Abstract
Cross-neutralising monoclonal antibodies against influenza hemagglutinin (HA) are of considerable interest as both therapeutics and diagnostic tools. We have recently described five different single domain antibodies (nanobodies) which share this cross-neutralising activity and suggest their small size, high stability, and cleft binding properties may present distinct advantages over equivalent conventional antibodies. We have used yeast display in combination with deep mutational scanning to give residue level resolution of positions in the antibody-HA interface which are crucial for binding. In addition, we have mapped positions within HA predicted to have minimal effect on antibody binding when mutated. Our cross-neutralising nanobodies were shown to bind to a highly conserved pocket in the HA2 domain of A(H1N1)pdm09 influenza virus overlapping with the fusion peptide suggesting their mechanism of action is through the inhibition of viral membrane fusion. We also note that the epitope overlaps with that of CR6261 and F10 which are human monoclonal antibodies in clinical development as immunotherapeutics. Although all five nanobodies mapped to the same highly conserved binding pocket we observed differences in the size of the epitope footprint which has implications in comparing the relative genetic barrier each nanobody presents to a rapidly evolving influenza virus. To further refine our epitope map, we have re-created naturally occurring mutations within this HA stem epitope and tested their effect on binding using yeast display. We have shown that a D46N mutation in the HA2 stem domain uniquely interferes with binding of R2b-E8. Further testing of this substitution in the context of full length purified HA from 1918 H1N1 pandemic (Spanish flu), 2009 H1N1 pandemic (swine flu) and highly pathogenic avian influenza H5N1 demonstrated binding which correlated with D46 whereas binding to seasonal H1N1 strains carrying N46 was absent. In addition, our deep sequence analysis predicted that binding to the emerging H1N1 strain (A/Christchurch/16/2010) carrying the HA2-E47K mutation would not affect binding was confirmed experimentally. This demonstrates yeast display, in combination with deep sequencing, may be able to predict antibody reactivity to emerging influenza strains so assisting in the preparation for future influenza pandemics.
Highlights
Influenza A virus remains a persistent threat to public health resulting in 200,000–500,000 deaths worldwide every year [1]
We sub-cloned a single open reading frame corresponding to the HA0 gene from A(H1N1)pdm09 into the yeast display vector pNIBS-5 fused to the C-terminus of the cell surface anchor protein Aga2p
With this in mind we have previously identified a panel of five cross-neutralising single domain antibodies to pandemic influenza A(H1N1)pdm09 virus hemagglutinin and highly pathogenic avian influenza H5N1 from an immune alpaca phage displayed library [8]
Summary
Influenza A virus remains a persistent threat to public health resulting in 200,000–500,000 deaths worldwide every year [1]. The advent of deep sequencing has allowed a more comprehensive analysis of the whole repertoire of mutations [45, 46] displayed on the yeast cell surface in addition to identifying residues that are likely to have minimal effect on binding when mutated This latter property has important implications when considering the scope of binding reactivity of individual monoclonal antibodies to a rapidly evolving target antigens. This is a very time consuming process requiring repeated cycles of growth in the presence of specific antibodies, sequencing of live virus to identify escape mutations followed by their re-introduction into infectious virus to confirm their significance This approach has been successful in locating antibody epitopes on influenza HA but has been limited to those epitopes which can be mutated without interfering with viral infection such as those in the variable head domain [47,48,49]. We demonstrate how deep mutational scanning can define residues likely to have minimal effect on binding and present examples which highlight the potential of this approach to predict antibody binding to new viruses
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
