Abstract
Influenza A virus (IAV) is a highly adaptable pathogen that poses a significant threat to human health. Genomic surveillance of IAVs is complex due to their broad host range, zoonotic potential, and rapid evolution. Strategies based on codon preference analysis have been successfully employed for the discrimination of IAVs with different host specificity in the past. Hence, monitoring changes in codon usage offers a promising strategy for tracking IAVs' host range and identifying significant epidemiological events. In this study, we developed a computational workflow for the stratification of IAVs based on codon usage profiles by analysing recent IAV-associated epidemiological emergencies: 1) the 2009 H1N1 pandemic in North America, 2) the H7N9 epidemic in China (2013-2017), and 3) the long-term circulation of H5N1 in domestic birds and its subsequent spillover to dairy cows. We explore the application of codon usage metrics for capturing patterns of viral diversification and expand previous related findings in the field. Our results uncovered important differences in genomic features, which are not always reflected in the clade-based nomenclature. Interestingly, a reduced set of amino acids and associated codons was sufficient to summarize salient patterns of IAV genomes across the 3 paradigmatic cases herein considered, suggesting shared evolutionary signatures across IAV serotypes. Codon usage-based stratification effectively highlighted key epidemiological events and enabled detailed comparisons of genomic features across IAV serotypes. The approach developed in this work provides a scalable framework for IAV genomic surveillance, offering insights into viral evolution and shared patterns of codon usage preferences. Its general applicability makes it suitable for extending to other Influenza A serotypes, particularly those for which available genomic data are limited or a reference nomenclature is not established.