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High-performance metabolic profiling with dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) for study of the exposome

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Abstract

Studies of gene–environment (G × E) interactions require effective characterization of all environmental exposures from conception to death, termed the exposome. The exposome includes environmental exposures that impact health. Improved metabolic profiling methods are needed to characterize these exposures for use in personalized medicine. In the present study, we compared the analytic capability of dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) to previously used liquid chromatography-FTMS (LC-FTMS) analysis for high-throughput, top-down metabolic profiling. For DC-FTMS, we combined data from sequential LC-FTMS analyses using reverse phase (C18) chromatography and anion exchange (AE) chromatography. Each analysis was performed with electrospray ionization in the positive ion mode and detection from m/z 85 to 850. Run time for each column was 10 min with gradient elution; 10 μl extracts of plasma from humans and common marmosets were used for analysis. In comparison to analysis with the AE column alone, addition of the second LC-FTMS analysis with the C18 column increased m/z feature detection by 23–36%, yielding a total number of features up to 7,000 for individual samples. Approximately 50% of the m/z matched to known chemicals in metabolomic databases, and 23% of the m/z were common to analyses on both columns. Database matches included insecticides, herbicides, flame retardants, and plasticizers. Modularity clustering algorithms applied to MS-data showed the ability to detection clusters and ion interactions. DC-FTMS thus provides improved capability for high-performance metabolic profiling of the exposome and development of personalized medicine.

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References

  • Aronson, K. J., Wilson, J. W., Hamel, M., et al. (2010). Plasma organochlorine levels and prostate cancer risk. Journal of Exposure Science and Environmental Epidemiology, 20, 434–445.

    Article  PubMed  CAS  Google Scholar 

  • Brigham, K. L. (2010). Predictive health: the imminent revolution in health care. Journal of the American Geriatrics Society, 58(Suppl 2), S298–S302.

    Article  PubMed  Google Scholar 

  • Brown, M., Dunn, W. B., Dobson, P., et al. (2009). Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. The Analyst, 134, 1322–1332.

    Article  PubMed  CAS  Google Scholar 

  • Buscher, J. M., Czernik, D., Ewald, J. C., Sauer, U., & Zamboni, N. (2009). Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Analytical Chemistry, 81, 2135–2143.

    Article  PubMed  CAS  Google Scholar 

  • Crews, B., Wikoff, W. R., Patti, G. J., et al. (2009). Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data. Analytical Chemistry, 81, 8538–8544.

    Article  PubMed  CAS  Google Scholar 

  • Ellis, D. I., Dunn, W. B., Griffin, J. L., Allwood, J. W., & Goodacre, R. (2007). Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics, 8, 1243–1266.

    Article  PubMed  CAS  Google Scholar 

  • Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M., & Milgram, E. (2009). Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Analytical Chemistry, 81, 6656–6667.

    Article  PubMed  CAS  Google Scholar 

  • Gilmour, M. I., Jaakkola, M. S., London, S. J., Nel, A. E., & Rogers, C. A. (2006). How exposure to environmental tobacco smoke, outdoor air pollutants, and increased pollen burdens influences the incidence of asthma. Environmental Health Perspectives, 114, 627–633.

    Article  PubMed  CAS  Google Scholar 

  • Haouala, A., Zanolari, B., Rochat, B., et al. (2009). Therapeutic Drug Monitoring of the new targeted anticancer agents imatinib, nilotinib, dasatinib, sunitinib, sorafenib and lapatinib by LC tandem mass spectrometry. Journal of Chromatography B. Analytical Technologies in the Biomedical and Life Sciences, 877, 1982–1996.

    Article  CAS  Google Scholar 

  • Hartigan, J. A., & Wong, M. A. (1979). A k-means clustering algorithm. Applied Statistics, 28, 100–108.

    Article  Google Scholar 

  • Hodel, E. M., Zanolari, B., Mercier, T., et al. (2009). A single LC-tandem mass spectrometry method for the simultaneous determination of 14 antimalarial drugs and their metabolites in human plasma. Journal of Chromatography B. Analytical Technologies in the Biomedical and Life Sciences, 877, 867–886.

