Mutator alleles have been found at considerable frequencies in populations of infectious and commensal bacteria (Matic et al., 1997; LeClerc et al., 1996; Oliver et al., 2000), viruses (Suárez et al., 1992), and pathogenic fungi (Healey et al., 2016; Billmyre et al., 2017; Boyce et al., 2017). Mutators are also believed to be widespread in many cancers (Loeb, 2011; Lengauer et al., 1997), and have been repeatedly observed to overtake microbial populations during laboratory evolution experiments (Sniegowski et al., 1997; Shaver et al., 2002; Barrick et al., 2009; Notley-McRobb et al., 2002; Pal et al., 2007; Mao et al., 1997; Raynes and Sniegowski, 2014; Voordeckers et al., 2015). Yet, unlike directly beneficial mutations that are favored by natural selection because they increase an organism’s reproductive success (i.e., its fitness), mutator mutations generally do not appear to be inherently advantageous (Raynes and Sniegowski, 2014), except potentially in some viruses (Furió et al., 2005; Furió et al., 2007). Instead, mutators experience indirect selection, mediated by persistent statistical associations with fitness-affecting mutations elsewhere in the genome. As a result, mutators may invade an adapting population by hitchhiking (Smith and Haigh, 1974) with linked beneficial mutations even when they have no effect on fitness of their own (Sniegowski et al., 2000).

Whether or not mutators can successfully hitchhike to fixation has often been described as depending on the initial prevalence of mutator alleles in a population - most commonly referred to as frequency or number dependence (Raynes and Sniegowski, 2014; Sniegowski et al., 2000). This view holds that to replace the resident non-mutators, mutators must generate a beneficial mutation that escapes genetic drift and sweeps to fixation before their non-mutator competitors do. Accordingly, it has been proposed that mutators may be expected to invade (i.e., are favored by selection) only when already present in sufficient numbers to produce the successful beneficial mutation first, and lose their advantage (i.e., are disfavored by selection) when too rare to do so (reviewed in Raynes and Sniegowski, 2014; Sniegowski et al., 2000).

This frequency-dependent interpretation of mutator success has been primarily motivated by mutator dynamics observed in experimental studies of laboratory microbial populations. Most famously, in a series of pioneering experiments, Lin Chao and colleagues showed that mutator strains of the bacterium E. coli could supplant otherwise isogenic non-mutator strains by hitchhiking with beneficial mutations when initialized above a critical threshold frequency but would decline when initialized below it (Chao and Cox, 1983Reproduced in Figure 1A; Chao et al., 1983). Since then, a similar pattern has been recapitulated in several other studies in E. coli and S. cerevisiae (Thompson et al., 2006; Gentile et al., 2011; de Visser and Rozen, 2006; Le Chat et al., 2006). Critically, a frequency-dependent framing of indirect selection on mutators implies a change in the sign or the strength of indirect selection with frequency. Here, we use stochastic, agent-based computer simulations to demonstrate that on the contrary, indirect selection on mutators is independent of frequency.

Figure 1 with 1 supplement see all

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Figure 1—source data 1

Numerical data represented in Figure 1.

Data set includes mutator frequencies in randomly-chosen individual realizationss and mutator frequencies averaged across all realizations.

https://doi.org/10.7554/eLife.51177.005

Download elife-51177-fig1-data1-v2.csv

Our computer simulations (Raynes, 2019) model asexual populations that mimic microbial evolution experiments under generally-accepted parameter values (Raynes et al., 2018). Figure 1B shows mutator frequency dynamics in randomly chosen simulations initialized across four log-orders of starting frequency, $x_0$, which recapitulate experimental observations of the critical frequency threshold for hitchhiking reproduced in Figure 1A. As in Figure 1A, single, randomly-chosen realizations (i.e., simulation replicates) started below a threshold frequency end in mutator loss, while randomly-chosen realizations started above end in fixation (Figure 1B).

Critically, fixation of an allele in a finite population is a probabilistic process influenced both by selection and random genetic drift, and even beneficial mutations will frequently be lost by chance alone. As such, whether an allele is truly favored or disfavored by selection can only be ascertained by evaluating its expected behavior averaged across many replicate, independent realizations. Indeed, if we consider the expected mutator frequency averaged across many replicate simulations, the threshold-frequency effect disappears (Figure 1C). Instead, the average mutator frequency ultimately rises above the starting frequency at all $x_0$, suggesting that mutators are, in fact, favored by selection in these populations regardless of starting frequency. (For more on why mutators are favored in large populations such as these see Raynes et al., 2018). The transient decline in average frequency seen in Figure 1C reflects selection against the deleterious load inherent to mutators (Kimura, 1967), and will be explored in a forthcoming publication].

