Identification of candidate genes involved in coronary artery calcification by transcriptome sequencing of cell lines

BMC Genomics

Leslie G Biesecker⁴  Peter J Munson¹  James C Mullikin⁴  Eric D Green⁴  Andrew E Arai³  Marcus Y Chen³  XiaoQing Zhao²  Qi Cheng²  Frank D Kolodgie²  NISC Comparative Sequencing Program⁴  Shih-Queen Lee-Lin⁴  Larry N Singh⁴  Abdel G Elkahloun⁴  David S Accame⁴  Praveen F Cherukuri⁴  Jennifer J Barb¹  Shurjo K Sen⁴ 

[1] Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA;CVPath Institute, Inc, Gaithersburg, MD 20878, USA;National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA;National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA

关键词: Transcriptome profiling;    Lymphoblastoid cell lines;    RNA-Seq;    Coronary artery calcification;   

Others  :  1217681
DOI  :  10.1186/1471-2164-15-198

 received in 2012-10-11, accepted in 2014-03-04,  发布年份 2014

【 摘 要 】

Background

Massively-parallel cDNA sequencing (RNA-Seq) is a new technique that holds great promise for cardiovascular genomics. Here, we used RNA-Seq to study the transcriptomes of matched coronary artery disease cases and controls in the ClinSeq® study, using cell lines as tissue surrogates.

Results

Lymphoblastoid cell lines (LCLs) from 16 cases and controls representing phenotypic extremes for coronary calcification were cultured and analyzed using RNA-Seq. All cell lines were then independently re-cultured and along with another set of 16 independent cases and controls, were profiled with Affymetrix microarrays to perform a technical validation of the RNA-Seq results. Statistically significant changes (p < 0.05) were detected in 186 transcripts, many of which are expressed at extremely low levels (5–10 copies/cell), which we confirmed through a separate spike-in control RNA-Seq experiment. Next, by fitting a linear model to exon-level RNA-Seq read counts, we detected signals of alternative splicing in 18 transcripts. Finally, we used the RNA-Seq data to identify differential expression (p < 0.0001) in eight previously unannotated regions that may represent novel transcripts. Overall, differentially expressed genes showed strong enrichment (p = 0.0002) for prior association with cardiovascular disease. At the network level, we found evidence for perturbation in pathways involving both cardiovascular system development and function as well as lipid metabolism.

Conclusions

We present a pilot study for transcriptome involvement in coronary artery calcification and demonstrate how RNA-Seq analyses using LCLs as a tissue surrogate may yield fruitful results in a clinical sequencing project. In addition to canonical gene expression, we present candidate variants from alternative splicing and novel transcript detection, which have been unexplored in the context of this disease.

