We suspected that this mutant cloud would be spread out further in genotype space—indicating greater standing diversity—for populations with a higher mutation rate.
To test whether this was indeed the case, we first defined the center of an evolving population as its consensus sequence and then computed the average distance of each population to this consensus. Similarly, higher mutation rates led to higher levels of mean nucleotide site diversity Fig 3C.
We also expected evolving replicate populations with higher mutation rates to accumulate more high frequency derived alleles than those with lower mutation rates because populations with more variation are expected to adapt faster. Most new mutations are thought to be effectively neutral or deleterious, and only a small fraction are beneficial in a given environment [ 1 ]. To identify putatively beneficial mutations in our replicate populations, we developed a statistical test that identifies genes in which more replicate populations contain high frequency derived alleles of any one gene than one would expect by chance alone Methods, S9 Fig.
In addition to identifying beneficial mutations, this approach can also identify artifacts such as mutational hotspots or the violation of independence across samples. Our test identified 20 genes with putatively beneficial mutations. Most of the mutations are nonsynonymous or nonsense mutations, and are thus likely to affect gene function S9A Fig. The two most commonly mutated genes were pykF and topA , which encode pyruvate kinase and topoisomerase A, respectively.
Pyruvate kinase is a key enzyme in glycolysis, and topoisomerase A can affect the superhelicity of DNA. Both genes have repeatedly acquired beneficial mutations in previous experiments with E. Similarly, mutations in cspC , a stress protein, confer a fitness advantage for E. Finally, mutations in the RNA polymerase gene rpoC and the cytoskeletal gene mreB have also been commonly found in laboratory evolution [ 62 , 67 , 68 ]. Surprisingly, multiple MR XL replicates showed the same nucleotide change in 12 of the 20 putatively beneficial genes.
As previously discussed, some mutations arose in the ancestor MR XL population before we split it into its replicate populations S7 Fig. Only two genes yfeZ and rrlH showed no evidence for such identical, pre-existing mutations, although such mutations may have existed below the detection limit of our sequencing coverage.
We cannot conclude that these 12 putatively beneficial genes have a beneficial effect in the MR XL populations, because our statistical test relies on the assumption that the mutations occurred and were subject to selection independently.
To know with certainty the phenotypic effects of any of these mutations would require additional empirical data from allelic replacement experiments. Thus far, the only phenotype we studied was population growth in one environment—the glucose minimal medium in which we conducted the entire experiment. To expand our analysis to other environments, we used Biolog Phenotype MicroArrays, which help measure the growth and respiration activity of a bacterial strain in multiple environments [ 69 ], but see [ 70 ] for caveats.
These microarrays determine the ability of our strains to grow in the presence of 96 stressful compounds that include antibiotics and heavy metals. We exposed our evolving replicate populations to these stressors only after completion of laboratory evolution, i.
One possible explanation is that the MR XL strain is inherently more sensitive to novel environments, including the medium used in the assay. The remaining populations grew in 42—60 In order to identify any link between mutation rate and growth in these 96 environments for the MR S , MR M , and MR L replicates, we identified the molecules in which an evolved replicate population grew better and worse than its ancestor S11 Fig. In sum, these analyses establish no simple association between ancestral mutation rate and stress tolerance after evolution.
Nitrofurantoin is a nitrofuran antibiotic with multiple mechanisms of action. Resistance to nitrofurantoin is conferred by mutations in two genes nfsA and nfsB , and has a fitness cost in the absence of the antibiotic [ 71 ]. Thus, resistance mutations are unlikely to exist at appreciable levels as part of the standing variation in populations not exposed to nitrofurantoin.
For both nitrofurantoin-containing and acidic media, we computed the fold change in growth the cell density after 24 hours of the evolved populations relative to their ancestors, controlling for changes in carrying capacity see Methods , Fig 4 , S12 Fig. We found that replicate populations with the highest mutation rates grew more slowly in nitrofurantoin than their ancestor.
The ancestral mutation rate also affected growth in acidic media. In sum, growth in two stressful conditions, nitrofurantoin-containing and acidic media, improved with increasing mutation rates and thus increasing diversity , except for the MR XL replicates which showed a relative reduction in growth. See S12 Fig for data on additional timepoints, and nitrofurantoin and pH conditions. To this end, we used fluctuation assays for mutations that cause rifampicin resistance, and estimated the genomic mutation rate U using Drake's approach [ 74 ].
Having estimated the mutation rates for the ancestor and evolved populations, we also wanted to examine whether prominent theoretical models that predict declines in mean population fitness at high mutation rates apply to our populations S2 Text. While some of the models we studied e. Each evolved strain's mean change in mutation rate is shown as the percentage of its ancestor's mutation rate.
