RNA-sequencing reveals that STRN, ZNF484 and WNK1 add to the value of mitochondrial MT-COI and COX10 as markers of unstable coronary artery disease

English version

Autoři: Paul Holvoet ^aff001; Bernward Klocke ^aff002; Maarten Vanhaverbeke ^aff003; Roxane Menten ^aff001; Peter Sinnaeve ^aff003; Emma Raitoharju ^aff004; Terho Lehtimäki ^aff004; Niku Oksala ^aff006; Christian Zinser ^aff002; Stefan Janssens ^aff003; Karin Sipido ^aff001; Leo-Pekka Lyytikainen ^aff004; Stefano Cagnin ^aff007
Působiště autorů: Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium ^aff001; Intrexon Bioinformatics Germany, Munich, Germany ^aff002; Department of Clinical Cardiology, UZ Leuven, Leuven, Belgium ^aff003; Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland ^aff004; Finnish Cardiovascular Research Centre, Faculty of Medicine and Life Sciences University of Tampere, Tampere, Finland ^aff005; Division of Vascular Surgery, Department of Surgery, Tampere University Hospital, Tampere, Finland ^aff006; Department of Biology, CRIBI Biotechnology Centre, Padova, Italy ^aff007; CIR-Myo Myology Centre, University of Padova, Padova, Italy ^aff008
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0225621

Souhrn

Markers in monocytes, precursors of macrophages, which are related to CAD, are largely unknown. Therefore, we aimed to identify genes in monocytes predictive of a new ischemic event in patients with CAD and/or discriminate between stable CAD and acute coronary syndrome. We included 66 patients with stable CAD, of which 24 developed a new ischemic event, and 19 patients with ACS. Circulating CD14+ monocytes were isolated with magnetic beads. RNA sequencing analysis in monocytes of patients with (n = 13) versus without (n = 11) ischemic event at follow-up and in patients with ACS (n = 12) was validated with qPCR (n = 85). MT-COI, STRN and COX10 predicted new ischemic events in CAD patients (power for separation at 1% error rate of 0.97, 0.90 and 0.77 respectively). Low MT-COI and high STRN were also related to shorter time between blood sampling and event. COX10 and ZNF484 together with MT-COI, STRN and WNK1 separated ACS completely from stable CAD patients. RNA expressions in monocytes of MT-COI, COX10, STRN, WNK1 and ZNF484 were independent of cholesterol lowering and antiplatelet treatment. They were independent of troponin T, a marker of myocardial injury. But, COX10 and ZNF484 in human plaques correlated to plaque markers of M1 macrophage polarization, reflecting vascular injury. Expression of MT-COI, COX10, STRN and WNK1, but not that of ZNF484, PBMCs paired with that in monocytes. The prospective study of relation of MT-COI, COX10, STRN, WNK1 and ZNF484 with unstable CAD is warranted.

Klíčová slova:

Gene expression – Cytokines – Blood – Coronary heart disease – Macrophages – Monocytes – RNA sequencing – Stable coronary artery disease

Zdroje

1. Lenfant C. Chest pain of cardiac and noncardiac origin. Metabolism. 2010;59 Suppl 1:S41–6. Epub 2010/09/21. doi: 10.1016/j.metabol.2010.07.014 20837193.

2. Sinnaeve PR, Donahue MP, Grass P, Seo D, Vonderscher J, Chibout SD, et al. Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PLoS One. 2009;4(9):e7037. Epub 2009/09/15. doi: 10.1371/journal.pone.0007037 19750006; PubMed Central PMCID: PMC2736586.

3. Wallace DC. A mitochondrial paradigm of metabolic and degenerative diseases, aging, and cancer: a dawn for evolutionary medicine. Annu Rev Genet. 2005;39:359–407. Epub 2005/11/16. doi: 10.1146/annurev.genet.39.110304.095751 16285865; PubMed Central PMCID: PMC2821041.

4. Wallace DC. The mitochondrial genome in human adaptive radiation and disease: on the road to therapeutics and performance enhancement. Gene. 2005;354:169–80. Epub 2005/07/19. doi: 10.1016/j.gene.2005.05.001 16024186.

5. Holvoet P, Vanhaverbeke M, Bloch K, Baatsen P, Sinnaeve P, Janssens S. Low MT-CO1 in Monocytes and Microvesicles Is Associated With Outcome in Patients With Coronary Artery Disease. J Am Heart Assoc. 2016;5(12). Epub 2016/12/07. doi: 10.1161/JAHA.116.004207 27919931; PubMed Central PMCID: PMC5210432.

