Butyrate Suppresses the Proliferation of Colorectal Cancer Cells via Targeting Pyruvate Kinase M2 and Metabolic Reprogramming
2018; Elsevier BV; Volume: 17; Issue: 8 Linguagem: Inglês
10.1074/mcp.ra118.000752
ISSN1535-9484
AutoresQingran Li, Lijuan Cao, Yang Tian, Pei Zhang, Chujie Ding, Wenjie Lu, Chenxi Jia, Chang Shao, Wenyue Liu, Dong Wang, Hui Ye, Haiping Hao,
Tópico(s)Amino Acid Enzymes and Metabolism
ResumoButyrate is a short chain fatty acid present in a high concentration in the gut lumen. It has been well documented that butyrate, by serving as an energetic metabolite, promotes the proliferation of normal colonocytes while, by serving as a histone deacetylase inhibitor, epigenetically suppressing the proliferation of cancerous counterparts undergoing the Warburg effect. However, how butyrate interrupts the metabolism of colorectal cancer cells and ultimately leads to the suppression of cell proliferation remains unclear. Here, we employed a metabolomics-proteomics combined approach to explore the link between butyrate-mediated proliferation arrest and cell metabolism. A metabolomics study revealed a remodeled metabolic profile with pronounced accumulation of pyruvate, decreased glycolytic intermediates upstream of pyruvate and reduced levels of nucleotides in butyrate-treated HCT-116 cells. Supplementation of key metabolite intermediates directly affected cancer-cell metabolism and modulated the suppressive effect of butyrate in HCT-116 cells. By a Drug Affinity Responsive Target Stability (DARTS)-based quantitative proteomics approach, we revealed the M2 isoform of a pyruvate kinase, PKM2, as a direct binding target of butyrate. Butyrate activates PKM2 via promoting its dephosphorylation and tetramerization and thereby reprograms the metabolism of colorectal cancer cells, inhibiting the Warburg effect while favoring energetic metabolism. Our study thus provides a mechanistic link between PKM2-induced metabolic remodeling and the antitumorigenic function of butyrate and demonstrates a widely applicable approach to uncovering unknown protein targets for small molecules with biological functions. Butyrate is a short chain fatty acid present in a high concentration in the gut lumen. It has been well documented that butyrate, by serving as an energetic metabolite, promotes the proliferation of normal colonocytes while, by serving as a histone deacetylase inhibitor, epigenetically suppressing the proliferation of cancerous counterparts undergoing the Warburg effect. However, how butyrate interrupts the metabolism of colorectal cancer cells and ultimately leads to the suppression of cell proliferation remains unclear. Here, we employed a metabolomics-proteomics combined approach to explore the link between butyrate-mediated proliferation arrest and cell metabolism. A metabolomics study revealed a remodeled metabolic profile with pronounced accumulation of pyruvate, decreased glycolytic intermediates upstream of pyruvate and reduced levels of nucleotides in butyrate-treated HCT-116 cells. Supplementation of key metabolite intermediates directly affected cancer-cell metabolism and modulated the suppressive effect of butyrate in HCT-116 cells. By a Drug Affinity Responsive Target Stability (DARTS)-based quantitative proteomics approach, we revealed the M2 isoform of a pyruvate kinase, PKM2, as a direct binding target of butyrate. Butyrate activates PKM2 via promoting its dephosphorylation and tetramerization and thereby reprograms the metabolism of colorectal cancer cells, inhibiting the Warburg effect while favoring energetic metabolism. Our study thus provides a mechanistic link between PKM2-induced metabolic remodeling and the antitumorigenic function of butyrate and demonstrates a widely applicable approach to uncovering unknown protein targets for small molecules with biological functions. Butyrate is a short-chain fatty acid (SCFA) 1The abbreviations used are:SCFAshort chain fatty acidHDAChistone deacetylaseBSAbovine serum albuminTCA cycletricarboxylic-acid cycleHRMShigh resolution mass spectrometryDARTSdrug affinity responsive target stabilityPKM2pyruvate kinase M2CCK-8cell counting kit 8BCABicinchoninic acidSDS-PAGEsodium dodecyl sulfate polyacrylamide gel electrophoresisSEMstandard error of the meanANOVAanalysis of variancePVDFpolyvinylidene difluoridePCAprincipal component analysisOPLS-DAorthogonal projections to latent structures discriminant analysisG6Pglucose-6-phosphate3PG3-phosphoglyceric acidPEPphosphoenolpyruvic acidR5Pribulose-5-phosphate5′-IMPinosine 5′-monophosphate5′-GMPguanosine 5′-monophosphate5′-UMPuridine 5′-monophosphatePYRpyruvateLACLactateAc-CoAacetyl coenzyme APPPpentose phosphate pathwayLDHlactate dehydrogenasePTMpost-translational modificationinternal standardIS. 1The abbreviations used are:SCFAshort chain fatty acidHDAChistone deacetylaseBSAbovine serum albuminTCA cycletricarboxylic-acid cycleHRMShigh resolution mass spectrometryDARTSdrug affinity responsive target stabilityPKM2pyruvate kinase M2CCK-8cell counting kit 8BCABicinchoninic acidSDS-PAGEsodium dodecyl sulfate polyacrylamide gel electrophoresisSEMstandard error of the meanANOVAanalysis of variancePVDFpolyvinylidene difluoridePCAprincipal component analysisOPLS-DAorthogonal projections to latent structures discriminant analysisG6Pglucose-6-phosphate3PG3-phosphoglyceric acidPEPphosphoenolpyruvic acidR5Pribulose-5-phosphate5′-IMPinosine 5′-monophosphate5′-GMPguanosine 5′-monophosphate5′-UMPuridine 5′-monophosphatePYRpyruvateLACLactateAc-CoAacetyl coenzyme APPPpentose phosphate pathwayLDHlactate dehydrogenasePTMpost-translational modificationinternal standardIS. produced by the fermentation of dietary fiber via microbiota in the lumen of the colon. It is usually present at a high concentration (>10 mm) in the colon and uptaken into the epithelium cells of the colon via a monocarboxylate transporter (1Tan J. McKenzie C. Potamitis M. Thorburn A.N. Mackay C.R. Macia L. The role of short-chain fatty acids in health and disease.Adv. Immunol. 2014; 121: 91-119Crossref PubMed Scopus (1208) Google Scholar). Butyrate then serves as the primary energy source (∼70%) for colonocytes by undergoing ß-oxidation in mitochondria and supports metabolic homeostasis (2Koh A. De Vadder F. Kovatcheva-Datchary P. Backhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites.Cell. 2016; 165: 1332-1345Abstract Full Text Full Text PDF PubMed Scopus (2731) Google Scholar). In contrast to its role in fueling normal colonocytes, recent studies have shown that butyrate exhibits an antitumorigenic function by inhibiting the proliferation or inducing the apoptosis of colorectal cancer cells (3Fung K.Y. Cosgrove L. 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The niacin/butyrate receptor GPR109A suppresses mammary tumorigenesis by inhibiting cell survival.Cancer Res. 2014; 74: 1166-1178Crossref PubMed Scopus (80) Google Scholar). short chain fatty acid histone deacetylase bovine serum albumin tricarboxylic-acid cycle high resolution mass spectrometry drug affinity responsive target stability pyruvate kinase M2 cell counting kit 8 Bicinchoninic acid sodium dodecyl sulfate polyacrylamide gel electrophoresis standard error of the mean analysis of variance polyvinylidene difluoride principal component analysis orthogonal projections to latent structures discriminant analysis glucose-6-phosphate 3-phosphoglyceric acid phosphoenolpyruvic acid ribulose-5-phosphate inosine 5′-monophosphate guanosine 5′-monophosphate uridine 5′-monophosphate pyruvate Lactate acetyl coenzyme A pentose phosphate pathway lactate dehydrogenase post-translational modification IS. short chain fatty acid histone deacetylase bovine serum albumin tricarboxylic-acid cycle high resolution mass spectrometry drug affinity responsive target stability pyruvate kinase M2 cell counting kit 8 Bicinchoninic acid sodium dodecyl sulfate polyacrylamide gel electrophoresis standard error of the mean analysis of variance polyvinylidene difluoride principal component analysis orthogonal projections to latent