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Cited by 83Viral Infectious Diseases and Gene Expression in InsectsGlycosylation and Glycoproteins ResearchMicrobial Metabolic Engineering and Bioproduction

Towards controlling the glycoform: A model framework linking extracellular metabolites to antibody glycosylation

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Jedrzejewski, Philip, Del Val, Ioscani, Constantinou, Antony, Dell, Anne, Haslam, Stuart, Polizzi, Karen, Kontoravdi, Cleo·2014·International Journal of Molecular Sciences
DOI 10.3390/ijms15034492PMC PMC3975410
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Abstract

Glycoproteins represent the largest group of the growing number of biologically-derived medicines. The associated glycan structures and their distribution are known to have a large impact on pharmacokinetics. A modelling framework was developed to provide a link from the extracellular environment and its effect on intracellular metabolites to the distribution of glycans on the constant region of an antibody product. The main focus of this work is the mechanistic in silico reconstruction of the nucleotide sugar donor (NSD) metabolic network by means of 34 species mass balances and the saturation kinetics rates of the 60 metabolic reactions involved. NSDs are the co-substrates of the glycosylation process in the Golgi apparatus and their simulated dynamic intracellular concentration profiles were linked to an existing model describing the distribution of N-linked glycan structures of the antibody constant region. The modelling framework also describes the growth dynamics of the cell population by means of modified Monod kinetics. Simulation results match well to experimental data from a murine hybridoma cell line. The result is a modelling platform which is able to describe the product glycoform based on extracellular conditions. It represents a first step towards the in silico prediction of the glycoform of a biotherapeutic and provides a platform for the optimisation of bioprocess conditions with respect to product quality.

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  • NSDs are synthesised intracellularly from extracellular metabolites such as glucose, glutamine, galactose, and uridine through a sequence of cytosolic enzymatic reactions, and are subsequently consumed in the Golgi apparatus during glycosylation.

    “NSDs are synthesised intracellularly from extracellular metabolites such as glucose, glutamine, galactose, and uridine through a sequence of cytosolic enzymatic reactions, and are subsequently consumed in the Golgi apparatus during glycosylation [20,21,25].”

    From Soft sensing of intracellular states for CHO cell bioprocessing with Ensemble Kalman Filters (2026) · cites this paper as [21]

