The influenza vaccine must be reformulated every year because influenza viruses continuously mutate, accumulating changes in the genomic regions that encode the proteins recognized by the human immune system. When the vaccine strains selected by the World Health Organization do not closely match the viruses actually circulating in a given season, vaccine effectiveness drops significantly. That mismatch has been a recurring and costly problem, and improving the accuracy of strain selection is one of the most important challenges in influenza vaccine science. Researchers at the University of Helsinki have now developed a genomic approach to real time influenza tracking and prediction that could meaningfully improve the process.
Why the Influenza Vaccine Must Change Every Year
Unlike vaccines for diseases caused by stable pathogens, the influenza vaccine cannot be a fixed, long lasting product. Influenza viruses have an unusually high mutation rate, and new genetic variants emerge continuously across global populations. Each year, the WHO analyzes surveillance data from around the world to predict which strains will be dominant in the coming season and recommends specific virus strains for vaccine manufacturers to use.
This process is inherently uncertain. The strains selected months in advance for production must closely match the strains that eventually circulate, and when they do not, the result is antigenic mismatch, a situation in which the immune response primed by the influenza vaccine does not adequately protect against the actual infecting strain. Improving the predictive accuracy of this selection process would directly translate into better protected populations and fewer influenza related hospitalizations and deaths.
How Genomic Tracking Could Improve Influenza Vaccine Strain Selection
Researchers from the Institute for Molecular Medicine Finland at the University of Helsinki, working in collaboration with researchers from Singapore and the United Kingdom, analyzed thousands of complete genome sequences of influenza A H1N1 and A H3N2 strains collected from multiple geographic regions. Using biostatistical methods, the team identified genetic variants that altered the structure of influenza proteins and appeared at high frequency across the strains studied.
These variants, which the team designated evolutionary markers, appeared to carry consistent information about the direction of viral evolution over time. Both H1N1 and H3N2 subtypes were found to carry their own distinct sets of these evolutionary markers, and tracking changes in marker patterns across viral populations provided a reliable signal for predicting where the virus was heading.
Critically, many of these evolutionary markers were absent from the virus strains used to formulate the influenza vaccine during the previous season, which the researchers believe contributed to the reduced vaccine efficacy observed. The research team proposed that future influenza vaccine candidates should be selected from strains containing all of the identified evolutionary markers rather than relying solely on traditional surveillance methods.
The Potential Impact on Influenza Vaccine Effectiveness
The researchers estimated that if vaccination efficacy increased by 50 percent as a result of better strain selection, up to 8,000 people in Finland alone could be spared from influenza infection each year. Scaled to global influenza burden, the potential public health impact of more accurate strain selection is substantial.
Denis Kainov, who led the genomic tracking research team, noted that the methodology developed offers a simple approach to reliable real time tracking and prediction of influenza viral evolution based on whole genome sequences. The approach is designed to be practical and applicable to the existing WHO strain selection process, supplementing rather than replacing current surveillance infrastructure.
What This Means for the Future of Influenza Vaccine Development
Genomic surveillance of influenza is not new, but the ability to rapidly identify evolutionary markers with predictive value across multiple subtypes and geographic regions represents a meaningful methodological advance. As whole genome sequencing becomes faster and less expensive, integrating this kind of marker based tracking into routine influenza surveillance is increasingly feasible.
If validated in subsequent seasons and adopted by vaccine advisory bodies, this approach could reduce the frequency of vaccine mismatch events and improve the consistency of influenza vaccine protection year over year. For populations most vulnerable to influenza, including the elderly, immunocompromised individuals, and those with chronic health conditions, more effective vaccines would have direct and measurable health benefits.
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For the full source, see the original article at EurekAlert.