structures discriminant analysis glucose-6-phosphate 3-phosphoglyceric acid phosphoenolpyruvic acid ribulose-5-phosphate inosine 5′-monophosphate guanosine 5′-monophosphate uridine 5′-monophosphate pyruvate Lactate acetyl coenzyme A pentose phosphate pathway lactate dehydrogenase post-translational modification IS. Although complicated, previous research generally attributed the cancer suppressive effect of butyrate to its functioning as a G-protein coupled receptor (GPR) 109a ligand or a histone deacetylase (HDAC) inhibitor (8Thangaraju M. Cresci G.A. Liu K. Ananth S. Gnanaprakasam J.P. Browning D.D. Mellinger J.D. Smith S.B. Digby G.J. Lambert N.A. Prasad P.D. Ganapathy V. GPR109A is a G-protein-coupled receptor for the bacterial fermentation product butyrate and functions as a tumor suppressor in colon.Cancer Res. 2009; 69: 2826-2832Crossref PubMed Scopus (447) Google Scholar, 9Singh N. Gurav A. Sivaprakasam S. Brady E. Padia R. Shi H. Thangaraju M. Prasad P.D. Manicassamy S. Munn D.H. Lee J.R. Offermanns S. Ganapathy V. Activation of Gpr109a, receptor for niacin and the commensal metabolite butyrate, suppresses colonic inflammation and carcinogenesis.Immunity. 2014; 40: 128-139Abstract Full Text Full Text PDF PubMed Scopus (1271) Google Scholar, 10Donohoe D.R. Collins L.B. Wali A. Bigler R. Sun W. Bultman S.J. The Warburg effect dictates the mechanism of butyrate-mediated histone acetylation and cell proliferation.Mol. Cell. 2012; 48: 612-626Abstract Full Text Full Text PDF PubMed Scopus (525) Google Scholar, 11Donohoe D.R. Holley D. Collins L.B. Montgomery S.A. Whitmore A.C. Hillhouse A. Curry K.P. Renner S.W. Greenwalt A. Ryan E.P. Godfrey V. Heise M.T. Threadgill D.S. Han A. Swenberg J.A. Threadgill D.W. Bultman S.J. A gnotobiotic mouse model demonstrates that dietary fiber protects against colorectal tumorigenesis in a microbiota- and butyrate-dependent manner.Cancer Discov. 2014; 4: 1387-1397Crossref PubMed Scopus (279) Google Scholar). As an HDAC inhibitor, specifically, butyrate regulates the expression of various cell fate-related genes, such as Fas and Wnt10B, while also mediating FOXO3-dependent expression of Gadd45b, Cdkn1a, and Cdkn1c, subsequently leading to inhibited proliferation and apoptosis in colorectal cancer cells (10Donohoe D.R. Collins L.B. Wali A. 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Cancer cells, unlike their normal counterparts, are known to undergo the Warburg effect and preferentially use glucose instead of butyrate as the energy source, which favors their survival and rapid growth (14Ward P.S. Thompson C.B. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate.Cancer Cell. 2012; 21: 297-308Abstract Full Text Full Text PDF PubMed Scopus (2172) Google Scholar, 15Louis P. Hold G.L. Flint H.J. The gut microbiota, bacterial metabolites and colorectal cancer.Nat. Rev. Microbiol. 2014; 12: 661-672Crossref PubMed Scopus (1528) Google Scholar). The reprogrammed metabolic pattern adapts cancer cells to a hypoxic microenvironment and proliferation-required biomass accumulation by regulating the expression levels and activities of key enzymes involved in cancer metabolism (10Donohoe D.R. Collins L.B. Wali A. Bigler R. Sun W. Bultman S.J. The Warburg effect dictates the mechanism of butyrate-mediated histone acetylation and cell proliferation.Mol. Cell. 2012; 48: 612-626Abstract Full Text Full Text PDF PubMed Scopus (525) Google Scholar, 16Cairns R.A. Harris I.S. Mak T.W. Regulation of cancer cell metabolism.Nat. Rev. Cancer. 2011; 11: 85-95Crossref PubMed Scopus (3564) Google Scholar). It has been previously proposed that butyrate fuels the tricarboxylic-acid cycle (TCA cycle) in normal colonocytes but accumulates in cancerous colonocytes undergoing the Warburg effect, thereby functioning as an HDAC inhibitor to inhibit the proliferation of colorectal cancer cells. However, the direct mechanistic link between metabolic adaptation and proliferative suppression by butyrate remains elusive. Because the continuous and rapid proliferation of cancer cells depends on the capacity of metabolic reprogramming (17Boroughs L.K. DeBerardinis R.J. Metabolic pathways promoting cancer cell survival and growth.Nat. Cell Biol. 2015; 17: 351-359Crossref PubMed Scopus (871) Google Scholar, 18Vernieri C. Casola S. Foiani M. Pietrantonio F. de Braud F. Longo V. Targeting Cancer Metabolism: Dietary and Pharmacologic Interventions.Cancer Discov. 