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Protein glycosylation fingerprints are widely recognized as potential markers for disease states, and indeed differential glycosylation has been identified in multiple types of autoimmune diseases and several types of cancer. However, releasing the glycans leave the glycoproteins unknown; therefore, there exists a need for high-throughput methods that allow quantification of site- and protein-specific glycosylation patterns from complex biological mixtures. In this study, a targeted multiple reaction monitoring (MRM)-based method for the protein- and site-specific quantitation involving serum proteins immunoglobulins A, G and M, alpha-1-antitrypsin, transferrin, alpha-2-macroglobulin, haptoglobin, alpha-1-acid glycoprotein and complement C3 was developed. The method is based on tryptic digestion of serum glycoproteins, followed by immediate reverse phase UPLC-QQQ-MS analysis of glycopeptides. To quantitate protein glycosylation independent of the protein serum concentration, a nonglycosylated peptide was also monitored. Using this strategy, 178 glycopeptides and 18 peptides from serum glycoproteins are analyzed with good repeatability (interday CVs of 3.65-21-92%) in a single 17 min run. To assess the potential of the method, protein glycosylation was analyzed in serum samples from ovarian cancer patients and controls. A training set consisting of 40 cases and 40 controls was analyzed, and differential analyses were performed to identify aberrant glycopeptide levels. All findings were validated in an independent test set (n = 44 cases and n = 44 controls). In addition to the differential glycosylation on the immunoglobulins, which was reported previously, aberrant glycosylation was also observed on each of the glycoproteins, which could be corroborated in the test set. This report shows the development of a method for targeted protein- and site-specific glycosylation analysis and the potential of such methods in biomarker development.

Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer