Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

Abstract

The growing threat of drug resistance has inspired a surge in evolution-based strategies for optimizing the efficacy of antibiotics. One promising approach involves harnessing collateral sensitivity-the increased susceptibility to one drug accompanying resistance to a different drug-to mitigate the spread of resistance. Unfortunately, because the mechanisms of collateral sensitivity are diverse and often poorly understood, the systematic design of multi-drug treatments based on these evolutionary trade-offs is extraordinarily difficult. In this work, we provide an extensive phenotypic characterization of collateral drug effects in E. faecalis, a gram-positive species among the leading causes of nosocomial infections. By combining parallel experimental evolution with high-throughput dose-response measurements, we provide quantitative profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict, as even mutants selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity. Overall, variability in collateral profiles is strongly correlated with the final level of resistance to the selecting drug. In addition, collateral effects to certain drugs (e.g. ceftriaxone) are considerably more variable than those to other drugs (e.g. fosfomycin), even for drugs from the same class. Remarkably, however, the sensitivity profiles cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the underlying statistical structure in the collateral profiles, we develop a simple mathematical framework based on a Markov decision process (MDP) to identify optimal antibiotic cycling policies that maximize expected collateral sensitivity. Importantly, these cycles can be tuned to optimize long-term treatment outcomes, leading to drug sequences that may produce long-term collateral sensitivity at the expense of short-term collateral resistance.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

“…In recent years, significant efforts have been devoted to designing evolutionarily sound strategies that balance short-term drug efficacy with the long-term potential to develop resistance. These approaches describe a number of different factors that could modulate resistance evolution, including interactions between bacterial cells [3][4][5][6][7][8], synergy with the immune system [9], spatial heterogeneity [10][11][12][13][14][15], epistasis between resistance mutations [16,17], precise temporal scheduling [18][19][20][21], and statistical correlations between resistance profiles for different drugs [22][23][24][25][26][27][28][29][30][31].…”

Genomic evolution of antibiotic resistance is contingent on genetic background following a long-term experiment withEscherichia coli

“…Alternatively, it might be possible to extend the usefulness of existing therapeutics by channeling evolution toward drug susceptible states with an improved understanding of the factors that shape evolutionary trajectories (8)(9)(10)(11). To that end, the phenotypic and genetic repeatability of resistance evolution has motivated several studies (12)(13)(14)(15)(16)(17)(18)(19). In particular, two landmark studies evaluated the reproducibility of Escherichia coli populations evolving in and adapting to increasing antibiotic concentrations in spatially homogeneous (12) and structured environments (15).…”

“…Yet differences in genetic background can make this approach difficult in practice. For example, replicate E. coli and Enterococcus faecalis populations can take different mutational paths to increased resistance that change their collateral responses to second-line antibiotics (18,19). Despite stochasticity at the level of individual strains, one can still exploit statistical patterns in resistance profiles across many replicates to optimize drug-cycling protocols (18).…”

“…For example, replicate E. coli and Enterococcus faecalis populations can take different mutational paths to increased resistance that change their collateral responses to second-line antibiotics (18,19). Despite stochasticity at the level of individual strains, one can still exploit statistical patterns in resistance profiles across many replicates to optimize drug-cycling protocols (18). Recent work showed that such an approach could drive populations to eventual long-term susceptibility through intermediate states of high-level resistance (18).…”

“…Despite stochasticity at the level of individual strains, one can still exploit statistical patterns in resistance profiles across many replicates to optimize drug-cycling protocols (18). Recent work showed that such an approach could drive populations to eventual long-term susceptibility through intermediate states of high-level resistance (18).…”