Mutation Supply and Relative Fitness Shape the Genotypes of Ciprofloxacin-Resistant Escherichia coli

Abstract

Ciprofloxacin is an important antibacterial drug targeting Type II topoisomerases, highly active against Gram-negatives including Escherichia coli. The evolution of resistance to ciprofloxacin in E. coli always requires multiple genetic changes, usually including mutations affecting two different drug target genes, gyrA and parC. Resistant mutants selected in vitro or in vivo can have many different mutations in target genes and efflux regulator genes that contribute to resistance. Among resistant clinical isolates the genotype, gyrA S83L D87N, parC S80I is significantly overrepresented suggesting that it has a selective advantage. However, the evolutionary or functional significance of this high frequency resistance genotype is not fully understood. By combining experimental data and mathematical modeling, we addressed the reasons for the predominance of this specific genotype. The experimental data were used to model trajectories of mutational resistance evolution under different conditions of drug exposure and population bottlenecks. We identified the order in which specific mutations are selected in the clinical genotype, showed that the high frequency genotype could be selected over a range of drug selective pressures, and was strongly influenced by the relative fitness of alternative mutations and factors affecting mutation supply. Our data map for the first time the fitness landscape that constrains the evolutionary trajectories taken during the development of clinical resistance to ciprofloxacin and explain the predominance of the most frequently selected genotype. This study provides strong support for the use of in vitro competition assays as a tool to trace evolutionary trajectories, not only in the antibiotic resistance field.

Keywords: ciprofloxacin; clinical isolates; modeling evolution; multistep evolution; population bottleneck.

Fig. 1

Competitive fitness of the gyrA S83L mutant as function of ciprofloxacin concentration in competition against (A) gyrA D87G; (B) gyrA D87Y; and (C) gyrA D87N. Values are mean ± standard deviation.

Fig. 2

Competitive fitness of the gyrA S83L parC S80I double mutant as function of ciprofloxacin concentration in competition against (A) gyrA S83L D87N; (B) gyrA S83L ΔacrR; (C) gyrA S83L ΔmarR; (D) gyrA S83L parC S80R; and (E) gyrA S83L parC E84K. Values are mean ± standard deviation.

Fig. 3

Competitive fitness of the gyrA S83L D87N parC S80I triple mutant as function of ciprofloxacin concentration in competition against (A) gyrA S83L D87Y parC S80I and (B) gyrA S83L D87G parC S80I. Values are mean ± standard deviation.

Fig. 4

Outcome of population modeling at ciprofloxacin concentration of 4 mg/L. The resistance development model (Materials and Methods) was run for 70 distinct combinations of bottleneck sizes and stepwise increases in ciprofloxacin concentrations with a total population size of 4 × 10⁹ (A,B) or 4 × 10¹⁰ (C,D). Results are averages of 1000 independent runs. (A, C) Proportion of runs that survive up to a concentration of 4 mg/L. (B, D) Proportion of runs that develop the clinically relevant genotype gyrA S83L D87N, parC S80I.

Fig. 5

Trajectories of resistance development. The resistance development model (Materials and Methods) was run fifty times with a bottleneck of 4 × 10⁸ cells per passage and a 1.1-fold increase per cycle in ciprofloxacin to a final concentration of 4 mg/L. Filled squares show genotypes of strains that appear during the evolution. All strains are places at their respective MIC values and labeled in the order of mutation appearance (A: gyrA S83L, B: gyrA D87N, C: parC S80I, D: ΔmarR, and E: ΔacrR). Lines that connect two mutants indicate a mutational event and lines with an open end indicate lineages that went extinct. Numbers on top of the lines show percentage of runs that followed the particular trajectory. The main trajectory (wild type to clinical relevant triple mutant ACB) is highlighted with in grey.