(494f) Metabolic Model-Guided Glycoengineering in Escherichia coli

Protein glycosylation, the post-translational covalent attachment of oligosaccharide groups to specific amino acid residues, is required of many therapeutic proteins. Currently, eukaryotes possessing native glycosylation machinery serve as the preferred production host of therapeutic glycoproteins. The discovery of bacterial glycosylation in the pathogen Campylobacter jejuni and the subsequent transfer of these pathways into E. coli has spurred interest in producing non-native glycans in a more genetically tractable host. Recently, human-like glycan production was achieved in E. coli with the recombinant expression of a synthetic glycosylation pathway. Producing glycoproteins from prokaryotic hosts continues to suffer from several limitations including insufficient yield. Removal of competing metabolic pathways and bottlenecks that diminish glycan precursor formation may improve glycosylation efficiency. Metabolic model-guided engineering approaches may aid in the design of strains displaying improved glycoprotein production. Genome-scale metabolic reconstructions of industrial microbes are commonly used to identify genetic perturbations that will produce a desired biochemical production phenotype. In this study, we modify the existing genome-scale E. coli model iAF1260 (1260 open reading frames) to include a variety of glycosylation pathways, including those for C. jejuni and human-like glycans. Added reactions include the biochemical transformations associated with glycan biosynthesis and flipping into the periplasm, as well as the expression and glycan conjugation of a target protein. Optimal gene knockout designs were identified using constraint-based modeling in combination with a bilevel optimization problem which sought to couple glycan production to a cellular growth objective. Identified strains were constructed that displayed increased glycan synthesis and varying levels of glycoprotein production. Factors affecting glycosylation efficiency for a variety of glycoprotein targets are discussed.