DISSERTATION RESEARCH: Quantitative test of evolutionary bet-hedging theory in a mirobial model system

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This project will provide understanding of the fundamental principles that influence adaptation in changing environments. Evolutionary theory predicts that natural selection in changing environments will favor traits so long as they confer a net benefit over time. This context of adaptation in changing environments is central to understanding many traits, such as the ability to sense and respond to the environment, and drug resistance of pathogenic microorganisms. This research will therefore help to elucidate the evolutionary processes underlying medically important traits. The researchers will also develop and distribute a lesson plan aimed at teaching the basic tenets of adaptation in changing environments to high school students, thus helping to raise public awareness about a biologically relevant problem.Biological traits such as phenotypic plasticity and bet-hedging are presumed to evolve by increasing the long-term geometric mean fitness of a lineage. This framework serves as a useful description of the average selective advantage of traits in a variable environment. However, there are known theoretical limitations with this approach since it fails to explicitly capture evolutionary dynamics of evolving populations and thus does not provide a mechanistic description of the evolutionary origin and maintenance of traits such as bet-hedging. Using laboratory yeast populations adapted to rapid periods of environmental stress, the researchers will experimentally measure evolutionary dynamics of competing bet-hedging strategies in a variable environment. The researchers will use a combination of genome sequencing and reverse genetics to construct a panel of fluorescently labeled yeast strains that express differing bet-hedging strategies. By carefully tracking the abundance of competing strains during the course of competition experiments in variable environments, the researchers will directly compare evolutionary changes in the laboratory to the predictions of computational models of adaptation in varying environments. In doing so, the research will generate data necessary to directly test of the geometric mean fitness concept and will experimentally determine the extent of its limitations for describing adaptation to varying environments.