Research Overview
Malaria infects around 300 million people each year, killing nearly 655,000 people. Resistance to all five classes of antimalarial drugs is known, emphasizing the need to better understand drug resistance, infection complexity, and evolution of adaptive parasite mutations. We have developed methods to isolate individual malaria parasites of P. falciparum, and P. vivax, and we are using these tools to understand how complex malaria infections are — a major black box in malaria epidemiology.
Malaria infections frequently contain multiple distinct genetic backgrounds. This complexity challenges standard genetic analysis and prevents direct haplotype capture. The single cell sequencing approach we developed comprises isolation of individual malaria parasites by cell sorting, amplification of genetic material using whole genome amplification, and whole genome sequencing.
Bioinformatic approaches allow us to identify genetic variation from next generation sequencing data. The extreme nucleotide bias of the malaria parasite genome (~80% AT-rich) requires extensive optimization and validation of analytical tools. These pipelines have become integral to our projects to understand parasite genetic variation. The genome of P. falciparum poses a particularly challenging target for genomics. It is highly repetitive and the most
AT-rich genome sequenced to date. My lab has developed methods for accurate scoring of genetic variants from microarray and next generation sequencing platforms for P. falciparum. We apply these methods to population-scale datasets to identify the targets of natural selection and map the emergence of drug resistance.
Drug resistance to artemisinin, currently the global front-line treatment for malaria infections, was first observed in 2004, and has rapidly spread through South-East Asia. Given that the loss of previous anti-malarial drugs has resulted in a substantial increase in mortality, it is imperative that we rapidly characterize drug resistance. Using population genetic methods, we identified a region of chromosome 13 in the parasite genome as a major determinant of artemisinin resistance. This was subsequently confirmed as the kelch gene. Employing this system, we have developed a method to rapidly map resistance by pooled sequencing of resistant and sensitive parasites. Our approach led to direct implication of kelch in a cost- effective and accurate way and opens the door for rapid identification.