Because mutation rates changed between the beginning and the end of the experiment, we wondered whether the final mutation rates were correlated with our measured phenotypes.
We found significant correlations between a replicate's mutation rate and its effective population size, standing genetic diversity, and number of high frequency derived alleles, but no correlations between a replicate's mutation rate and its final relative fitness, or normalized cell density after 24 hours of growth in acidic medium or medium containing nitrofurantoin Spearman's rank correlation, S13 Fig. Interpretation of these results requires caution for two reasons. First, for any one population, we do not know exactly when during the generations of evolution the mutation rate changed from its ancestral value.
Second, we compared the mutation rate of a single randomly-selected clone from populations which can have considerable genetic diversity, and thus potentially also show diversity in mutation rates. Despite these caveats, we found that the correlations between a representative clone's mutation rate and our other metrics are consistent with our previous analyses and figures Fig 4 , S2 Fig , S12 Fig , which simply considered the effects of ancestral mutation rate strain identity.
We call the set of genes potentially involved in modulating the mutation rate the "mutation rate genome". We wondered whether this part of the whole genome was a preferential target of mutation or selection in our experiments. To find out, we first identified a set of 96 genes potentially involved in modulating the mutation rate S2 Table from the literature and EcoCyc [ 49 , 75 — 77 ].
If mutations or selection did not preferentially affect the mutation rate genome, the amount of genetic change we observe in it would be proportional to its length relative to the rest of the genome. This is indeed the case: We counted the number of synonymous mutations occurring at any frequency in any replicate population at generation , and observed no statistically significant increase in the incidence of such genetic change in the mutation rate genome for any of our evolving strains S14A Fig.
We also found no difference in mean diversity between synonymous sites in the mutation rate genome relative to the rest of the genome S14B Fig. Although the mutation rate genome is not a preferential target of genetic change, its genes still accumulated many non-synonymous and nonsense changes, which are the kinds of changes that are especially likely to affect protein function S15 Fig. Mutations in ten genes met these criteria rpoS , umuC , dinB , dinG , dps , glyS , glyW , mutL , phr , and vsr , and two were found in multiple replicate populations rpoS : 7 of 8; umuC : 2 of 8.
Populations with rpoS mutations can hold a fitness advantage in nutrient-limiting environments [ 79 ], but at a cost to fitness in a variety of stressful environments [ 28 , 80 ].
We found the same rpoS ND mutation in 2. This mutation reached Thus, the mutation was likely distributed to the eight replicate populations from the ancestor, and either increased in frequency due to its direct fitness effects, or because it was hitchhiking with a beneficial mutation.
Each of the remaining genes with high frequency mutant alleles in a single replicate population were involved in DNA repair and replication dinB , dinG , glyS , glyW , mutL , phr , vsr or protection of DNA in stationary phase dps in a single replicate population and could have also affected the evolved mutation rate. Here, we studied the effects of mutational pressure on evolutionary adaptation and the evolution of the mutation rate itself.
To this end, we engineered four isogenic E. At the opposite extreme was our strain with the highest ancestral mutation rate MR XL. We originally expected this strain to have a mutation rate approximately fold higher than wildtype [ 35 ], consistent with the large effects that mutations in the dnaQ and mutL genes have on the mutation rate [ 75 ].
The discrepancy could in principle be due to the acquisition of an anti-mutator allele during the transfer of the strain between laboratory locations. Alternatively, our mutation rate could be an underestimate for technical reasons discussed in the Methods.
The mutation rate for our MR XL strain was also somewhat lower than that of a hypermutable clone which spontaneously evolved from an E. The mutation rate of our hypermutable MR XL strain is low enough that we expected its populations to be viable [ 21 ]. We first characterized the general patterns of adaptation in our four strains, and found that their fitness increased significantly by generation for all replicate populations. Previous experimental evolution studies in constant environments have observed fitness gains that are initially large but decrease over time [ 17 , 18 , 83 , 84 ], which is consistent with diminishing returns epistasis, in which the size of the fitness gain in an evolving population depends on its current fitness, such that populations with lower fitness can improve their fitness to a greater extent [ 85 , 86 ].
However, our fitness trajectories differ from those predicted by diminishing returns epistasis in two ways. First, they do not show a decreasing fitness gain over time [ 18 ]. Second, the mean fitness of replicate populations with small or modestly high mutation rates MR S , MR M did not immediately improve, but unexpectedly remained largely unchanged for the first generations compared to [ 87 ].
