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Field-deployed Metabolomics for Assessing Waters Impacted by Point and Non-Point Sources of Contamination
Citation:
Collette, Tim, G. Ankley, J. Cavallin, E. Durhan, K. Jensen, M. Kahl, L. Makynen, D. Skelton, Q. Teng, Dan Villeneuve, AND D. Ekman. Field-deployed Metabolomics for Assessing Waters Impacted by Point and Non-Point Sources of Contamination. Presented at 34th Annual SETAC North America, Nashville, TN, November 17 - 21, 2013.
Impact/Purpose:
Presentation given at SETAC North America 34th in Nashville, TN (Nov 17-21, 2013)
Description:
Metabolomics is becoming well-established for studying chemical contaminant-induced alterations to normal biological function. For example, the literature contains a wealth of laboratory-based studies involving analysis of samples from organisms exposed to individual chemical toxicants. These lab studies have demonstrated the ability to rapidly screen and prioritize individual chemicals for adverse effects, and also to inform with regard to toxic modes-of-action. However, metabolomics has rarely been used for characterizing the impacts of exposure to complex “real-world” chemical mixtures, or for biomonitoring in the natural environment. This is unfortunate, because metabolomics is well suited for these applications as well. For example, metabolomics is applicable to virtually any species because a sequenced genome is not required. Also, it can be applied with relatively low per-sample cost, is “open-ended” (requiring no pre-selection of targets), and highly reproducible with modern NMR and MS instruments. Recognizing that these are considerable advantages for in situ effects-based monitoring, we have been conducting numerous biomonitoring studies with metabolomics in, and around, the Great Lakes. Most of this work involves caged fathead minnows (Pimephales promelas), which are strategically deployed at various sites in relation to point and non-point sources of contamination (e.g., WWTPs, agricultural operations, etc.) For much of this work, the metabolomic data is linked with other ‘omic measurements, classical whole-animal outcomes, and site characterization and chemical monitoring data. These studies – which will be discussed here – clearly indicate the unique potential for effects-based monitoring with metabolomics.
