Graphical Modeling of Gene Expression in Monocytes Suggests Molecular Mechanisms Explaining Increased Atherosclerosis in Smokers

PLoS ONE

Volumn 8, Issue 1, 2013, Pages

  Verdugo, Ricardo A ^a,d   Zeller, Tanja ^b   Rotival, Maxime ^a,e   Wild, Philipp S ^c   Münzel, Thomas ^c   Lackner, Karl J ^c   Weidmann, Henri ^a   Ninio, Ewa ^a   Trégouët, David Alexandre ^a   Cambien, François ^a   Blankenberg, Stefan ^b   Tiret, Laurence ^a  

^a INSERM   (France)

^b UNIVERSITY HEART CENTER HAMBURG   (Germany)

^c UNIVERSITY MEDICAL CENTER MAINZ   (Germany)

^d UNIVERSITY OF CHILE   (Chile)

^e HAMMERSMITH HOSPITAL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CALCIUM; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR GAMMA;

ADULT; ALGORITHM; ARTICLE; ATHEROSCLEROSIS; ATHEROSCLEROTIC PLAQUE; BOOTSTRAPPING; CAROTID ATHEROSCLEROSIS; CONTROLLED STUDY; FEMALE; GAS6 GENE; GENE; GENE EXPRESSION; GENETIC ASSOCIATION; HUMAN; HUMAN CELL; INDEPENDENT COMPONENT ANALYSIS; LYMPHOCYTE; MAJOR CLINICAL STUDY; MALE; MONOCYTE; PPARG GENE; SASH1 GENE; SLC39A8 GENE; SMOKING;

ALGORITHMS; ATHEROSCLEROSIS; CAROTID ARTERIES; CATION TRANSPORT PROTEINS; FEMALE; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMANS; INTERCELLULAR SIGNALING PEPTIDES AND PROTEINS; LIKELIHOOD FUNCTIONS; MALE; MIDDLE AGED; MODELS, GENETIC; MONOCYTES; MULTIGENE FAMILY; PLAQUE, ATHEROSCLEROTIC; PPAR GAMMA; PREGNANCY; RISK FACTORS; SMOKING; TRANSCRIPTOME; TUMOR SUPPRESSOR PROTEINS;

NICOTIANA TABACUM;

EID: 84872796718     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0050888     Document Type: Article

References (64)

1. McGill HC Jr, (1990) Smoking and the pathogenesis of atherosclerosis. Adv Exp Med Biol 273: 9-16.
2. Teo KK, Ounpuu S, Hawken S, Pandey MR, Valentin V, et al. (2006) Tobacco use and risk of myocardial infarction in 52 countries in the INTERHEART study: a case-control study. Lancet 368: 647-658 doi:10.1016/S0140-6736(06)69249-0.
3. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, et al. (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 364: 937-952 doi:10.1016/S0140-6736(04)17018-9.
4. Campbell SC, Moffatt RJ, Stamford BA, (2008) Smoking and smoking cessation-The relationship between cardiovascular disease and lipoprotein metabolism: A review. Atherosclerosis 201: 225-235 doi:10.1016/j.atherosclerosis.2008.04.046.
5. Gastaldelli A, Folli F, Maffei S, (2010) Impact of tobacco smoking on lipid metabolism, body weight and cardiometabolic risk. Curr Pharm Des 16: 2526-2530.
6. Puranik R, Celermajer DS, (2003) Smoking and endothelial function. Prog Cardiovasc Dis 45: 443-458 doi:10.1053/pcad.2003.YPCAD13.
7. Dumeaux V, Olsen KS, Nuel G, Paulssen RH, Børresen-Dale A-L, et al. (2010) Deciphering normal blood gene expression variation-The NOWAC postgenome study. PLoS Genet 6: e1000873 doi:10.1371/journal.pgen.1000873.
8. Charlesworth JC, Curran JE, Johnson MP, Göring HH, Dyer TD, et al. (2010) Transcriptomic epidemiology of smoking: the effect of smoking on gene expression in lymphocytes. BMC Med Genomics 3: 29 doi:10.1186/1755-8794-3-29.
9. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, et al. (2010) Genetics and beyond-the transcriptome of human monocytes and disease susceptibility. PLoS ONE 5: e10693 doi:10.1371/journal.pone.0010693.
10. Csiszar A, Podlutsky A, Wolin MS, Losonczy G, Pacher P, et al. (2009) Oxidative stress and accelerated vascular aging: implications for cigarette smoking. Front Biosci 14: 3128-3144.
11. Sopori M, (2002) Effects of cigarette smoke on the immune system. Nat Rev Immunol 2: 372-377 doi:10.1038/nri803.
12. Shipley B (2002) Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference. 1st ed. Cambridge University Press. 330 p.
13. Spirtes P, (2010) Introduction to Causal Inference. J Mach Learn Res 99: 1643-1662.
14. Murphy KP (1998) Inference and Learning in Hybrid Bayesian Networks. Univeristy of California Berkeley. p. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.6529.
15. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, et al. (2006) ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics 7: S7.
16. Watkinson J, Liang K-C, Wang X, Zheng T, Anastassiou D, (2009) Inference of regulatory gene interactions from expression data using three-way mutual information. Ann N Y Acad Sci 1158: 302-313 doi:10.1111/j.1749-6632.2008.03757.x.
17. Sales G, Romualdi C, (2011) parmigene- a parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics Available: http://www.ncbi.nlm.nih.gov/pubmed/21531770. Accessed 2011 May 12.
18. Peters SA, den Ruijter HM, Bots ML, Moons KG, (2012) Improvement in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis;: a systematic review. Heart 98: 177-184.
19. Johnson HM, Douglas PS, Srinivasan SR, Bond MG, Tang R, et al. (2007) Predictors of carotid intima-media thickness progression in young adults: the Bogalusa Heart Study. Stroke 38: 900-5.
20. Hyvärinen A, Oja E, (2000) Independent component analysis: algorithms and applications. Neural Networks 13: 411-430 doi:16/S0893-6080(00)00026-5.
21. Rotival M, Zeller T, Wild PS, Maouche S, Szymczak S, et al. (2011) Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans. PLoS Genet 7: e1002367 doi:10.1371/journal.pgen.1002367.
22. Kalisch M, Bühlmann P, (2007) Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm. J Mach Learn Res 8: 613-636.
23. Staels B, (2005) PPARgamma and atherosclerosis. Curr Med Res Opin 21 Suppl 1: S13-20 doi:10.1185/030079905X36440.
24. Wright WR, Parzych K, Crawford D, Mein C, Mitchell JA, et al. (2012) Inflammatory Transcriptome Profiling of Human Monocytes Exposed Acutely to Cigarette Smoke. PLoS One 7doi:10.1371/journal.pone.0030120.
25. Nebert DW, Dalton TP, (2006) The role of cytochrome P450 enzymes in endogenous signalling pathways and environmental carcinogenesis. Nat Rev Cancer 6: 947-960 doi:10.1038/nrc2015.
26. Bruchova H, Vasikova A, Merkerova M, Milcova A, Topinka J, et al. (2010) Effect of maternal tobacco smoke exposure on the placental transcriptome. Placenta 31: 186-191 doi:10.1016/j.placenta.2009.12.016.
27. Conway DE, Sakurai Y, Weiss D, Vega JD, Taylor WR, et al. (2009) Expression of CYP1A1 and CYP1B1 in human endothelial cells: regulation by fluid shear stress. Cardiovascular research 81: 669.
28. Tang Y, Scheef EA, Gurel Z, Sorenson CM, Jefcoate CR, et al. (2010) CYP1B1 and endothelial nitric oxide synthase combine to sustain proangiogenic functions of endothelial cells under hyperoxic stress. Am J Physiol, Cell Physiol 298: C665-678 doi:10.1152/ajpcell.00153.2009.
29. Shah CA, Wang H, Bei L, Platanias LC, Eklund EA, (2011) HoxA10 regulates transcription of the gene encoding transforming growth factor beta2 (TGFbeta2) in myeloid cells. J Biol Chem 286: 3161-3176 doi:10.1074/jbc.M110.183251.
30. Lindsey S, Huang W, Wang H, Horvath E, Zhu C, et al. (2007) Activation of SHP2 protein-tyrosine phosphatase increases HoxA10-induced repression of the genes encoding gp91(PHOX) and p67(PHOX). J Biol Chem 282: 2237-49.
31. Zhang J, Burridge KA, Friedman MH, (2008) In vivo differences between endothelial transcriptional profiles of coronary and iliac arteries revealed by microarray analysis. Am J Physiol Heart Circ Physiol 295: H1556-1561 doi:10.1152/ajpheart.00540.2008.