To confirm that selection on mutators is independent of starting frequency, we measured the fixation probability of a mutator allele, $P_{fix}\left(x_0\right)$, at each initial frequency, $x_0$ simulated in Figure 1. Given the stochasticity of the fixation process (and following Good and Desai, 2016; Raynes et al., 2018; Wylie et al., 2009), we compare $P_{fix}\left(x_0\right)$ to the probability of fixation of a neutral allele, given simply by $x_0$. If a mutator is favored, we expect it to fare better than neutral (i.e., ${\displaystyle P_{fix}\left(x_0\right)>x_0}$), and worse than neutral (i.e., ${\displaystyle P_{fix}\left(x_0\right)<x_0}$) if disfavored. As Figure 2 shows, $P_{fix}\left(x_0\right)$ exceeds the fixation probability of a neutral allele for all $x_0$, as anticipated in Figure 1C and confirming that the sign of selection on mutators does not depend on starting frequency.

Figure 2

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Figure 2—source data 1

Numerical data represented in Figure 2.

Data set includes fixation probabilities of a mutator allele at each initial frequency shown.

https://doi.org/10.7554/eLife.51177.007

Download elife-51177-fig2-data1-v2.csv

Furthermore, while $P_{fix}\left(x_0\right)$ of a mutator increases with $x_0$, it does so exactly as expected for a frequency-independent mutation. Under frequency-independent selection $P_{fix}\left(x_0\right)$ is simply the probability that at least one of the $x_{0}N$ alleles reaches fixation (where N is the population size). By definition of frequency-independent selection, the per-capita fixation probability is a constant, written, $P_{fix}\left(x_0=1/N\right)$. Correspondingly, $P_{fix}\left(x_0\right)$ for any $x_0$ can be calculated as

As the orange line in Figure 2 shows, $P_{fix}\left(x_0\right)$ calculated with Equation 1 is indistinguishable from $P_{fix}\left(x_0\right)$ observed in simulations, confirming that the per-capita fixation probability is independent of $x_0$ and equal to $P_{fix}\left(x_0=1/N\right)$ at any $x_0$. Thus, while the expected fixation probability of a mutator increases with $x_0$, the per-capita fixation probability remains unchanged, confirming that individual mutators do not become more likely to hitchhike to fixation when present at higher frequencies in a population.

Why then do mutators in experimental populations appear destined to go extinct when initially rare (e.g. Chao and Cox, 1983)? Given that this behavior has been documented across different systems and selective environments (as well as in our stochastic simulations in Figure 1B), it seems unlikely to depend on any shared biological property of the experimental systems. Consider, however, that the per-capita fixation probability of a mutator is relatively low – in our simulations, operating under realistic parameter values, $P_{fix}\left(x_0=1/N\right)$ = 5.6×10⁻⁴. Thus even when mutators are favored, most experimental replicates with rare mutators are expected to end in mutator extinction, and only those started at frequencies higher than roughly $1/{[NP}_{fix}\left(x_0=1/N\right)]$ are expected to end mostly with mutator fixation. Considering only a single or even a few realizations at each starting frequency (as in Figure 1A or B) would, most likely, result in observing only the most expected outcome for each $x_0$. Indeed, all experimental studies that have documented the frequency-based transition included only a few populations at each starting frequency. Such limited sampling across a broad range of starting frequencies in these experiments would explain the sharp transition between fixation at high frequencies and loss at lower ones even when selection is frequency-independent (see also Tanaka et al., 2003). We expect that observing the dynamics in Figure 1C would be possible with more experimental replication, which, however, may not always be experimentally feasible.

In fact, the critical frequency-dependent transition observed in Figure 1A and 1B is not unique to mutators. Recall that $P_{fix}\left(x_0\right)$ of any mutation not under frequency-dependent selection, nevertheless, increases with starting frequency, $x_0$ (Equation 1). For example, even for a directly beneficial mutation, the probability of fixation from low frequencies is relatively low (Figure 3A Inset), Accordingly, as Figure 3A illustrates, single realizations of the dynamics of a directly beneficial mutation also exhibit a threshold-like switch from fixation to loss. In contrast, expected frequency dynamics averaged across many independent realizations confirm that beneficial mutations are favored by selection independent of starting frequency (Figure 3B). Indeed, only for mutations under truly frequency-dependent selection do both the individual realizations (Figure 3C) and the expected dynamics averaged across many realizations (Figure 3D) exhibit an actual frequency-dependent transition.

Figure 3

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Figure 3—source data 1

Numerical data represented in Figure 3.

