Smoking-Induced Expression of the GPR15 Gene Indicates Its Potential Role in Chronic Inflammatory Pathologies
2015; Elsevier BV; Volume: 185; Issue: 11 Linguagem: Inglês
10.1016/j.ajpath.2015.07.006
ISSN1525-2191
AutoresGea Kõks, Mari‐Liis Uudelepp, Maia Limbach, Pärt Peterson, Ene Reimann, Sulev Kõks,
Tópico(s)Asthma and respiratory diseases
ResumoDespite the described clear epigenetic effects of smoking, the effect of smoking on genome-wide gene expression in the blood is obscure. We therefore studied the smoking-induced changes in the gene-expression profile of the peripheral blood. RNA was extracted from the whole blood of 48 individuals with a detailed smoking history (24 never-smokers, 16 smokers, and 8 ex-smokers). Gene-expression profiles were evaluated with RNA sequencing, and results were analyzed separately in 24 men and 24 women. In the male smokers, 13 genes were statistically significantly (false-discovery rate <0.1) differentially expressed; in female smokers, 5 genes. Although most of the differentially expressed genes were different between the male and female smokers, the G-protein–coupled receptor 15 gene (GPR15) was differentially expressed in both male and female smokers compared with never-smokers. Analysis of GPR15 methylation identified significantly greater hypomethylation in smokers compared with that in never-smokers. GPR15 is the chemoattractant receptor that regulates T-cell migration and immunity. Up-regulation of GPR15 could explain to some extent the health hazards of smoking with regard to chronic inflammatory diseases. Despite the described clear epigenetic effects of smoking, the effect of smoking on genome-wide gene expression in the blood is obscure. We therefore studied the smoking-induced changes in the gene-expression profile of the peripheral blood. RNA was extracted from the whole blood of 48 individuals with a detailed smoking history (24 never-smokers, 16 smokers, and 8 ex-smokers). Gene-expression profiles were evaluated with RNA sequencing, and results were analyzed separately in 24 men and 24 women. 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Predictors of global methylation levels in blood DNA of healthy subjects: a combined analysis.Int J Epidemiol. 2012; 41: 126-139Crossref PubMed Scopus (169) Google Scholar Focus on the methylation of particular gene loci has identified several regions as differentially methylated between smokers and never-smokers. The CpG site in the COMT gene at position -193 is methylated in 22.2% of smokers and 18.3% of never-smokers.17Xu Q. Ma J.Z. Payne T.J. Li M.D. Determination of Methylated CpG Sites in the Promoter Region of Catechol-O-Methyltransferase (COMT) and their Involvement in the Etiology of Tobacco Smoking.Front Psychiatry. 2010; 1: 16PubMed Google Scholar Similarly, in smokers, significant hypomethylation of the nontranslated region in the MAOA gene has been reported.18Philibert R.A. Beach S.R. Gunter T.D. Brody G.H. Madan A. Gerrard M. 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The effect of smoking on MAOA promoter methylation in DNA prepared from lymphoblasts and whole blood.Am J Med Genet B Neuropsychiatr Genet. 2010; 153B: 619-628PubMed Google Scholar Smoking induced hypomethylation in the MAOB promoter region, with a high monoamine oxidase protein concentration.19Launay J.M. Del Pino M. Chironi G. Callebert J. Peoc'h K. Megnien J.L. Mallet J. Simon A. Rendu F. Smoking induces long-lasting effects through a monoamine-oxidase epigenetic regulation.PLoS One. 2009; 4: e7959Crossref PubMed Scopus (107) Google Scholar Studies of genome-wide methylation have identified distinct regions affected by smoking and smoking cessation.20Tsaprouni L.G. Yang T.P. Bell J. Dick K.J. Kanoni S. Nisbet J. Vinuela A. Grundberg E. Nelson C.P. Meduri E. Buil A. Cambien F. Hengstenberg C. Erdmann J. Schunkert H. Goodall A.H. Ouwehand W.H. Dermitzakis E. Spector T.D. Samani N.J. Deloukas P. 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Tobacco-smoking-related differential DNA methylation: 27K discovery and replication.Am J Hum Genet. 2011; 88: 450-457Abstract Full Text Full Text PDF PubMed Scopus (511) Google Scholar In another study with the same methylation array (27K BeadChip; Illumina, San Diego, CA), the F2RL3 finding was replicated, and additionally a novel association at the G-protein–coupled receptor 15 gene (GPR15) locus was identified.21Wan E.S. Qiu W. Baccarelli A. Carey V.J. Bacherman H. Rennard S.I. Agusti A. Anderson W. Lomas D.A. Demeo D.L. Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome.Hum Mol Genet. 2012; 21: 3073-3082Crossref PubMed Scopus (230) Google Scholar Recently, higher-density arrays have been used. An epigenome-wide association study in 374 Europeans replicated the smoking-related hypomethylation of F2RL3 and identified three additional loci, including the aryl hydrocarbon receptor repressor gene (AHRR).23Shenker N.S. Polidoro S. van Veldhoven K. Sacerdote C. Ricceri F. Birrell M.A. Belvisi M.G. Brown R. Vineis P. Flanagan J.M. Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking.Hum Mol Genet. 2013; 22: 843-851Crossref PubMed Scopus (311) Google Scholar Another study in a larger cohort that used the same 450K methylation chip identified several smoking-dependent loci.24Zeilinger S. Kuhnel B. Klopp N. Baurecht H. Kleinschmidt A. Gieger C. Weidinger S. Lattka E. Adamski J. Peters A. Strauch K. Waldenberger M. Illig T. Tobacco smoking leads to extensive genome-wide changes in DNA methylation.PLoS One. 2013; 8: e63812Crossref PubMed Scopus (517) Google Scholar In addition to the confirmation of the AHRR methylation, at least eight additional loci were found. Methylation was found to depend on the cessation time and pack-years of smoking.24Zeilinger S. Kuhnel B. Klopp N. Baurecht H. Kleinschmidt A. Gieger C. Weidinger S. Lattka E. Adamski J. Peters A. Strauch K. Waldenberger M. Illig T. Tobacco smoking leads to extensive genome-wide changes in DNA methylation.PLoS One. 2013; 8: e63812Crossref PubMed Scopus (517) Google Scholar All of these studies indicate the broad effects of smoking on the genome and cell physiology. Although the clear effects of smoking on epigenetics have been described, the effects of smoking on genome-wide gene expression in blood is not often studied. Our goals were to describe the smoking-induced changes in the gene-expression profile of the blood and to identify methylated regions that match the altered RNA levels. We analyzed RNA and DNA extracted from the whole blood of donors at the Estonian Genome Center, University of Tartu (Tartu, Estonia). The study cohort was derived from the Estonian Biobank of the Estonian Genome Center.25Leitsalu L. Haller T. Esko T. Tammesoo M.L. Alavere H. Snieder H. Perola M. Ng P.C. Magi R. Milani L. Fischer K. Metspalu A. Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu.Int J Epidemiol. 2015; 44: 1137-1147Crossref PubMed Scopus (171) Google Scholar The Ethics Review Committee on Human Research of the University of Tartu approved the protocols and informed-consent forms used in this study. All of the participants signed a written informed-consent form. The Estonian Biobank cohort is a volunteer-based sample of the Estonian resident adult population (aged over 18 years). The current number of participants (52,000) represents 5% of the adult population of Estonia, making it ideally suited to population-based studies. The Biobank stores DNA and peripheral blood mononuclear cells from donors, along with data on lifestyle (eg, smoking and alcohol-intake habits, physical activity). Forty-eight samples (from 24 men and 24 women) were used for gene-expression profiling in the present study. For collecting whole-blood samples, Tempus blood RNA tubes (Thermo Fisher Scientific Inc., Waltham, MA) were used. The samples were stored at −20°C until RNA extraction. For total RNA extraction, the combination of TRIzol reagent (Thermo Fisher Scientific) and the RNeasy Mini Kit (Qiagen, Hilden, Germany) was used. The protocol was as follows—after samples were thawed and mixed the in Tempus tube, blood was transferred to an empty 50-mL tube. The Tempus tube was additionally washed with 3 mL of phosphate-buffered saline. The sample in the 50-mL tube was mixed and centrifuged at 4°C for 60 minutes at 3000 × g. The supernatant was discarded (the invisible precipitation was at the bottom of the tube), and the tube without the cap was placed onto the clean tissue, upside down, for 2 minutes. TRIzol reagent (1 mL) was added into the tube, mixed, and incubated for 5 minutes at room temperature. The sample was lifted to a new 1.5-mL tube, and 200 μL of chloroform was added. The sample was mixed with vortexing for 15 seconds, incubated at room temperature for 2 to 3 minutes, and centrifuged at 4°C for 15 minutes at 12,000 × g. Five hundred microliters of the upper (clear) phase was lifted to a new 1.5-mL tube, and an equal amount of isopropanol was added to the sample. The sample was mixed with vortexing for 15 seconds, incubated at room temperature for 10 minutes, and centrifuged at 4°C for 10 minutes at 12,000 × g. The invisible RNA sediment was at the bottom. The supernatant was discarded, and 1 mL of freshly prepared 75% ethanol was added and centrifuged at 4°C for 5 minutes at 7500 × g. The supernatant was discarded and the ethanol wash step was repeated once more. After the second wash, the tube was dried with open cap at room temperature for 5 minutes. The RNA was eluted in 50 μL of nuclease-free water and incubated at 55°C for 5 minutes. The DNase treatment was conducted with an Ambion Turbo DNA-free kit (Thermo Fisher Scientific) according to the manufacturer's protocol. The final volume of DNase-treated total RNA was 50 μL. The total RNA was then cleaned with an RNeasy Mini Kit. Three hundred fifty microliters of RLT buffer was added and the sample was mixed, followed by the addition of 1225 μL of 100% ethanol and mixing. The sample was centrifuged through RNeasy Mini Kit columns for 15 seconds at 8000 × g and washed twice with 500 μL of buffer RPE. The RNA was eluted in 50 μL of nuclease-free water. The quality of total RNA was evaluated with an Agilent 2100 Bioanalyzer and the RNA 6000 Nano kit (Agilent Technologies Inc., Santa Clara, CA); the RNA Integrity Number of all of the samples was >5. Total RNA extraction was conducted at the Estonian Genome Center. From there, 2 μg of total RNA per individual was transported to the Core Facility of Clinical Genomics, University of Tartu, where all of the following procedures were conducted. Total RNA from whole blood consists of up to 70% of Ig mRNA; a Globin Clear Human kit (Thermo Fisher Scientific) was applied to purify the samples from globin mRNA. After Globin Clear treatment, nearly 1.5 μg of RNA was left. The RNA Integrity Number remained >5. The RNA quality was assessed using the Agilent 2100 Bioanalyzer and the RNA 6000 Nano kit (Agilent Technologies). Whole transcriptome RNAseq libraries for 48 RNA samples were prepared. Fifty nanograms of each Globin Clear kit–treated total RNA sample was taken as library input. The rest of the RNA material was stored at −80°C. For library preparation, the Ovation RNAseq V2 Kit (NuGen, Emeryville, CA) together with the 5500 Series Fragment Library Core Kit (Thermo Fisher Scientific) were used according to the manufacturers' protocols. An automated SOLiD EZ Bead System and EZ Bead E80 System Consumables (Thermo Fisher Scientific) were applied for emulsion PCR. With each template preparation, the pool of 12 libraries was used (marked with barcoding sequences to distinguish the samples on data analysis). All together, four template preparations were achieved. The samples were sequenced with SOLiD 5500 xl platform (Thermo Fisher Scientific) on two flowchips. For each sample, at least 40 million mappable reads were received, which is enough for gene-expression analysis and also fusion and exon junction analysis. Paired-end chemistry for barcoded libraries was used, which provides up to 110 Bp (75 Bp forward and 35 Bp reverse) per one paired-end read. We also compared methylation patterns between the smokers and never-smokers using MassArray EpiTyper DNA methylation technology (Agena Bioscience, San Diego, CA). Samples were prepared using an EpiTyper T Complete Reagent Set according to the manufacturer's instructions (Agena Bioscience). The bisulfite-treated DNA (25 ng) was amplified with Hot FirePol DNA Polymerase (Solis BioDyne, Tartu, Estonia), and CpG methylation was determined by the MassArray Analyzer 4 (Agena Bioscience). The specific primers were designed with the EpiDesigner software beta version (Agena Bioscience), and the primer sequences for GPR15 were 5′-AGGAAGAGAGTATTGTTTTTTTGGGTGGATAAAGA-3′ and 5′-CAGTAATACGACTCACTATAGGGAGAAGGCTCAATAACAAATCACAATACTCAACAAAA-3′. Raw reads were color-space mapped to the human genome hg19 reference using a