Empirical determinants of adaptive mutations in yeast experimental evolution

Abstract

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.\n\nAUTHOR SUMMARYWhole genome sequencing of thousands of cancer genomes has been conducted to characterize variants including point mutations and structural changes, providing a large catalogue of critical polymorphisms associated with tumorigenesis. Despite the high prevalence of mutations in cancer and technological advances in their genotyping, cancer genetics still presents many open questions regarding the prediction of selection and the functional impact of mutations on cellular fitness. Long term experimental evolution using model organisms has allowed the selection for strains bearing recurrent and rare mutations, mimicking the genetic aberrations acquired by tumor cells. Here, we evaluate the functional impact of thousands of single gene losses and amplifications on the cellular fitness of yeast. Our results show that hundreds of beneficial mutations are possible during adaptation but not all of them have been selected in evolution experiments so far performed. Together, our results provide evidence that 50% of the mutations found in experimentally evolved populations are advantageous, and that alternative mutations with improved fitness could be selected in the absence of the main adaptive mutations with higher fitness.\n\nBLURBA combined view of potential adaptive mutations, generated by a systematic screening approach, coupled with the mutational spectrum derived from experimentally evolved yeast reveals the usage of accessible evolutionary solutions.