Mutations are a double-edged sword. At the molecular level, they usually arise as a spontaneous consequence of DNA replication errors or damage and are the ultimate cause of genetic diseases (Iossifov et al., 2014; Sebat et al., 2007; Antonarakis and Beckmann, 2006; Crow, 1997; Nei, 1983). All organisms have evolved complex mechanisms for keeping mutation rates low and safeguarding their genetic information as it is passed from generation to generation (Beckman and Loeb, 1993; Eisen and Hanawalt, 1999); in multicellular organisms, these mechanisms also safeguard somatic tissues from mutations that can cause cancer and age-related decline (Alexandrov et al., 2013; Loeb, 2016; Risques and Kennedy, 2018). A low mutation rate is essential for long-term population survival, and the larger and more complex a genome is, the lower the mutation rate must be to prevent deleterious mutations from arising faster than natural selection can eliminate them (Eigen, 1971; Drake, 1991; Sung et al., 2012a; Acosta et al., 2015). Over long time scales, however, mutations also serve as the raw material for evolution. Although beneficial mutations are rare occurrences, they are essential for the acquisition of novel phenotypes and adaptations (Gompel et al., 2005; McGregor et al., 2007).

A large body of theory has been written to describe how natural selection might act on the mutation rate to balance these beneficial and deleterious effects (Sturtevant, 1937; Kimura, 1967; Leigh,, 1970; Johnson, 1999; André and Godelle, 2006; Sung et al., 2012a). One prediction is that organisms living in more changeable environments might evolve higher mutation rates than organisms living in more stable environments, assuming that the environment determines whether a higher rate of beneficial mutations is likely to counterbalance a higher rate of deleterious mutations. This prediction has been borne out in laboratory evolution experiments, where mutator phenotypes sometimes emerge in populations that are forced to tolerate challenging conditions (Good et al., 2017; Tenaillon et al., 2016; Couce et al., 2017) and mutator strains are often observed to take over chemostat populations by producing beneficial mutations at a higher rate than competing non-mutator strains (Chao and Cox, 1983). Many mutator phenotypes in E. coli have been linked to defects in DNA repair enzymes (Horst et al., 1999). Mutator phenotypes also commonly occur in cancer (Loeb, 2001; Prindle et al., 2010), likely either because of relaxed selection against cellular dysfunction or because it is beneficial for cancer cells to adapt rapidly to their aggressive growth niche. However, it is less clear how much mutation rate variation exists within and between natural populations, and if such variation exists, whether it is maintained by natural selection. The drift barrier hypothesis predicts that mutator alleles will usually be deleterious because they produce more damaging mutations than beneficial ones, but that mutator alleles with relatively small effects may persist in populations because they are not deleterious enough to be efficiently eliminated (Lynch et al., 2016).

Although next-generation sequencing has rapidly increased our ability to measure the genetic variation that currently exists within populations, the extent of mutation rate variation is still more difficult and expensive to measure. One of the original methods for measuring mutation rates is the Luria-Delbrück fluctuation assay (Lang and Murray, 2008; Gou et al., 2019; Luria and Delbrück, 1943), in which a population of microorganisms is allowed to grow clonally for a controlled length of time, then challenged with a form of artificial selection that kills most cells except for those that have happened to acquire specific resistance mutations. The mutation rate can then be calculated from the number of colonies that manage to grow after this artificial selection is imposed.

Although fluctuation assays are an elegant and efficient way for measuring the mutation rates of specific reporter genes, the results are potentially sensitive to the reporter gene being used and where it is located within the genome (Lang and Murray, 2008; Lang and Murray, 2011); in addition, they are not applicable to multicellular organisms. These drawbacks have motivated the development of newer methods that take advantage of high-throughput sequencing, such as mutation accumulation (MA) assays in which a laboratory population is serially bottlenecked for many generations, eliminating most effects of natural selection and allowing mutations to be directly counted by sequencing at the end of the experiment. MA studies have been used to estimate mutation rates in a wide variety of organisms (Lynch et al., 2008; Sharp et al., 2018; Zhu et al., 2014; Farlow et al., 2015; Wang et al., 2019; Keightley et al., 2009), including a recent study of nine heterozygous S. cerevisiae strains (Dutta et al., 2021). However, higher throughput MA experiments require DNA repair genes to be knocked out to induce higher mutation rates that can be accurately measured with fewer generations of labor-intensive propagation, as in Liu and Zhang, 2021.

