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Mar Environ Res. 2014 Sep;100:57-67. doi: 10.1016/j.marenvres.2014.03.007. Epub 2014 Mar 20.

Machine learning approaches to investigate the impact of PCBs on the transcriptome of the common bottlenose dolphin (Tursiops truncatus).

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Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy; Marine Biomedicine and Environmental Science Center, Medical University of South Carolina, Hollings Marine Laboratory, Charleston, SC 29412, USA. Electronic address:
NOAA, National Ocean Service, Hollings Marine Laboratory, Charleston, SC 29412, USA.
National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, SC 29412, USA.
NOAA, National Marine Fisheries Service, Office of Protected Species, Silver Spring, MD 20910, USA.
Chicago Zoological Society, c/o Mote Marine Laboratory, Sarasota, FL 34236, USA.
NOAA, National Marine Fisheries Service, Southeast Fisheries Science Center, Lafayette, LA 70506, USA.
NOAA, National Marine Fisheries Service, Southeast Fisheries Science Center, Beaufort, NC 28516, USA.


As top-level predators, common bottlenose dolphins (Tursiops truncatus) are particularly sensitive to chemical and biological contaminants that accumulate and biomagnify in the marine food chain. This work investigates the potential use of microarray technology and gene expression profile analysis to screen common bottlenose dolphins for exposure to environmental contaminants through the immunological and/or endocrine perturbations associated with these agents. A dolphin microarray representing 24,418 unigene sequences was used to analyze blood samples collected from 47 dolphins during capture-release health assessments from five different US coastal locations (Beaufort, NC, Sarasota Bay, FL, Saint Joseph Bay, FL, Sapelo Island, GA and Brunswick, GA). Organohalogen contaminants including pesticides, polychlorinated biphenyl congeners (PCBs) and polybrominated diphenyl ether congeners were determined in blubber biopsy samples from the same animals. A subset of samples (n = 10, males; n = 8, females) with the highest and the lowest measured values of PCBs in their blubber was used as strata to determine the differential gene expression of the exposure extremes through machine learning classification algorithms. A set of genes associated primarily with nuclear and DNA stability, cell division and apoptosis regulation, intra- and extra-cellular traffic, and immune response activation was selected by the algorithm for identifying the two exposure extremes. In order to test the hypothesis that these gene expression patterns reflect PCB exposure, we next investigated the blood transcriptomes of the remaining dolphin samples using machine-learning approaches, including K-nn and Support Vector Machines classifiers. Using the derived gene sets, the algorithms worked very well (100% success rate) at classifying dolphins according to the contaminant load accumulated in their blubber. These results suggest that gene expression profile analysis may provide a valuable means to screen for indicators of chemical exposure.


Biotoxin exposure; Bottlenose dolphin; Ecogenomics; Environmental contaminant exposure; Machine learning; Transcriptome

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