While delayed adaptive response is consistent with a lower overall beneficial mutation supply rate, it may not be sufficient to explain our observations. We expected to wait just 44 generations for a new beneficial mutation to establish in our slowest-evolving replicate population S3 Text.
An instance of such contingent evolution has been documented in E. We next characterized the effect of mutational pressure on adaptation.
We found that strains with higher ancestral mutation rates increased in fitness more than those with lower mutation rates, except for MR XL populations, which we will discuss below. These observations are in agreement with theory [ 15 , 91 ] and previous experimental studies which found that large asexual populations of E.
We do not actually observe the loss of fitness on average across the MR XL replicate populations, but rather a prolonged period in which fitness remains unchanged as a whole. Interestingly, however, the fitness of several MR XL replicate populations decreases from its maximum and arrives at a value that is approximately equal to that of the ancestral population. This is reminiscent of models of extreme mutational pressure developed over the past forty years that predict reduced adaptation and eventual extinction [ 19 , 20 , 22 , 92 — 94 ].
However, these models predict a loss of fitness only at higher mutation rates than we observed, and require unrealistic assumptions S2 Text , together emphasizing the importance of additional theoretical work. Another possibility is Hill-Robertson interference [ 7 ], which can reduce the rate of adaptive evolution by background selection—negative selection against deleterious alleles that removes the most deleterious lineages from a population—and can reduce genetic diversity [ 8 , 12 ].
Empirical evidence supports the action of this mechanism in natural populations of several eukaryotic species reviewed in [ 13 , 14 ]. However, because background selection removes deleterious mutations from a population, it cannot alone reduce the fitness of a population and it can therefore not explain the loss of fitness we observed in the three MR XL replicates. While a lowering of the mutation rate has been previously observed [ 46 — 48 , 50 ] and predicted to be favored in some conditions [ 38 , 40 , 42 , 43 , 95 ], its extent and consistency across multiple of our evolving populations is remarkable.
The mutation rate decrease probably did not occur very early during evolution, because the MR XL populations show greater genetic diversity than all other populations throughout the experiment Fig 3. The decreasing mutation rate, together with the observation that the MR XL populations failed to adapt after more than generations, suggests that the maladaptive effects of hypermutation begin at even lower mutation rates than those in our initial MR XL strain.
While we cannot predict whether our hypermutable populations would eventually go extinct, the observation that their mutation rate can decrease adaptively makes this less likely. Indeed, recent mutation accumulation experiments with small bacterial populations suggested that populations with higher mutation rates tend to go extinct more often and have reduced fitness than populations with lower mutation rates [ 47 ].
However, we cannot definitively identify the proximal mechanisms driving the drop in mutation rates using bioinformatics alone. Future experimental studies to evaluate the effect of each "mutate rate genome" mutant allele on the mutation rate and fitness would be necessary.
We emphasize that all our experiments use asexual populations, and that the evolutionary dynamics of mutation rates and adaptation may be different in sexual, recombining populations.
For example, in our non-recombining populations, any mutator allele remains completely linked to the mostly deleterious mutations it helps bring forth, resulting in indirect negative selection on the mutator allele. However, such an allele and its associated mutations can become unlinked in recombining populations, which reduces the strength of indirect selection on the mutator allele see [ 33 , 39 ] for reviews.
Additionally, beneficial and deleterious alleles can become unlinked in recombining populations, which can lead to increased levels of adaptation and diversity see [ 13 , 14 ] for reviews. We also characterized the effect of mutational pressure on the ability of an evolving population to grow better or worse than its ancestor in a variety of chemically novel environments, which contain chemical agents that include heavy metal stressors, antibiotics, or acids.
Importantly, our populations were never exposed to any of these conditions during the evolution experiment. A priori , we reasoned that two outcomes were possible. First, populations with high mutation rates may grow better in novel environments, because they can accumulate more beneficial mutations while evolving in their original environment, and these mutations may also be beneficial in novel environments through pleiotropy.
High mutation rate populations can also generate more genotypic diversity, which in turn increases the chances that a population harbors a clone with a latent beneficial mutation that allows it to grow better in a novel environment. Such latent beneficial mutations can indeed occur, as demonstrated by the classic fluctuation test, which relies on such mutations to estimate mutation rates towards resistance to lethal selection [ 96 , 97 ].
Second, populations with high mutation rates may grow worse in novel environments, because they may accumulate more mutations that are either beneficial or neutral in the current environment, but deleterious in a novel environment. Such latent deleterious mutations do indeed exist [ 36 , 70 , 98 ]. In sum, strains with high mutational pressure may harbor more latent beneficial alleles, but also more latent deleterious alleles, and it is not clear a priori which dominates in their effect on fitness.
