Identification of Early Warning Indicators of Environmental Stress in the ACE Basin (South Carolina) by Using Gene Expression Profiles From Oysters, Crassostrea virginicaEPA Grant Number: U916185
Title: Identification of Early Warning Indicators of Environmental Stress in the ACE Basin (South Carolina) by Using Gene Expression Profiles From Oysters, Crassostrea virginica
Investigators: Jenny, Matthew J.
Institution: University of South Carolina at Columbia
EPA Project Officer: Michaud, Jayne
Project Period: January 1, 2003 through January 1, 2006
Project Amount: $124,000
RFA: STAR Graduate Fellowships (2003) Recipients Lists
Research Category: Academic Fellowships , Biology/Life Sciences , Fellowship - Biochemistry, Molecular Biology, Cell Biology, Development Biology, and Genetics
The objective of this research project is to test the hypothesis that the physiological status of oysters is reflected in specific patterns of gene expression, which manifest via the interplay of internal processes in organisms and external environmental stressors.
Long-term chronic exposures to metals and organic pollutants from point and nonpoint sources (i.e., aerial deposition, boating activities) threaten estuarine ecosystems, including the ACE Basin, South Carolina. Crassostrea virginica, a common species found along the Gulf of Mexico and Atlantic seaboard, is an important indicator species of estuarine health, and a potentially valuable model for evaluating the relationship between ecosystems and human activities. "Transcript profiling" will be used to assess and compare the expression of approximately 2,000 genes, including a family of metallothionein isoforms, in oysters from sites located primarily in the ACE Basin and a small number of highly contaminated sites in Charleston Harbor (Charleston, SC). In addition to the gene expression profiles, additional indicators of cell damage (glutathione, lipid peroxidation, lysosomal destabilization), tissue metals analysis, and environmental parameters (water and sediment quality) will be assessed. Artificial neural network techniques (ANN) will be used to analyze this multiparametric dataset. With the ability to store and utilize experience-based knowledge, ANNs can extract "signal" from "noise," making them ideally suited for large-scale ecological monitoring. The functional genomics approach will validate and expand traditional ecological models by incorporating gene expression patterns as part of the assessment strategy. Gene microarrays are sensitive, rapid-screening techniques capable of advancing current environmental assessment tools. The success of future conservation and management practices relies on a greater understanding of the effects of environmental pressures on organismal health. Gene expression profiles will be used to identify potential early warning indicators of long-term chronic stress.