【 授权许可】

   
2014 Sen et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Koren O, Spor A, Felin J, Fåk F, Stombaugh J, Tremaroli V, Behre CJ, Knight R, Fagerberg B, Ley RE, Bäckhed F: Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc Natl Acad Sci USA 2011, 108(Suppl 1):4592-4598.
• [2]Lusis AJ, Mar R, Pajukanta P: Genetics of atherosclerosis. Annu Rev Genomics Hum Genet 2004, 5:189-218.
• [3]Roberts R, McPherson R, Stewart AFR: Genetics of atherosclerosis. In Cardiovascular Genetics and Genomics. Edited by Roden D. Oxford, UK: Wiley-Blackwell; 2009:151-166.
• [4]Bijnens AP, Lutgens E, Ayoubi T, Kuiper J, Horrevoets AJ, Daemen MJ: Genome-wide expression studies of atherosclerosis: critical issues in methodology, analysis, interpretation of transcriptomics data. Arterioscler Thromb Vasc Biol 2006, 26(6):1226-1235.
• [5]Skogsberg J, Lundstrom J, Kovacs A, Nilsson R, Noori P, Maleki S, Kohler M, Hamsten A, Tegner J, Bjorkegren J: Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes. PLoS Genet 2008, 4(3):e1000036.
• [6]Shendure J: The beginning of the end for microarrays? Nat Methods 2008, 5(7):585-587.
• [7]Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, Foster E, Hlatky MA, Hodgson JM, Kushner FG, Lauer MS, Shaw LJ, Smith SC Jr, Taylor AJ, Weintraub WS, Wenger NK, Jacobs AK: 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2010, 122(25):2748-2764.
• [8]Bullaughey K, Chavarria CI, Coop G, Gilad Y: Expression quantitative trait loci detected in cell lines are often present in primary tissues. Hum Mol Genet 2009, 18(22):4296-4303.
• [9]Veyrieras JB, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, Stephens M, Pritchard JK: High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet 2008, 4(10):e1000214.
• [10]Biesecker LG, Mullikin JC, Facio FM, Turner C, Cherukuri PF, Blakesley RW, Bouffard GG, Chines PS, Cruz P, Hansen NF, Teer JK, Maskeri B, Young AC, Manolio TA, Wilson AF, Finkel T, Hwang P, Arai A, Remaley AT, Sachdev V, Shamburek R, Cannon RO, Green ED, NISC Comparative Sequencing Program: The ClinSeq project: piloting large-scale genome sequencing for research in genomic medicine. Genome Res 2009, 19(9):1665-1674.
• [11]Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R: Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990, 15(4):827-832.
• [12]Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25(9):1105-1111.
• [13]Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26(6):841-842.
• [14]Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010, 28(5):511-515.
• [15]Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, Foster E, Hlatky MA, Hodgson JM, Kushner FG, Lauer MS, Shaw LJ, Smith SC Jr, Taylor AJ, Weintraub WS, Wenger NK, Jacobs AK, Smith SC Jr, Anderson JL, Albert N, Buller CE, Creager MA, Ettinger SM, Guyton RA, Halperin JL, Hochman JS, Kushner FG, Nishimura R, Ohman EM, Page RL, et al.: 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2010, 56(25):e50-e103.
• [16]Folsom AR, Kronmal RA, Detrano RC, O'Leary DH, Bild DE, Bluemke DA, Budoff MJ, Liu K, Shea S, Szklo M, Tracy RP, Watson KE, Burke GL: Coronary artery calcification compared with carotid intima-media thickness in the prediction of cardiovascular disease incidence: the Multi-Ethnic Study of Atherosclerosis (MESA). Arch Intern Med 2008, 168(12):1333-1339.
• [17]Piers LH, Salachova F, Slart RH, Vliegenthart R, Dikkers R, Hospers FA, Bouma HR, Zeebregts CJ, Willems TP, Oudkerk M, Zijlstra F, Tio RA: The role of coronary artery calcification score in clinical practice. BMC Cardiovasc Disord 2008, 8:38. BioMed Central Full Text
• [18]McClelland RL, Chung H, Detrano R, Post W, Kronmal RA: Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2006, 113(1):30-37.
• [19]Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinforma 2010, 11:94. BioMed Central Full Text
• [20]Robinson MD, McCarthy DJ, Smyth GK: edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26(1):139-140.
• [21]Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A: Differential expression in RNA-seq: a matter of depth. Genome Res 2011, 21(12):2213-2223.
• [22]Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, Boysen C, Hunkapiller K, Jensen RV, Knight CR, Lee KY, Ma Y, Maqsodi B, Papallo A, Peters EH, Poulter K, Ruppel PL, Samaha RR, Shi L, Yang W, Zhang L, Goodsaid FM: Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 2006, 24(9):1115-1122.
• [23]Raghavachari N, Barb J, Yang Y, Liu P, Woodhouse K, Levy D, O’Donnell C, Munson PJ, Kato G: A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease. BMC Med Genomics 2012, 5(1):28. BioMed Central Full Text
• [24]Gene Expression in early and advanced atherosclerotic plaque from human carotid [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28829 webcite]
• [25]Halpert I, Sires UI, Roby JD, Potter-Perigo S, Wight TN, Shapiro SD, Welgus HD, Wickline SA, Parks WC: Matrilysin is expressed by lipid-laden macrophages at sites of potential rupture in atherosclerotic lesions and localizes to areas of versican deposition, a proteoglycan substrate for the enzyme. Proc Natl Acad Sci USA 1996, 93:9748-9753.
• [26]Williams H, Johnson JL, Jackson CL, White SJ, George SJ: MMP-7 mediates cleavage of N-cadherin and promotes smooth muscle cell apoptosis. Cardiovasc Res 2010, 87(1):137-146.
• [27]Nilsson L, Jonasson L, Nijm J, Hamsten A, Eriksson P: Increased plasma concentration of matrix metalloproteinase-7 in patients with coronary artery disease. Clin Chem 2006, 52(8):1522-1527.
• [28]Clark MB, Amaral PP, Schlesinger FJ, Dinger ME, Taft RJ, Rinn JL, Ponting CP, Stadler PF, Morris KV, Morillon A, Rozowsky JS, Gerstein MB, Wahlestedt C, Hayashizaki Y, Carninci P, Gingeras TR, Mattick JS: The reality of pervasive transcription. PLoS Biol 2011, 9(7):e1000625-e1001102. discussion
• [29]Takeuchi F, Yokota M, Yamamoto K, Nakashima E, Katsuya T, Asano H, Isono M, Nabika T, Sugiyama T, Fujioka A, Awata N, Ohnaka K, Nakatochi M, Kitajima H, Rakugi H, Nakamura J, Ohkubo T, Imai Y, Shimamoto K, Yamori Y, Yamaguchi S, Kobayashi S, Takayanagi R, Ogihara T, Kato N: Genome-wide association study of coronary artery disease in the Japanese. Eur J Hum Genet 2012, 20(3):333-340.
• [30]Hägg S, Skogsberg J, Lundström J, Noori P, Nilsson R, Zhong H, Maleki S, Shang MM, Brinne B, Bradshaw M, Bajic VB, Samnegård A, Silveira A, Kaplan LM, Gigante B, Leander K, de Faire U, Rosfors S, Lockowandt U, Liska J, Konrad P, Takolander R, Franco-Cereceda A, Schadt EE, Ivert T, Hamsten A, Tegnér J, Björkegren J: Multi-organ expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM domain binding 2: the Stockholm Atherosclerosis Gene Expression (STAGE) study. PLoS Genet 2009, 5(12):e1000754.
• [31]Illumina HumanCVD Genotyping BeadChip [http://www.illumina.com/documents/products/datasheets/datasheet_humancvd.pdf webcite]
• [32]Bis JC, Kavousi M, Franceschini N, Isaacs A, Abecasis GR, Schminke U, Post WS, Smith AV, Cupples LA, Markus HS, Schmidt R, Huffman JE, Lehtimäki T, Baumert J, Münzel T, Heckbert SR, Dehghan A, North K, Oostra B, Bevan S, Stoegerer EM, Hayward C, Raitakari O, Meisinger C, Schillert A, Sanna S, Völzke H, Cheng YC, Thorsson B, Fox CS, et al.: Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque. Nat Genet 2011, 43(10):940-947.
• [33]Robertson AK, Hansson GK: T cells in atherogenesis: for better or for worse? Arterioscler Thromb Vasc Biol 2006, 26(11):2421-2432.
• [34]Rutsch F, Nitschke Y, Terkeltaub R: Genetics in arterial calcification: pieces of a puzzle and cogs in a wheel. Circ Res 2011, 109(5):578-592.