Murine colon proteome and characterization of the protein pathways
© Magdeldin et al.; licensee BioMed Central Ltd. 2012
Received: 31 January 2012
Accepted: 24 July 2012
Published: 28 August 2012
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.
KeywordsColon Proteome Mass spectrometry HPLC
The essential role of colon in normal physiology includes absorption of vitamins, salts, nutrients and water which is summarized as the final stage digestive process [1, 2]. Together with its ability to process indigestible fibers (in certain species) , it hosts a wide range of useful microbiota which live in a symbiotic relationship providing a proper absorption and wastes elimination . Recruitment of proteomics in colon studies has been an interest for many researchers especially gastroenterologists . However, overwhelming majorities of these reports were focusing on proteome of disordered colon tissues rather than its normal state. Owing to the Pubmed-Medline search result with the keywords “Colon” and “Proteomics” on June 2012, 300 literatures investigated diseased-state colon proteome (mainly colorectal cancer), while less than 5 partially reported healthy one. To this end, the availability of comprehensive whole tissue- colon proteome remains a critical demand. In a former report by Gourley et al…, the authors compared colonic gastrointestinal mucosa by 2 different approaches; 2D and 2DLC. Integration of both analyses resulted in identification of 568 proteins . Another 2DE study examining colonic crypts identified approximately 800 proteins , while other comparative experiments compared healthy intestinal scraping versus cell lines (Caco-2) , aged intestinal epithelia  or colorectal cancer [9–11]. Given the necessity of disclosing colon proteome in its normal state to overcome currently insufficient global understanding of physiological and pathophysiological colonic tissues profiles, our impetus was to provide a comprehensive whole tissue colon proteome as a mainstay for further colon research analysis.
Materials and methods
Three healthy normal C57BL/6 J male mice (8 weeks old) bred at the animal Care Research Center (Niigata University, Japan) was used in the current study. Mice were housed in individual cages in sterile environment with 12 hour light cycle and ad lib access to standard chow and filtered water. Experimental animal were treated in accordance with ethics of animal center committee at school of medical and dental sciences, Niigata University [approval number 2009–32].
Tissue processing and protein extraction
Mice were sacrificed by decapitation and colon; 3 cm above the rectum were longitudinally cut, and rinsed in ice-cold PBS buffer. Tissues were sliced into small pieces prior to homogenization. For colonic protein extraction, 100 mg of wet tissue were homogenized in home- made lysis solution consists of (9.8 M urea, 2% nonidet, 0.2% Ampholite; pH 3–10, 12 μl/ml Destreak buffer, 0.5 μg/ml E-64, 0.5 mM PMSF, 40 μg/ml TLCK, 1.0 μg/μl chymostatin, 0.5 mM EDTA, 0.01% bromophenol blue, and 2 μg/μl aprotonin). Samples were homogenized using Polytron PT1200 homogenizer (Kinematica AG, Switzerland) 5–10 bursts with 45 s interval in ice. Homogenized samples were kept in 37 °C for 1 h with occasional vortex, centrifuged at 12,000 rpm for 20 m. Protein assay for the extracts was carried out using Ramagli’s modified method of Bradford (Bio-Rad, Japan) with bovine serum albumin as a standard .
Protein fractionation, tryptic digestion
Samples were acetone precipitated and reconstituted in SDS sample buffer with 2 β mercaptoethanol (final dilution 4%) . Ten μg of colon protein extract from each sample were run on 12.5% SDS-PAGE. Gel was stained with Coomassie Brilliant Blue stain (CBB R-250, Wako, Japan). Each lane was sliced into 14 consecutive slices (2 cm/slice). Samples were reduced with 10 mM dithiothreitol (DTT), alkylated with 55 mM iodoacetamide (IAA), and digested with 6 ng/μl of trypsin overnight . Peptide was extracted with 0.3% formic acid and 5 μl (0.25 μg digested protein) from each sample was loaded onto nano-LC-ESI-IT-TOF-MS/MS (Hitachi NanoFrontier LD., Tokyo, Japan).
Reversed- phase capillary Lc-Ms/Ms analysis
Digested peptides were purified and concentrated on a trap column; monolith trap C18-50-150 (Merck, Darmstadt, Germany). Peptides were separated using the C18 separation column; monocap for Fast-Flow, 0.05 × 150 mm. The injected peptides were eluted with 7.5-70% gradient with solvent B (H2O/ACN = 98/2 in 0.1%HCOOH) for 120 minutes at 200 nl/minute. Nano-LC-ESI-IT-TOF-MS/MS was performed on the top of two ions in each MS scan. Dynamic exclusion and repeat settings ensured each ion was selected only once and excluded in the subsequent parent ion selection. Precursor ions were selected using the following MS to MS/MS switch criteria: ion range m/z 100–1800, charge state 2–5, and former target ion were excluded for 20 ms. Collision ion dissociation (CID) was performed using nitrogen as collision gas. Data were merged using Mascot daemon (V 2.0) .
Ms/Ms data processing and protein identification
Peak lists were generated using NanoFrontier LD data processing software (V 1.0). Product ion data were searched against Mouse International protein index (IPI_mouse; version 3.71, 169347 entries) using a locally stored copy of the Mascot search engine (version 2.2.1, Matrix Science, London, UK) . The following parameters were used for database search: MudPIT scoring, precursor mass tolerance 0.3 Da, product ion mass tolerance 0.3 Da, 2 missed cleavages allowed, fully tryptic peptides only, fixed modification of Carbamoidomethyl (C), variable modifications of glutamine (Gln) to pyroglutamate (pyro-Glu) (N-term Q); glutamate (Glu) to pyroglutamate (pyro-Glu) (N-term E), Oxidation of histidine and tryptophan (HW); Oxidation of methionine (M), mass values of monoisotopic and peptide charge state of 2+ and 3+. Protein was accepted if at least 2 peptides passed identity and homology threshold of Mascot (MOWSE) algorithm considering that if multiple spectra were identified to match precisely the same sequence and charge state of a given peptide, only the spectrum with highest score was retained. The false discovery rate (FDR) against reversed decoy database was below 2%.
Relative protein abundance and gene ontology (GO) annotation
To estimate protein contents in the analyzed samples, normalized spectral abundance factor (NSAF) for each protein was calculated , in which the total number of tandem mass spectra (SpC) matching peptides of a given protein was divided by its protein length (L), then divided by the sum of (SpC/L) for all uniquely identified proteins in each dataset. Protein abundance factor (PAF) was calculated for each protein where PAF of a given protein is expressed as the total number of non redundant spectra normalized to the molecular weight (KDa) of the cognate protein (104) . Moreover, protein weight% was estimated based on algorithms of exponentially modified protein abundance factor (emPAI) . These parameters were used to rank proteins according to their relative abundance. In addition, enrichment, depletion analysis and functional annotation network for GO terms were visualized and statistically evaluated using BiNGO (v2.3)  and ClueGO (v1.1) plug-ins  integrated in Cytoscape (v 2.6.3) . To access over- and under- represented GO hierarchies, both plug-ins were setup to two sided hypergeometric statistical testing with significance level (P < 0.05). False discovery rate (FDR) correction was calculated using Benjamini and Hochberg multiple testing correction . A customized and updated GO slim file (OBO v1.2; 32150 term) and gene annotation file were downloaded from GO consortium  and Kyoto encyclopedia of genes and genomes (KEGG) pathway databases , respectively and used in the current analysis. For secondary structure prediction of membrane and globular proteins, Gravy index (average hydrophobicity or hydrophilicity scores) were measured using Kyte-Doolittle and Hopp Woods formula .