We conducted two tests on how mutational pressure can affect growth in novel conditions. However, it yielded a very clear pattern for our MR XL populations: They were not able to grow in any one of these environments, which illustrates that at the highest mutation rates we consider, latent deleterious mutations outweigh beneficial ones in both the ancestor and evolved populations.
One possible explanation is that the MR XL strain is inherently more sensitive to novel environments, including the assay environment.
Because the MR XL ancestor population could not grow at all, we were unable to further quantify the effect of the highest mutation rate in these 96 novel environments. In the second test, we periodically measured growth of all 32 replicate populations relative to their ancestors in two stressful conditions: the antibiotic nitrofurantoin a specific narrow stressor and an acidic medium a broader stressor.
For both, we found that strains with higher ancestral mutation rates could grow better than those with lower mutation rates, except for MR XL replicate populations, which grew worst of all populations. This experiment shows that latent beneficial alleles may predominate at low and intermediate mutational pressure, but no longer at high mutational pressure.
Our observations are consistent with a previous study showing that multidrug resistance in E. In sum, a modest increase in mutation rates can provide an evolutionary advantage in both the constant environment of our long-term laboratory evolution experiment and in novel environments.
These mutation rates are below those commonly considered to limit adaptation, and highlight the need for additional theoretical work. Our observations show that biological systems may be more sensitive to mutational pressure than simple theoretical models suggest, at least when the effects of mutations are allowed to accumulate over many generations.
This observation may improve the prospects of using elevated mutagenesis to drive pathogen or tumor populations to extinction [ 20 , — ], if high mutation rates can be sustained for a sufficiently long amount of time. We utilized four isogenic E. Strain genotypes are summarized in Table 1. This gene is involved in the methyl-directed mismatch repair system.
We constructed the mutator strain MR L by replacing the mutL region in MR M with the mutL region from ES4 with a kanamycin resistance gene inserted upstream of the region, using the method of Datsenko and Wanner [ ]. We then excised the kanamycin resistance gene using pCP20 [ ], which left a small scar immediately upstream of the mutL gene.
We confirmed the mutation rates of these ancestral strains using fluctuation tests [ ] see "Mutation rate measurements and calculations" for details , and found that the MR M , MR L , and MR XL strains had , , and fold higher mutation rates to rifampicin resistance than MR S. In additional to these strains, we used the strain E. See Fig 1 for an overview.
Each plate held 24 populations arranged in a checkerboard pattern, such that each well was surrounded only by wells with blank medium, and the populations were assigned to the 24 wells at random by a custom R script.
We diluted each culture ,fold every 24 hours into fresh DM medium, which allows almost 17 generations of growth per day. We delayed the start of the MR S replicates by 63 days for technical reasons. We controlled for contamination in several ways. Second, we periodically checked each evolving culture for contamination by confirming its resistance profile and approximate mutation rate using spot tests.
MR S and MR XL replicates can grow on tetracycline, and the replicates with higher mutation rates display more colonies on rifampicin. Third, we examined the genome sequence data for cross-contamination, but detected no evidence for cross-contamination in it. For populations that do not have a constant number of cells, the effective population size is given by the harmonic mean of population sizes over the course of the dilution and growth cycles of the experiment. Previous studies have estimated the effective population size only from the size of the bottleneck measured during one dilution [ , ].
In contrast, because we recorded the census size of the population at carrying capacity N max every 7 days, we were able to estimate the effective population size as the harmonic mean of the population sizes both at the beginning and at the end of a cycle of growth and dilution. To obtain N max , d at any one day d , we counted the number of cells in each evolving replicate population in stationary phase just before transferring the population into fresh media.
We discarded plates with fewer than 20 or more than colonies for the purpose of this analysis. Thus, during each generation g of each growth cycle, a population assumed population sizes. We then determined the nominal effective population size N e of a replicate population during its entire lab evolution as which is the harmonic mean of all the population sizes. We calculated it for all 25 days on which we collected population size data.
The number 18 corresponds to the total number of generations g for which we computed population sizes during any one of these 25 days. We also estimated the effect of linkage on reducing the effective population size due to background selection or interference selection [ 53 — 55 , ]. We periodically obtained a proxy for the fitness of the evolving strains by measuring growth curves of the archived populations.
