Cost of Antibiotic Resistance and the Geometry of Adaptation

Abstract

The distribution of effects of beneficial mutations is key to our understanding of biological adaptation. Yet, empirical estimates of this distribution are scarce, and its functional form is largely unknown. Theoretical models of adaptation predict that the functional form of this distribution should depend on the distance to the optimum. Here, we estimate the rate and distribution of adaptive mutations that compensate for the effect of a single deleterious mutation, which causes antibiotic resistance. Using a system with multiple molecular markers, we estimate the distribution of fitness effects of mutations at two distances from the adaptive peak in 60 populations of Escherichia coli. We find that beneficial mutations, which can contribute to compensatory evolution, occur at very high rates, of the order of 10−5 per genome per generation and can be detected within a few tens of generations. They cause an average fitness increase of 2.5% and 3.6%, depending on the cost of resistance, which is expected under Fisher's geometrical model of adaptation. Moreover, we provide the first description of the distribution of beneficial mutations, segregating during the process of compensatory evolution, to antibiotic resistances bearing different costs. Hence, these results have important implications to understanding the spread and maintenance of antibiotic resistance in bacteria.

Fisher’s Geometrical Model Emerges as a Property of Complex Integrated Phenotypic Networks

“…In such experiments, the fitness of replicate populations reaches a plateau [which may sometimes vary (Schoustra et al 2009)] or at least shows a striking deceleration. Consistent with this finding, negative epistasis among beneficial mutations has been repeatedly reported: beneficial mutations tend to be less advantageous when arising from fitter parents (MacLean et al 2010;Chou et al 2011 ;Khan et al 2011;Sousa et al 2011). These observations are obviously suggestive of the existence of an optimum for fitness, at some local scale at least.…”

“…In principle, the FGM can be used to predict how the DFE is affected by any environmental or genotypic context (epistasis), with any type of nonsilent mutation. Empirical support of the model's predictions has recently accumulated (Martin and Lenormand 2006a,b;Martin et al 2007;MacLean et al 2010;Sousa et al 2011;Weinreich and Knies 2013), although it was sometimes relatively indirect. The most quantitative tests (Martin et al 2007;MacLean et al 2010;Sousa et al 2011), and hence those with most statistical power, used the model to predict how the DFE is affected by epistasis.…”

“…Empirical support of the model's predictions has recently accumulated (Martin and Lenormand 2006a,b;Martin et al 2007;MacLean et al 2010;Sousa et al 2011;Weinreich and Knies 2013), although it was sometimes relatively indirect. The most quantitative tests (Martin et al 2007;MacLean et al 2010;Sousa et al 2011), and hence those with most statistical power, used the model to predict how the DFE is affected by epistasis. More generally, the FGM indeed predicts both the pervasiveness and the diminishing-return form of epistasis, as documented repeatedly in experimental evolution (e.g., MacLean et al 2010;Chou et al 2011;Khan et al 2011;Sousa et al 2011).…”

“…The most quantitative tests (Martin et al 2007;MacLean et al 2010;Sousa et al 2011), and hence those with most statistical power, used the model to predict how the DFE is affected by epistasis. More generally, the FGM indeed predicts both the pervasiveness and the diminishing-return form of epistasis, as documented repeatedly in experimental evolution (e.g., MacLean et al 2010;Chou et al 2011;Khan et al 2011;Sousa et al 2011). A model of pleiotropic mutations affecting the distance to an optimum is also qualitatively consistent with the prevalence of antagonistic pleiotropic effects affecting unused functions during long-term adaptation (as observed in Cooper and Lenski 2000).…”

The effect of induced mutations on quantitative traits in Arabidopsis thaliana: Natural versus artificial conditions

“…Previous work with an earlier generation of these mutant lines (and the mutant lines of several other A. thaliana ecotypes) suggested that beneficial mutations occurred, but that the magnitude of deleterious mutations was greater (Stearns & Fenster, 2016). This study, when considered in the context of the previous A. thaliana mutation accumulation studies, suggests that high magnitude deleterious mutations are more common than high magnitude beneficial mutations and that adaptation likely occurs due to small or intermediate effect beneficial mutations, as suggested by Fisher (1930) and Kimura (1983) and supported experimentally (Barrett, MacLean, & Bell, 2006; Heilbron et al., 2014; Sousa, Magalhaes, & Gordo, 2012). The large deleterious mutations that may be affecting these lines would likely be removed from the population via selection and would not contribute significantly to standing genetic variation.…”