Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

Abstract

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. We have previously shown that in obligate mutualisms the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners, setting the community antibiotic tolerance at that of the weakest link species. In this study, we extended that hypothesis to test whether obligate cross-feeding would limit the extent and mechanisms of antibiotic resistance evolution. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.\n\nSignificance statementLittle is known about how ecological interactions between bacteria influence the evolution of antibiotic resistance. We tested the impact of metabolic interactions on resistance evolution in an engineered two-species bacterial community. Through experimental and modeling work, we found that obligate metabolic interdependency slows the rate of resistance acquisition, and can change the type and magnitude of resistance mutations that evolve. This work suggests that resistance evolution may be slowed by targeting both a pathogen and its metabolic partners with antibiotics. Additionally, we showed that community context can generate novel trajectories through which antibiotic resistance evolves.