RT Journal Article
SR Electronic
T1 Exploiting evolutionary non-commutativity to prevent the emergence of bacterial antibiotic resistance
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 007542
DO 10.1101/007542
A1 Daniel Nichol
A1 Peter Jeavons
A1 Alexander G. Fletcher
A1 Robert A. Bonomo
A1 Philip K. Maini
A1 Jerome L. Paul
A1 Robert A. Gatenby
A1 Alexander R.A. Anderson
A1 Jacob G. Scott
YR 2014
UL http://biorxiv.org/content/early/2014/07/29/007542.abstract
AB Statement of impact In a time when we receive almost daily warnings of a ‘post-antibiotic era’ from the CDC and other groups, and drug development is stalling, we find ourselves in desperate need for novel strategies in the fight against bacterial evolution. Herein, we abstract the process of evolution on a fitness landscape to a Markov chain and, using this abstraction, demonstrate how different orderings of commonly used antibiotic therapies can steer bacterial evolution to genotypes from which highly resistant states are inaccessible. These results suggest a strategy by which, using drugs which may have been been considered less efficacious, we can prevent the emergence of resistance before it arises.Abstract The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. In the present study we develop a Markov Chain model of evolution in asexually reproducing populations which we use to illustrate that different selection pressures do not commute. We demonstrate that the emergence of resistant individuals can be both hindered and promoted by careful orderings of drug application. This suggests a new strategy in the war against antibiotic therapy resistant organisms: rational drug ordering to shepherd evolution through genotype space to states corresponding to greater sensitivity to antibiotic treatment. The model we present is an encoding of the ‘Strong Selection Weak Mutation’ model of evolution on fitness landscapes within a Markov Chain, which associates the global properties of the fitness landscape with the algebraic properties of the Markov Chain transition matrix. Through this association we derive results on the non-commutativity and irreversibility of natural selection.