Evolution of Antibiotic Resistance through a Multi-Peaked Adaptive Landscape

Abstract

Antibiotic resistance is an evolving threat to public health. Understanding the evolution of antibiotic resistance at the genetic level is critical to develop novel strategies to diagnose and treat antibiotic resistant infections. We recently developed an automated microbial selection device, the “morbidostat”, which is used to study the evolution of antibiotic resistance in dynamically sustained drug selection. The morbidostat adjusts drug concentrations to maintain nearly constant inhibition of bacterial growth even as evolving bacterial populations acquire higher resistance. using the morbidostat and next generation sequencing, we identified striking features in the evolution of trimethoprim resistance in five E. coli populations evolving in parallel. We found that resistance was acquired in a stepwise manner, through multiple mutations almost exclusively restricted to the gene encoding trimethoprim's target, dihydrofolate reductase (DHFR). Multiple distinct genotypes produced very similar trimethoprim resistant phenotypes, with each highly evolved strain each containing four mutations from a set of six possibilities, that were acquired in a non-random order. Never were more than four mutations acquired, despite sustained selection for further increases in drug resistance, indicating that these genotypes were local adaptive peaks.In order to understand how the adaptive landscape of drug resistance contained multiple peaks, all combinatorial sets of adaptive mutations in the DHFR gene (96 strains) were constructed and characterized. These measurements showed that resistance evolves through an almost maximally rugged adaptive landscape with direct and indirect trajectories leading to distinct peaks. The ruggedness was not explained by pairwise incompatibilities between mutations, instead indicating ‘high-order' genetic interactions between mutations. These high-order interactions were responsible for the existence of multiple adaptive peaks. One mutation was seen to have the power to control the adaptive landscape: its presence or absence largely defined the ruggedness or smoothness of the adaptive landscape.