To evolve to meet environmental challenges, organisms must acquire new genetic mutations. Understanding the spectrum of effects caused by individual mutations is therefore central to predicting the paths evolution might take. Many organisms face the additional challenge of fluctuating environmental conditions, where mutations that are beneficial in one condition may not be advantageous in others. The fitness effects of particular mutations, and their dependence on the environmental conditions, can be systematically measured in laboratory microbial populations. Previous large-scale studies using this approach have primarily characterized disadvantageous mutations. Yet despite their rarity, beneficial mutations are the primary drivers of evolution. This project will use laboratory evolution and recently-developed sequencing technology to measure the effects of thousands of novel beneficial mutations in several environments. The work will produce a large, publicly-available dataset detailing how the effects of beneficial mutations change with the environmental condition, which will help illuminate the patterns underlying mutational effects, facilitating the modeling of evolution in fluctuating environmental conditions.
Mutations are the raw material that drives evolution. Understanding the competitive fitness benefit conferred by new mutations is important to quantitative modeling of evolution. Past work has made significant progress towards characterizing the costs and benefits associated with mutations. To understand evolution in fluctuating conditions, however, it is also important to know how the fitness effects of mutations depend on the growth condition. This project will use massively-parallel lineage tracking to characterize the effects of thousands of individual, spontaneously arising beneficial mutations across multiple environmental conditions. This broad statistical dataset will enable mathematical modeling of evolution in fluctuating conditions. The project will additionally address the genetic basis of evolutionary tradeoffs, using whole genome sequencing to identify hundreds of mutations with benefits in some conditions but costs in others. Ultimately, this project will contribute to the theoretical understanding of evolution in fluctuating conditions and the biological basis of evolutionary tradeoffs.
