Project description:Consumer-resource interactions are a central issue in evolutionary and community ecology because they play important roles in selection and population regulation. Most consumers encounter resource variation at multiple scales, and respond through phenotypic plasticity in the short term or evolutionary divergence in the long term. The key traits for these responses may influence resource acquisition, assimilation and/or allocation. To identify candidate genes, we experimentally assayed genome-wide gene expression in pond and lake Daphnia ecotypes exposed to alternate resource environments. One was a simple, high-quality laboratory diet, Ankistrodesmus falcatus. The other was the complex natural seston from a large lake. In temporary ponds, Daphnia generally experience high-quality, abundant resources, whereas lakes provide low-quality, seasonally shifting resources that are chronically limiting. For both ecotypes, we used replicate clones drawn from a number of separate populations. We compared gene expression in whole Daphnia pulex that had been raised in the lab for 10 days, and then exposed to alternate resource environments for 24 hours. One resource environment was a 24 hour continuation of the lab resource, a satiating level of Ankistrodesmus falcatus. The alternate environment was the natural seston present in the epilimnion of Lake Murray, South Carolina. Two ecotypes were analyzed, one adapted to large lakes, and one adapted to temporary ponds. For each ecotype, eight replicate clones were used. Clones of the lake ecotype were isolated from eight independent lakes, clones of the pond ecotype were isolated from six different ponds. The total number of arrays is 16 (8 replicate clones x 2 ecotypes) x 2 resource environments). Total RNA was extracted from eight whole organisms pooled together. Pools were then converted to cDNA and labelled with a single round of amplification. For array hybridizations, samples from the two resource environments were paired for each clone, and dyes were swapped across clones.

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.

Dataset Information

Water Ecosystem

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets