Systems genetics of artemisinin resistance
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ABSTRACT The ability to conduct experimental genetic crosses in malaria using humanized mice provides exciting opportunities for genetic analysis of Plasmodium falciparum. Systems genetics approaches can complement these efforts, because transcription of RNA, translation into proteins and metabolism of these proteins, provide the critical intermediate steps that link genotype and phenotype. We will characterize the transcriptional, proteomic and metabolomic signatures of both parental parasites and progeny from three genetic crosses generated by Core A (Experimental Genetic Crosses). To generate the material needed, we will grow and harvest tightly synchronized parasite cultures (2 replicates) at 6 hr intervals throughout the parasites lifecycle for extraction of mRNA, proteins and metabolites: RNAseq, proteomic and metabolomic characterization will then be conducted by Core C (Genomics). The systems datasets generated will be used to ask both applied and fundamental questions about parasite biology: (i) We hypothesis that `omic' data collected will provide key insights into the mechanism of resistance, the metabolic networks involved in resistance and cost of resistance. Systems genetic analyses in a linkage mapping framework will show how additional loci impact artemisinin resistance, and the reasons that kelch13 alleles vary in level of resistance observed. (ii) we hypothesize that the systems biology paradigms emerging from studies of model organism will fit poorly for malaria. P. falciparum is fundamentally different from models organism in having just-in-time cascades of mRNA production across the lifecycle and few encoded transcription factors. We will conduct genome wide QTL analyses of mRNA, proteins and metabolites to investigate the interplay and feedback between genes, mRNA, proteins and metabolites to test this hypothesis. These analyses will explore the level at which protein abundance is regulated, and the relative importance of SNPs, microsatellites and copy number variation in driving regulatory evolution. These analyses will involve strong collaboration with Core B (Data Integration and Analysis Core) and RP02 (Drug Resistance Profiling and QTL mapping) in Notre Dame. The RNAseq, proteomics and metabolomics data will be made publically available for use by the malaria research community.
