underlying mutator allele is likely active in nature. We use a plasmid complementation test to show that AAR and AEQ share a mutator allele in the DNA repair gene OGG1 , which excises 8-oxoguanine lesions that can cause C > A mutations if left unrepaired.

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  1. Version published to 10.7554/elife.68285 on eLife
  2. Version published to 10.1101/2021.01.11.425955v2 on bioRxiv
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    Reply to the reviewers

    Response to reviewers

    We first thank Review Commons for recruiting such knowledgeable reviewers to comment on our manuscript. We appreciate their diverse set of useful and constructive comments, which should help us improve the manuscript substantially. Please see our response to each reviewer’s comments below.

    Reviewer #1:

    **Summary:** The authors describe a useful modified fluctuation assay that couples conventional mutation rate analysis with mutational spectrum characterization of forward mutations at the S. cerevisiae CAN1 locus. They nicely showed that wild yeast isolates display a wide range of mutation rates with strains AAR and AEQ displaying rates ~10-fold higher than the control lab strain. These two strains also showed a bias for C>A mutations, and were the only strains analyzed that had a mutation spectrum statistically different from the lab control. Together, these data provide a compelling proof-of-principle of the applicability of the modified fluctuation analysis approach described in this manuscript. Overall, the manuscript is very well written, and the work reported in it does represent a valuable contribution to the field. However, two primary shortcomings were identified that can be addressed to strengthen the conclusions prior to publication. Both points described below pertain to the analysis of the possible C>A specific mutator phenotype in strains AAR and AEQ.

    __Response: __

    We thank the reviewer for this positive response. We have made a plan, detailed below, to address the shortcomings the reviewer has highlighted.

    **Major comments:**

    1. The work presented in the manuscript does suggest that these two haploids are likely to display the C>A mutator phenotype. Yet, the authors fell short of providing a full and unambiguous demonstration that would elevate the significance of their discovery. They could have directly tested the predicted C>A specific mutator phenotype by conducting additional experiments, one of which is relatively simple. Specifically, they could have performed a simple reversion-based mutation assay to validate the reported C>A mutator phenotype displayed by AAR and AEQ. For example, into AAR, AEQ, and a wild type control, the authors could introduce an engineered auxotrophic marker allele (e.g., ura3 mutation) caused by an A to C substitution, which upon mutation back to A restores prototrophic growth in minimal media (ie. reversion from ura3-C to URA3-A). Such specific reversible allele should be relatively easy to integrate into the AAR and AEQ genomes, as well as in the control strain. Based on the authors' prediction, AAR and AEQ should display a very large increase (far higher than 10 fold) in the reversion rate when compared to a control haploid. To demonstrate the specificity of the mutation spectrum, the authors could test the reversion rates of a different engineered allele requiring a reversion mutation in the opposite direction (ie. reversion from ura3-A to URA3-C). If the AAR and AEQ mutator is specific C>A, one would predict that all three strains should have similar mutation rates for a reversion in the A>C direction. This additional genetic work would thoroughly validate the central discovery and would reinforce the usefulness of the method described in the manuscript.

    Alternatively, a conventional mutation accumulation and whole genome re-sequencing experiment with parallel lines of AAR, AEQ and a control strain would also very effectively validate the C>A mutator prediction, and it would also answer the authors' discussion point about specificity to the CAN1 locus. However, it would be more costly and much more time consuming.

    __Response: __

    We thank the reviewer for these detailed, clear suggestions regarding additional methodology for further validating our results. We appreciate that parallel independent validations always add credibility to unexpected results like the ones presented in our manuscript. We’ve been considering these suggestions seriously, but our concern is that it is much less straightforward to engineer the genomes of these wild yeast than one might expect based on experiments with standard laboratory strains. Unforeseen roadblocks related to the biology of AAR and AEQ could end up making the URA3 reversion assay take even longer than an MA study. As we understand it, the two main concerns that might necessitate this additional undertaking are that either our novel assay for ascertaining mutations in CAN1 doesn’t work properly, or that the mosaic beer strains mutate significantly differently outside CAN1. Below we describe revisions to the text that we think will clearly represent these caveats and the relatively modest uncertainty associated with them.