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  1. [2]Kyriakopoulos S. Kontoravdi C. Analysis of the landscape of biologically-derived pharmaceuticals in Europe: Dominant production systems molecule types on the rise and approval trends Eur. J. Pharm. Sci. 2013 48 428 441 23262060
  2. [3]Shields R.L. Lai J. Keck R. O’Connell L.Y. Hong K. Meng Y.G. Weikert S.H.A. Presta L.G. Lack of fucose on human IgG1 N-linked oligosaccharide improves binding to human Fc gamma RIII and antibody-dependent cellular toxicity J. Biol. Chem. 2002 277 26733 26740 11986321
  3. [4]Matsumiya S. Yamaguchi Y. Saito J. Nagano M. Sasakawa H. Otaki S. Satoh M. Shitara K. Kato K. Structural comparison of fucosylated and nonfucosylated Fc fragments of human immunoglobulin G1 J. Mol. Biol. 2007 368 767 779 17368483
  4. [5]Hodoniczky J. Zheng Y.Z. James D.C. Control of recombinant monoclonal antibody effector functions by Fc N-glycan remodeling in vitro Biotechnol. Progr. 2005 21 1644 1652 16321047
  5. [7]Goetze A.M. Liu Y.D. Zhang Z.Q. Shah B. Lee E. Bondarenko P.V. Flynn G.C. High-mannose glycans on the Fc region of therapeutic IgG antibodies increase serum clearance in humans Glycobiology 2011 21 949 959 21421994
  6. [8]Mullard A. Can next-generation antibodies offset biosimilar competition? Nat. Rev. Drug Discov. 2012 11 426 428 22653202
  7. [11]Krambeck F.J. Bennun S.V. Narang S. Choi S. Yarema K.J. Betenbaugh M.J. A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data Glycobiology 2009 19 1163 1175 19506293 PMC2757573
  8. [12]Del Val I.J. Kontoravdi C. Nagy J.M. Towards the implementation of quality by design to the production of therapeutic monoclonal antibodies with desired glycosylation patterns Biotechnol. Progr. 2010 26 1505 1527 20665659
  9. [13]Hills A.E. Patel A. Boyd P. James D.C. Metabolic control of recombinant monoclonal antibody N-glycosylation in GS-NS0 cells Biotechnol. Bioeng. 2001 75 239 251 11536148
  10. [14]Grainger R.K. James D.C. CHO cell line specific prediction and control of recombinant monoclonal antibody N-glycosylation Biotechnol. Bioeng. 2013 110 2970 2983 23737295
  11. [15]Wong N.S.C. Wati L. Nissom P.M. Feng H.T. Lee M.M. Yap M.G.S. An investigation of intracellular glycosylation activities in CHO Cells: Effects of nucleotide sugar precursor feeding Biotechnol. Bioeng. 2010 107 321 336 20506284
  12. [16]Rathore A.S. Winkle H. Quality by design for biopharmaceuticals Nat. Biotechnol. 2009 27 26 34 19131992
  13. [17]del Val I.J. Nagy J.M. Kontoravdi C. A dynamic mathematical model for monoclonal antibody N-linked glycosylation and nucleotide sugar donor transport within a maturing Golgi apparatus Biotechnol. Progr. 2011 27 1730 1743 21956887
  14. [19]Xie L.Z. Wang D.I.C. Stoichiometric analysis of animal-cell growth and its application in medium design Biotechnol. Bioeng. 1994 43 1164 1174 18615530
  15. [20]Nolan R.P. Lee K. Dynamic model of CHO cell metabolism Metab. Eng. 2011 13 108 124 20933095
  16. [21]Bonarius H.P. Hatzimanikatis V. Meesters K.P. de Gooijer C.D. Schmid G. Tramper J. Metabolic flux analysis of hybridoma cells in different culture media using mass balances Biotechnol. Bioeng. 1996 50 299 318 18626958
  17. [24]Springer M.S. Stanhope M.J. Madsen O. de Jong W.W. Molecules consolidate the placental mammal tree Trends Ecol. Evol. 2004 19 430 438 16701301
  18. [25]Rijcken W.R.P. Overdijk B. Vandeneijnden D.H. Ferwerda W. The effect of increasing nucleotide sugar concentrations on the incorporation of sugars into glycoconjugates in rat hepatocytes Biochem. J. 1995 305 865 870 7848287 PMC1136339
  19. [26]Apweiler R. Hermjakob H. Sharon N. On the frequency of protein glycosylation as deduced from analysis of the SWISS-PROT database Bba-Gen. Subj. 1999 1473 4 8 10580125
  20. [27]Chen N. Koumpouras G.C. Polizzi K.M. Kontoravdi C. Genome-based kinetic modeling of cytosolic glucose metabolism in industrially relevant cell lines: Saccharomyces cerevisiae and Chinese hamster ovary cells Bioproc. Biosyst. Eng. 2012 35 1023 1033 22286123
  21. [28]Wong D.C.F. Wong N.S.C. Goh J.S.Y. May L.M. Yap M.G.S. Profiling of N-glycosylation gene expression in CHO cell fed-batch cultures Biotechnol. Bioeng. 2010 107 516 528 20521304
  22. [29]Frame K.K. Hu W.S. Cell-Volume Measurement as an estimation of mammalian-cell biomass Biotechnol. Bioeng. 1990 36 191 197 18595067
  23. [30]Lee M.S. Lee G.M. Hyperosmotic pressure enhances immunoglobulin transcription rates and secretion rates of KR12H-2 transfectoma Biotechnol. Bioeng. 2000 68 260 268 10745194
  24. [31]Fujita N. Tamura A. Higashidani A. Tonozuka T. Freeze H.H. Nishikawa A. The relative contribution of mannose salvage pathways to glycosylation in PMI-deficient mouse embryonic fibroblast cells FEBS J. 2008 275 788 798 18215164
  25. [34]Kontoravdi C. Asprey S.P. Pistikopoulos E.N. Mantalaris A. Application of global sensitivity analysis to determine goals for design of experiments: An example study on antibody-producing cell cultures Biotechnol. Progr. 2005 21 1128 1135 16080692
  26. [36]Goudar C. Biener R. Boisart C. Heidemann R. Piret J. de Graaf A. Konstantinov K. Metabolic flux analysis of CHO cells in perfusion culture by metabolite balancing and 2D [C-13 H-1] COSY NMR spectroscopy Metab. Eng. 2010 12 138 149 19896555
  27. [38]Li G.Y. Hu J.S. Wang S.W. Georgopoulos P.G. Schoendorf J. Rabitz H. Random sampling-high dimensional model representation (RS-HDMR) and orthogonality of its different order component functions J. Phys. Chem. A 2006 110 2474 2485 16480307
  28. [39]Li G.Y. Rabitz H. Ratio control variate method for efficiently determining high-dimensional model representations J. Comput. Chem. 2006 27 1112 1118 16691567
  29. [40]Feil B. Kucherenko S. Shah N. Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation Proceedings of the Simul: 2009 First International Conference on Advances in System Simulation Porto, Portugal 20–25 September 2009 12 17
  30. [42]Geiger T. Velic A. Macek B. Lundberg E. Kampf C. Nagaraj N. Uhlen M. Cox J. Mann M. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse Mol. Cell. Proteomics 2013 12 1709 1722 23436904 PMC3675825
  31. [43]Baycin-Hizal D. Tabb D.L. Chaerkady R. Chen L. Lewis N.E. Nagarajan H. Sarkaria V. Kumar A. Wolozny D. Colao J. Proteomic analysis of Chinese hamster ovary cells J. Proteome Res. 2012 11 5265 5276 22971049 PMC3772721
  32. [45]Milla M.E. Clairmont C.A. Hirschberg C.B. Reconstitution into proteoliposomes and partial purification of the Golgi apparatus membrane UDP-galactose UDP-xylose and UDP-glucuronic acid transport activities J. Biol. Chem. 1992 267 103 107 1730575
  33. [46]Dietmair S. Timmins N.E. Gray P.P. Nielsen L.K. Kromer J.O. Towards quantitative metabolomics of mammalian cells: Development of a metabolite extraction protocol Anal. Biochem. 2010 404 155 164 20435011
  34. [47]Del Val I.J. Kyriakopoulos S. Polizzi K.M. Kontoravdi C. An optimized method for extraction and quantification of nucleotides and nucleotide sugars from mammalian cells Anal. Biochem. 2013 443 172 180 24036437
  35. [48]Dell A. Reason A.J. Khoo K.H. Panico M. McDowell R.A. Morris H.R. Mass spectrometry of carbohydrate-containing biopolymers Methods Enzymol. 1994 230 108 132 8139492
  36. [49]Jang-Lee J. North S.J. Sutton-Smith M. Goldberg D. Panico M. Morris H. Haslam S. Dell A. Glycomic profiling of cells and tissues by mass spectrometry: Fingerprinting and sequencing methodologies Methods Enzymol. 2006 415 59 86 17116468
  37. [50]Damerell D. Ceroni A. Maass K. Ranzinger R. Dell A. Haslam S.M. The GlycanBuilder and GlycoWorkbench glycoinformatics tools: Updates and new developments Biol. Chem. 2012 393 1357 1362 23109548
  38. [51]Ceroni A. Maass K. Geyer H. Geyer R. Dell A. Haslam S.M. GlycoWorkbench: A tool for the computer-assisted annotation of mass spectra of glycans J. Proteome Res. 2008 7 1650 1659 18311910

