A team of experts, including scientists from The University of Texas Medical Branch at Galveston, is proposing a more effective way of selecting the seasonal influenza vaccine and has potentially identified a novel influenza virus.

In a paper published in the open-access journal Frontiers in Microbiology a multidisciplinary team argues a new bioinformatics approach could be a better tool for selecting the needed flu vaccines than the currently used physlogenetic analyses based on homology. This type of analysis looks at genetic changes of closely related influenza strains and is currently used to monitor virus evolution.

The influenza virus evolves and changes quickly, so each year the World Health Organization recommends the needed influenza vaccine to match the predominant strains of the virus in circulation. It takes about six months to produce the vaccine but by the time it reaches the public the vaccine may no longer match and be effective against the evolving influenza viruses, said Slobodan Paessler, professor and director of the Galveston National Laboratory Preclinical Studies Core and Animal Biosafety Level 3, Institute for Human Infections and Immunity at UTMB.

“Last year was a pretty bad year,” Paessler said. “This year it could be very similar.”

In most years, the flu vaccine is expected to be between 50 and 70 percent effective. But in the last flu season, the vaccine was only about 20 percent effective and this year could be equally problematic, he said.

The current genetic analyses used to monitor virus evolution cannot distinguish and predict important mutations in the influenza virus that would have an impact on vaccine efficacy, Paessler said.

So, by using the research team's proposed bioinformatics approach, which is led by Dr. Veljkovic from Vinca Institute in Belgrade, Serbia, and by focusing on the portion of the virus responsible for binding to cells, scientists could still identify the predominant strains as well as some outliers that would be “resistant” to seasonal vaccine, Paessler said.

Paessler and his team put their system to the test on a newly identified H3N2v virus that has pandemic potential this flu season, according to the paper.

“The goal is to combine real-time data and register changes in the genome that are important for the function of the virus protein and to go away from the classical model that has failed year after year,” he said. “This new model allows you to know when some major change occurs and allows you to start preparing new vaccine(s).”

Other authors of this study include Veljko Veljkovic, Sanja Glisic, Jelena Prljic, Vladimir R. Perovic and Nevena Veljkovic from the Center for Multidisciplinary Research and the VINCA Institute of Nuclear Sciences at University of Belgrade; and Matthew Scotch from the Department of Biomedical Informatics and the Center for Environmental Security, Biodesign Institute and Security and Defense Systems Initiative at Arizona State University.