Functional network of glycan-related molecules: glyco-net in glycoconjugate data bank

Functional network in posttranslational modifications: Glyco-Net in Glycoconjugate Data Bank

Elucidating pathways related to posttranslational modifications (PTMs) such as glycosylation is of growing importance in post-genome science and technology. Graphical networks describing the relationships among glycan-related molecules, including genes, proteins, lipids, and various biological events, are considered extremely valuable and convenient tools for the systematic investigation of PTMs. Glyco-Net (http://bibi.sci.hokudai.ac.jp/functions/) can dynamically make network figures among various biological molecules and biological events. A certain molecule or event is expressed with a node, and the relationship between the molecule and the event is indicated by arrows in the network figures. In this chapter, we mention the features and current status of the Glyco-Net and a simple example of the search with the Glyco-Net.

Nano-HPLC-MS of glycopeptides obtained after nonspecific proteolysis

Liquid chromatography-tandem stage mass spectrometry of glycopeptides is a powerful tool for the site-specific glycosylation analysis of glycoproteins. Using fetuin as a model substance, we describe a protocol for glycopeptide dissection using nonspecific proteolysis by proteinase K. Proteolysis is achieved using dissolved or immobilized enzyme. For glycopeptide separation three different nanoHPLC separation principles are compared, namely hydrophilic interaction liquid chromatography (HILIC), C18-reverse phase (RP), and graphitized carbon HPLC. Chromatographically resolved glycopeptides are analyzed by nano-electrospray ionization multistage mass spectrometry for identification of the glycan as well as the peptide moiety. Using this approach, site-specific information on protein glycosylation is obtained.

Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations

N-glycans play an essential role in biological process and are associated with age, gender, and body mass parameters in Caucasian populations, whereas no study has been reported in Chinese populations. To investigate the correlation between N-glycan structures and metabolic syndrome (MetS) components, we conducted a population-based study in 212 Chinese Han individuals. The replication study was performed on 520 unrelated individuals from a Croatian island Korčula. The most prominent observation was the consistent positive correlations between different forms of triantennary glycans and negative correlations between glycans containing core-fucose with MetS components including BMI, SBP, DBP, and fasting plasma glucose (FPG) simultaneously. Significant differences in a number of N-glycan traits were also detected between normal and abnormal groups of BMI, BP, and FPG, respectively. In the multivariate analysis, the level of monosialylated glycans (structure loadings = -0.776) was the most correlated with the MetS related risk factors, especially with SBP (structure loadings = 0.907). Results presented here are showing that variations in the composition of the N-glycome in human plasma could represent the alternations of human metabolism and could be potential biomarkers of MetS.

Systems glycobiology: biochemical reaction networks regulating glycan structure and function

There is a growing use of bioinformatics based methods in the field of Glycobiology. These have been used largely to curate glycan structures, organize array-based experimental data and display existing knowledge of glycosylation-related pathways in silico. Although the cataloging of vast amounts of data is beneficial, it is often a challenge to gain meaningful mechanistic insight from this exercise alone. The development of specific analysis tools to query the database is necessary. If these queries can integrate existing knowledge of glycobiology, new insights may be gained. Such queries that couple biochemical knowledge and mathematics have been developed in the field of Systems Biology. The current review summarizes the current state of the art in the application of computational modeling in the field of Glycobiology. It provides (i) an overview of experimental and online resources that can be used to construct glycosylation reaction networks, (ii) mathematical methods to formulate the problem including a description of ordinary differential equation and logic-based reaction networks, (iii) optimization techniques that can be applied to fit experimental data for the purpose of model reconstruction and for evaluating unknown model parameters, (iv) post-simulation analysis methods that yield experimentally testable hypotheses and (v) a summary of available software tools that can be used by non-specialists to perform many of the above functions.