2016; 6: 1315-1333Crossref PubMed Scopus (111) Google Scholar), we reason that the proliferation-suppressive effect of butyrate might be directly connected with its capability of regulating the metabolism of cancerous colonocytes. It is possible that butyrate exerts differential influences on the metabolism between normal cells and their cancerous counterparts, which thereby explains the "butyrate paradox." We thus sought to explore the mechanistic relationship between butyrate-inhibited colorectal cancer cell proliferation and their corresponding metabolic programs and to identify potential protein targets mediating such a link. In this regard, a high-resolution mass spectrometry (HRMS)-based metabolomic technique was employed to probe the metabolic changes in HCT-116 cells elicited by butyrate. Metabolomics data revealed butyrate-mediated metabolic remodeling and underlined the significantly perturbed metabolic pathways, providing clues to the protein targets responsible for butyrate-induced suppressive proliferation. Subsequently, a target identification approach was employed to uncover the binding proteins of butyrate and reveal whether any of those are involved in the butyrate-affected metabolism. Because of the simple structure of butyrate, we employed a derivatization-free proteomics approach, namely, drug affinity responsive target stability (DARTS), to characterize the protein targets of butyrate instead of conventional affinity-based methods that require the derivatization of butyrate (19Lomenick B. Hao R. Jonai N. Chin R.M. Aghajan M. Warburton S. Wang J. Wu R.P. Gomez F. Loo J.A. Wohlschlegel J.A. Vondriska T.M. Pelletier J. Herschman H.R. Clardy J. Clarke C.F. Huang J. Target identification using drug affinity responsive target stability (DARTS).Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 21984-21989Crossref PubMed Scopus (560) Google Scholar, 20Pai M.Y. Lomenick B. Hwang H. Schiestl R. McBride W. Loo J.A. Huang J. Drug affinity responsive target stability (DARTS) for small-molecule target identification.Methods Mol. Biol. 2015; 1263: 287-298Crossref PubMed Scopus (164) Google Scholar). In contrast with the conventional DARTS approach that directly assigns the in-gel digested drug-stabilizing protein of highest abundance as the target, we employed a label-free quantitative proteomics approach and spiked in a coeluting exogenous protein as an internal standard (IS) for reliable and accurate quantitation of in-gel digested target proteins. The combined utility of the biomics approach thus allows for identifying butyrate-bound proteins as potential targets mediating cancerous cell proliferation. Here, with the metabolism-guided proteomics approach, we found that, upon butyrate treatment, pronounced elevation of pyruvate and decreased levels of other glycolytic intermediates upstream of pyruvate were noted in HCT116 cells. The DARTS-based quantitative proteomics results and biochemical assays both suggested that butyrate binds to and activates pyruvate kinase isoform 2 (PKM2), which is subsequently responsible for reversing the metabolic advantages gained by cancerous colonocytes and ultimately leads to proliferation arrest. Our study thus provides a mechanistic perspective by linking PKM2-mediated metabolic remodeling with the antitumorigenic function of butyrate. We first validated the inhibitory effect of butyrate on the proliferation of colorectal cancer cells using HCT116, HT29 and LoVo cell lines. Cell viability was determined via cell counting kit-8 (CCK-8) using increasing doses of butyrate. For each condition, five independent biological