Skip to main content
SpringerLink
Log in

How Gene Networks Can Uncover Novel CVD Players

  • Lipids (J Ordovas, L Parnell, Section Editors)
  • Published:
Current Cardiovascular Risk Reports Aims and scope Submit manuscript

Abstract

Cardiovascular diseases (CVD) are complex, involving numerous biological entities from genes and small molecules to organ function. Placing these entities in networks where the functional relationships among the constituents are drawn can aid in our understanding of disease onset, progression, and prevention. While networks, or interactomes, are often classified by a general term, say lipids or inflammation, it is a more encompassing class of network that is more informative in showing connections among the active entities and allowing better hypotheses of novel CVD players to be formulated. A range of networks will be presented whereby the potential to bring new objects into the CVD milieu will be exemplified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Rhinn H, Fujita R, Qiang L, Cheng R, Lee JH, Abeliovich A. Integrative genomics identifies APOE ε4 effectors in Alzheimer’s disease. Nature. 2013;500:45–50.

    Article  CAS  PubMed  Google Scholar 

  2. Erbilgin A, Civelek M, Romanoski CE, Pan C, Hagopian R, Berliner JA, et al. Identification of CAD candidate genes in GWAS loci and their expression in vascular cells. J Lipid Res. 2013;54:1894–905.

    Article  CAS  PubMed  Google Scholar 

  3. Zhang X, Johnson AD, Hendricks AE, Hwang SJ, Tanriverdi K, Ganesh SK, et al. Genetic associations with expression for genes implicated in GWAS studies for atherosclerotic cardiovascular disease and blood phenotypes. Hum Mol Genet. 2014;23:782–95.

    Google Scholar 

  4. Stranger BE, Raj T. Genetics of human gene expression. Curr Opin Genet Dev. 2013;23:627–34.

    Article  CAS  PubMed  Google Scholar 

  5. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008;40:189–97.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature. 2010;466:714–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Parnell LD, Lee YC, Lai CQ. Adaptive genetic variation and heart disease risk. Curr Opin Lipidol. 2010;21:116–22.

    Article  CAS  PubMed  Google Scholar 

  8. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45:1238–43.

    Article  CAS  PubMed  Google Scholar 

  9. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5. This project will make significant contributions to describing genes whose expression in any of >40 tissues is regulated in part by common genetic variation.

    Article  Google Scholar 

  10. Rung J, Cauchi S, Albrechtsen A, Shen L, Rocheleau G, Cavalcanti-Proença C, et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet. 2009;41:1110–5.

    Article  CAS  PubMed  Google Scholar 

  11. Richardson K, Lai CQ, Parnell LD, Lee YC, Ordovas JM. A genome-wide survey for SNPs altering microRNA seed sites identifies functional candidates in GWAS. BMC Genomics. 2011;12:504.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Najafi-Shoushtari SH, Kristo F, Li Y, Shioda T, Cohen DE, Gerszten RE, et al. MicroRNA-33 and the SREBP host genes cooperate to control cholesterol homeostasis. Science. 2010;328:1566–9.

    Article  CAS  PubMed  Google Scholar 

  13. Rayner KJ, Suárez Y, Dávalos A, Parathath S, Fitzgerald ML, Tamehiro N, et al. MiR-33 contributes to the regulation of cholesterol homeostasis. Science. 2010;328:1570–3.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Allen RM, Marquart TJ, Albert CJ, Suchy FJ, Wang DQ, Ananthanarayanan M, et al. miR-33 controls the expression of biliary transporters, and mediates statin- and diet-induced hepatotoxicity. EMBO. Mol Med. 2012;4:882–95.

    CAS  Google Scholar 

  15. Dávalos A, Goedeke L, Smibert P, Ramírez CM, Warrier NP, Andreo U, et al. miR-33a/b contribute to the regulation of fatty acid metabolism and insulin signaling. Proc Natl Acad Sci U S A. 2011;108:9232–7.