    Article  CAS  Google Scholar 

  • Howell, G., III, & Mangum, L. (2010). Exposure to bioaccumulative organochlorine compounds alters adipogenesis, fatty acid uptake, and adipokine production in NIH3T3-L1 cells. Toxicology in Vitro, 25(1), 394–402.

    Article  PubMed  Google Scholar 

  • Institute of Laboratory Animal Resources. (1996). Guide for the care and use of laboratory animals. Washington, DC: National Academy Press.

    Google Scholar 

  • Johnson, J. M., Strobel, F. H., Reed, M., Pohl, J., & Jones, D. P. (2008). A rapid LC-FTMS method for the analysis of cysteine, cystine and cysteine/cystine steady-state redox potential in human plasma. Clinica Chimica Acta, 396, 43–48.

    Article  CAS  Google Scholar 

  • Johnson, J. M., Yu, T., Strobel, F. H., & Jones, D. P. (2010). A practical approach to detect unique metabolic patterns for personalized medicine. Analyst, 135, 2864–2870.

    Article  PubMed  CAS  Google Scholar 

  • Jones, D. P., Park, Y., Gletsu-Miller, N., et al. (2011). Dietary sulfur amino acid effects on fasting plasma cysteine/cystine redox potential in humans. Nutrition, 27, 199–205.

    Article  PubMed  CAS  Google Scholar 

  • Kaufman, L., & Rousseeuw, P. J. (2005). Finding groups in data: an introduction to cluster analysis. New York: Wiley.

    Google Scholar 

  • Kind, T., Scholz, M., & Fiehn, O. (2009). How large is the metabolome? a critical analysis of data exchange practices in chemistry. PLoS One, 4, e5440.

    Article  PubMed  Google Scholar 

  • Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78, 1464–1480.

    Article  Google Scholar 

  • Lawton, K. A., Berger, A., Mitchell, M., et al. (2008). Analysis of the adult human plasma metabolome. Pharmacogenomics, 9, 383–397.

    Article  PubMed  CAS  Google Scholar 

  • Lewis, C. M., Whitwell, S. C., Forbes, A., et al. (2007). Estimating risks of common complex diseases across genetic and environmental factors: the example of Crohn disease. Journal of Medical Genetics, 44, 689–694.

    Article  PubMed  CAS  Google Scholar 

  • Loscalzo, J., Kohane, I., & Barabasi, A. L. (2007). Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Molecular systems biology, 3, 124.

    Article  PubMed  Google Scholar 

  • Mar, J. C., Wells, C. A., & Quackenbush, J. (2011). Defining an informativeness metric for clustering gene expression data. Bioinformatics, 27(8), 1094–1100.

    Article  PubMed  CAS  Google Scholar 

  • Marshall, A. G., & Hendrickson, C. L. (2008). High-resolution mass spectrometers. Annual review of Analytical Chemistry (Palo Alto Calif), 1, 579–599.

    Article  CAS  Google Scholar 

  • McLachlan, G. J., Bean, R. W., & Peel, D. (2002). A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18, 413–422.

    Article  PubMed  CAS  Google Scholar 

  • Miura, D., Tsuji, Y., Takahashi, K., Wariishi, H., & Saito, K. (2010). A strategy for the determination of the elemental composition by fourier transform ion cyclotron resonance mass spectrometry based on isotopic peak ratios. Analytical Chemistry, 82, 5887–5891.

    Article  PubMed  CAS  Google Scholar 

  • Nordstrom, A., Want, E., Northen, T., Lehtio, J., & Siuzdak, G. (2008). Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics. Analytical Chemistry, 80, 421–429.

    Article  PubMed  Google Scholar 

  • Olsen, J. V., Schwartz, J. C., Griep-Raming, J., et al. (2009). A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed. Molecular and Cellular Proteomics, 8, 2759–2769.

