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Identification of sialylated glycoproteins from metabolically oligosaccharide engineered pancreatic cells

Overview of attention for article published in Clinical Proteomics, April 2015
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Title
Identification of sialylated glycoproteins from metabolically oligosaccharide engineered pancreatic cells
Published in
Clinical Proteomics, April 2015
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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The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
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 %
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%