Modeling Glycan Processing Reveals Golgi-Enzyme Homeostasis upon Trafficking Defects and Cellular Differentiation

TY - JOUR

T1 - Modeling Glycan Processing Reveals Golgi-Enzyme Homeostasis upon Trafficking Defects and Cellular Differentiation

AU - Fisher, Peter

AU - Spencer, Hannah

AU - Thomas-Oates, Jane

AU - Wood, A. Jamie

AU - Ungar, Daniel

N1 - Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

PY - 2019/4/23

Y1 - 2019/4/23

N2 - The decoration of proteins by carbohydrates is essential for eukaryotic life yet heterogeneous due to a lack of biosynthetic templates. This complex carbohydrate mixture-the glycan profile-is generated in the compartmentalized Golgi, in which level and localization of glycosylation enzymes are key determinants. Here, we develop and validate a computational model for glycan biosynthesis to probe how the biosynthetic machinery creates different glycan profiles. We combined stochastic modeling with Bayesian fitting that enables rigorous comparison to experimental data despite starting with uncertain initial parameters. This is an important development in the field of glycan modeling, which revealed biological insights about the glycosylation machinery in altered cellular states. We experimentally validated changes in N-linked glycan-modifying enzymes in cells with perturbed intra-Golgi-enzyme sorting and the predicted glycan-branching activity during osteogenesis. Our model can provide detailed information on altered biosynthetic paths, with potential for advancing treatments for glycosylation-related diseases and glyco-engineering of cells.

AB - The decoration of proteins by carbohydrates is essential for eukaryotic life yet heterogeneous due to a lack of biosynthetic templates. This complex carbohydrate mixture-the glycan profile-is generated in the compartmentalized Golgi, in which level and localization of glycosylation enzymes are key determinants. Here, we develop and validate a computational model for glycan biosynthesis to probe how the biosynthetic machinery creates different glycan profiles. We combined stochastic modeling with Bayesian fitting that enables rigorous comparison to experimental data despite starting with uncertain initial parameters. This is an important development in the field of glycan modeling, which revealed biological insights about the glycosylation machinery in altered cellular states. We experimentally validated changes in N-linked glycan-modifying enzymes in cells with perturbed intra-Golgi-enzyme sorting and the predicted glycan-branching activity during osteogenesis. Our model can provide detailed information on altered biosynthetic paths, with potential for advancing treatments for glycosylation-related diseases and glyco-engineering of cells.

KW - COG complex

KW - glycan processing

KW - Golgi apparatus

KW - Markov chain Monte Carlo

KW - mass spectrometry

KW - MSCs

UR - http://www.scopus.com/inward/record.url?scp=85064428399&partnerID=8YFLogxK

U2 - 10.1016/j.celrep.2019.03.107

DO - 10.1016/j.celrep.2019.03.107

M3 - Article

C2 - 31018136

AN - SCOPUS:85064428399

SN - 2211-1247

VL - 27

SP - 1231

EP - 1243

JO - Cell reports

JF - Cell reports

IS - 4

ER -