Understanding Drug Resistance in Plasmodium falciparum through Genetic Crosses and Global Metabolomics
Knowledge of genetic determinants of the metabolic state of Plasmodium falciparum can reveal crucial details about the evolution of antimalarial drug resistance. There is a growing demand for high resolution data to quantify and characterize the enzymatic and metabolic status of the human malaria parasite. Global metabolomics offers an unbiased, real-time readout of cellular activity and metabolic flux regulation, promising insights into parasitic drug responsive biology and pathophysiology. Quantitative trait locus mapping of metabolite profiles from untargeted mass spectrometry can uncover genetic loci influencing metabolite levels and parasite physiology. I extracted metabolites from two P. falciparum genetic crosses at three erythrocytic cell cycle stages. Individual mass signatures from this highly inclusive dataset map to all chromosomes in the genome in an asymmetrical manner. My approach is also enhanced by the incorporation of network theory and graphs to model metabolic pathways. The resolution of a metabolic network offers insights into the pathophysiology of the parasite. An understanding of these metabolites and metabolic pathways could serve to guide chemotherapeutic approaches to multiple cellular targets and identifying strategies to circumvent antimalarial drug resistance.
Generating a cross between strains with different phenotypes and studying intermediary phenotypes of recombinant progeny has been a highly effective approach for studying the genetic basis of drug resistance. The data is subjected to linkage analysis to study inheritance patterns to pinpoint genes associated with a phenotype of interest. Traditionally, genetic crosses in P. falciparum have relied on chimpanzees for liver-stage parasite development, however due to the extensive labor and overwhelming issues like cost and ethics, the chimp-based crosses are no longer feasible. Here, we demonstrate that genetic crosses can be made in a humanized mouse model. I characterized four genetic crosses derived from this model, identified recombinant progeny using microsatellite markers, constructed a linkage map for one of them with SNP genotyping data, performed phenotyping and QTL analyses. We generated the first experimental crosses using parent lines recently isolated from clinical patients in geographical regions with emerging drug resistance. Generation of crosses using recently isolated parasites will allow for a quicker understanding of the mechanisms of resistance development.
History
Date Created
2018-04-09Date Modified
2018-11-05Defense Date
2018-04-03Research Director(s)
Michael FerdigCommittee Members
Michael Pfrender Nitesh Chawla David SeversonDegree
- Doctor of Philosophy
Degree Level
- Doctoral Dissertation
Program Name
- Biological Sciences