Murine colon proteome and characterization of the protein pathways

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Abstract

Background

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.

Results

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 p I 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.

Conclusions

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.

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Most cited references 15

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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Rapid identification of proteins by peptide-mass fingerprinting.

D Pappin, P. Hojrup, A Bleasby (1993)

Developments in 'soft' ionisation techniques have revolutionized mass-spectro-metric approaches for the analysis of protein structure. For more than a decade, such techniques have been used, in conjuction with digestion b specific proteases, to produce accurate peptide molecular weight 'fingerprints' of proteins. These fingerprints have commonly been used to screen known proteins, in order to detect errors of translation, to characterize post-translational modifications and to assign diulphide bonds. However, the extent to which peptide-mass information can be used alone to identify unknown sample proteins, independent of other analytical methods such as protein sequence analysis, has remained largely unexplored. We report here on the development of the molecular weight search (MOWSE) peptide-mass database at the SERC Daresbury Laboratory. Practical experience has shown that sample proteins can be uniquely identified from a few as three or four experimentally determined peptide masses when these are screened against a fragment database that is derived from over 50 000 proteins. Experimental errors of a few Daltons are tolerated by the scoring algorithms, thus permitting the use of inexpensive time-of-flight mass spectrometers. As with other types of physical data, such as amino-acid composition or linear sequence, peptide masses provide a set of determinants that are sufficiently discriminating to identify or match unknown sample proteins. Peptide-mass fingerprints can prove as discriminating as linear peptide sequences, but can be obtained in a fraction of the time using less protein. In many cases, this allows for a rapid identification of a sample protein before committing it to protein sequence analysis. Fragment masses also provide information, at the protein level, that is complementary to the information provided by large-scale DNA sequencing or mapping projects.

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Gastrointestinal microflora, food components and colon cancer prevention.

Cindy Davis, John A Milner (2009)

Evidence that the intestinal microbiota is intrinsically linked with overall health, including cancer risk, is emerging. Moreover, its composition is not fixed but can be influenced by several dietary components. Dietary modifiers, including the consumption of live bacteria (probiotics) and indigestible or limited digestible food constituents such as oligosaccharides (prebiotics) and polyphenols or both (synbiotics), are recognized modifiers of the numbers and types of microbes and have been reported to reduce colon cancer risk experimentally. Microorganisms also have the ability to generate bioactive compounds from food components. Examples include equol from isoflavones, enterodiol and enterolactone from lignans and urolithins from ellagic acid, which have also been demonstrated to retard experimentally induced cancers. The gastrointestinal microbiota can also influence both sides of the energy balance equation, namely, as a factor influencing energy utilization from the diet and as a factor that influences host genes that regulate energy expenditure and storage. Because of the link between obesity and cancer incidence and mortality, this complex complexion deserves greater attention. Overall, a dynamic interrelationship exists between the intestinal microbiota and colon cancer risk, which can be modified by dietary components and eating behaviors.

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Author and article information

Journal

Journal ID (nlm-ta): BioData Min

Journal ID (iso-abbrev): BioData Min

Title: BioData Mining

Publisher: BioMed Central

ISSN (Electronic): 1756-0381

Publication date Collection: 2012

Publication date (Electronic): 28 August 2012

Volume: 5

Page: 11

Affiliations

[1 ]Department of Structural Pathology, Institute of Nephrology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan

[2 ]Department of Physiology, Faculty of Veterinary Medicine, Suez Canal University, Suez Canal, Egypt

[3 ]Department of Intensive Care Unit, West-China Hospital, Sichuan University, Sichuan, China

[4 ]Animal resource branch, Center of Bio-based researches, Brain research Institute, Niigata University, Niigata, Japan

[5 ]Department of Microbiology and Immunology, Faculty of Pharmacy, Suez Canal University, Suez Canal, Egypt

[6 ]Department of Nephrology, Tokyo Medical and Dental University, Tokyo, Japan

Article

Publisher ID: 1756-0381-5-11

DOI: 10.1186/1756-0381-5-11

PMC ID: 3497880

PubMed ID: 22929016

SO-VID: e68e7866-14b5-47a3-8efa-d06ad1f6d26d

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Murine colon proteome and characterization of the protein pathways

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Abstract

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Most cited references 15

Gene Ontology: tool for the unification of biology

Rapid identification of proteins by peptide-mass fingerprinting.

Gastrointestinal microflora, food components and colon cancer prevention.

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