replicates were used. Next, we evaluated the capability of butyrate in reprogramming cell metabolism via metabolomics. Five independent biological replicate experiments were performed. For subsequent determination of potential targets of butyrate via a DARTS assay, lysates of HCT116 cells were incubated with butyrate, digested by Pronase, and separated by SDS-PAGE. Proteins showing higher stability upon butyrate binding were visualized by densitometry after Coomassie blue staining (three independent biological replicates). To reliably quantify proteins from the targeted bands, we spiked an exogenous protein in each lysate sample as an IS and separated the IS-spiked samples by SDS-PAGE followed by excision, in-gel digestion and LC-MS analysis. The label-free proteomics approach was employed to quantify the identified proteins from the specific protein bands collected across four samples including the control sample and three butyrate-treated samples of different doses (three biological replicates). Relative quantitative analysis indicated potential target proteins of butyrate that showed significantly increased abundance in a butyrate dose-dependent manner. The target proteins identified via the proteomics approach were further confirmed by immunoblotting (n = 3, biological replicates). For the determination of cell proliferation via BrdU cell proliferation kits, cells were seeded and treated in 96-well plates (n = 4, biological replicates). The data are presented as the mean ± standard error of the mean (S.E.). Statistical differences between two groups were determined by Student's t-test, whereas comparisons among three or more groups were analyzed by one-way ANOVA followed by the Tukey-Kramer test. p < 0.05 was considered statistically significant. Sodium butyrate, hypoxanthine, sodium pyruvate, l-serine, shikonin, ADP, NADH, PEP, lactate dehydrogenase (LDH) and protease inhibitor mixture were purchased from Sigma-Aldrich (St. Louis, MO). TEPP-46 was purchased from Merck Millipore (Darmstadt, Germany). Unless indicated, other chemical reagents were all obtained from Sinopharm Chemical Reagents (Shanghai, China). Human colorectal cancer cell lines HCT116, HT29 and LoVo were all purchased from ATCC (Manassas, VA), among which the HCT116 and HT29 cells were cultured in McCoy's 5A medium (Sigma-Aldrich), and LoVo cells were cultured in DMEM/F12 medium (GIBCO, Gland Island, NY). Media were all supplemented with 10% fetal bovine serum (FBS, Biological Industries, Kibbutz, Israel), 100 unit/ml penicillin and 1 μg/ml streptomycin, and cells were maintained in a humidified incubator with 5% CO2 at 37 °C. Cells were lysed on ice with NP-40 buffer (Beyotime, Jiangsu, China) supplemented with a 1% protease inhibitor mixture. The total protein concentration was quantified with a bicinchoninic acid (BCA) assay kit (Beyotime) for normalization of each sample. Cell lysates were then loaded and separated on 8–12% SDS-PAGE and transferred onto polyvinylidene difluoride (PVDF) membranes. The immunoblots were blocked by 5% nonfat milk and incubated with the indicated primary-antibody solution at 4 °C overnight followed by incubation with peroxidase-conjugated secondary antibodies. The resulting bands were detected using chemiluminescent reagents on a ChemiDoc XRS system (Bio-Rad, Hercules, CA), and the relative quantification of selected bands was accomplished by densitometry via ImageLab (Bio-Rad). Details of the antibodies are provided in supplemental Table S1, Supporting Information (SI). The activity of pyruvate kinase (PK) was determined by LDH-dependent conversion of NADH to NAD+. Cells were lysed with NP-40 lysis buffer supplemented with a 1% protease inhibitor mixture. The total protein concentration was determined by a BCA assay kit and diluted to 1 μg/μl with lysis buffer. A 200 μl reaction mixture consisting of 50 mm Tris-HCl (pH = 7.5), 5 mm MgCl2, 100 mm KCl, 1 mm ADP, 0.5 mm PEP, 0.2 mm NADH and 8 units of LDH was added to 5 μg cell lysate in each well in 96-well plates. Upon