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Using networks to analyze rapid genetic recombination in malaria parasites

Daniel Larremore

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Thursday, April 9, 2015 - 2:00pm

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Newton Lab

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Despite extensive research and public health efforts, there remain hundreds of millions of malaria cases annually, causing over half a million deaths, mostly children. Key to malaria's ongoing transmission is the fact that humans develop only a weak immunity, stemming from the parasite’s evasion of the immune system by sequential expression of camouflage-like proteins on the surface of infected red blood cells. The genetic variation within the camouflage-encoding var genes is sufficiently high dimensional that immunity to a single camouflage variant doesn't hinder future infections. What’s more, each parasite genome contains ~60 different var genes, which rapidly recombine, precluding the use of traditional phylogenetic techniques. I will present a series of investigations to understand the key mechanisms and constraints underlying the ongoing evolution of var genes. We first developed a framework capable of mapping rapidly recombining genes to networks in which evolutionary constraints are revealed in large-scale network structures. Applying this approach to multiple genomes, we identified the parts of the camouflage proteins that evolve differently than others. To improve the quality of network community detection, we developed a bipartite stochastic block model, and then applied it to an expanded data set including var genes from ape-infecting malaria parasites. This revealed the deep origins of the malaria parasite's current immune evasion strategy, which evolved tens of millions of years ago in an ancient ancestor of extant malaria species. This frames the current adaptive struggle in humans in a broader evolutionary context, with implications for parasite population genetics as malaria prevention efforts shift toward elimination.