    To further justify the soundness of our claim that AAR and AEQ have distinctive mutation rates and spectra, we plan to add additional discussion of the validation approaches that are presented in the manuscript to verify the accuracy of our pipeline. Although the ability of fluctuation assays to estimate mutation rates is well established, the identification of the spectra using our next-generation-sequencing-based pipeline is novel, so we used Sanger sequencing to validate the exact de novo mutations it ascertained in a select control strain. Our Sanger sequencing test found our assay to have an undetectably low false positive rate and a false negative rate that was much too low to account for the differences we measured between AAR, AEQ, and the standard lab strains. The fact that we also observed similar mutation spectra from control lab strains used in previous CAN1-based studies further demonstrates the reliability of our method, and it is notable that most natural isolates were measured to have very similar mutation spectra to lab strains (Figure 4 and Supplementary Figure S8-S9). We agree that further validation would be needed to read much into the more subtle differences in mutation rates and spectra that we saw hints of between other strains, and for that reason, we focused this paper on the differences that well exceed what we measured to be our measurement pipeline’s margin of error.

    It is true that the genome-wide mutation rate might differ somewhat from the mutation rate at the CAN1-locus, but the mutation spectrum at the CAN1 locus measured in a previous study (Lang and Murray, 2008) was very similar to the genome-wide mutation spectra obtained from MA studies (Sharp et al., 2018), with just a small overall increase of mutations with C/G nucleotides (the second to last paragraph on page 17 and Supplementary Figure S13). Moreover, we have avoided making any claims of seeing distinct mutation rates or spectra based on “apples-to-oranges” comparisons between mutation spectra measured at CAN1 and spectra measured across the whole genome.

    We also note that the enrichment of C>A mutations in AEQ and AAR is not only observed from our *de novo *mutation data in CAN1, but also seen in rare natural polymorphisms genome-wide (Figure 1B, 5A,B). Rare natural polymorphisms are recent mutations that occurred during the history of the strain, and the fact that they disproportionately enrich in C>A mutations in these strains indirectly shows that the C>A enrichment occurs not only at CAN1, as measured in our experiment, but has also been occurring during natural mutation accumulation genome-wide.

    The second concern is in regard to the relatively extensive conclusions drawn about the possible evolutionary significance of the possible C>A mutator in AAR and AEQ. The authors should be more cautious and conservative in the proposed interpretation. As the authors note:

    'Three of the four C>A-enriched mosaic beer strains, AAR, AEQ, and SACE_YAG, are all haploid derivatives of the [highly heterozygous] diploid Saccharomyces cerevisiae var diastaticus strain CBS1782, which was isolated in 1952 from super-attenuated beer.'

    From this statement, and because the paper cited provided few details on the isolation of CBS1782, it is presumed that these haploid derivatives were most likely isolated as recombinant spores. Furthermore, it is unclear when this isolation occurred, and for how many generations strains AAR and AEQ have been propagated in a haploid state.

    Herein lies a critical point: AAR and AEQ were recently derived from a diploid background with a "high level of heterozygosity". In a heterozygous diploid context, deleterious point mutations (and any resulting mutator phenotypes) would likely be masked by the presence of wild-type alleles. Now, as haploids, they express a novel genotype (i.e., combination of defective or incompatible parental alleles), which manifests as a mutator phenotype. In this respect, AAR and AEQ appear analogous to the spore derivatives of the incompatible cMLH1-kPMS1 isolate referred to in the manuscript as a notable exception. The analysis of strains harboring incompatible MLH1-PMS1 mutations by Raghavan et al. demonstrated that the heterozygous diploid parents were not themselves mutators, but that haploid spores which had inherited the pair of incompatible alleles displayed mutator phenotype. Collectively, while it can certainly be argued that the strains AAR and AEQ (like the MLH1/PMS1 incompatible strains) are mutators now, this fact alone does not support the conclusion that they have adapted to survive the expression of an extant mutator phenotype. This premise could be tested by analyzing the mutation rates/spectra of four new spores derived from a single tetrad of CBS 1782. Do the four sibling spores display similar or different mutational rates and spectra? If all four spores from a single tetrad exhibit the 10-fold increase in CAN1 mutation rate and the C>A transversion bias, then it can be inferred that the diploid parent is also a mutator in the same manner. Further direct analysis of mutation rates and spectrum in the parent diploid CBS 1782 would complete the work. This finding would be quite significant, and would provide strong evidence that wild strains can in fact tolerate the expression of a chronic mutator allele.

