Current Cardiovascular Risk Reports

, Volume 5, Issue 1, pp 52–61 | Cite as

Systems Biology Approaches for Investigating the Relationship Between Lipids and Cardiovascular Disease

  • Gemma M. Kirwan
  • Diego Diez
  • Jesper Z. Haeggström
  • Susumu Goto
  • Craig E. Wheelock
Article
  • 88 Downloads

Abstract

Systems biology is an emerging field that offers promise in exploring the inter-connectivity and causality between biological pathways. This review focuses on systems biology approaches in cardiovascular disease and on the role of inflammatory lipid mediators in atherosclerosis. The basic concepts of systems biology are presented, with a focus on the integration of “omics” data from multiple technology platforms, applications of multivariate analysis, and network theory. A particular emphasis is placed on the role of multivariate statistics in analyzing data from omics platforms. An overview of selected systems biology-specific bioinformatics tools is provided, with a focus on applications that explore the role of lipids in cardiovascular systems. Systems biology offers the promise of increased insight into the biological pathways involved in cardiovascular disease and in unraveling the mechanistic relationships arising from lipid-artery interactions that lead to immune and inflammatory responses and the onset of disease.

Keywords

Systems biology Lipidomics Cardiovascular disease Atherosclerosis Lipid Eicosanoid Inflammation Multivariate statistics PCA OPLS 

