New research from the University of Pennsylvania is beginning to crack the code of which strain of flu will be prevalent in a given year, with major implications for global public health preparedness. Joshua Plotkin and Sergey Kryazhimskiy, both at the University of Pennsylvania, conducted the research with colleagues at McMaster University and the Institute for Information Transmission Problems of the Russian Academy of Sciences. Plotkin believes that his group's 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. Says Plotkin:
"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...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,"
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.
In class, we discussed the making of the flu vaccine, as well as difficulties in guessing what strains can will be prevalent in the upcoming year. Though I think the methods employed above will be useful in predicting genetic drift, we still have no reliable way of anticipating either reassortment or the next big zoonosis.
full article: http://www.sciencedaily.com/releases/2011/02/110217171336.htm