Project description:Draft genome sequence of a high virulent multidrug resistent ESBL-producing Enterobacter hormaechei isolated from a canine urine sample
Project description:Epigenetic variation has the potential to control environmentally dependent development and contribute to phenotypic responses to local environments. Environmental epigenetic studies of sexual organisms confirm the responsiveness of epigenetic variation, which should be even more important when genetic variation is lacking. A previous study of an asexual snail, Potamopyrgus antipodarum, demonstrated that different populations derived from a single clonal lineage differed in both shell phenotype and methylation signature when comparing lake versus river populations. Here, we examine methylation variation among lakes that differ in environmental disturbance and pollution histories. The differential DNA methylation regions (DMRs) identified among the different lake comparisons suggested a higher number of DMRs and variation between rural Lake 1 and one urban Lake 2 and between the two urban Lakes 2 and 3, but limited variation between the rural Lake 1 and urban Lake 3. DMR genomic characteristics and gene associations were investigated. Observations suggest there is no effect of geographic distance or any consistent pattern of DMRs between urban and rural lakes. Environmental factors may influence epigenetic response.
Project description:Rainbow darter (Etheostoma caeruleum) are a small benthic fish found in North America. This species is sensitive to sewage effluent in the environment, showing the presence of intersex in up to 80% of males in near-field areas in the Grand River, ON. To learn more about the molecular events associated with intersex, we developed a customized oligonucleotide microarray (4x180K) with next generation sequencing (454 Roche) to characterize molecular responses in the gonad. Transcriptomics was performed on both males and females from both a reference site and a polluted site. Males with and without intersex from the polluted site were compared to the control males. Rainbow darter were sampled from from the Grand River in May 2011. Fish were selected according to the location, gonad maturity, and intersex index. Reference fish were taken from the upstream to the urban area; exposed fish were taken from downstream of from Kitchener MWWE treatment plant.
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.