Glyco-Mapper: A Chinese hamster ovary (CHO) genome-specific glycosylation prediction tool

Abstract

Glyco-Mapper is a novel systems biology product quality prediction tool created using a new framework termed: Discretized Reaction Network Modeling using Fuzzy Parameters (DReaM-zyP). Within Glyco-Mapper, users fix the nutrient feed composition and the glycosylation reaction fluxes to fit the model glycoform to the reference experimental glycoform, enabling cell-line specific glycoform predictions as a result of cell engineering strategies. Glyco-Mapper accurately predicts glycoforms associated with genetic alterations that result in the appearance or disappearance of one or more glycans with an accuracy, sensitivity, and specificity of 96%, 85%, and 97%, respectively, for publications between 1999 and 2014. The modeled glycoforms span a large range of glycoform engineering strategies, including the altered expression of glycosylation, nucleotide sugar transport, and metabolism genes, as well as an altered nutrient feeding strategy. A glycoprotein-producing CHO cell line reference glycoform was modeled and a novel Glyco-Mapper prediction was experimentally confirmed with an accuracy and specificity of 95% and 98%, respectively. Glyco-Mapper is a product quality prediction tool that provides a streamlined way to design host cell line genomes to achieve specific product quality attributes.

CHOmpact: a reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

“…Our flux balance model (Figure 1) is based on previous work for GS-CHO cells (Carinhas et al, 2013), which has been expanded to include the aspartate-malate (Asp-Mal) shuttle (Mulukutla et al, 2012; Nolan and Lee, 2011), the urea cycle (Zamorano et al, 2010), de novo serine synthesis from glycolytic intermediates, and ATP synthesis via oxidative phosphorylation. The pathway for nucleotide sugar donor biosynthesis (Kremkow and Lee, 2018) has also been included.…”

Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication

“…Their value, however, is seen in their ability to handle larger networks with minimal parameterization. The low parameterization and scalability of these models accommodate diverse approaches to compute fluxes, such as flux balance modeling, probabilistic learning [58,59], multiomic comparison, and other linearized approaches [61]. The reduced complexity of linearized glycan biosynthesis models allows for the simulation of multiple glycogene knockouts [62], tolerance of glycan structure and reaction uncertainty [60], and analysis of many types of glycosylation including human milk oligosaccharide biosynthesis [60,63], O-linked glycans [50,64], GAGs [58], and glycolipids [65].…”

“…Similarly, expression-modulating miRNAs are also a useful proxy for glycan biosynthesis [75,76]. The ability of a genome to express a glycan has been predicted from glycogene presence in minimally characterized organisms and states like bacteria and archaea [77], CHO cells [61], and cells with GT knockouts [64]. Novel glycan-glycogene relationships have been elucidated using a genome-wide association study of serum glycosylation [78].…”

Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy

“…This modeling framework has been used to predict how a cell line can be engineered in biosimilar design [ 89 ]. In another study, an N-linked glycosylation model [ 90 ] of Chinese hamster ovary (CHO) was developed that includes all CHO N-glycosylation genes, as well as metabolic genes related to nucleotide sugar synthesis, transport, and glycosylation. This model uses reaction flux flow stoichiometry, discrete variable state parameters, and mass balances to estimate the possible glycosylation patterns of therapeutic protein.…”