New research is beginning to crack the problem of which strain of flu will be prevalent in a given year, with major implications for global public health preparedness. A computational study of 40 years of flu genomes offers a new way of looking at mutations: by cataloging pairs of genetic changes that have occurred in rapid succession, observing that a mutation in one half of the pair can act as an early warning sign of a mutation about to occur in the other.
Tracking single mutations in a vacuum is not always enough to understand how the flu virus evolves. Sometimes a mutation is functional or adaptive only if it’s in the context of a certain genetic background – that is, if the protein already has some other mutation. The influence such combinations have on an organism’s adaptive fitness is known as epistasis. If you see a mutation occur in Site A and then very soon after you see a mutation in Site B, and this pattern happens repeatedly, then you have some evidence that A and B influence fitness epistatically. The first mutation might be useless on its own, but it might be a prerequisite for the second mutation to be useful. The first mutation is like giving you a nail, and the second one is like giving you a hammer.
Because the studied mutations generally affect the surface proteins that determine whether the virus can enter and infect human cells, being able to predict what mutations are likely to happen in the near future has lifesaving applications. Tens of thousands of Americans, and hundreds of thousands worldwide, die of seasonal flu complications every year. Flu vaccine production is labor intensive and time consuming; to have enough supplies ready for the flu season, public health groups like the Centers for Disease Control and the World Health Organization must make an educated guess as to which strain is likely to be the most active several months in advance. Observing the leading site of an epistatic pair could give them a head start.
Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins. (2011) PLoS Genet 7(2): e1001301. doi:10.1371/journal.pgen.1001301
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 dN=dSv1, 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.