Examining the conditionality and time dynamics of evolutionary constraint in yeast

Project Summary The idea of evolutionary constraint is central to understanding of how populations have and will adapt(ed) in response to heterogeneous selective pressures. Constraints therefore determine how organisms with relevance to human health, like pathogens and cancers, adapt to different drugs, hosts, or environments. Constraints can

be formed by strict trade-offs, when mutations adaptive in one environment have antagonistically pleiotropic costs in alternate environments, or by more dynamic processes, when the ability to select for hypothetically possible costless generalism is limited by mutational accessibility, speed of selection, or ecological opportunity

for selection. These overlapping processes can make evolutionary constraint difficult to measure and these different processes difficult to disentangle. Despite the centrality of constraint to evolutionary theory, these difficulties mean that the processes forming constraints at different levels remain poorly understood—

leaving open questions about how they affect evolution. In this proposal, I leverage high throughput microbial experimental evolution lineage tracking methods that provide unprecedented power to measure the distributions of fitness effects for mutations in multiple environments. These methods allow us to generate quantitative insights into the dynamics of

evolutionary constraint. Aim 1 will ask whether constraints in laboratory experimental evolution resemble natural variation by measuring the joint distribution of fitness effects (JDFE) for natural isolates of S. cerevisiae in two nutrient environments mimicking natural conditions (synthetic wine must and synthetic beer wort) and then

experimentally evolving a barcoded laboratory strain in these and an alternating media environment to determine whether the laboratory evolved JDFE resembles the naturally evolved JDFE. Aim 2 will explore how evolutionary constraints change over time by leveraging a rebarcoding system to experimentally evolve S.

cerevisiae in the same conditions as Aim 1 for 5 evolutionary `steps' (~400 generations) and measure the JDFE at each `step'. Most laboratory experimental evolution examines shorter time scales, but we know from long-term experiments and natural observations that evolutionary dynamics change over time as populations near fitness

peaks and/or change their environment. Thus, Aim 2 will allow us to quantitatively measure how constraints change over time. Through this work, I will build a powerful system for conducting high-throughput, long term evolution in multiple environments. The proposed research will significantly expand my training in the areas of quantitative genetics,

population genetics, and molecular biology while encouraging increasing independence. It will therefore compliment my PhD training in evolutionary ecology to allow me to build an integrative research program that links genetic, evolutionary, and ecological processes to understand how constraints determine eco-

evolutionary dynamics. Additionally, I will participate in professional development and outreach activities.