Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1012128 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25897129_1.fastq.gz | Fastqsanger.gz | |||
| SRR25897129_2.fastq.gz | Fastqsanger.gz | |||
| SRR25897130_1.fastq.gz | Fastqsanger.gz | |||
| SRR25897130_2.fastq.gz | Fastqsanger.gz | |||
| SRR25897131_1.fastq.gz | Fastqsanger.gz |
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Project description:Analysis of microbial gene expression in response to physical and chemical gradients forming in the Columbia River, estuary, plume and coastal ocean was done in the context of the environmental data base. Gene expression was analyzed for 2,234 individual genes that were selected from fully sequenced genomes of 246 prokaryotic species (bacteria and archaea) as related to the nitrogen metabolism and carbon fixation. Seasonal molecular portraits of differential gene expression in prokaryotic communities during river-to-ocean transition were created using freshwater baseline samples (268, 270, 347, 002, 006, 207, 212). Total RNA was isolated from 64 filtered environmental water samples collected in the Columbia River coastal margin during 4 research cruises (14 from August, 2007; 17 from November, 2007; 18 from April, 2008; and 16 from June, 2008), and analyzed using microarray hybridization with the CombiMatrix 4X2K format. Microarray targets were prepared by reverse transcription of total RNA into fluorescently labeled cDNA. All samples were hybridized in duplicate, except samples 212 and 310 (hybridized in triplicate) and samples 336, 339, 50, 152, 157, and 199 (hybridized once). Sample location codes: number shows distance from the coast in km; CR, Columbia River transect in the plume and coastal ocean; NH, Newport Hydroline transect in the coastal ocean at Newport, Oregon; AST and HAM, Columbia River estuary locations near Astoria (river mile 7-9) and Hammond (river mile 5), respectively; TID, Columbia River estuary locations in the tidal basin (river mile 22-23); BA, river location at Beaver Army Dock (river mile 53) near Quincy, Oregon; UP, river location at mile 74.
2010-09-10 | E-GEOD-18303 | biostudies-arrayexpress
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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Project description:Freshwater environments such as rivers receive effluent discharges from wastewater treatment plants, representing a potential hotspot for antibiotic resistance genes (ARGs). These effluents also contain low levels of different antimicrobials including biocides and antibiotics such as sulfonamides that can be frequently detected in rivers. The impact of such exposure on ARG prevalence and microbial diversity of riverine environment is unknown, so the aim of this study was to investigate the release of a sub-lethal concentration (<4 g L-1) of the sulfonamide compound sulfamethoxazole (SMX) on the river bacterial microbiome using a microflume system. This system was a semi-natural in-vitro microflume using river water (30 L) and sediment, with circulation to mimic river flow. A combination of ‘omics’ approaches were conducted to study the impact of SMX exposure on the microbiomes within the microflumes. Metaproteomics did not show differences in ARGs expression with SMX exposure in water.
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