Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part, this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems, and perhaps more important, is the insensitivity of the biological end points that we have used to assess these impacts. In this study, we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land-use practices in the surrounding watershed using advanced machine-learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data show that gill tissues are far more responsive and provide superior discrimination of land-use classes than hepatopancreas and that transcripts encoding proteins involved in energy production, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P-450.
A transcriptomic analysis of land-use impacts on the oyster, Crassostrea virginica, in the South Atlantic bight
MANCIA, Annalaura;
2009
Abstract
Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part, this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems, and perhaps more important, is the insensitivity of the biological end points that we have used to assess these impacts. In this study, we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land-use practices in the surrounding watershed using advanced machine-learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data show that gill tissues are far more responsive and provide superior discrimination of land-use classes than hepatopancreas and that transcripts encoding proteins involved in energy production, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P-450.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
