Glycomics is rapidly emerging as a new paradigm for biomarker discovery. Diseases as diverse as infection and cancer are known to involve changes in glycosylation. Glycans on cell surfaces are important for understanding nearly all cell surface interactions. They are key targets for drugs and may yield cell-specific therapeutics. In addition, they are also shed and can give indications of the changes in glycosylation associated with the disease. The study of glycosylation of cell surfaces is still in its infancy. The majority of the research has employed fluorescently labeled lectins providing few structural details. In this proposal, we will develop techniques to study surface glycans by using methods that release them specifically and examine them with high sensitivity. In the process, we will develop comprehensive methods to determine glycan structures and micoheterogeneity. To achieve these tasks, we will develop a high throughput method for glycomics analysis that will rapidly identify glycan structures. This goal would have seemed impossible given the complexity and the heterogeneity of glycan structures. However, we will develop a method with a constructed database at its core that will serve as template with descriptors including liquid chromatography retention time, accurate mass, and tandem mass spectrometry to identify individual glycan (or oligosaccharide) structures. The creation of this database will form the kernel of a comprehensive database will allow routine analysis of oligosaccharide possible. The development of methods for the rapid identification will significantly advance glycobiology research. Additionally, the analysis of glycans by liquid chromatography will provide a new method for biomarker discovery by allowing the analysis of structural isomers thereby increasing the richness of the compound pool.
A method for the deep structural analysis of glycans on cell surface will be developed that will allow differentiation of specific cancer types based on their glycan profile. This research will lead to potentially new biomarkers for cancer-specific therapeutics.
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