    __Response: __

    We thank the reviewer for suggesting additional study of the ancestral diploid strain CBS 1782, and we agree this could add a lot to the manuscript, especially given the high level of heterozygosity in the diploid and the link to the previous MLH1-PMS1 incompatibility story. We have obtained a sample of CBS 1782 and plan to knock out its HO locus using CRISPR, perform tetrad dissection of spores freshly derived from the diploid, and then measure mutation rates and spectra in all four segregants derived from a single tetrad (provided that all four spores end up growing). We plan to collect and sequence about 50 mutations to get qualitative results on the mutation rates and spectra of these segregants. We also plan to sequence the whole genome of the strain CBS 1782 and examine polymorphisms together with the 1011 strains to check for any signal of C>A enrichment. We recognize that our pipeline as currently implemented will not let us directly measure the mutation spectrum of the diploid, which is inaccessible to our pipeline given its two functional copies of CAN1 and the recessive nature of canavanine resistance. That being said, the elevation of the C>A fraction in natural polymorphisms found in AAR and AEQ provides evidence for prolonged activity of the mutator phenotype in the wild and/or in the domesticated environment from which CBS 1782 was derived. However, we acknowledge we have limited information about how these haploids were propagated before they were banked.

    **Minor comments:** A final, relatively minor point. That the new haploids AAR and AEQ show distinct mutation rates and spectra opens the door to an interesting line of inquiry, which may help to identify the causative mutator allele in a manner more efficient than searching for missense mutations. It is stated, and it is understandable, that the identification of the possible causal mutations is beyond the scope of the present manuscript. In this spirit, it would be much more appropriate to restrict such considerations to the Discussion section. Specifically, while the authors make a plausible case for OGG1 being a candidate gene responsible for the C>A mutator phenotype, no experimental demonstration was attempted. As such, that text segment should be moved from the Results to the Discussion section.

    __Response: __

    We agree with the reviewer of lacking genetic evidence on *OGG1 *in the current manuscript and we will move that section from the results to the discussion. Future work is underway to test and identify the causal loci for the mutator phenotype.

    Reviewer #1 (Significance (Required)): As stated in the summary section above, the manuscript by Jiang et al represents a substantial contribution to the fields of genome stability and genome evolution. The method described is likely to be useful beyond budding yeast. The work will be appreciated by a broad audience of geneticists. The additional work and text modifications proposed above would likely further elevate the impact of this work.

    __Response: __

    We are very grateful for this generous assessment and we likewise hope our planned revisions will further elevate the paper’s potential impact.

    Reviewer #2:

    Mutation is a fundamental force in organismal evolution, and therefore understanding the evolution of mutational mechanisms are important in evolutionary studies. In this manuscript, the authors used strains of S. cerevisiae as a model system to study the variations of rates and spectra in mutations with bioinformatic and experimental approaches. First, the authors analyzed the polymorphism data from 1011 strains by PCA analysis and show the variations in spectra. Second, the authors used fluctuation test combined with deep sequencing of the resistance gene to identify mutation rates and spectra in 18 strains, which show ~10-fold mutation rate variations and increased C-to-A mutations in two strains.

    For the second part, the experimental procedures and statistical analysis are mostly solid. For the first part, as what authors said in the introduction, polymorphism is not equal to the mutation spectra. I think the authors did a good job by being cautious in the wording and having no over-inference after the analysis. It is thus inevitable that the conclusion of this part sounds mostly descriptive. The overall writing is very clear. I will recommend the publication in field-specific journals.