Data set includes frequencies of a beneficial mutation and a frequncy-dependent mutation in randomly-chosen individual realizations and averaged across all replicate realizations.

https://doi.org/10.7554/eLife.51177.009

Download elife-51177-fig3-data1-v2.csv

In summary, our results demonstrate that neither the strength nor the sign of selection on mutators depend on initial frequency or number. Instead, we show that in populations favoring higher mutation rates, mutators consistently fare better than the neutral expectation (Figure 1 and Figure 2) regardless of starting frequency. Most importantly, the per-capita probability of fixation remains unchanged with frequency. We conclude that the frequency threshold observed in earlier experiments is, therefore, an artifact of limited experimental sampling rather than a frequency-dependent change in selective effect.

Individual-based, stochastic simulations employed here have been previously described (Raynes et al., 2018). In brief, we consider haploid asexual populations of constant size, N, evolving in discrete, non-overlapping generations according to the Wright-Fisher model (Ewens, 2004). Populations are composed of genetic lineages - subpopulations of individuals with the same genotype. A genotype is modeled as an array of 99 fitness-affecting loci and 1 mutation rate modifier locus, which in a mutator state raises the genomic mutation rate m-fold. For computational efficiency, simulations in Figure 1 assume constant fitness effects: beneficial mutations at the fitness loci increase fitness by a constant effect s_ben, while deleterious mutations decrease fitness by a constant effect s_del. We assume additive fitness effects and so calculate fitness of a lineage with x beneficial and y deleterious mutations as $w_{xy}=1+{xs}_{ben}-{ys}_{del}$. In simulations in Figure 1—figure supplement 1, beneficial and deleterious fitness effects are randomly drawn from an exponential distribution with the mean s_ben = 0.1 and s_del = -0.1 respectively. Simulations start with the mutator allele at a frequency of $x_0$ and continue until it either fixes or is lost from a population.

Every generation the size of each lineage i is randomly sampled from a multinomial distribution with expectation $N·f_i·(w_i/\stackrel{-}{w})$, where $f_i$ is the frequency of the lineage in the previous generation, w_i is the lineage’s fitness, and $\stackrel{-}{w}$ is the average fitness of the population ($\stackrel{-}{w}=\sum f_i\bullet w_i$). Upon reproduction, each lineage acquires a random number of fitness-affecting mutations M, drawn from a Poisson distribution with mean equal to the product of its size and its total per-individual mutation rate, $\left(U_{ben}+U_{del}\right)$, where U_ben and U_del are the deleterious and beneficial mutation rates respectively. The number of beneficial and deleterious mutations is then drawn from a binomial distribution with n=M and P = $U_{ben}/\left(U_{ben}+U_{del}\right)$ and new mutations are assigned to randomly chosen non-mutated fitness loci.

Simulation code is available at https://github.com/yraynes/Mutator-Frequency (copy archived at https://github.com/elifesciences-publications/Mutator-Frequency).

1. 1. Barrick JE
   2. Yu DS
   3. Yoon SH
   4. Jeong H
   5. Oh TK
   6. Schneider D
   7. Lenski RE
   8. Kim JF

   (2009) Genome evolution and adaptation in a long-term experiment with Escherichia coli

   Nature 461:1243–1247.

   https://doi.org/10.1038/nature08480

   • PubMed
   • Google Scholar
2. 1. Billmyre RB
   2. Clancey SA
   3. Heitman J

   (2017) Natural mismatch repair mutations mediate phenotypic diversity and drug resistance in Cryptococcus deuterogattii

   eLife 6:e28802.

   https://doi.org/10.7554/eLife.28802

   • PubMed
   • Google Scholar
3. 1. Boyce KJ
   2. Wang Y
   3. Verma S
   4. Shakya VPS
   5. Xue C
   6. Idnurm A

   (2017) Mismatch repair of DNA replication errors contributes to microevolution in the pathogenic fungus Cryptococcus neoformans

   mBio 8:e00595.

   https://doi.org/10.1128/mBio.00595-17

   • PubMed
   • Google Scholar
4. 1. Chao L
   2. Vargas C
   3. Spear BB
   4. Cox EC

   (1983) Transposable elements as mutator genes in evolution

   Nature 303:633–635.

   https://doi.org/10.1038/303633a0

   • PubMed
   • Google Scholar
5. 1. Chao L
   2. Cox EC

   (1983)

   Competition between high and low mutating strains of Escherichia coli

   Evolution 37:125–134.

