Power of metabolomics in biomarker discovery and mining mechanisms of obesity
A. Zhang
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Harbin, China
Search for more papers by this authorH. Sun
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Harbin, China
Search for more papers by this authorCorresponding Author
X. Wang
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Harbin, China
Address for correspondence: Professor Xijun Wang, National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, Key Pharmacometabolomics Platform of Chinese Medicines, Heping Road 24, Harbin 150040, China.
E-mail: [email protected], [email protected], [email protected]
Search for more papers by this authorA. Zhang
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Harbin, China
Search for more papers by this authorH. Sun
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Harbin, China
Search for more papers by this authorCorresponding Author
X. Wang
National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, and Key Pharmacometabolomics Platform of Chinese Medicines, Harbin, China
Address for correspondence: Professor Xijun Wang, National TCM Key Lab of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, Key Pharmacometabolomics Platform of Chinese Medicines, Heping Road 24, Harbin 150040, China.
E-mail: [email protected], [email protected], [email protected]
Search for more papers by this authorSummary
Obesity, the prevalence of which is increasing rapidly worldwide, is recognized as a risk factor for diabetes, cardiovascular disease, liver disease and renal disease. Unfortunately, the mechanisms underlying it have not been well characterized. Fortunately, metabolomics – the systematic study of metabolites, which are small molecules generated by the process of metabolism – has been important in elucidating the pathways underlying obesity. Small-molecule metabolites have an important role in biological system and represent attractive candidates to understand obesity phenotypes. Metabolomic analysis is a valid and powerful tool with which to further define the mechanisms. Recent attention has focused on identifying biomarkers that would propose a better non-invasive way to detect or visualize obesity and prevent its events. The discovery of the biomarkers has become a key breakthrough towards a better molecular understanding of obesity. Thus, this review covers how recent metabolomic studies have advanced biomarker discovery and the elucidation of mechanisms underlying obesity and its comorbidities. The importance of identifying metabolic markers and pathways of disease-associated intermediate phenotypes is also emphasized. These biomarkers would be applicable as diagnostic tools in a personalized healthcare setting and may also open door to biomarker discovery, disease diagnosis and novel therapeutic avenues.
References
- 1 Choi JH, Banks AS, Estall JL et al. Anti-diabetic drugs inhibit obesity-linked phosphorylation of PPARgamma by Cdk5. Nature 2010; 466: 451–456.
- 2 Henao-Mejia J, Elinav E, Jin C et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 2012; 482: 179–185.
- 3 World Health Organization (WHO). Global Strategy on Diet, Physical Activity and Health. 2011. [WWW document]. URL http://www.who.int/dietphysicalactivity/childhood/en/ (accessed 18 December 2012).
- 4 Houtkooper RH, Auwerx J. Obesity: new life for antidiabetic drugs. Nature 2010; 466: 443–444.
- 5 Friedman JM. Obesity: causes and control of excess body fat. Nature 2009; 459: 340–342.
- 6 Wang X, Zhang A, Han Y et al. Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease. Mol Cell Proteomics 2012; 11: 370–380.
- 7 Zhang A, Sun H, Wang X. Saliva metabolomics opens door to biomarker discovery, disease diagnosis, and treatment. Appl Biochem Biotechnol 2012; 168: 1718–1727.
- 8 Wang X, Yang B, Sun H, Zhang A. Pattern recognition approaches and computational systems tools for ultra performance liquid chromatography-mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets. Anal Chem 2012; 84: 428–439.
- 9 Wang X, Wang H, Zhang A et al. Metabolomics study on the toxicity of aconite root and its processed products using ultraperformance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition approach and ingenuity pathways analysis. J Proteome Res 2012; 11: 1284–1301.
- 10 Zhang A, Sun H, Han Y et al. Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach. Analyst 2012; 137: 4200–4208.
- 11 Yuan M, Breitkopf SB, Yang X, Asara JM. A positive/negative ion-switching, targeted mass spectrometry- basedmetabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat Protoc 2012; 7: 872–881.
- 12 Czupryn A, Zhou YD, Chen X et al. Transplanted hypothalamic neurons restore leptin signaling and ameliorate obesity in db/db mice. Science 2011; 334: 1133–1137.
- 13 Park EJ, Lee JH, Yu GY et al. Dietary and genetic obesity promote liver inflammation and tumorigenesis by enhancing IL-6 and TNF expression. Cell 2010; 140: 197–208.
- 14 Leslie M. Circadian rhythms. Sleep study suggests triggers for diabetes and obesity. Science 2012; 336: 143.
- 15 Yuliana ND, Jahangir M, Korthout H, Choi YH, Kim HK, Verpoorte R. Comprehensive review on herbal medicine for energy intake suppression. Obes Rev 2011; 12: 499–514.
- 16 Pardo M, Roca-Rivada A, Seoane LM, Casanueva FF. Obesidomics: contribution of adipose tissue secretome analysis to obesity research. Endocrine 2012; 41: 374–383.
- 17 Zhang A, Sun H, Wu X, Wang X. Urine metabolomics. Clin Chim Acta 2012; 414C: 65–69.
- 18 Zhang A, Sun H, Wang P, Han Y, Wang X. Modern analytical techniques in metabolomics analysis. Analyst 2012; 137: 293–300.
- 19 Zhang A, Sun H, Wang X. Serum metabolomics as a novel diagnostic approach for disease: a systematic review. Anal Bioanal Chem 2012; 404: 1239–1245.
- 20 Wang X, Yang B, Zhang A, Sun H, Yan G. Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach. J Proteomics 2012; 75: 1411–1427.
- 21 Wang X, Zhang A, Sun H. Future perspectives of Chinese medical formulae: chinmedomics as an effector. OMICS 2012; 16: 414–421.
- 22 Dong H, Zhang A, Sun H et al. Ingenuity pathways analysis of urine metabolomics phenotypes toxicity of Chuanwu in Wistar rats by UPLC-Q-TOF-HDMS coupled with pattern recognition methods. Mol Biosyst 2012; 8: 1206–1221.
- 23 Zhang A, Sun H, Wang P, Han Y, Wang X. Recent and potential developments of biofluid analyses in metabolomics. J Proteomics 2012; 75: 1079–1088.
- 24 Fukushima A, Kusano M, Nakamichi N et al. Impact of clock-associated Arabidopsis pseudo-response regulators in metabolic coordination. Proc Natl Acad Sci U S A 2009; 106: 7251–7256.
- 25 Catchpole GS, Beckmann M, Enot DP et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci U S A 2009; 102: 14458–14462.
- 26 Gjesing AP, Pedersen O. ‘Omics’-driven discoveries in prevention and treatment of type 2 diabetes. Eur J Clin Invest 2012; 42: 579–588.
- 27 Ugarte M, Brown M, Hollywood KA, Cooper GJ, Bishop PN, Dunn WB. Metabolomic analysis of rat serum in streptozotocin-induced diabetes and after treatment with oral triethylenetetramine (TETA). Genome Med 2012; 4: 35.
- 28 Mihalik SJ, Michaliszyn SF, de las Heras J et al. Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care 2012; 35: 605–611.
- 29 McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care 2011; 34: 2624–2630.
- 30 Mintz-Oron S, Meir S, Malitsky S, Ruppin E, Aharoni A, Shlomi T. Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc Natl Acad Sci U S A 2012; 109: 339–344.
- 31 Liu JY, Li N, Yang J et al. Metabolic profiling of murine plasma reveals an unexpected biomarker in rofecoxib-mediated cardiovascular events. Proc Natl Acad Sci U S A 2010; 107: 17017–17022.
- 32 Park JM, Kim TY, Lee SY. Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses. Proc Natl Acad Sci U S A 2010; 107: 14931–14936.
- 33 Wei H, Pasman W, Rubingh C et al. Urine metabolomics combined with the personalized diagnosis guided by Chinese medicine reveals subtypes of pre-diabetes. Mol Biosyst 2012; 8: 1482–1491.
- 34 Lanza IR, Zhang S, Ward LE, Karakelides H, Raftery D, Nair KS. Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes. PLoS ONE 2010; 5: e10538.
- 35 Duggan GE, Hittel DS, Hughey CC, Weljie A, Vogel HJ, Shearer J. Differentiating short- and long-term effects of diet in the obese mouse using (1) H-nuclear magnetic resonance metabolomics. Diabetes Obes Metab 2011; 13: 859–862.
- 36 Tsutsui H, Maeda T, Min JZ et al. Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry. Clin Chim Acta 2011; 412: 861–872.
- 37 Morris C, O'Grada C, Ryan M et al. The relationship between BMI and metabolomic profiles: a focus on amino acids. Proc Nutr Soc 2012; 71: 634–638.
- 38 Oberbach A, Blüher M, Wirth H et al. Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes. J Proteome Res 2011; 10: 4769–4788.
- 39 Kim HJ, Kim JH, Noh S et al. Metabolomic analysis of livers and serum from high-fat diet induced obese mice. J Proteome Res 2011; 10: 722–731.
- 40 Kim JY, Park JY, Kim OY et al. Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res 2010; 9: 4368–4375.
- 41 He Q, Ren P, Kong X et al. Comparison of serum metabolite compositions between obese and lean growing pigs using an NMR-based metabonomic approach. J Nutr Biochem 2012; 23: 133–139.
- 42 Wahl S, Yu Z, Kleber M et al. Childhood obesity is associated with changes in the serum metabolite profile. Obes Facts 2012; 5: 660–670.
- 43 Jung JY, Kim IY, Kim YN et al. 1H NMR-based metabolite profiling of diet-induced obesity in a mouse mode. BMB Rep 2012; 45: 419–424.
- 44 Kim SH, Yang SO, Kim HS, Kim Y, Park T, Choi HK. 1H-nuclear magnetic resonance spectroscopy-based metabolic assessment in a rat model of obesity induced by a high-fat diet. Anal Bioanal Chem 2009; 395: 1117–1124.
- 45 Calvani R, Miccheli A, Capuani G et al. Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. Int J Obes (Lond) 2010; 34: 1095–1098.
- 46 Cohen DA, Babey SH. Candy at the cash register–a risk factor for obesity and chronic disease. N Engl J Med 2012; 367: 1381–1383.