    Article  PubMed  CAS  Google Scholar 

  • Peng, J., Oo, M. L., & Andersen, J. K. (2010). Synergistic effects of environmental risk factors and gene mutations in Parkinson’s disease accelerate age-related neurodegeneration. Journal of Neurochemistry, 115(6), 1363–1373.

    Article  PubMed  CAS  Google Scholar 

  • Sandanger, T. M., Brustad, M., Sandau, C. D., & Lund, E. (2006). Levels of persistent organic pollutants (POPs) in a coastal northern Norwegian population with high fish-liver intake. Journal of Environmental Monitoring, 8, 552–557.

    Article  PubMed  CAS  Google Scholar 

  • Soltow, Q. A., Jones, D. P., & Promislow, D. E. (2010). A network perspective on metabolism and aging. Integrative and comparative biology, 50, 844–854.

    Article  PubMed  Google Scholar 

  • Sreekumar, A., Poisson, L. M., Rajendiran, T. M., et al. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457, 910–914.

    Article  PubMed  CAS  Google Scholar 

  • Stone, E. A., & Ayroles, J. F. (2009). Modulated modularity clustering as an exploratory tool for functional genomic inference. PLoS Genetics, 5, e1000479.

    Article  PubMed  Google Scholar 

  • Takahashi, H., Kai, K., Shinbo, Y., et al. (2008). Metabolomics approach for determining growth-specific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry. Analytical and Bioanalytical Chemistry, 391, 2769–2782.

    Article  PubMed  CAS  Google Scholar 

  • Tjernberg, A., Markova, N., Griffiths, W. J., & Hallen, D. (2006). DMSO-related effects in protein characterization. Journal of Biomolecular Screening: the Official Journal of the Society for Biomolecular Screening, 11, 131–137.

    Article  CAS  Google Scholar 

  • Voit, E. O. (2009). A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view. Mathematical Biosciences, 217, 11–18.

    Article  PubMed  Google Scholar 

  • Wang, H., Yang, X. B., Liu, A. L., et al. (2007). Significant positive correlation of plasma BPDE-albumin adducts to urinary 1-hydroxypyrene in coke oven workers. Biomedical and Environmental Sciences, 20, 179–183.

    PubMed  CAS  Google Scholar 

  • Want, E. J., O’Maille, G., Smith, C. A., et al. (2006). Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Analytical Chemistry, 78, 743–752.

    Article  PubMed  CAS  Google Scholar 

  • Wild, C. P. (2005). Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer epidemiology, Biomarkers and Prevention, 14, 1847–1850.

    Article  PubMed  CAS  Google Scholar 

  • Yanes, O., Tautenhahn, R., Patti, G. J., & Siuzdak, G. (2011). Expanding coverage of the metabolome for global metabolite profiling. Analytical Chemistry, 83, 2152–2161.

    Article  PubMed  CAS  Google Scholar 

  • Yu, T., Park, Y., Johnson, J. M., & Jones, D. P. (2009). apLCMS–adaptive processing of high-resolution LC/MS data. Bioinformatics, 25, 1930–1936.

    Article  PubMed  CAS  Google Scholar 

  • Ziech, D., Franco, R., Pappa, A., et al. (2010). The role of epigenetics in environmental and occupational carcinogenesis. Chemico Biological Interactions, 188, 340–349.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank Jennifer M. Johnson, M.S., for her technical help with the internal standard and acquisition of human reference samples. This work was supported by research NIH grants P01ES016731 (DPJ), R01AG038746 (DPJ), R01ES011195 (DPJ) and P51RR000168 (KGM).

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Correspondence to Dean P. Jones.

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Soltow, Q.A., Strobel, F.H., Mansfield, K.G. et al. High-performance metabolic profiling with dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) for study of the exposome. Metabolomics 9 (Suppl 1), 132–143 (2013). https://doi.org/10.1007/s11306-011-0332-1

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