501516 Modifying IgG Glycan Profile Using a High-Throughput Multi-Gene Assembly Platform

Christopher Stach1, Tung S. Le2, Meghan G. McCann3, Xinning Chen4, Liang Zhao4, Michael J. Smanski5 and Wei-Shou Hu3, (1)Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Twin Cities, Minneapolis, MN, (2)Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, (3)Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, (4)State Key Laboratory of Bioreactor Engineering, East China University of Science & Technology, Shanghai, China, (5)Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Twin Cities, St. Paul, MN

Protein N-glycosylation is a post-translational modification that is crucial for protein folding, biological activity, half-life and stability. It is one of the most complex post-translational modifications in that it involves a complex network of enzymes that may act on identical substrates to generate many different glycosylation patterns. A drawback of protein expression in mammalian cells is the presence of heterogeneous populations of the same protein due to differences in protein N-glycosylation. The complex nature of glycosylation makes it a good target for control using genetic engineering approaches. While pipelines exist for the generation of multi-gene constructs they are limited in their utility. Utilizing the latest DNA synthesis and assembly technology we developed a platform for combinatorial assembly of multi-gene constructs for use in altering glycosylation of immunoglobulin G (IgG) produced in chinese hamster ovary cells (CHO). A kinetic based model of N-glycosylation was used to predict N-glycosylation genes responsible for generating targeted glycosylation profiles of IgG. Expression of selected glycosylation genes from multi-gene constructs led to altered glycan profiles of IgG demonstrating the efficacy of our platform and validating predictions of our model.

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