Results and discussion
Mouse colon proteome
Using protein data sets generated from 42 slice analysis, we created a proteome catalogue of mouse colon by merging and refining outputs from redundancy and low confidence protein candidates with 1 peptide match. Protein candidate was accepted and considered a confident hit if at least 2 corresponding peptides passed identity and homology threshold of Mascot (MOWSE) algorithm . Over 48.000 Ms/Ms spectra corresponding to 1237 protein candidate were detected and used to configure murine colon proteome (Additional file 2). Ms/Ms spectra of annotated peptides are freely accessible through proteomic data repository, PRIDE, the PRoteomic IDEntification database; http://www.ebi.ac.uk/pride/ under accession number . Constructed dataset was used for further characterization of mouse colon proteins.
Characteristics of murine colon proteins
Subcellular localization of identified proteins
GO annotation and functional network analysis
Regulation of actin cytoskeleton/tight junction and related family groups
Representing over one third (563) of identified terms, as illustrated in Figure 6, this family holds 43 GO terms (shown by nodes). Most prominent is actin cytoskeleton which contains 47 identifiers. A wide variety of essential pathways could be recognized in this group. For example; MAPK (proliferation and apoptosis), VEGF (angiogenesis), Toll-like and T-like receptors (Immune barrier) signaling pathways and others, which reflect the active metabolic processes took place in the colonic cells. Moreover, several protein candidates for cancer pathways could be reported. Most notably Mapk8 and 9, Rac1 and 2, Rhoa for colorectal cancer, Cdc42, Fh1, Rac1, Rap1a, Tceb2 for renal cell carcinoma, Cdc42, Mapk8and 9, Pld1, Rac1and 2for pancreatic carcinoma and Fgfr2, Gstp1, Hsp90aa1, Hsp90ab1, Hsp90b1 for prostate cancer.
Glycolysis and Glyconeogenesis and related family groups
Major 2 ubiquitous processes that involve glucose breakdown (glycolysis) and its generation form non carbohydrate sources (glyconeogenesis) were detected. In colon proteome database, we reported 500 identifiers representing 30.5% of total colon proteome representing Glycolysis pathway. This family holds wide members of enzymes including aldolases A and B, alcohol dehydrogenases family members, enolases (α, ß, and γ), lactate dehydrogenases, and others.
Proximal tubule bicarbonate reclamation and related family groups
This family includes ATPase, Na+/K + transporters [alpha1-4], glutamate dehydrogenase and malate dehydrogenase 1 (NAD) and representing 9.6% of colon database. These catalytic enzymes are essential for exchanging sodium and potassium ions and providing energy for active transport of various nutrients in the gut. Other ATPase transporters were also identified which are contributors in salivary and gastric acid secretion such as (ATP1b1and ATP4a).
Pyruvate metabolism and related family groups
An essential group family which has key enzymes in citric acid cycle including dehydrogenases such as pyruvate, lactate, malate dehydrogenase. This group is mainly responsible for cellular respiration and release of energy via NADH. We were able also to recognize a wide variety of enzymes that contribute in amino acid metabolism and participating in cysteine, methionine, valine, leucine and isoleucine, arginine, proline, histidine and tryptophane synthesis and breakdown including LAP3, OAT, GOT2, and ALDH2 (Additional file 3). Several enzymes that share in fatty acid metabolism and elongation were also reported such as acetyl CoA acyltransferese 1 and 2, alcohol dehydrogenase 1 and 2 and others.
Glutathione metabolism and related family groups
Includes glutathione peroxidase (GPX 1–5) and glutathione S- transferases (GST 1–5) enzymes that protect cells and other enzymes form oxidative damage by catalyzing the reduction of hydrogen peroxide, lipid peroxides and organic hydroperoxides.
Representing less than 15% of colon database and including chemokine signaling pathway, nicotinate and nicotinamide metabolism, amino sugar and nucleotide sugar metabolism, phenylalanine metabolism and amobiasis.