    __Response: __

    We thank the reviewer for these positive comments. We will address each minor point below.

    **Minor comments:** P9 - It is very hard to not wonder how the 16 strains were picked in the fluctuation tests. Some comments on that will be appreciated. E.g., was that informed by the results of Fig 1?

    __Response: __

    We actually did not pick strains based on the results of Figure 1, one reason being that the CAN1 reporter method only works on haploid strains with a canavanine sensitivity phenotype. We also restricted our analysis to strains without known aneuploidies to maximize our ability to accurately measure the spectra of the strains’ polymorphisms. When possible, given these constraints, we included at least two randomly selected strains from each clade of the 1011 collection whenever possible. These constraints are currently explained on the second to last paragraph on page 9, and will be explained in more detail in revision.

    P17- In the paragraph "natural selection might contribute ..." , is there any example of "certain mutation types are more often beneficial than others"?

    __Response: __

    One example of this is that transitions are more often synonymous than transversions are (Freeland and Hurst, 1998), and mutations that create or destroy CpG sites are more likely to alter gene regulation than other mutation types are (in species other than yeast where CpGs are methylated). We recognize that these effects are likely not large, which is one reason we don’t think natural selection is a great explanation for mutation spectrum difference among groups.We will mention these examples explicitly in the revised text.

    P20 - Extra ')' in the sentence "Adjacent indels were merged if their frequencies differed by less than 10%)."

    __Response: __

    We will fix this in revision.

    In the discussion, it might be good to add a paragraph to compare the rate and spectra reported here and the ones found by MA and then NGS approach(e.g., Zhu et al. 2014).

    __Response: __

    We’ll be sure to add a reference to the Zhu et al. (2014) spectrum in the discussion, extending our existing comparison of mutation spectra previously reported using CAN1 (Lang and Murray, 2008) and the MA approach (Sharp et al., 2018) (currently discussed on the second to last paragraph on page 17, Supplementary Figure S13). Our CAN1 method also obtains results that are consistent with the Lang et al 2008 study on the same control strain (the last paragraph on page 11).

    Reviewer #2 (Significance (Required)): The significance of this manuscript will be relatively specific to evolutionary biologists and geneticists, especially those who use yeasts as a model system. For example, I expect the variation of mutation rates and spectra found in this manuscript will impact the following population-genetic analysis in this collection of 1011 strains and motivate more studies on the molecular machineries which affect mutation rates and spectra.

    In addition, in terms of methodological novelty, adding a novel step of reporter-gene sequencing is a reasonable way to get some information on mutation spectra as it is less labor-intensive than NGS of MAs. Other statistical or experimental procedures in this manuscript mostly follow the approaches which have been developed in previous literature and thus show not much novelty.

    __Response: __

    We thank the reviewer for this positive assessment. Since evolutionary biology, population genetics, and model organism genetics are three of eLife’s major focus areas, we are hoping to communicate our results to this journal’s broad audience rather than restrict ourselves to a journal focusing too narrowly on just one of these focus areas.

    Reviewer #3:

    **Summary** The authors show that certain yeast strains have altered mutation rates/bias. The study is well motivated, genetic variation in mutation rates are not easily uncovered, and capitalizes on yeast and a high-throughput mutation rate/bias method that validates findings of C>A bias from yeast polymorphism data. The results are solid and clearly presented and I have no major concerns.

    __Response: __

    We are very grateful for this positive response. Please find our response to each minor comment below.

    **Major comments** None

    **Minor comments** Should have comma: "In addition, environmental ..."

    Response:

    We will fix this in revision.

    Using S. paradoxus to classify derived vs ancestral alleles may not work as well as allele frequency. A 1/100 rare variant is 100x more likely derived than common variant. But with S. paradoxus divergence of say 5%, 5% polymorphic sites are misclassified or NA. Of course, since you used both, this is not a concern. But the number of variants included/excluded in each analysis should be reported. Also, I was a bit surprised that the rare variants are more noisy since most variants are rare.

