Skip to main content
SpringerLink
Log in

Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes

  • Protocol
  • First Online:
Glycosylation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2370))

  • 1301 Accesses

Abstract

Modeling glycan biosynthesis is becoming increasingly important due to the far-reaching implications that glycosylation can exhibit, from pathologies to biopharmaceutical manufacturing. Here we describe a stochastic simulation approach, to overcome the deterministic nature of previous models, that aims to simulate the action of glycan modifying enzymes to produce a glycan profile. This is then coupled with an approximate Bayesian computation methodology to systematically fit to empirical data in order to determine which set of parameters adequately describes the organization of enzymes within the Golgi. The model is described in detail along with a proof of concept and therapeutic applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Takahashi M, Tsuda T, Ikeda Y et al (2003) Role of N-glycans in growth factor signaling. Glycoconj J 20:207–212

    Article  Google Scholar 

  2. Gu J, Isaji T, Xu Q et al (2012) Potential roles of N-glycosylation in cell adhesion. Glycoconj J 29:599–607

    Article  CAS  Google Scholar 

  3. Xu C, Ng DTW (2015) Glycosylation-directed quality control of protein folding. Nat Rev Mol Cell Biol 16:742–752

    Article  CAS  Google Scholar 

  4. Chang IJ, He M, Lam CT (2018) Congenital disorders of glycosylation. Ann Transl Med 6:477–477

    Article  CAS  Google Scholar 

  5. Pinho SS, Reis CA (2015) Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer 15:540–555

    Article  CAS  Google Scholar 

  6. Nairn AV, Aoki K, Dela Rosa M et al (2012) Regulation of glycan structures in murine embryonic stem cells: combined transcript profiling of glycan-related genes and glycan structural analysis. J Biol Chem 287:37835–37856

    Article  CAS  Google Scholar 

  7. Fisher P, Ungar D (2016) Bridging the gap between glycosylation and vesicle traffic. Front Cell Dev Biol 4:15

    Article  Google Scholar 

  8. Rabouille C, Hui N, Hunte F et al (1995) Mapping the distribution of Golgi enzymes involved in the construction of complex oligosaccharides. J Cell Sci 108:1617–1627

    Article  CAS  Google Scholar 

  9. Umaña P, Bailey JE (1997) A mathematical model of N-linked glycoform biosynthesis. Biotechnol Bioeng 55:890–908

    Article  Google Scholar 

  10. Krambeck FJ, Betenbaugh MJ (2005) A mathematical model of N-linked glycosylation. Biotechnol Bioeng 92:711–728

    Article  CAS  Google Scholar 

  11. Spahn PN, Hansen AH, Hansen HG et al (2016) A Markov chain model for N-linked protein glycosylation - towards a low-parameter tool for model-driven glycoengineering. Metab Eng 33:52–66

    Article  CAS  Google Scholar 

  12. Fisher P, Spencer H, Thomas-Oates J et al (2019) Modeling glycan processing reveals Golgi-enzyme homeostasis upon trafficking defects and cellular differentiation. Cell Rep 27:1231–1243

    Article  CAS  Google Scholar 

  13. Glick BS, Elston T, Oster G (1997) A cisternal maturation mechanism can explain the asymmetry of the Golgi stack. FEBS Lett 414:177–181

    Article  CAS  Google Scholar 

  14. Ungar D, Oka T, Brittle EE et al (2002) Characterization of a mammalian Golgi-localized protein complex, COG, that is required for normal Golgi morphology and function. J Cell Biol 157:405–415

    Article  CAS  Google Scholar 

  15. Harris SL, Waters MG (1996) Localization of a yeast early Golgi mannosyltransferase, Och1p, involves retrograde transport. J Cell Biol 132:985–998

    Article  CAS  Google Scholar 

  16. Morré DJ, Ovtracht L (1977) Dynamics of the Golgi apparatus: membrane differentiation and membrane flow. Int Rev Cytol Suppl 5:61–188

