Although Plasmodium vivax parasites are the predominant cause of malaria outside of sub-Saharan Africa, they not always prioritised by elimination programmes. P. vivax is resilient and poses challenges through its ability to re-emerge from dormancy in the human liver. With observed growing drug-resistance and the increasing reports of life-threatening infections, new tools to inform elimination efforts are needed. In order to halt transmission, we need to better understand the dynamics of transmission, the movement of parasites, and the reservoirs of infection in order to design targeted interventions. The use of molecular genetics and epidemiology for tracking and studying malaria parasite populations has been applied successfully in P. falciparum species and here we sought to develop a molecular genetic tool for P. vivax. By assembling the largest set of P. vivax whole genome sequences (n = 433) spanning 17 countries, and applying a machine learning approach, we created a 71 SNP barcode with high predictive ability to identify geographic origin (91.4%). Further, due to the inclusion of markers for within population variability, the barcode may also distinguish local transmission networks. By using P. vivax data from a low-transmission setting in Malaysia, we demonstrate the potential ability to infer outbreak events. By characterising the barcoding SNP genotypes in P. vivax DNA sourced from UK travellers (n = 132) to ten malaria endemic countries predominantly not used in the barcode construction, we correctly predicted the geographic region of infection origin. Overall, the 71 SNP barcode outperforms previously published genotyping methods and when rolled-out within new portable platforms, is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria.
The zoonotic Plasmodium knowlesi parasite is a growing public health concern in Southeast Asia, especially in Malaysia, where elimination of P. falciparum and P. vivax malaria has been the focus of control efforts. Understanding of the genetic diversity of P. knowlesi parasites can provide insights into its evolution, population structure, diagnostics, transmission dynamics, and the emergence of drug resistance. Previous work has revealed that P. knowlesi fall into three main sub-populations distinguished by a combination of geographical location and macaque host (Macaca fascicularis and M. nemestrina). It has been shown that Malaysian Borneo groups display profound heterogeneity with long regions of high or low divergence resulting in mosaic patterns between sub-populations, with some evidence of chromosomal-segment exchanges. However, the genetic structure of non-Borneo sub-populations is less clear. By gathering one of the largest collections of P. knowlesi whole-genome sequencing data, we studied structural genomic changes across sub-populations, with the analysis revealing differences in Borneo clusters linked to mosquito-related stages of the parasite cycle, in contrast to differences in host-related stages for the Peninsular group. Our work identifies new genetic exchange events, including introgressions between Malaysian Peninsular and M. nemestrina-associated clusters on various chromosomes, including in parasite invasion genes (DBP[Formula: see text], NBPX[Formula: see text] and NBPX[Formula: see text]), and important proteins expressed in the vertebrate parasite stages. Recombination events appear to have occurred between the Peninsular and M. fascicularis-associated groups, including in the DBP[Formula: see text] and DBP[Formula: see text] invasion associated genes. Overall, our work finds that genetic exchange events have occurred among the recognised contemporary groups of P. knowlesi parasites during their evolutionary history, leading to apparent mosaicism between these sub-populations. These findings generate new hypotheses relevant to parasite evolutionary biology and P. knowlesi epidemiology, which can inform malaria control approaches to containing the impact of zoonotic malaria on human communities.