References

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

  1. 1.
    van der Greef J, Martin S, Juhasz P et al.: The art and practice of systems biology in medicine: mapping patterns of relationships. J Proteome Res 2007, 6: 1540-59.CrossRefPubMedGoogle Scholar
  2. 2.
    Lusis AJ, Weiss JN: Cardiovascular Networks: Systems-Based Approaches to Cardiovascular Disease. Circulation 2010, 121: 157-170.CrossRefPubMedGoogle Scholar
  3. 3.
    Rosamond W, Flegal K, Furie K et al.: Heart Disease and Stroke Statistics 2008 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2008, 117: e25-e146.CrossRefPubMedGoogle Scholar
  4. 4.
    Libby P: Inflammation in atherosclerosis. Nature 2002, 420: 868-874.Google Scholar
  5. 5.
    Lusis A, Attie A, Reue K: Metabolic syndrome: from epidemiology to systems biology. Nature Reviews Genetics 2008, 9: 819-830.CrossRefPubMedGoogle Scholar
  6. 6.
    Hansson G, Libby P: The immune response in atherosclerosis: a double-edged sword. Nat Rev Immunol 2006, 6: 508-519.CrossRefPubMedGoogle Scholar
  7. 7.
    Maxfield F, Tabas I: Role of cholesterol and lipid organization in disease. Nature 2005, 438: 612-621.CrossRefPubMedGoogle Scholar
  8. 8.
    Li A, Glass C: The macrophage foam cell as a target for therapeutic intervention. Nat Med. 2002, 8: 1235-1242.CrossRefPubMedGoogle Scholar
  9. 9.
    Shai I, Spence J, Schwarzfuchs D et al.: Dietary intervention to reverse carotid atherosclerosis. Circulation 2010, 121: 1200-1208.CrossRefPubMedGoogle Scholar
  10. 10.
    Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994, 344: 1383-9.Google Scholar
  11. 11.
    Qiu H, Gabrielsen A, Agardh H et al.: Expression of 5-lipoxygenase and leukotriene A4 hydrolase in human atherosclerotic lesions correlates with symptoms of plaque instability Proc. Natl. Acad. Sci. U. S. A. 2006, 103: 8161-8166.CrossRefGoogle Scholar
  12. 12.
    Praticò D, Dogné J: Vascular biology of eicosanoids and atherogenesis. Expert Rev Cardiovasc Ther. 2009, 7: 1079-1089.CrossRefPubMedGoogle Scholar
  13. 13.
    Poeckel D, Funk C: The 5-lipoxygenase/leukotriene pathway in preclinical models of cardiovascular disease. Cardiovasc Res. 2010, 86: 243-253.CrossRefPubMedGoogle Scholar
  14. 14.
    Chorro F, Such-Belenguer L, López-Merino V: Animal models of cardiovascular disease. Rev Esp Cardiol. 2009, 62: 69-84.CrossRefPubMedGoogle Scholar
  15. 15.
    Joyce AR, Palsson BO: The model organism as a system: integrating ‘omics’ data sets. Nat Rev Mol Cell Biol 2006, 7: 198-210.CrossRefPubMedGoogle Scholar
  16. 16.
    van den Berg R, Hoefsloot H, Westerhuis J et al.: Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7: 142.CrossRefPubMedGoogle Scholar
  17. 17.
    Bonferroni C, Teoria statistica delle classi e calcolo delle probabilit. 1936, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze. p. 3-62.Google Scholar
  18. 18.
    Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 1995, 57: 289-300.Google Scholar
  19. 19.
    Higdon R, van Belle G, Kolker E: A note on the false discovery rate and inconsistent comparisons between experiments. Bioinformatics 2008, 24: 1225-1228.CrossRefPubMedGoogle Scholar
  20. 20.
    Ioannidis J: Why most published research findings are false. PLoS Medicine 2005, 2: e124.CrossRefPubMedGoogle Scholar
  21. 21.
    Trygg J, Wold S: Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics 2002, 16: 119-128.CrossRefGoogle Scholar
  22. 22.
    Stacklies W, Redestig H, Scholz M et al.: pcaMethods—a bioconductor package providing PCA methods for incomplete data. Bioinformatics 2007, 23: 1164-1167.CrossRefPubMedGoogle Scholar
  23. 23.
    • Teul J, Rupérez F, Garcia A et al.: Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints. J Proteome Res. 2009, 8: 5580-5589. The authors demonstrate good use of multivariate analysis to explore correlations between plasma metabolites in healthy subjects and patients with carotid atherosclerosis. Google Scholar
  24. 24.
    Madsen R, Lundstedt T, Trygg J: Chemometrics in metabolomics--a review in human disease diagnosis. Anal Chim Acta 2010, 659: 23-33.CrossRefPubMedGoogle Scholar
  25. 25.
    Barabasi AL, Oltvai ZN: Network biology: understanding the cell's functional organization. Nat Rev Genet 2004, 5: 101-13.CrossRefPubMedGoogle Scholar
  26. 26.
    Schadt EE, Friend SH, Shaywitz DA: A network view of disease and compound screening. Nat Rev Drug Discov. 2009, 8: 286-295.CrossRefPubMedGoogle Scholar
  27. 27.
    • Diez D, Wheelock A, Goto S et al.: The use of network analyses for elucidating mechanisms in cardiovascular disease. Mol Biosyst. 2010, 6: 289-304. This reference provides a solid overview of systems biology and network analysis (in the context of cardiovascular disease), including an example network constructed using microarray data from carotid endarterectomies from the Karolinska University Hospital. Google Scholar
  28. 28.
    Kelder T, Conklin B, Evelo C, Pico A: Finding the right questions: exploratory pathway analysis to enhance biological discovery in large datasets. PLoS One 2010, 8: e10000472.Google Scholar
  29. 29.
    Brindle J, Antti H, Holmes E et al.: Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med. 2002, 8: 1439-1444.CrossRefPubMedGoogle Scholar
  30. 30.
    Kirschenlohr H, Griffin J, Clarke S et al.: Proton NMR analysis of plasma is a weak predictor of coronary artery disease. Nat Med. 2006, 12: 705-710.CrossRefPubMedGoogle Scholar
  31. 31.
    Orešič M, Clish C, Davidov E et al.: Phenotype characterisation using integrated gene transcript, protein and metabolite profiling. Appl Bioinformatics 2004, 3: 205-217.CrossRefPubMedGoogle Scholar
