How does mutation affect evolution

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. In this analysis, we chose the mutation rate classes as fixed effects, and the identity of the original glycerol stock and of the well plate as random effects. We used the R package multcomp v1. We sequenced samples from the four ancestral populations day 0, generation 7 and from each of the 32 evolving replicate populations at days 63, , and generations , , and For simplicity, we hereafter designate these time points as generations 0, , , and No , with modifications as previously described [ ].

Importantly, we used no PCR steps in preparing the libraries. We used breseq v0. We developed scripts in R to identify the alterations that occurred in the evolved populations, but were not fixed in their ancestors. Because DNA is double-stranded, the remaining possible point mutations are covered by their reverse complements, e. We computed the relative frequencies of each mutational class for each replicate population, and used these to perform a principal component analysis PCA in R with prcomp, which uses singular value decomposition for the PCA.

We defined the center of the mutational cloud of genomes as the location in genotype space defined by the majority allele at each site. We define the population spread metric C as the average of C n over all sites in the genome. A related quantity is the approximate sequence distance D that an evolving population has moved from its ancestral genotype, i. In other words, D corresponds to the total number of sites at which the majority allele is different from the ancestral allele.

We also computed each population's average genome-scale nucleotide site diversity [ , ] using the pairwise alignment position nucleotide counting approach [ , ]. We estimated the proportion of pairwise nucleotide differences at each site n as where m p is the number of reads corresponding to the majority allele and m is the total number of reads at site n. We estimated the average nucleotide diversity for the L positions in our genome having non-zero coverage as.

In this analysis, we chose the mutation rate classes as fixed effects, and the time points and each of the 32 evolving replicates as random effects. We identified putatively beneficial mutations as mutations that occurred in a genomic region more often than one would expect by chance alone. To identify such mutations, we used a numerical approach that focuses on a given gene g among a larger set of genes or genomic regions G e.

If all sites in the genomes of all samples were equally likely to experience a mutation, and if different genes were likely to experience mutations only in proportion to their length, then the probability p g that any one gene g receives such a mutation in any given replicate would depend only on the length of the gene l g ,.

We computed the probability of observing the n g mutations in any given set of replicates as the probability that gene g was mutated in each member of the set of replicates times the probability that it was not mutated in any of the other replicates. This quantity P g is our null expectation that two replicates acquire mutations in gene g , if each replicate population's mutations were randomly distributed across its genome. We were interested in genes containing mutations in improbably many replicate populations, which we identified as those genes having less than a 0.

Similarly, the probability of observing a mutation in exactly 3 replicate populations is given by. We estimated the mutation rate of a single clone isolated from each ancestor and from each evolved replicate population through fluctuation assays that screened for mutants resistant to rifampicin [ ], which can be caused by mutations in the rpoB gene.

Specifically, we performed the following procedure for each replicate population. We obtained the genomic mutation rate U using Drake's approach [ 74 ] by first determining the "correction factor" C , which counts the number of single nucleotide mutations in rpoB that show rifampicin resistance.

This mutation rate may be an underestimate because we neglected other types of mutations e. Our phenotype screening revolved around the density of cells after growth in various chemicals. We resuspended the colonies from the final third round plates in IF-0 solution Biolog, Inc. To determine the minimum threshold for detection of growth in a given compound C , we computed the absolute difference between the readings in a given well across all pairs of samples i , j after 10 minutes before cells had started to grow and divide , i.

The values of A i-j , C , 10m quantify the expected experimental noise of wells with no growth. Each compound in the Biolog Phenotype MicroArrays we used occurs in four wells at increasing concentrations. For further analysis, we used data only from the concentration the well that showed the highest variation in the difference between matched evolved and ancestor strains across all samples. We considered a sample to have evolved tolerance to a compound C if it improved its phenotype after generations of evolution more than expected based on experimental noise, i.

Likewise, we considered that a sample had lost tolerance if its phenotype had degenerated after generations of evolution, i. We note that both cellular growth and respiration contribute to the Biolog phenotype B S , C , because respiration can occur independently of cellular growth [ 70 , ].

We were also interested in observing the evolutionary dynamics of phenotypes over time. The phenotypes we selected for this analysis are the cell density after 24 hours of growth of the evolved populations relative to their ancestors in two conditions: a narrow antibiotic nitrofurantoin stress, and a broader environmental low pH stress. Specifically, we chose DM medium with 1. Nitrofurantoin is one of the phenotypes where evolved populations gained tolerance in the Biolog analyses, and acid stress has been well-studied in E.

