Membrane Protein Profiling of Human Colon Reveals Distinct Regional Differences
2014; Elsevier BV; Volume: 13; Issue: 9 Linguagem: Inglês
10.1074/mcp.m114.040204
ISSN1535-9484
AutoresSjoerd van der Post, Gunnar C. Hansson,
Tópico(s)Glycosylation and Glycoproteins Research
ResumoThe colonic epithelium is a highly dynamic system important for the regulation of ion and water homeostasis via absorption and secretion and for the maintenance of a protective barrier between the outer milieu and the inside of the body. These processes are known to gradually change along the length of the colon, although a complete characterization at the protein level is lacking. We therefore analyzed the membrane proteome of isolated human (n = 4) colonic epithelial cells from biopsies obtained via routine colonoscopy for four segments along the large intestine: ascending, transverse, descending, and sigmoid colon. Label-free quantitative proteomic analyses using high-resolution mass spectrometry were performed on enriched membrane proteins. The results showed a stable level for the majority of membrane proteins but a distinct decrease in proteins associated with bacterial sensing, cation transport, and O-glycosylation in the proximal to distal regions. In contrast, proteins involved in microbial defense and anion transport showed an opposing gradient and increased toward the distal end. The gradient of ion-transporter proteins could be directly related to previously observed ion transport activities. All individual glycosyltransferases required for the O-glycosylation of the major colonic mucin MUC2 were observed and correlated with the known glycosylation variation along the colon axis. This is the first comprehensive quantitative dataset of membrane protein abundance along the human colon and will add to the knowledge of the physiological function of the different regions of the colonic mucosa. Mass spectrometry data have been deposited to the ProteomeXchange with the identifier PXD000987. The colonic epithelium is a highly dynamic system important for the regulation of ion and water homeostasis via absorption and secretion and for the maintenance of a protective barrier between the outer milieu and the inside of the body. These processes are known to gradually change along the length of the colon, although a complete characterization at the protein level is lacking. We therefore analyzed the membrane proteome of isolated human (n = 4) colonic epithelial cells from biopsies obtained via routine colonoscopy for four segments along the large intestine: ascending, transverse, descending, and sigmoid colon. Label-free quantitative proteomic analyses using high-resolution mass spectrometry were performed on enriched membrane proteins. The results showed a stable level for the majority of membrane proteins but a distinct decrease in proteins associated with bacterial sensing, cation transport, and O-glycosylation in the proximal to distal regions. In contrast, proteins involved in microbial defense and anion transport showed an opposing gradient and increased toward the distal end. The gradient of ion-transporter proteins could be directly related to previously observed ion transport activities. All individual glycosyltransferases required for the O-glycosylation of the major colonic mucin MUC2 were observed and correlated with the known glycosylation variation along the colon axis. This is the first comprehensive quantitative dataset of membrane protein abundance along the human colon and will add to the knowledge of the physiological function of the different regions of the colonic mucosa. Mass spectrometry data have been deposited to the ProteomeXchange with the identifier PXD000987. The physiology and architecture of the human gastrointestinal tract differ along its axis, where the stomach and small intestine are responsible for digestion followed by nutrient absorption. The colon forms the last part of the digestive tract and is required for the reabsorption of the large volumes of fluid and ions from material that has passed through the small intestine. In addition, it also functions as a large anaerobic bioreactor in which the gut microbiota degrade host-indigestible polysaccharides and glycans into short-chain fatty acids that are used as an energy source (1.de Graaf A.A. Maathuis A. de Waard P. Deutz N.E.P. Dijkema C. De Vos W.M. Venema K. Profiling human gut bacterial metabolism and its kinetics using [U-13C]glucose and NMR.NMR Biomed. 