    Response:

    We agree that the heuristic of classifying rare alleles as derived will do the right thing the majority of the time, but this could potentially create artifactual differences between the mutation spectra of different populations because the exact ratio of rare derived alleles to common derived alleles depends on the population’s demographic history and true site frequency spectrum. If two populations had the same mutation spectrum but very different proportions of variants that are polarized incorrectly, this could create the appearance of a mutation spectrum difference where none exists. In the revision, we will be sure to report the total number of variants filtered because of the variation present in S. paradoxus.

    The reviewer is right to point out that rare variants are generally more abundant than common variants, but this pattern is much more pronounced in a species like humans that has undergone recent population expansion than it appears to be in S. cerevisiae, which appears to have a higher proportion of older, shared variation. We hope this clarifies why the rare variant mutation spectrum PCA appears noisier than the plot made from variation across more frequency categories.

    In regards to variation in mutation rate based on canavanine resistanct. There is a caveat that some strains may be more canavanine resistant - due to differences in transporter abundanced or some other aspect of metabolism. Thus, the same mutation would survive and grow (barely) in one strain background, but not another. This caveat is very unlikely to have much of an impact but it would be worth discussing.

    Response:

    Thanks for pointing this out. We also considered the possibility that our mutation rate estimates could be confounded by slight differences in canavanine resistance between strains, and will address this point in the discussion.

    The explanation for synonymous mutations is hitchhikers or errors. However, they could also disrupt translation, here's one possibility PMC4552401.

    Response:

    Thanks for pointing this out. We will expand our statement on the possible significance of synonymous mutations to include modification of transcription and translation efficiency.

    Are there CAN allele differences between strains? If there are some, it might be worth mentioning why you do/don't think this influences the mutation rate. E.g. CGG is one step from stop but CGT is not.

    Response:

    The reviewer makes a good point that there are segregating differences among these strains in the sequence of CAN1. We plan to add an analysis where we calculate the number of opportunities for missense mutations and nonsense in each strain, as a function of its CAN1 sequence, to put a bound on the amount that these differences could affect our estimates of mutation rates in each strain.

    For the allele counts in Figure 5B. 2 indicates a variant is present in one strain so there are only 9 mutations present in AAR and not found in ANY other strain or just not found in the four listed? Likewise AAR has 36 for count 4, meaning that there are 36 variants present in AAR and one other strain, where other strains are just the 4 shown in the table, or other strains being any of the 1011?

    Response:

    The allele count in Figure 5B represents the number of times the derived allele is present in the whole population. In this case, the whole population refers to the 1011 strains minus 336 strains that are so closely related to other strains in the panel that they are effectively duplicates. An allele of count 2 might be homozygous in AAR and absent from all other strains, or present as one heterozygous copy in AAR as well as one heterozygous copy in another strain. We will explain this more clearly in the revised manuscript.

    "To our knowledge, this is one of the first" This is an odd way to put it and could be rephrased. As it stand you are either the first and not knowledgeable or knowledgeable and not the first.

    Response:

    Thanks. We will revise this to state that to our knowledge, we are the first to report such a discovery.

    "humans, great apes, .." Could you put the citations in the discussion too. I was a little surprised there was no mention of C>A bias as it relates to studies in bacteria and cancer, where there has been a lot of work on mutational spectra. A comment on this literature or whether the C>A biases are not found elsewhere would be nice.

    Response:

    We will add citations and discussion of bacteria and cancer in the revised manuscript. The reviewer is right to point out that C>A mutations do come up in cancer signatures, for example in familial adenomatous polyposis disorders where excision repair of 8-oxoguanine is compromised.

    Reviewer #3 (Significance (Required)):

    I am an evolutionary geneticist with expertise in genomics and bioinformatics. In addition to reviewing papers I also regularly handle papers as an editor. The manuscript provides rare insight into population variation in mutation rates. While differences in mutational biases are well known between species and in some cases within a species, we typically don't know what causes this biases. Environmental factors are often thought to be involved; this work clearly shows that genetic (mutator strains) exist and impact polymorphism in yeast. The manuscript does a nice job in the introduction of explaining the background on mutation rate research and motivation for the work. It also clear explains the advantage of an experimental highthroughput mutation rate/spectra approach. Thus, I believe this new angle on a long-standing problem will be of interest to the community of evolutionary geneticists outside of yeast researchers.

    Response:

    We appreciate this very generous assessment, thank you!

    Reference

    Freeland, S. J. and Hurst, L. D. (1998) ‘The genetic code is one in a million’, Journal of molecular evolution, 47(3), pp. 238–248.

    Lang, G. I. and Murray, A. W. (2008) ‘Estimating the Per-Base-Pair Mutation Rate in the Yeast Saccharomyces cerevisiae’, Genetics, 178(1), pp. 67–82.

    Sharp, N. P. et al. (2018) ‘The genome-wide rate and spectrum of spontaneous mutations differ between haploid and diploid yeast’, Proceedings of the National Academy of Sciences of the United States of America, 115(22), pp. E5046–E5055.

    Zhu, Y. O. et al. (2014) ‘Precise estimates of mutation rate and spectrum in yeast’, Proceedings of the National Academy of Sciences of the United States of America, 111(22), pp. E2310–8.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary

    The authors show that certain yeast strains have altered mutation rates/bias. The study is well motivated, genetic variation in mutation rates are not easily uncovered, and capitalizes on yeast and a high-throughput mutation rate/bias method that validates findings of C>A bias from yeast polymorphism data. The results are solid and clearly presented and I have no major concerns.

    Major comments

    None

    Minor comments

    Should have comma: "In addition, environmental ..."

    Using S. paradoxus to classify derived vs ancestral alleles may not work as well as allele frequency. A 1/100 rare variant is 100x more likely derived than common variant. But with S. paradoxus divergence of say 5%, 5% polymorphic sites are misclassified or NA. Of course, since you used both, this is not a concern. But the number of variants included/excluded in each analysis should be reported. Also, I was a bit surprised that the rare variants are more noisy since most variants are rare.

    In regards to variation in mutation rate based on canavanine resistanct. There is a caveat that some strains may be more canavanine resistant - due to differences in transporter abundanced or some other aspect of metabolism. Thus, the same mutation would survive and grow (barely) in one strain background, but not another. This caveat is very unlikely to have much of an impact but it would be worth discussing.

    The explanation for synonymous mutations is hitchhikers or errors. However, they could also disrupt translation, here's one possibility PMC4552401.

    Are there CAN allele differences between strains? If there are some, it might be worth mentioning why you do/don't think this influences the mutation rate. E.g. CGG is one step from stop but CGT is not.

    For the allele counts in Figure 5B. 2 indicates a variant is present in one strain so there are only 9 mutations present in AAR and not found in ANY other strain or just not found in the four listed? Likewise AAR has 36 for count 4, meaning that there are 36 variants present in AAR and one other strain, where other strains are just the 4 shown in the table, or other strains being any of the 1011?

    "To our knowledge, this is one of the first" This is an odd way to put it and could be rephrased. As it stand you are either the first and not knowledgeable or knowledgeable and not the first.

    "humans, great apes, .." Could you put the citations in the discussion too. I was a little surprised there was no mention of C>A bias as it relates to studies in bacteria and cancer, where there has been a lot of work on mutational spectra. A comment on this literature or whether the C>A biases are not found elsewhere would be nice.

    Significance

    I am an evolutionary geneticist with expertise in genomics and bioinformatics. In addition to reviewing papers I also regularly handle papers as an editor. The manuscript provides rare insight into population variation in mutation rates. While differences in mutational biases are well known between species and in some cases within a species, we typically don't know what causes this biases. Environmental factors are often thought to be involved; this work clearly shows that genetic (mutator strains) exist and impact polymorphism in yeast. The manuscript does a nice job in the introduction of explaining the background on mutation rate research and motivation for the work. It also clear explains the advantage of an experimental highthroughput mutation rate/spectra approach. Thus, I believe this new angle on a long-standing problem will be of interest to the community of evolutionary geneticists outside of yeast researchers.

