Title |
Identification of sialylated glycoproteins from metabolically oligosaccharide engineered pancreatic cells
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Published in |
Clinical Proteomics, April 2015
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DOI | 10.1186/s12014-015-9083-8 |
Pubmed ID | |
Authors |
Yuan Tian, Ruben T Almaraz, Caitlin H Choi, Qing Kay Li, Christopher Saeui, Danni Li, Punit Shah, Rahul Bhattacharya, Kevin J Yarema, Hui Zhang |
Abstract |
In this study, we investigated the use of metabolic oligosaccharide engineering and bio-orthogonal ligation reactions combined with lectin microarray and mass spectrometry to analyze sialoglycoproteins in the SW1990 human pancreatic cancer line. Specifically, cells were treated with the azido N-acetylmannosamine analog, 1,3,4-Bu3ManNAz, to label sialoglycoproteins with azide-modified sialic acids. The metabolically labeled sialoglyproteins were then biotinylated via the Staudinger ligation, and sialoglycopeptides containing azido-sialic acid glycans were immobilized to a solid support. The peptides linked to metabolically labeled sialylated glycans were then released from sialoglycopeptides and analyzed by mass spectrometry; in parallel, the glycans from azido-sialoglycoproteins were characterized by lectin microarrays. This method identified 75 unique N-glycosite-containing peptides from 55 different metabolically labeled sialoglycoproteins of which 42 were previously linked to cancer in the literature. A comparison of two of these glycoproteins, LAMP1 and ORP150, in histological tumor samples showed overexpression of these proteins in the cancerous tissue demonstrating that our approach constitutes a viable strategy to identify and discover sialoglycoproteins associated with cancer, which can serve as biomarkers for cancer diagnosis or targets for therapy. |
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Geographical breakdown
Country | Count | As % |
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Unknown | 30 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 5 | 17% |
Student > Bachelor | 4 | 13% |
Professor | 4 | 13% |
Researcher | 3 | 10% |
Student > Master | 2 | 7% |
Other | 1 | 3% |
Unknown | 11 | 37% |
Readers by discipline | Count | As % |
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Chemistry | 6 | 20% |
Agricultural and Biological Sciences | 5 | 17% |
Biochemistry, Genetics and Molecular Biology | 3 | 10% |
Engineering | 2 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
Other | 1 | 3% |
Unknown | 12 | 40% |