Fitness Landscapes Reveal Simple Strategies for Steering Evolution to Minimize Antibiotic Resistance

Abstract

The evolution of antibiotic resistance presents a practical and theoretical challenge: the design of strategies that limit the risk of evolved resistance while effectively treating current patients. Sequentially cycling antibiotics has been proposed as a way to slow the evolution of resistance by reducing the extent of adaptation to a given drug, and clinical trials have demonstrated its effectiveness in some settings. Empirical fitness landscapes in theory allow the sequence of drugs to be refined to maximize tradeoffs between drugs and thereby slow adaptation even further. Using the measured growth rates of 16 genotypes of Escherichia coli in the presence of 15 β-lactam antibiotics, we test an adaptive strategy, based on a Markov chain transition matrix, to select drug sequences that continuously minimize resistance. Cycling is never selected over the long term. Instead, monotherapy with the antibiotic that permits the least growth in its landscape's absorbing state is rapidly selected from different starting conditions. Analysis of a synthetic fitness landscape shows that cycling drugs that induce sensitivity to one other could, in theory, outperform monotherapy. These results underscore the importance of considering the specific topologies of fitness landscape in determining whether to cycle drugs and suggest a general computational approach to identify high-performing, practical strategies to manage resistance.

Inferring genetic interactions from comparative fitness data

“…Second, several studies compare fitness only between mutational neighbors, that is, genotypes which differ at exactly one locus. The resulting partial orders are referred to as fitness graphs and have recently been used extensively ( Ogbunugafor et al, 2016 ; Wu et al, 2016 ; Smith and Cobey, 2016 ; Mira et al, 2015 ).…”

“…In addition to the theoretical work mentioned above, rank order arguments have been used for developing antimicrobial treatment strategies ( Smith and Cobey, 2016 ; Nichol et al, 2015 ; Mira et al, 2015 ; Goulart et al, 2013 ). However, the full potential of rank order consideration for the comprehensive analysis of epistatic gene interactions in general -locus genetic systems has not been exploited.…”

Inferring Genetic Interactions From Comparative Fitness Data

“…Second, several studies compare fitness only between mutational neighbors, i.e., genotypes which differ at exactly one locus. The resulting partial orders are referred to as fitness graphs and have recently been used extensively (Ogbunugafor, Wylie, et al 2016;Wu et al 2016;Smith and Cobey 2016;Mira et al 2015).…”

“…In addition to the theoretical work mentioned above, rank order arguments have been used for developing antimicrobial treatment strategies (Smith and Cobey 2016;Nichol et al 2015;Mira et al 2015;Goulart et al 2013). However, the full potential of rank order consideration for the comprehensive analysis of epistatic gene interactions in general n-locus genetic systems has not been exploited.…”

Steering Phages to Combat Bacterial Pathogens

“…In either case, target bacteria not eliminated by the phage persist but either cause far less damage to the host or are more sensitive to antibiotics. The notion of 'steering' has previously been discussed in the context of preventing or guiding the emergence of drug resistance in microbial pathogens [24,25] and cancers [26]. Phage steering differs by a focus on actively selecting for specific phage resistances that are correlated with improved infection outcomes.…”