Glycosylation is one of the most important post-translational modifications (PTMs) of proteins and has great biological significance. Carbohydrate-protein interactions are involved in many cellular events such as protein localization, protein-protein interactions, immune responses, cell division, tumor immunology, and cell signaling. Therefore, profiling of glycoproteins is imperative in the discovery of disease biomarkers and clinical diagnosis.
This thesis describes the development of a new method for enrichment of glycopeptides, glycolipids, and N-glycans using filters and lectins. Chapter 1 explains the significance of glycosylation, advantages and disadvantages associated with current techniques for extraction of glycoproteome, and the newly developed method for glycoproteome analysis. Chapter 2 discusses the development of the method for simultaneous extraction and analysis of phospho- and glycoproteome in a one-batch sample from nephrocalcinosis tissues of phytate-fed rats. Chapter 3 reports integrated analysis of global proteome, phosphoproteome, and glycoproteome that enables complementary interpretation of disease-related protein networks. This study shows integrated analysis of protein expression, phosphorylation, and N glycosylation by serial enrichment of phosphorylation and N glycosylation (SEPG) from the same tissue samples. Furthermore, an automated high throughput sample preparation protocol for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of glycopeptides is outlined in Chapter 4. It describes an automated glycopeptide preparation workflow for a 96-well plate liquid-handling robotic system. In Chapter 5, the use of filter aided capture and elution protocol for concurrent preparation of N-glycan and O-glycopeptides for LC-MS/MS analysis from a single experimental batch is explained. Chapter 6 narrates a novel method for ganglioside enrichment from mouse brain (sialic acid containing ganglioside lipid capturing method [SG LC]). In Chapter 7, proteome analysis of mouse adipose and colon tissues using a novel integrated data processing pipeline is elucidated. The pipeline utilizes two open-source software: iPE-MMR for mass number correction and iProphet to combine several search results.