Combining genetic and experimental data into models about the influenza virus can help predict more accurately which strains will be most common during the next winter, says a study published recently in eLife.
The models could make the design of flu vaccines more accurate, providing fuller protection against a virus that causes around half a million deaths each year globally.
Vaccines are the best protection we have against the flu. But the virus changes its appearance to our immune system every year, requiring researchers to update the vaccine to match. Since a new vaccine takes almost a year to make, flu researchers must predict which flu viruses look the most like the viruses of the future.
The gold-standard ways of studying influenza involve laboratory experiments looking at a key molecule that coats the virus called haemagglutinin. But these methods are labor-intensive and take a long time. Researchers have focused instead on using computers to predict how the flu virus will evolve from the genetic sequence of haemagglutinin alone, but these data only give part of the picture.
“The influenza research community has long recognized the importance of taking into account physical characteristics of the flu virus, such as how haemagglutinin changes over time, as well as genetic information,” explains lead author John Huddleston, a Ph.D. student in the Bedford Lab at Fred Hutchinson Cancer Research Center and Molecular and Cell Biology Program at the University of Washington, Seattle, US. “We wanted to see whether combining genetic sequence-only models of influenza evolution with other high-quality experimental measurements could improve the forecasting of the new strains of flu that will emerge one year down the line.”
Huddleston and the team looked at different components of virus ‘fitness’ – that is, how likely the virus is to