Classification of Mucin-Type O-Glycopeptides Using Higher-Energy Collisional Dissociation in Mass Spectrometry.

Changes in mucin-type O-glycosylation of human proteins affect protein function, immune response, and cancer progression. Since O-glycoproteins are characterized by the microheterogeneity of diverse O-glycans with no conserved sequence and the macroheterogeneity of multiple glycosylation sites on serine and/or threonine in human proteins, the assessment of different mucin types, such as Tn-antigen, core 1, and core 2, and their extended core types in O-glycopeptides, is extremely challenging. Here, we present an O-GlycoProteome Analyzer (O-GPA) that automatically classifies mucin-type O-glycosylation using higher-energy collisional dissociation (HCD) in mass spectrometry. First, we estimated the number of GlcNAc residues using the intensity ratio of GlcNAc-specific fragment ions (HexNAc-CH6O3 and HexNAc-2H2O) over GalNAc-specific fragment ions (HexNAc-C2H6O3 and HexNAc-C2H4O2) in the HCD spectrum. Furthermore, we classified the different mucin types of O-glycopeptides from characteristic B2 (HexNAc2) or Y2α (PEP + HexNAc2), and Y2ß (PEP + HexNAcHex) fragment ions, along with the number of GlcNAc. Furthermore, O-GPA automatically determined single or multiple O-glycosylation, regardless of the mucin types. The mucin type of O-glycopeptides from human urine and plasma was confirmed with an overall accuracy of 96%. We found 97 core 1, 56 core 2, 13 extended core 1, and 12 extended core 2 glycopeptides from urine; and 22 core 1, 13 core 2, 7 extended core 1, 1 extended core 2, and 1 Tn-antigen from plasma. Our strategy can be used to successfully characterize specific mucin types of O-glycoproteins in human biological samples.

Glycoproteomic Alterations in Drug-Resistant Nonsmall Cell Lung Cancer Cells Revealed by Lectin Magnetic Nanoprobe-Based Mass Spectrometry.

Understanding the functional role of glycosylation-mediated pathogenesis requires deep characterization of glycoproteome, which remains extremely challenging due to the inherently complex nature of glycoproteins. We demonstrate the utility of lectin-magnetic nanoprobe (MNP@lectin) coupled to Orbitrap HCD-CID-MS/MS for complementary glycotope-specific enrichment and site-specific glycosylation analysis of the glycoproteome. By three nanoprobes, MNP@ConA, MNP@AAL, and MNP@SNA, our results revealed the first large-scale glycoproteome of nonsmall cell lung cancer (NSCLC) with 2290 and 2767 nonredundant glycopeptides confidently identified (Byonic score ≥100) in EGFR-TKI-sensitive PC9 and -resistant PC9-IR cells, respectively, especially with more fucosylated and sialylated glycopeptides in PC9-IR cells. The complementary enrichment was demonstrated with only five glycopeptides commonly enriched in three MNP@lectins. Glycotope specificity of 79 and 62% for enrichment was achieved using MNP@AAL and MNP@SNA, respectively. Label-free quantitation revealed predominant fucosylation in PC9-IR cells, suggesting its potential role associated with NSCLC resistance. Moreover, without immunoprecipitation, this multilectin nanoprobe allows the sensitive identification of 51 glycopeptides from 10 of 12 reported sites from onco-protein EGFR. Our results not only demonstrated a sensitive approach to study the vastly under-represented N-glycoprotome but also may pave the way for a glycoproteomic atlas to further explore the site-specific function of glycoproteins associated with drug resistance in NSCLC.

Glycomics using mass spectrometry.

Mass spectrometry plays an increasingly important role in structural glycomics. This review provides an overview on currently used mass spectrometric approaches such as the characterization of glycans, the analysis of glycopeptides obtained by proteolytic cleavage of proteins and the analysis of glycosphingolipids. The given examples are demonstrating the application of mass spectrometry to study glycosylation changes associated with congenital disorders of glycosylation, lysosomal storage diseases, autoimmune diseases and cancer.

Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry.

Targeted glycoproteomics represents an attractive approach for conducting peripheral blood based cancer biomarker discovery due to the well-known altered pattern of protein glycosylation in cancer and the reduced complexity of the resultant glycoproteome. Here we report its application to a set of pooled nonsmall cell lung cancer (NSCLC) case sera (9 adenocarcinoma and 6 squamous cell carcinoma pools from 54 patients) and matched controls pools, including 8 clinical control pools with computed tomography detected nodules but being nonmalignant as determined by biopsy from 54 patients, and 8 matched healthy control pools from 106 cancer-free subjects. The goal of the study is to discover biomarkers that may enable improved early detection and diagnosis of lung cancer. Immunoaffinity subtraction was used to first deplete the topmost abundant serum proteins; the remaining serum proteins were then subjected to hydrazide chemistry based glycoprotein capture and enrichment. Hydrazide resin in situ trypsin digestion was used to release nonglycosylated peptides. Formerly N-linked glycosylated peptides were released by peptide-N-glycosidase F (PNGase F) treatment and were subsequently analyzed by liquid chromatography (LC)-tandem mass spectrometry (MS/MS). A MATLAB based in-house tool was developed to facilitate retention time alignment across different LC-MS/MS runs, determination of precursor ion m/z values and elution profiles, and the integration of mass chromatograms based on determined parameters for identified peptides. A total of 38 glycopeptides from 22 different proteins were significantly differentially abundant across the case/control pools (P < 0.01, Student's t test) and their abundances led to a near complete separation of case and control pools based on hierarchical clustering. The differential abundances of three of these candidate proteins were verified by commercially available ELISAs applied in the pools. Strong positive correlations between glycopeptide mass chromatograms and ELISA-measured protein abundance was observed for all of the selected glycoproteins.