An alternative source of information about mutational processes in strains with low mutation rates is genetic variation among related individuals who share common ancestors. Polymorphic sites are easier and cheaper to discover than new mutations, because they are present at a higher density within the genome and often shared among several individuals. Mining polymorphisms for information about mutation rates can be difficult since their abundance is affected by genetic drift and natural selection (Scally and Durbin, 2012; Ségurel et al., 2014; Zhu et al., 2017), but despite these limitations, they have provided surprisingly strong evidence for the existence of historical changes to the mutation spectrum, meaning the tendency of mutations to occur most often in certain nucleotide contexts (Hwang and Green, 2004). In humans, for example, Europeans and South Asians have a significantly higher proportion of TCC> TTC mutations than other human groups (Harris, 2015; Harris and Pritchard, 2017), a pattern that is difficult to explain without a recent population-specific increase in the rate of this type of mutation. This pattern might have been caused by either a genetic mutator or an environmental mutagen, but is not explicable by the action of selection or drift or any other process that simply modulates the retention or loss of genetic variation.

Polymorphism data has revealed that each human population and great ape species appears to have a distinctive triplet mutation spectrum, which implies that genetic and/or environmental mutators likely emerge relatively often and act within localized populations to increase mutation rates in specific sequence contexts (Harris and Pritchard, 2017; Goldberg and Harris, 2021). However, identifying these hypothetical mutators is a challenging proposition, not least because some population-specific signatures such as the human TCC> TTC enrichment appear to be relics of mutators that are no longer active. A recent study of de novo mutations in diverse human families found some evidence of mutation rate variation between human populations (Kessler et al., 2020), but argued that most of this variation was driven by the environment rather than genetics. Given that humans from different populations tend to be born and raised in different environments, it is extremely challenging to determine the degree to which genetics and/or the environment are responsible for variation of the rates and spectrum of de novo mutations accumulating within human populations today.

More is known about the genetic architecture of mutagenesis in model organisms, including the single-celled organism Saccharomyces cerevisiae, where it is tractable to disentangle genetic mutator effects from environmental ones by accumulating mutations on different genetic backgrounds in controlled laboratory environments (Serero et al., 2014; Lang et al., 2013; Stirling et al., 2014; Huang et al., 2003; Herr et al., 2011). Many S. cerevisiae mutator alleles have been discovered using genetic screens, which involve creating libraries of artificial mutants in the lab and determining which ones have high mutation rates (Stirling et al., 2014). Mutation rates can be elevated by up to a thousand-fold in lines where DNA proofreading and repair capabilities are artificially knocked out (Herr et al., 2011; Serero et al., 2014; Lang et al., 2013), and quantitative trait loci with more modest effects have been found to underlie a five-fold range of mutation rate variation among a few natural S. cerevisiae strains (Gou et al., 2019). A more complex mutator phenotype has been observed as a result of epistasis between two incompatible alleles found as natural variation in the mismatch repair genes MLH1 and PMS1, although the natural isolates in which these alleles are found appear to have acquired compensatory variants that suppress this mutator phenotype (Bui et al., 2017; Argueso et al., 2003; Raghavan et al., 2018; Heck et al., 2006). An extreme case of long-term maintenance of a hypermutation lineage comes from phylogenetic analysis of bipolar budding yeasts Hanseniaspora which shows large amounts of gene loss, accelerated evolutionary rates and altered mutation spectra (Steenwyk et al., 2019).

Mild environmental stressors, such as high salt, ethanol, and heat, can also alter the mutation rate of S. cerevisiae (Liu and Zhang, 2019; Voordeckers et al., 2020) as well as Arabidopsis thaliana (Jiang et al., 2014; Belfield et al., 2021). The same environmental perturbations can cause detectable changes to both species’ mutation spectra. The mutation spectrum has also been observed to depend on whether S. cerevisiae is replicating in a haploid or diploid state (Sharp et al., 2018). In addition, environmental mutagens, more complex ploidy, and genetic mutation rate modifiers could all conceivably affect the mutation spectrum of natural variation as it accumulates. However, no study to our knowledge has looked at whether any mutational signatures measured in the laboratory are capable of explaining natural mutation spectrum variation observed in polymorphism data from a model species.

Recently, comprehensive sampling efforts have produced a collection of 1011 natural isolates of S. cerevisiae (Peter et al., 2018). This is a uniquely powerful system containing abundant natural variation that accumulated within diverse environments during the recent and ancient evolution of S. cerevisiae, and the panel is also amenable for experimental accumulation of mutations over laboratory growth. Many genetic polymorphisms differentiate these strains, and these are relics of mutations that accumulated over many generations on divergent genetic backgrounds adapted to diverse environmental conditions, ranging from forests to beverage fermentation pipelines. Both environmental mutagens and genetic mutators may have created differences among the mutation spectra of these 1011 strains, but only genetically determined mutation spectrum differences should have the potential to be reproduced in the spectra of mutations accumulated in a controlled lab environment.

We hypothesized that yeast strains with outlying spectra of natural polymorphisms are more likely to have distinct de novo mutation spectra than strains whose polymorphisms have indistinguishable mutation spectra. The same hypothesis underlies previous inferences of de novo mutation spectrum variation from polymorphism data (Harris, 2015; Harris and Pritchard, 2017; Dumont, 2019; Goldberg and Harris, 2021), and rare mutations found in mouse colonies (Dumont, 2019), but this hypothesis has been difficult to test in vertebrate species. To enable such direct testing in S. cerevisiae, we describe a new Luria-Delbrück-based assay that efficiently measures the spectra of de novo mutations in haploid strains using pooled amplicon sequencing. We then use this assay to identify strains with reproducibly measurable mutator phenotypes that explain the spectrum biases of these strains’ polymorphisms. Some proportion of natural mutation spectrum variation might not be reproducible in the lab if it is driven by environmental mutagens, bioinformatic artifacts, or extinct genetic mutators, but our assay has the potential to identify which gradients of mutation spectrum variance are driven by extant genotypic differences.

In this study, we compare the mutation spectra of natural variants and de novo mutations within diverse strains of S. cerevisiae. We find both population genetic and experimental evidence pointing to a recent increase in the rate of C > A mutations within one clade as a result of a naturally occurring genetically encoded mutator phenotype.

Our highly accurate pipeline uses a classical fluctuation assay to quantify de novo mutation rate in the reporter gene CAN1, then utilizes sequencing of pooled mutants to measure mutation spectra. Sanger validations confirmed that this approach has an undetectably low false positive rate and a very low false negative rate. The reliability of our pipeline is further supported by concordance between our mutation spectrum measurements and previous measurements made from the same lab strains (Lang and Murray, 2008). Using this method, we identified two extreme mutation spectrum outliers—the Mosaic beer strains AEQ and AAR—as well as several strains with more subtle mutation spectrum phenotypes that are good candidates for future investigation.

Although the mutation spectrum variation from natural polymorphisms we report here is reminiscent of observed mutation spectrum variation among humans, great apes, and laboratory mouse strains, we note that S. cerevisiae mutation spectra separate less by population compared to vertebrate mutation spectra. While it is possible to infer a human genome’s continental group of origin using its mutation spectrum alone, the same is not generally true in S. cerevisiae. Several factors, which are not mutually exclusive, might underlie this difference. One is the presence of complex population structure and pervasive gene flow between S. cerevisiae clades (Schacherer et al., 2009; Peter et al., 2018; Liti et al., 2009). Another factor is the small size of the S. cerevisiae genome; each strain has two orders of magnitude fewer derived alleles than most vertebrate genomes have. This limits our ability to detect how flanking base pairs affect the mutation rate of each site in the genome and constrains us to study a lower dimensional mutation spectrum than the context-dependent ones studied in species with larger genomes.

