Metabolomics

, Volume 5, Issue 1, pp 44–58 | Cite as

A new approach to toxicity testing in Daphnia magna: application of high throughput FT-ICR mass spectrometry metabolomics

  • Nadine S. Taylor
  • Ralf J. M. Weber
  • Andrew D. Southam
  • Tristan G. Payne
  • Olga Hrydziuszko
  • Theodoros N. Arvanitis
  • Mark R. Viant
Original Article

Abstract

Currently there is a surge of interest in exploiting toxicogenomics to screen the toxicity of chemicals, enabling rapid and accurate categorisation into classes of defined mode-of-action (MOA), and prioritising chemicals for further testing. Direct infusion FT-ICR mass spectrometry-based metabolomics can provide a sensitive and unbiased analysis of metabolites in only 15 mins and therefore has considerable potential for chemical screening. The water flea, Daphnia magna, is an OECD test species and is utilised internationally for toxicity testing. However, no metabolomics studies of this species have been reported. Here we optimised and evaluated the effectiveness of FT-ICR mass spectrometry metabolomics for toxicity testing in D. magna. We confirmed that high-quality mass spectra can be recorded from as few as 30 neonates (<24 h old; 224 μg dry mass) or a single adult daphnid (301 μg dry mass). An OECD 24 h acute toxicity test was conducted with neonates at copper concentrations of 0, 5, 10, 25, 50 μg l−1. A total of 5447 unique peaks were detected reproducibly, of which 4768 were assigned at least one empirical formula and 1017 were putatively identified based upon accurate mass measurements. Significant copper-induced changes to the daphnid metabolome, consistent with the documented MOA of copper, were detected thereby validating the approach. In addition, N-acetylspermidine was putatively identified as a novel biomarker of copper toxicity. Collectively, our results highlight the excellent sensitivity, reproducibility and mass accuracy of FT-ICR mass spectrometry, and provide strong evidence for its applicability to high-throughput screening of chemical toxicity in D. magna.

Keywords

Metabonomics Environmental metabolomics Water flea Tiered testing Risk assessment SIM-stitching 

Supplementary material

11306_2008_133_MOESM1_ESM.doc (1.7 mb)
(DOC 1788 kb)

