High-throughput concentration-response analysis for omics datasets
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Periodical |
| Magazine / Source | Toxicol. Environ. Chem. |
| MU Faculty or unit | |
| Citation | |
| web | http://onlinelibrary.wiley.com/doi/10.1002/etc.3025/abstract?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+27th+February+from+09:00-14:00+GMT+/+04:00-09:00+EST+/+17:00-22:00+SGT+for+essential+maintenance.++Apologies+for+the+inconveni |
| Doi | http://dx.doi.org/10.1002/etc.3025 |
| Field | Environment influence on health |
| Keywords | Ecotoxicogenomics; Biostatistics; Dose-response modeling; Mixture toxicity; Zebrafish embryo; Myriophyllum |
| Description | Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level. |
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