  32. 32.
    Davidov E, Clish C, Oresic M et al.: Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model. OMICS 2004, 8: 267-288.CrossRefPubMedGoogle Scholar
  33. 33.
    Clish C, Davidov E, Oresic M et al.: Integrative biological analysis of the APOE*3-leiden transgenic mouse. OMICS 2004, 8: 3-13.CrossRefPubMedGoogle Scholar
  34. 34.
    de Roos B, Rucklidge G, Reid M et al.: Divergent mechanisms of cis9, trans11- and trans10, cis12-conjugated linoleic acid affecting insulin resistance and inflammation in apolipoprotein E knockout mice: a proteomics approach. FASEB J 2005, 19: 1746-1748.PubMedGoogle Scholar
  35. 35.
    Cheng K, Benson G, Grimsditch D et al.: A metabolomic study of the LDL receptor null mouse fed a high-fat diet reveals profound perturbations in choline metabolism that are shared with ApoE null mice. Physiol Genomics 2010, 41: 224-231.CrossRefGoogle Scholar
  36. 36.
    Pietiläinen K, Sysi-Aho M, Rissanen A et al.: Acquired Obesity Is Associated with Changes in the Serum Lipidomic Profile Independent of Genetic Effects—A Monozygotic Twin Study. PLoS One 2007, 2: e218.CrossRefPubMedGoogle Scholar
  37. 37.
    King JY FR, Tabibiazar R, Spin JM, Chen MM, Kuchinsky A, Vailaya A, Kincaid R, Tsalenko A, Deng DX, Connolly A, Zhang P, Yang E, Watt C, Yakhini Z, Ben-Dor A, Adler A, Bruhn L, Tsao P, Quertermous T, Ashley EA.: Pathway analysis of coronary atherosclerosis. Physiol Genomics 2005, 23: 103-118.Google Scholar
  38. 38.
    Tseng H, Juan H, Huang H et al.: Lipopolysaccharide-stimulated responses in rat aortic endothelial cells by a systems biology approach. Proteomics 2006, 6: 5915-5928.CrossRefPubMedGoogle Scholar
  39. 39.
    Laaksonen R, Katajamaa M, Päivä H et al.: A Systems Biology Strategy Reveals Biological Pathways and Plasma Biomarker Candidates for Potentially Toxic Statin-Induced Changes in Muscle. PLoS One 2006, 1: e97.CrossRefPubMedGoogle Scholar
  40. 40.
    •• Skogsberg J, Lundström J, Kovacs A et al.: Transcriptional Profiling Uncovers a Network of Cholesterol-Responsive Atherosclerosis Target Genes. PLoS Genetics 2008, 4: e1000036. The authors examined the progression of atherosclerosis lesions in mice and discovered a regulatory gene network centered around poliovirus receptor-related 2 (PVRL2) and hydroxysteroid dehydrogenase-like 2 (HSDL2). There is little literature regarding these genes and nodes, and it presents an exciting starting point for future studies on atherosclerotic lesion development. Google Scholar
  41. 41.
    Kleemann R, Verschuren L, van Erk M et al.: Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis. Genome Biol. 2007, 8: R200.CrossRefPubMedGoogle Scholar
  42. 42.
    Wheelock C, Wheelock A, Kawashima S et al.: Systems biology approaches and pathway tools for investigating cardiovascular disease. Mol Biosyst. 2009, 5: 588-602.CrossRefPubMedGoogle Scholar
  43. 43.
    •• van Erk M, Wopereis S, Rubingh C et al.: Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study. BMC Med Genomics 2010, 3: 5. The authors examined obesity associated inflammation (cardiovascular disease related) following administration of the anti-inflammatory drug dicloflenac to overweight test subjects. This study combined results from transcriptomics, proteomics, metabolomics, oxylipin, and RNA data using multivariate analysis and Metacore v4.7. Google Scholar
  44. 44.
    •• Inouye M, Silander K, Hamalainen E et al.: An immune response network associated with blood lipid levels. PLoS Genetics 2010, 6: e1001113. The authors created a comprehensive study that implicates a previously uncharacterized tissue-specific gene network, the Lipid Leukocyte module, to be associated with blood lipid mediation and inflammation response. Google Scholar
  45. 45.
    Back M, Bu DX, Branstrom R et al.: Leukotriene B4 signaling through NF-kappaB-dependent BLT1 receptors on vascular smooth muscle cells in atherosclerosis and intimal hyperplasia. Proc Natl Acad Sci U S A 2005, 102: 17501-6.CrossRefPubMedGoogle Scholar
  46. 46.
    Yu Y, Lucitt MB, Stubbe J et al.: Prostaglandin F2alpha elevates blood pressure and promotes atherosclerosis. Proc Natl Acad Sci U S A 2009, 106: 7985-90.CrossRefPubMedGoogle Scholar
  47. 47.
    Samuelsson B, Dahlen SE, Lindgren JA et al.: Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science 1987, 237: 1171-6.CrossRefPubMedGoogle Scholar
  48. 48.
    • Buczynski M, Dumlao D, Dennis E: Thematic Review Series: Proteomics. An integrated omics analysis of eicosanoid biology. J. Lipid Res. 2009, 50: 1015-1038. The authors have prepared a detailed and thorough review pertaining to eicosanoid systems biology. It provides an excellent reference for future proteomic analysis of eicosanoids and their associated biological networks. Google Scholar
  49. 49.
    Mitchell J, Warner T: COX isoforms in the cardiovascular system: understanding the activities of non-steroidal anti-inflammatory drugs. Nature Reviews Drug Discovery 2006, 5: 75-86.CrossRefPubMedGoogle Scholar
  50. 50.
    Gertow K, Nobili E, Folkersen L et al.: Expression of 12- and 15-lipoxygenase mRNAs in human carotid atherosclerotic lesions: associations with cerebrovascular symptoms. Submitted to Atherosclerosis 2010.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Gemma M. Kirwan
    • 1
  • Diego Diez
    • 1
  • Jesper Z. Haeggström
    • 2
  • Susumu Goto
    • 1
  • Craig E. Wheelock
    • 1
    • 2
  1. 1.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityUjiJapan
  2. 2.Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry IIKarolinska InstitutetStockholmSweden

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