<p> Despite significant surveillance and vaccine development efforts, Influenza A viruses remain a significant challenge to human health. A critical aspect of routine surveillance concerns strain specificity towards sialylated glycan structures on the cell surface and avoidance of host decoy structures on secreted mucins. Although methods of assessing glycan-binding phenotype, such as glycan microarray technology, have generated significant advances in our understanding of these interactions, the inherent complexity of this multivalent recognition event and the glycocalyx itself necessitate additional methods that better recapitulate the full molecular context of viral binding. Here in, I present multivalent glycopolymers displaying sialoglycans to better recapitulate native IAV receptor binding events in a chemically-controlled manner. This biologically inspired chemical approach facilitates the collection of quantitative whole-virus binding to our multivalent scaffolds presented either on glass slide/bead arrays or in soluble form, which can be combined to assess viral binding preference across varying geometric parameters including glycan density, valency, and scaffold length. In addition, I report the utilization of lipid bearing sialoglycopolymers to remodel the glycocalyx of mammalian cells as a tool to investigate viral recognition of sialylated glycan structures on living cell surfaces. In this bottom-up approach, we can readily control the glycan structure installation and Influenza receptor display on the cell membrane. Importantly, these glycomaterials appear able to restore viral adhesion to sialic acid deficient cells. By combining existing technologies with this method, we aim to better uncover structural features that enhance influenza’s primary recognition of host cells.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:13806200 |
| Date | 06 April 2019 |
| Creators | Fisher, Christopher James |
| Publisher | University of California, San Diego |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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