2010; 23: 2-12Crossref PubMed Scopus (59) Google Scholar). The human gut hosts 1013 to 1014 commensal bacteria, of which the majority are found in the colon (2.Luckey T.D. Introduction to intestinal microecology.Am. J. Clin. Nutrition. 1972; 25: 1292-1294Crossref PubMed Scopus (184) Google Scholar). In this symbiotic system, direct interaction between bacteria and epithelium is prevented by the continuous secretion of a dense mucus layer. The organization and composition of this mucus layer vary along the digestive tract (3.Hansson G.C. Role of mucus layers in gut infection and inflammation.Curr. 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Gene expression data do not support such a sharp border and suggest a gradual change in gene expression along the human colon (14.LaPointe L.C. Dunne R. Brown G.S. Worthley D.L. Molloy P.L. Wattchow D. Young G.P. Map of differential transcript expression in the normal human large intestine.Physiol. Genom. 2008; 33: 50-64Crossref PubMed Scopus (64) Google Scholar, 15.Birkenkamp-Demtroder K. Olesen S.H. Sørensen F.B. Laurberg S. Laiho P. Aaltonen L.A. Orntoft T.F. Differential gene expression in colon cancer of the caecum versus the sigmoid and rectosigmoid.Gut. 2005; 54: 374-384Crossref PubMed Scopus (195) Google Scholar). Little is known about the global proximal–distal variation in protein levels, and this has not been studied via proteomics approaches. Targeted studies at the protein level have shown that several transporters are differentially expressed, such as monocarboxylic acid transporter 1 and Na+/H+ exchanger 3 (NHE3) 1The abbreviations used are: NHE, Na+/H+ exchanger; MHC, major histocompatibility complex. 1The abbreviations used are: NHE, Na+/H+ exchanger; MHC, major histocompatibility complex. (16.Cuff M.A. Lambert D.W. Shirazi-Beechey S.P. Substrate-induced regulation of the human colonic monocarboxylate transporter, MCT1.J. Physiol. 2002; 539: 361-371Crossref PubMed Scopus (161) Google Scholar, 17.Jakab R.L. Collaco A.M. Ameen N.A. Physiological relevance of cell-specific distribution patterns of CFTR, NKCC1, NBCe1, and NHE3 along the crypt-villus axis in the intestine.AJP Gastrointest. Liver Physiol. 2011; 300: G82-G98Crossref PubMed Scopus (94) Google Scholar). Monocarboxylic acid transporter 1 is required for butyrate transport, and butyrate is found at the highest concentration in the proximal colon. 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Intestinal epithelial cells: regulators of barrier function and immune homeostasis.Nat. Rev. Immunol. 2014; 14: 141-153Crossref PubMed Scopus (1640) Google Scholar), although the enriched membrane fraction analyzed contained most membrane proteins after the removal of nuclei and mitochondria. Mass spectrometry analyses were performed on ascending, transverse, descending, and sigmoid colon for both characterization of the protein composition and quantification using a label-free approach. We show that various biological processes were found to differ between the distal and proximal colon, such as metabolism, antigen presentation, protein O-glycosylation, and ion transport. This extensive dataset emphasizes that the colon is a more dynamic organ than often assumed. Macroscopically normal biopsies from ascending, descending, transverse, and sigmoid colon (two biopsies from each colon region) were obtained from four patients referred for routine colonoscopy for diagnostic purposes (∼3-mm diameter). Biopsies were frozen in liquid nitrogen and stored at −80 °C until use. Approval was granted by the Human Research Ethical Committee, Gothenburg University, and written informed consent was obtained from all study subjects. Epithelial cells were isolated as described in Ref. 23.Whitehead R.H. Brown A. Bhathal P.S. A method for the isolation and culture of human colonic crypts in collagen gels.In Vitro Cell. Dev. Biol. 1987; 23: 436-442Crossref PubMed Scopus (102) Google Scholar, with slight modifications. Briefly, tissues were washed in PBS for 5 min and then incubated in PBS containing 3 mm EDTA and 1 mm DTT at 4 °C for 60 min while gently shaken. The solution was replaced with fresh PBS, and epithelial cells were dissociated from the tissue by vigorous shaking for 30 s. The remaining tissue was removed from the solution using a forceps, and cells were pelleted via centrifugation at 500 rpm (5415R, Eppendorf, Hamburg, Germany). Pelleted cells were resolved in 500 μl of 2 m NaCl, 1 mm EDTA in 10 mm HEPES, pH 7.4, containing complete protease inhibitor mixture (Roche) and lysed by means of tip-probe