References

  1. Ankley, G. T., Miracle, A., Perkins, E. J., & Daston, G. P. (2008). Genomics in regulatory ecotoxicology: Applications and challenges. Boca Raton, London: CRC Press.Google Scholar
  2. Arambasic, M. B., Bjelic, S., & Subakov, G. (1995). Acute toxicity of heavy metals (copper, lead and zinc), phenol and sodium on Allium cepa L., Lepidium sativum L. and Daphnia magna ST-comparative investigations and the practical applications. Water Research, 29, 497–503. doi:10.1016/0043-1354(94)00178-A.CrossRefGoogle Scholar
  3. Baldovin, A., Wu, W., Centner, V., et al. (1996). Feature selection for the discrimination between pollution types with partial least squares modelling. Analyst (London), 121, 1603–1608. doi:10.1039/an9962101603.CrossRefGoogle Scholar
  4. Barata, C., Navarro, J. C., Varo, I., et al. (2005). Changes in antioxidant enzyme activities, fatty acid composition and lipid peroxidation in Daphnia magna during the aging process. Comparative Biochemistry and Physiology B: Comparative Biochemistry, 140, 81–90. doi:10.1016/j.cbpc.2004.09.025.CrossRefGoogle Scholar
  5. 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, 57, 289–300.Google Scholar
  6. Benson, W. H., Gallagher, K., & McClintock, J. T. (2007). U.S. Environmental Protection Agency’s activities to prepare for regulatory and risk assessment applications of genomics information. Environmental and Molecular Mutagenesis, 48, 359–362. doi:10.1002/em.20302.PubMedCrossRefGoogle Scholar
  7. Berlett, B. S., & Stadtman, E. R. (1997). Protein oxidation in aging, disease, and oxidative stress. The Journal of Biological Chemistry, 272, 20313–20316. doi:10.1074/jbc.272.33.20313.PubMedCrossRefGoogle Scholar
  8. Bligh, E. G., & Dyer, W. J. (1959). A rapid method of total lipid extraction and purification. Canadian Journal of Biochemistry and Physiology, 37, 911–917.PubMedGoogle Scholar
  9. Bopp, S. K., Abicht, H. K., & Knauer, K. (2008). Copper-induced oxidative stress in rainbow trout gill cells. Aquatic Toxicology (Amsterdam, Netherlands), 86, 197–204. doi:10.1016/j.aquatox.2007.10.014.Google Scholar
  10. Breitling, R., Pitt, A. R., & Barrett, M. P. (2006). Precision mapping of the metabolome. Trends in Biotechnology, 24, 543–548. doi:10.1016/j.tibtech.2006.10.006.PubMedCrossRefGoogle Scholar
  11. Breitling, R., Vitkup, D., & Barrett, M. P. (2008). New surveyor tools for charting microbial metabolic maps. Nature Reviews. Microbiology, 6, 156–161. doi:10.1038/nrmicro1797.PubMedCrossRefGoogle Scholar
  12. Brown, S. C., Kruppa, G., & Dasseux, J. L. (2005). Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrometry Reviews, 24, 223–231. doi:10.1002/mas.20011.PubMedCrossRefGoogle Scholar
  13. Casero, R. A., & Pegg, A. E. (1993). Spermidine spermine N1-acetyltransferase—the turning-point in polyamine metabolism. The FASEB Journal, 7, 653–661.PubMedGoogle Scholar
  14. 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, 9–27. doi:10.1021/tx700335d.PubMedCrossRefGoogle Scholar
  15. Coffino, P., & Poznanski, A. (1991). Killer polyamines. Journal of Cellular Biochemistry, 45, 54–58. doi:10.1002/jcb.240450112.PubMedCrossRefGoogle Scholar
  16. Colbourne, J. K., Singan, V. R., & Gilbert, D. G. (2005). wFleaBase: The Daphnia genomics information system. http://wfleabase.org/.
  17. Connon, R., Hooper, H. L., Sibly, R. M., et al. (2008). Linking molecular and population stress responses in Daphnia magna exposed to cadmium. Environmental Science and Technology, 42, 2181–2188. doi:10.1021/es702469b.PubMedCrossRefGoogle Scholar
  18. De Coen, W. M., & Janssen, C. R. (2003). A multivariate biomarker-based model predicting population-level responses of Daphnia magna. Environmental Toxicology and Chemistry, 22, 2195–2201. doi:10.1897/02-223.PubMedCrossRefGoogle Scholar
