Empirical determinants of adaptive mutations in yeast experimental evolution

High-throughput sequencing technologies have enabled expansion of the scope of genetic screens to identify mutations that underlie quantitative phenotypes, such as fitness improvements that occur during the course of experimental evolution. This new capability has allowed us to describe the relationship between fitness and genotype at a level never possible before, and ask deeper questions, such as how genome structure, available mutation spectrum, and other factors drive evolution. Here we combined functional genomics and experimental evolution to first map on a genome scale the distribution of potential beneficial mutations available as a first step to an evolving population and then compare these to the mutations actually observed in order to define the constraints acting upon evolution. We first constructed a single-step fitness landscape for the yeast genome by using barcoded gene deletion and overexpression collections, competitive growth in continuous culture, and barcode sequencing. By quantifying the relative fitness effects of thousands of single-gene amplifications or deletions simultaneously we revealed the presence of hundreds of accessible evolutionary paths. To determine the actual mutation spectrum used in evolution, we built a catalog of >1000 mutations selected during experimental evolution. By combining both datasets, we were able to ask how and why evolution is constrained. We identified adaptive mutations in laboratory evolved populations, derived mutational signatures in a variety of conditions and ploidy states, and determined that half of the mutations accumulated positively affect cellular fitness. We also uncovered hundreds of potential beneficial mutations never observed in the mutational spectrum derived from the experimental evolution catalog and found that those adaptive mutations become accessible in the absence of the dominant adaptive solution. This comprehensive functional screen explored the set of potential adaptive mutations on one genetic background, and allows us for the first time at this scale to compare the mutational path with the actual, spontaneously derived spectrum of mutations.