6. Chapman AR, Lee KK, McAllister DA, Cullen L, Greenslade JH, Parsonage W, et al. Association of High-Sensitivity Cardiac Troponin I Concentration With Cardiac Outcomes in Patients With Suspected Acute Coronary Syndrome. JAMA. 2017;318(19):1913–24. Epub 2017/11/12. doi: 10.1001/jama.2017.17488 29127948; PubMed Central PMCID: PMC5710293.

7. Park KC, Gaze DC, Collinson PO, Marber MS. Cardiac troponins: from myocardial infarction to chronic disease. Cardiovasc Res. 2017;113(14):1708–18. Epub 2017/10/11. doi: 10.1093/cvr/cvx183 29016754; PubMed Central PMCID: PMC5852618.

8. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947–58. Epub 2003/11/25. doi: 10.1021/ci034160g 14632445.

9. Cagnin S, Biscuola M, Patuzzo C, Trabetti E, Pasquali A, Laveder P, et al. Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries. BMC Genomics. 2009;10:13. Epub 2009/01/13. doi: 10.1186/1471-2164-10-13 19134193; PubMed Central PMCID: PMC2654039.

10. Stary HC, Chandler AB, Dinsmore RE, Fuster V, Glagov S, Insull W Jr., et al. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation. 1995;92(5):1355–74. Epub 1995/09/01. doi: 10.1161/01.cir.92.5.1355 7648691.

11. Oksala N, Levula M, Airla N, Pelto-Huikko M, Ortiz RM, Jarvinen O, et al. ADAM-9, ADAM-15, and ADAM-17 are upregulated in macrophages in advanced human atherosclerotic plaques in aorta and carotid and femoral arteries—Tampere vascular study. Ann Med. 2009;41(4):279–90. Epub 2009/03/03. doi: 10.1080/07853890802649738 19253070.

12. Niinisalo P, Oksala N, Levula M, Pelto-Huikko M, Jarvinen O, Salenius JP, et al. Activation of indoleamine 2,3-dioxygenase-induced tryptophan degradation in advanced atherosclerotic plaques: Tampere vascular study. Ann Med. 2010;42(1):55–63. Epub 2009/11/28. doi: 10.3109/07853890903321559 19941414.

13. Oksala N, Parssinen J, Seppala I, Raitoharju E, Kholova I, Hernesniemi J, et al. Association of neuroimmune guidance cue netrin-1 and its chemorepulsive receptor UNC5B with atherosclerotic plaque expression signatures and stability in human(s): Tampere Vascular Study (TVS). Circ Cardiovasc Genet. 2013;6(6):579–87. Epub 2013/10/15. doi: 10.1161/CIRCGENETICS.113.000141 24122613.

14. Raitoharju E, Seppala I, Lyytikainen LP, Levula M, Oksala N, Klopp N, et al. A comparison of the accuracy of Illumina HumanHT-12 v3 Expression BeadChip and TaqMan qRT-PCR gene expression results in patient samples from the Tampere Vascular Study. Atherosclerosis. 2013;226(1):149–52. Epub 2012/11/28. doi: 10.1016/j.atherosclerosis.2012.10.078 23177970.

15. Poyton RO, McEwen JE. Crosstalk between nuclear and mitochondrial genomes. Annu Rev Biochem. 1996;65:563–607. Epub 1996/01/01. doi: 10.1146/annurev.bi.65.070196.003023 8811190.

16. Villani G, Attardi G. In vivo control of respiration by cytochrome c oxidase in wild-type and mitochondrial DNA mutation-carrying human cells. Proc Natl Acad Sci U S A. 1997;94(4):1166–71. Epub 1997/02/18. doi: 10.1073/pnas.94.4.1166 9037024; PubMed Central PMCID: PMC19762.

17. Holvoet P, Lee DH, Steffes M, Gross M, Jacobs DR Jr., Association between circulating oxidized low-density lipoprotein and incidence of the metabolic syndrome. JAMA. 2008;299(19):2287–93. Epub 2008/05/22. doi: 10.1001/jama.299.19.2287 18492970; PubMed Central PMCID: PMC2562739.

18. Park K, Gross M, Lee DH, Holvoet P, Himes JH, Shikany JM, et al. Oxidative stress and insulin resistance: the coronary artery risk development in young adults study. Diabetes Care. 2009;32(7):1302–7. Epub 2009/04/25. doi: 10.2337/dc09-0259 19389821; PubMed Central PMCID: PMC2699736.

19. Acin-Perez R, Bayona-Bafaluy MP, Bueno M, Machicado C, Fernandez-Silva P, Perez-Martos A, et al. An intragenic suppressor in the cytochrome c oxidase I gene of mouse mitochondrial DNA. Hum Mol Genet. 2003;12(3):329–39. Epub 2003/01/30. doi: 10.1093/hmg/ddg021 12554686.

