Synthetic glycoconjugates for the precise detection of toxins and pathogens

ENVR 181

Suri S. Iyer, iyersi@email.uc.edu1, Ramesh R. Kale1, Duane M. Hatch, hatchdm@email.uc.edu1, Colleen McGannon2, Cynthia Fuller-Schaefer2, Agnese Jurkeva1, Michael J. Flagler3, H. Brian Halsall1, William R. Heineman1, and Alison A. Weiss3. (1) Department of Chemistry, University of Cincinnati, 404 Crosley, Cincinnati, OH 45221, (2) Department of Molecular Genetics & Microbiology, University of Cincinnati, OH, (3) Department of Molecular Genetics, Biochemistry & Microbiology, Cincinnati, OH 45267
Current challenges to minimize health risks resulting from a potential bioterrorist attack necessitate the development of autonomous environmental monitoring systems and compact portable biosensors. Most point-of-care diagnostics use antibodies as capture and recognition elements. However, monoclonal antibodies are expensive and exhibit poor shelf life at standard operating temperatures. Unlike antibodies, cell surface oligosaccharides are robust molecules and not prone to facile decomposition. They are natural receptors for several cellular processes such as proliferation, differentiation, migration and changes in cell shape. However, carbohydrates have not been used as recognition motifs in biosensor technologies because of their apparent lack of selectivity.

Here, we present that it is possible to tailor sugars to achieve high selectivity and sensitivity. Using a modular synthetic approach, we have synthesized a panel of high affinity glycoconjugates for the differentiation of closely related serotypes of Shiga toxin. In addition to the structure of the recognition motif, we observe that the spacer length also plays a significant role in defining the binding efficiency. We have also synthesized a robust, tailored biotinylated glycoconjugate and used it as a capture ligand instead of antibodies in a live E. coli detection assay. Lower limits of detection were achieved with the synthetic glycoconjugate compared to commercial antibodies. These robust, synthetic recognition elements can be directly incorporated onto existing biosensor platforms.