Abstract
Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or 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) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This revision addresses the criticisms raised by reviewers. Specifically: * We now extend the theory to the concurrent mutations regime * We use existing data from E. coli knock-out collection and from deep mutational scanning to show that resistance mutations have diverse collateral effects. * Refocused paper on collateral sensitivity/resistance * Added a section on ranking drug pairs and a section on estimating JDFEs.