20. Fukuda R, Zhang H, Kim JW, Shimoda L, Dang CV, Semenza GL. HIF-1 regulates cytochrome oxidase subunits to optimize efficiency of respiration in hypoxic cells. Cell. 2007;129(1):111–22. Epub 2007/04/10. doi: 10.1016/j.cell.2007.01.047 17418790.

21. Holvoet P, Vanhaverbeke M, Geeraert B, De Keyzer D, Hulsmans M, Janssens S. Low Cytochrome Oxidase 1 Links Mitochondrial Dysfunction to Atherosclerosis in Mice and Pigs. PLoS One. 2017;12(1):e0170307. Epub 2017/01/26. doi: 10.1371/journal.pone.0170307 28122051; PubMed Central PMCID: PMC5266248.

22. Diaz F, Thomas CK, Garcia S, Hernandez D, Moraes CT. Mice lacking COX10 in skeletal muscle recapitulate the phenotype of progressive mitochondrial myopathies associated with cytochrome c oxidase deficiency. Hum Mol Genet. 2005;14(18):2737–48. Epub 2005/08/17. doi: 10.1093/hmg/ddi307 16103131; PubMed Central PMCID: PMC2778476.

23. Tarasenko TN, Pacheco SE, Koenig MK, Gomez-Rodriguez J, Kapnick SM, Diaz F, et al. Cytochrome c Oxidase Activity Is a Metabolic Checkpoint that Regulates Cell Fate Decisions During T Cell Activation and Differentiation. Cell Metab. 2017;25(6):1254–68 e7. Epub 2017/06/08. doi: 10.1016/j.cmet.2017.05.007 28591633; PubMed Central PMCID: PMC5562283.

24. Tan H, Yang K, Li Y, Shaw TI, Wang Y, Blanco DB, et al. Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation. Immunity. 2017;46(3):488–503. Epub 2017/03/14. doi: 10.1016/j.immuni.2017.02.010 28285833; PubMed Central PMCID: PMC5466820.

25. Geng S, Chen K, Yuan R, Peng L, Maitra U, Diao N, et al. The persistence of low-grade inflammatory monocytes contributes to aggravated atherosclerosis. Nat Commun. 2016;7:13436. Epub 2016/11/09. doi: 10.1038/ncomms13436 27824038; PubMed Central PMCID: PMC5105176.

26. Hulsmans M, Geeraert B, Arnould T, Tsatsanis C, Holvoet P. PPAR agonist-induced reduction of Mcp1 in atherosclerotic plaques of obese, insulin-resistant mice depends on adiponectin-induced Irak3 expression. PLoS One. 2013;8(4):e62253. Epub 2013/04/27. doi: 10.1371/journal.pone.0062253 23620818; PubMed Central PMCID: PMC3631170.

27. Schieffer B, Luchtefeld M. Emerging role of chemokine receptor 7 in atherosclerosis. Trends Cardiovasc Med. 2011;21(8):211–6. Epub 2012/08/21. doi: 10.1016/j.tcm.2012.05.012 22902067.

28. Mueller PA, Zhu L, Tavori H, Huynh K, Giunzioni I, Stafford JM, et al. Deletion of Macrophage Low-Density Lipoprotein Receptor-Related Protein 1 (LRP1) Accelerates Atherosclerosis Regression and Increases C-C Chemokine Receptor Type 7 (CCR7) Expression in Plaque Macrophages. Circulation. 2018;138(17):1850–63. Epub 2018/05/26. doi: 10.1161/CIRCULATIONAHA.117.031702 29794082; PubMed Central PMCID: PMC6343494.

29. Lahav-Ariel L, Caspi M, Nadar-Ponniah PT, Zelikson N, Hofmann I, Hanson KK, et al. Striatin is a novel modulator of cell adhesion. FASEB J. 2018:fj201801882R. Epub 2018/12/29. doi: 10.1096/fj.201801882R 30592649.

30. Garza AE, Pojoga LH, Moize B, Hafiz WM, Opsasnick LA, Siddiqui WT, et al. Critical Role of Striatin in Blood Pressure and Vascular Responses to Dietary Sodium Intake. Hypertension. 2015;66(3):674–80. Epub 2015/07/15. doi: 10.1161/HYPERTENSIONAHA.115.05600 26169051; PubMed Central PMCID: PMC4537321.

31. Zheng S, Chen X, Hong S, Long L, Xu Y, Simoncini T, et al. 17beta-Estradiol inhibits vascular smooth muscle cell migration via up-regulation of striatin protein. Gynecol Endocrinol. 2015;31(8):618–24. Epub 2015/07/30. doi: 10.3109/09513590.2015.1021325 26220767.

