A novel systems‐level approach to unravel the regulation and biosynthetic steps of glycosylation

Abstract

Protein glycosylation is fundamentally important to most biological processes, and an understanding of how it is regulated is fundamental to our understanding of many diseases and the development of biopharmaceuticals. However, the diversity and complexity of glycosylation have made it difficult to control glycosylation and identify how glycan synthesis is regulated. To address this challenge, we have developed a computational platform using probabilistic modeling of glycan synthesis, which allows us to study how glycosylation is regulated, and to guide glycoengineering efforts. Specifically, our computational approach first leverages known enzyme specificities or possible biosynthetic pathways based on glycan structures. This is used to map out all possible glycans that can be synthesized. Next, glycoprofiling and other omics data are used from control samples to train the model and quantify how glycosyltransferases and metabolic pathways contribute to glycan synthesis under normal conditions. Here I will discuss a few applications of this modeling framework for glycoengineering and biological discovery to identify how regulatory proteins control glycosylation in the cell. First, by coupling the framework with a genetic algorithm, we can predict the specific genetic and bioprocess modifications that are necessary to obtain a desired glycoprofile. Second, the framework can be used to gain deeper insights into how glycosylation is controlled by more distal regulators in the secretory pathway. Third, we can use the framework to identify which glycosyltransferases are associated with specific biosynthetic steps in incompletely characterized classes of glycans, such as human milk oligosaccharides. Thus, despite the complexity of glycosylation, novel systems biology approaches now provide tools to unravel its regulation, discover novel insights regarding their synthesis, and aid us in glycoengineering. Support or Funding Information The Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517) and from NIH (R35 GM119850 and R21 HD080682) Our novel modeling framework enables the integration of glycan profiling with other omics data (e.g., transcriptomics, proteomics, or measured genetic variants), to understand which steps of glycosylation are regulated, predict novel glycosyltransferases for poorly characterized classes of glycans, or guide glycoengineering efforts. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.