sonication (T-8, Turrax, IKA, Staufen, Germany). Membrane proteins were extracted as described in Ref. 24.Lu A. Wiœniewski J.R. Mann M. Comparative proteomic profiling of membrane proteins in rat cerebellum, spinal cord, and sciatic nerve.J. Proteome Res. 2009; 8: 2418-2425Crossref PubMed Scopus (39) Google Scholar. Briefly, proteins were pelleted via centrifugation at 130,000 × g for 20 min in a tabletop ultracentrifuge (Optima MAX, Beckman Coulter, Fullerton, CA) and dissolved once in 0.1 m Na2CO3, twice in 1 mm EDTA, pH 11.3, and finally in 5 m urea, 100 mm NaCl, 10 mm HEPES, pH 7.4, with pelleting via ultracentrifugation between each step. The final pellet was washed twice with 1 ml of 0.1 m Tris/HCl, pH 7.6, and centrifuged for 10 min at 20,000 × g. Proteins were solubilized in 0.1 m DTT, 4% SDS, 0.1 m Tris/HCl, pH 7.6, added on 30,000-kDa cutoff filters (NanoSep, Pall, Ann Arbor, MI), and digested according to the filter-aided sample preparation method (25.Wiœniewski J.R. Zougman A. Nagaraj N. Mann M. Universal sample preparation method for proteome analysis.Nat. Methods. 2009; 6: 359-362Crossref PubMed Scopus (5043) Google Scholar) using two-step digestion with endoproteinase Lys-C (Wako, Richmond, VA) overnight followed by trypsin (Promega, Madison, WI) for 4 h, both at room temperature. The concentration of eluted peptides was determined by means of Qubit fluorescent measurement (Invitrogen). 10 μg of each sample was fractionated on a ZIC-HILIC column (3.5 μm, SeQuant, Umeå, Sweden) packed in a fused silica capillary (150 mm × 0.32 mm inner diameter) connected to an Ettan LC (Amersham Biosciences). The following buffers were used: A, 5 mm ammonium acetate in 0.5% formic acid, 95% acetonitrile; and B, 5 mm ammonium acetate. Peptides were eluted using a gradient of 5% to 50% B, and the absorbance was monitored at 280 nm. Six fractions were collected, dried under vacuum, and reconstituted in 15 μl of 0.1% TFA. Sample injection and nano-liquid chromatography were performed using an HTC-PAL autosampler (CTC Analytics, Zwingen, Switzerland) equipped with a Cheminert valve (0.25-mm bore, C2V-1006D-CTC, Valco Instruments, Schenkon, Switzerland) connected to an Agilent 1100 Series degasser and capillary pump (Agilent, Palo Alto, CA). Five microliters of the protein digest mixture was trapped on a fritted pre-column (4 cm × 100 μm inner diameter) packed with 2 cm of 5-μm Reprosil-Pur C18-AQ particles (Dr. Maisch, Ammerbuch, Germany) connected between two MicroTee connectors (Upchurch, Oak Harbor, WA) in a valve switching configuration. The analytical column consisted of a fused silica capillary (15 cm × 75 μm inner diameter, 10 μm tip, New Objective, Woburn, MA) packed with the 3-μm Reprosil-Pur C18-AQ particles (Dr. Maisch). After sample loading in buffer A (0.2% formic acid), the peptides were separated using a piece-linear gradient (10% to 40% B over 60 min and 40% to 70% B over 15 min) with mobile phase B (80% acetonitrile in 0.2% formic acid) at a flow rate of ∼300 nl/min. Mass spectrometry analysis was performed on an LTQ-Orbitrap XL (Thermo) operated in a data-dependent mode automatically switching between scan modes, performing MS/MS on the six most intense ions per precursor scan. MS scans in the mass range of m/z 350–1600 were obtained in the Orbitrap at a resolution of 60,000 measured at 400 m/z, using the lock-mass feature for internal calibration (m/z 371.101). MS/MS fragmentation scans were obtained in the ion trap using collision-induced dissociation of 30% followed by a 60-s exclusion time. Raw spectral data were converted using MaxQuant version 1.3.0.5 (26.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar), identified by the integrated database search engine Andromeda (27.Cox J. Neuhauser N. Michalski A. Scheltema R.A. Olsen J.V. Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment.J. Proteome Res. 2011; 10: 1794-1805Crossref PubMed Scopus (3450) Google Scholar), and searched against the human Swiss-Prot protein database (release 2013 3, 21,324 entries) combined with a database of common contaminates concatenated with the same sequence database in reversed order for false discovery rate estimation. The following parameters were used for searches: (i) two missed cleavages, trypsin; (ii) precursor tolerance of 20 ppm in the first search used for recalibration, followed by 7 ppm for the main search and 0.5 Da for fragment ions; (iii) carbamidomethyl cysteine (fixed), oxidized methionine, and acetylated protein N-terminal (variable); (iv) a