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.
Completed project
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.
Project partners
- 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
Project resources
- Statistical method for fitness landscape inference from experimental evolution data
- Statistical method for analysis of deep mutational scanning fitness data
Publications
- Nature Ecology & Evolution, 2020. Dysbiosis personalizes fitness effect of antibiotic resistance in the mammalian gut
- PLOS Biology, 2020. Low mutational load and high mutation rate variation in gut commensal bacteria
- Evolution, 2019. Environment changes epistasis to alter trade‐offs along alternative evolutionary paths
- Evolution, 2019. How phenotypic convergence arises in experimental evolution
- Heredity, 2018. The fitness landscape of the codon space across environments
- Evol Ecol, 2020. The effect of environmental heterogeneity on the fitness of antibiotic resistance mutations in Escherichia coli.
- Can J Microbiol. 2018. Plasmid persistence: costs, benefits, and the plasmid paradox.
- Genome Biol Evol. 2018. Fitness Tradeoffs of Antibiotic Resistance in Extraintestinal Pathogenic Escherichia coli.
- Mol Biol Evol. 2021. The Adaptive Potential of the Middle Domain of Yeast Hsp90.
- Elife. 2020. Comprehensive fitness maps of Hsp90 show widespread environmental dependence.
- EPL, 2018 Playing evolution in the laboratory: From the first major evolutionary transition to global warming.
- Peer Community in Evolutionary Biology, 2018. Let’s move beyond costs of resistance!
- Heredity (Edinb). 2018. The fitness landscape of the codon space across environments.
- Evolutionary Applications. 2020. Identifying the drivers of computationally detected correlated evolution among sites under antibiotic selection
- Nat Ecol Evol. 2020. Dysbiosis individualizes the fitness effect of antibiotic resistance in the mammalian gut.
- Antimicrob Agents Chemother. 2020. Radial Expansion Facilitates the Maintenance of Double Antibiotic Resistances.
- Trends Microbiol. 2018. Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance.
