Development of a molecular barcoding tool for Plasmodium malaria

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Malaria caused by Plasmodium parasites kills ~600,000 people per year, and increased population mobility through international air travel carries further risks of re-introducing parasites to elimination areas and dispersing drug resistant parasites to new regions. Identifying genetic markers that quickly and accurately identifies the malaria species that cause human disease (e.g. P.falciparum, P.vivax, P.malariae, P.ovale, P.knowlesi) and the geographic origin of infections would be a valuable tool for locating the source of outbreaks imported to malaria elimination countries. This research project will entail DNA analysis of plasmodial mitochondria (Mt) and the apicoplast (Ap) sequences to create reliable genetic barcodes for identifying species and tracking the geographical movement of malaria, potentially for an operational context. Previous work in P.falciparum (n=711) found evidence that the organellar genomes are non-recombining and co-inherited, and the high degree of linkage produces a panel of relatively few markers that is geographically informative (Preston et al, 2014). Our project will build on this insight, focusing on all Plasmodia that cause human disease, and involve the: (1) Sequencing of Mt and Ap genomes from dry blood filter papers containing Plasmodium isolate DNA, sourced from malaria endemic areas;(2) Characterisation of all genomic variation in the Mt/Ap, and a population genetic analysis to understand diversity;(3) Characterisation of a genetic barcode to identify different Plasmodium species and geographical regions; (4) Development of a rapid barcoding tool using TwistDx RPA technology that could be applied in the field. Objective (1) will involve applying the selective whole genome amplification method (Leichty AR et al, 2014) on field-collected filter paper samples (n=500, sourced from Malaria Centre collaborators - Drs. Cally Roper and Colin Sutherland), followed by sequencing on the MiSeq technology (LSHTM). The resulting raw sequencing data will be complemented by those from published studies (n=2,500, 15 populations, www.malariagen.net). Analysis of the sequencing data will be performed on the LSHTM computing cluster with established pipelines (see Campino et al, 2016), and involve both alignment and assembly of sequences. For (2), genomic variants (e.g. SNPs) will be characterized from the alignments, and used to construct phylogenetic trees, calculate genetic diversity by gene and population, and identify genetic regions under selection. For (3), molecular barcoding polymorphisms will be established using phylogenetic-based (e.g. internal node SNP characterization), population genetic (e.g. population differentiation Fst) and statistical (e.g. random forest) methods. Validation of the Mt/Ap barcoding markers will be performed by analysis of newly published data and additional sequencing. For (4), the barcode and any additional known drug resistance markers will be incorporated into the RPA technology at TwistDx. The resulting prototype will be tested on control and malaria samples. Once validated, we will attempt to prospectively trial the tool in sites of collaborators that are involved in malaria elimination activities (e.g. Northern Kenya - Dr. Harold Ocholla; Amazon Brazil - Dr. Simone Santos). All objectives will lead to scientific publications. The work combines bioinformatics, molecular biology, epidemiology and translational activities, including diagnostic development and testing.