Metabonomics-based omics study and atherosclerosis

Journal of Clinical Bioinformatics

Volumn 1, Issue 1, 2011, Pages

  Wu, Duo jiao ^a   Zhu, Bi jun ^a   Wang, Xiang dong ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

atherosclerosis; inflammation; metabolic disturbances; metabolomics; Metabonomics

Indexed keywords

EID: 84866104335     PISSN: None     EISSN: 20439113     Source Type: Journal    
DOI: 10.1186/2043-9113-1-30     Document Type: Review

References (38)

1. Kitano H. Systems biology: a brief overview. Science 2002, 295:1662-1664. 10.1126/science.1069492, 11872829.
2. Ambrose JA, Srikanth S. Vulnerable plaques and patients: improving prediction of future coronary events. American Journal of Medicine 2010, 123:10-16. 10.1016/j.amjmed.2009.07.019, 20102983.
3. Naghavi M, Libby P, Falk E. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part II. Circulation 2003, 108:1772-1778. 10.1161/01.CIR.0000087481.55887.C9, 14557340.
4. Nicholson JK. Global systems biology, personalized medicine and molecular epidemiology. Mol Syst Biol 2006, 2:1-6.
5. Lewis GD, Asnani A, Gerszten RE. Application of metabolomics to cardiovascular biomarker and pathway discovery. Journal of the American College of Cardiology 2008, 52:117-123. 10.1016/j.jacc.2008.03.043, 3204897, 18598890.
6. Mimmi MC, Picotti P, Corazza A, Betto E. High-performance metabolic marker assessment in breast cancer tissue by mass spectrometry. Clin Chem Lab Med 2011, 49:317-324. 10.1515/CCLM.2011.060, 21143022.
7. Patterson AD, Bonzo JA, Li F, Krausz KW, Eichler GS, Aslam S. Metabolomics Reveals Attenuation of the SLC6A20 Kidney Transporter in Nonhuman Primate and Mouse Models of Type 2 Diabetes Mellitus. J Biol Chem 2011, 286:19511-19522. 10.1074/jbc.M111.221739, 21487016.
8. Cavill R, Keun HC, Holmes E, Lindon JC, Nicholson JK, Ebbels TM. Genetic algorithms for simultaneous variable and sample selection in metabonomics. Bioinformatics 2008, 25:112-118.
9. Yang J, Song SL, Castro-Perez J, Plumb RS, Xu GW. Metabonomics and its Applications. Sheng Wu Gong Cheng Xue Bao 2005, 21:1-5.
10. Nicholson JK, Lindon JC. Systems biology metabonomics. Nature 2008, 455:1054-1056. 10.1038/4551054a, 18948945.
11. Chen CL, Liu IH, Fliesler SJ, Han X, Huang SS, Huang JS. Cholesterol suppresses cellular TGF-beta responsiveness: implications in atherogenesis. Journal of Cell Science 2007, 120:3509-3521. 10.1242/jcs.006916, 2045688, 17878231.
12. Fessler MB, Rudel LL, Brown JM. Toll-like receptor signaling links dietary fatty acids to the metabolic syndrome. Current Opinion in Lipidology 2009, 20:379-385. 10.1097/MOL.0b013e32832fa5c4, 3099529, 19625959.
13. Lindon JC, Nicholson JK, Holmes E. The handbook of metabonomics and metabolomics 2006. Elsevier Science.
14. Dieterle F, Riefke B, Schlotterbeck G, Ross A, Senn H, Amberg A. NMR and MS methods for metabonomics. Methods Mol Biol 2011, 691:385-415. 10.1007/978-1-60761-849-2_24, 20972767.
15. Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 2006, 78:779-787. 10.1021/ac051437y, 16448051.
16. Katajamaa M, Miettinen J, Oresic M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006, 22:634-636. 10.1093/bioinformatics/btk039, 16403790.
17. Lommen A. MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data processing. Anal Chem 2009, 81:3079-3086. 10.1021/ac900036d, 19301908.
18. Baran R, Kochi H, Saito N, Suematsu M, Soga T, Nishioka T, Robert M, Tomita M. MathDAMP: a package for differential analysis of metabolite profiles. BMC Bioinformatics 2006, 7:530. 10.1186/1471-2105-7-530, 1764210, 17166258.
19. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA 2003, 100:9440-9445. 10.1073/pnas.1530509100, 170937, 12883005.
20. Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics. J Proteome Res 2007, 6:469-479. 10.1021/pr060594q, 17269704.
