Understanding Evolutionary Dynamics of Influenza to Inform and Improve Vaccine Strain Selection

H1N1 Influenza Particles. Credit: NIAID


The National Institute of Allergy and Infectious Diseases (NIAID) is seeking proposals to support research to improve understanding of the evolutionary dynamics of seasonal influenza to increase our capacity to predict the emergence of new antigenic variants and more accurately select strains for use in the seasonal influenza vaccine.

Seasonal influenza viruses infect 5-15% of the global population each year, with epidemics occurring annually during the winter months in temperate regions and during the rainy season in tropical areas. The annual nature of epidemics is due to the ability of the virus to rapidly evolve, resulting in antigenically drifted strains. Selection for new antigenic strains that can escape pre-existing human immunity leads to the extinction of previously circulating variants when new ones emerge. The mechanisms driving this process and their importance at the within-host, between-host and population level are not well understood. As a result, our ability to project future evolutionary trajectories is limited.

Vaccines remain the most important tool for preventing influenza infection. However, viral evolution can have enormous impacts on vaccine effectiveness. Current methods of vaccine production rely on the identification of strains from each of the four types of influenza that circulate in the human population (influenza A H3N2 and H1N1 subtypes, and influenza B Yamagata and Victoria lineages) that are likely to dominate in the upcoming season. In order to accommodate the egg-based manufacturing process that is most commonly used to produce seasonal influenza vaccines, strains must be selected 6-9 months in advance of the flu season. Antigenic changes that occur during this window cannot be incorporated into the vaccine; major reductions in vaccine effectiveness result in years when vaccines are not well-matched to the circulating strains. Notable vaccine mismatches have occurred in at least one component of the vaccine in 4 of the past 15 years.

Recent technological advancements make now an opportune time to stimulate research in this area. Increases in computational power have led to the development of more sophisticated models that can incorporate prior knowledge and integrate various data types. Improved sequencing capabilities have resulted in major increases in the quantity of viral samples that are being characterized each season from around the globe. This initiative encourages fundamental research on seasonal influenza evolution with a focus on developing tools and predictive models with the ultimate goal of improving vaccine strain selection.

This initiative will support studies of seasonal influenza evolution to accurately predict evolutionary trajectories of circulating influenza viruses and improve strain selection for the seasonal influenza vaccine. Specifically, this initiative will support research to understand the evolutionary mechanisms that shape genetic and antigenic change in areas including:

  • Molecular basis of antigenic drift
  • Immunodominance of B cell responses to infection/vaccination, selection pressures on presentation of T cell epitopes and response to internal epitopes
  • How population structure and transmission bottlenecks shape genetic diversity
  • Connecting within-host selection with between-host transmission and population level dynamics
  • Understanding the frequency of emergence of new genetic variants, constraints on immune escape and rate of antigenic evolution, and importance of genetic background
  • Connecting viral genotype with phenotype
  • Develop new tools to assess immune and vaccine-driven selection pressure and accurately estimate the fitness of circulating viruses.
  • Predict the next dominant seasonal influenza virus strain through the development and/or optimization of models that estimate the influence of pre-existing immunity on virus evolution, and the use of new and/or refined platforms that integrate novel data streams into evolutionary and epidemiological models.

Additional details are available via RFA-AI-20-055. Letters of Intent are due no later than January 11, 2021.

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