Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analysed by direct-infusion mass spectrometry based metabolomics and lipidomics

Metabolomics

Volumn 10, Issue 6, 2014, Pages 1050-1058

  Southam, Andrew D ^a   Lange, Anke ^b   Al Salhi, Raghad ^c   Hill, Elizabeth M ^c   Tyler, Charles R ^b   Viant, Mark R ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b UNIVERSITY OF EXETER   (United Kingdom)

^c UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Endometabolome; Exposome; Lipidome; Rutilus rutilus; Xenometabolome

Indexed keywords

STEROID; XENOBIOTIC AGENT;

ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTEMIA; ARTICLE; CHIRONOMUS; CONTROLLED STUDY; DATA PROCESSING; DIRECT INFUSION MASS SPECTROMETRY; EFFLUENT; ENDOCRINE DISEASE; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL MONITORING; EXTRACTION AND EXTRACTS; FEMALE; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; LIPIDOMICS; LIQUID CHROMATOGRAPHY; MALE; MASS SPECTROMETRY; METABOLOME; METABOLOMICS; NONHUMAN; PRINCIPAL COMPONENT ANALYSIS; SENSITIVITY ANALYSIS; SOLID PHASE EXTRACTION; TANDEM MASS SPECTROMETRY; WASTE WATER EFFLUENT;

EID: 84919950718     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-014-0693-3     Document Type: Article

References (26)

1. Al-Salhi, R., Abdul-Sada, A., Lange, A., Tyler, C. R., & Hill, E. M. (2012). The xenometabolome and novel contaminant markers in fish exposed to a wastewater treatment works effluent. Environmental Science and Technology,46, 9080–9088.
2. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate—a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Methodological,57, 289–300.
3. Borg, B. (1994). Androgens in teleost fishes. Comparative Biochemistry and Physiology C-Pharmacology Toxicology & Endocrinology,109, 219–245.
4. Bundy, J. G., Davey, M. P., & Viant, M. R. (2009). Environmental metabolomics: A critical review and future perspectives. Metabolomics,5, 3–21.
5. Calabrese, E. J., & Baldwin, L. A. (2003). The hormetic dose–response model is more common than the threshold model in toxicology. Toxicological Sciences,71, 246–250.
6. 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 H-1 NMR metabonomics. Analytical Chemistry,78, 4281–4290.
7. Draper, J., Lloyd, A., Goodacre, R., & Beckmann, M. (2013). Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: A review. Metabolomics,9, 4–29.
8. Flores-Valverde, A. M., & Hill, E. M. (2008). Methodology for profiling the steroid metabolome in animal tissues using ultraperformance liquid chromatography-electrospray-time-of-flight mass spectrometry. Analytical Chemistry,80, 8771–8779.
9. Flores-Valverde, A. M., Horwood, J., & Hill, E. M. (2010). Disruption of the steroid metabolome in fish caused by exposure to the environmental estrogen 17 alpha-ethinylestradiol. Environmental Science and Technology,44, 3552–3558.
10. Han, J., Danell, R. M., Patel, J. R., Gumerov, D. R., Scarlett, C. O., Speir, J. P., et al. (2008). Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics,4, 128–140.
11. Hines, A., Staff, F. J., Widdows, J., Compton, R. M., Falciani, F., & Viant, M. R. (2010). Discovery of metabolic signatures for predicting whole organism toxicology. Toxicological Sciences,115, 369–378.
12. Holmes, E., Loo, R. L., Cloarec, O., Coen, M., Tang, H., Maibaum, E., et al. (2007). Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Analytical Chemistry,79, 2629–2640.
13. Jobling, S., Burn, R. W., Thorpe, K., Williams, R., & Tyler, C. (2009). Statistical modeling suggests that antiandrogens in effluents from wastewater treatment works contribute to widespread sexual disruption in fish living in english rivers. Environmental Health Perspectives,117, 797–802.
14. Lin, L., Yu, Q., Yan, X., Hang, W., Zheng, J., Xing, J., et al. (2010). Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. Analyst,135, 2970–2978.
15. Liney, K. E., Hagger, J. A., Tyler, C. R., Depledge, M. H., Galloway, T. S., & Jobling, S. (2006). Health effects in fish of long-term exposure to effluents from wastewater treatment works. Environmental Health Perspectives,114, 81–89.
16. Parsons, H. M., Ludwig, C., Gunther, U. L., & Viant, M. R. (2007). Improved classification accuracy in 1-and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation. BMC Bioinformatics,8, 234.
17. Payne, T. G., Southam, A. D., Arvanitis, T. N., & Viant, M. R. (2009). A signal filtering method for improved quantification and noise discrimination in Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data. Journal of the American Society for Mass Spectrometry,20, 1087–1095.
18. Rostkowski, P., Horwood, J., Shears, J. A., Lange, A., Oladapo, F. O., Besselink, H. T., et al. (2011). Bioassay-directed identification of novel antiandrogenic compounds in bile of fish exposed to wastewater effluents. Environmental Science and Technology,45, 10660–10667.
19. Routledge, E. J., Sheahan, D., Desbrow, C., Brighty, G. C., Waldock, M., & Sumpter, J. P. (1998). Identification of estrogenic chemicals in STW effluent. 2. In vivo responses in trout and roach. Environmental Science and Technology,32, 1559–1565.
20. Southam, A. D., Lange, A., Hines, A., Hill, E. M., Katsu, Y., Iguchi, T., et al. (2011). Metabolomics reveals target and off-target toxicities of a model organophosphate pesticide to roach (Rutilus rutilus): Implications for biomonitoring. Environmental Science and Technology,45, 3759–3767.
21. Southam, A. D., Payne, T. G., Cooper, H. J., Arvanitis, T. N., & Viant, M. R. (2007). Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method. Analytical Chemistry,79, 4595–4602.
22. Van Aggelen, G., Ankley, G. T., Baldwin, W. S., Bearden, D. W., Benson, W. H., Chipman, J. K., et al. (2010). Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: Hurdles, achievements, and future outlook. Environmental Health Perspectives,118, 1–5.
23. Weber, R. J. M., & Viant, M. R. (2010). MI-Pack: Increased confidence of metabolite identification in mass spectra by integrating accurate masses and metabolic pathways. Chemometrics and Intelligent Laboratory Systems,104, 75–82.
24. Wu, H. F., Southam, A. D., Hines, A., & Viant, M. R. (2008). High-throughput tissue extraction protocol for NMRand MS-based metabolomics. Analytical Biochemistry,372, 204–212.
25. Zhang, Y., Qiu, L., Wang, Y., Qin, X., & Li, Z. (2014). High-throughput and high-sensitivity quantitative analysis of serum unsaturated fatty acids by chip-based nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry: Early stage diagnostic biomarkers of pancreatic cancer. Analyst,139, 1697–1706.
26. Zhang, A. H., Sun, H., Wang, P., Han, Y., & Wang, X. J. (2012). Modern analytical techniques in metabolomics analysis. Analyst,137, 293–300.