Relative abundance of identified murine colon proteome
Comparison of murine colon proteome to gene expression database of mouse colon
Recent advances in mass spectrometric methodologies enabled direct analysis of complex protein mixtures in a shotgun approach for global protein identification and biomarker discovery. Presented data in this article, provides not only a normal comprehensive colon proteome database, but also, various label free quantification methods for researcher’s guidance especially when monitoring cancer- related colon protein expression. Furthermore, a functional network analysis of colon proteome is believed to provide a valuable piece of information for clarifying the relationship between possible predicted biomarkers. For instance, several candidates of gastrointestinal tract carcinoma showed correlated pattern; Mapk8 and Rac1,2 of colorectal cancer, cdc42 of renal cell carcinoma, pld1 in pancreatic cancer and Hsp90b1 and hsp90ab1 in prostate cancer. These data might anticipate in elucidating cell signaling and pathophysiological pathways.
The present research reports a comprehensive whole-tissue colon proteome catalogue consists of 1237 high confidence candidate protein with FDR < 2, together with its characteristics, relative expression, and functional pathway analysis which depicts unbiased database. Moreover, the colon protein profile shown in this report represents, to the best of our knowledge, a reference point for further comparative studies and better understanding protein expression patterns induced not only in normal physiological status but also in commonly diseased conditions as well.
Liquid chromatography- electospray ionization- time of flight
Sodium dodecyl sulfate polyacrylamide gel electrophoresis
MOWSE scoring, Molecular weight search scoring
Gene ontology, colloidal ion dissociation
Normalized spectral abundance factor
false discovary rate
Protein abundance factor
exponentially modified protein abundance factor.
This work was supported by JSPS (Japan Society for Promotion of Science) Grant-in-Aid for scientific research (B) to SM (23790933) and Grant-in-Aid for scientific research (B) to TY (21390262) from Ministry of Education, Culture, Sports, Science and Technology of Japan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- Rose RC: Water-soluble vitamin absorption in intestine. Annu Rev Physiol. 1980, 42: 157-171. 10.1146/annurev.ph.42.030180.001105.View ArticlePubMed
- Weiser MM, Bloor JH, Dasmahapatra A: Intestinal calcium absorption and vitamin D metabolism. J Clin Gastroenterol. 1982, 4: 75-86. 10.1097/00004836-198202000-00014.View ArticlePubMed
- Donini LM, Savina C, Cannella C: Nutrition in the elderly: role of fiber. Arch Gerontol Geriatr. 2009, 49 (Suppl 1): 61-69.View ArticlePubMed
- Davis CD, Milner JA: Gastrointestinal microflora, food components and colon cancer prevention. J Nutr Biochem. 2009, 20: 743-752. 10.1016/j.jnutbio.2009.06.001.PubMed CentralView ArticlePubMed
- Gourley GR, Yang L, Higgins L, Riviere MA, David LL: Proteomic analysis of biopsied human colonic mucosa. J Pediatr Gastroenterol Nutr. 2010, 51: 46-54. 10.1097/MPG.0b013e3181c15f43.View ArticlePubMed
- Patel BB, Li XM, Dixon MP, Blagoi EL, Seeholzer SH, Chen Y, Miller CG, He YA, Tetruashvily M, Chaudhry AH: Searchable high-resolution 2D gel proteome of the human colon crypt. J Proteome Res. 2007, 6: 2232-2238. 10.1021/pr060641e.View ArticlePubMed
- Lenaerts K, Bouwman FG, Lamers WH, Renes J, Mariman EC: Comparative proteomic analysis of cell lines and scrapings of the human intestinal epithelium. BMC genomics. 2007, 8: 91-10.1186/1471-2164-8-91.PubMed CentralView ArticlePubMed
- Yi H, Li XH, Yi B, Zheng J, Zhu G, Li C, Li MY, Zhang PF, Li JL, Chen ZC, Xiao ZQ: Identification of Rack1, EF-Tu and Rhodanese as aging-related proteins in human colonic epithelium by proteomic analysis. J Proteome Res. 2010, 9: 1416-1423. 10.1021/pr9009386.View ArticlePubMed
- Mazzanti R, Solazzo M, Fantappie O, Elfering S, Pantaleo P, Bechi P, Cianchi F, Ettl A, Giulivi C: Differential expression proteomics of human colon cancer. Am J Physiol. 2006, 290: G1329-G1338. 10.1152/ajprenal.00284.2005.
