The genome-wide HaploInsufficieny Profiling (HIPHOP) technique has been validated as a method to quantify the relative abundance of uniquely tagged yeast deletion strains using a microarray readout. The massive throughput of next generation sequencing presents a new technology for assessing HIPHOP profiles. I developed a new method called Barcode analysis by Sequencing (Bar-seq) that applies deep sequencing to genome-scale fitness. I show that Bar-seq outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex genome-scale fitness assay, Bar-seq quantitatively identifies drug-targets, exceeding the performance of the microarray assay. I also established that Bar-seq is well suited to a multiplex format and provides a dramatic increase in throughput. I used the genome-wide HIPHOP assay and other functional genomics tools to explore the mechanisms underlying drug-drug synergies. Drug combination therapy, and synergistic combinations in particular, have several advantages over monotherapies. Synergistic drug combinations allow the dose of each agent to be reduced, often with the benefit of diminishing side effects while maintaining efficacy and decreasing the chances of drug resistance. I used my yeast model to identify synergistic drug combinations and found that inhibitors of ergosterol biosynthesis are highly synergistic with several agents, including those targeting other points within the same pathway. I also devised a method that enriches for synergistic interactions during screening of compound combinations. This new synergy prediction method can aid in the rapid identification of anti-proliferative combinations and can be readily applied to other organisms for further characterization and/or confirmation. Finally, I examined synergistic combination HIPHOP profiles and identified Gene Ontology enrichments that are combination-specific.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/31941 |
| Date | 11 January 2012 |
| Creators | Smith, Andrew Michael |
| Contributors | Giaever, Guri, Boone, Charles |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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