Project description:Transcriptional profiling of mussel (Mytilus galloprovincialis) digestive gland tissue comparing control tissue with tissue obtained from animals exposed for four days to sublethal amounts of Nickel, Chlorpyrifos, or their mixture. Background: Mixtures of chemicals present in aquatic environments may elicit toxicity due to additive or synergistic effects among the constituents or ‘vice versa’ the adverse outcome may be reduced by antagonistic interactions. Deviations from additivity should be explained either by the perturbations of toxicokinetic parameters and/or chemical toxicodynamics. We addressed this important question in marine mussels exposed subchronically to a binary mixture made of two wide-spread pollutants: the heavy metal Nickel and the organic phosphorus pesticide Chlorpyrifos. To this aim, we proposed the use of a systems approach based on the evaluation and integration of different disciplines, i.e. high throughput gene expression profiling, functional genomics, stress biomakers and toxicokinetics. Results: Stress biomarkers showed statistically significant antagonistic deviations from the reference model systems to predict mixture toxicity which are based either on simple additivity or non-interaction. While toxicokinetic modeling did not explain mixture interactions, gene expression profiling and further functional genomics analysis provided clues that the decrement of toxicity may arise from the development of specific toxicodynamics. Multivariate statistics of microarray and quantitative PCR data showed two separate patterns for the single chemicals and a composite complex profile for the mixture suggesting the occurrence of interactive molecular responses. The latter signature accounted for differentially expressed genes whose relative expression values were either in trend or in contrast with those found due to single substance treatments and a relevant set of sequences which were exclusive of the mixture gene list. Conclusion: The functional genomics assessment fits with biological data to indicate the occurrence of different toxicodynamic events and ‘in general’ a decrease of toxicity, driven by the mitigation or even abolishment of lysosomal responses such as the lipid metabolic disorder observed exclusively in single chemical-exposed samples. Furthermore, our results emphasized the importance of the application of mechanistic approaches and the power of systems assessment to study toxicological responses in ecological relevant organisms. Mussel MytArray 1.0 (Platform GPL1799)-based arrays: Three-condition experiment. Dual color competitive hybridizations. Common reference (vehicle treated animals, 0.02% Dimethyl sulfoxide); Pools of six animals. Biological replicates: 4 controls, 4 Nickel, 4 Chlorpyrifos. One replicate per array. Mussel MytArray 1.1 (Platform GPL10269)-based arrays: Four-condition experiment. Dual color competitive hybridizations. Common reference (vehicle treated animals, 0.02% Dimethyl sulfoxide); Pools of six animals. Biological replicates: 5 controls, 1 Nickel, 3 Chlorpyrifos, 5 mixture. One replicate per array.

Project description:The aim of this mRNA expression profiling experiment was to screen for ecotoxicogenomic fingerprints in zebrafish (Danio rerio) embryos as aquatic vertebrate non-target model exposed to sub lethal concentrations of the heavily used organophosphate insecticide Chlorpyrifos (CAS 2921-88-2). The Insecticide Resistance Action Committee (IRAC) classified Chlorpyrifos after its mode of action (MoA) in the target organism as an acetylcholinesterase (AChE) inhibitor (Group 1B). The goal is to identify toxicogenomic profiles with predictive character and potential molecular key events (KE) explaining upstream adverse effects in aquatic non-target organisms. This will provide useful information to refine and improve existing adverse outcome pathways (AOP). Furthermore, integrating the obtained profiles for this and other tested chemicals in a collective database will enable us in the future to derive predictions about the ecotoxicological hazard for chemcials with unknown apical effects, based on similarly altered transcriptomic and proteomic profiles. In a modified version of the zebrafish embryo toxicity test (OECD 236), 15 fertilized eggs were exposed to two different sub lethal concentrations of 6PTU for 96 hours under semi-static conditions. Each test comprised of a low exposure (LE, 0.75 mg/L), high exposure (HE, 3 mg/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 data normalization and differential gene expression analysis with DESeq2.

Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results: Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their intended chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions: The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin Since this study was conducted in several phases, three different version of Agilent zebrafish two color microarrays were used based on their availability at the time. These include G2518A (designID 013223) and G2519F (designID 015064, 019161). There were a total of 58 treatment conditions with various combinations of chemical, tissue type, exposure time, and gender. Each condition contained eight to 12 independent samples, half from chemical-treated fish and half from water- control fish.

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RNA-seq of immortalized rat thyrocytes (PCCl3) treated with two thyroid disrupting chemicals (ethylenethiourea and chlorpyrifos) against untreated controls

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