While gene flow and genome size might be responsible for the relatively modest magnitude of mutation spectrum divergence between most strains of S. cerevisiae, it is also possible that DNA replication and repair are intrinsically more uniform in S. cerevisiae than in vertebrates, perhaps because of the greater efficiency of selection against weakly deleterious mutator alleles in a unicellular organism that exists at large effective population sizes and often reproduces asexually. Asexual reproduction should theoretically increase the efficiency of selection against mutator alleles because deleterious variants created by the mutator cannot recombine onto other genetic backgrounds; on the other hand, it can also limit the efficiency of selection against individual deleterious mutations by permanently tethering them to particular genetic backgrounds. Further measurements of mutation spectrum variation within other species will be needed to determine whether the stability observed here is indeed characteristic of unicellular eukaryotes. If mutation spectra tend to be stable within species that have low mutation rates and strong selection against mutation rate modifiers, we might expect to see even less mutation spectrum variation among populations of ciliates like Paramecium and Tetrahymena, whose mutation rates are substantially lower than that of S. cerevisiae (Sung et al., 2012b).

Many questions about mutation spectrum variation within S. cerevisiae and other species remain unresolved and present important avenues for future work. For example, it is unclear whether rare polymorphisms in the Mixed origin and Mosaic region three clades are enriched for C > A mutations due to the same genetic mechanisms active in AEQ and AAR. Other genes might underlie the mutation spectrum differences observed among other strains, though our analyses suggest that some mutation spectrum gradients that dominate the common variation PCA are unlikely to be explained by extant mutators. One such gradient is the A > G enrichment in the African beer yeast clade, which is less pronounced in our rare variant PCA (Figure 1B) compared to our PCA extracted from variation of all frequencies (Figure 1A). This is somewhat reminiscent of the frequency distribution of the TCC> TTC mutation ‘pulse’ that distinguishes Europeans and South Asians from other human populations, and may suggest that the African beer A > G enrichment was caused by an extinct mutator allele or a mutagen found in a past environment.

Natural selection might contribute to the mutation spectrum variation within S. cerevisiae if certain mutation types are more often beneficial than others and if such asymmetries vary between populations. One example of how natural selection can affect mutation spectrum variation is that transitions are more often synonymous than transversions are (Freeland and Hurst, 1998), leading to more frequent selection against transversions in genic regions. However, we note that most of the gradient structure observed in our analyses can be reproduced with synonymous mutations alone, meaning that selection is unlikely to explain much of the natural yeast mutation spectrum variation we observe.

The C > A mutations enriched in AEQ, AAR, and their relatives might be a signature of oxidative stress damage; such mutations are a known signature of failure to repair 8-oxoguanine lesions, which is consistent with a causal role for these strains’ missense substitution in the oxidative stress response gene OGG1 as verified through our OGG1 plasmid assay. A recent study knocked out OGG1 in human cells and found that this elevated the rate of a C > A-dominated mutational signature known in the Cosmic catalog as SBS18 (Zou, 2020). SBS18 was initially identified in tumors from individuals with pathogenic variation in Mutyh (Viel et al., 2017), a direct interacting partner of OGG1 in the 8-oxoguanine repair pathway that has also been implicated in a C > A-dominated germline mutational signature in mice (Sasani et al., 2021).

Although our OGG1 plasmid assay provides compelling evidence that the 8-oxoguanine repair pathway plays a role in the AAR/AEQ mutator phenotype, we note that C > A mutations do not comprise all of the excess mutations measured in AEQ and AAR. Compared to LCTL1, the ratio of C > A mutations to C > T mutations is elevated roughly threefold in AEQ and AAR, which is less than the 10-fold overall elevation of the mutation rate in these strains. These mutation data imply that all mutation types have higher rates in AEQ and AAR compared to other S. cerevisiae strains, not just C > A. Further work will be required to verify whether the C > A enrichment and broad-spectrum mutation rate increase are driven by the same biochemical mechanism.