  5. Review Commons

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    Referee #2

    Evidence, reproducibility and clarity

    Mutation is a fundamental force in organismal evolution, and therefore understanding the evolution of mutational mechanisms are important in evolutionary studies. In this manuscript, the authors used strains of S. cerevisiae as a model system to study the variations of rates and spectra in mutations with bioinformatic and experimental approaches. First, the authors analyzed the polymorphism data from 1011 strains by PCA analysis and show the variations in spectra. Second, the authors used fluctuation test combined with deep sequencing of the resistance gene to identify mutation rates and spectra in 18 strains, which show ~10-fold mutation rate variations and increased C-to-A mutations in two strains.

    For the second part, the experimental procedures and statistical analysis are mostly solid. For the first part, as what authors said in the introduction, polymorphism is not equal to the mutation spectra. I think the authors did a good job by being cautious in the wording and having no over-inference after the analysis. It is thus inevitable that the conclusion of this part sounds mostly descriptive. The overall writing is very clear. I will recommend the publication in field-specific journals.

    Minor comments:

    P9 - It is very hard to not wonder how the 16 strains were picked in the fluctuation tests. Some comments on that will be appreciated. E.g., was that informed by the results of Fig 1?

    P17- In the paragraph "natural selection might contribute ..." , is there any example of "certain mutation types are more often beneficial than others"?

    P20 - Extra ')' in the sentence "Adjacent indels were merged if their frequencies differed by less than 10%)." In the discussion, it might be good to add a paragraph to compare the rate and spectra reported here and the ones found by MA and then NGS approach(e.g., Zhu et al. 2014).

    Significance

    The significance of this manuscript will be relatively specific to evolutionary biologists and geneticists, especially those who use yeasts as a model system. For example, I expect the variation of mutation rates and spectra found in this manuscript will impact the following population-genetic analysis in this collection of 1011 strains and motivate more studies on the molecular machineries which affect mutation rates and spectra.

    In addition, in terms of methodological novelty, adding a novel step of reporter-gene sequencing is a reasonable way to get some information on mutation spectra as it is less labor-intensive than NGS of MAs. Other statistical or experimental procedures in this manuscript mostly follow the approaches which have been developed in previous literature and thus show not much novelty.

  6. Review Commons

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    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    The authors describe a useful modified fluctuation assay that couples conventional mutation rate analysis with mutational spectrum characterization of forward mutations at the S. cerevisiae CAN1 locus. They nicely showed that wild yeast isolates display a wide range of mutation rates with strains AAR and AEQ displaying rates ~10-fold higher than the control lab strain. These two strains also showed a bias for C>A mutations, and were the only strains analyzed that had a mutation spectrum statistically different from the lab control. Together, these data provide a compelling proof-of-principle of the applicability of the modified fluctuation analysis approach described in this manuscript. Overall, the manuscript is very well written, and the work reported in it does represent a valuable contribution to the field. However, two primary shortcomings were identified that can be addressed to strengthen the conclusions prior to publication. Both points described below pertain to the analysis of the possible C>A specific mutator phenotype in strains AAR and AEQ.

    Major comments:

    1. The work presented in the manuscript does suggest that these two haploids are likely to display the C>A mutator phenotype. Yet, the authors fell short of providing a full and unambiguous demonstration that would elevate the significance of their discovery. They could have directly tested the predicted C>A specific mutator phenotype by conducting additional experiments, one of which is relatively simple. Specifically, they could have performed a simple reversion-based mutation assay to validate the reported C>A mutator phenotype displayed by AAR and AEQ. For example, into AAR, AEQ, and a wild type control, the authors could introduce an engineered auxotrophic marker allele (e.g., ura3 mutation) caused by an A to C substitution, which upon mutation back to A restores prototrophic growth in minimal media (ie. reversion from ura3-C to URA3-A). Such specific reversible allele should be relatively easy to integrate into the AAR and AEQ genomes, as well as in the control strain. Based on the authors' prediction, AAR and AEQ should display a very large increase (far higher than 10 fold) in the reversion rate when compared to a control haploid. To demonstrate the specificity of the mutation spectrum, the authors could test the reversion rates of a different engineered allele requiring a reversion mutation in the opposite direction (ie. reversion from ura3-A to URA3-C). If the AAR and AEQ mutator is specific C>A, one would predict that all three strains should have similar mutation rates for a reversion in the A>C direction. This additional genetic work would thoroughly validate the central discovery and would reinforce the usefulness of the method described in the manuscript.

