Statistical method developed may be used to identify sites of importance for viral adaptation.
A group of investigators report developing a new phylogenetic method for detecting epistasis. They applied the technique to the surface proteins of the influenza A virus, identifying and characterizing hundreds of epistatic mutations in these proteins.
The researchers were able to pinpoint the specific epistatic mutations that were recently shown, experimentally, to confer resistance to Tamiflu. They believe that their results may help predict the course of influenza’s antigenic evolution and thus help select more appropriate vaccines and drugs.
The study was conducted by a team of scientists from the Kharkevich Institute, McMaster University, and University of Pennsylvania. Their work was published February 17 in PLoS Genetics in a paper titled “Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins.”
Epistasis describes nonadditive interactions among genetic sites: the consequence of a mutation at one site may depend on the status of the genome at other sites. In an extreme case, a mutation may have no effect if it arises on one genetic background, but a strong effect on another background.
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.
The researchers thus developed a 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 idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic, the investigators explain.
The method was applied to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a nonepistatic null distribution, the scientists say that they detected substantial amounts of epistasis and determined the identities of putatively epistatic pairs of sites.
In particular, using sequence data alone, their method identified epistatic interactions between specific sites in neuraminidase that have recently been demonstrated in vitro to confer resistance to Tamiflu. These epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today.
The research team concludes that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites that would normally not be identified as positively selected can facilitate viral adaptation through epistatic interactions with their partner sites.