Although our estimates of de novo mutation spectra are all based on the CAN1 locus, we note that CAN1 mutation spectra are generally similar to the genome-wide spectra obtained from MA experiments (Zhu et al., 2014; Sharp et al., 2018), except that CAN1 spectra have more mutations at C/G base pairs and relatively fewer mutations at A/T base pairs (Figure 4—figure supplement 7). The 2676 and 1866 missense and nonsense mutations in our dataset contain numerous instances of all six mutation types that comprise our summary mutation spectrum, suggesting that CAN1 contains many opportunities to ascertain the full spectrum observed genomewide. We noticed that the CAN1 spectra we measured from the LCTL2 strain showed a slightly higher proportion of C > A mutations than that measured from the same strain by Lang and Murray, 2008 also using a CAN1 reporter. This could be due to the fact that Lang et al. used ten times the concentration of canavanine that we used, which might mean that some mutants could have grown after plating on our canavanine media (Shor et al., 2013). The fact that we do not see elevated C > A mutations in LCTL1 suggests that this could be a LCTL2 strain-specific behavior.

In summary, the results presented in this paper provide some of the most direct evidence to date that eukaryotic mutation spectra are variable within species (Harris, 2015; Harris and Pritchard, 2017; Dumont, 2019; Goldberg and Harris, 2021). It has been proposed that the best explanation for such mutation spectrum heterogeneity is the frequent emergence of nearly neutral mutator alleles that turn over rapidly as a consequence of weak purifying selection on the mutation rate. Our de novo mutation spectrum measurements provide experimental verification of this claim, showing that at least one mutational signature whose activity varies among natural yeast strains is likely caused by an extant mutator allele.

Although our results show that the mutation spectrum bias shared by certain Mosaic beer yeast is genetically encoded, it is worth noting that this C > A gradient is not the principal axis of mutation spectrum variation in the 1011 yeast genomes that we computed from all variants (Figure 1A). It remains to be seen how many other mutator or antimutator alleles might exist within this strain collection and to what extent they can explain the mutation spectrum variation observed among strains from different environments. A broader question still is whether the forces that created S. cerevisiae’s mutation spectrum variation are similar to the forces that shaped the distinctive mutation spectra of different human populations and great ape species. If we can identify the genes that underlie natural yeast mutator phenotypes such as the one described in this study, it will likely be more straightforward to test these genes for mutator activity in humans and other species than to discover mutator alleles via any kind of agnostic genome scan.

Sequencing data have been uploaded to the SRA and approved (Accession numbers PRJNA691686).

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Author details

1. Pengyao Jiang

   Department of Genome Sciences, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Validation

   Competing interests

   No competing interests declared

2. Anja R Ollodart

   1. Department of Genome Sciences, University of Washington, Seattle, United States
   2. Molecular and Cellular Biology Program, University of Washington, Seattle, United States

   Contribution

   Methodology, Resources

   Competing interests

   No competing interests declared

3. Vidha Sudhesh

   Department of Genome Sciences, University of Washington, Seattle, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Alan J Herr (deceased)

   Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States

   Contribution

   , Methodology, Supervision, Funding acquisition, Validation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9498-0972

5. Maitreya J Dunham

   Department of Genome Sciences, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Writing – review and editing, Methodology, Supervision, Funding acquisition, Validation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9944-2666

6. Kelley Harris

   1. Department of Genome Sciences, University of Washington, Seattle, United States
   2. Department of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, United States

   Contribution

   Conceptualization, Writing – review and editing, Investigation, Methodology, Project administration, Supervision, Software, Visualization, Writing – original draft, Validation

   For correspondence

   harriske@uw.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0302-2523

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