maximum of four modifications per peptide allowed; and (v) match between runs of 2 min. Relative protein quantification was performed based on the extracted ion chromatograms over the elution time window of each identified peptide. Peptide signals were combined for all identified charge states and variable modifications. The match-between-runs feature was used to determine whether peptides also occurred in the same retention time window in adjacent fractions, and the total sum was used for quantification. Identifications and quantifications were combined using the "identify" module in MaxQuant, applying a false discovery rate for both peptide and protein identifications of 1% based on the reversed peptide identifications (cutoff score > 50.83); protein identification was based on a minimum of one unique peptide, and proteins were grouped when based on the same set of peptides. Annotated spectra for all protein identifications based on a single peptide used for quantification are provided as supplemental Fig. S1. Non-unique peptides were strictly used for the quantification of the protein with the most identified peptides (26.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar). The intensity data were converted so that the sum of each protein over the four segments was 1, allowing for comparison between patient datasets. Additional available protein information was retrieved through the mapping feature of UniProt, and functional analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (28.Huang D.W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat. Protoc. 2008; 4: 44-57Crossref Scopus (25346) Google Scholar). Membrane proteins were predicted using TMHMM 2.0 (29.Sonnhammer E.L. von Heijne G. Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences.Proc. Int. Conf. Intell. Syst. Mol. Biol. 1998; 6: 175-182PubMed Google Scholar). Protein abundance factors were calculated by dividing the summed peptide intensities for each protein by the number of theoretically observable peptides of all fully tryptic peptides between 700 and 2500 Da; missed cleavages were neglected, and only carbamidomethylation of cysteine was considered as a fixed modification (30.Schwanhäusser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4059) Google Scholar). A paired t test was performed to determine statistical significance between the segments with a false discovery rate value of 0.05. Further data analysis and statistical analysis were performed using the R language and environment for statistical computing. The mass spectrometry data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) with the dataset identifier PXD000987. For each collected sample, one reference biopsy per segment was fixed overnight using methanol-Carnoy (31.Puchtler H. Waldrop F.S. Meloan S.N. Terry M.S. Conner H.M. Methacarn (methanol-Carnoy) fixation.Histochem. Cell Biol. 1970; 21: 97-116Crossref Scopus (194) Google Scholar), paraffin embedded, and sectioned at 4 μm. After tissue rehydration and antigen retrieval, sections were incubated overnight with rabbit anti-GCNT3 (1:200; HPA011154, Atlas Antibodies, Stockholm, Sweden) or rabbit anti-B4GALNT2 (1:200; HPA015721, Atlas Antibodies). Secondary Alexa-555-conjugated antibodies were used for detection with anti-mouse anti-rabbit (1:1000; Invitrogen), Hoechst 33258 nuclear counterstained, and imaged using an LSM 700 Axio Examiner Z.1 confocal imaging system with identical settings for all sections. We aimed to characterize the epithelial cell membrane protein composition and segmental protein levels along the length of the normal human colon to gain insight into the dynamic and distinct regional protein level differences. The proteins were isolated from two 1-mm-sized routine biopsies of ascending, transverse, descending, and sigmoid colon segments, covering the full length of the colon. These biopsies were collected from four different patients without any known colon diseases and with macroscopically normal mucosa. The epithelial cells were isolated, and membrane proteins were isolated and digested using the filter-aided sample preparation method and offline prefractionated using ZIC-HILIC chromatography prior to mass spectrometry analysis. An overview of the sample preparation method is presented in Fig. 1. The mass spectrometry analysis identified between 2598 and 2682 proteins per patient based on the combined identifications of the four segments with a false discovery rate of 1% at both protein and peptide levels. Of the total identifications, 87% were based on at least two unique peptides (peptide spectra matches) with an average of seven unique peptides per protein (median = 4). When we focused only on proteins identified in all patients, a total of 2508 unique proteins were selected for further data analysis (Fig. 2A, supplemental Table S1). 