  19. Deleebeeck, N. M., De Schamphelaere, K. A., Heijerick, D. G., Bossuyt, B. T., & Janssen, C. R. (2008). The acute toxicity of nickel to Daphnia magna: Predictive capacity of bioavailability models in artificial and natural waters. Ecotoxicology and Environmental Safety, 70, 67–78. doi:10.1016/j.ecoenv.2007.05.002.PubMedCrossRefGoogle Scholar
  20. De Meester, L., & Vanoverbeke, J. (1999). An uncoupling of male and sexual egg production leads to reduced inbreeding in the cyclical parthenogen Daphnia. Proceedings of the Biological Sciences, 266, 2471–2477. doi:10.1098/rspb.1999.0948.PubMedCrossRefGoogle Scholar
  21. De Schamphelaere, K. A. C., & Janssen, C. R. (2004). Effects of dissolved organic carbon concentration and source, pH, and water hardness on chronic toxicity of copper to Daphnia magna. Environmental Toxicology and Chemistry, 23, 1115–1122. doi:10.1897/02-593.PubMedCrossRefGoogle Scholar
  22. 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 1H NMR metabonomics. Analytical Chemistry, 78, 4281–4290. doi:10.1021/ac051632c.PubMedCrossRefGoogle Scholar
  23. Dunn, W. B. (2008). Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Physical Biology, 5, 11001. doi:10.1088/1478-3975/5/1/011001.PubMedCrossRefGoogle Scholar
  24. Ekman, D. R., Teng, Q., Villeneuve, D. L., et al. (2008). Investigating compensation and recovery of fathead minnow (Pimephales promelas) exposed to 17 alpha-ethynylestradiol with metabolite profiling. Environmental Science and Technology, 42, 4188–4194. doi:10.1021/es8000618.PubMedCrossRefGoogle Scholar
  25. Gaetke, L. M., & Chow, C. K. (2003). Copper toxicity, oxidative stress, and antioxidant nutrients. Toxicology, 189, 147–163. doi:10.1016/S0300-483X(03)00159-8.PubMedCrossRefGoogle Scholar
  26. Han, J., Danell, R. M., Patel, J. R., et al. (2008). Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics, 4, 128–140. doi:10.1007/s11306-008-0104-8.PubMedCrossRefGoogle Scholar
  27. Heckmann, L.-R., Sibly, R. M., Connon, R., et al. (2008). Systems biology meets stress ecology: Linking molecular and organismal stress responses in Daphnia magna. Genome Biology, 9, R40. doi:10.1186/gb-2008-9-2-r40.PubMedCrossRefGoogle Scholar
  28. Heijerick, D. G., Janssen, C. R., & De Coen, W. M. (2003). The combined effects of hardness, pH, and dissolved organic carbon on the chronic toxicity of Zn to D. magna: Development of a surface response model. Archives of Environmental Contamination and Toxicology, 44, 210–217. doi:10.1007/s00244-002-2010-9.CrossRefGoogle Scholar
  29. Ikenaka, Y., Eun, H., Ishizaka, M., & Miyabara, Y. (2006). Metabolism of pyrene by aquatic crustacean, Daphnia magna. Aquatic Toxicology (Amsterdam, Netherlands), 80, 158–165. doi:10.1016/j.aquatox.2006.08.005.Google Scholar
  30. Kanehisa, M., Goto, S., Hattori, M., et al. (2006). From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Research, 34, D354–D357. doi:10.1093/nar/gkj102.PubMedCrossRefGoogle Scholar
  31. Kind, T., & Fiehn, O. (2007). Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics, 8, 105. doi:10.1186/1471-2105-8-105.PubMedCrossRefGoogle Scholar
  32. Knops, M., Altenburger, R., & Segner, H. (2001). Alterations of physiological energetics, growth and reproduction of Daphnia magna under toxicant stress. Aquatic Toxicology (Amsterdam, Netherlands), 53, 79–90. doi:10.1016/S0166-445X(00)00170-3.Google Scholar
  33. Linder, M. C., & Hazegh-Azam, M. (1996). Copper biochemistry and molecular biology. The American Journal of Clinical Nutrition, 63, 797S–811S.PubMedGoogle Scholar
  34. Makarov, A., Denisov, E., Lange, O., & Horning, S. (2006). Dynamic range of mass accuracy in LTQ orbitrap hybrid mass spectrometer. Journal of the American Society for Mass Spectrometry, 17, 977–982. doi:10.1016/j.jasms.2006.03.006.PubMedCrossRefGoogle Scholar
