Quantifying evolutionary solutions to fitness tradeoffs in fluctuating environments

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Project Summary One lesson of experimental evolution is that beneficial, large-effect mutations appear rapidly in simple laboratory environments. Given that such mutations are possible and thus have not already fixed in wild populations, it is likely that they help the organism to specialize to a particular environment and may display pleiotropic tradeoffs that make them deleterious in other environments. Evidence for pleiotropy is common, and unlike constant laboratory environments, natural settings likely require organisms to solve evolutionary tradeoffs in order to persist through intermittently harsh conditions. Despite the central importance to biology and medicine of the effect of environmental fluctuations on evolution, the field has been limited by the ability to track large numbers of evolutionary paths in multiple environmental conditions. Here, we propose to address this problem through use of a high-throughput system of DNA-barcoded yeast. By evolving Saccharomyces cerevisiae populations in environments that impose known biological tradeoffs on their fitness optimization, and by tracking the emergence and spread of hundreds of thousands of adaptive mutations in both fluctuating and constant environments, we will learn how adaptation to fluctuating environments differs from strategies that appear in the component constant environments. In Aim 1, we will test the hypothesis that fluctuating environments lead to generalists that overcome biological tradeoffs by optimizing mean fitness across conditions, while constant environments produce specialists that are more fit in particular environments but have lower mean fitness. After identifying generalists and specialists, we will re-barcode a collection of them in Aim 2 for subsequent evolution in environments that fluctuate both temporally and spatially, allowing the mutants to either diversify further or invade each other’s niches. These experiments will test the hypothesis that character displacement and specialization should evolve as a result of competition. This research and training plan will prepare me to become a leader in the intersecting fields of microbial ecology and evolution. I will ultimately apply the insights I gain from these experiments with a highly precise model system to complex multi-species communities. My PhD training was in community ecology, and here I will learn to work with a cutting-edge barcoding technology, as well as study molecular biology and population genetics. Additionally, I will participate in professional development activities and do public outreach.