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Gene Expression Signatures and the Spectrum of Coronary Artery Disease

  • Kevin A. Friede
  • Geoffrey S. Ginsburg
  • Deepak VooraEmail author
Article

Abstract

Over the past 10–15 years, developments in gene expression profiling have opened new arenas for the discovery of important factors in the pathogenesis of numerous disease processes, including coronary artery disease. Messenger RNA and microRNA are differentially expressed in patients with coronary plaques, acute plaque rupture, and response to well-established treatments for acute coronary syndromes. In this review, we will explore recent developments in messenger RNA and microRNA technology at each stage of a patient’s progression through the natural history of cardiovascular disease, including evaluation of risk factors, prediction and detection of coronary artery disease and acute coronary syndromes, and finally, response to treatments for coronary artery disease and its sequelae including congestive heart failure.

Keywords

Gene expression profiling mRNA MicroRNA Coronary artery disease 

Abbreviations

ACE

Angiotensin-converting enzyme

ARB

Angiotensin receptor blocker

CVD

Cardiovascular disease

CAD

Coronary artery disease

CHF

Congestive heart failure

CNV

Copy number variation

CYP

Cytochrome P450

ICD

Implantable cardioverter-defibrillator

JNK

c-Jun N-terminal kinase

LAD

Left anterior descending coronary artery

MACE

Major adverse cardiac events

miRNA/miR

MicroRNA

MI

Myocardial infarction

mRNA

Messenger RNA

NSTEMI

Non-ST-elevation myocardial infarction

PBMC

Peripheral blood mononuclear cell

ROC

Receiver operating characteristic

SNP

Single nucleotide polymorphism

STEMI

ST-elevation myocardial infarction

UA

Unstable angina

Notes

Compliance with Ethical Standards

Funding

This review article did not receive any outside funding.

Conflict of Interest

Drs. Friede, Ginsburg, and Voora each declare that they no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., et al. (2007). Genomewide association analysis of coronary artery disease. New England Journal of Medicine, 357(5), 443–453. doi: 10.1056/NEJMoa072366.PubMedCentralPubMedGoogle Scholar
  2. 2.
    Helgadottir, A., Thorleifsson, G., Manolescu, A., Gretarsdottir, S., Blondal, T., et al. (2007). A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science, 316(5830), 1491–1493. doi: 10.1126/science.1142842.PubMedGoogle Scholar
  3. 3.
    Shen, G.-Q., Li, L., Rao, S., Abdullah, K. G., Ban, J. M., et al. (2008). Four SNPs on chromosome 9p21 in a South Korean population implicate a genetic locus that confers high cross-race risk for development of coronary artery disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 28(2), 360–365.PubMedGoogle Scholar
  4. 4.
    Shen, G.-Q., Rao, S., Martinelli, N., Li, L., Olivieri, O., et al. (2008). Association between four SNPs on chromosome 9p21 and myocardial infarction is replicated in an Italian population. Journal of Human Genetics, 53(2), 144–150.PubMedGoogle Scholar
  5. 5.
    Assimes, T. L., Knowles, J. W., Basu, A., Iribarren, C., Southwick, A., et al. (2008). Susceptibility locus for clinical and subclinical coronary artery disease at chromosome 9p21 in the multi-ethnic ADVANCE study. Human Molecular Genetics, 17(15), 2320–2328.PubMedCentralPubMedGoogle Scholar
  6. 6.
    Kathiresan, S., Voight, B. F., Purcell, S., Musunuru, K., Ardissino, D., et al. (2009). Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nature Genetics, 41(3), 334–341.PubMedGoogle Scholar
  7. 7.
    Johnson, A. D., Hwang, S.-J., Voorman, A., Morrison, A., Peloso, G. M., et al. (2013). Resequencing and clinical associations of the 9p21.3 region: a comprehensive investigation in the Framingham heart study. Circulation, 127(7), 799–810. doi: 10.1161/circulationaha.112.111559.PubMedCentralPubMedGoogle Scholar
  8. 8.
    Shia, W.-C., Ku, T.-H., Tsao, Y.-M., Hsia, C.-H., Chang, Y.-M., et al. (2011). Genetic copy number variants in myocardial infarction patients with hyperlipidemia. BMC Genomics, 12(Suppl 3), S23.PubMedCentralPubMedGoogle Scholar
  9. 9.
    Ripatti, S., Tikkanen, E., Orho-Melander, M., Havulinna, A. S., Silander, K., et al. (2010). A multilocus genetic risk score for coronary heart disease: case–control and prospective cohort analyses. The Lancet, 376(9750), 1393–1400. doi: 10.1016/S0140-6736(10)61267-6.Google Scholar
  10. 10.
    Aziz, H., Zaas, A., & Ginsburg, G. S. (2007). Peripheral blood gene expression profiling for cardiovascular disease assessment. Genomic Medecine, 1(3–4), 105–112.Google Scholar
  11. 11.
    Suresh, R., Li, X., Chiriac, A., Goel, K., Terzic, A., et al. (2014). Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction. Journal of Molecular and Cellular Cardiology, 74, 13–21.PubMedGoogle Scholar
  12. 12.
    Wettinger, S. B., Doggen, C. J., Spek, C. A., Rosendaal, F. R., & Reitsma, P. H. (2005). High throughput mRNA profiling highlights associations between myocardial infarction and aberrant expression of inflammatory molecules in blood cells. Blood, 105(5), 2000–2006.PubMedGoogle Scholar
  13. 13.
    Small, E. M., & Olson, E. N. (2011). Pervasive roles of microRNAs in cardiovascular biology. Nature, 469(7330), 336–342.PubMedCentralPubMedGoogle Scholar
  14. 14.
    van Rooij, E., Marshall, W. S., & Olson, E. N. (2008). Toward MicroRNA–based therapeutics for heart disease the sense in antisense. Circulation Research, 103(9), 919–928.PubMedCentralPubMedGoogle Scholar
  15. 15.
    Frost, R. J., & van Rooij, E. (2010). miRNAs as therapeutic targets in ischemic heart disease. Journal of Cardiovascular Translational Research, 3(3), 280–289.PubMedGoogle Scholar
  16. 16.
    Lewis, D. A., Stashenko, G. J., Akay, O. M., Price, L. I., Owzar, K., et al. (2011). Whole blood gene expression analyses in patients with single versus recurrent venous thromboembolism. Thrombosis Research, 128(6), 536–540. doi: 10.1016/j.thromres.2011.06.003.PubMedCentralPubMedGoogle Scholar
  17. 17.
    Qin, J., Liang, H., Shi, D., Dai, J., Xu, Z., et al. (2015). A panel of microRNAs as a new biomarkers for the detection of deep vein thrombosis. Journal of Thrombosis and Thrombolysis, 39(2), 215–221. doi: 10.1007/s11239-014-1131-0.PubMedGoogle Scholar
  18. 18.
