Genomics and Transcriptomics Approaches to Understanding Drug Resistance Mechanisms in the Malaria Parasite \u3cem\u3ePlasmodium falciparum\u3c/em\u3e

The malaria parasite Plasmodium falciparum is responsible for about 500,000 deaths a year and is evolving resistance to the front-line treatment of artemisinin-based combination therapy. Resistance is currently confined to South East Asia, however millions of lives will be at risk if resistance spreads to Africa. Understanding the mechanism of resistance to artemisinins would aid containment strategies to prevent the spread of artemisinin resistance. There is also an urgent need to accelerate drug discovery since drug resistance has already been documented to all existing antimalarials. Here, I report on our efforts to understand the function of the gene k13, the gene with the strongest association with artemisinin resistance, and the potential genetic mechanisms associated with resistance to atovaquone, another widely used antimalarial. To precisely study the transcriptome characteristics of an isogenic k13 dysregulation mutant and wild type strain, I developed a new computational algorithm called Dephasing Identifier (DI) that is capable of identifying the genes dysregulated in cell cycle shifts. DI is designed to solve the problem of pinpointing important patterns in complex genomics data with temporal sequences that cannot be resolved by standard pair-wise comparison methods, by using an innovative method that leverages external reference data for systematic comparisons. In the k13 study, I demonstrated that the algorithm identifies co- regulated gene sets that have consistent annotated functions. The DI algorithm successfully identified aberrantly early DNA replication as the driving process of transcriptome changes in the mutant. To understand genome-wide changes that occurred in a set of atovaquone resistance stains, I analyzed whole genome sequencing data previously generated for a P. falciparum strain that underwent in vitro atovaquone selection to create atovaquone resistant strains. I systematically analyzed the genomes of these strains to search for significant genetic changes associated with atovaquone resistance; and used stringent criteria to identify genes involved in regulating transcription and protein modifications as acquiring non- synonymous mutations. Additionally, copy number variations in plasmepsin genes, a family known to be involved in resistance, were found in the resistant strains. In summary, genomics and transcriptomics technologies can be used to rapidly identify resistance mechanisms allowing for faster adjustment of current containment strategies. Future research on the critical targets identified in this study can aid new drug discovery efforts and novel control strategies