Influenza A viruses (IAVs) have caused large losses of life around the world and continue to present a great public health challenge. IAVs can cause infections in birds, sea mammals, lower mammals (e.g., pigs, dogs, and horses), and humans. Previous studies have demonstrated that the structures of the carbohydrate receptors determine influenza host and tissue tropisms. Thus, it is necessary to understand the receptor- binding properties for IAVs and monitor changes to them, especially for IAVs at the animal?human interface. However, this understanding is hampered by our lack of detailed knowledge of IAV glycan substructures; most of our knowledge is limited to SA2,3Gal-like and SA2,6Gal-like structures. The goals of this project are to develop and validate a machine learning method to identify host-specific glycan substructures for IAVs by using glycan array data and to identify and validate the glycan motifs associated with the host tropisms of IAVs, including those for zoonotic IAVs. The study will focus on natural hosts of IAVs: humans, swine, canines, equines, and various avian species, including common domestic poultry species and wild bird species. We expect to identify structural determinants for receptor binding with human-, swine-, canine-, and avian-origin IAVs. Such knowledge will help us understand the factors that contribute to influenza infection and transmission and thereby facilitate development of an effective influenza vaccine to prevent virus infection and block virus transmission. This knowledge will also help us develop rapid assays for monitoring emerging influenza threats at the animal?human interface. We also expect to develop a computational method for identifying glycan motifs associated with influenza host tropisms; this method will be able to be adapted to determine functional glycan motifs for other proteins, lectins, antibodies, antisera, and microorganisms, including those of other infectious pathogens, by using glycan arrays.
This project will develop and apply a computational tool to identify glycan motifs associated with influenza host tropisms, and the derived knowledge will help us develop effective strategies for influenza prevention and control.