Maxmapper algorithm implemented in Lifescope software version 2.5.1 (Life Technologies Corporation, Carlsbad, California). Mapping to multiple locations was permitted. The quality threshold was set at 10, providing a mapping confidence of >90. Reads with a Phred score <10 were filtered out. The mean mapping quality was 30. RNA content and gene-based annotation were analyzed with whole-transcriptome workflow. Raw sequencing data with appropriate experimental information are available from the Gene Expression Omnibus repository (www.ncbi.nlm.nih.gov/geo; accession number GSE68549). We analyzed RNA samples of blood from 24 smokers and 24 never-smokers with SOLiD 5500xl RNA sequencing technology. There were three main groups: smokers (n = 16), ex-smokers (n = 8) and individuals who had never smoked (n = 14) (Table 1). We analyzed men and women separately and left the data from the ex-smokers out of the initial statistical comparison. The data from ex-smokers were used only for illustrative purposes.Table 1Characteristics of the Subjects Enrolled in the Present StudyCharacteristicMenWomenNumber of participants2424Age, years52.3 ± 17.953.0 ± 17.9Smoking status, n Smoker88 Ex-smoker80 Never-smoker816Years of smoking33 ± 17.820 ± 13.9Height, cm177.6 ± 7.2163.2 ± 6.4Weight, kg88.0 ± 21.669.5 ± 14.1Body mass index, kg/m227.8 ± 5.826.1 ± 5.4Quantitative data are expressed as means ± SEM. Open table in a new tab Quantitative data are expressed as means ± SEM. Non-normalized raw counts were used for the edgeR package version 3.2.0 (Bioconductor, http://bioconductor.org) to perform differential gene-expression analysis after quality control of samples. edgeR performs model-based scale normalization, estimates dispersions, and applies negative binomial modeling. edgeR is a flexible tool for RNAseq data analysis to identify differentially expressed genes.26McCarthy D.J. Chen Y. Smyth G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.Nucleic Acids Res. 2012; 40: 4288-4297Crossref PubMed Scopus (2787) Google Scholar, 27Robinson M.D. McCarthy D.J. Smyth G.K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data.Bioinformatics. 2010; 26: 139-140Crossref PubMed Scopus (20814) Google Scholar It implements negative binomial model fitting, followed by testing procedures for determining differential expression. To detect differentially expressed genes, we used negative binomial fitting followed by Fisher exact testing. False-discovery rate adjustment was used for multiple-testing correction.28Storey J.D. Tibshirani R. Statistical significance for genomewide studies.Proc Natl Acad Sci U S A. 2003; 100: 9440-9445Crossref PubMed Scopus (7083) Google Scholar A false-discovery rate threshold of 0.1 for statistical significance was applied. Genes with greater differential expression were defined with a threshold of log fold-change 0.5 (ie, 50% change between experimental conditions). We analyzed men and women as separate data sets (24 individuals each) (Table 1). The basic characteristics of the study groups are listed in the Table 1. For each sample, at least 40 million mappable reads were received, which is enough for gene-expression analysis and also fusion and exon junction analysis. Paired-end chemistry for barcoded libraries were used, which gives up to 110 Bp (75 Bp forward and 35 Bp reverse) per one paired-end read. RNA sequencing provided high-quality reads with good similarity between different samples. Multidimensional scaling analysis of fold-change differences in gene expression indicated good separation of study groups by sex (Figure 1). Multidimensional scaling analysis showed the sample distances and illustrated the similarity of samples based on the biological coefficient of variation. As we saw strong separation of samples by sex, to reduce confounding effects, it was better to analyze samples separately by sex. Therefore, in further statistical analysis we used data from men and women separately. In women, a comparison of gene-expression profiles between smokers and never-smokers revealed differential expression (false-discovery rate <0.1) of five genes: FKBP10, GPR128, GPR15, L1TD1, and SMOC1 (Table 2). Of these, the only gene found to be related to smoking in earlier studies was GPR15. Other genes were not identified in earlier studies as related