4.7 Article

Significance of metabolite extraction method for evaluating sulfamethazine toxicity in adult zebrafish using metabolomics

期刊

ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
卷 127, 期 -, 页码 127-134

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ecoenv.2016.01.015

关键词

Environmental metabolomics; Danio rerio; Antibiotics

资金

  1. National Institute for Environmental Research [NIER-SP2014-188]
  2. KHIDI [HI14C2686]
  3. National Research Foundation of Korea (NRF) of Korea University [NRF-2014R1A1A2053787]
  4. Korea University

向作者/读者索取更多资源

Recently, environmental metabolomics has been introduced as a next generation environmental toxicity method which helps in evaluating toxicity of bioactive compounds to non-target organisms. In general, efficient metabolite extraction from target cells is one of the keys to success to better understand the effects of toxic substances to organisms. In this regard, the aim of this study is (1) to compare two sample extraction methods in terms of abundance and quality of metabolites and (2) investigate how this could lead to difference in data interpretation using pathway analysis. For this purpose, the antibiotic sulfamethazine and zebrafish (Danio rerio) were selected as model toxic substance and target organism, respectively. The zebrafish was exposed to four different sulfamethazine concentrations (0, 10, 30, and 50 mg/L) for 72 h. Metabolites were extracted using two different methods (Bligh and Dyer and solid phase extraction). A total of 13,538 and 12,469 features were detected using quadrupole time-of-flight liquid chromatography mass spectrometry (QTOF LC-MS). Of these metabolites, 4278 (Bligh and Dyer) and 332 (solid phase extraction) were found to be significant after false discovery rate adjustment at a significance threshold of 0.01. Metlin and KEGG pathway analysis showed comprehensive information from fish samples extracted using Bligh and Dyer compared to solid phase extraction. This study shows that proper selection of sample extraction method is critically important for interpreting and analyzing the toxicity data of organisms when metabolomics is applied. (C) 2016 Elsevier Inc. All rights reserved.

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