Abstract Details
Name
Iterative rational design of a ferritin pan–group 1 influenza nanoparticle vaccine informed by immune feedback and mechanistic analysis of cross reactivity
Presenter
Kawkab Kanjo, University of Calgary
Co-Author(s)
• Kawkab Kanjo: Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada • Opeyemi Ernest Oludada: Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada • Gustavo Sganzerla Martinez: Department of Immunology and Microbiology, Dalhousie University, Halifax, NS, Canada • Zahed Khatooni: Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, Saskatoon, SK, Canada • Kevin Gough: School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom • Brian Mark: Faculty of Science, University of Manitoba, Winnipeg, Manitoba, Canada • Alyson Kelvin: Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada
Abstract Category
Fighting & Responding
Abstract
Emerging influenza A viruses (IAV), especially H9N2 and H5N1, pose serious health risks to public health as well as animals as they continue to spill over from wild birds. Current influenza vaccines provide narrow strain-specific protection and require regular updates to match circulating variants. To overcome these limitations, rational multivalent display of antigens on ferritin nanoparticles has been proposed as a strategy to enhance antibody cross-reactivity. Here, we aim to develop broadly protective vaccines against group-1 IAV through the rational design of ferritin nanoparticles that co-display eight trimeric hemagglutinins (HAs), representatives of H1, H2, H5, and H9 subtypes. The representative HAs were selected using in-house developed informatics pipeline that scores antigens based on their genetic and antigenic relatedness, determined using Multiple Sequence Alignment, Phylogenetic analysis (IQ-Tree2) and in silico T cell and B cell epitope prediction tools such as NetMHCPan EL 4.1, NetMHCIIPan 4.0, BepiPred-2.0, Discotope 2.0, and AI-based B cell conformational epitopes prediction model that is trained on thousands of antibody-antigen structures and validated on antibody-HA complex structures. Sequences with the best conservation and predicted immunogenicity scores were expressed as ferritin-fusion constructs in mammalian system and evaluated for immunogenicity and cross-protection in murine models using in-house developed panels of recombinant antigens and influenza viruses. The phage display of cross-reactive B cell repertoire, coupled with deep sequencing, will be used to determine the mechanism underlying the broad protection. Crystal structure determination to map the epitopes of broadly reactive antibodies will be carried out to guide vaccine design improvement through immunefocusing.
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