Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins

Abstract

The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.

A genotype network reveals homoplastic cycles of convergent evolution in influenza A (H3N2) haemagglutinin

“…More generally, the genotype network approach reveals the extent of homoplasy to be more extreme than hitherto realized. Previous analyses indicated homoplastic changes between leaf sequences on different branches of a HA phylogenetic tree, usually separated by multiple further additional amino acid changes [39,41,42], but convergent changes in this dataset occur in the same square, only one amino acid change apart. I note that the amino acid changes that occur in squares are suggestive of epistatic interactions [13,42,49 -52], which demonstrably exist in influenza HA evolution [42] and can be a source of homoplasy.…”

Historical variations in mutation rate in an epidemic pathogen, Yersinia pestis

“…Diversifying selection might have resulted in frequent homoplasies, as has been observed for mutations to antibiotic resistance (34) and resistance to antivirals (35), but only 28 SNPs were homoplastic in the 133 genomes (SI Appendix, Table S2). Diversifying selection is also frequently hallmarked by genes with multiple, clustered nonsynonymous SNPs (nsSNPs).…”

Weak Selection and Protein Evolution

“…Such patterns support within-protein epistatic interactions but do not distinguish among CN, CM, and CWS scenarios. Mapping the temporal order of substitutions (given dense sampling of species) can identify sequentially occurring substitutions consistent with CM or CWS (Bazykin et al 2006;Kryazhimskiy et al 2011;Osada and Akashi 2012), but distinguishing between these scenarios may be limited to particular biological contexts.…”