    Google Scholar 

  17. Ungar D (2009) Golgi linked protein glycosylation and associated diseases. Semin Cell Dev Biol 20:762–769

    Article  CAS  Google Scholar 

  18. Arigoni-Affolter I, Scibona E, Lin C-W et al (2019) Mechanistic reconstruction of glycoprotein secretion through monitoring of intracellular N-glycan processing. Sci Adv 5:eaax8930

    Article  CAS  Google Scholar 

  19. McDonald AG, Hayes JM, Bezak T et al (2014) Galactosyltransferase 4 is a major control point for glycan branching in N-linked glycosylation. J Cell Sci 127:5014–5026

    PubMed  PubMed Central  Google Scholar 

  20. Sou SN, Jedrzejewski PM, Lee K et al (2017) Model-based investigation of intracellular processes determining antibody Fc-glycosylation under mild hypothermia. Biotechnol Bioeng 114:1570–1582

    Article  CAS  Google Scholar 

  21. Kotidis P, Jedrzejewski P, Sou SN et al (2019) Model-based optimization of antibody galactosylation in CHO cell culture. Biotechnol Bioeng 116:1612–1626

    Article  CAS  Google Scholar 

  22. Jimenez del Val I, Nagy JM, Kontoravdi C (2011) A dynamic mathematical model for monoclonal antibody N-linked glycosylation and nucleotide sugar donor transport within a maturing Golgi apparatus. Biotechnol Prog 27:1730–1743

    Article  CAS  Google Scholar 

  23. Wada Y, Azadi P, Costello CE et al (2007) Comparison of the methods for profiling glycoprotein glycans—HUPO human disease glycomics/proteome initiative multi-institutional study. Glycobiology 17:411–422

    Article  CAS  Google Scholar 

  24. Gillespie DT (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22:403–434

    Article  CAS  Google Scholar 

  25. Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8:3–30

    Article  Google Scholar 

  26. Marjoram P, Molitor J, Plagnol V, Tavaré S (2003) Markov chain Monte Carlo without likelihoods. Proc Natl Acad Sci U S A 100:15324–15328

    Article  CAS  Google Scholar 

  27. Sherlock C, Thiery AH, Roberts GO, Rosenthal JS (2015) On the efficiency of pseudo-marginal random walk metropolis algorithms. Ann Stat 43:238–275

    Article  Google Scholar 

  28. Toni T, Stumpf MPH (2009) Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics 26:104–110

    Article  Google Scholar 

  29. Fisher P, Thomas-Oates J, Wood AJ, Ungar D (2019) The N-glycosylation processing potential of the mammalian Golgi apparatus. Front Cell Dev Biol 7:157

    Article  Google Scholar 

  30. Elbein AD, Solf R, Dorling PR, Vosbeck K (1981) Swainsonine: an inhibitor of glycoprotein processing. Proc Natl Acad Sci U S A 78:7393–7397

    Article  CAS  Google Scholar 

  31. Zeevaert R, Foulquier F, Jaeken J, Matthijs G (2008) Deficiencies in subunits of the conserved oligomeric Golgi (COG) complex define a novel group of congenital disorders of glycosylation. Mol Genet Metab 93:15–21

    Article  CAS  Google Scholar 

  32. Ng BG, Freeze HH (2018) Perspectives on glycosylation and its congenital disorders. Trends Genet 34:466–476

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biology, University of York, York, UK

    Ben West & Daniel Ungar

  2. Departments of Biology and Mathematics, University of York, York, UK

    A. Jamie Wood

Authors
  1. Ben West
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Jamie Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Ungar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Ungar .

Editor information

Editors and Affiliations

  1. School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland

    Gavin P. Davey

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

West, B., Wood, A.J., Ungar, D. (2022). Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes. In: Davey, G.P. (eds) Glycosylation. Methods in Molecular Biology, vol 2370. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1685-7_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1685-7_10

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1684-0

  • Online ISBN: 978-1-0716-1685-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation