Fitness is strongly influenced by rare mutations of large effect in a microbial mutation accumulation experiment

Abstract

Our understanding of the evolutionary consequences of mutation relies heavily on estimates of the rate and fitness effect of spontaneous mutations generated by mutation accumulation (MA) experiments. We performed a classic MA experiment in which frequent sampling of MA lines was combined with whole genome resequencing to develop a high-resolution picture of the effect of spontaneous mutations in a hypermutator (ΔmutS) strain of the bacterium Pseudomonas aeruginosa. After ∼644 generations of mutation accumulation, MA lines had accumulated an average of 118 mutations, and we found that average fitness across all lines decayed linearly over time. Detailed analyses of the dynamics of fitness change in individual lines revealed that a large fraction of the total decay in fitness (42.3%) was attributable to the fixation of rare, highly deleterious mutations (comprising only 0.5% of fixed mutations). Furthermore, we found that at least 0.64% of mutations were beneficial and probably fixed due to positive selection. The majority of mutations that fixed (82.4%) were base substitutions and we failed to find any signatures of selection on nonsynonymous or intergenic mutations. Short indels made up a much smaller fraction of the mutations that were fixed (17.4%), but we found evidence of strong selection against indels that caused frameshift mutations in coding regions. These results help to quantify the amount of natural selection present in microbial MA experiments and demonstrate that changes in fitness are strongly influenced by rare mutations of large effect.

Keywords: Pseudomonas aeruginosa; experimental evolution; hypermutator; spontaneous mutation; whole genome resequencing.

Figure 1

Types of mutations accumulated. (A) The distribution of accumulated mutations according to type of mutation. Indels <10 base pairs long were considered to be “short.” (B) Further information on the effects of point mutations.

Figure 2

Average fitness decays in mutation accumulation lines. Plotted points show the mean fitness (± SE) of hypermutator lines (solid symbols, n = 8) and control lines (shaded symbols, n = 4) that were passaged through 28 daily bottlenecks, which correspond to ∼644 generations of mutation accumulation. The fitness of hypermutator lines rapidly declined, but the fitness of control lines did not change over the course of the experiment (ANOVA: F_1,3 = 0.436, P = 0.556). Note that in some MA lines, fitness decayed to the point where it was not possible to measure fitness reliably, but these data are included to prevent bias.

Figure 3

Fitness trajectories for individual mutation accumulation lines. The mean (± SE; n = 6) fitness of individual hypermutator lines through time. Red data points indicate that fitness is too low to measure accurately. The mean fitness (± SE; n = 6) of individual hypermutator lines through time. Red data points indicate that fitness is too low to measure accurately. The y-axis of each plot is scaled differently to maximize the resolution of evolutionary dynamics within a single line.

Figure 4

Changes in fitness for individual “steps.” The distribution of fitness changes for each step in the mutation accumulation experiment across all eight hypermutator lines. Each step represents the difference in fitness between successive assays for an MA line (∼8.4 mutations accumulated/step). The solid line depicts no change in fitness and the area between the dashed shaded lines is the area in which N_es < 1, where N_e is the harmonic mean of population size over time (although this may be an underestimate) (Otto and Whitlock 1997).

Figure 5

The distribution of indel mutations in proteins. Comparison between the observed and expected position of frameshifts in coding regions. Proteins were divided into three equal pieces and we counted the number of frameshifts (overlapping with homopolymeric tracts) that fell in each section. Expected frequencies were computed by counting the number of homopolymeric tracts in the P. aeruginosa PAO1 proteome that fall in each section. The differences between observed and expected values were statistically significant for the N-terminal third of proteins (one-sided exact binomial test: P = 0.037).