to smoking.Table 2Gene Expression of Smokers and Never-Smokers among WomenGene nameSymbollogFClogCPMPFDRG-protein–coupled receptor 128GPR1284.361.271.31 × 10−70.003G-protein–coupled receptor 15GPR151.752.353.36 × 10−70.004LINE-1 type transposase domain-containing protein 1L1TD11.631.368.88 × 10−60.063SPARC-related modular calcium binding protein 1SMOC1−1.97−0.371.08 × 10−50.063FK506 binding protein 10, 65 kDaFKBP10−2.04−0.432.11 × 10−50.097Long intergenic nonprotein–coding RNA 518C6orf218−3.15−1.094.29 × 10−50.142Prostaglandin D2 synthase, 21 kDa (brain)PTGDS−1.461.873.81 × 10−50.142Uncharacterized locus MGC21881MGC21881−2.16−0.256.56 × 10−50.189RNAseq data were analyzed with edgeR software package version 3.2.0.FDR, false-discovery rate (P value corrected for multiple testing); LINE, long interspersed nuclear element; logFC, log fold-change (smokers – never-smokers); logCPM, gene expression level in log of counts per million; SPARC, secreted protein, acidic, cysteine-rich protein. Open table in a new tab RNAseq data were analyzed with edgeR software package version 3.2.0. FDR, false-discovery rate (P value corrected for multiple testing); LINE, long interspersed nuclear element; logFC, log fold-change (smokers – never-smokers); logCPM, gene expression level in log of counts per million; SPARC, secreted protein, acidic, cysteine-rich protein. In men, a comparison of the gene-expression profiles between smokers and never-smokers identified differential expression (false-discovery rate <0.1) of 15 genes: C4BPA, C8orf42, CCDC3, CCR8, CD177, CREG1, FRMD4B, FSTL1, GPR15, MYCT1, NFIA, PLCH1, and TPM1 (Table 3). Again, the only gene found in earlier studies to be related to smoking was GPR15. Other genes were not identified in earlier studies as related to smoking. The gene-expression levels of four genes related to smoking status (smokers, ex-smokers, and never-smokers) are illustrated in Figure 2. The lymphocyte antigen 6 complex, locus G6C (LY6G6C) was significantly down-regulated in smokers when we analyzed the entire study sample (men and women together). GPR15 expression correlated well with smoking status. The highest expression was in smokers, and the lowest was in never-smokers. The expression level was in between in ex-smokers (Figure 2). Similarly, expression of the CCDC3 gene followed the smoking status with the lowest level in smokers, intermediate level in ex-smokers, and the greatest level in never-smokers (Figure 2). Interestingly, the expression levels of MYCT1 and LY6G6C were reduced only in smokers and not in ex-smokers.Table 3Differential Gene Expression of Smokers and Never-Smokers among MenGene nameGenelogFClogCPMPFDRG-protein–coupled receptor 15GPR152.612.963.21 × 10−137.42 × 10−9Myc target 1MYCT1−1.064.381.59 × 10−60.02Coiled-coil domain-containing protein 3CCDC3−3.591.005.19 × 10−60.03Cellular repressor of E1A-stimulated genes 1CREG1−0.748.304.52 × 10−60.03Chemokine (C-C motif) receptor 8CCR81.871.747.26 × 10−60.03Phospholipase C, η1PLCH1−1.042.981.04 × 10−50.04Complement component 4 binding protein αC4BPA−3.222.602.01 × 10−50.07Testis development–related proteinC8orf42−1.241.412.48 × 10−50.07CD177 moleculeCD177−2.602.774.10 × 10−50.09FERM domain-containing protein 4BFRMD4B−0.685.763.43 × 10−50.09Follistatin-like 1FSTL1−1.083.834.83 × 10−50.09Nuclear factor I/ANFIA−0.746.204.49 × 10−50.09Tropomyosin 1 (α)TPM1−0.796.693.97 × 10−50.09RNAseq data were analyzed with edgeR package.FDR, false-discovery rate (P value corrected for multiple testing); FERM, 4.1, ezrin, radixin, moesin protein; logFC, log fold-change (smokers – never-smokers); logCPM, gene expression level in log of counts per million. Open table in a new tab RNAseq data were analyzed with edgeR package. FDR, false-discovery rate (P value corrected for multiple testing); FERM, 4.1, ezrin, radixin, moesin protein; logFC, log fold-change (smokers – never-smokers); logCPM, gene expression level in log of counts per million. The heatmap of gene-expression data based on 50 genes with the lowest P values illustrated a clear smoking-related expressional pattern (Figure 3). Male smokers were clustered into two groups with characteristic gene-expression profiles. GPR15, RTKN2, USP46, CCR4, and CCR8 formed a cluster o
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