Abstract
Investigation of the diversity of malaria parasite antigens can help prioritize and validate them as vaccine candidates and identify the most common variants for inclusion in vaccine formulations. Studies of vaccine candidates of the most virulent human malaria parasite,Plasmodium falciparum, have focused on a handful of well-known antigens, while several others have never been studied. Here we examine the global diversity and population structure of leading vaccine candidate antigens ofP.falciparumusing the MalariaGEN Pf3K (version 5.1) resource, comprising more than 2600 genomes from 15 malaria endemic countries. A stringent variant calling pipeline was used to extract high quality antigen gene ‘haplotypes’ from the global dataset and a new R-package namedVaxPackwas used to streamline population genetic analyses. In addition, a newly developed algorithm that enables spatial averaging of selection pressure on 3D protein structures was applied to the dataset. We analysed the genes encoding 23 leading and novel candidate malaria vaccine antigens includingcsp,trap,eba175,ama1,rh5, andCelTOS. Our analysis shows that current malaria vaccine formulations are based on rare haplotypes and thus may have limited efficacy against natural parasite populations. High levels of diversity with evidence of balancing selection was detected for most of the erythrocytic and pre-erythrocytic antigens. Measures of natural selection were then mapped to 3D protein structures to predict targets of functional antibodies. For some antigens, geographical variation in the intensity and distribution of these signals on the 3D structure suggests adaptation to different human host or mosquito vector populations. This study provides an essential framework for the diversity ofP.falciparumantigens to be considered in the design of the next generation of malaria vaccines.
Funder
national health and medical research council
National Institutes of Health
Publisher
Public Library of Science (PLoS)
Subject
Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics
Cited by
13 articles.
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