    Julià, A., Erra, A., Palacio, C., Tomas, C., Sans, X., et al. (2009). An eight-gene blood expression profile predicts the response to Infliximab in rheumatoid arthritis. PLoS ONE, 4(10), e7556. doi: 10.1371/journal.pone.0007556.PubMedCentralPubMedGoogle Scholar
  19. 19.
    Khot, U. N., Khot, M. B., Bajzer, C. T., et al. (2003). Prevalence of conventional risk factors in patients with coronary heart disease. JAMA, 290(7), 898–904. doi: 10.1001/jama.290.7.898.PubMedGoogle Scholar
  20. 20.
    Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., et al. (1998). Prediction of coronary heart disease using risk factor categories. Circulation, 97(18), 1837–1847. doi: 10.1161/01.cir.97.18.1837.PubMedGoogle Scholar
  21. 21.
    Lloyd-Jones, D. M., Nam, B., D’Agostino, R. B., Sr., et al. (2004). Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA, 291(18), 2204–2211. doi: 10.1001/jama.291.18.2204.PubMedGoogle Scholar
  22. 22.
    Murabito, J. M., Pencina, M. J., Nam, B., et al. (2005). SIbling cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults. JAMA, 294(24), 3117–3123. doi: 10.1001/jama.294.24.3117.PubMedGoogle Scholar
  23. 23.
    Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., et al. (2013). 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. doi: 10.1161/01.cir.0000437741.48606.98.Google Scholar
  24. 24.
    Ridker, P., Buring, J. E., Rifai, N., & Cook, N. R. (2007). Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds risk score. JAMA, 297(6), 611–619. doi: 10.1001/jama.297.6.611.PubMedGoogle Scholar
  25. 25.
    Cook, N. R., Paynter, N. P., Eaton, C. B., Manson, J. E., Martin, L. W., et al. (2012). Comparison of the Framingham and Reynolds risk scores for global cardiovascular risk prediction in the multiethnic women’s health initiative. Circulation, 125(14), 1748–1756. doi: 10.1161/circulationaha.111.075929.PubMedCentralPubMedGoogle Scholar
  26. 26.
    Blankenberg, S., Zeller, T., Saarela, O., Havulinna, A. S., Kee, F., et al. (2010). Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, risk, genetics, archiving, and monograph (MORGAM) biomarker project. Circulation, 121(22), 2388–2397. doi: 10.1161/circulationaha.109.901413.PubMedGoogle Scholar
  27. 27.
    Melander, O., Newton-Cheh, C., Almgren, P., et al. (2009). Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA, 302(1), 49–57. doi: 10.1001/jama.2009.943.PubMedCentralPubMedGoogle Scholar
  28. 28.
    Brindle, J. T., Antti, H., Holmes, E., Tranter, G., Nicholson, J. K., et al. (2002). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine, 8(12), 1439–1445.PubMedGoogle Scholar
  29. 29.
    Mäkinen, V.-p., Soininen, P., Forsblom, C., Parkkonen, M., Ingman, P., et al. (2008). 1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death. Molecular Systems Biology, 4, 167. doi: 10.1038/msb4100205.PubMedCentralPubMedGoogle Scholar
  30. 30.
    Lodovici, M., Luceri, C., De Filippo, C., Romualdi, C., Bambi, F., et al. (2007). Smokers and passive smokers gene expression profiles: correlation with the DNA oxidation damage. Free Radical Biology and Medicine, 43(3), 415–422. doi: 10.1016/j.freeradbiomed.2007.04.018.PubMedGoogle Scholar
  31. 31.
    Lampe, J. W., Stepaniants, S. B., Mao, M., Radich, J. P., Dai, H., et al. (2004). Signatures of environmental exposures using peripheral leukocyte gene expression: tobacco smoke. Cancer Epidemiology, Biomarkers & Prevention, 13(3), 445–453.Google Scholar
  32. 32.
    Beineke, P., Fitch, K., Tao, H., Elashoff, M. R., Rosenberg, S., et al. (2012). A whole blood gene expression-based signature for smoking status. BMC Medical Genomics, 5(1), 58.PubMedCentralPubMedGoogle Scholar
  33. 33.
    Hopkins, P. N. (2013). Molecular Biology of Atherosclerosis 93. vol 3. doi: 10.1152/physrev.00004.2012.
  34. 34.
    Misu, H., Takamura, T., Matsuzawa, N., Shimizu, A., Ota, T., et al. (2007). Genes involved in oxidative phosphorylation are coordinately upregulated with fasting hyperglycaemia in livers of patients with type 2 diabetes. Diabetologia, 50(2), 268–277. doi: 10.1007/s00125-006-0489-8.PubMedGoogle Scholar
  35. 35.
    Takamura, T., Honda, M., Sakai, Y., Ando, H., Shimizu, A., et al. (2007). Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetes. Biochemical and Biophysical Research Communications, 361(2), 379–384. doi: 10.1016/j.bbrc.2007.07.006.PubMedGoogle Scholar
  36. 36.
