Fishing for contaminants: identification of three mechanism specific transcriptome signatures using Danio rerio embryos

  • Jonas Hausen
  • Jens C. Otte
  • Jessica Legradi
  • Lixin Yang
  • Uwe Strähle
  • Martina Fenske
  • Markus Hecker
  • Song Tang
  • Monika Hammers-Wirtz
  • Henner Hollert
  • Steffen H. Keiter
  • Richard Ottermanns
Effect-related evaluation of anthropogenic trace substances, -concepts for genotoxicity, neurotoxicity and, endocrine effects

Abstract

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.

Keywords

Transcriptomics Methylmercury Aroclor 1254 Chlorpyrifos Ecotoxicogenomics Pathway network analysis 

Supplementary material

11356_2017_8977_MOESM1_ESM.csv (9 kb)
ESM 1List of all GO-groups and contained functional terms. A table of all significantly enriched GO-terms as well as their respective GO-groups for all three treatments. Table includes p-values and Benjamini-Hochberg corrected p-values of each term. (CSV 9 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jonas Hausen
    • 1
  • Jens C. Otte
    • 2
  • Jessica Legradi
    • 3
  • Lixin Yang
    • 2
  • Uwe Strähle
    • 2
  • Martina Fenske
    • 4
  • Markus Hecker
    • 5
  • Song Tang
    • 5
  • Monika Hammers-Wirtz
    • 6
  • Henner Hollert
    • 1
  • Steffen H. Keiter
    • 1
    • 7
  • Richard Ottermanns
    • 1
  1. 1.Institute for Environmental ResearchRWTH Aachen UniversityAachenGermany
  2. 2.Institute of Toxicology and GeneticsKarlsruhe Institute of TechnologyEggenstein-LeopoldshafenGermany
  3. 3.Environment and HealthVU AmsterdamAmsterdamthe Netherlands
  4. 4.Fraunhofer Institute for Molecular Biology and Applied Ecology IMEProject Group for Translational Medicine and PharmacologyAachenGermany
  5. 5.School of Environment and SustainabilityUniversity of SaskatchewanSaskatoonCanada
  6. 6.Research Institute for Ecosystem Analysis and Assessment - gaiacAachenGermany
  7. 7.Man-Technology-Environment Research CentreÖrebro UniversityÖrebroSweden

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