Murine colon proteome and characterization of the protein pathways


Most of the current proteomic researches focus on proteome alteration due to pathological disorders (i.e.: colorectal cancer) rather than normal healthy state when mentioning colon. As a result, there are lacks of information regarding normal whole tissue- colon proteome. We report here a detailed murine (mouse) whole tissue- colon protein reference dataset composed of 1237 confident protein (FDR < 2) with comprehensive insight on its peptide properties, cellular and subcellular localization, functional network GO annotation analysis, and its relative abundances. The presented dataset includes wide spectra of pI and Mw ranged from 3–12 and 4–600 KDa, respectively. Gravy index scoring predicted 19.5% membranous and 80.5% globularly located proteins. GO hierarchies and functional network analysis illustrated proteins function together with their relevance and implication of several candidates in malignancy such as Mitogen- activated protein kinase (Mapk8, 9) in colorectal cancer, Fibroblast growth factor receptor (Fgfr 2), Glutathione S-transferase (Gstp1) in prostate cancer, and Cell division control protein (Cdc42), Ras-related protein (Rac1,2) in pancreatic cancer. Protein abundances calculated with 3 different algorithms (NSAF, PAF and emPAI) provide a relative quantification under normal condition as guidance. This highly confidence colon proteome catalogue will not only serve as a useful reference for further experiments characterizing differentially expressed proteins induced from diseased conditions, but also will aid in better understanding the ontology and functional absorptive mechanism of the colon as well.

DOI: 10.1186/1756-0381-5-11

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@inproceedings{Magdeldin2012MurineCP, title={Murine colon proteome and characterization of the protein pathways}, author={Sameh Magdeldin and Yutaka Yoshida and Huiping Li and Yoshitaka Maeda and Munesuke Yokoyama and Shymaa Enany and Ying Zhang and Bo Xu and Hidehiko Fujinaka and Eishin Yaoita and Sei Sasaki and Tadashi Yamamoto}, booktitle={BioData Mining}, year={2012} }