    Ma, J., Dempsey, A. A., Stamatiou, D., Marshall, K. W., & Liew, C.-C. (2007). Identifying leukocyte gene expression patterns associated with plasma lipid levels in human subjects. Atherosclerosis, 191(1), 63–72. doi: 10.1016/j.atherosclerosis.2006.05.032.PubMedGoogle Scholar
  37. 37.
    Freedman, J. E., Larson, M. G., Tanriverdi, K., O’Donnell, C. J., Morin, K., et al. (2010). Relation of platelet and leukocyte inflammatory transcripts to body mass index in the Framingham heart study. Circulation, 122(2), 119–129. doi: 10.1161/circulationaha.109.928192.PubMedCentralPubMedGoogle Scholar
  38. 38.
    Grayson, B. L., Wang, L., & Aune, T. M. (2011). Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes. Genes and Immunity, 12(5), 341–351.PubMedCentralPubMedGoogle Scholar
  39. 39.
    Zampetaki, A., Kiechl, S., Drozdov, I., Willeit, P., Mayr, U., et al. (2010). Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circulation Research, 107(6), 810–817.PubMedGoogle Scholar
  40. 40.
    Fichtlscherer, S., De Rosa, S., Fox, H., Schwietz, T., Fischer, A., et al. (2010). Circulating microRNAs in patients with coronary artery disease. Circulation Research, 107(5), 677–684. doi: 10.1161/circresaha.109.215566.PubMedGoogle Scholar
  41. 41.
    Hoekstra, M., van der Lans, C. A., Halvorsen, B., Gullestad, L., Kuiper, J., et al. (2010). The peripheral blood mononuclear cell microRNA signature of coronary artery disease. Biochemical and Biophysical Research Communications, 394(3), 792–797.PubMedGoogle Scholar
  42. 42.
    Sondermeijer, B. M., Bakker, A., Halliani, A., de Ronde, M. W. J., Marquart, A. A., et al. (2011). Platelets in patients with premature coronary artery disease exhibit upregulation of miRNA340* and miRNA624*. PLoS ONE, 6(10), e25946. doi: 10.1371/journal.pone.0025946.PubMedCentralPubMedGoogle Scholar
  43. 43.
    Becker, D. M., Segal, J., Vaidya, D., et al. (2006). SEx differences in platelet reactivity and response to low-dose aspirin therapy. JAMA, 295(12), 1420–1427. doi: 10.1001/jama.295.12.1420.PubMedGoogle Scholar
  44. 44.
    Shen, H., Herzog, W., Drolet, M., Pakyz, R., Newcomer, S., et al. (2009). Aspirin resistance in healthy drug-naïve men versus women (From the Heredity and Phenotype Intervention [HAPI] Heart Study). The American Journal of Cardiology, 104(4), 606–612. doi: 10.1016/j.amjcard.2009.04.027.PubMedCentralPubMedGoogle Scholar
  45. 45.
    Johnson, A. D., Yanek, L. R., Chen, M.-H., Faraday, N., Larson, M. G., et al. (2010). Genome-wide meta-analyses identifies 7 loci associated with platelet aggregation in response to agonists. Nature Genetics, 42(7), 608–613. doi: 10.1038/ng.604.PubMedCentralPubMedGoogle Scholar
  46. 46.
    Voora, D., Cyr, D., Lucas, J., Chi, J.-T., Dungan, J., et al. (2013). Aspirin exposure reveals novel genes associated with platelet function and cardiovascular events. Journal of the American College of Cardiology, 62(14), 1267–1276. doi: 10.1016/j.jacc.2013.05.073.PubMedCentralPubMedGoogle Scholar
  47. 47.
    Rosenberg, S., Elashoff, M. R., Beineke, P., Daniels, S. E., Wingrove, J. A., et al. (2010). Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Annals of Internal Medicine, 153(7), 425–434. doi: 10.1059/0003-4819-153-7-201010050-00005.PubMedCentralPubMedGoogle Scholar
  48. 48.
    Thomas, G. S., Voros, S., McPherson, J. A., Lansky, A. J., Winn, M. E., et al. (2013). A blood-based gene expression test for obstructive coronary artery disease tested in symptomatic nondiabetic patients referred for myocardial perfusion imaging the COMPASS study. Circulation. Cardiovascular Genetics, 6(2), 154–162. doi: 10.1161/circgenetics.112.964015.PubMedGoogle Scholar
  49. 49.
    Taurino, C., Miller, W., Mcbride, M., McClure, J., Khanin, R., et al. (2010). Gene expression profiling in whole blood of patients with coronary artery disease. Clinical Science (London), 119, 335–343.Google Scholar
  50. 50.
    Wingrove, J. A., Daniels, S. E., Sehnert, A. J., Tingley, W., Elashoff, M. R., et al. (2008). Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circulation. Cardiovascular Genetics, 1(1), 31–38. doi: 10.1161/circgenetics.108.782730.PubMedGoogle Scholar
  51. 51.
    Calverley, D. C., Casserly, I. P., Choudhury, Q. G., Phang, T. L., Gao, B., et al. (2010). Platelet gene expression as a biomarker risk stratification tool in acute myocardial infarction: A pilot investigation. Clinical Medicine Insights: Blood Disorders 2010 (2193-CMBD-Platelet-Gene-Expression-as-a-Biomarker-Risk-Stratification-Tool-in-Ac.pdf):9.Google Scholar
  52. 52.
    Healy, A. M., Pickard, M. D., Pradhan, A. D., Wang, Y., Chen, Z., et al. (2006). Platelet expression profiling and clinical validation of myeloid-related protein-14 as a novel determinant of cardiovascular events. Circulation, 113(19), 2278–2284. doi: 10.1161/circulationaha.105.607333.PubMedGoogle Scholar
  53. 53.
    Kiliszek, M., Burzynska, B., Michalak, M., Gora, M., Winkler, A., et al. (2012). Altered gene expression pattern in peripheral blood mononuclear cells in patients with acute myocardial infarction. PLoS One, 7(11), e50054. doi: 10.1371/journal.pone.0050054.PubMedCentralPubMedGoogle Scholar
  54. 54.
    Cheng, Y., Tan, N., Yang, J., Liu, X., Cao, X., et al. (2010). A translational study of circulating cell-free microRNA-1 in acute myocardial infarction. Clinical Science (London), 119(2), 87–95. doi: 10.1042/CS20090645.Google Scholar
  55. 55.
    Gidlöf, O., Andersson, P., van der Pals, J., Götberg, M., & Erlinge, D. (2011). Cardiospecific microRNA plasma levels correlate with troponin and cardiac function in patients with ST elevation myocardial infarction, are selectively dependent on renal elimination, and can be detected in urine samples. Cardiology, 118(4), 217–226.PubMedGoogle Scholar
  56. 56.
    D’Alessandra, Y., Devanna, P., Limana, F., Straino, S., Di Carlo, A., et al. (2010). Circulating microRNAs are new and sensitive biomarkers of myocardial infarction. European Heart Journal, 31(22), 2765–2773. doi: 10.1093/eurheartj/ehq167.PubMedCentralPubMedGoogle Scholar
  57. 57.
    Cheng, Y., Wang, X., Yang, J., Duan, X., Yao, Y., et al. (2012). A translational study of urine miRNAs in acute myocardial infarction. Journal of Molecular and Cellular Cardiology, 53(5), 668–676.PubMedCentralPubMedGoogle Scholar
  58. 58.
    Eitel, I., Adams, V., Dieterich, P., Fuernau, G., de Waha, S., et al. (2012). Relation of circulating MicroRNA-133a concentrations with myocardial damage and clinical prognosis in ST-elevation myocardial infarction. American Heart Journal, 164(5), 706–714.PubMedGoogle Scholar
  59. 59.
    Zampetaki, A., Willeit, P., Tilling, L., Drozdov, I., Prokopi, M., et al. (2012). Prospective study on circulating microRNAs and risk of myocardial infarction. Journal of the American College of Cardiology, 60(4), 290–299. doi: 10.1016/j.jacc.2012.03.056.PubMedGoogle Scholar
  60. 60.
    Olivieri, F., Antonicelli, R., Lorenzi, M., D’Alessandra, Y., Lazzarini, R., et al. (2013). Diagnostic potential of circulating miR-499-5p in elderly patients with acute non ST-elevation myocardial infarction. International Journal of Cardiology, 167(2), 531–536. doi: 10.1016/j.ijcard.2012.01.075.PubMedGoogle Scholar
  61. 61.
    Widera, C., Gupta, S. K., Lorenzen, J. M., Bang, C., Bauersachs, J., et al. (2011). Diagnostic and prognostic impact of six circulating microRNAs in acute coronary syndrome. Journal of Molecular and Cellular Cardiology, 51(5), 872–875. doi: 10.1016/j.yjmcc.2011.07.011.PubMedGoogle Scholar
  62. 62.
    Long, G., Wang, F., Duan, Q., Yang, S., Chen, F., et al. (2012). Circulating miR-30a, miR-195 and let-7b associated with acute myocardial infarction. PLoS ONE, 7(12), e50926. doi: 10.1371/journal.pone.0050926.PubMedCentralPubMedGoogle Scholar
  63. 63.
    Meder, B., Keller, A., Vogel, B., Haas, J., Sedaghat-Hamedani, F., et al. (2011). MicroRNA signatures in total peripheral blood as novel biomarkers for acute myocardial infarction. Basic Research in Cardiology, 106(1), 13–23. doi: 10.1007/s00395-010-0123-2.PubMedGoogle Scholar
  64. 64.
    Kuwabara, Y., Ono, K., Horie, T., Nishi, H., Nagao, K., et al. (2011). Increased microRNA-1 and microRNA-133a levels in serum of patients with cardiovascular disease indicate myocardial damage. Circulation. Cardiovascular Genetics, 4(4), 446–454. doi: 10.1161/circgenetics.110.958975.PubMedGoogle Scholar
  65. 65.
    Wang, G.-K., Zhu, J.-Q., Zhang, J.-T., Li, Q., Li, Y., et al. (2010). Circulating microRNA: a novel potential biomarker for early diagnosis of acute myocardial infarction in humans. European Heart Journal, 31(6), 659–666.PubMedGoogle Scholar
  66. 66.
    Wang, R., Li, N., Zhang, Y., Ran, Y., & Pu, J. (2011). Circulating microRNAs are promising novel biomarkers of acute myocardial infarction. Internal Medicine, 50(17), 1789–1795.PubMedGoogle Scholar
  67. 67.
    Devaux, Y., Vausort, M., Goretti, E., Nazarov, P. V., Azuaje, F., et al. (2012). Use of circulating microRNAs to diagnose acute myocardial infarction. Clinical Chemistry, 58(3), 559–567.PubMedGoogle Scholar
  68. 68.
    Ai, J., Zhang, R., Li, Y., Pu, J., Lu, Y., et al. (2010). Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. Biochemical and Biophysical Research Communications, 391(1), 73–77.PubMedGoogle Scholar
  69. 69.
    Adachi, T., Nakanishi, M., Otsuka, Y., Nishimura, K., Hirokawa, G., et al. (2010). Plasma microRNA 499 as a biomarker of acute myocardial infarction. Clinical Chemistry, 56(7), 1183–1185.PubMedGoogle Scholar
  70. 70.
    De Rosa, S., Fichtlscherer, S., Lehmann, R., Assmus, B., Dimmeler, S., et al. (2011). Transcoronary concentration gradients of circulating microRNAs. Circulation, 124(18), 1936–1944.PubMedGoogle Scholar
  71. 71.
    Oerlemans, M. I., Mosterd, A., Dekker, M. S., de Vrey, E. A., van Mil, A., et al. (2012). Early assessment of acute coronary syndromes in the emergency department: the potential diagnostic value of circulating microRNAs. EMBO Molecular Medicine, 4(11), 1176–1185.PubMedCentralPubMedGoogle Scholar
  72. 72.
    Huang, S., Chen, M., Li, L., He, M., Hu, D., et al. (2014). Circulating microRNAs and the occurrence of acute myocardial infarction in Chinese populations. Circulation. Cardiovascular Genetics. doi: 10.1161/circgenetics.113.000294.PubMedCentralGoogle Scholar
  73. 73.
    Vogel, B., Keller, A., Frese, K. S., Kloos, W., Kayvanpour, E., et al. (2013). Refining diagnostic microRNA signatures by whole-miRNome kinetic analysis in acute myocardial infarction. Clinical Chemistry, 59(2), 410–418.PubMedGoogle Scholar
  74. 74.
    Jansen, F., Yang, X., Proebsting, S., Hoelscher, M., Przybilla, D., et al. (2014). MicroRNA expression in circulating microvesicles predicts cardiovascular events in patients with coronary artery disease. Journal of the American Heart Association, 3(6), e001249. doi: 10.1161/jaha.114.001249.PubMedCentralPubMedGoogle Scholar
  75. 75.
    Amsterdam, E. A., Wenger, N. K., Brindis, R. G., Casey, D. E., Ganiats, T. G., et al. (2014). 2014 AHA/ACC guideline for the management of patients with non–ST-elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Journal of the American College of Cardiology, 64(24), e139–e228. doi: 10.1016/j.jacc.2014.09.017.PubMedGoogle Scholar
  76. 76.
    Willeit, P., Zampetaki, A., Dudek, K., Kaudewitz, D., King, A., et al. (2013). Circulating microRNAs as novel biomarkers for platelet activation. Circulation Research, 112(4), 595–600.PubMedGoogle Scholar
  77. 77.
    de Boer, H. C., van Solingen, C., Prins, J., Duijs, J. M., Huisman, M. V., et al. (2013). Aspirin treatment hampers the use of plasma microRNA-126 as a biomarker for the progression of vascular disease. European Heart Journal:eht007.Google Scholar
  78. 78.
    Zhang, Y.-Y., Zhou, X., Ji, W.-J., Shi, R., Lu, R.-Y., et al. (2014). Decreased circulating microRNA-223 level predicts high on-treatment platelet reactivity in patients with troponin-negative non-ST elevation acute coronary syndrome. Journal of Thrombosis and Thrombolysis, 38(1), 65–72.PubMedGoogle Scholar
  79. 79.
    Kaudewitz, D., Lee, R., Willeit, P., McGregor, R., Markus, H. S., et al. (2013). Impact of intravenous heparin on quantification of circulating microRNAs in patients with coronary artery disease. Thrombosis and Haemostasis, 110(3), 609–615.PubMedGoogle Scholar
  80. 80.
    Chyrchel, B., Totoń-Żurańska, J., Kruszelnicka, O., Chyrchel, M., Mielecki, W., et al. (2014). Association of plasma miR-223 and platelet reactivity in patients with coronary artery disease on dual antiplatelet therapy: a preliminary report. Platelets. doi: 10.3109/09537104.2014.974527.PubMedGoogle Scholar
  81. 81.
    Wallentin, L., Becker, R. C., Budaj, A., Cannon, C. P., Emanuelsson, H., et al. (2009). Ticagrelor versus clopidogrel in patients with acute coronary syndromes. New England Journal of Medicine, 361(11), 1045–1057. doi: 10.1056/NEJMoa0904327.PubMedGoogle Scholar
  82. 82.
    Voora, D., & Ginsburg, G. S. (2012). Clinical application of cardiovascular pharmacogenetics. Journal of the American College of Cardiology, 60(1), 9–20. doi: 10.1016/j.jacc.2012.01.067.PubMedGoogle Scholar
  83. 83.
    Temesvari, M., Kobori, L., Paulik, J., Sarvary, E., Belic, A., et al. (2012). Estimation of drug-metabolizing capacity by cytochrome P450 genotyping and expression. The Journal of Pharmacology and Experimental Therapeutics, 341(1), 294–305. doi: 10.1124/jpet.111.189597.PubMedGoogle Scholar
  84. 84.
    Furukawa, M., Nishimura, M., Ogino, D., Chiba, R., Ikai, I., et al. (2004). Cytochrome p450 gene expression levels in peripheral blood mononuclear cells in comparison with the liver. Cancer Science, 95(6), 520–529.PubMedGoogle Scholar
  85. 85.
    Minami, Y., Satoh, M., Maesawa, C., Takahashi, Y., Tabuchi, T., et al. (2009). Effect of atorvastatin on microRNA 221/222 expression in endothelial progenitor cells obtained from patients with coronary artery disease. European Journal of Clinical Investigation, 39(5), 359–367.PubMedGoogle Scholar
  86. 86.
    Takahashi, Y., Satoh, M., Minami, Y., Tabuchi, T., Itoh, T., et al. (2010). Expression of miR-146a/b is associated with the Toll-like receptor 4 signal in coronary artery disease: effect of renin-angiotensin system blockade and statins on miRNA-146a/b and Toll-like receptor 4 levels. Clinical Science (London), 119, 395–405.Google Scholar
  87. 87.
    Devaux, Y., Bousquenaud, M., Rodius, S., Marie, P.-Y., Maskali, F., et al. (2011). Transforming growth factor β receptor 1 is a new candidate prognostic biomarker after acute myocardial infarction. BMC Medical Genomics, 4(1), 83.PubMedCentralPubMedGoogle Scholar
  88. 88.
    Matsumoto, S., Sakata, Y., Suna, S., Nakatani, D., Usami, M., et al. (2013). Circulating p53-responsive microRNAs are predictive indicators of heart failure after acute myocardial infarction. Circulation Research, 113(3), 322–326.PubMedGoogle Scholar
  89. 89.
    Zile, M. R., Mehurg, S. M., Arroyo, J. E., Stroud, R. E., DeSantis, S. M., et al. (2011). Relationship between the temporal profile of plasma microRNA and left ventricular remodeling in patients after myocardial infarction. Circulation. Cardiovascular Genetics, 4(6), 614–619. doi: 10.1161/circgenetics.111.959841.PubMedCentralPubMedGoogle Scholar
  90. 90.
    Matsumoto, S., Sakata, Y., Nakatani, D., Suna, S., Mizuno, H., et al. (2012). A subset of circulating microRNAs are predictive for cardiac death after discharge for acute myocardial infarction. Biochemical and Biophysical Research Communications, 427(2), 280–284.PubMedGoogle Scholar
  91. 91.
    Gidlöf, O., Smith, J. G., Miyazu, K., Gilje, P., Spencer, A., et al. (2013). Circulating cardio-enriched microRNAs are associated with long-term prognosis following myocardial infarction. BMC Cardiovascular Disorders, 13(1), 12.PubMedCentralPubMedGoogle Scholar
  92. 92.
    van Rooij, E., Sutherland, L. B., Liu, N., Williams, A. H., McAnally, J., et al. (2006). A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proceedings of the National Academy of Sciences, 103(48), 18255–18260. doi: 10.1073/pnas.0608791103.Google Scholar
  93. 93.
    Dickinson, B. A., Semus, H. M., Montgomery, R. L., Stack, C., Latimer, P. A., et al. (2013). Plasma microRNAs serve as biomarkers of therapeutic efficacy and disease progression in hypertension-induced heart failure. European Journal of Heart Failure, 15(6), 650–659. doi: 10.1093/eurjhf/hft018.PubMedGoogle Scholar
  94. 94.
    Akat, K. M., Moore-McGriff, D. V., Morozov, P., Brown, M., Gogakos, T., et al. (2014). Comparative RNA-sequencing analysis of myocardial and circulating small RNAs in human heart failure and their utility as biomarkers. Proceedings of the National Academy of Sciences, 111(30), 11151–11156. doi: 10.1073/pnas.1401724111.Google Scholar
  95. 95.
    Lara-Pezzi, E., Gómez-Salinero, J., Gatto, A., & García-Pavía, P. (2013). The alternative heart: impact of alternative splicing in heart disease. Journal of Cardiovascular Translational Research, 6(6), 945–955.PubMedGoogle Scholar
  96. 96.
    Kittleson, M. M., Ye, S. Q., Irizarry, R. A., Minhas, K. M., Edness, G., et al. (2004). Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. Circulation, 110(22), 3444–3451. doi: 10.1161/01.cir.0000148178.19465.11.PubMedGoogle Scholar
  97. 97.
    Tilemann, L., Ishikawa, K., Weber, T., & Hajjar, R. J. (2012). Gene therapy for heart failure. Circulation Research, 110(5), 777–793. doi: 10.1161/circresaha.111.252981.PubMedCentralPubMedGoogle Scholar
  98. 98.
    Marfella, R., Di Filippo, C., Potenza, N., Sardu, C., Rizzo, M. R., et al. (2013). Circulating microRNA changes in heart failure patients treated with cardiac resynchronization therapy: responders vs. non‐responders. European Journal of Heart Failure, 15(11), 1277–1288.PubMedGoogle Scholar
  99. 99.
