Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence

Abstract

Effective treatment of bacterial infections proves increasingly challenging due to the emergence of bacterial variants that endure antibiotic exposure. Antibiotic resistance and persistence have been identified as two major bacterial survival mechanisms, and several studies have shown a rapid and strong selection of resistance or persistence mutants under repeated drug treatment. Yet, little is known about the impact of the environmental conditions on resistance and persistence evolution and the potential interplay between both phenotypes. Based on the distinct growth and survival characteristics of resistance and persistence mutants, we hypothesized that the antibiotic dose and availability of nutrients during treatment might play a key role in the evolutionary adaptation to antibiotic stress. To test this hypothesis, we combined high-throughput experimental evolution with a mathematical model of bacterial evolution under intermittent antibiotic exposure. We show that high nutrient levels during antibiotic treatment promote selection of high-level resistance, but that resistance mainly emerges independently of persistence when the antibiotic concentration is sufficiently low. At higher doses, resistance evolution is facilitated by the preceding or concurrent selection of persistence mutants, which ensures survival of populations in harsh conditions. Collectively, our experimental data and mathematical model elucidate the evolutionary routes toward increased bacterial survival under different antibiotic treatment schedules, which is key to designing effective antibiotic therapies.

Keywords: antibiotics; experimental evolution; mathematical modeling; persistence; resistance.

Figure 1

Resistance and persistence levels of experimentally evolved populations. (A) Setup of the evolution experiments. Populations founded from an ancestral E. coli strain (MIC = 2 μg/ml) were exposed to daily, 5-h treatments with varying amikacin (AMK) doses and nutrient levels, intermitted with periods of antibiotic-free growth. Nutrient levels were varied by diluting stationary phase cultures in fresh, nutrient-rich growth medium, using different dilution factors. In each condition, 24 parallel populations were evolved for 11–16 treatment cycles. (B) Resistance and persistence levels (expressed as log₂- and log₁₀-transformed fold changes relative to the ancestral level, respectively) of evolved populations, measured under the same conditions for all populations (see Materials and Methods). Horizontal, filled bars depict the proportion of populations that did not go extinct during the evolution experiment. Only populations for which resistance and persistence level measurements were available are shown. (C) Resistance and persistence levels averaged over all surviving populations per condition.

Figure 2

Modeled effects of nutrients and antibiotic doses on bacterial survival. (A) Schematic representation of the mathematical model of bacterial evolution. Cells can grow or die, with the net growth rate g depending on the antibiotic dose and nutrient level, and switch to and from the nongrowing persister state with rates a and b, respectively. Initially, all cells belong to the ancestral class, but they can mutate to a class with a higher persistence level (i.e. switching rate a) or resistance level (i.e. MIC value), or both, with mutation rates μ_P, μ_R, and μ_RP, respectively. (B) Theoretical pharmacodynamic curves for ancestral cells (i.e. E. coli wild-type strain with MIC = 2 μg/ml) and different resistant cells with gradually increasing MIC (4–8-16–32 μg/ml). Growth rates represent the change in log₁₀(cell density) per hour, with negative rates corresponding to cell death. The MIC of a strain corresponds to the antibiotic concentration for which the growth rate equals 0. Persister cells are assumed to neither grow nor die. (C) Simulated antibiotic survival of mutant populations after one treatment cycle. Simulations were initiated with isogenic populations of mutant strains with increasing resistance (y-axis in top graphs) or persistence (y-axis in bottom graphs) levels. These populations were exposed in silico to one cycle of growth (19 h) followed by antibiotic killing (5 h) with varying antibiotic concentrations at either low (left) or high (right) nutrient levels. De novo mutations were not considered in these simulations. MIC values and switching rates are expressed as log₂- resp. log₁₀-transformed fold changes relative to the ancestral level.

Figure 3

Resistance and persistence evolution in simulated populations. (A) Number of simulated populations evolving only resistance, only persistence, or both resistance and persistence, in various treatment conditions. 1000 populations were simulated per condition. The total number of populations that did not go extinct is indicated. (B) Resistance and persistence levels of evolved populations (expressed as log₂- resp. log₁₀-transformed fold changes relative to the ancestral level), averaged over all surviving populations per condition. Dark-gray squares correspond to conditions where all populations went extinct.

Figure 4

Resistance and persistence over time in experimentally evolved and simulated populations. (A) Resistance and persistence levels over time of experimentally evolved populations. Phenotypes were assessed at different time points by measuring persistence and resistance levels of three clones for two populations per condition. No measurements were performed for populations evolved with 12.5 μg/ml amikacin and a nutrient level of 0.25, as no evolutionary adaptation was observed for this condition (Fig. S3). Log(fold change) corresponds to log₁₀(fold change) for persistence levels and log₂(fold change) for resistance levels. Solid lines connect the means on log scale through time. (B) Fraction of simulated populations with increased resistance or persistence. A population was classified as having evolved resistance or persistence when at least 30% of the cells resided in mutant classes characterized by increased resistance or persistence, respectively. Only populations that survived until the end of the simulation are shown. The resistance and persistence curves overlap for the following conditions: 50 μg/ml—0.9; 200 μg/ml—0.8; 200 μg/ml—0.9.

Figure 5

Evolutionary trajectories for simulated populations. Muller plots and corresponding evolutionary paths for a set of simulated populations, representative for different scenarios: (A) only resistance evolved (antibiotic concentration = 12.5 μg/ml; nutrient level = 0.8), (B) only persistence evolved (antibiotic concentration = 400 μg/ml; nutrient level = 0.6), (C) persistence and resistance evolved simultaneously (antibiotic concentration = 100 μg/ml; nutrient level = 0.4), and (D) persistence evolved first, followed by an increase in resistance and persistence (antibiotic concentration = 200 μg/ml; nutrient level = 0.6).