21. Frolkis A, Knox C, Lim E. SMPDB: The Small Molecule Pathway Database. Nucleic Acids Research 2010, 38:D480-487. 10.1093/nar/gkp1002, 2808928, 19948758.
22. Wishart DS, Knox C, Guo AC. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Research 2009, 37:D603-610. 10.1093/nar/gkn810, 2686599, 18953024.
23. Wishart DS. Human Metabolome Database: completing the 'human parts list'. Pharmacogenomics 2007, 8:683-686. 10.2217/14622416.8.7.683, 18240899.
24. Watson AD. Lipidomics: a global approach to lipid analysis in biological systems. Journal of Lipid Research 2006, 47:2101-2111. 10.1194/jlr.R600022-JLR200, 16902246.
25. Clish CB, Davidov E, Oresic M. Integrative biological analysis of the APOE*3-leiden transgenic mouse. OMICS 2004, 8:3-13. 10.1089/153623104773547453, 15107233.
26. Martin JC, Canlet C, Delplanque B. 1H NMR metabonomics can differentiate the early atherogenic effect of dairy products in hyperlipidemic hamsters. Atherosclerosis 2009, 206:127-133. 10.1016/j.atherosclerosis.2009.01.040, 19324361.
27. Zhang F, Jia Z, Gao P. Metabonomics study of atherosclerosis rats by ultra fast liquid chromatography coupled with ion trap-time of flight mass spectrometry. Talanta 2009, 79:836-844. 10.1016/j.talanta.2009.05.010, 19576453.
28. Mayr M, Chung YL, Mayr U. Proteomic and metabolomic analyses of atherosclerotic vessels from apolipoprotein E-deficient mice reveal alterations in inflammation, oxidative stress, and energy metabolism. Arteriosclerosis, Thrombosis, and Vascular Biology 2005, 25:2135-2142. 10.1161/01.ATV.0000183928.25844.f6, 16123314.
29. Bryson JM, Cooney GJ, Wensley VR, Phuyal JL, Caterson ID. The effects of the inhibition of fatty acid oxidation on pyruvate dehydrogenase complex activity in tissues of lean and obese mice. International journal of obesity and related metabolic disorders 1996, 20:738-744.
30. Lynch RM, Paul RJ. Compartmentation of glycolytic and glycogenolytic metabolism in vascular smooth muscle. Science 1983, 222:1344-1346. 10.1126/science.6658455, 6658455.
31. Teul J, Rupérez FJ, Garcia A. Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints. Journal of Proteome Research 2009, 8:5580-55896. 10.1021/pr900668v, 19813770.
32. Chen X, Liu L, Palacios G, Gao J, Zhang N, Li G, Lu J, Song T, Zhang Y, Lv H. Plasma metabolomics reveals biomarkers of the atherosclerosis. Sep Sci 2010, 33:2776-2783.
33. Febbraio MA, Hiscock N, Sacchetti M, Fischer CP, Pedersen BK. Interleukin-6 is a novel factor mediating glucose homeostasis during skeletal muscle contraction. Diabetes 2004, 53:1643-1648. 10.2337/diabetes.53.7.1643, 15220185.
34. Kleemann R, Verschuren L, van Erk MJ. Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis. Genome Biology 2007, 8:R200. 10.1186/gb-2007-8-9-r200, 2375038, 17892536.
35. Li N, Liu JY, Timofeyev V. Beneficial effects of soluble epoxide hydrolase inhibitors in myocardial infarction model: Insight gained using metabolomic approaches. Journal of Molecular and Cellular Cardiology 2009, 47:835-845. 10.1016/j.yjmcc.2009.08.017, 19716829.
36. Liu JY, Yang J, Inceoglu B. Inhibition of soluble epoxide hydrolase enhances the anti-inflammatory effects of aspirin and 5-lipoxygenase activation protein inhibitor in a murine model. Biochemical Pharmacology 2010, 79:880-887. 10.1016/j.bcp.2009.10.025, 19896470.
37. Buschow SI, Lasonder E, van Deutekom HW. Dominant processes during human dendritic cell maturation revealed by integration of proteome and transcriptome at the pathway level. Journal of Proteome Research 2010, 9:1727-1737. 10.1021/pr9008546, 20131907.
38. Nibbe RK, Koyutürk M, Chance MR. An integrative-omics approach to identify functional sub-networks in human colorectal cancer. PLoS Computational Biology 2010, 6:e1000639. 10.1371/journal.pcbi.1000639, 2797084, 20090827.