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Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis


Cardiovascular disease is a strongly heritable trait. Despite application of the latest genomic technologies, the genetic architecture of disease risk remains poorly defined, and mechanisms underlying this susceptibility are incompletely understood. In this study, we performed genome-wide mapping of heart disease-related metabolites measured in the blood as the genetic traits of interest (instead of the disease itself), in a large cohort of 3512 patients at risk of heart disease from the CATHGEN study. Our goal was to discover new cardiovascular disease genes and thereby mechanisms of disease pathogenesis by understanding the genes that regulate levels of these metabolites. These analyses identified novel genetic variants associated with metabolite levels and with cardiovascular disease itself. Importantly, by utilizing an unbiased systems-based approach integrating genetics, gene expression, epigenetics and metabolomics, we uncovered a novel pathway of heart disease pathogenesis, that of endoplasmic reticulum (ER) stress, represented by elevated levels of circulating short-chain dicarboxylacylcarnitine (SCDA) metabolites.


Vyšlo v časopise: Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis. PLoS Genet 11(11): e32767. doi:10.1371/journal.pgen.1005553
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005553

Souhrn

Cardiovascular disease is a strongly heritable trait. Despite application of the latest genomic technologies, the genetic architecture of disease risk remains poorly defined, and mechanisms underlying this susceptibility are incompletely understood. In this study, we performed genome-wide mapping of heart disease-related metabolites measured in the blood as the genetic traits of interest (instead of the disease itself), in a large cohort of 3512 patients at risk of heart disease from the CATHGEN study. Our goal was to discover new cardiovascular disease genes and thereby mechanisms of disease pathogenesis by understanding the genes that regulate levels of these metabolites. These analyses identified novel genetic variants associated with metabolite levels and with cardiovascular disease itself. Importantly, by utilizing an unbiased systems-based approach integrating genetics, gene expression, epigenetics and metabolomics, we uncovered a novel pathway of heart disease pathogenesis, that of endoplasmic reticulum (ER) stress, represented by elevated levels of circulating short-chain dicarboxylacylcarnitine (SCDA) metabolites.


Zdroje

1. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316(5830):1491–3. 17478679

2. Gieger C, Geistlinger L, Altmaier E, Hrabe de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS genetics. 2008;4(11):e1000282. doi: 10.1371/journal.pgen.1000282 19043545

3. Shah AA, Craig DM, Sebek JK, Haynes C, Stevens RC, Muehlbauer MJ, et al. Metabolic profiles predict adverse events after coronary artery bypass grafting. The Journal of thoracic and cardiovascular surgery. 2012;143(4):873–8. doi: 10.1016/j.jtcvs.2011.09.070 22306227

4. Shah SH, Bain JR, Muehlbauer MJ, Stevens RD, Crosslin DR, Haynes C, et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circulation Cardiovascular genetics. 2010;3(2):207–14. doi: 10.1161/CIRCGENETICS.109.852814 20173117

5. Shah SH, Sun JL, Stevens RD, Bain JR, Muehlbauer MJ, Pieper KS, et al. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. American heart journal. 2012;163(5):844–50 e1. doi: 10.1016/j.ahj.2012.02.005 22607863

6. Shah SH, Hauser ER, Bain JR, Muehlbauer MJ, Haynes C, Stevens RD, et al. High heritability of metabolomic profiles in families burdened with premature cardiovascular disease. Molecular systems biology. 2009;5:258. doi: 10.1038/msb.2009.11 19357637

7. Bhattacharya S, Granger CB, Craig D, Haynes C, Bain J, Stevens RD, et al. Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization. Atherosclerosis. 2014;232(1):191–6. doi: 10.1016/j.atherosclerosis.2013.10.036 24401236

8. Szebenyi G, Wigley WC, Hall B, Didier A, Yu M, Thomas P, et al. Hook2 contributes to aggresome formation. BMC cell biology. 2007;8:19. 17540036

9. Wang Y, Wan B, Li D, Zhou J, Li R, Bai M, et al. BRSK2 is regulated by ER stress in protein level and involved in ER stress-induced apoptosis. Biochemical and biophysical research communications. 2012;423(4):813–8. doi: 10.1016/j.bbrc.2012.06.046 22713462

10. Liu S, Lv J, Han L, Ichikawa T, Wang W, Li S, et al. A pro-inflammatory role of deubiquitinating enzyme cylindromatosis (CYLD) in vascular smooth muscle cells. Biochemical and biophysical research communications. 2012;420(1):78–83. doi: 10.1016/j.bbrc.2012.02.118 22406061

11. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(43):15545–50. 16199517

12. Maeda Y, Ito T, Suzuki A, Kurono Y, Ueta A, Yokoi K, et al. Simultaneous quantification of acylcarnitine isomers containing dicarboxylic acylcarnitines in human serum and urine by high-performance liquid chromatography/electrospray ionization tandem mass spectrometry. Rapid communications in mass spectrometry: RCM. 2007;21(5):799–806. 17279485

13. Johnson DW. Synthesis of dicarboxylic acylcarnitines. Chemistry and physics of lipids. 2004;129(2):161–71. 15081857

14. Reyes-Turcu FE, Ventii KH, Wilkinson KD. Regulation and cellular roles of ubiquitin-specific deubiquitinating enzymes. Annual review of biochemistry. 2009;78:363–97. doi: 10.1146/annurev.biochem.78.082307.091526 19489724

15. Smith NL, Felix JF, Morrison AC, Demissie S, Glazer NL, Loehr LR, et al. Association of genome-wide variation with the risk of incident heart failure in adults of European and African ancestry: a prospective meta-analysis from the cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium. Circulation Cardiovascular genetics. 2010;3(3):256–66. doi: 10.1161/CIRCGENETICS.109.895763 20445134

