Imported cases present a considerable challenge to the elimination of malaria. Traditionally, patient travel history has been used to identify imported cases, but the long-latency liver stages confound this approach in Plasmodium vivax. Molecular tools to identify and map imported cases offer a more robust approach, that can be combined with drug resistance and other surveillance markers in high-throughput, population-based genotyping frameworks. Using a machine learning approach incorporating hierarchical FST (HFST) and decision tree (DT) analysis applied to 831 P. vivax genomes from 20 countries, we identified a 28-Single Nucleotide Polymorphism (SNP) barcode with high capacity to predict the country of origin. The Matthews correlation coefficient (MCC), which provides a measure of the quality of the classifications, ranging from −1 (total disagreement) to 1 (perfect prediction), exceeded 0.9 in 15 countries in cross-validation evaluations. When combined with an existing 37-SNP P. vivax barcode, the 65-SNP panel exhibits MCC scores exceeding 0.9 in 17 countries with up to 30% missing data. As a secondary objective, several genes were identified with moderate MCC scores (median MCC range from 0.54-0.68), amenable as markers for rapid testing using low-throughput genotyping approaches. A likelihood-based classifier framework was established, that supports analysis of missing data and polyclonal infections. To facilitate investigator-lead analyses, the likelihood framework is provided as a web-based, open-access platform (vivaxGEN-geo) to support the analysis and interpretation of data produced either at the 28-SNP core or full 65-SNP barcode. These tools can be used by malaria control programs to identify the main reservoirs of infection so that resources can be focused to where they are needed most.

Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection’s country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.

Plasmodium vivax is the most widespread and difficult to treat cause of human malaria. The development of vaccines against the blood stages of P. vivax remains a key objective for the control and elimination of vivax malaria. Erythrocyte binding-like (EBL) protein family members such as Duffy binding protein (PvDBP) are of critical importance to erythrocyte invasion and have been the major target for vivax malaria vaccine development. In this study, we focus on another member of EBL protein family, P. vivax erythrocyte binding protein (PvEBP). PvEBP was first identified in Cambodian (C127) field isolates and has subsequently been showed its preferences for binding reticulocytes which is directly inhibited by antibodies. We analysed PvEBP sequence from 316 vivax clinical isolates from eight coun

Plasmodium vivax is the most widespread cause of human malaria. Recent reports of drug resistant vivax malaria and the challenge of eradicating the dormant liver forms increase the importance of vaccine development against this relapsing disease. P. vivax reticulocyte binding protein 1a (PvRBP1a) is a potential vaccine candidate, which is involved in red cell tropism, a crucial step in the merozoite invasion of host reticulocytes. As part of the initial evaluation of the PvRBP1a vaccine candidate, we investigated its genetic diversity and antigenicity using geographically diverse clinical isolates. We analysed pvrbp1a genetic polymorphisms using 202 vivax clinical isolates from six countries. Pvrbp1a was separated into six regions based on specific domain features, sequence conserved/polymorphic regions, and the reticulocyte binding like (RBL) domains. In the fragmented gene sequence analysis, PvRBP1a region II (RII) and RIII (head and tail structure homolog, 152–625 aa.) showed extensive polymorphism caused by random point mutations. The haplotype network of these polymorphic regions was classified into three clusters that converged to independent populations. Antigenicity screening was performed using recombinant proteins PvRBP1a-N (157–560 aa.) and PvRBP1a-C (606–962 aa.), which contained head and tail structure region and sequence conserved region, respectively. Sensitivity against PvRBP1a-N (46.7%) was higher than PvRBP1a-C (17.8%). PvRBP1a-N was reported as a reticulocyte binding domain and this study identified a linear epitope with moderate antigenicity, thus an attractive domain for merozoite invasion-blocking vaccine development. However, our study highlights that a global PvRBP1a-based vaccine design needs to overcome several difficulties due to three distinct genotypes and low antigenicity levels.

Challenges in understanding the origin of recurrent Plasmodium vivax infections constrains the surveillance of antimalarial efficacy and transmission of this neglected parasite. Recurrent infections within an individual may arise from activation of dormant liver stages (relapse), blood-stage treatment failure (recrudescence) or new inoculations (reinfection). Molecular inference of familial relatedness (identity-by-descent or IBD) based on whole genome sequence data, together with analysis of the intervals between parasitaemic episodes (time-to-event analysis), can help resolve the probable origin of recurrences. Whole genome sequencing of predominantly low-density P. vivax infections is challenging, so an accurate and scalable genotyping method to determine the origins of recurrent parasitaemia would be of significant benefit. We have developed a P. vivax genome-wide informatics pipeline to select specific microhaplotype panels that can capture IBD within small, amplifiable segments of the genome. Using a global set of 615 P. vivax genomes, we derived a panel of 100 microhaplotypes, each comprising 3-10 high frequency SNPs within <200 bp sequence windows. This panel exhibits high diversity in regions of the Asia-Pacific, Latin America and the horn of Africa (median HE = 0.70-0.81) and it captured 89% (273/307) of the polyclonal infections detected with genome-wide datasets. Using data simulations, we demonstrate lower error in estimating pairwise IBD using microhaplotypes, relative to traditional biallelic SNP barcodes. Our panel exhibited high accuracy in predicting the country of origin (median Matthews correlation coefficient >0.9 in 90% countries tested) and it also captured local infection outbreak and bottlenecking events. The informatics pipeline is available open-source and yields microhaplotypes that can be readily transferred to high-throughput amplicon sequencing assays for surveillance in malaria-endemic regions.