  35. Martins, J., Oliva Teles, L., & Vasconcelos, V. (2007a). Assays with Daphnia magna and Danio rerio as alert systems in aquatic toxicology. Environment International, 33, 414–425. doi:10.1016/j.envint.2006.12.006.PubMedCrossRefGoogle Scholar
  36. Martins, J., Soares, M. L., Saker, M. L., Olivateles, L., & Vasconcelos, V. M. (2007b). Phototactic behavior in Daphnia magna Straus as an indicator of toxicants in the aquatic environment. Ecotoxicology and Environmental Safety, 67, 417–422. doi:10.1016/j.ecoenv.2006.11.003.PubMedCrossRefGoogle Scholar
  37. Nicholson, J. K., Connelly, J., Lindon, J. C., & Holmes, E. (2002). Metabonomics: A platform for studying drug toxicity and gene function. Nature Reviews. Drug Discovery, 1, 153–161. doi:10.1038/nrd728.PubMedCrossRefGoogle Scholar
  38. OECD. (1998). Guidelines for testing of chemicals, no. 211—Daphnia magna reproduction test (p. 21). Organisation for Economic Cooperation and Development.Google Scholar
  39. OECD. (2004). Guidelines for Testing of Chemicals, No. 202—Daphnia sp.acute immobilisation test (p. 12). Organisation for Economic Cooperation and Development.Google Scholar
  40. Olsen, J. V., de Godoy, L. M. F., Li, G., Macek, B., Mortensen, P., Pesch, R., et al. (2005). Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Molecular & Cellular Proteomics, 4, 2010–2021. doi:10.1074/mcp.T500030-MCP200.CrossRefGoogle Scholar
  41. Parsons, H. M., Ludwig, C., Günther, 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. doi:10.1186/1471-2105-8-234.PubMedCrossRefGoogle Scholar
  42. Poynton, H. C., Varshavsky, J. R., Chang, B., et al. (2007). Daphnia magna ecotoxicogenomics provides mechanistic insights into metal toxicity. Environmental Science and Technology, 41, 1044–1050. doi:10.1021/es0615573.PubMedCrossRefGoogle Scholar
  43. REACH. (2006). Registration, evaluation, authorisation and restriction of chemicals REACH, regulation no. 1907/2006. The European Parliament and The Council of The European Union.Google Scholar
  44. Robosky, L. C., Robertson, D. G., Baker, J. D., Rane, S., & Reily, M. D. (2002). In vivo toxicity screening programs using metabonomics. Combinatorial Chemistry & High Throughput Screening, 5, 651–662.Google Scholar
  45. Sandbacka, M., Christianson, I., & Isomaa, B. (2000). The acute toxicity of surfactants on fish cells, Daphnia magna and fish-a comparative study. Toxicology In Vitro, 14, 61–68. doi:10.1016/S0887-2333(99)00083-1.PubMedCrossRefGoogle Scholar
  46. Sangster, T. P., Wingate, J. E., Burton, L., Teichert, F., & Wilson, I. D. (2007). Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry. Rapid Communications in Mass Spectrometry, 21, 2965–2970. doi:10.1002/rcm.3164.PubMedCrossRefGoogle Scholar
  47. Santojanni, A., Gorbi, G., & Sartore, F. (1998). Prediction of fecundity in chronic toxicity tests on Daphnia magna. Water Research, 32, 3146–3156. doi:10.1016/S0043-1354(98)00052-9.CrossRefGoogle Scholar
  48. Smolders, R., Baillieul, M., & Blust, R. (2005). Relationship between the energy status of Daphnia magna and its sensitivity to environmental stress. Aquatic Toxicology (Amsterdam, Netherlands), 73, 155–170. doi:10.1016/j.aquatox.2005.03.006.Google Scholar
  49. Soetaert, A., Vandenbrouck, T., van der Ven, K., et al. (2007a). Molecular responses during cadmium-induced stress in Daphnia magna: Integration of differential gene expression with higher-level effects. Aquatic Toxicology (Amsterdam, Netherlands), 83, 212–222. doi:10.1016/j.aquatox.2007.04.010.Google Scholar
  50. Soetaert, A., van der Ven, K., Moens, L. N., et al. (2007b). Daphnia magna and ecotoxicogenomics: Gene expression profiles of the anti-ecdysteroidal fungicide fenarimol using energy-, molting- and life stage-related cDNA libraries. Chemosphere, 67, 60–71. doi:10.1016/j.chemosphere.2006.09.076.PubMedCrossRefGoogle Scholar
