Literature DB >> 9184832

A mathematical model of sialylation of N-linked oligosaccharides in the trans-Golgi network.

T J Monica1, D C Andersen, C F Goochee.   

Abstract

A mathematical model is developed of the compartmentalized sialylation of N-linked oligosaccharides in order to understand and predict the outcome of sialylation reactions. A set of assumptions are presented, including Michaelis-Menten-type dependency of reaction rate on the concentration of the glycoprotein substrate. The resulting model predicts the heterogeneous outcome of a posttranslational oligosaccharide biosynthesis step, a critical aspect that is not accounted for in the modeling of the cotranslational attachment of oligosaccharides to glycosylation sites (Shelikoff et al., Biotech. Bioeng., 50, 73-90, 1996) or general models of the secretion process (Noe and Delenick, J. Cell Sci., 92, 449-459, 1989). In the steady-state for the likely case where the concentration of substrate is much less than the Km of the sialyltransferase, the model predicts that the extent of sialylation, x, will depend upon the enzyme concentration, enzyme kinetic parameters and substrate residence time in the reaction compartment. The value of x predicted by the model using available literature data is consistent with the values of x that have been recently determined for the glycoproteins CD4 (Spellman et al., Biochemistry, 30, 2395-2406, 1991) and t-PA (Spellman et al., J. Biol. Chem., 264, 14100-14111, 1989) secreted by Chinese hamster ovary cells. For the unsaturated case, the model also predicts that x is independent of the concentration of secreted glycoprotein in the Golgi. The general modeling approach outlined in this article may be applicable to other glycosylation reactions and posttranslational modifications.

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Year:  1997        PMID: 9184832     DOI: 10.1093/glycob/7.4.515

Source DB:  PubMed          Journal:  Glycobiology        ISSN: 0959-6658            Impact factor:   4.313


  9 in total

1.  Metabolic flux increases glycoprotein sialylation: implications for cell adhesion and cancer metastasis.

Authors:  Ruben T Almaraz; Yuan Tian; Rahul Bhattarcharya; Elaine Tan; Shih-Hsun Chen; Matthew R Dallas; Li Chen; Zhen Zhang; Hui Zhang; Konstantinos Konstantopoulos; Kevin J Yarema
Journal:  Mol Cell Proteomics       Date:  2012-03-28       Impact factor: 5.911

Review 2.  Systems glycobiology: biochemical reaction networks regulating glycan structure and function.

Authors:  Sriram Neelamegham; Gang Liu
Journal:  Glycobiology       Date:  2011-03-24       Impact factor: 4.313

Review 3.  Harnessing cancer cell metabolism for theranostic applications using metabolic glycoengineering of sialic acid in breast cancer as a pioneering example.

Authors:  Haitham A Badr; Dina M M AlSadek; Motawa E El-Houseini; Christopher T Saeui; Mohit P Mathew; Kevin J Yarema; Hafiz Ahmed
Journal:  Biomaterials       Date:  2016-11-25       Impact factor: 12.479

Review 4.  Understanding glycomechanics using mathematical modeling: a review of current approaches to simulate cellular glycosylation reaction networks.

Authors:  Apurv Puri; Sriram Neelamegham
Journal:  Ann Biomed Eng       Date:  2011-11-17       Impact factor: 3.934

5.  Modulation of circulatory residence of recombinant acetylcholinesterase through biochemical or genetic manipulation of sialylation levels.

Authors:  T Chitlaru; C Kronman; M Zeevi; M Kam; A Harel; A Ordentlich; B Velan; A Shafferman
Journal:  Biochem J       Date:  1998-12-15       Impact factor: 3.857

6.  Systems-level modeling of cellular glycosylation reaction networks: O-linked glycan formation on natural selectin ligands.

Authors:  Gang Liu; Dhananjay D Marathe; Khushi L Matta; Sriram Neelamegham
Journal:  Bioinformatics       Date:  2008-10-07       Impact factor: 6.937

7.  A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data.

Authors:  Frederick J Krambeck; Sandra V Bennun; Someet Narang; Sean Choi; Kevin J Yarema; Michael J Betenbaugh
Journal:  Glycobiology       Date:  2009-06-08       Impact factor: 4.313

Review 8.  Metabolic glycoengineering: sialic acid and beyond.

Authors:  Jian Du; M Adam Meledeo; Zhiyun Wang; Hargun S Khanna; Venkata D P Paruchuri; Kevin J Yarema
Journal:  Glycobiology       Date:  2009-08-12       Impact factor: 4.313

Review 9.  What can mathematical modelling say about CHO metabolism and protein glycosylation?

Authors:  Sarah N Galleguillos; David Ruckerbauer; Matthias P Gerstl; Nicole Borth; Michael Hanscho; Jürgen Zanghellini
Journal:  Comput Struct Biotechnol J       Date:  2017-01-28       Impact factor: 7.271

  9 in total

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