For each time point, we restarted all evolving populations as well as three replicates from each ancestral population and three replicates of wild type E. During this time, we read the absorbance at nm every 10 minutes. We fit the classic logistic equation describing population growth to the data [ ], using the Growthcurver R package [ ], and defined the relative fitness of each population as r evo —r anc.
Here, r evo is the growth rate of the evolved population and r anc is the mean growth rate of the three replicates of the ancestor grown in the same plate reported in units of cell divisions per hour. We measured each growth curve three times. We used the R package lme4 v1. In this analysis, we chose the mutation rate classes as fixed effects, and the replicate population as well as the well plate as random effects.
For all linear mixed effects analyses conducted in this paper, we observed no deviations from homoscedasticity according to Levene's test for homogeneity of variance [ ] implemented in the R package car v2. Also, all residuals were normally distributed unless otherwise specified. We obtained significance values using a likelihood ratio test of the full model against a null model that did not contain the fixed effects. Using the data from the above growth curve experiments, we also compared the fitness of the ancestor populations against each other by obtaining the relative fitness of the ancestors as r anc —r K12 , where r anc is the growth rate of the ancestor population and r K12 is the mean growth rate of the three replicates of E.
We performed a linear mixed effects analysis of the relationship between the ancestral fitness relative to E. The number of alleles in a population will be related to the size of the population.
Mutation rates are calculated in units of generations, either per individual, per base pair, or per spore. A mutation rate of 1 x 10 -6 can mean that a mutation for a particular gene will occur once every million cells per generation, or once in every million base pairs of DNA per generation. The only mutations that are passed to progeny are those that occur in reproductive cells, such as fungal spores or virus particles or sperm or eggs. A mutation rate of 1 x 10 -6 also implies that the mutation occurs at a frequency of one in every million individuals in a population.
Mutation rates vary across genes and organisms, but they are usually low and can be considered rare events in most cases Flor , Zimmer , Gassman et al. This means that, on average, in a population of one million individuals spores, bacterial cells, or virus particles , you can expect to find one mutant for any given locus per generation.
In a population of 10 million individuals, you would expect to find 10 mutants for any locus. And in a population of 1 billion individuals, you expect to find mutants for any locus. Consider the barley powdery mildew pathogen Blumeria graminis f. With a mutation rate of 10 -6 at avirulence loci, there would be approximately 10 7 virulent mutant spores produced in each hectare each day.
These virulent mutants can travel out of a field planted to a susceptible barley cultivar and infect a neighboring field planted to a resistant barley cultivar.
The virulent mutants that have lost the elicitor encoded by the avirulence allele can infect the resistant cultivar and produce a new generation of virulent progeny. This process appears to have happened many times with powdery mildew and rust fungi in agricultural ecosystems, leading eventually to boom-and-bust cycles.
Thus mutation is the critical first stage in producing the "bust. In general, large populations are expected to have more alleles than small populations because there are more mutants present for selection or genetic drift to operate on. This is one reason to keep pathogen population sizes as low as possible in agroecosystems. In addition, large populations usually contain more alleles because they experience less genetic drift.
Genetic drift leads to a reduction in the number of alleles in a population. Finally, the diversity of alleles at a locus will be affected by the length of time a population occupies a particular area. Over thousands of generations, many mutations will be introduced into a population and some of these will increase to a detectable frequency as a result of selection or genetic drift. Both of these processes may take a long time to make a measurable increase in allele diversity. This concept of a "center of genetic diversity" is used to identify the center of origin of a host plant and its pathogens.
Email Facebook Twitter. More Details Evo Examples Teaching Resources Read more about how mutations are random and the famous Lederberg experiment that demonstrated this.
Learn more about mutation in context: Evolution at the scene of the crime , a news brief with discussion questions. A chink in HIV's evolutionary armor , a news brief with discussion questions. Previous Genetic variation. Genetic variations can arise from gene variants also called mutations or from a normal process in which genetic material is rearranged as a cell is getting ready to divide known as genetic recombination.
Genetic variations that alter gene activity or protein function can introduce different traits in an organism. If a trait is advantageous and helps the individual survive and reproduce, the genetic variation is more likely to be passed to the next generation a process known as natural selection. Over time, as generations of individuals with the trait continue to reproduce, the advantageous trait becomes increasingly common in a population, making the population different than an ancestral one.
Sometimes the population becomes so different that it is considered a new species. Not all variants influence evolution. Only hereditary variants , which occur in egg or sperm cells, can be passed to future generations and potentially contribute to evolution. Also, many genetic changes have no impact on the function of a gene or protein and are not helpful or harmful. In addition, the environment in which a population of organisms lives is integral to the selection of traits.
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