   • Google Scholar
6. 1. de Visser JA
   2. Rozen DE

   (2006) Clonal interference and the periodic selection of new beneficial mutations in Escherichia coli

   Genetics 172:2093–2100.

   https://doi.org/10.1534/genetics.105.052373

   • PubMed
   • Google Scholar
7. Book

   1. Ewens W

   (2004)

   Mathematical Population Genetics

   New York: Springer.

   • Google Scholar
8. 1. Furió V
   2. Moya A
   3. Sanjuán R

   (2005) The cost of replication fidelity in an RNA virus

   PNAS 102:10233–10237.

   https://doi.org/10.1073/pnas.0501062102

   • PubMed
   • Google Scholar
9. 1. Furió V
   2. Moya A
   3. Sanjuán R

   (2007) The cost of replication fidelity in human immunodeficiency virus type 1

   Proceedings of the Royal Society B: Biological Sciences 274:225–230.

   https://doi.org/10.1098/rspb.2006.3732

   • Google Scholar
10. 1. Gentile CF
    2. Yu SC
    3. Serrano SA
    4. Gerrish PJ
    5. Sniegowski PD

    (2011) Competition between high- and higher-mutating strains of Escherichia coli

    Biology Letters 7:422–424.

    https://doi.org/10.1098/rsbl.2010.1036

    • PubMed
    • Google Scholar
11. 1. Good BH
    2. Desai MM

    (2016) Evolution of mutation rates in rapidly adapting asexual populations

    Genetics 204:1249–1266.

    https://doi.org/10.1534/genetics.116.193565

    • PubMed
    • Google Scholar
12. 1. Healey KR
    2. Zhao Y
    3. Perez WB
    4. Lockhart SR
    5. Sobel JD
    6. Farmakiotis D
    7. Kontoyiannis DP
    8. Sanglard D
    9. Taj-Aldeen SJ
    10. Alexander BD
    11. Jimenez-Ortigosa C
    12. Shor E
    13. Perlin DS

    (2016) Prevalent mutator genotype identified in fungal pathogen Candida Glabrata promotes multi-drug resistance

    Nature Communications 7:11128.

    https://doi.org/10.1038/ncomms11128

    • PubMed
    • Google Scholar
13. 1. Kimura M

    (1962)

    On the probability of fixation of mutant genes in a population

    Genetics 47:713–719.

    • PubMed
    • Google Scholar
14. 1. Kimura M

    (1967) On the evolutionary adjustment of spontaneous mutation rates

    Genetical Research 9:23–34.

    https://doi.org/10.1017/S0016672300010284

    • Google Scholar
15. 1. Le Chat L
    2. Fons M
    3. Taddei F

    (2006) Escherichia coli mutators: selection criteria and migration effect

    Microbiology 152:67–73.

    https://doi.org/10.1099/mic.0.28418-0

    • PubMed
    • Google Scholar
16. 1. LeClerc JE
    2. Li B
    3. Payne WL
    4. Cebula TA

    (1996) High mutation frequencies among Escherichia coli and Salmonella pathogens

    Science 274:1208–1211.

    https://doi.org/10.1126/science.274.5290.1208

    • PubMed
    • Google Scholar
17. 1. Lengauer C
    2. Kinzler KW
    3. Vogelstein B

    (1997) Genetic instability in colorectal cancers

    Nature 386:623–627.

    https://doi.org/10.1038/386623a0

    • PubMed
    • Google Scholar
18. 1. Loeb LA

    (2011) Human cancers express mutator phenotypes: origin, consequences and targeting

    Nature Reviews Cancer 11:450–457.

    https://doi.org/10.1038/nrc3063

    • PubMed
    • Google Scholar
19. 1. Mao EF
    2. Lane L
    3. Lee J
    4. Miller JH

    (1997) Proliferation of mutators in A cell population

    Journal of Bacteriology 179:417–422.

    https://doi.org/10.1128/jb.179.2.417-422.1997

    • PubMed
    • Google Scholar
20. 1. Matic I
    2. Radman M
    3. Taddei F
    4. Picard B
    5. Doit C
    6. Bingen E
    7. Denamur E
    8. Elion J

    (1997) Highly variable mutation rates in commensal and pathogenic Escherichia coli

    Science 277:1833–1834.

    https://doi.org/10.1126/science.277.5333.1833

    • PubMed
    • Google Scholar
21. 1. Notley-McRobb L
    2. Seeto S
    3. Ferenci T

    (2002)

    Enrichment and elimination of mutY mutators in Escherichia coli populations

    Genetics 162:1055–1062.

