Abstract 3221: Bioinformatic approaches for large-scale identification and relative quantitation of N-glycosylation sites from breast cancer cell lines.

Introduction: Protein glycosylation and N-glycopeptide site occupancy can be used as disease indicators differentiating normal and cancer cells. The analysis of glycoproteins introduces additional computational challenges to the workflow. In this study, we used the high resolution and accurate mass features of the Q-Exactive Orbitrap mass spectrometer combined with glycopeptide enrichment to analyze glycoprotein changes in a breast cancer cell model. We employed BYONIC, a bioinformatics search engine optimized for glycoprotein analysis. We are in the process of comparing these results with workflows involving MASCOT to identify false positive rates from the dataset. In addition; we will determine relative quantitation of the deamidated peptides with Pinpoint. Methods: We compared the expression of glycoproteins between a “benign” (MSF10A) and breast cancer (HCC70) cell line. Glycans of secreted and cell surface glycoproteins of breast cell lines (MCF10A and HCC70) were oxidized with periodate and cells lysed with a non-ionic detergent buffer. Lysates were mixed with hydrazide-modified magnetic beads to capture glycoproteins, and glycopeptides generated with trypsin and PNGaseF (results in delta mass of 0.984 for deamidated peptides). Glycopeptides were analyzed by LC/MS/MS on a Q Exactive in data dependent acquisition mode set to top12 HCD. Preliminary Results Mascot and Byonic will be used to evaluate the identification of the deamidated peptides. Currently, using MASCOT on the two cell lines, we identified 500 glycoproteins, The averaged mascot ion (e.g., ion scores of 81.0 vs. 69.1 for 2+ ions, and 67.7 vs. 53.3 for 3+ ions) gave identifications within 1% false discovery rate for the deamidated peptides searched against the 22,763 proteins related to human taxonomy via Swissprot database search. The bioinformatic approaches for identifying deamidated peptides will be evaluated with Byonic compared to the MASCOT results based on the number of identified N-linked glycopeptides; the false positive rates; speed of analysis; identification of multiple consensus sites (NX-S/T/Y) on same peptide and overall dynamic range of the identified deamidated peptides. For other comparable searches, the Byonic output with the same database search was considerably faster (10s of seconds versus several minutes for the MASCOT search). Byonic streamlines PTM searches by applying specific number of modifications per peptide to avoid combinatorial explosion; increasing the search efficiency. Citation Format: Sucharita M. Dutta, Ten-Yang Yen, John O. Semmes, Bruce Macher. Bioinformatic approaches for large-scale identification and relative quantitation of N-glycosylation sites from breast cancer cell lines. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3221. doi:10.1158/1538-7445.AM2013-3221