To control for changes in cell density at stationary phase, we also performed a control measurement in the standard medium, DM Specifically, we measured the growth of evolved replicate populations at days 28, 63, 91, , , and generations , , , , , and , hereafter designated as generations , , , , , and We incubated the resulting well plates for 24 hours, and then measured the absorbance at nm.

In order to quantify the relationship between the normalized fold-change in cell density G and ancestral mutation rate, we performed a linear mixed effects analysis using the R package lme4 v1. For the nitrofurantoin and pH stressors, we used data from the experimental condition with the most variability between replicates 2. We tested for homoscedasticity using the R package car v 2. We counted the number of cells in stationary phase just before our daily transfer at regular intervals.

Each point is the average cell density of an evolving replicate population at a given generation. One standard deviation above and below the mean is depicted with a shaded line.

We counted the number of cells at regular intervals, and used these counts to estimate A the nominal effective population size N e for each replicate population. Because our populations are asexual, the effects of selection on polymorphisms linked to neutral sites will make drift at neutral sites appear much stronger than indicated by these estimates.

To account for such effects, we also made rough estimates of the effect of linkage on the effective population size using two published methods further described in Methods , which compute the "Gordo" N e B , and the "Good" N e C.

Together, panels B and C suggest that the effective population size may be much smaller than the nominal population size. Each circle shows the N e estimate of a replicate population, the center line of the box plot is the median value, and the top and bottom edges of the box correspond to the first and third quartiles. A Fitness differences between ancestral replicate populations and E. Each circle shows the growth rate of a replicate population for a given strain horizontal axis minus the growth rate of E.

Overall, 54 experimental estimates were made for each strain. B Fitness differences between each evolving replicate population and a common reference strain E.

Shaded areas indicate one s. A C The fitness difference between each evolving replicate population and its ancestor and its change over time is depicted in separate panels for each strain and replicate. Panels corresponding to the replicates randomly chosen for further characterization in Biolog plates are outlined with a heavy black border. B Variance in relative fitness for the replicate populations of each strain. Strains with higher ancestral mutation rates have more variability in the relative fitness of their evolving populations than those with lower mutation rates.

Each line in a given panel shows the frequency of one SNP in one replicate population vertical axis at generations 0, , , and horizontal axis. The color of the line indicates the type of SNP. Types of SNPs with likely functional consequences are emphasized in brown nonsense mutations and green nonsynonymous mutations. The frequency of newly-arising SNPs after one day of growth in the ancestral populations. Several of the observed SNPs, particularly those occurring at higher frequencies, may have been transferred to the eight replicates.

A Nucleotide changes are depicted along the horizontal axis. For each type of mutation, we computed how often it occurred at any time point during the evolution experiment relative to all other types Methods.

B The mutational spectra from replicate populations evolved from ancestors with different mutation rates do not clearly separate when projected onto the first two principal components PC1 and PC2 in a principal component analysis Methods.

Because we evolved eight replicate populations for each strain, each vertical stack of dots can harbor at most eight dots. For many genes, all MR XL replicates share the same nucleotide change, which likely already occurred in the shared ancestor.

A Genes with different mutations in the same gene in different replicates, and B genes where all the MR XL replicates share the same nucleotide change the nucleotide changes found in the MR L replicate populations for betI and torA are not the same as found in the MR XL populations. Each circle corresponds to one evolving replicate population.

The size of a circle is proportional to the frequency at which a mutation is found in a population, and can change over time horizontal axes. All replicates for all strains circles inside each panel are depicted for each gene labeled in the top, left of each panel.

Importantly, all tested ancestor and evolved MR XL strains failed to grow in every one of the 96 environments. Each circle represents the ancestor's density horizontal axes and the evolved replicate population's density vertical axes in a particular environment. Points above the diagonal line correspond to conditions in which an evolved replicate population outperformed its ancestor; points below the line correspond to conditions in which an evolved replicate population underperformed its ancestor.

We consider a population to have evolved tolerance to a condition when its density is larger than the ancestral density in the same condition, excluding differences attributable to experimental noise.

Conversely, we consider a population as having experienced decay if its density after evolution is smaller than that of its ancestor see Methods. Both gains and decays are indicated by solid circles. Open circles indicate that no gain or decay was detected for that condition, or that the difference between the evolved and ancestral cell density could be due to experimental noise.

We plotted each property in a pairwise fashion to identify correlations between properties. Each property is listed on the diagonal "log U " is the logarithm of the genomic mutation rate, "relative fitness" is the evolved growth rate relative to the ancestor, "N e " is the effective population size, "cell density at " is the absorbance reading at nm at generation after 24 hours of growth in minimal medium, "log derived alleles " is the logarithm of the number of high frequency derived alleles at generation , "log cloud size " is the logarithm of the population's average distance to the center of the cloud at generation , "log pH cell density " is the logarithm of the normalized fold change in cell density after 24 hours of growth in acidic media at pH 5.

Pairwise comparisons are plotted below the diagonal; each circle corresponds to a different replicate population. The Spearman correlation coefficient of each panel below the diagonal is reported in the corresponding panel above the diagonal. A We calculated the percentage of synonymous nucleotide changes at any frequency that occurred within genes belonging to the mutation rate genome vertical axes during the evolution experiment horizontal axes at any frequency in each evolving replicate population circles.

Horizontal gray lines indicate the percentage of coding regions in the E. B We calculated the mean synonymous nucleotide site diversity and its standard error Methods. The mean synonymous nucleotide site diversity for the mutation rate genome is depicted in the right panel, and for all other genes in the left panel. Note that no or very few sites may contribute to average diversity at low mutation rates.

Shaded areas indicate one standard error of the mean. Each circle corresponds to a putatively function-altering mutation nonsynonymous or nonsense mutations in protein-coding genes, or any mutation in tRNA-encoding genes in one evolving replicate population. All replicates for all strains circles inside each panel are depicted for each gene labeled on the top left of each panel.

SNPs occurring in intergenic regions are annotated with the nearest 5' and 3' genes. The columns are as follows: "Strain" is the identity of the ancestral strain e. Mutation rates were measured at generations 0 "Ancestor" replicates and all other replicates. Abstract Mutation is fundamental to evolution, because it generates the genetic variation on which selection can act. Author summary Mutation is of central importance in biology.

Introduction Mutation is fundamental to evolution. Results The experimental design is summarized in Fig 1. Download: PPT. Population sequencing at regular intervals We sequenced a sample of each heterogeneous evolving population rather than a clone isolated from each population, so that we could estimate the genetic diversity within each sequenced population. Higher mutation rates lead to larger mutant clouds and more high frequency derived alleles One can view an evolving population as a cloud of mutant individuals in sequence space.

Fig 3. Replicate populations with higher mutation rates have increased genetic diversity and more high frequency derived alleles. Beneficial mutations Most new mutations are thought to be effectively neutral or deleterious, and only a small fraction are beneficial in a given environment [ 1 ]. Growth and survival in stressful conditions Thus far, the only phenotype we studied was population growth in one environment—the glucose minimal medium in which we conducted the entire experiment.

Fig 4. Cell density after 24 hours of growth in stressful conditions increased with increasing mutation rate, except for MR XL replicate populations. Changes in the mutation rate genome We call the set of genes potentially involved in modulating the mutation rate the "mutation rate genome". Discussion Here, we studied the effects of mutational pressure on evolutionary adaptation and the evolution of the mutation rate itself. Methods Bacterial strains We utilized four isogenic E.

Evolution experiment See Fig 1 for an overview. Effective population size 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.

Fitness measurements We periodically obtained a proxy for the fitness of the evolving strains by measuring growth curves of the archived populations. Sequencing We sequenced samples from the four ancestral populations day 0, generation 7 and from each of the 32 evolving replicate populations at days 63, , and generations , , and We estimated the average nucleotide diversity for the L positions in our genome having non-zero coverage as We used the R package lme4 v1.

Identification of putatively beneficial mutations We identified putatively beneficial mutations as mutations that occurred in a genomic region more often than one would expect by chance alone. Mutation rate measurements and calculations We estimated the mutation rate of a single clone isolated from each ancestor and from each evolved replicate population through fluctuation assays that screened for mutants resistant to rifampicin [ ], which can be caused by mutations in the rpoB gene.

