Metabolomics

, Volume 9, Issue 1, pp 88–100 | Cite as

Metabonomic investigation of rat tissues following intravenous administration of cyanidin 3-glucoside at a physiologically relevant dose

  • Andreja Vanzo
  • Matthias Scholz
  • Mattia Gasperotti
  • Federica Tramer
  • Sabina Passamonti
  • Urska Vrhovsek
  • Fulvio Mattivi
Original Article
First Online:
Received:
Accepted:

Abstract

Anthocyanins, which are dietary flavonoids occurring in fruit and beverages, are reported to have a beneficial impact on a wide range of chronic diseases, such as cardiovascular, neurodegenerative and neoplastic diseases. To understand the underlying mechanisms, a biochemical description of the changes in cell metabolism caused by anthocyanins can be provided by metabonomic studies. The aim of this study was to detect changes in the profiles of metabolites induced by the administration of cyanidin 3-glucoside to adult male rats. A physiological dose of cyanidin 3-glucoside was intravenously administered, and blood, kidneys and liver were collected after 5 min. The tissues were rapidly frozen in liquid nitrogen, stored briefly at −80 °C, homogenised under cryogenic conditions and extracted in ice-cold methanol:water (95:5, v/v). The extracts were then analysed using UPLC/QTOF-MS. Multivariate statistical analysis of the data was performed using orthogonal projections to latent structures-discriminant analysis (OPLS-DA). Discriminating variables were compared to the in-house standard database, considering matches in retention times, parent mass ions, mass fragment patterns and isotopic patterns. This metabolomic approach made it possible to identify as many as eight metabolite markers, including bile acids, reduced and oxidised glutathione and some lipids. Such changes suggest that cyanidin 3-glucoside has a major effect on tissue antioxidant status as well as on energy and glucose metabolism.

Keywords

Anthocyanins Cyanidin 3-glucoside Metabolomics Metabonomics Wistar rats Ultra performance liquid chromatography Quadrupole-time-of-flight mass spectrometry 
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Notes

Acknowledgments

The authors gratefully thank Domenico Masuero for his expert assistance in MS analysis. The study was carried out with support of: the Slovenian Research Agency (project: Z4-2280), the ADP2010 MetaQuality projects, funded by the Autonomous Province of Trento, Italy and the “Integrated and Sustainable Vine-Wine Management (GISVI)” project (L.R. 26/2010 – Support for the production and exploitation of knowledge) funded by the Autonomous Region of Friuli Venezia Giulia.

Supplementary material

11306_2012_430_MOESM1_ESM.doc (86 kb)
Supplementary material 1 (DOC 86 kb)
11306_2012_430_MOESM2_ESM.doc (94 kb)
Supplementary material 2 (DOC 94 kb)

References

  1. Adhikari, D. P., Francis, J. A., Schutzki, R. E., Chandra, A., & Nair, M. G. (2005). Quantification and characterisation of cyclo-oxygenase and lipid peroxidation inhibitory anthocyanins in fruits of Amelanchier. Phytochemical Analysis, 16(3), 175–180.PubMedCrossRefGoogle Scholar
  2. Ali, K., Iqbal, M., Korthout, H., Maltese, F., Fortes, A., Pais, M., et al. (2012). NMR spectroscopy and chemometrics as a tool for anti-TNFα activity screening in crude extracts of grapes and other berries. Metabolomics. doi: 10.1007/s11306-012-0406-8.
  3. Attilio, R. (2007). Absorption, transport, and tissue delivery of vitamin E. Molecular Aspects of Medicine, 28(5–6), 423–436.Google Scholar
  4. Ballatori, N., Krance, S. M., Notenboom, S., Shi, S., Tieu, K., & Hammond, C. L. (2009). Glutathione dysregulation and the etiology and progression of human diseases. Biological Chemistry, 390(3), 191–214.PubMedCrossRefGoogle Scholar
  5. Basu, A., Rhone, M., & Lyons, T. J. (2010). Berries: emerging impact on cardiovascular health. Nutrition Reviews, 68(3), 168–177.PubMedCrossRefGoogle Scholar
  6. Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(8–10), 341–351.CrossRefGoogle Scholar
  7. Cassidy, A., O’Reilly, É. J., Kay, C., Sampson, L., Franz, M., Forman, J. P., et al. (2011). Habitual intake of flavonoid subclasses and incident hypertension in adults. The American Journal of Clinical Nutrition, 93(2), 338–347.PubMedCrossRefGoogle Scholar
  8. Cevallos-Cevallos, J. M., Reyes-De-Corcuera, J. I., Etxeberria, E., Danyluk, M. D., & Rodrick, G. E. (2009). Metabolomic analysis in food science: a review. Trends in Food Science & Technology, 20(11–12), 557–566.CrossRefGoogle Scholar
  9. Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78(1–2), 103–112.CrossRefGoogle Scholar
  10. Coen, M., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2008). NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chemical Research in Toxicology, 21(1), 9–27.PubMedCrossRefGoogle Scholar
  11. Cole, S. P. C., & Deeley, R. G. (2006). Transport of glutathione and glutathione conjugates by MRP1. Trends in Pharmacological Sciences, 27(8), 438–446.PubMedCrossRefGoogle Scholar
  12. Dawson, P. A., Hubbert, M. L., & Rao, A. (2010). Getting the mOST from OST: Role of organic solute transporter, OSTα-OSTβ, in bile acid and steroid metabolism. Biochimica et Biophysica Acta, 1801(9), 994–1004.PubMedCrossRefGoogle Scholar
  13. De Vos, R. C. H., Moco, S., Lommen, A., Keurentjes, J. J. B., Bino, R. J., & Hall, R. D. (2007). Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nature Protocols, 2(4), 778–791.PubMedCrossRefGoogle Scholar
  14. Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26(1), 51–78.PubMedCrossRefGoogle Scholar
  15. Ennulat, D., Magid-Slav, M., Rehm, S., & Tatsuoka, K. S. (2010). Diagnostic performance of traditional hepatobiliary biomarkers of drug-induced liver injury in the rat. Toxicological Sciences, 116(2), 397–412.PubMedCrossRefGoogle Scholar
  16. Fiehn, O. (2001). Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comparative and Functional Genomics, 2(3), 155–168.PubMedCrossRefGoogle Scholar
  17. Fiehn, O. (2002). Metabolomics—The link between genotypes and phenotypes. Plant Molecular Biology, 48(1), 155–171.PubMedCrossRefGoogle Scholar
  18. Gika, H., & Theodoridis, G. (2011). Sample preparation prior to the LC-MS-based metabolomics/metabonomics of blood-derived samples. Bioanalysis, 3(14), 1647–1661.PubMedCrossRefGoogle Scholar
  19. Hanhineva, K., Törrönen, R., Bondia-Pons, I., Pekkinen, J., Kolehmainen, M., Mykkänen, H., et al. (2010). Impact of dietary polyphenols on carbohydrate metabolism. International Journal of Molecular Sciences, 11(4), 1365–1402.PubMedCrossRefGoogle Scholar
  20. Hertog, M. G., Feskens, E. J., Hollman, P. C., Katan, M. B., & Kromhout, D. (1993). Dietary antioxidant flavonoids and risk of coronary heart disease: The Zutphen Elderly Study. Lancet, 342(8878), 1007–1011.PubMedCrossRefGoogle Scholar
  21. Ichiyanagi, T. (2008). Bioavailability and metabolic fate of anthocyanins. Functional food and health. ACS Symposium Series, 993, 48–61.CrossRefGoogle Scholar
  22. Jackson, J. E. (1991). User’s guide to principal components. New York: Wiley.Google Scholar
  23. Kay, C. D. (2006). Aspects of anthocyanin absorption, metabolism and pharmacokinetics in humans. Nutrition Research Reviews, 19(01), 137–146.PubMedCrossRefGoogle Scholar
  24. Kind, T., & Fiehn, O. (2009). What are the obstacles for an integrated system for comprehensive interpretation of cross-platform metabolic profile data? Bioanalysis, 1(9), 1511–1514.PubMedCrossRefGoogle Scholar
  25. Klaassen, C. D., & Aleksunes, L. M. (2010). Xenobiotic, bile acid, and cholesterol transporters: function and regulation. Pharmacological Reviews, 62(1), 1–96.PubMedCrossRefGoogle Scholar
  26. Kostrubsky, V. E., Strom, S. C., Hanson, J., Urda, E., Rose, K., Burliegh, J., et al. (2003). Evaluation of hepatotoxic potential of drugs by inhibition of bile-acid transport in cultured primary human hepatocytes and intact rats. Toxicological Sciences, 76(1), 220–228.PubMedCrossRefGoogle Scholar
  27. Koulman, A., Lane, G., Harrison, S., & Volmer, D. (2009). From differentiating metabolites to biomarkers. Analytical and Bioanalytical Chemistry, 394(3), 663–670.PubMedCrossRefGoogle Scholar
  28. Lefebvre, P., Cariou, B., Lien, F., Kuipers, F., & Staels, B. (2009). Role of bile acids and bile acid receptors in metabolic regulation. Physiological Reviews, 89(1), 147–191.PubMedCrossRefGoogle Scholar
  29. Lichtenstein, A. H., Appel, L. J., Brands, M., Carnethon, M., Daniels, S., Franch, H. A., et al. (2006). Diet and lifestyle recommendations revision 2006. Circulation, 114(1), 82–96.PubMedCrossRefGoogle Scholar
  30. Manach, C., & Donovan, J. L. (2004). Pharmacokinetics and metabolism of dietary flavonoids in humans. Free Radical Research, 38(8), 771–785.PubMedCrossRefGoogle Scholar
  31. Manach, C., Williamson, G., Morand, C., Scalbert, A., & Remesy, C. (2005). Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies. American Journal of Clinical Nutrition, 81(1), 230S–242S.PubMedGoogle Scholar
  32. Masson, P., Alves, A. C., Ebbels, T. M. D., Nicholson, J. K., & Want, E. J. (2010). Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS. Analytical Chemistry, 82(18), 7779–7786.PubMedCrossRefGoogle Scholar
  33. McGhie, T. K., & Walton, M. C. (2007). The bioavailability and absorption of anthocyanins: Towards a better understanding. Molecular Nutrition & Food Research, 51(6), 702–713.CrossRefGoogle Scholar
  34. Minami, Y., Kasukawa, T., Kakazu, Y., Iigo, M., Sugimoto, M., Ikeda, S., et al. (2009). Measurement of internal body time by blood metabolomics. Proceedings of the National Academy of Sciences, 106(24), 9890–9895.CrossRefGoogle Scholar
  35. Moazzami, A. A., Andersson, R., & Kamal-Eldin, A. (2011). Changes in the metabolic profile of rat liver after α-tocopherol deficiency as revealed by metabolomics analysis. NMR in Biomedicine, 24(5), 499–505.PubMedCrossRefGoogle Scholar
  36. Moco, S., Vervoort, J., Moco, S., Bino, R. J., De Vos, R. C. H., & Bino, R. (2007). Metabolomics technologies and metabolite identification. TrAC Trends in Analytical Chemistry, 26(9), 855–866.CrossRefGoogle Scholar
  37. Mursu, J., Nurmi, T., Tuomainen, T.-P., Salonen, J. T., Pukkala, E., & Voutilainen, S. (2008). Intake of flavonoids and risk of cancer in Finnish men: The Kuopio Ischaemic Heart Disease Risk Factor Study. International Journal of Cancer, 123(3), 660–663.CrossRefGoogle Scholar
  38. Nicholson, J. K., Lindon, J. C., & 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(11), 1181–1189.PubMedCrossRefGoogle Scholar
  39. Prior, R. L. (2003). Fruits and vegetables in the prevention of cellular oxidative damage. American Journal of Clinical Nutrition, 78(3 Suppl.), 570S–578S.PubMedGoogle Scholar
  40. Purucker, E., Marschall, H.-U., Geier, A., Gartung, C., & Matern, S. (2002). Increase in renal glutathione in cholestatic liver disease is due to a direct effect of bile acids. American Journal of Physiology, 283(6), F1281–F1289.PubMedGoogle Scholar
  41. Qiu, Y., Cai, G., Su, M., Chen, T., Liu, Y., Xu, Y., et al. (2010). Urinary metabonomic study on colorectal cancer. Journal of Proteome Research, 9(3), 1627–1634.PubMedCrossRefGoogle Scholar
  42. Robertson, D. G. (2005). Metabonomics in toxicology: A review. Toxicological Sciences, 85(2), 809–822.PubMedCrossRefGoogle Scholar
  43. Römisch-Margl, W., Prehn, C., Bogumil, R., Röhring, C., Suhre, K., & Adamski, J. (2011). Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics, 1–10. doi:  10.1007/s11306-011-0293-4.
  44. Roux, A., Lison, D., Junot, C., & Heilier, J.-F. (2011). Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: A review. Clinical Biochemistry, 44(1), 119–135.PubMedCrossRefGoogle Scholar
  45. Sana, T. R., Waddell, K., & Fischer, S. M. (2008). A sample extraction and chromatographic strategy for increasing LC/MS detection coverage of the erythrocyte metabolome. Journal of Chromatography B, 871(2), 314–321.CrossRefGoogle Scholar
  46. Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B., van Ommen, B., et al. (2009). Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics, 5(4), 435–458.PubMedCrossRefGoogle Scholar
  47. Shin, M. H., Lee, D. Y., Liu, K.-H., Fiehn, O., & Kim, K. H. (2010). Evaluation of sampling and extraction methodologies for the global metabolic profiling of Saccharophagus degradans. Analytical Chemistry, 82(15), 6660–6666.PubMedCrossRefGoogle Scholar
  48. Spormann, T. M., Albert, F. W., Rath, T., Dietrich, H., Will, F., Stockis, J.-P., et al. (2008). Anthocyanin/polyphenolic-rich fruit juice reduces oxidative cell damage in an intervention study with patients on hemodialysis. Cancer Epidemiology, Biomarkers and Prevention, 17(12), 3372–3380.PubMedCrossRefGoogle Scholar
  49. Stella, C., Beckwith-Hall, B., Cloarec, O., Holmes, E., Lindon, J. C., Powell, J., et al. (2006). Susceptibility of human metabolic phenotypes to dietary modulation. Journal of Proteome Research, 5(10), 2780–2788.PubMedCrossRefGoogle Scholar
  50. St-Pierre, M. V., Kullak-Ublick, G. A., Hagenbuch, B., & Meier, P. J. (2001). Transport of bile acids in hepatic and non-hepatic tissues. Journal of Experimental Biology, 204(10), 1673–1686.PubMedGoogle Scholar
  51. Theodoridis, G., Gika, H., Franceschi, P., Caputi, L., Arapitsas, P., Scholz, M., et al. (2012). LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation. Metabolomics, 8(2), 175–185.CrossRefGoogle Scholar
  52. Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabonomics. Journal of Proteome Research, 6(2), 469–479.PubMedCrossRefGoogle Scholar
  53. Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119–128.CrossRefGoogle Scholar
  54. U.S. Department of Agriculture, A. R. S. (2007). USDA Database for the flavonoid content of selected Foods, Release 2.1. Accessed May 4, 2011, from http://www.nal.usda.gov/fnic/foodcomp/Data/Flav/Flav02-1.pdf.
  55. U.S. Department of Health & Human services, & U.S. Department of Agriculture (2010). Dietary Guidelines for Americans, 2010. Accessed Oct 3, 2011 from http://health.gov/dietaryguidelines/dga2010/DietaryGuidelines2010.pdf.
  56. Vanzo, A., Vrhovsek, U., Tramer, F., Mattivi, F., & Passamonti, S. (2011). Exceptionally fast uptake and metabolism of cyanidin 3-glucoside by rat kidneys and liver. Journal of Natural Products, 74(5), 1049–1054.PubMedCrossRefGoogle Scholar
  57. Villas-Bôas, S. G. (2006). Sampling and sample preparation. In S. G. Villas-Bôas, U. Roessner, M. A. E. Hansen, J. Smedsgaard, & J. Nielsen (Eds.), Metabolome analysis: An introduction (pp. 39–82). New Yersey: Wiley.Google Scholar
  58. WHO (2004). Global strategy on diet, physical activity and health. Accessed September 2, 2011, from http://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf.
  59. Wiklund, S., Johansson, E., Sjostrom, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., et al. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry, 80(1), 115–122.PubMedCrossRefGoogle Scholar
  60. Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., et al. (2009). HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research, 37(suppl 1), D603–D610.PubMedCrossRefGoogle Scholar
  61. Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., et al. (2007). HMDB: The human metabolome database. Nucleic Acids Research, 35(suppl 1), D521–D526.PubMedCrossRefGoogle Scholar
  62. Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (vol. 6, pp. 581–591). New York: Willey.Google Scholar
  63. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.CrossRefGoogle Scholar
  64. Wu, X., Beecher, G. R., Holden, J. M., Haytowitz, D. B., Gebhardt, S. E., & Prior, R. L. (2006). Concentrations of anthocyanins in common foods in the United States and estimation of normal consumption. Journal of Agricultural and Food Chemistry, 54(11), 4069–4075.PubMedCrossRefGoogle Scholar
  65. Yang, H., Pang, W., Lu, H., Cheng, D., Yan, X., Cheng, Y., et al. (2011). Comparison of metabolic profiling of cyanidin-3-O-galactoside and extracts from blueberry in aged mice. Journal of Agricultural and Food Chemistry, 59(5), 2069–2076.PubMedCrossRefGoogle Scholar
  66. Yin, P., Zhao, X., Li, Q., Wang, J., Li, J., & Xu, G. (2006). Metabonomics study of intestinal fistulas based on ultraperformance liquid chromatography coupled with Q-TOF mass spectrometry (UPLC/Q-TOF MS). Journal of Proteome Research, 5(9), 2135–2143.PubMedCrossRefGoogle Scholar
  67. Zelena, E., Dunn, W. B., Broadhurst, D., Francis-McIntyre, S., Carroll, K. M., Begley, P., et al. (2009). Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Analytical Chemistry, 81(4), 1357–1364.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Andreja Vanzo
    • 1
    • 2
    • 3
  • Matthias Scholz
    • 4
  • Mattia Gasperotti
    • 2
  • Federica Tramer
    • 3
  • Sabina Passamonti
    • 3
  • Urska Vrhovsek
    • 2
  • Fulvio Mattivi
    • 2
  1. 1.Central LaboratoryAgricultural Institute of SloveniaLjubljanaSlovenia
  2. 2.Department of Food Quality and NutritionFondazione Edmund Mach, Centro Ricerca e InnovazioneSan Michele all’AdigeItaly
  3. 3.Department of Life SciencesUniversity of TriesteTriesteItaly
  4. 4.Department of Computational BiologyFondazione Edmund Mach, Centro Ricerca e InnovazioneSan Michele all’AdigeItaly

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