    Article  PubMed Central  PubMed  Google Scholar 

  16. Rottiers V, Obad S, Petri A, McGarrah R, Lindholm MW, Black JC, et al. Pharmacological Inhibition of a MicroRNA Family in Nonhuman Primates by a Seed-Targeting 8-Mer AntimiR. Sci Transl Med. 2013;5:212ra162. Several groups have been investigating gene-microRNA-cholesterol networks. This report describes the latest in exploiting those networks for therapeutic purposes.

    Article  PubMed  Google Scholar 

  17. Richardson K, Nettleton JA, Rotllan N, Tanaka T, Smith CE, Lai CQ, et al. Gain-of-function lipoprotein lipase variant rs13702 modulates lipid traits through disruption of a microRNA-410 seed site. Am J Hum Genet. 2013;92:5–14.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472:57–63.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Bennett BJ, de Aguiar Vallim TQ, Wang Z, Shih DM, Meng Y, Gregory J, et al. Trimethylamine-N-oxide, a metabolite associated with atherosclerosis, exhibits complex genetic and dietary regulation. Cell Metab. 2013;17:49–60.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19:576–85.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0–The Human Metabolome Database in 2013. Nucleic Acids Res. 2013;41:D801–7.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, et al. The human urine metabolome. PLoS One. 2013;8:e73076.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Trupp M, Zhu H, Wikoff WR, Baillie RA, Zeng ZB, Karp PD, et al. Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS One. 2012;7:e38386. Metabolomics is an expanding wing of omics research. This study showcases the potential benefits of integrating small molecule measures in a study of a commonly prescribed hypolipidemic therapeutic.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Magnusson M, Lewis GD, Ericson U, Orho-Melander M, Hedblad B, Engström G, et al. A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J. 2013;34:1982–9.

    Article  CAS  PubMed  Google Scholar 

  25. Holzer RG, Park EJ, Li N, Tran H, Chen M, Choi C, et al. Saturated fatty acids induce c-Src clustering within membrane subdomains, leading to JNK activation. Cell. 2011;147:173–84.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Liu HY, Collins QF, Moukdar F, Zhuo D, Han J, Hong T, et al. Suppression of hepatic glucose production by human neutrophil alpha-defensins through a signaling pathway distinct from insulin. J Biol Chem. 2008;283:12056–63.

    Article  CAS  PubMed  Google Scholar 

  27. Khaliullina H, Bilgin M, Sampaio JL, Shevchenko A, Eaton S. Lipoproteins carry endocannabinoids that inhibit the Hedgehog pathway. bioRxiv. 2013. doi:10.1101/000570.

    Google Scholar 

  28. Teperino R, Amann S, Bayer M, McGee SL, Loipetzberger A, Connor T, et al. Hedgehog partial agonism drives Warburg-like metabolism in muscle and brown fat. Cell. 2012;151:414–26.

    Article  CAS  PubMed  Google Scholar 

  29. Thomas G, Betters JL, Lord CC, Brown AL, Marshall S, Ferguson D, et al. The serine hydrolase ABHD6 is a critical regulator of the metabolic syndrome. Cell Rep. 2013;5:508–20.

    Article  CAS  PubMed  Google Scholar 

  30. McCurley A, Pires PW, Bender SB, Aronovitz M, Zhao MJ, Metzger D, et al. Direct regulation of blood pressure by smooth muscle cell mineralocorticoid receptors. Nat Med. 2012;18:1429–33.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Newfell BG, Iyer LK, Mohammad NN, McGraw AP, Ehsan A, Rosano G, et al. Aldosterone regulates vascular gene transcription via oxidative stress-dependent and -independent pathways. Arterioscler Thromb Vasc Biol. 2011;31:1871–80.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Schnoes KK, Jaffe IZ, Iyer L, Dabreo A, Aronovitz M, Newfell B, et al. Rapid recruitment of temporally distinct vascular gene sets by estrogen. Mol Endocrinol. 2008;22:2544–56.

