Predictable properties of fitness landscapes induced by adaptational tradeoffs
Abstract
Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape.
Data availability
Experimental data have been deposited in Edinburgh DataShare
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Data from: Interplay in the Selection of Fluoroquinolone Resistance and Bacterial FitnessPLOS Pathogens, 10.1371/journal.ppat.1000541.
Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft (CRC 1310)
- Joachim Krug
H2020 European Research Council (ERC Consolidator Grant 682237 EVOSTRUC)
- Rosalind J Allen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Richard A Neher, University of Basel, Switzerland
Publication history
- Received: January 14, 2020
- Accepted: May 5, 2020
- Accepted Manuscript published: May 19, 2020 (version 1)
- Version of Record published: June 16, 2020 (version 2)
Copyright
© 2020, Das et al.
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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