DOI QR코드

DOI QR Code

Nutritional Metabolomics

영양 대사체학

  • 홍영식 (한국기초과학지원연구원 자기공명연구단)
  • Received : 2014.02.03
  • Accepted : 2014.02.05
  • Published : 2014.02.28

Abstract

Metabolomics is the study of changes in the metabolic status of an organism as a consequence of drug treatment, environmental influences, nutrition, lifestyle, genetic variations, toxic exposure, disease, stress, etc, through global or comprehensive identification and quantification of every single metabolite in a biological system. Since most chronic diseases have been demonstrated to be linked to nutrition, nutritional metabolomics has great potential for improving our understanding of the relationship between disease and nutritional status, nutrient, or diet intake by exploring the metabolic effects of a specific food challenge in a more global manner, and improving individual health. In particular, metabolite profiling of biofluids, such as blood, urine, or feces, together with multivariate statistical analysis provides an effective strategy for monitoring human metabolic responses to dietary interventions and lifestyle habits. Therefore, studies of nutritional metabolomics have recently been performed to investigate nutrition-related metabolic pathways and biomarkers, along with their interactions with several diseases, based on animal-, individual-, and population-based criteria with the goal of achieving personalized health care in the future. This article introduces analytical technologies and their application to determination of nutritional phenotypes and nutrition-related diseases in nutritional metabolomics.

References

  1. Nicholson JK, Lindon JC. 2008. Systems biology: Metabonomics. Nature 455: 1054-1056. https://doi.org/10.1038/4551054a
  2. Opinion. 2010. 2020 visions. Nature 463: 26-32. https://doi.org/10.1038/463026a
  3. Oliver SG, Winson MK, Kell DB, Baganz F. 1998. Systematic functional analysis of the yeast genome. Trends Biotechnol 16: 373-378. https://doi.org/10.1016/S0167-7799(98)01214-1
  4. Nicholson JK, Lindon JC, Holmes E. 1999. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29: 1181-1189. https://doi.org/10.1080/004982599238047
  5. Saini A. 2012. Metabolomics. London's Olympic drug testing lab to become national phenome center. Science 337:513 https://doi.org/10.1126/science.337.6094.513
  6. Nicholson JK, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon JC. 2012. Metabolic phenotyping in clinical and surgical environments. Nature 491: 384-392. https://doi.org/10.1038/nature11708
  7. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM. 2009. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457: 910-914. https://doi.org/10.1038/nature07762
  8. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O'Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE. 2011. Metabolite profiles and the risk of developing diabetes. Nat Med 17: 448-453 https://doi.org/10.1038/nm.2307
  9. Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, Pettersson S. 2012. Host-gut microbiota metabolic interactions. Science 336: 1262-1267. https://doi.org/10.1126/science.1223813
  10. Kinross J, Nicholson JK. 2012. Gut microbiota: Dietary and social modulation of gut microbiota in the elderly. Nat Rev Gastroenterol Hepatol 9: 563-564. https://doi.org/10.1038/nrgastro.2012.169
  11. Holmes E, Li JV, Marchesi JR, Nicholson JK. 2012. Gut microbiota composition and activity in relation to host metabolic phenotype and disease risk. Cell Metab 16: 559-564. https://doi.org/10.1016/j.cmet.2012.10.007
  12. Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, Nicholson JK. 2007. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2: 2692-2703. https://doi.org/10.1038/nprot.2007.376
  13. Want EJ, Wilson ID, Gika H, Theodoridis G, Plumb RS, Shockcor J, Holmes E, Nicholson JK. 2010. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc 5: 1005-1018. https://doi.org/10.1038/nprot.2010.50
  14. Beckonert O, Coen M, Keun HC, Wang Y, Ebbels TM, Holmes E, Lindon JC, Nicholson JK. 2010. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc 5: 1019-1032. https://doi.org/10.1038/nprot.2010.45
  15. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis- McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R. 2100. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6: 1060-1083.
  16. Pearce JT, Athersuch TJ, Ebbels TM, Lindon JC, Nicholson JK, Keun HC. 2008. Robust algorithms for automated chemical shift calibration of 1D $^{1}H$ NMR spectra of blood serum. Anal Chem 80: 7158-7162. https://doi.org/10.1021/ac8011494
  17. Savorani F, Tomasi G, Engelsen SB. 2010. icoshift: A versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson 202: 190-202. https://doi.org/10.1016/j.jmr.2009.11.012
  18. Veselkov KA, Lindon JC, Ebbels TM, Crockford D, Volynkin VV, Holmes E, Davies DB, Nicholson JK. 2009. Recursive segment-wise peak alignment of biological $^{1}H$ NMR spectra for improved metabolic biomarker recovery. Anal Chem 81: 56-66. https://doi.org/10.1021/ac8011544
  19. Hong YS, Coen M, Rhode CM, Reily MD, Robertson DG, Holmes E, Lindon JC, Nicholson JK. 2009. Chemical shift calibration of $^{1}H$ MAS NMR liver tissue spectra exemplified using a study of glycine protection of galactosamine toxicity. Magn Reson Chem 47: S47-S53. https://doi.org/10.1002/mrc.2521
  20. Craig A, Cloarec O, Holmes E, Nicholson JK, Lindon JC. 2006. Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem 78: 2262-2267. https://doi.org/10.1021/ac0519312
  21. Dieterle F, Ross A, Schlotterbeck G, Senn H. 2006. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in $^{1}H$ NMR metabonomics. Anal Chem 78: 4281-4290. https://doi.org/10.1021/ac051632c
  22. Savorani F, Rasmussen MA, Mikkelson MS, Engelsen SB. 2013. A primer to nutritional metabolomics by NMR spectroscopy and chemometrics. Food Res Int 54: 1131-1145. https://doi.org/10.1016/j.foodres.2012.12.025
  23. McNiven EM, German JB, Slupsky CM. 2011. Analytical metabolomics: nutritional opportunities for personalized health. J Nutr Biochem 22: 995-1002. https://doi.org/10.1016/j.jnutbio.2011.05.016
  24. Brennan L. 2013. Metabolomics in nutrition research: current status and perspectives. Biochem Soc Trans 41: 670-673 https://doi.org/10.1042/BST20120350
  25. Ismail NA, Posma JM, Frost G, Holmes E, Garcia-Perez I. 2013. The role of metabonomics as a tool for augmenting nutritional information in epidemiological studies. Electrophoresis 34: 2776-2786.
  26. Montoliu I, Genick U, Ledda M, Collino S, Martin FP, le Coutre J, Rezzi S. 2013. Current status on genome-metabolome- wide associations: an opportunity in nutrition research. Genes Nutr 8: 19-27. https://doi.org/10.1007/s12263-012-0313-7
  27. Jones DP, Park Y, Ziegler TR. 2012. Nutritional metabolomics: progress in addressing complexity in diet and health. Annu Rev Nutr 32: 183-202. https://doi.org/10.1146/annurev-nutr-072610-145159
  28. Solanky KS, Bailey NJ, Beckwith-Hall BM, Davis A, Bingham S, Holmes E, Nicholson JK, Cassidy A. 2003. Application of biofluid $^{1}H$ nuclear magnetic resonancebased metabonomic techniques for the analysis of the biochemical effects of dietary isoflavones on human plasma profile. Anal Biochem 323: 197-204. https://doi.org/10.1016/j.ab.2003.08.028
  29. Solanky KS, Bailey NJ, Beckwith-Hall BM, Bingham S, Davis A, Holmes E, Nicholson JK, Cassidy A. 2005. Biofluid $^{1}H$ NMR-based metabonomic techniques in nutrition research - metabolic effects of dietary isoflavones in humans. J Nutr Biochem 16: 236-244. https://doi.org/10.1016/j.jnutbio.2004.12.005
  30. Holmes E, Loo RL, Stamler J, Bictash M, Yap IK, Chan Q, Ebbels T, De Iorio M, Brown IJ, Veselkov KA, Daviglus ML, Kesteloot H, Ueshima H, Zhao L, Nicholson JK, Elliott P. 2008. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453: 396-400. https://doi.org/10.1038/nature06882
  31. Lenz EM, Bright J, Wilson ID, Hughes A, Morrisson J, Lindberg H, Lockton A. 2004. Metabonomics, dietary influences and cultural differences: a $^{1}H$ NMR-based study of urine samples obtained from healthy British and Swedish subjects. J Pharm Biomed Anal 36: 841-849. https://doi.org/10.1016/j.jpba.2004.08.002
  32. Pere-Trepat E, Ross AB, Martin FP, Rezzi S, Kochhar S, Hasselbalch AL, Kyvik KO, Sorensen TIA. 2010. Chemometric strategies to assess metabonomic imprinting of food habits in epidemiological studies. Chemom Intell Lab Syst 104: 95-100. https://doi.org/10.1016/j.chemolab.2010.06.001
  33. Dumas ME, Maibaum EC, Teague C, Ueshima H, Zhou B, Lindon JC, Nicholson JK, Stamler J, Elliott P, Chan Q, Holmes E. 2006. Assessment of analytical reproducibility of $^{1}H$ NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem 78: 2199-2208. https://doi.org/10.1021/ac0517085
  34. Martin FP, Rezzi S, Pere-Trepat E, Kamlage B, Collino S, Leibold E, Kastler J, Rein D, Fay LB, Kochhar S. 2009. Metabolic effects of dark chocolate consumption on energy, gut microbiota, and stress-related metabolism in free-living subjects. J Proteome Res 8: 5568-5579. https://doi.org/10.1021/pr900607v
  35. Martin FP, Montoliu I, Nagy K, Moco S, Collino S, Guy P, Redeuil K, Scherer M, Rezzi S, Kochhar S. 2012. Specific dietary preferences are linked to differing gut microbial metabolic activity in response to dark chocolate intake. J Proteome Res 11: 6252-6263.
  36. Martin FP, Antille N, Rezzi S, Kochhar S. 2012. Everyday eating experiences of chocolate and non-chocolate snacks impact postprandial anxiety, energy and emotional states. Nutrients 4: 554-567. https://doi.org/10.3390/nu4060554
  37. Rezzi S, Martin FP, Shanmuganayagam D, Colman RJ, Nicholson JK, Weindruch R. 2009. Metabolic shifts due to long-term caloric restriction revealed in nonhuman primates. Exp Gerontol 44: 356-362. https://doi.org/10.1016/j.exger.2009.02.008
  38. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS Jr, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP. 2009. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9: 311-326. https://doi.org/10.1016/j.cmet.2009.02.002
  39. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabe de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T. 2010. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5: e13953. https://doi.org/10.1371/journal.pone.0013953
  40. Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz CA, Tanner CJ, Kuchibhatla M, Houmard JA, Newgard CB, Kraus WE. 2009. Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care 32: 1678-1683. https://doi.org/10.2337/dc08-2075
  41. Boulange CL, Claus SP, Chou CJ, Collino S, Montoliu I, Kochhar S, Holmes E, Rezzi S, Nicholson JK, Dumas ME, Martin FP. 2013. Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways. J Proteome Res 12: 1956-1968. https://doi.org/10.1021/pr400051s
  42. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, Palma MJ, Roberts LD, Dejam A, Souza AL, Deik AA, Magnusson M, Fox CS, O'Donnell CJ, Vasan RS, Melander O, Clish CB, Gerszten RE, Wang TJ. 2012. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125: 2222-2231. https://doi.org/10.1161/CIRCULATIONAHA.111.067827
  43. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, DuGar B, Feldstein AE, Britt EB, Fu X, Chung YM, Wu Y, Schauer P, Smith JD, Allayee H, Tang WHW, DiDonato JA, Lusis AJ, Hazen SL. 2011. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472: 57-63. https://doi.org/10.1038/nature09922
  44. Tang WHW, Wang Z, Levison BS, Koeth RA, Britt EB, Fu X, Wu YW, Hazen SL. 2013. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med 368: 1575-1584. https://doi.org/10.1056/NEJMoa1109400
  45. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, Britt EB, Fu X, Wu Y, Li L, Smith JD, DiDonato JA, Chen J, Li H, Wu GD, Lewis JD, Warrier M, Brown JM, Krauss RM, Tang WHW, Bushman FD, Lusis AJ, Hazen SL. 2013. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med 19: 576-585. https://doi.org/10.1038/nm.3145
  46. Kim J, Choi JN, Choi JH, Cha YS, Muthaiya MJ, Lee CH. 2013. Effect of fermented soybean product (Cheonggukjang) intake on metabolic parameters in mice fed a high-fat diet. Mol Nutr Food Res 57: 1886-1891.
  47. Fardet A, Llorach R, Martin JF, Besson C, Lyan B, Pujos- Guillot E, Scalbert A. 2008. A liquid chromatography quadrupole time-of-flight (LC-QTOF)-based metabolomic approach reveals new metabolic effects of catechin in rats fed high-fat diets. J Proteome Res 7: 2388-2398. https://doi.org/10.1021/pr800034h
  48. Martin FP, Sprenger N, Yap IK, Wang Y, Bibiloni R, Rochat F, Rezzi S, Cherbut C, Kochhar S, Lindon JC, Holmes E, Nicholson JK. 2009. Panorganismal gut microbiome- host metabolic crosstalk. J Proteome Res 8: 2090- 2105. https://doi.org/10.1021/pr801068x
  49. Waldram A, Holmes E, Wang Y, Rantalainen M, Wilson ID, Tuohy KM, McCartney AL, Gibson GR, Nicholson JK. 2009. Top-down systems biology modeling of host metabotype- microbiome associations in obese rodents. J Proteome Res 8: 2361-2375. https://doi.org/10.1021/pr8009885
  50. An Y, Xu W, Li H, Lei H, Zhang L, Hao F, Duan Y, Yan X, Zhao Y, Wu J, Wang Y, Tang H. 2013. High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats. J Proteome Res 12: 3755-3768. https://doi.org/10.1021/pr400398b
  51. Wang Y, Utzinger J, Saric J, Li JV, Burckhardt J, Dirnhofer S, Nicholson JK, Singer BH, Brun R, Holmes E. 2008. Global metabolic responses of mice to Trypanosoma brucei brucei infection. Proc Natl Acad Sci USA 105: 6127-6132. https://doi.org/10.1073/pnas.0801777105
  52. Yap IK, Li JV, Saric J, Martin FP, Davies H, Wang Y, Wilson ID, Nicholson JK, Utzinger J, Marchesi JR, Holmes E. 2008. Metabonomic and microbiological analysis of the dynamic effect of vancomycin-induced gut microbiota modification in the mouse. J Proteome Res 7: 3718-3728. https://doi.org/10.1021/pr700864x
  53. Martin FP, Verdu EF, Wang Y, Dumas ME, Yap IK, Cloarec O, Bergonzelli GE, Corthesy-Theulaz I, Kochhar S, Holmes E, Lindon JC, Collins SM, Nicholson JK. 2006. Transgenomic metabolic interactions in a mouse disease model: interactions of Trichinella spiralis infection with dietary Lactobacillus paracasei supplementation. J Proteome Res 5: 2185- 2193. https://doi.org/10.1021/pr060157b
  54. Martin FP, Wang Y, Sprenger N, Holmes E, Lindon JC, Kochhar S, Nicholson JK. 2007. Effects of probiotic Lactobacillus paracasei treatment on the host gut tissue metabolic profiles probed via magic-angle-spinning NMR spectroscopy. J Proteome Res 6: 1471-1481. https://doi.org/10.1021/pr060596a
  55. Hong YS, Hong KS, Park MH, Ahn YT, Lee JH, Huh CS, Lee J, Kim IK, Hwang GS, Kim JS. 2011. Metabonomic understanding of probiotic effects in humans with irritable bowel syndrome. J Clin Gastroenterol 45: 415-425. https://doi.org/10.1097/MCG.0b013e318207f76c
  56. Nicholson JK, Holmes E, Wilson ID. 2005. Gut microorganisms, mammalian metabolism and personalized health care. Nat Rev Microbiol 3: 431-438. https://doi.org/10.1038/nrmicro1152

Cited by

  1. Discrimination model of cultivation area of Corni Fructus using a GC-MS-Based metabolomics approach vol.29, pp.1, 2016, https://doi.org/10.5806/AST.2016.29.1.1
  2. Comparative Analysis of Cultivation Region of Angelica gigas Using a GC-MS-Based Metabolomics Approach vol.24, pp.2, 2016, https://doi.org/10.7783/KJMCS.2016.24.2.93