32. Burridge KA, Friedman MH, (2010) Environment and vascular bed origin influence differences in endothelial transcriptional profiles of coronary and iliac arteries. Am J Physiol Heart Circ Physiol 299: H837-846 doi:10.1152/ajpheart.00002.2010.
33. Zhou Y, Jorgensen EM, Gan Y, Taylor HS, (2011) Cigarette smoke increases progesterone receptor and homeobox A10 expression in human endometrium and endometrial cells: a potential role in the decreased prevalence of endometrial pathology in smokers. Biol Reprod 84: 1242-1247 doi:10.1095/biolreprod.110.087494.
34. Waterworth DM, Ricketts SL, Song K, Chen L, Zhao JH, et al. (2010) Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arterioscler Thromb Vasc Biol 30: 2264-2276 doi:10.1161/ATVBAHA.109.201020.
35. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707-713 doi:10.1038/nature09270.
36. Wain LV, Verwoert GC, O'Reilly PF, Shi G, Johnson T, et al. (2011) Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet 43: 1005-1011 doi:10.1038/ng.922.
37. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. (2011) Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478: 103-109 doi:10.1038/nature10405.
38. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937-948 doi:10.1038/ng.686.
39. Yang Q, Kathiresan S, Lin J-P, Tofler GH, O'Donnell CJ, (2007) Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study. BMC Med Genet 8 Suppl 1: S12 doi:10.1186/1471-2350-8-S1-S12.
40. Dalton TP, He L, Wang B, Miller ML, Jin L, et al. (2005) Identification of mouse SLC39A8 as the transporter responsible for cadmium-induced toxicity in the testis. Proc Natl Acad Sci USA 102: 3401-3406 doi:10.1073/pnas.0406085102.
41. He L, Girijashanker K, Dalton TP, Reed J, Li H, et al. (2006) ZIP8, member of the solute-carrier-39 (SLC39) metal-transporter family: characterization of transporter properties. Mol Pharmacol 70: 171-180 doi:10.1124/mol.106.024521.
42. Napolitano JR, Liu M-J, Bao S, Crawford M, Nana-Sinkam P, et al. (2012) Cadmium-mediated toxicity of lung epithelia is enhanced through NF-κB-mediated transcriptional activation of the human zinc transporter ZIP8. American Journal of Physiology Lung Cellular and Molecular Physiology Available: http://www.ncbi.nlm.nih.gov/pubmed/22345571. Accessed 2012 Feb 24.
43. Tjwa M, Moons L, Lutgens E, (2009) Pleiotropic role of growth arrest-specific gene 6 in atherosclerosis. Curr Opin Lipidol 20: 386-392 doi:10.1097/MOL.0b013e328330982e.
44. Clauser S, Meilhac O, Bièche I, Raynal P, Bruneval P, et al. (2012) Increased secretion of Gas6 by smooth muscle cells in human atherosclerotic carotid plaques. Thromb Haemost 107: 140-149 doi:10.1160/TH11-05-0368.
45. Murphy AJ, Westerterp M, Yvan-Charvet L, Tall AR, (2012) Anti-atherogenic mechanisms of high density lipoprotein: Effects on myeloid cells. Biochim Biophys Acta 1821: 513-521 doi:10.1016/j.bbalip.2011.08.003.
46. Norata GD, Pirillo A, Catapano AL, (2011) HDLs, immunity, and atherosclerosis. Curr Opin Lipidol 22: 410-416 doi:10.1097/MOL.0b013e32834adac3.
47. Pearl J (2009) Causality: Models, Reasoning and Inference. 2nd ed. Cambridge University Press.
48. Spirtes P, Glymour C, Scheines R (2001) Causation, Prediction, and Search. Second ed. The MIT Press. 565 p.
49. Pearl J, (2010) The foundations of causal inference. Sociological Methodology 40: 75-149 doi:10.1111/j.1467-9531.2010.01228.x.
50. Barbosa-Morais NL, Dunning MJ, Samarajiwa SA, Darot JFJ, Ritchie ME, et al. (2010) A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res 38: e17 doi:10.1093/nar/gkp942.
51. Lin SM, Du P, Huber W, Kibbe WA, (2008) Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res 36: e11 doi:10.1093/nar/gkm1075.
52. R Development Core Team (2011) R: A Language and Environment for Statistical Computing Vienna, Austria: R Foundation for Statistical Computing. Available: http://www.R-project.org.
53. Lunceford JK, Chen G, Hu PH, Mehrotra DV, (2010) Evaluating surrogate variables for improving microarray multiple testing inference. Pharmaceut Statist pp. n/a-n/a doi:10.1002/pst.466.
54. Hardin JW, Hilbe JM (2007) Generalized Linear Models and Extensions, Second Edition. 2nd ed. Stata Press. 387 p.
55. Cameron AC, Windmeijer FAG, (1996) R-squared measures for count data regression models with applications to health-care utilization. Journal of Business & Economic Statistics pp. 209-220.
56. Neter J, Wasserman W, Kutner MH (1996) Applied linear statistical models. Fourth ed. Irwin Homewood, IL. 1408 p. Available: http://books.google.fr/books/about/Applied_linear_statistical_models.html?id=q2sPAQAAMAAJ&redir_esc=y.
57. Benjamini Y, Hochberg Y, (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57: 289-300.
58. Alexa A, Rahnenführer J, Lengauer T, (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22: 1600-1607 doi:10.1093/bioinformatics/btl140.
59. Marchini JL, Heaton C, Ripley BD (2010) fastICA: FastICA Algorithms to perform ICA and Projection Pursuit. Available: http://CRAN.R-project.org/package=fastICA.
60. Schwarz G, (1978) Estimating the Dimension of a Model. The Annals of Statistics 6: 461-464 doi:10.2307/2958889.
61. Chickering DM (1995) A transformational characterization of equivalent Bayesian network structures. UAI'95. pp. 87-98.
62. Friedman N, Linial M, Nachman I, Pe'er D, (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7: 601-620 doi:10.1089/106652700750050961.
63. Carey V, Long L, Gentleman R (2010) RBGL: An interface to the BOOST graph library. R package version 1.
64. Maathuis MH, Kalisch M, Bühlmann P, (2009) Estimating high-dimensional intervention effects from observational data. The Annals of Statistics 37: 3133-3164.