Quantifying the reassortment potential of influenza viruses for pandemic risk prediction

Influenza A virus (IAV) reassortment – the exchange of genes between segmented coinfecting viruses – is a major driver of viral evolution and pandemic emergence. However, we are currently unable to predict which IAV pairs will produce compatible reassortant genotypes or which genotypes pose the greatest risk of emergence. In this fellowship, I will dissect the biological and evolutionary barriers that govern viral reassortment, using biosafe single-cycle infectious IAVs (sciIAVs) and a novel application of single-cell sequencing for the identification of individual viral genotypes in mixed populations. I hypothesise that reassortment is shaped by complex interactions of evolutionary, functional, and mechanistic constraints whose influence varies across viral lineages. Defining these interactions will explain why some coinfecting IAVs readily generate reassortants while others do not. To investigate this, I will quantify reassortment across a phylogenetically diverse panel of human, avian, and swine sciIAVs, and characterise reassortant phenotypes to link genotype to function. I will use these data to develop machine-learning models that predict reassortment compatibility and the pandemic risk of specific strains. These models will be embedded within IAV risk-assessment platforms at UKHSA and APHA to enable real-time prioritisation of high-risk IAVs before novel reassortants arise, transforming pandemic preparedness from reactive to predictive.