- Rho JH, Qin S, Wang JY, Roehrl MH: Proteomic expression analysis of surgical human colorectal cancer tissues: up-regulation of PSB7, PRDX1, and SRP9 and hypoxic adaptation in cancer. J Proteome Res. 2008, 7: 2959-2972. 10.1021/pr8000892.PubMed CentralView ArticlePubMed
- Zhao L, Liu L, Wang S, Zhang YF, Yu L, Ding YQ: Differential proteomic analysis of human colorectal carcinomacell lines metastasis-associated proteins. J Cancer Res Clin Oncol 2007.
- Ramagli LS, Rodriguez LV: Quantitation of microgram amounts of protein in two-dimensional polyacrylamide gel electrophoresis sample buffer. Electrophoresis. 1985, 6: 559-563. 10.1002/elps.1150061109.View Article
- Magdeldin S, Li H, Yoshida Y, Satokata I, Maeda Y, Yokoyama M, Enany S, Zhang Y, Xu B, Fujinaka H: Differential proteomic shotgun analysis elucidates involvement of water channel aquaporin 8 in presence of alpha-amylase in the colon. J Proteome Res. 2010, 9: 6635-6646. 10.1021/pr100789v.View ArticlePubMed
- Shevchenko A, Wilm M, Vorm O, Mann M: Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal Chem. 1996, 68: 850-858. 10.1021/ac950914h.View ArticlePubMed
- Perkins DN, Pappin DJ, Creasy DM, Cottrell JS: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999, 20: 3551-3567. 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2.View ArticlePubMed
- Pappin DJ, Hojrup P, Bleasby AJ: Rapid identification of proteins by peptide-mass fingerprinting. Curr Biol. 1993, 3: 327-332. 10.1016/0960-9822(93)90195-T.View ArticlePubMed
- Liu H, Sadygov R, Yates J: A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem. 2004, 76: 4193-4201. 10.1021/ac0498563.View ArticlePubMed
- Powell DW, Weaver CM, Jennings JL, McAfee KJ, He Y, Weil PA, Link AJ: Cluster analysis of mass spectrometry data reveals a novel component of SAGA. Mol Cell Biol. 2004, 24: 7249-7259. 10.1128/MCB.24.16.7249-7259.2004.PubMed CentralView ArticlePubMed
- Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J, Mann M: Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics. 2005, 4: 1265-1272. 10.1074/mcp.M500061-MCP200.View ArticlePubMed
- Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics (Oxford, England). 2005, 21: 3448-3449. 10.1093/bioinformatics/bti551.View Article
- Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pages F, Trajanoski Z, Galon J: ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics (Oxford, England). 2009, 25: 1091-1093. 10.1093/bioinformatics/btp101.View Article
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.PubMed CentralView ArticlePubMed
- Hochberg Y, Benjamini Y: More powerful procedures for multiple significance testing. Stat Med. 1990, 9: 811-818. 10.1002/sim.4780090710.View ArticlePubMed
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMed CentralView ArticlePubMed
- Aoki KF, Kanehisa M: Using the KEGG database resource. Curr Protoc Bioinformatics. 2005, Chapter 1:Unit 1 12
- Kyte J, Doolittle RF: A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982, 157: 105-132. 10.1016/0022-2836(82)90515-0.View ArticlePubMed
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