Will a cancer develop drug resistance? Will a virus jump hosts and cause a new pandemic? The root of these and other public health concerns is evolution. The principles that govern evolution are well understood, yet we cannot predict it and answer the questions posed above. We might be interested in two kinds of evolutionary predictions. How would a phenotype change over time? And which genetic changes would cause this evolution? In many populations, adaptation is driven by new mutations. A major challenge is that the effects of new mutations on phenotypes and on fitness depend on the genetic background in which they arise. Such dependencies, called ?epistasis?, complicate evolutionary prediction at both the phenotypic and genetic levels. The simplest phenotype to predict is fitness. To do so, we need to know how new mutations affect fitness. This information is contained in the quantity called the distribution of fitness effects of new mutations (DFE). Epistasis can cause the DFE to vary from on genotype to another, and we do not understand how it varies. Objective 1 of this research is predict fitness evolution. To achieve it, we will measure how DFEs vary across genotypes that arise in evolution and find regularities in this variation. Predicting genetic evolution is more difficult because there are usually too many different adaptive mutations that can arise in the population. Epistasis complicates the situation further. If there is no epistasis or if epistasis is such that all mutations beneficial in one genotype are also beneficial in all other genotypes, there would be only one (i.e., highly predictable) eventual genetic outcome of adaptive evolution, but the number of mutational paths leading to it would be very large. Predicting which path any given population takes would likely be impossible. On the other hand, if epistasis makes certain mutations that are beneficial in some genotypes deleterious in others, the number of mutational paths accessible to natural selection would decrease, making them more predictable. However, these paths could lead to different eventual genotypes making the outcomes of evolution less predicta- ble. The prevalence of different types of epistasis is unknown. Objective 2 of this research is to understand how predictable mutational paths and evolutionary outcomes are. To achieve it, we will measure how the effects of mutations vary across genotypes and quantify different types of epistasis. We will approach our objectives in experimental yeast populations using several novel genetic engineering and sequencing techniques. We will estimate the DFEs in multiple yeast strains using barcode lineage tracking, as well as chemical and insertion mutagenesis coupled with barcode sequencing-based fitness assays. We will meas- ure the fitness effects of hundreds of individual mutations using a novel CRISPEY method, full-genome sequenc- ing of adaptive mutants and insertion mutagenesis. Finally, we will synthesize these data in a model of a ?fitness landscape? and attempt to predict evolution of our populations. 1

Public Health Relevance

PI: Kryazhimskiy, Sergey Project Narrative Evolution of drug resistance in many bacterial pathogens and cancers and the evolution of immune escape and host range expansion in viruses are often driven by new mutations. Our ability to predict such harmful evolution hinges on the understanding of how new mutations affect fitness in different genetic backgrounds. This project will provide an empirical characterizations of these effects, which will form the basis for developing predictive models of evolutionary outcomes in microbial and viral populations. 1

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genetic Variation and Evolution Study Section (GVE)
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Janes, Daniel E
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University of California, San Diego
Schools of Arts and Sciences
La Jolla
United States
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