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Assessments in biocides with omics approaches to ecosystem

  • Ma, Seohee (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Yoon, Dahye (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Kim, Hyunsu (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Lee, Hyangjin (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Kim, Seonghye (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Lee, Huichan (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Kim, Jieun (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Lee, Soojin (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Lee, Yunsuk (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Lee, Yujin (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University) ;
  • Kim, Suhkmann (Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, Pusan National University)
  • Received : 2017.11.16
  • Accepted : 2018.12.01
  • Published : 2018.12.20

Abstract

Benzisothiazolinone (BIT) is the preservative that is widely used in industrial and household products. In this study, zebrafish (Danio rerio) was exposed to BIT at different concentrations (control, 0.5 g/L, 1.0 g/L and 2.0 g/L) for 72 hours. The techniques of nuclear magnetic resonance (NMR) spectroscopy were applied to analyze the effects of BIT on zebrafish. The advantages of NMR are the minimal sample preparation and high reproducibility of experimental results. With the multivariate statistical analysis, dimethylamine, N-acetylaspartate, glycine and histidine were identified as an important metabolite in differentiating between the control and BIT-exposed group. This study will improve the understanding the metabolite changes in the zebrafish in response to BIT exposure.

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Figure 1. Representative 1H-NMR spectrum of aqueous extracts of zebrafish. High and low field spectra were magnified. Ala, Gln and Glu indicate alanine, glutamine and glutamate, respectively.

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Figure 2. Data normalization plot (control-based z-score).

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Figure 2. PLS-DA score plot of zebrafish exposed to BIT (●, control; ●, exposure).

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Figure 4. VIP plot. The VIP plot displays the top 15 most important metabolite features identified by PLS-DA. (Metabolites of VIP > 1).

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Figure 5. ROC curve. The ROC curves are the comparison of control and BIT exposed group. AUC values: Dimethylamine (0.96) and N-Acetylaspartate (0.92).