brief mixing, samples were measured at 37 °C with an interval of 10 s until the OD340 value was constant. The fluorescent intensity at 100 s was recorded and used to calculate the PK activity using the following equation: Activity (U/μl) = (A0 s – A100 s) × 200/6.22 × 100 × 5. Approximately 1×107 of untreated colorectal cancer cells were lysed with NP-40 lysis buffer for 30 min at 4 °C. Sodium butyrate dissolved in 1× TNC buffer (50 mm Tris, 50 mm NaCl, 10 mm CaCl2, pH = 7.4) was added into the cell lysate (500 μg total protein, 5 mg/ml) to reach the indicated concentrations and gently mixed. The lysates were then placed at room temperature for 2 h to allow sufficient ligand-protein target interactions, and then digested by Pronase (Roche, Basel, Swiss, dissolved in 1× TNC buffer) at a 1:500 ratio (wt/wt) for precisely 30 min. Proteolysis was quenched by mixing the lysate with 4× loading buffer (Bio-Rad) and 5 min of boiling. Predenatured bovine serum albumin (BSA) was aliquoted, and then spiked into each sample to reach an equal concentration of 15 μg/ml in each sample, which subsequently serves as an IS for the quantitative analysis of target proteins (Fig. 4A). For the DARTS assay of recombinant PKM2 protein, the protein was diluted with 1× TNC buffer to 100 μg/ml. Subsequently, 10 μg of the protein lysate was incubated with butyrate at the indicated concentrations at room temperature for 2 h, followed by digestion with Pronase at a 1:750 ratio (wt/wt) for 30 min. Proteolysis was quenched by mixing the lysate with 4× loading buffer and 5 min of boiling. The production of recombinant PKM2 protein is detailed in the Supporting Methods. The resulting samples were separated by SDS-PAGE for the visualization of target proteins. The SDS-PAGE separated gel was stained by Coomassie blue, and the images were captured by the ChemiDoc XRS system. Gel bands that displayed significant abundance changes following butyrate incubation were manually excised, destained, reduced, alkylated and in-gel digested by sequencing-grade trypsin (Promega, Madison, WI). The digests were then extracted and desalted on C18 Ziptips (Millipore, Milford, MA), vacuum dried, and reconstituted in water containing 0.1% formic acid. An online nanoACQUITY UPLC system coupled-SYNAPT G2-Si mass spectrometer (Waters, Manchester, UK) was used for LC-MS/MS analysis. A trapping column (Acquity UPLC M-Class 0.18×20 mm, 5 μm 100 Å, C18, Waters) and analytical column (Acquity UPLC M-Class 0.075×150 mm, 1.8 μm HSS T3, Waters) were employed. Mobile phases A and B consist of 0.1% (v/v) formic acid in water and 0.1% (v/v) formic acid in acetonitrile, respectively. A 90 min-length gradient of 1–40% acetonitrile at a flow rate of 300 nL/min was used for separation. The scan time of MS was set as 0.2 s, the full MS scan range was set to 350–1500 m/z with a scan time of 0.2 s, and MS/MS scan range was set to 50–2000 m/z. The top 10 abundant precursors were subjected to MS/MS fragmentation with a ramp CE set between low energy (14–19 eV) and elevated energy (60–90 eV) using a scan time of 0.15 s per function. PEAKS Studio version 8.5 (Bioinformatics Solution, Inc., Waterloo, ON, Canada) was used for the generation of peaks and identification of protein from MS/MS spectra against the Human UniProt database (release 2017.10 with 20,316 entries). The sequence of bovine serum albumin (BSA, P02769 ALBU_BOVIN) was manually added into the database. The specificity of the enzyme was set to trypsin, a maximum of two missed cleavages was acceptable, carbamidomethylation of cysteine was set as a fixed modification, oxidation of methionine was included as the variable modification, and the mass tolerance for precursor ions and fragment ions were set to 20 ppm and 0.05 Da, respectively. A false discovery rate of 1% was used to filter assigned peptides, and protein identifications with ≥2 unique peptides were kept. The identified proteins are detailed in supplemental Table S2, SI. Proteins displaying top abundance from the gel