    Article  CAS  PubMed  Google Scholar 

  33. Howard BV, Rossouw JE. Estrogens and cardiovascular disease risk revisited: the Women's Health Initiative. Curr Opin Lipidol. 2013;24:493–9.

    Article  CAS  PubMed  Google Scholar 

  34. Quehenberger O, Dennis EA. The human plasma lipidome. N Engl J Med. 2011;365:1812–23.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Kind T, Liu KH, Lee do Y, DeFelice B, Meissen JK, Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods. 2013;10:755–8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, et al. Plasma lipid profiling in a large population-based cohort. J Lipid Res. 2013;54:2898–908. The identified relationships between different lipids and disease, sex and age do much to demystify the complexity of this class of molecule.

    Article  CAS  PubMed  Google Scholar 

  37. Tracy RP, Lemaitre RN, Psaty BM, Ives DG, Evans RW, Cushman M, et al. Relationship of C-reactive protein to risk of cardiovascular disease in the elderly. Results from the Cardiovascular Health Study and the Rural Health Promotion Project. Arterioscler Thromb Vasc Biol. 1997;17:1121–7.

    Article  CAS  PubMed  Google Scholar 

  38. Obin M, Parnell LD, Ordovas JM. The emerging relevance of the gut microbiome in cardiometabolic health. Curr Cardiovasc Risk Rep. 2013;7:425–6.

    Article  Google Scholar 

  39. Jacobs AS, Ayinde HO, Lee DL. Inflammatory Biomarkers and Cardiovascular Complications in Sickle Cell Disease: A Review. Curr Cardiovasc Risk Rep. 2013;7:368–77.

    Article  Google Scholar 

  40. Ordovás JM, Robertson R, Cléirigh EN. Gene-gene and gene-environment interactions defining lipid-related traits. Curr Opin Lipidol. 2011;22:129–36.

    Article  PubMed  Google Scholar 

  41. Ordovas-Montanes JM, Ordovas JM. Cholesterol, Inflammasomes, and Atherogenesis. Curr Cardiovasc Risk Rep. 2012;6:45–52.

    Article  PubMed Central  PubMed  Google Scholar 

  42. Weber C, Noels H. Atherosclerosis: current pathogenesis and therapeutic options. Nat Med. 2011;17:1410–22.

    Article  CAS  PubMed  Google Scholar 

  43. Metabolic Syndrome ePoster. Nat Med 2011;17.

  44. McManus DD, Beaulieu LM, Mick E, Tanriverdi K, Larson MG, Keaney Jr JF, et al. Relationship among circulating inflammatory proteins, platelet gene expression, and cardiovascular risk. Arterioscler Thromb Vasc Biol. 2013;33:2666–73.

    Article  CAS  PubMed  Google Scholar 

  45. Okser S, Pahikkala T, Aittokallio T. Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives. BioData Min. 2013;6:5.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Ma L, Brautbar A, Boerwinkle E, Sing CF, Clark AG, Keinan A. Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations. PLoS Genet. 2012;8:e1002714.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Turner SD, Berg RL, Linneman JG, Peissig PL, Crawford DC, Denny JC, et al. Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS One. 2011;6:e19586.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376:1393–400.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Morrison AC, Bare LA, Chambless LE, Ellis SG, Malloy M, Kane JP, et al. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2007;166:28–35.

    Article  PubMed  Google Scholar 

  50. Brautbar A, Pompeii LA, Dehghan A, Ngwa JS, Nambi V, Virani SS, et al. A genetic risk score based on direct associations with coronary heart disease improves coronary heart disease risk prediction in the Atherosclerosis Risk in communities (ARIC), but not in the Rotterdam and Framingham Offspring, Studies. Atherosclerosis. 2012;223:421–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Qi L, Cornelis MC, Zhang C, van Dam RM, Hu FB. Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men. Am J Clin Nutr. 2009;89:1453–8.