  51. Soga, T., Baran, R., Suematsu, M., et al. (2006). Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption. The Journal of Biological Chemistry, 281, 16768–16776. doi:10.1074/jbc.M601876200.PubMedCrossRefGoogle Scholar
  52. 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. doi:10.1021/ac062446p.PubMedCrossRefGoogle Scholar
  53. Stohs, S. J., & Bagchi, D. (1995). Oxidative mechanisms in the toxicity of metal ions. Free Radical Biology and Medicine, 18, 321–336. doi:10.1016/0891-5849(94)00159-H.PubMedCrossRefGoogle Scholar
  54. Sugimoto, H., Matsuzaki, S., Hamana, K., Yamada, S., & Kobayashi, S. (1991). Alpha-tocopherol and superoxide-dismutase suppress and diethyldithiocarbamate and phorone enhance the lipopolysaccharide-induced increase in N1-acetylspermidine concentrations in mouse-liver. Circulatory Shock, 33, 171–177.PubMedGoogle Scholar
  55. Sumner, L. W., Amberg, A., Barrett, D., et al. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics, 3, 211–221. doi:10.1007/s11306-007-0082-2.CrossRefGoogle Scholar
  56. 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. doi:10.1007/s00216-008-2195-5.PubMedCrossRefGoogle Scholar
  57. Tsui, M. T., & Wang, W. X. (2007). Biokinetics and tolerance development of toxic metals in Daphnia magna. Environmental Toxicology and Chemistry, 26, 1023–1032. doi:10.1897/06-430R.1.PubMedCrossRefGoogle Scholar
  58. US-EPA. (2004). Potential implications of genomics for regulatory and risk assessment applications at EPA (p. 70). United States Environmental Protection Agency.Google Scholar
  59. Viant, M. R., Bundy, J. G., Pincetich, C. A., de Ropp, J. S., & Tjeerdema, R. S. (2005). NMR-derived developmental metabolic trajectories: An approach for visualizing the toxic actions of trichloroethylene during embryogenesis. Metabolomics, 1, 149–158. doi:10.1007/s11306-005-4429-2.CrossRefGoogle Scholar
  60. Viant, M. R., Pincetich, C. A., & Tjeerdema, R. S. (2006). Metabolic effects of dinoseb, diazinon and esfenvalerate in eyed eggs and alevins of Chinook salmon (Oncorhynchus tshawytscha) determined by 1H NMR metabolomics. Aquatic Toxicology (Amsterdam, Netherlands), 77, 359–371. doi:10.1016/j.aquatox.2006.01.009.Google Scholar
  61. Watanabe, H., Takahashi, E., Nakamura, Y., et al. (2007). Development of a Daphnia magna DNA microarray for evaluating the toxicity of environmental chemicals. Environmental Toxicology and Chemistry, 26, 669–676. doi:10.1897/06-075R.1.PubMedCrossRefGoogle Scholar
  62. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., et al. (2008). Assessment of PLSDA cross validation. Metabolomics, 4, 81–89. doi:10.1007/s11306-007-0099-6.CrossRefGoogle Scholar
  63. Wu, H., Southam, A. D., Hines, A., & Viant, M. R. (2008). High throughput tissue extraction protocol for NMR- and MS-based metabolomics. Analytical Biochemistry, 372, 204–212. doi:10.1016/j.ab.2007.10.002.PubMedCrossRefGoogle Scholar
  64. Zhang, L. K., Rempel, D., Pramanik, B. N., & Gross, M. L. (2005). Accurate mass measurements by Fourier transform mass spectrometry. Mass Spectrometry Reviews, 24, 286–309. doi:10.1002/mas.20013.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Nadine S. Taylor
    • 1
  • Ralf J. M. Weber
    • 2
  • Andrew D. Southam
    • 1
  • Tristan G. Payne
    • 3
  • Olga Hrydziuszko
    • 2
  • Theodoros N. Arvanitis
    • 3
  • Mark R. Viant
    • 1
    • 2
  1. 1.School of BiosciencesUniversity of BirminghamBirminghamUK
  2. 2.Centre for Systems BiologyUniversity of BirminghamBirminghamUK
  3. 3.School of Electronic, Electrical & Computer EngineeringUniversity of BirminghamBirminghamUK

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