Advertisement

Metabolomics

, Volume 8, Supplement 1, pp 52–63 | Cite as

Data handling for interactive metabolomics: tools for studying the dynamics of metabolome-macromolecule interactions

  • Clare A. DaykinEmail author
  • Rasmus Bro
  • Florian Wulfert
Original Article

Abstract

All published metabolomics studies investigate changes in either absolute or relative quantities of metabolites. However, blood plasma, one of the most commonly studied biofluids for metabolomics applications, is a complex, heterogeneous mixture of lipoproteins, proteins, small organic molecules and ions which together undergo a variety of possible molecular interactions including metal complexation, chemical exchange processes, micellular compartmentation of metabolites, enzyme-mediated biotransformations and small-molecule-macromolecule binding. In particular, many low molecular weight (MW) compounds (including drugs) can exist both ‘free’ in solution and bound to proteins or within organised aggregates of macromolecules. To study the effects of e.g. disease on these interactions we suggest that new approaches are needed. We have developed a technique termed ‘interactive metabolomics’ or i-metabolomics. i-metabolomics can be defined as: “The study of interactions between low MW biochemicals and macromolecules in heterogeneous biosamples such as blood plasma, without pre-selection of the components of interest”. Standard 1D NMR experiments commonly used in metabolomics allow metabolite concentration differences between samples to be investigated because the intensity of each peak depends on the concentration of the compound in question. On the other hand, the instrument can be set-up to measure molecular interactions by monitoring the diffusion coefficients of molecules. According to the Stokes–Einstein equation, the diffusion coefficient of a molecule is inversely proportional to its effective size, as represented by the hydrodynamic radius. Therefore, when low MW compounds are non-covalently bound to proteins, the observed diffusion coefficient for the compound will be intermediate between those of its free and bound forms. By measuring diffusion by NMR, the degree of protein binding can be estimated for either low MW endogenous biochemicals or xenobiotics. This type of experiment is referred to as either Diffusion-Ordered Spectroscopy (DOSY) or Diffusion-Edited Spectroscopy, depending on the type of post-acquisition data processing applied to the spectra. Results presented in this paper demonstrate approaches for the non-selective modelling of metabolite-macromolecule interactions (i-metabolomics), whilst additionally highlighting some of the all too frequently ignored issues associated with interpretation of data derived from profiling of blood plasma.

Keywords

NMR spectroscopy DOSY Diffusion-Edited Spectroscopy PCA PARAFAC Interactive metabolomics Plasma 

Notes

Acknowledgments

The authors would like to thank Dr. Jonathan Byrne (School of Pharmacy, University of Nottingham, now School of Biosciences, University of Birmingham) for his contribution to the early stages of this project, Angela Savage (School of Pharmacy, University of Nottingham) for technical assistance during this work. Professor Gareth Morris and Dr. Matteis Nilsson (University of Manchester) are also gratefully acknowledged for fruitful discussions on the initial set up of the diffusion experiments. Finally, the EPSRC are thanked for funding of the work (EPSRC Grant number EP/F014767/1).

