(523b) Systems Engineering N-Glycans of Recombinant Therapeutic Proteins Produced in Mammalian Cells

Authors: 
McCann, M. G., University of Minnesota, Twin Cities
Le, T. S., University of Minnesota, Twin Cities
Stach, C., University of Minnesota, Twin Cities
Chen, X., East China University of Science & Technology
Somia, N., University of Minnesota, Twin Cities
Zhao, L., East China University of Science & Technology
Smanski, M. J., University of Minnesota, Twin Cities
Hu, W. S., University of Minnesota, Twin Cities
Glycan patterns on therapeutic proteins affect the stability, half-life, and biological activities of those medicines and are an important quality attribute. The glycosyltransferase reactions form a complex network and are distributed into compartmentalized Golgi apparatus. Furthermore, long pathways are involved in the synthesis of nucleotide sugar precursors requisite for glycosylation, and the transport rate of those precursors into Golgi apparatus is controlled by specific transporters.

We have taken a systems engineering approach to engineer the glycosylation pathway in CHO cells to modulate the N-Glycan patterns in recombinant immunoglobulin G (IgG). Using a network visualization tool developed in our lab (GlycoVis) and a reaction network model to analyze the N-Glycan profile of a recombinant IgG, the potential limiting step of galactosylation was identified and the components of the glycosylation network, including glycan synthesis, nucleotide sugar synthesis and transporter were compiled. Transcript levels of the candidate genes were evaluated for the merit of genetic manipulation. Candidate genes, individually as well as in the form of cassettes of multiple genes with potential synergistic effects, were introduced into CHO for overexpression. Resulting N-Glycan patterns of recombinant protein produced in glycoengineered CHO cells were analyzed by HPLC using 2-AB labeling. Several gene construct gave the desired the results. The results also serve to refine the model parameters and sharpen its prediction. Such a systems approach will enhance our capability to steer N-Glycan patterns and enhance the control of the quality of therapeutic proteins.