Integrative prediction of seasonal influenza evolution by genotype, phenotype, and geography

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Project Summary/Abstract The rapid evolution of seasonal influenza requires the development of a new influenza vaccine by the World Health Organization (WHO) every one to two years. This evolution occurs through a process of antigenic drift where amino acid mutations in the hemagglutinin (HA) surface protein allow currently circulating viruses to evade adaptive immunity against previous vaccine viruses. Therefore, globally successful seasonal influenza viruses are often antigenically distinct from previous lineages. High-quality experimental assays for antigenic drift are laborious and low-throughput, leading researchers to develop computational models that can predict the success of influenza viruses from HA sequence data alone. Since the publication of these original sequence- only models in 2014, there have been significant advances in influenza virology and computational methods that could benefit influenza predictive models. Specifically, there are now computational methods to measure antigenic drift by accurately inferring missing measurements in HI assays, high-throughput mutagenesis assays to measure functional constraints on mutations in HA, research supporting the importance of proteins other than HA for influenza's fitness, and detailed analysis of influenza's variable geographic circulation. I propose to create a new predictive model of influenza evolution that integrates these modern, biologically-informed fitness metrics into a single framework. These new metrics will build on dense, high-quality HI assays from collaborators at the Centers for Disease Control and Prevention (CDC), deep mutational scanning assays of seasonal influenza from collaborators in Dr. Jesse Bloom's lab, a curated database of whole genome sequences for influenza, and empirical estimates of influenza's global migration rates. This new predictive model will improve the accuracy of predictions about which viruses are most likely to succeed in future influenza seasons. These improved predictions will inform recommendations by Dr. Bedford to the WHO at annual vaccine design meetings and, thereby, effect improvements in vaccine efficacy and reduce influenza-related morbidity and mortality in human populations.