A codon usage-based approach for the stratification of Influenza A
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Background: Influenza A virus (IAV) is a highly adaptable pathogen that poses a significant threat to human health. Genomic surveillance of IAVs is a complex task due to its broad host range, its zoonotic potential, and rapid evolution. Strategies based on codon preference analysis have been successfully employed for the categorization of IAVs with different host specificity in the past. Hence, monitoring codon usage adaptation, where viral codon preferences align with host preferences to enhance replication efficiency, offers a promising strategy for tracking IAV and identifying significant epidemiological events. Results: In this study, we develop a computational workflow for the analysis and stratification of IAVs based on codon usage profiles across large datasets. Using three key case studies--the 2009 H1N1 pandemic, the H7N9 epidemic in China (2013–2017), and the long-term circulation of H5N1 in domestic birds--we demonstrate the applicability of codon usage metrics for capturing patterns of viral adaptation and genomic diversification. Our approach uncovered interesting genomic features, which are not always reflected by the clade-based nomenclature. Interestingly a reduced set of amino acids and associated codons was sufficient to summarize salient patterns of IAV genomes, suggesting shared evolutionary pressures across IAV serotypes. Conclusion: Codon usage-based stratification effectively highlighted key epidemiological events and enabled detailed comparisons of genomic features across IAV serotypes. This approach provides a scalable framework for IAV genomic surveillance, offering insights into viral evolution and adaptation. 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.