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Linkage of genomic biomarkers to whole organism endpoints in a Toxicity Identification Evaluation (TIE).
Citation:
Biales, A., M. Kostich, R. Flick, D. Bencic, K. Ho, R. Burgess, M. Perron, L. Portis, M. Pelletier, AND M. Reiss. Linkage of genomic biomarkers to whole organism endpoints in a Toxicity Identification Evaluation (TIE). ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 47(3):1306-1312, (2013).
Impact/Purpose:
This paper describes the incorporation of a transcriptomic method into a traditional Toxicity Identification Evaluation (TIE). As explained by the authors, TIE methods have been important for identifying the source of toxicity in mixtures; however, the bioassays employed in these methods have changed little since the inception of the technique. This paper describes one of the first attempts to add a complementary assay, in this case a transcriptomic method, to the TIE procedure which enhances the ability of the predicative power of the method. In addition the paper describes a bioinformatics method to detect a sub-set of the best predictor genes from a list of 129 differentially expressed probes. This subset of 13 genes became the classifier set for subsequent analysis of contaminated sediments.
Description:
Aquatic organisms are exposed to many toxic chemicals and interpreting the cause and effect relationships between occurrence and impairment is difficult. Toxicity Identification Evaluation (TIE) provides a systematic approach for identifying responsible toxicants. TIE relies on relatively uninformative and potentially insensitive toxicological endpoints. Gene expression analysis may provide needed sensitivity and specificity aiding in the identification of primary toxicants. The current work aims to determine the added benefit of integrating gene expression endpoints into the TIE process. A cDNA library and a custom microarray were constructed for the marine amphipod Ampelisca abdita. Phase 1 TIEs were conducted using 10% and 40% dilutions of acutely toxic sediment. Gene expression was monitored in survivors and controls. An expression-based classifier was developed and evaluated against control organisms, organisms exposed to low or medium toxicity diluted sediment, and chemically selective manipulations of highly toxic sediment. The expression-based classifier correctly identified organisms exposed to toxic sediment even when little mortality was observed, suggesting enhanced sensitivity of the TIE process. The ability of the expression-based endpoint to correctly identify toxic sediment was lost concomitantly with acute toxicity when organic contaminants were removed. Taken together, this suggests that gene expression enhances the performance of the TIE process.