Acknowledgement

Supported by : Pusan National University

References

  1. M. D. Lundov, L. Moesby, C. Zachariae, and J. D. Johansen, Contact Derm. 60, 70 (2009) https://doi.org/10.1111/j.1600-0536.2008.01501.x
  2. Danish EPA, Survey and health assessment of preservatives in toys 124, 56 (2014)
  3. Danish EPA, Survey and health assessment of preservatives in toys 124, 38 (2014)
  4. E. Garcia-Hidalgo, V. Sottas, N. von Goetz, U. Hauri, C. Bogdal, and K. Hungerbuhler, Contact Derm. 76, 96 (2017) https://doi.org/10.1111/cod.12700
  5. H. Segner, Comp. Biochem. Physiol. C Toxicol. Pharmacol. 149, 187 (2009) https://doi.org/10.1016/j.cbpc.2008.10.099
  6. A. J. Hill, H. Teraoka, W. Heideman, and R. E. Peterson, Toxicol. Sci. 86, 6 (2005) https://doi.org/10.1093/toxsci/kfi110
  7. P. McGrath and C. Q. Li, Drug Discov. Today 13, 394 (2008) https://doi.org/10.1016/j.drudis.2008.03.002
  8. L. M. Samuelsson, L. Forlin, G. Karlsson, M. Adolfsson-Erici, and D. G. Larsson, Aquat. Toxicol. 78, 341 (2006) https://doi.org/10.1016/j.aquatox.2006.04.008
  9. D. Yoon, J. Choi, H. Choi, and S. Kim, J. Korean Magn. Reson. Soc. 20, 13 (2016) https://doi.org/10.6564/JKMRS.2016.20.1.013
  10. D. Yoon, M. Lee, S. Kim, and S. Kim, J. Korean Magn. Reson. Soc. 17, 1 (2013)
  11. J. L. Griffin, Curr. Opin. Chem. Biol. 7, 648 (2003) https://doi.org/10.1016/j.cbpa.2003.08.008
  12. O. Beckonert, H. C. Keun, T. M. Ebbels, J. Bundy, E. Holmes, J. C. Lindon, and J. K. Nicholson, Nat. Protoc. 2, 2692 (2007) https://doi.org/10.1038/nprot.2007.376
  13. OECD guideline for the testing of chemicals: Fish, Acute Toxicity Test, Guideline 203, Organization for Economic Co-operation and Development (2014)
  14. E. G. Bligh and W. J. Dyer, Can. J. Biochem. Physiol. 37, 911 (1959) https://doi.org/10.1139/y59-099
  15. A. M. Weljie, J. Newton, P. Mercier, E. Carlson, and C. M. Slupsky, Anal. Chem. 78, 4430 (2006) https://doi.org/10.1021/ac060209g
  16. E. Szymanska, E. Saccenti, A. K. Smilde, and J. A. Westerhuis, Metabolomics 8, 3 (2012) https://doi.org/10.1007/s11306-011-0330-3
  17. M. Barker and W. Rayens, J. Chemom. 17, 166 (2003) https://doi.org/10.1002/cem.785
  18. E. J. van Velzen, J. A. Westerhuis, J. P. van Duynhoven, F. A. van Dorsten, H. C. Hoefsloot, D. M. Jacobs, S. Smit, R. Draijer, C. I. Kroner, and A. K. Smilde, J. Proteome Res. 7, 4483 (2008) https://doi.org/10.1021/pr800145j
  19. D. G. Altman and J.M. Bland, Br. Med. J. 308, 1552 (1994) https://doi.org/10.1136/bmj.308.6943.1552
  20. T. Fawcett, Machine Learning, 31, 1 (2004)
  21. J. Davis and M. Goadrich, Proceedings of the 23rd International Conference on Machine Learning, 233 (2006)
  22. J. Zhou, B. Chen and Z. Cai, Environ. Sci. Pollut. Res. Int. 22, 5092 (2015) https://doi.org/10.1007/s11356-014-3859-7
  23. J. W. Lee, J. W. Lee, K. Kim, Y. J. Shin, J. Kim, S. Kim, H. Kim, P. Kim, and K. Park, J. Hazard. Mater. 340, 231 (2017) https://doi.org/10.1016/j.jhazmat.2017.06.058
  24. M. Teng, W. Zhu, D. Wang, S. Qi, Y. Wang, J. Yan, K. Dong, M. Zheng, and C. Wang, Aquat. Toxicol. 194, 112 (2018) https://doi.org/10.1016/j.aquatox.2017.11.009
  25. S. Y. Kim, B. Y. Choe, H. S. Lee, D. W. Lee, K. N. Ryu, J. S. Park, C. S. Yin, K. S. Hong, C. H. Lee, and C. B. Choi, Neurochem. J. 5, 270 (2011) https://doi.org/10.1134/S1819712411040088
  26. C. Demougeot, P. Garnier, C. Mossiat, N. Bertrand, M. Giroud, A. Beley, and C. Marie, J. Neurochem. 77, 408 (2001) https://doi.org/10.1046/j.1471-4159.2001.00285.x
  27. T. N. Sager, S. Topp, L. Torup, L. G. Hanson, B. Egestad, and A. Moller, Brain Res. 892, 166 (2001) https://doi.org/10.1016/S0006-8993(00)03274-1
  28. T. E. Bates, M. Strangward, J. Keelan, G. P. Davey, P. M. Munro, and J. B. Clark, Neuroreport 7, 1397 (1996) https://doi.org/10.1097/00001756-199605310-00014
  29. G. B. Downes and M. Granato, J. Neurobiol. 66, 437 (2006) https://doi.org/10.1002/neu.20226
  30. A. Roberts, Brain Res. Bull. 53, 585 (2000) https://doi.org/10.1016/S0361-9230(00)00392-0
  31. A. Roberts, S. R. Soffe, E. S. Wolf, M. Yoshida, and F. Y. Zhao, Ann. N. Y. Acad. Sci. 860, 19 (1998) https://doi.org/10.1111/j.1749-6632.1998.tb09036.x
  32. S. Grillner, Nat. Rev. Neurosci. 4, 573 (2003)
  33. O. Kiehn and S. J. Butt, Prog. Neurobiol. 70, 347 (2003) https://doi.org/10.1016/S0301-0082(03)00091-1
  34. L. P. Cofiel and R. Mattioli, Braz. J. Med. Biol. Res. 42, 128 (2009) https://doi.org/10.1590/S0100-879X2009000100018
  35. Y. Bessho, E. Iwakoshi-Ukena, T. Tachibana, S. Maejima, S. Taniuchi, K. Masuda, K. Shikano, K. Kondo, M. Furumitsu, and K. Ukena, Neurosci. Lett. 578, 106 (2014) https://doi.org/10.1016/j.neulet.2014.06.048
  36. A. N. Fonteh, R. J. Harrington, A. Tsai, P. Liao, and M. G. Harrington, Amino Acids 32, 213 (2007) https://doi.org/10.1007/s00726-006-0409-8
  37. H. Tohgi, T. Abe, K. Hashiguchi, S. Takahashi, Y. Nozaki, and T. Kikuchi, Neurosci. Lett. 126, 155 (1991) https://doi.org/10.1016/0304-3940(91)90542-2