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

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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.

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References

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

  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.

    Article  PubMed  Google Scholar 

  2. Lusis AJ, Weiss JN: Cardiovascular Networks: Systems-Based Approaches to Cardiovascular Disease. Circulation 2010, 121: 157-170.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  4. Libby P: Inflammation in atherosclerosis. Nature 2002, 420: 868-874.

    CAS  Google Scholar 

  5. Lusis A, Attie A, Reue K: Metabolic syndrome: from epidemiology to systems biology. Nature Reviews Genetics 2008, 9: 819-830.

    Article  CAS  PubMed  Google Scholar 

  6. Hansson G, Libby P: The immune response in atherosclerosis: a double-edged sword. Nat Rev Immunol 2006, 6: 508-519.

    Article  CAS  PubMed  Google Scholar 

  7. Maxfield F, Tabas I: Role of cholesterol and lipid organization in disease. Nature 2005, 438: 612-621.

    Article  CAS  PubMed  Google Scholar 

  8. Li A, Glass C: The macrophage foam cell as a target for therapeutic intervention. Nat Med. 2002, 8: 1235-1242.

    Article  CAS  PubMed  Google Scholar 

  9. Shai I, Spence J, Schwarzfuchs D et al.: Dietary intervention to reverse carotid atherosclerosis. Circulation 2010, 121: 1200-1208.

    Article  PubMed  Google Scholar 

  10. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994, 344: 1383-9.

  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.

    Article  CAS  Google Scholar 

  12. Praticò D, Dogné J: Vascular biology of eicosanoids and atherogenesis. Expert Rev Cardiovasc Ther. 2009, 7: 1079-1089.

    Article  PubMed  Google Scholar 

  13. Poeckel D, Funk C: The 5-lipoxygenase/leukotriene pathway in preclinical models of cardiovascular disease. Cardiovasc Res. 2010, 86: 243-253.

    Article  CAS  PubMed  Google Scholar 

  14. Chorro F, Such-Belenguer L, López-Merino V: Animal models of cardiovascular disease. Rev Esp Cardiol. 2009, 62: 69-84.

    Article  PubMed  Google Scholar 

  15. Joyce AR, Palsson BO: The model organism as a system: integrating ‘omics’ data sets. Nat Rev Mol Cell Biol 2006, 7: 198-210.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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. Higdon R, van Belle G, Kolker E: A note on the false discovery rate and inconsistent comparisons between experiments. Bioinformatics 2008, 24: 1225-1228.

    Article  CAS  PubMed  Google Scholar 

  20. Ioannidis J: Why most published research findings are false. PLoS Medicine 2005, 2: e124.

    Article  PubMed  Google Scholar 

  21. Trygg J, Wold S: Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics 2002, 16: 119-128.

    Article  CAS  Google Scholar 

  22. Stacklies W, Redestig H, Scholz M et al.: pcaMethods—a bioconductor package providing PCA methods for incomplete data. Bioinformatics 2007, 23: 1164-1167.

    Article  CAS  PubMed  Google Scholar 

  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.

  24. Madsen R, Lundstedt T, Trygg J: Chemometrics in metabolomics--a review in human disease diagnosis. Anal Chim Acta 2010, 659: 23-33.

    Article  CAS  PubMed  Google Scholar 

  25. Barabasi AL, Oltvai ZN: Network biology: understanding the cell's functional organization. Nat Rev Genet 2004, 5: 101-13.

    Article  CAS  PubMed  Google Scholar 

  26. Schadt EE, Friend SH, Shaywitz DA: A network view of disease and compound screening. Nat Rev Drug Discov. 2009, 8: 286-295.

    Article  CAS  PubMed  Google Scholar 

  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.

  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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  33. Clish C, Davidov E, Oresic M et al.: Integrative biological analysis of the APOE*3-leiden transgenic mouse. OMICS 2004, 8: 3-13.

    Article  CAS  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  47. Samuelsson B, Dahlen SE, Lindgren JA et al.: Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science 1987, 237: 1171-6.

    Article  CAS  PubMed  Google Scholar 

  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.

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

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Acknowledgments

This research was supported by VINNOVA and JSPS under the Sweden-Japan Research Cooperative Program, the Center for Allergy Research, the Åke Wibergs Stiftelse, the Jeanssons Stiftelse, and the Swedish Research Council. GMK was supported by a JSPS Postdoctoral Fellowship, and CEW was supported by a research fellowship from the Center for Allergy Research.

Disclosure

Gemma M. Kirwan reports no potential conflict of interest relevant to this article. Diego Diez reports no potential conflict of interest relevant to this article. Jesper Z. Haeggström reports no potential conflict of interest relevant to this article. Susumu Goto reports no potential conflict of interest relevant to this article. Craig E. Wheelock reports no potential conflict of interest relevant to this article.

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Authors and Affiliations

  1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan

    Gemma M. Kirwan, Diego Diez, Susumu Goto & Craig E. Wheelock

  2. Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, SE-171 77, Stockholm, Sweden

    Jesper Z. Haeggström & Craig E. Wheelock

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  1. Gemma M. Kirwan
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  2. Diego Diez
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Correspondence to Craig E. Wheelock.

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Kirwan, G.M., Diez, D., Haeggström, J.Z. et al. Systems Biology Approaches for Investigating the Relationship Between Lipids and Cardiovascular Disease. Curr Cardio Risk Rep 5, 52–61 (2011). https://doi.org/10.1007/s12170-010-0144-2

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