View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

Antibiotic resistant infections are predicted to kill millions of people per year within the next 30 years. As the rate of new antibiotic development dwindles, the outlook of many single drug treatments is grim. This major challenge has sparked interest in developing multi-drug treatments which utilize current antibiotics together to effectively eliminate patients’ infections before dual (or higher order) resistance emerges. Successful multi-drug treatments rely on the phenomenon known as collateral sensitivity – when evolution of resistance to one drug leads to the simultaneous decrease in resistance to another.
However, if mutations with diverse collateral effects are available, the treated population may evolve either collateral sensitivity or its opposite - collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE), which describes the probability of a new mutation having any given set of selection coefficients across the antibiotic environments of interest. We then develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank candidate drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.

The population genetics of collateral resistance and sensitivity