1729 proteins were identified in all patients and in all four segments. Only a small variation in identified proteins was observed, suggesting notable homogeneity in human colon membrane proteins. This group of proteins was for 96% of the proteins based on two or more unique peptides (median = 7) and showed strong correlation among the four segments. This group of proteins was used to quantify the membrane proteins (supplemental Table S2). To address the similarity among the different patients and segment samples, we performed hierarchical clustering of the protein intensities for all shared identified proteins. The sigmoid and ascending samples were both separately grouped with all four samples together, and the two central segments were mixed with a higher correlation toward the sigmoid colon (Fig. 2B).Fig. 2Proteomics analysis of colonic epithelium. A, the combined analyses of the four segments identified 2508 proteins, of which 1729 were shared among all patients. B, hierarchical clustering of the normalized intensities of proteins identified in all segments for each patient (n = 4) grouping all ascending and sigmoid samples together. C, the relative abundance of each protein was estimated by dividing the summed peptide intensities by the number of theoretical observable tryptic peptides. The data are presented as the mean ± S.E. (n = 4), and the approximate abundance range spans over 5 orders of magnitude. D, the 25 most abundant proteins from the abundance estimation curve. The most abundant were mainly mitochondrial proteins involved in ATP synthesis, of which 15 contained transmembrane-spanning domains. E, the 25 least abundant proteins included isoforms and soluble cytoplasmic proteins.View Large Image Figure ViewerDownload Hi-res image Download (PPT) To assess the dynamic range of the analysis, we estimated the relative abundance of each protein based on the sum of peptide ion intensities per protein divided by the number of theoretical tryptic peptides (700–2500 Da) to normalize for varying protein length (30.Schwanhäusser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4059) Google Scholar). The proteins were ranked depending on their estimated abundance and spanned over 5 orders of magnitude between the highest and lowest abundant protein, showing the depth of our analysis (Fig. 2C). A majority of the 25 most abundant proteins originated from the mitochondria comprising parts of the ATP synthesis and cytochrome C oxidase complexes (Fig. 2D). The abundance of proteins belonging to the mitochondrial respiratory chain suggests that the highly biological active colonic cells require vast amounts of energy. The bottom part of the abundance plot consists mainly of soluble proteins, as the applied sample preparation method favors hydrophobic proteins (Fig. 2E). The relative abundance estimation can therefore be used only for hydrophobic and transmembrane-spanning proteins. In standard proteomics workflows, membrane proteins are often underrepresented because of their amphiphilic properties. Various methods for enriching membrane proteins have been developed, and here we used an established method based on sodium carbonate washes at high pH combined with ultracentrifugation (32.Fujiki Y. Hubbard A. Fowler S. Lazarow P. Isolation of intracellular membranes by means of sodium carbonate treatment: application to endoplasmic reticulum.J. Cell Biol. 1982; 93: 97Crossref PubMed Scopus (1382) Google Scholar, 33.Wu C.C. MacCoss M.J. Howell K.E. Yates J.R. A method for the comprehensive proteomic analysis of membrane proteins.Nat. Biotechnol. 2003; 21: 532-538Crossref PubMed Scopus (606) Google Scholar). We identified a total of 2508 proteins from the four colon segments, of which 1098 (44%) were predicted to contain transmembrane-spanning domains based on the TMHMM model (29.Sonnhammer E.L. von Heijne G. Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences.Proc. Int. Conf. Intell. Syst. Mol. Biol. 1998; 6: 175-182PubMed Google Scholar), exceeding the 20% to 30% membrane proteins predicted in the human genome (34.Wallin E. Heijne, von G. Genome-wide analysis of integral membrane proteins from eubacterial
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