Phenotype screening Our phenotype screening revolved around the density of cells after growth in various chemicals. Supporting information. S1 Fig. Cell density vertical axes after 24 hours of growth as a function of generation time horizontal axes. S2 Fig. Effective population size N e of replicate populations over the course of the evolution experiment.

S3 Fig. S4 Fig. S5 Fig. Percentage of the genome with no sequencing coverage for all sequenced populations. S6 Fig. Frequency and type of SNP in each evolving population over time. S7 Fig. S8 Fig. The mutational spectra at four-fold degenerate sites for each evolving replicate strain.

S9 Fig. The number of replicates for which at least half of the population harbors any mutation in a putatively beneficial gene. S10 Fig. The evolutionary dynamics of mutations in the eight putatively beneficial genes.

S11 Fig. Cell density of two randomly selected evolved and ancestral strains in 96 different environments on Biolog plates. S12 Fig. The fold-change in cell density after 24 hours of growth of evolved replicate populations relative to their ancestor in media with nitrofurantoin and low pH media. S13 Fig. Comparisons of multiple population properties matrix diagonal for the experimental data from each evolved replicate population. S14 Fig. No evidence that the mutation rate genome is preferentially subject to genetic change.

S15 Fig. The evolutionary dynamics of possibly function-altering mutations in the mutation rate genome. S1 Table. S2 Table. Genes putatively involved in modulating the mutation rate. S3 Table. Genomic mutation rates. S1 Text. Area Under the Curve AUC is a complementary fitness metric that also demonstrates reduced adaptation at very high mutation rates.

S2 Text. Applicability of several theoretical models predicting loss or reduction of adaptation at high mutation rates. S3 Text. The waiting time for the establishment of a new beneficial allele. References 1. The distribution of fitness effects of new mutations. Nat Rev Genet. Kunkel TA, Bebenek K. DNA replication fidelity. Annu Rev Biochem. DNA mismatch repair. Fisher RA. The genetical theory of natural selection. Oxf Univ Press. Muller H. Some genetic aspects of sex.

Am Nat. View Article Google Scholar 6. Felsenstein J. The evolutionary advantage of recombination. Hill WG, Robertson A. The effect of linkage on limits to artificial selection. Genet Res. The effect of deleterious mutations on neutral molecular variation. Johnson T, Barton NH. The effect of deleterious alleles on adaptation in asexual populations.

Beneficial mutation selection balance and the effect of linkage on positive selection. Barton NH. Genetic linkage and natural selection. Charlesworth B. The effects of deleterious mutations on evolution at linked sites. Genomic signatures of selection at linked sites: unifying the disparity among 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. Some differences introduced by variants may help an organism survive in one setting but not in another—for example, resistance to a certain bacteria is only advantageous if that bacteria is found in a particular location and harms those who live there.

So why do some harmful traits, like genetic diseases, persist in populations instead of being removed by natural selection? There are several possible explanations, but in many cases, the answer is not clear. For some conditions, such as the neurological condition Huntington disease , signs and symptoms occur later in life, typically after a person has children, so the gene variant can be passed on despite being harmful.

For other harmful traits, a phenomenon called reduced penetrance , in which some individuals with a disease-associated variant do not show signs and symptoms of the condition, can also allow harmful genetic variations to be passed to future generations. For some conditions, having one altered copy of a gene in each cell is advantageous, while having two altered copies causes disease. The best-studied example of this phenomenon is sickle cell disease : Having two altered copies of the HBB gene in each cell results in the disease, but having only one copy provides some resistance to malaria.

This disease resistance helps explain why the variants that cause sickle cell disease are still found in many populations, especially in areas where malaria is prevalent. Other chapters in Help Me Understand Genetics.

What is the new frequency of A 2 after one generation of mutation? We find that there is not much change in the frequency of A 2 after one generation of mutation. In general, after t generations, the frequency of the A 1 wild-type allele will be. To calculate the number of generations required to change allele frequencies by a given amount, solve for t, which gives:. We can use this formula to calculate the number of generations needed to change allele frequencies under the assumption that mutation is the only evolutionary force acting on a population.

To move the frequency of A 1 from 1. To move it from 0. In general, as the frequency of the wild-type allele decreases, it takes longer to accomplish the same amount of change.

This simple model should convince you that mutation is a very weak force when it comes to changing allele frequencies. But mutation is very important for introducing new alleles new DNA sequences into populations. 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.