    Article  CAS  PubMed  Google Scholar 

  52. Qi Q, Chu AY, Kang JH, Jensen MK, Curhan GC, Pasquale LR, et al. Sugar-sweetened beverages and genetic risk of obesity. N Engl J Med. 2012;367:1387–96.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Rukh G, Sonestedt E, Melander O, Hedblad B, Wirfält E, Ericson U, et al. Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmö Diet and Cancer Study. Genes Nutr. 2013;8:535–47.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Li S, Zhao JH, Luan J, Ekelund U, Luben RN, Khaw KT, et al. Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med. 2010;7:e1000332.

    Article  PubMed Central  PubMed  Google Scholar 

  55. Ahmad S, Rukh G, Varga TV, Ali A, Kurbasic A, Shungin D, et al. Gene × physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry. PLoS Genet. 2013;9:e1003607.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Qi Q, Li Y, Chomistek AK, Kang JH, Curhan GC, Pasquale LR, et al. Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation. 2012;126:1821–7.

    Article  PubMed Central  PubMed  Google Scholar 

  57. Lee YC, Lai CQ, Ordovas JM, Parnell LD. A Database of Gene-Environment Interactions Pertaining to Blood Lipid Traits, Cardiovascular Disease and Type 2 Diabetes. J Data Min Genomics Proteomics. 2011;2:106.

    Google Scholar 

  58. de la Fuente A. From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. Trends Genet. 2010;26:326–33.

    Article  PubMed  Google Scholar 

  59. Lusis AJ, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet. 2008;9:819–30.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  60. Civelek M, Lusis AJ. Systems genetics approaches to understand complex traits. Nat Rev Genet. 2013;15:34–48. This important review explores the means by which complex traits can be mapped and dissected for the quantitative understanding of biology and disease.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work is supported in part by National Institutes of Health (5R21HL114238-02) to LDP; National Institutes of Health (1R21AR055228-01A1, HL54776), the National Institute of Diabetes and Digestive and Kidney Diseases (DK075030) and the US Department of Agriculture Research Service (53-K06-5-10 and 58–1950-9-001) to JMO. This research has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. PIOF-GA-2010-272581 to PC-A. Tufts Center for Neuroscience Research P30 NS047243 provided support to LKI. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The USDA is an equal opportunity provider and employer.

Compliance with Ethics Guidelines

ᅟ

Conflict of Interest

Laurence D Parnell declares that he has no conflict of interest.

Patricia Casas-Agustench declares that she has no conflict of interest.

Lakshmanan K Iyer declares that he has no conflict of interest.

Jose M Ordovas declares that he has no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by the author.

Author information

Authors and Affiliations

  1. Nutritional Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA, 02111, USA

    Laurence D. Parnell & Jose M. Ordovas

  2. Instituto Madrileño de Estudios Avanzados (IMDEA) Alimentación, CEI UAM + CSIC, C/ Faraday, 7, 1ª planta D1.11, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Km.15, Madrid, 28049, Spain

    Patricia Casas-Agustench & Jose M. Ordovas

  3. Tufts Center for Neuroscience Research, Tufts University School of Medicine, 136 Harrison Ave, Boston, MA, 02111, USA

    Lakshmanan K. Iyer

  4. Molecular Cardiology Research Institute, Tufts Medical Center, 15 Kneeland Street, Boston, MA, 02111, USA

    Lakshmanan K. Iyer

Authors
  1. Laurence D. Parnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patricia Casas-Agustench
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lakshmanan K. Iyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jose M. Ordovas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laurence D. Parnell.

Additional information

This article is part of the Topical Collection on Lipids

Rights and permissions

Reprints and permissions

About this article

Cite this article

Parnell, L.D., Casas-Agustench, P., Iyer, L.K. et al. How Gene Networks Can Uncover Novel CVD Players. Curr Cardiovasc Risk Rep 8, 372 (2014). https://doi.org/10.1007/s12170-013-0372-3

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s12170-013-0372-3

Keywords

Search

Navigation