16. McKeown L, Moss NK, Turner P, Li J, Heath N, Burke D, et al. Platelet-derived growth factor maintains stored calcium through a nonclustering Orai1 mechanism but evokes clustering if the endoplasmic reticulum is stressed by store depletion. Circulation research. 2012;111(1):66–76. doi: 10.1161/CIRCRESAHA.111.263616 22556336

17. Jang JW, Lee WY, Lee JH, Moon SH, Kim CH, Chung HM. A novel Fbxo25 acts as an E3 ligase for destructing cardiac specific transcription factors. Biochemical and biophysical research communications. 2011;410(2):183–8. doi: 10.1016/j.bbrc.2011.05.011 21596019

18. Kitagawa K, Skowyra D, Elledge SJ, Harper JW, Hieter P. SGT1 encodes an essential component of the yeast kinetochore assembly pathway and a novel subunit of the SCF ubiquitin ligase complex. Molecular cell. 1999;4(1):21–33. 10445024

19. Libert R, Van Hoof F, Laus G, De Nayer P, Habib Jiwan JL, de Hoffmann E, et al. Identification of ethylsuccinylcarnitine present in some human urines. Clinica chimica acta; international journal of clinical chemistry. 2005;355(1–2):145–51. 15820489

20. Ranek MJ, Terpstra EJ, Li J, Kass DA, Wang X. Protein kinase g positively regulates proteasome-mediated degradation of misfolded proteins. Circulation. 2013;128(4):365–76. doi: 10.1161/CIRCULATIONAHA.113.001971 23770744

21. Herrmann J, Lerman LO, Lerman A. On to the road to degradation: atherosclerosis and the proteasome. Cardiovascular research. 2010;85(2):291–302. doi: 10.1093/cvr/cvp333 19815565

22. Vieira O, Escargueil-Blanc I, Jurgens G, Borner C, Almeida L, Salvayre R, et al. Oxidized LDLs alter the activity of the ubiquitin-proteasome pathway: potential role in oxidized LDL-induced apoptosis. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2000;14(3):532–42.

23. Kikuchi J, Furukawa Y, Kubo N, Tokura A, Hayashi N, Nakamura M, et al. Induction of ubiquitin-conjugating enzyme by aggregated low density lipoprotein in human macrophages and its implications for atherosclerosis. Arteriosclerosis, thrombosis, and vascular biology. 2000;20(1):128–34. 10634809

24. Herrmann J, Saguner AM, Versari D, Peterson TE, Chade A, Olson M, et al. Chronic proteasome inhibition contributes to coronary atherosclerosis. Circulation research. 2007;101(9):865–74. 17823377

25. Herrmann J, Wohlert C, Saguner AM, Flores A, Nesbitt LL, Chade A, et al. Primary proteasome inhibition results in cardiac dysfunction. European journal of heart failure. 2013;15(6):614–23. doi: 10.1093/eurjhf/hft034 23616520

26. Feng B, Zhang Y, Mu J, Ye Z, Zeng W, Qi W, et al. Preventive effect of a proteasome inhibitor on the formation of accelerated atherosclerosis in rabbits with uremia. Journal of cardiovascular pharmacology. 2010;55(2):129–38. doi: 10.1097/FJC.0b013e3181c87f8e 19935080

27. Versari D, Herrmann J, Gossl M, Mannheim D, Sattler K, Meyer FB, et al. Dysregulation of the ubiquitin-proteasome system in human carotid atherosclerosis. Arteriosclerosis, thrombosis, and vascular biology. 2006;26(9):2132–9. 16778122

28. Marfella R, D'Amico M, Di Filippo C, Baldi A, Siniscalchi M, Sasso FC, et al. Increased activity of the ubiquitin-proteasome system in patients with symptomatic carotid disease is associated with enhanced inflammation and may destabilize the atherosclerotic plaque: effects of rosiglitazone treatment. Journal of the American College of Cardiology. 2006;47(12):2444–55. 16781372

29. Dombroski BA, Nayak RR, Ewens KG, Ankener W, Cheung VG, Spielman RS. Gene expression and genetic variation in response to endoplasmic reticulum stress in human cells. American journal of human genetics. 2010;86(5):719–29. doi: 10.1016/j.ajhg.2010.03.017 20398888

30. Shah SH, Granger CB, Hauser ER, Kraus WE, Sun JL, Pieper K, et al. Reclassification of cardiovascular risk using integrated clinical and molecular biosignatures: Design of and rationale for the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Horizon 1 Cardiovascular Disease Study. American heart journal. 2010;160(3):371–9 e2. doi: 10.1016/j.ahj.2010.06.051 20826242

31. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics. 2007;81(3):559–75. 17701901

32. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–1. doi: 10.1093/bioinformatics/btq340 20616382

33. Chen W, Gao G, Nerella S, Hultman CM, Magnusson PK, Sullivan PF, et al. MethylPCA: a toolkit to control for confounders in methylome-wide association studies. BMC bioinformatics. 2013;14:74. doi: 10.1186/1471-2105-14-74 23452721

34. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547–8. doi: 10.1093/bioinformatics/btn224 18467348

35. Pidsley R, CC YW, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC genomics. 2013;14:293. doi: 10.1186/1471-2164-14-293 23631413

36. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189–96. doi: 10.1093/bioinformatics/bts680 23175756

37. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015;43(7):e47. doi: 10.1093/nar/gkv007 25605792

38. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC bioinformatics. 2012;13:86. doi: 10.1186/1471-2105-13-86 22568884

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Genetika Reprodukčná medicína

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PLOS Genetics


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