Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa

Abstract

Because adaptation depends upon the fixation of novel beneficial mutations, the fitness effects of beneficial mutations that are substituted by selection are key to our understanding of the process of adaptation. In this study, we experimentally investigated the fitness effects of beneficial mutations that are substituted when populations of the pathogenic bacterium Pseudomonas aeruginosa adapt to the antibiotic rifampicin. Specifically, we isolated the first beneficial mutation to be fixed by selection when 96 populations of three different genotypes of P. aeruginosa that vary considerably in fitness in the presence of rifampicin were challenged with adapting to a high dose of this antibiotic. The simple genetics of rifampicin resistance allowed us to determine the genetic basis of adaptation in the majority of our populations. We show that the average fitness effects of fixed beneficial mutations show a simple and clear pattern of diminishing returns, such that selection tends to fix mutations with progressively smaller effects as populations approach a peak on the adaptive landscape. The fitness effects of individual mutations, on the other hand, are highly idiosyncratic across genetic backgrounds, revealing pervasive epistasis. In spite of this complexity of genetic interactions in this system, there is an overall tendency toward diminishing-returns epistasis. We argue that a simple overall pattern of diminishing-returns adaptation emerges, despite pervasive epistasis between beneficial mutations, because many beneficial mutations are available, and while the fitness landscape is rugged at the fine scale, it is smooth and regular when we consider the average over possible routes to adaptation. In the context of antibiotic resistance, these results show that acquiring mutations that confer low levels of antibiotic resistance does not impose any constraint on the ability to evolve high levels of resistance.

Figure 1.—

Diminishing returns from beneficial mutations. (A–C) The distribution of fitness effects of beneficial mutations that were fixed by natural selection when three different strains of P. aeruginosa PAO1, each carrying a different missense mutation in RNA polymerase (A455T, A455G, or C1550T), were challenged with adapting to culture medium containing a high dose (120 mg/liter) of rifampicin. (D) A linear regression of the average (±95% C.I.; n = 93, 95, or 96) fitness effect of beneficial mutations that were fixed by selection as a function of the fitness of the genetic background in which the mutations were fixed.

Figure 2.—

Idiosyncratic fitness effects of individual fixed beneficial mutations in different genetic backgrounds. This figure shows the mean (±SEM; n ≥ 4) fitness effect of beneficial mutations across genetic backgrounds. (A) Mutations that were fixed in both A455T (high initial fitness) and A455G (low initial fitness). (B) Mutations that were fixed in both C1550T (intermediate initial fitness) and A455G (low initial fitness). (C) The fitness effects of mutations that were fixed in the A455T and C1550T backgrounds. The dashed line is a plot of y = x; mutations that fall along this line have additive effects across backgrounds, mutations that fall above the line show diminishing returns, and mutations that fall below this line show escalating returns.

Figure 3.—

Complex epistatic effects of mutation A1562G. This figure shows the mean (±SEM) fitness effect of the A1562G mutation across genetic backgrounds. The A1562G mutation has a different effect on fitness in each genetic background, as determined by a one-way ANOVA (F_2,136 = 10.5, P < 0.0001) followed by a Tukey test (P < 0.05).

Figure 4.—

Diminishing returns from beneficial mutations in Fisher's geometric model. This figure shows how the fitness effects of beneficial mutations that are substituted by natural selection change as a population approaches an adaptive peak in Fisher's geometric model (solid line). The dashed line shows the linear regression of ΔW against W inferred from our experimental data (Figure 1D).