Genetic Analysis of Drug Resistance in Plasmodium falciparum

Malaria remains the most prominent cause of child mortality worldwide. Infections resulting from Plasmodium falciparum kill more than one million children in Africa alone and cause an estimated half-billion episodes of disease each year. The current paradigm of antimalarial drug resistance, that a single gene determines resistance, neglects the complexity with which drug resistance evolves. Although, major gene effects such as the chloroquine resistance (CQR) determinant, pfcrt, have been identified, epidemiological studies identify a range of drug responses indicative of a complex trait that is impacted by multiple genes. Understanding the bases of these traits using a more robust quantitative approach will lead to the identification of novel genes and their underlying role in drug resistance. In the first study we re-examined response to chloroquine (CQ) and CQ reversal agents in the progeny of the HB3 and Dd2 cross. Quantitative trait loci analyses (QTL) identify a major role of pfcrt chloroquine resistance, but additional loci were found that account for the three-fold quantitative variation of response in CQR progeny. We find that the degree of CQ reversibility by reversal agents is independent of CQR response, whereas reversed CQ response is dependent on levels of inherent CQR response. The identified additional loci highlight the background complexity to provide clues to the evolution of CQR and to facilitate the improved design of combination therapies that can reverse CQR. In the second study, we performed a QTL analysis to identify genetic loci that account for the strongly correlated quantitative sensitivities mefloquine and artemisinin, the components of the widely used combination therapy in multi-drug resistant (MDR) regions. We identified two QTL that are shared for both drug responses that point to genes controlling these correlated responses. We identified underlying gene polymorphisms in candidate genes at each QTL and address the application of these findings in surveillance and anticipation of imminent drug resistance. In the third study, we used an alternative approach to identify the gene polymorphisms underlying antimalarial drug resistance. Isogenic parasite lines were screened for polymorphisms using an integrative genomics approach. Comparative genome hybridizations and expression arrays identified co-adapted gene complexes associated with in vitro laboratory selections by CQ and quinine. We demonstrate the feasibility and utility of this approach to identify structural and transcriptional gene polymorphisms that will provide insights to unresolved questions of drug resistance evolution.