Antimicrobial resistance poses a serious challenge to health care worldwide. One common approach to tackling resistance is to stop using a particular antimicrobial for a period of months or years, in the hope that resistance will decrease.
However, such attempts to control resistance by stopping antimicrobial use have met with mixed success. Failures of a critical assumption underlying such strategies – that resistant strains suffer a disadvantage in the absence of drug (the “cost of resistance”) – may be responsible for difficulties in controlling resistance by cessation of drug use.
The PREPARE consortium was convened to develop an experimental and theoretical framework for understanding, and ultimately predicting, the conditions under which resistance will persist. Specifically, we set out to address the roles of host genetic background and environment in determining the costs of resistance. That is, we asked whether there are particular environments, or particular bacterial strains, in which we would expect to see resistance persist.
We found that there are indeed strains, environments, and combinations thereof, where resistance confers no cost. This suggests that resistance could persist in some real-world settings, even when antimicrobial usage has been stopped or paused. Unfortunately, we found that it is very difficult to predict which environments or strains might act as reservoirs for resistance. Novel mathematical models do show promise in increasing our ability to predict persistence. The ability to make such predictions will assist policy-makers in formulating strategies for controlling the spread of resistance.
- Alex Wong, Carleton University, Canada (Coordinator)
- Claudia Bank, Instituto Gulbenkian de Ciência, Portugal
- Thomas Bataillon, University of Aarhus, Denmark
- Isabel Gordo, Instituto Gulbenkian de Ciência, Portugal
- Rees Kassen, University of Ottawa, Canada
- Statistical method for fitness landscape inference from experimental evolution data
- Statistical method for analysis of deep mutational scanning fitness data
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