Glycobiology Advance Access originally published online on June 8, 2009
Glycobiology 2009 19(11):1163-1175; doi:10.1093/glycob/cwp081
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A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data
2 Department of Chemical and Biomolecular Engineering, Johns Hopkins University 3400 North Charles Street, Baltimore, MD 21218
3 ReacTech Inc., 810 Cameron Street, Alexandria, VA 22314
4 Department of Biomedical Engineering, Johns Hopkins University 3400 North Charles Street, Baltimore, MD 21218
5 Medimmune, LLC, One MedImmune Way, Gaithersburg, MD 20878, USA
1 To whom correspondence should be addressed: Tel: +703-549-9767; Fax: +703-652-4571; e-mail: fjkrambeck{at}reactech.net
Received on April 1, 2009; revised on June 3, 2009; accepted on June 3, 2009
Effective representation and characterization of biosynthetic pathways of glycosylation can be facilitated by mathematical modeling. This paper describes the expansion of a previously developed detailed model for N-linked glycosylation with the further application of the model to analyze MALDI-TOF mass spectra of human N-glycans in terms of underlying cellular enzyme activities. The glycosylation reaction network is automatically generated by the model, based on the reaction specificities of the glycosylation enzymes. The use of a molecular mass cutoff and a network pruning method typically limits the model size to about 10,000 glycan structures. This allows prediction of the complete glycan profile and its abundances for any set of assumed enzyme concentrations and reaction rate parameters. A synthetic mass spectrum from model-calculated glycan profiles is obtained and enzyme concentrations are adjusted to bring the theoretically calculated mass spectrum into agreement with experiment. The result of this process is a complete characterization of a measured glycan mass spectrum containing hundreds of masses in terms of the activities of 19 enzymes. In addition, a complete annotation of the mass spectrum in terms of glycan structure is produced, including the proportions of isomers within each peak. The method was applied to mass spectrometric data of normal human monocytes and monocytic leukemia (THP1) cells to derive glycosyltransferase activity changes underlying the differences in glycan structure between the normal and diseased cells. Model predictions could lead to a better understanding of the changes associated with disease states, identification of disease-associated biomarkers, and bioengineered glycan modifications.
Key words: automatic glycan annotation / glycosylation enzyme activity / mass spectrum / mathematical model / monocytic leukemia