    • PubMed
    • Google Scholar
22. 1. Oliver A
    2. Cantón R
    3. Campo P
    4. Baquero F
    5. Blázquez J

    (2000) High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection

    Science 288:1251–1253.

    https://doi.org/10.1126/science.288.5469.1251

    • PubMed
    • Google Scholar
23. 1. Pal C
    2. Maciá MD
    3. Oliver A
    4. Schachar I
    5. Buckling A

    (2007) Coevolution with viruses drives the evolution of bacterial mutation rates

    Nature 450:1079–1081.

    https://doi.org/10.1038/nature06350

    • PubMed
    • Google Scholar
24. 1. Raynes Y
    2. Wylie CS
    3. Sniegowski PD
    4. Weinreich DM

    (2018) Sign of selection on mutation rate modifiers depends on population size

    PNAS 115:3422–3427.

    https://doi.org/10.1073/pnas.1715996115

    • PubMed
    • Google Scholar
25. Software

    1. Raynes Y

    (2019) Mutator-Frequency, version 3b4c41d

    Github.

    https://github.com/yraynes/Mutator-Frequency
26. 1. Raynes Y
    2. Sniegowski PD

    (2014) Experimental evolution and the dynamics of genomic mutation rate modifiers

    Heredity 113:375–380.

    https://doi.org/10.1038/hdy.2014.49

    • PubMed
    • Google Scholar
27. 1. Shaver AC
    2. Dombrowski PG
    3. Sweeney JY
    4. Treis T
    5. Zappala RM
    6. Sniegowski PD

    (2002)

    Fitness evolution and the rise of mutator alleles in experimental Escherichia coli populations

    Genetics 162:557–566.

    • PubMed
    • Google Scholar
28. 1. Smith JM
    2. Haigh J

    (1974) The hitch-hiking effect of a favourable gene

    Genetical Research 23:23–35.

    https://doi.org/10.1017/S0016672300014634

    • PubMed
    • Google Scholar
29. 1. Sniegowski PD
    2. Gerrish PJ
    3. Lenski RE

    (1997) Evolution of high mutation rates in experimental populations of E. coli

    Nature 387:703–705.

    https://doi.org/10.1038/42701

    • PubMed
    • Google Scholar
30. 1. Sniegowski PD
    2. Gerrish PJ
    3. Johnson T
    4. Shaver A

    (2000) The evolution of mutation rates: separating causes from consequences

    BioEssays 22:1057–1066.

    https://doi.org/10.1002/1521-1878(200012)22:12<1057::AID-BIES3>3.0.CO;2-W

    • PubMed
    • Google Scholar
31. 1. Suárez P
    2. Valcárcel J
    3. Ortín J

    (1992)

    Heterogeneity of the mutation rates of influenza A viruses: isolation of mutator mutants

    Journal of Virology 66:2491–2494.

    • PubMed
    • Google Scholar
32. 1. Tanaka MM
    2. Bergstrom CT
    3. Levin BR

    (2003)

    The evolution of mutator genes in bacterial populations: the roles of environmental change and timing

    Genetics 164:843–854.

    • PubMed
    • Google Scholar
33. 1. Thompson DA
    2. Desai MM
    3. Murray AW

    (2006) Ploidy controls the success of mutators and nature of mutations during budding yeast evolution

    Current Biology 16:1581–1590.

    https://doi.org/10.1016/j.cub.2006.06.070

    • PubMed
    • Google Scholar
34. 1. Voordeckers K
    2. Kominek J
    3. Das A
    4. Espinosa-Cantú A
    5. De Maeyer D
    6. Arslan A
    7. Van Pee M
    8. van der Zande E
    9. Meert W
    10. Yang Y
    11. Zhu B
    12. Marchal K
    13. DeLuna A
    14. Van Noort V
    15. Jelier R
    16. Verstrepen KJ

    (2015) Adaptation to high ethanol reveals complex evolutionary pathways

    PLOS Genetics 11:e1005635.

    https://doi.org/10.1371/journal.pgen.1005635

    • PubMed
    • Google Scholar
35. 1. Wylie CS
    2. Ghim CM
    3. Kessler D
    4. Levine H

    (2009) The fixation probability of rare mutators in finite asexual populations

    Genetics 181:1595–1612.

    https://doi.org/10.1534/genetics.108.094532

    • PubMed
    • Google Scholar

Author details

1. Yevgeniy Raynes

   Department of Ecology and Evolutionary Biology, Center for Computational Molecular Biology, Brown University, Providence, United States

   Contribution

   Conceptualization, Software, Investigation, Writing—original draft, Writing—review and editing

   For correspondence

   yevgeniy_raynes@brown.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1608-9479

2. Daniel Weinreich

   Department of Ecology and Evolutionary Biology, Center for Computational Molecular Biology, Brown University, Providence, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1424-7541

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