Project description:A variety of contaminants find their way to the marine sediments from different sources, and these contaminants can pose serious risks to the natural marine flora and fauna. For example, pyrethroids, which are a potent pesticide family, are often used in agriculture fields worldwide, and these find their way into the marine environment through run off. Further, pyrethroids are used in farmed Atlantic salmon cages in Chile, Great Britain and Norway. Ammonia is another contaminant that is used in agriculture in form of ammonia-rich fertilizer and can be carried during run-offs to localized rivers and streams. Ammonia is also detectable after emission of effluents from sewage treatment plants and industrial plants like oil refineries and meat processing plants. Contaminants may have short and long term effects on non-target organisms living in the water column or in the marine sediment. Importantly, the sediment ecosystem houses a variety of plants, animals and crustaceans, including the American lobster Homarus americanus. Lobster is the most fished crustacean in New Brunswick and Quebec and its resale and exportation produced over $1.6 billion in 2011. Due to its economic and environmental importance, it is essential to study the effects of contaminants present in its ecosystem. Sediment samples are often used as pollution markers during toxicity testing due to their tendency to accumulate hydrophobic contaminants. To better understand the possible effects of contaminants in sediment, a total gene expression study was developed using the marine amphipod Eohaustorius estuarius. A 10 day spike-in exposure was performed using ammonia and two pyrethroids, namely cypermethrin and deltamethrin. As pyrethroids and ammonia are known to have vastly different mechanisms of action in living organisms, we compared global gene expression patterns following exposure to ammonia against the patterns observed following exposure to pyrethroids. Total gene expression was measured by oligonucleotide microarray. The expression of five genes of interest involved in different biological processes such as metabolism, transcription, translation, immunity and stress, which were found to be differently expressed by microarray, was validated by RT-qPCR. A set of genes was identified that showed differential expression levels in a treatment-dependent manner, thus further highlighting the different mechanisms of action of ammonia and pyrethroids in the marine sediment. This study provides a proof of concept for the use of DNA microarrays with model crustaceans for the study of marine sediment contaminants.

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.

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