References

  1. Barton, R. H., Waterman, D., Bonner, F. W., et al. (2010). The influence of EDTA and citrate anticoagulant addition to human plasma on information recovery from NMR-based metabolic profiling studies. Molecular BioSystems, 6, 215–224.PubMedCrossRefGoogle Scholar
  2. Bell, J. D., Brown, J. C. C., Kubal, G., & Sadler, P. J. (1988). NMR invisible lactate. FEBS Letters, 235, 81–86.PubMedCrossRefGoogle Scholar
  3. Chen, A. D., & Shapiro, M. J. (2000). NOE pumping. 2. A high-throughput method to determine compounds with binding affinity to macromolecules by NMR. Journal of the American Chemical Society, 122, 414–415.CrossRefGoogle Scholar
  4. Cohen, Y., Avram, L., & Frish, L. (2005). Diffusion NMR spectroscopy in supramolecular and combinatorial chemistry: An old parameter—new insights. Angewandte Chemie International Edition, 44, 520–554.CrossRefGoogle Scholar
  5. Connor, S. C., Nicholson, J. K., & Everett, J. E. (1987). Spin-echo proton NMR spectroscopy of urine samples: Water suppression via a urea dependant T2 relaxation process. Magnetic Resonance in Medicine, 4, 461–470.PubMedCrossRefGoogle Scholar
  6. Daykin, C. A. (2010). Interactive metabolomics: A powerful new technique. Book of abstracts metabolomics 2010. http://www.metabolomics2010.com/PDF/Metabolomics%202010%20abstract%20book.pdf. Accessed 1 July 2011.
  7. Daykin, C. A., Foxall, P. J. D., Connor, S. C., Lindon, J. C., & Nicholson, J. K. (2002). The comparison of plasma deproteinization methods for the detection of low-molecular-weight metabolites by 1H nuclear magnetic resonance spectroscopy. Analytical Biochemistry, 304, 220–230.PubMedCrossRefGoogle Scholar
  8. Delsuc, M. A., & Malliavin, T. E. (1998). Maximum entropy processing of DOSY NMR spectra. Analytical Chemistry, 70, 2146–2148.CrossRefGoogle Scholar
  9. Dieterle, F., Riefke, B., Schlotterbeck, G., et al. (2011). NMR and MS methods for metabonomics. Methods in Molecular Biology, 691, 385–415.PubMedCrossRefGoogle Scholar
  10. Dyrby, M., Petersen, M., Whittaker, A. K., et al. (2005). Analysis of lipoproteins using 2D diffusion-edited NMR spectroscopy and multi-way chemometrics. Analytica Chimica Acta, 531, 209–216.CrossRefGoogle Scholar
  11. Hajduk, P. J., Olejniczak, E. T., & Fesik, S. W. (1997). One-dimensional relaxation- and diffusion-edited NMR methods for screening compounds that bind to macromolecules. Journal of the American Chemical Society, 119, 12257–12261.CrossRefGoogle Scholar
  12. Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA working papers in phonetics, 16, no. 10085 (pp. 1–84). Ann Arbor, MI: University Microfilms. http://publish.uwo.ca/~harshman/wpppfac0.pdf.
  13. Huo, R., Geurts, C., Brands, J., Wehrens, R., & Buydens, L. M. C. (2006). Real-life applications of the MULVADO software package for processing DOSY NMR data. Magnetic Resonance in Chemistry, 44, 110–117.PubMedCrossRefGoogle Scholar
  14. Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley.CrossRefGoogle Scholar
  15. Lentner, C. (1984). Geigy scientific tables, Vol. 3: Physical chemistry composition of blood hematology somatometric data (8th revised ed.). Basel: Icon Learning Systems.Google Scholar
  16. Lin, M., Shapiro, M. J., & Wareing, J. R. (1997). Screening mixtures by affinity NMR. Journal of Organic Chemistry, 62, 8930–8931.CrossRefGoogle Scholar
  17. Liu, M. L., Mao, X. A., Ye, C. H., Huang, H., Nicholson, J. K., & Lindon, J. C. (1998). Improved WATERGATE pulse sequences for solvent suppression in NMR spectroscopy. Journal of Magnetic Resonance, 132, 125–129.CrossRefGoogle Scholar
  18. Liu, M. L., Nicholson, J. K., & Lindon, J. C. (1996). High resolution diffusion and relaxation edited one- and two-dimensional 1H NMR spectroscopy of biological fluids. Analytical Chemistry, 68, 3370–3376.PubMedCrossRefGoogle Scholar
  19. Liu, M. L., Nicholson, J. K., & Lindon, J. C. (1997a). Analysis of drug-protein binding using nuclear magnetic resonance based molecular diffusion measurements. Analytical Communications, 34, 225–228.CrossRefGoogle Scholar
