Project description:To determine gene expression differences in the olfactory epithelium of sea lamprey between sequential yet behaviorally distinct adult life history stages 2 samples: parasitic adults removed from fish in northern Lake Huron and Lake Michigan in February and March, and reproductive adults collected from Lake Huron and Lake Michigan tributaries in June
Project description:Transcripts of the gill epithelium from three different stocks of Atlantic salmon (Salmo salar) migrating from freshwater river to lake (Saimaa stock, SS), brackish water (Neva stock, NS) or seawater (Teno stock, TS) were compared at three successive developmental stages (parr, smolt and postsmolt) using the 16K GRASP cDNA microarray platform.
Project description:Today, many contaminants of emerging concern can be measured in waters across the United States, including the tributaries of the Great Lakes. However, just because the chemicals can be measured does not mean that they necessarily result in harm to fish and other aquatic species. Complicating risk assessment in these waters is the fact that aquatic species are encountering the chemicals as mixtures, which may have additive or synergistic risks that cannot be calculated using single chemical hazard and concentration-response information. We developed an in vitro effects-based screening approach to help us predict potential liver toxicity and cancer in aquatic organisms using water from specific Great Lakes tributaries: St. Louis River (MN), Bad River (WI), Fox River (WI), Manitowoc River (WI), Milwaukee River (WI), Indiana Harbor Canal (IN), St. Joseph River (MI), Grand River (MI), Clinton River (MI), River Rouge (MI), Maumee River (OH), Vermilion River (OH), Cuyahoga River (OH), Genesee River (NY), and Oswego River (NY). We exposed HepG2 cells for 48hrs to medium spiked with either field collected water (final concentration of environmental samples in the exposure medium were 75% of the field-collected water samples) or purified water. Using a deep neural network we clustered our collection sites from each tributary based on water chemistry. We also performed high throughput transcriptomics on the RNA obtained from the HepG2 cells. We used the transcriptomics data with our Bayesian Inferene for Sustance and Chemical Toxicity (BISCT) Bayesian Network for Steatosis to predict the probability of the field samples yielding a gene expression pattern consistent with predicting steatosis as an outcome. Surprisingly, we found that the probability of steatosis did not correspond to the surface water chemistry clustering. Our analysis suggests that chemical signatures are not informative in predicting biological effects. Furthermore, recent reports published after we obtained our samples, suggest that chemical levels in the sediment may be more relevant for predicting potential biological effects in the fish species developing tumors in the Great Lakes basin.
