ZFIN ID: ZDB-PUB-170410-2
Fishing for contaminants: identification of three mechanism specific transcriptome signatures using Danio rerio embryos
Hausen, J., Otte, J.C., Legradi, J., Yang, L., Strähle, U., Fenske, M., Hecker, M., Tang, S., Hammers-Wirtz, M., Hollert, H., Keiter, S.H., Ottermanns, R.
Date: 2017
Source: Environmental science and pollution research international   25(5): 4023-4036 (Journal)
Registered Authors: Fenske, Martina, Legradi, Jessica, Otte, Jens, Strähle, Uwe, Yang, Lixin
Keywords: Aroclor 1254, Chlorpyrifos, Ecotoxicogenomics, Methylmercury, Pathway network analysis, Transcriptomics
MeSH Terms:
  • Animals
  • Chlorodiphenyl (54% Chlorine)/adverse effects*
  • Chlorpyrifos/adverse effects*
  • Embryo, Nonmammalian/drug effects
  • Embryo, Nonmammalian/metabolism
  • Methylmercury Compounds/adverse effects*
  • Transcriptome/drug effects*
  • Water Pollutants, Chemical/adverse effects*
  • Zebrafish/metabolism*
PubMed: 28391457 Full text @ Environ. Sci. Pollut. Res. Int.
In ecotoxicology, transcriptomics is an effective way to detect gene expression changes in response to environmental pollutants. Such changes can be used to identify contaminants or contaminant classes and can be applied as early warning signals for pollution. To do so, it is important to distinguish contaminant-specific transcriptomic changes from genetic alterations due to general stress. Here we present a first step in the identification of contaminant class-specific transcriptome signatures. Embryos of zebrafish (Danio rerio) were exposed to three substances (methylmercury, chlorpyrifos and Aroclor 1254, each from 24 to 48 hpf exposed) representing sediment typical contaminant classes. We analyzed the altered transcriptome to detect discriminative genes significantly regulated in reaction to the three applied contaminants. By comparison of the results of the three contaminants, we identified transcriptome signatures and biologically important pathways (using Cytoscape/ClueGO software) that react significantly to the contaminant classes. This approach increases the chance of finding genes that play an important role in contaminant class-specific pathways rather than more general processes.