  20. Liu, M. L., Nicholson, J. K., Parkinson, J. A., & Lindon, J. C. (1997b). Measurement of biomolecular diffusion coefficients in blood plasma using two-dimensional H-1-H-1 diffusion-edited total-correlation NMR spectroscopy. Analytical Chemistry, 69, 1504–1509.PubMedCrossRefGoogle Scholar
  21. Loening, N. M., Keeler, J., & Morris, G. A. (2001). One-dimensional DOSY. Journal of Magnetic Resonance, 153, 103–112.PubMedCrossRefGoogle Scholar
  22. Moffat, A. C., Jackson, J. V., Moss, M. S., & Widdop, B. (1986). Clarke’s isolation and identification of drugs. London, UK: The Pharmaceutical Press.Google Scholar
  23. Morris, K. F., Stilbs, P., & Johnson, C. S. (1994). Analysis of mixtures based on molecular-size and hydrophobicity by means of diffusion-ordered 2D NMR. Analytical Chemistry, 66, 211–215.CrossRefGoogle Scholar
  24. Nicholson, J. K., & Gartland, K. P. R. (1989). 1H NMR studies on protein binding of histidine, tyrosine and phenylalanine in blood plasma. NMR in Biomedicine, 2, 77–82.PubMedCrossRefGoogle Scholar
  25. 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, 11, 1181–1189.CrossRefGoogle Scholar
  26. Nilsson, M., Botana, A., & Morris, G. A. (2009). T-1-diffusion-ordered spectroscopy: Nuclear magnetic resonance mixture analysis using parallel factor analysis. Analytical Chemistry, 81, 8119–8125.PubMedCrossRefGoogle Scholar
  27. Nilsson, M., & Morris, G. A. (2006). Correction of systematic errors in CORE processing of DOSY data. Magnetic Resonance in Chemistry, 44, 655.PubMedCrossRefGoogle Scholar
  28. Robertson, D. G., Watkins, P. B., & Reilly, M. D. (2011). Metabolomics in toxicology: Preclinical and clinical applications. Toxicological Sciences, 120(suppl 1), S146–S170.PubMedCrossRefGoogle Scholar
  29. Smith, L. M., Maher, A. D., Cloarec, O., et al. (2007). Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples. Analytical Chemistry, 79, 5682–5689.PubMedCrossRefGoogle Scholar
  30. Stockman, B. J., & Dalvit, C. (2002). NMR screening techniques in drug discovery and drug design. Progress in Nuclear Magnetic Resonance Spectroscopy, 41, 187–231.CrossRefGoogle Scholar
  31. Tietz, N. W. (1986). Textbook of clinical chemistry (p. 590). Philadelphia: Saunders.Google Scholar
  32. Tiziani, S., Einwas, A. H., Lodi, A., Ludwig, C., Bunce, C. M., Viant, M. R., et al. (2008). Optimized metabolite extraction from blood serum for H-1 nuclear magnetic resonance spectroscopy. Analytical Biochemistry, 377, 16–23.PubMedCrossRefGoogle Scholar
  33. Weigelt, J., van Dongen, M., Uppenberg, J., Schultz, J., & Wikstrom, M. (2002). Site-selective screening by NMR spectroscopy with labeled amino acid pairs. Journal of the American Chemical Society, 124, 2446–2447.PubMedCrossRefGoogle Scholar
  34. Windig, W., Antalek, B., Sorriero, L. J., Bijlsma, S., Louwerse, D. J., & Smilde, A. K. (1999). Applications and new developments of the direct exponential curve resolution algorithm (DECRA). Examples of spectra and magnetic resonance images. Journal of Chemometrics, 13, 95.CrossRefGoogle Scholar
  35. Zartler, E. R., Yan, J. L., Mo, H. P., Kline, A. D., & Shapiro, M. J. (2003). 1D NMR methods in ligand-receptor interactions. Current Topics in Medicinal Chemistry, 3, 25–37.PubMedCrossRefGoogle Scholar
  36. Zheng, G., Stait-Gardner, T., Kumar, P. G. A., Torres, A. M., & Price, W. S. (2008). PGSTE-WATERGATE: An STE-based PGSE NMR sequence with excellent solvent suppression. Journal of Magnetic Resonance, 191, 159–163.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Clare A. Daykin
    • 1
    Email author
  • Rasmus Bro
    • 2
  • Florian Wulfert
    • 1
    • 3
  1. 1.Division of Molecular and Cellular Science, School of PharmacyUniversity of NottinghamNottinghamUK
  2. 2.Department of Food Science, Faculty of Life SciencesUniversity of CopenhagenFrederiksberg CDenmark
  3. 3.Department of Biosciences, Faculty of Health and WellbeingSheffield Hallam UniversitySheffieldUK

Personalised recommendations