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Authors
Jedrzejewski, P., Del Val, I., Constantinou, A., Dell, A., Haslam, S., Polizzi, K., & Kontoravdi, C.
Affiliations
  • Imperial College London (GB)
  • Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK
Corresponding author
Kontoravdi, Cleo
ORCID
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2014-03-14
Volume
15
Issue
3
Pages
4492-4522
ISSN
1422-0067 (ISSN-L)
DOI
10.3390/ijms15034492
PMC
PMC3975410
OpenAlex
W2021861438
Cited by
83
References
2
Citations by year
2026: 4 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 1 · 2021: 7 · 2020: 8 · 2019: 8 · 2018: 10 · 2017: 9 · 2016: 11 · 2015: 8 · 2014: 4
FWCI
4.03
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Spiral (Imperial College London) (submittedVersion, cc-by)
Funders
  • Research Councils UK
  • Directorate for Biological Sciences
  • Engineering and Physical Sciences Research Council (1162114)
  • Biotechnology and Biological Sciences Research Council (BB/K016164/1, BB/I017011/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Viral Infectious Diseases and Gene Expression in Insects
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
antibody glycosylation, glycoproteins, biopharmaceuticals, mass balance model, saturation kinetics, Golgi apparatus, Monod kinetics, extracellular metabolites, bioprocess optimization, pharmacokinetics
MeSH terms
Algorithms, Animals, Antibodies, Cell Line, Computer Simulation, Extracellular Space, Glucose, Glutamine, Glycoproteins, Glycosylation, Golgi Apparatus, Hybridomas, Models, Biological, Nucleoside Diphosphate Sugars, Nucleotides, Polysaccharides, Time Factors, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Cell Proliferation, Mice, Metabolic Networks and Pathways

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