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High-Resolution Quantitative Analysis of Genetic Interactions


Genetic interactions describe how the presence of one gene affects the function of a second gene. Negative genetic interactions arise when two mutations together cause a stronger growth defect than expected based on the individual single mutations, while positive genetic interactions correspond to cases where the double mutant is either no sicker (epistatic) or healthier (suppressive) than the sickest single mutant.

Our lab has developed a high-throughput technology in budding yeast for quantitative analysis of the entire spectrum of genetic interactions, termed epistatic miniarray profile (E-MAP). The genetic profiles generated by this method provide highly specific readouts that can be used to identify genes that are functionally related. In this dissertation I describe my work related to the employment and extension of the E-MAP technology. With collaborators, I have interrogated several biological processes using E-MAPs, including the phosphorylation network, RNA processing, lipid metabolism, and the plasma membrane. We further extended the technology to facilitate analysis of higher-order genetic interactions by examination of triple mutants, and we developed an adaptation of E-MAP to function in E. coli.

My main focus was on the development of an important advance of the E-MAP technology, which allows us to address higher levels of complexity by examining genetic interactions of point mutant alleles of multi-functional genes. The technique, termed point mutant E-MAP (or pE-MAP), greatly increases the achievable resolution as it allows assignment of genetic relationships to individual residues and domains. We applied this system to genetically dissect RNA polymerase II (RNAPII) in budding yeast, and generated a pE-MAP comprising ~60,000 quantitative genetic interactions. Using these data, we assigned functions to RNAPII sub-domains and uncovered connections to protein complexes. The pE-MAP further allowed us to characterize connections related to RNAPII activity. These include an inverse relationship between in vitro transcription rate and in vivo splicing efficiency, classification of fast and slow mutants that shift transcription start upstream and downstream, respectively, and identification of Sub1 as a positive transcription factor that regulates start site selection and influences splicing. The pE-MAP approach provides a powerful strategy to understand other multi-functional machines at amino acid resolution.

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