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

Abstract

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.

Metabolic Flux Increases Glycoprotein Sialylation: Implications for Cell Adhesion and Cancer Metastasis

“…To date, the biosynthesis of cell surface displayed glycans has been thought to be controlled largely by individual glycosyltransferases based on the assumption that flux through the metabolic pathways that supply activated nucleotide sugar donors (the substrates for these enzymes) is not a limiting factor. For example, this premise has been used in mathematical models of sialylation (1), where concentrations of CMP-Neu5Ac in the lumen of the Golgi were assumed to be much higher than the K m of sialyltransferases (2). In the past several years, the idea that nucleotide sugars, exemplified by CMP-Neu5Ac (shown in Fig.…”

Glycoprotein Disease Markers and Single Protein-omics

“…Over 200 glycosyltransferases and many additional regulatory proteins expressed in tissue-specific patterns and levels participate in glycan synthesis, using the seven monosaccharides to build a diverse set of glycan structures (16). The variety of glycan structures on human proteins is not entirely known, although some estimates of the numbers exist (17). Each glycosylation site is typically occupied by a few major and additional minor glycan forms (18,19).…”

Bioinformatics for Glycan Structure Determination by Mass Spectrometry

“…On top of these approaches for data generation steps, advances in bioinformatics approaches for utilizing glycome data generated by the MS approaches are also expected. For example, recently reported studies of data visualization (55) and systems biology approaches (56)(57)(58)(59) will be pioneering works in such new areas in the near future. …”