RNA-seq of zebrafish embryos exposed to different sublethal concentrations of flutamide for 96 hours against non-treated control groups
Endocrine disruption (ED) can trigger far-reaching effects on environmental populations, justifying a refusal of market approval for chemicals with ED properties. For the assessment of ED effects on development and reproduction, regulatory decisions mostly rely on apical endpoints from in vivo testing with adult animals. Here, we present a rapid and reproducible mRNA expression profiling experiment for identifying comprehensive molecular fingerprints interfering with the sexual endocrine system in zebrafish (Danio rerio) embryos as an alternative to animal testing. For this, we have analysed flutamide as model substances for anti-androgenic perturbation in a modified zebrafish embryo toxicity test (zFET). These signatures allow for the identification of gene expression changes and the definition of solid biomarkers as tools in screening approaches and for integration in chronic toxicity studies for identifying suspect ED substances, in the fish early life-stage test (OECD TG 210). In a modified version of the zebrafish embryo toxicity test (OECD 236), 15 fertilized eggs were exposed to two different sub lethal concentrations of flutamide for 96 hours under semi-static conditions. Each test comprised of a low exposure (LE, 200 µg/L) and high exposure (HE, 2000 µg/L) and negative control (NC) group and was performed in triplicates. At 96 hours post fertilization (hpf), 10 larvae were randomly picked for each sample and pooled for RNA and protein extraction with NucleoSpin RNA/Protein kit (Macherey-Nagel). RNA quality was assessed with a 2100 Bioanalyzer system (Agilent) before coding RNA was purified (PolyA selection with TruSeq RNA Library Prep Kit v2) and sequenced on an Illumina HiSeq 4000 System (Illumina) in 50 bp single read mode, producing roughly 30 million reads per sample. Adapter sequences were removed with trimmomatic and sequences were aligned to the D.rerio reference genome GRCz11 with STAR. Counting of feature mapped reads was performed through featureCounts. Library gene count tables were then merged to a single count matrix as input for differential gene expression analysis with DESeq2.