bands were selected for relative quantification (the abundance levels of protein were ranked based on #unique peptides summarized by the Label Free module of PEAKS Studio). The label-free method was then employed. We selected quantifiable peptides and calculated the total values of the corresponding peak areas. The mass error tolerance for precursors and fragment ions was set at 20 ppm and 0.05 Da, respectively, and each experimental condition was set as a group for relative quantification. Criteria of the selected peptides for protein quantification included (1) -10lgP value > 30, (2) miss cleavage ≤ 2, (3) average ppm of peptides <15, and (4) displaying consistent chromatographic behaviors across all samples. The peptides identified from BSA were used for normalization. Therefore, the relative abundance of proteins in each sample was calculated by AR = ∑(Peak areas of peptide of target)/∑(Peak areas of peptides of IS Protein). Proteins that displayed increased abundances upon butyrate incubation were then assigned as potential target proteins. All raw mass spectrometric data were deposited into the MassIVE system and can be accessed at ftp://massive.ucsd.edu/MSV000082189. HCT116 cells were seeded and cultured in medium in 6-well plates, and subsequent metabolomic analysis was performed based on a previously reported method (21Wu M. Ye H. Shao C. Zheng X. Li Q. Wang L. Zhao M. Lu G. Chen B. Zhang J. Wang Y. Wang G. Hao H. Metabolomics-proteomics combined approach identifies differential metabolism-associated molecular events between senescence and apoptosis.J. Proteome Res. 2017; 16: 2250-2261Crossref PubMed Scopus (32) Google Scholar, 22Wang L. Ye H. Sun D. Meng T. Cao L. Wu M. Zhao M. Wang Y. Chen B. Xu X. Wang G. Hao H. Metabolic pathway extension approach for metabolomic biomarker identification.Anal. Chem. 2017; 89: 1229-1237Crossref PubMed Scopus (25) Google Scholar). Briefly, cells were extracted with 1 ml ice-cold 80% methanol containing 1.5 μg/ml 1,2-13C2-glutamine as an IS. The cell extracts were subsequently concentrated in a SPD2010–230 SpeedVac concentrator (Thermo Scientific, Holbrook, NY) and reconstituted in 100 μl deionized water before being subjected to LC-MS analysis. A 20 μl aliquot of the sample was separated by an Xbridge Amide HPLC column (3.5 μm, 4.6 mm × 100 mm, Waters, Milford, MA) in a LC-30A UFLC system (Shimadzu, Kyoto, Japan). The temperature of the column oven was set at 40 °C. The mobile phase A consisted of 5 mm ammonium acetate, 95% water and 5% acetonitrile (pH 9.0), and the mobile phase B was acetonitrile. The eluent was then introduced into a Triple TOF 5600 system (SCIEX, Framingham, MA) operated in the negative ion mode. ESI conditions were set as follows: Ion Source Gas 1 (GS1) at 33 psi, Ion Source Gas 2 (GS2) at 33 psi, Curtain gas (CUR) at 25 psi, source temperature at 475 °C and ion spray voltage floating at −4500 V. Mass spectrometer parameters were as follows: N2 as the collision gas, Q1 vacuum gauge: 2.2×10−5 torr, TOF vacuum 0.309×10−6 torr. Information-dependent acquisition (IDA) was employed for metabolite identification. The TOF/MS scan was set in the m/z range of 50 to 1000 Da, the declustering potential (DP) was set at −95 V, and the collision energy (CE) was set at −5 V. For the TOF-MS/MS scan, the IDA settings were set as follows: DP at −95 V, CE at −30 V with a spread of ± 10 V, charge state −1, intensity threshold at 500 cps, and maximum number of candidate ions at 4; isotopes within 4 Da were excluded. Accumulation times for TOF-MS and MS/MS scan were set at 250 ms and 100 ms, respectively. For metabolomic data processing, peak areas were extracted from raw data to generate a data matrix and normalized against IS and protein concentrations of each samples. Principal component analysis (PCA) was performed to determine the difference between the control versus the butyrate-treated groups. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was subsequentl
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