    Gao, G., Brahmanandam, V., Raicu, M., Gu, L., Zhou, L., et al. (2014). Enhanced risk profiling of implanted defibrillator shocks with circulating SCN5A mRNA splicing variants: a pilot trial. Journal of the American College of Cardiology, 63(21), 2261–2269.PubMedCentralPubMedGoogle Scholar
  100. 100.
    Heidecker, B., Kasper, E. K., Wittstein, I. S., Champion, H. C., Breton, E., et al. (2008). Transcriptomic biomarkers for individual risk assessment in new-onset heart failure. Circulation, 118(3), 238–246. doi: 10.1161/circulationaha.107.756544.PubMedCentralPubMedGoogle Scholar
  101. 101.
    Mehra, M. R., Benza, R., Deng, M. C., Russell, S., & Webber, S. (2004). Surrogate markers for late cardiac allograft survival. American Journal of Transplantation, 4(7), 1184–1191. doi: 10.1111/j.1600-6143.2004.00485.x.PubMedGoogle Scholar
  102. 102.
    Deng, M. C., Eisen, H. J., Mehra, M. R., Billingham, M., Marboe, C. C., et al. (2006). Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. American Journal of Transplantation, 6(1), 150–160. doi: 10.1111/j.1600-6143.2005.01175.x.PubMedGoogle Scholar
  103. 103.
    Van Huyen, J-P. D., Tible, M., Gay, A., Guillemain, R., Aubert, O., et al. (2014). MicroRNAs as non-invasive biomarkers of heart transplant rejection. European Heart Journal:ehu346.Google Scholar
  104. 104.
    Pham, M. X., Teuteberg, J. J., Kfoury, A. G., Starling, R. C., Deng, M. C., et al. (2010). Gene-expression profiling for rejection surveillance after cardiac transplantation. New England Journal of Medicine, 362(20), 1890–1900. doi: 10.1056/NEJMoa0912965.PubMedGoogle Scholar
  105. 105.
    Deng, M. C., Elashoff, B., Pham, M. X., Teuteberg, J. J., Kfoury, A. G., et al. (2014). Utility of gene expression profiling score variability to predict clinical events in heart transplant recipients. Transplantation, 97(6), 708.PubMedCentralPubMedGoogle Scholar
  106. 106.
    Kobashigawa, J., Patel, J., Azarbal, B., Kittleson, M., Chang, D., et al. (2015). Randomized pilot trial of gene expression profiling versus heart biopsy in the first year after heart transplant: early invasive monitoring attenuation through gene expression trial (EIMAGE). Circulation. Heart Failure. doi: 10.1161/circheartfailure.114.001658.PubMedGoogle Scholar
  107. 107.
    Zhang, C. (2010). MicroRNAs in vascular biology and vascular disease. Journal of Cardiovascular Translational Research, 3(3), 235–240.PubMedCentralPubMedGoogle Scholar
  108. 108.
    Eikmans, M., Rekers, N. V., Anholts, J. D. H., Heidt, S., & Claas, F. H. J. (2013). Blood cell mRNAs and microRNAs: optimized protocols for extraction and preservation. Blood, 121(11), e81–e89. doi: 10.1182/blood-2012-06-438887.PubMedGoogle Scholar
  109. 109.
    Koh, W., Pan, W., Gawad, C., Fan, H. C., Kerchner, G. A., et al. (2014). Noninvasive in vivo monitoring of tissue-specific global gene expression in humans. Proceedings of the National Academy of Sciences, 111(20), 7361–7366.Google Scholar
  110. 110.
    Huan, T., Zhang, B., Wang, Z., Joehanes, R., Zhu, J., et al. (2013). A systems biology framework identifies molecular underpinnings of coronary heart disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 33(6), 1427–1434. doi: 10.1161/atvbaha.112.300112.PubMedCentralPubMedGoogle Scholar
  111. 111.
    Joehanes, R., Ying, S., Huan, T., Johnson, A. D., Raghavachari, N., et al. (2013). Gene expression signatures of coronary heart disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 33(6), 1418–1426. doi: 10.1161/atvbaha.112.301169.PubMedCentralPubMedGoogle Scholar
  112. 112.
    Huan, T., Rong, J., Tanriverdi, K., Meng, Q., Bhattacharya, A., et al. (2015). Dissecting the roles of microRNAs in coronary heart disease via integrative genomic analyses. Arteriosclerosis, Thrombosis, and Vascular Biology, 35(4), 1011–1021. doi: 10.1161/atvbaha.114.305176.PubMedGoogle Scholar
  113. 113.
    Rosenberg, S., Elashoff, M. R., Beineke, P., Daniels, S. E., Wingrove, J. A., et al. (2010). Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Annals of Internal Medicine, 153(7), 425–434. doi: 10.7326/0003-4819-153-7-201010050-00005.PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Kevin A. Friede
    • 1
  • Geoffrey S. Ginsburg
    • 1
    • 2
  • Deepak Voora
    • 1
    • 2
    Email author
  1. 1.Department of MedicineDuke UniversityDurhamUSA
  2. 2.Center for Applied Genomics & Precision MedicineDuke UniversityDurhamUSA

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