32. Lu Q, Pallas DC, Surks HK, Baur WE, Mendelsohn ME, Karas RH. Striatin assembles a membrane signaling complex necessary for rapid, nongenomic activation of endothelial NO synthase by estrogen receptor alpha. Proc Natl Acad Sci U S A. 2004;101(49):17126–31. Epub 2004/12/01. doi: 10.1073/pnas.0407492101 15569929; PubMed Central PMCID: PMC534607.

33. Bellefroid EJ, Poncelet DA, Lecocq PJ, Revelant O, Martial JA. The evolutionarily conserved Kruppel-associated box domain defines a subfamily of eukaryotic multifingered proteins. Proc Natl Acad Sci U S A. 1991;88(9):3608–12. Epub 1991/05/01. doi: 10.1073/pnas.88.9.3608 2023909; PubMed Central PMCID: PMC51501.

34. Schirmer SH, Fledderus JO, van der Laan AM, van der Pouw-Kraan TC, Moerland PD, Volger OL, et al. Suppression of inflammatory signaling in monocytes from patients with coronary artery disease. J Mol Cell Cardiol. 2009;46(2):177–85. Epub 2008/12/09. doi: 10.1016/j.yjmcc.2008.10.029 19059264.

35. Margolin JF, Friedman JR, Meyer WK, Vissing H, Thiesen HJ, Rauscher FJ 3rd. Kruppel-associated boxes are potent transcriptional repression domains. Proc Natl Acad Sci U S A. 1994;91(10):4509–13. Epub 1994/05/10. doi: 10.1073/pnas.91.10.4509 8183939; PubMed Central PMCID: PMC43815.

36. Friedman JR, Fredericks WJ, Jensen DE, Speicher DW, Huang XP, Neilson EG, et al. KAP-1, a novel corepressor for the highly conserved KRAB repression domain. Genes Dev. 1996;10(16):2067–78. Epub 1996/08/15. doi: 10.1101/gad.10.16.2067 8769649.

37. Wolf G, Greenberg D, Macfarlan TS. Spotting the enemy within: Targeted silencing of foreign DNA in mammalian genomes by the Kruppel-associated box zinc finger protein family. Mob DNA. 2015;6:17. Epub 2015/10/06. doi: 10.1186/s13100-015-0050-8 26435754; PubMed Central PMCID: PMC4592553.

38. Ridker PM. Canakinumab for Residual Inflammatory Risk. Eur Heart J. 2017;38(48):3545–8. Epub 2017/12/28. doi: 10.1093/eurheartj/ehx723 29281019.

39. Ridker PM, Everett BM, Thuren T, MacFadyen JG, Chang WH, Ballantyne C, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med. 2017;377(12):1119–31. Epub 2017/08/29. doi: 10.1056/NEJMoa1707914 28845751.

40. Powell L, Wiederkehr RS, Damascus P, Fauvart M, Buja F, Stakenborg T, et al. Rapid and sensitive detection of viral nucleic acids using silicon microchips. Analyst. 2018;143(11):2596–603. Epub 2018/05/10. doi: 10.1039/c8an00552d 29741175.

41. Cornelis S, Fauvart M, Gansemans Y, Vander Plaetsen AS, Colle F, Wiederkehr RS, et al. Multiplex STR amplification sensitivity in a silicon microchip. Sci Rep. 2018;8(1):9853. Epub 2018/07/01. doi: 10.1038/s41598-018-28229-9 29959383; PubMed Central PMCID: PMC6026139.

42. Wang H, Zhang X, Xu X, Zhang Q, Wang H, Li D, et al. A portable microfluidic platform for rapid molecular diagnostic testing of patients with myeloproliferative neoplasms. Sci Rep. 2017;7(1):8596. Epub 2017/08/19. doi: 10.1038/s41598-017-08674-8 28819248; PubMed Central PMCID: PMC5561050.

43. Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. 2017;38(23):1805–14. Epub 2016/07/21. doi: 10.1093/eurheartj/ehw302 27436868; PubMed Central PMCID: PMC5837244.

44. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944. Epub 2017/04/05. doi: 10.1371/journal.pone.0174944 28376093; PubMed Central PMCID: PMC5380334.

45. Mandl KD, Manrai AK. Potential Excessive Testing at Scale: Biomarkers, Genomics, and Machine Learning. JAMA. 2019. Epub 2019/02/09. doi: 10.1001/jama.2019.0286 30735228.

RNA-sequencing reveals that STRN, ZNF484 and WNK1 add to the value of mitochondrial MT-COI and COX10 as markers of unstable coronary artery disease

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz