Advertisement

Automated Annotation of Microbial and Human Flavonoid-Derived Metabolites

  • Velitchka V. Mihaleva
  • Fatma Yelda Ünlü
  • Jacques VervoortEmail author
  • Lars Ridder
Chapter
Part of the Molecular and Integrative Toxicology book series (MOLECUL)

Abstract

Flavonoids are a class of natural compounds essentially produced by plants that are part of animal and human diets and have assumed health-promoting benefits. Upon human consumption, these flavonoids are to a modest extent absorbed in the small intestines. The major part arrives in the colon where the microflora utilises and converts the flavonoids to a wide range of products. Many of these products are absorbed in the major intestines and subsequently metabolised by the host. To understand the impact of the microflora on the metabolism and possible effects on human health, complete (and quantitative) identification of the microbial as well as human metabolic conversion products of flavonoids is required. This is a challenging task, as these bioconversion products are often present in relatively small amounts, making classical identification strategies based on (accurate) mass information or nuclear magnetic resonance, not straightforward. In the absence of reference compounds, annotation of a component may be achieved by detailed expert evaluation, e.g. by searching for similar fragmentation patterns in spectral databases of known compounds. However, such manual analysis is a tedious task, and in advanced metabolite profiling experiments, with large numbers of unknown metabolites, this is a major bottleneck. Therefore, new strategies are needed for quick and reliable identification of the diverse range of molecules in complex matrices (faeces, blood, urine). Intelligent software for annotation and identification of unknowns is crucial to fully exploit complex datasets. We developed a new software tool (MAGMA) for (sub)structure-based annotation of LC-MSn datasets which, combined with a newly established database for phenolic molecules (MetIDB), enables semiautomated identification of flavonoid derivatives.

Keywords

Flavonoids Identification Automation LC-MS NMR Microflora MAGMA PERCH NMR Software Microbiota MetIDB Profiling Metabolites Glucuronide Sulphate Valerolactone Hippuric acid Epicatechin Beta oxidation Alpha oxidation Lactone hydrolysis Gallocatechins Urolithin 

References

  1. 1.
    Harborne J. Encyclopedia of plant physiology. Berlin/Heidelberg/New York: Springer; 1980.Google Scholar
  2. 2.
    Moco S, Ross A. Can we use metabolomics to understand changes to gut microbiota. In: Martin F-PJ, Kochhar S, editors. Metabolomics and gut microbiota in nutrition and disease. Springer; 2014.Google Scholar
  3. 3.
    Rietjens IMCM, Sotoca AM, Vervoort J, Louisse J. Mechanisms underlying the dualistic mode of action of major soy isoflavones in relation to cell proliferation and cancer risks. Mol Nutr Food Res. 2013;57(1):100–13.CrossRefPubMedGoogle Scholar
  4. 4.
    van der Woude H, Alink GM, van Rossum BEJ, Walle K, van Steeg H, Walle T, et al. Formation of transient covalent protein and DNA adducts by quercetin in cells with and without oxidative enzyme activity. Chem Res Toxicol. 2005;18(12):1907–16.CrossRefPubMedGoogle Scholar
  5. 5.
    Mihaleva VV, te Beek TAH, van Zimmeren F, Moco S, Laatikainen R, Niemitz M, et al. MetIDB: a publicly accessible database of predicted and experimental 1H NMR spectra of flavonoids. Anal Chem. 2013;85:8700.CrossRefPubMedGoogle Scholar
  6. 6.
    Arita M, Suwa K. Search extension transforms Wiki into a relational system: a case for flavonoid metabolite database. Bio Data Min. 2008;1(1):7.CrossRefGoogle Scholar
  7. 7.
    de Boer VCJ, Dihal AA, van der Woude H, Arts ICW, Wolffram S, Alink GM, et al. Tissue distribution of quercetin in rats and pigs. J Nutr. 2005;135(7):1718–25.PubMedGoogle Scholar
  8. 8.
    van der Hooft JJJ, de Vos RCH, Mihaleva V, Bino RJ, Ridder L, de Roo N, et al. Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake. Anal Chem. 2012;84(16):7263–71.CrossRefPubMedGoogle Scholar
  9. 9.
    Aura A-M. Microbial metabolism of dietary phenolic compounds in the colon. Phytochem Rev. 2008;7(3):407–29.CrossRefGoogle Scholar
  10. 10.
    van Velzen EJJ, Westerhuis JA, van Duynhoven JPM, van Dorsten FA, GruМ€n CH, Jacobs DM, et al. Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites. J Proteome Res. 2009;8(7):3317–30.CrossRefPubMedGoogle Scholar
  11. 11.
    Hodek P, Trefil P, Stiborová M. Flavonoids-potent and versatile biologically active compounds interacting with cytochromes P450. Chem Biol Interact. 2002;139(1):1–21.CrossRefPubMedGoogle Scholar
  12. 12.
    Lampe JW. Interindividual differences in response to plant-based diets: implications for cancer risk. Am J Clin Nutr. 2009;89(5):1553S–7.CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Kelly GE, Nelson C, Waring MA, Joannou GE, Reeder AY. Metabolites of dietary (soya) isoflavones in human urine. Clin Chim Acta. 1993;223(1–2):9–22.CrossRefPubMedGoogle Scholar
  14. 14.
    Chang Y, Nair M, Nitiss J. Metabolites of daidzein and genistein and their biological activities. J Nat Prod. 1995;58(12):1901.CrossRefPubMedGoogle Scholar
  15. 15.
    Blaut M, Clavel T. Metabolic diversity of the intestinal microbiota: implications for health and disease. J Nutr. 2007;137(3):751S–5.PubMedGoogle Scholar
  16. 16.
    Rowland IR, Wiseman H, Sanders TAB, Adlercreutz H, Bowey EA. Interindividual variation in metabolism of Soy isoflavones and lignans: influence of habitual diet on equol production by the gut microflora. Nutr Cancer. 2000;36(1):27–32.CrossRefPubMedGoogle Scholar
  17. 17.
    Zhang Y, Song TT, Cunnick JE, Murphy PA, Hendrich S. Daidzein and genistein glucuronides in vitro are weakly estrogenic and activate human natural killer cells at nutritionally relevant concentrations. J Nutr. 1999;129(2):399–405.PubMedGoogle Scholar
  18. 18.
    Collino S, Martin F-P, Moco S. Metabonomics in clinical practice. In: Kochhar S, Martin F-P, editors. Metabonomics and gut microbiota in nutrition and disease. London: Springer; 2014.Google Scholar
  19. 19.
    Daykin CA, Van Duynhoven JPM, Groenewegen A, Dachtler M, Van Amelsvoort JMM, Mulder TPJ. Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans. J Agric Food Chem. 2005;53(5):1428–34.CrossRefPubMedGoogle Scholar
  20. 20.
    Van der Hooft JJJ, Mihaleva V, Bino RJ, de Vos RCH, Vervoort J. A strategy for fast structural elucidation of metabolites in small volume plant extracts using automated MS-guided LC-MS-SPE-NMR. Magn Res Chem. 2011;49(S1):S55–60.CrossRefGoogle Scholar
  21. 21.
    van der Hooft JJJ, Akermi M, Unlu FY, Mihaleva V, Roldan VG, Bino RJ, et al. Structural annotation and elucidation of conjugated phenolic compounds in black, green, and white tea extracts. J Agric Food Chem. 2012;60(36):8841–50.CrossRefPubMedGoogle Scholar
  22. 22.
    Hill AW, Mortishire-Smith RJ. Automated assignment of high-resolution collisionally activated dissociation mass spectra using a systematic bond disconnection approach. Rapid Commun Mass Spectrom. 2005;19(21):3111–8.CrossRefGoogle Scholar
  23. 23.
    Hill DW, Kertesz TM, Fontaine D, Friedman R, Grant DF. Mass spectral metabonomics beyond elemental formula: chemical database querying by matching experimental with computational fragmentation spectra. Anal Chem. 2008;80(14):5574–82.CrossRefPubMedGoogle Scholar
  24. 24.
    Heinonen M, Rantanen A, Mielikäinen T, Kokkonen J, Kiuru J, Ketola RA, et al. FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data. Rapid Commun Mass Spectrom. 2008;22(19):3043–52.CrossRefPubMedGoogle Scholar
  25. 25.
    Wolf S, Schmidt S, Muller-Hannemann M, Neumann S. In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinforma. 2010;11(1):148.CrossRefGoogle Scholar
  26. 26.
    Bonn B, Leandersson C, Fontaine F, Zamora I. Enhanced metabolite identification with MSE and a semi-automated software for structural elucidation. Rapid Commun Mass Spectrom. 2010;24(21):3127–38.CrossRefPubMedGoogle Scholar
  27. 27.
    Ridder L, van der Hooft JJJ, Verhoeven S, de Vos RCH, van Schaik R, Vervoort J. Substructure-based annotation of high-resolution multistage MSn spectral trees. Rapid Commun Mass Spectrom. 2012;26(20):2461–71.CrossRefPubMedGoogle Scholar
  28. 28.
    Ridder L, van der Hooft JJJ, Verhoeven S, de Vos RCH, Bino RJ, Vervoort J. Automatic chemical structure annotation of an LC–MSn based metabolic profile from green tea. Anal Chem. 2013;85(12):6033–40.CrossRefPubMedGoogle Scholar
  29. 29.
    Ridder L, Wang H, de Vlieg J, Wagener M. Revisiting the rule of five on the basis of pharmacokinetic data from rat. ChemMedChem. 2011;6(11):1967–70.CrossRefPubMedGoogle Scholar
  30. 30.
    Ridder L, Wagener M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem. 2008;3(5):821–32.CrossRefPubMedGoogle Scholar
  31. 31.
    Lehtivarjo J, Hassinen T, Korhonen SP, Perakyla M, Laatikainen R. 4D prediction of protein H-1 chemical shifts. J Biomol NMR. 2009;45(4):413–26.CrossRefPubMedGoogle Scholar
  32. 32.
    Gonzalez-Barrio R, Truchado P, Ito H, Espin JC, Tomas-Barberan FA. UV and MS identification of urolithins and nasutins, the bioavailable metabolites of ellagitannins and ellagic acid in different mammals. J Agric Food Chem. 2011;59(4):1152–62.CrossRefPubMedGoogle Scholar
  33. 33.
    Ito H, Iguchi A, Hatano T. Identification of urinary and intestinal bacterial metabolites of ellagitannin geraniin in rats. J Agric Food Chem. 2007;56(2):393–400.CrossRefPubMedGoogle Scholar
  34. 34.
    Nealmongkol P, Tangdenpaisal K, Sitthimonchai S, Ruchirawat S, Thasana N. Cu(I)-mediated lactone formation in subcritical water: a benign synthesis of benzopyranones and urolithins A-C. Tetrahedron. 2013;69:9277–83.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Velitchka V. Mihaleva
    • 1
    • 2
  • Fatma Yelda Ünlü
    • 1
  • Jacques Vervoort
    • 1
    • 2
    Email author
  • Lars Ridder
    • 1
    • 3
  1. 1.Laboratory of BiochemistryWageningen University and Research CenterWageningenThe Netherlands
  2. 2.Netherlands Metabolomics CentreLeidenThe Netherlands
  3. 3.Netherlands eScience CenterAmsterdamThe Netherlands

Personalised recommendations