The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.
Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 2015;13(11). doi:10.1371/journal.pbio.1002299
Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak, E., & Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299
Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.
G. Chevereau et al., “Quantifying the determinants of evolutionary dynamics leading to drug resistance,” PLoS Biology, vol. 13, no. 11. Public Library of Science, 2015.
Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 13(11), e1002299.
Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology, vol. 13, no. 11, e1002299, Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.
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