    Alternatively, a conventional mutation accumulation and whole genome re-sequencing experiment with parallel lines of AAR, AEQ and a control strain would also very effectively validate the C>A mutator prediction, and it would also answer the authors' discussion point about specificity to the CAN1 locus. However, it would be more costly and much more time consuming.

    1. The second concern is in regard to the relatively extensive conclusions drawn about the possible evolutionary significance of the possible C>A mutator in AAR and AEQ. The authors should be more cautious and conservative in the proposed interpretation. As the authors note:

    'Three of the four C>A-enriched mosaic beer strains, AAR, AEQ, and SACE_YAG, are all haploid derivatives of the [highly heterozygous] diploid Saccharomyces cerevisiae var diastaticus strain CBS1782, which was isolated in 1952 from super-attenuated beer.'

    From this statement, and because the paper cited provided few details on the isolation of CBS1782, it is presumed that these haploid derivatives were most likely isolated as recombinant spores. Furthermore, it is unclear when this isolation occurred, and for how many generations strains AAR and AEQ have been propagated in a haploid state.

    Herein lies a critical point: AAR and AEQ were recently derived from a diploid background with a "high level of heterozygosity". In a heterozygous diploid context, deleterious point mutations (and any resulting mutator phenotypes) would likely be masked by the presence of wild-type alleles. Now, as haploids, they express a novel genotype (i.e., combination of defective or incompatible parental alleles), which manifests as a mutator phenotype. In this respect, AAR and AEQ appear analogous to the spore derivatives of the incompatible cMLH1-kPMS1 isolate referred to in the manuscript as a notable exception. The analysis of strains harboring incompatible MLH1-PMS1 mutations by Raghavan et al. demonstrated that the heterozygous diploid parents were not themselves mutators, but that haploid spores which had inherited the pair of incompatible alleles displayed mutator phenotype. Collectively, while it can certainly be argued that the strains AAR and AEQ (like the MLH1/PMS1 incompatible strains) are mutators now, this fact alone does not support the conclusion that they have adapted to survive the expression of an extant mutator phenotype. This premise could be tested by analyzing the mutation rates/spectra of four new spores derived from a single tetrad of CBS 1782. Do the four sibling spores display similar or different mutational rates and spectra? If all four spores from a single tetrad exhibit the 10-fold increase in CAN1 mutation rate and the C>A transversion bias, then it can be inferred that the diploid parent is also a mutator in the same manner. Further direct analysis of mutation rates and spectrum in the parent diploid CBS 1782 would complete the work. This finding would be quite significant, and would provide strong evidence that wild strains can in fact tolerate the expression of a chronic mutator allele.

    Minor comments:

    A final, relatively minor point. That the new haploids AAR and AEQ show distinct mutation rates and spectra opens the door to an interesting line of inquiry, which may help to identify the causative mutator allele in a manner more efficient than searching for missense mutations. It is stated, and it is understandable, that the identification of the possible causal mutations is beyond the scope of the present manuscript. In this spirit, it would be much more appropriate to restrict such considerations to the Discussion section. Specifically, while the authors make a plausible case for OGG1 being a candidate gene responsible for the C>A mutator phenotype, no experimental demonstration was attempted. As such, that text segment should be moved from the Results to the Discussion section.

    Significance

    As stated in the summary section above, the manuscript by Jiang et al represents a substantial contribution to the fields of genome stability and genome evolution. The method described is likely to be useful beyond budding yeast. The work will be appreciated by a broad audience of geneticists. The additional work and text modifications proposed above would likely further elevate the impact of this work.

  7. Version published to 10.1101/2021.01.11.425955v1 on bioRxiv