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Sci Rep. 2015 Dec 11;5:18189. doi: 10.1038/srep18189.

Integrated analysis of global proteome, phosphoproteome, and glycoproteome enables complementary interpretation of disease-related protein networks.

Author information

1
Department of Pharmaceutics, College of Pharmacy, Gachon University, Incheon 406-799, Republic of Korea.
2
Department of Chemical Engineering, POSTECH, Pohang 790-784, Republic of Korea.
3
Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, Republic of Korea.
4
Department of Applied Chemistry, College of Applied Sciences, Kyung Hee University, Yong-in 446-701, Republic of Korea.
5
Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea.
6
National Cancer Center, Goyang 410-769, Republic of Korea.

Abstract

Multi-dimensional proteomic analyses provide different layers of protein information, including protein abundance and post-translational modifications. Here, we report an integrated analysis of protein expression, phosphorylation, and N-glycosylation by serial enrichments of phosphorylation and N-glycosylation (SEPG) from the same tissue samples. On average, the SEPG identified 142,106 unmodified peptides of 8,625 protein groups, 18,846 phosphopeptides (15,647 phosphosites), and 4,019 N-glycopeptides (2,634 N-glycosites) in tumor and adjacent normal tissues from three gastric cancer patients. The combined analysis of these data showed that the integrated analysis additively improved the coverages of gastric cancer-related protein networks; phosphoproteome and N-glycoproteome captured predominantly low abundant signal proteins, and membranous or secreted proteins, respectively, while global proteome provided abundances for general population of the proteome. Therefore, our results demonstrate that the SEPG can serve as an effective approach for multi-dimensional proteome analyses, and the holistic profiles of protein expression and PTMs enabled improved interpretation of disease-related networks by providing complementary information.

PMID:
26657352
PMCID:
PMC4676070
DOI:
10.1038/srep18189
[Indexed for MEDLINE]
Free PMC Article

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