The population genetics of collateral resistance and sensitivity
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.
Data availability
All code is available on GitHub. All data are available as Source Data files, included with the manuscript.
Article and author information
Author details
Funding
Burroughs Wellcome Fund (1010719.01)
- Sergey Kryazhimskiy
Alfred P. Sloan Foundation (FG-2017-9227)
- Sergey Kryazhimskiy
Hellman Foundation
- Sergey Kryazhimskiy
National Institutes of Health (1R01GM137112)
- Sergey Kryazhimskiy
National Institutes of Health (1T32GM133351-01)
- Sarah M Ardell
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Ardell & Kryazhimskiy
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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