The distribution of epistatic effects within and between genes

Johns Hopkins University, Baltimore MD

Investigators

Abstract

Mutations are an important part of evolution and genetic diseases. A mutation's effect on an organism can be positive, negative or neutral. However, mutations that have one type of effect in isolation can have the opposite effect when combined with another mutation elsewhere in the genome. This phenomenon, termed epistasis, clearly affects evolutionary mechanisms and genetic diseases, yet relatively little is known about its extent or degree of impact. This research will provide the most extensive systematic study of epistasis within and between two genes to date. The data collected and analyzed will inform future laboratory and modeling experiments to address important questions about evolution and the mechanisms underlying genetic disease. During the course of this research, two pre-doctoral students and one undergraduate will be trained in research. The research supported by the grant will be incorporated into a class on protein engineering taught by the principle investigator. This project will examine (1) the fitness distribution of nearly all single amino acid mutations for the bacterial genes TEM1 beta-lactamase (TEM-1) and the beta-lactamase inhibitor protein (BLIP), (2) the fitness distribution of an extensive set of 5000 double mutant alleles within each of those genes, (3) the fitness distribution of an extensive set of 5000 pairs of alleles of TEM-1 and BLIP in which each gene has one amino acid mutation. An index of fitness will be estimated for each line using novel DNA mutagenesis and band-pass genetic selection technologies developed by the principal investigator with the extensive use of next generation sequencing. The distribution of epistatic effects will be determined by a quantitative comparison of the fitness data from the single amino acid substitutions with the fitness data from those same substitutions in the double mutant lines. This distribution of fitness effects will be used assess the frequency and strength of epistatic interactions across the genome.