Project description:Toxicity of river sediments are assessed using whole sediment toxicity tests with benthic organisms. The challenge, however, is the differentiation between multiple effects caused by complex contaminant mixtures and the unspecific toxicity endpoints such as survival, growth or reproduction. Moreover, natural sediment properties, such as grain size distribution and organic carbon content, can influence the test parameters by masking pollutant toxicity. The use of gene expression profiling facilitates the identification of transcriptional changes at the molecular level that are specific to the bioavailable fraction of pollutants. The nematode Caenorhabditis elegans is ideally suited for these purposes, as (i) it can be exposed to whole sediments, and (ii) its genome is fully sequenced and widely annotated. In this pilot study we exposed C. elegans for 48 h to three sediments varying in degree of contamination with e.g. heavy metals and organic pollutants. Following the exposure period, gene expression was profiled using a whole genome DNA-microarray approach. Whole genome DNA microarray experiments were performed using a common reference design to identify differentially expressed genes in nematodes exposed to one of three river sediments of differing pollution level. Each sample consists of the 5 “biological replicates”.

2008-06-25 | E-GEOD-11837 | biostudies-arrayexpress

Gene expression profiling as a tool for sediment risk assessment: a pilot study using Caenorhabditis elegans whole genome microarrays

2008-06-25 | GSE11837 | GEO

Archaea in Pearl River estuarine sediments

Project description:Archaeal 16S rRNA genes in Pearl River estuarine sediments

| PRJNA479594 | ENA

Columbia River sediment HUM-V (Hyporheic Uncultured Microbial and Viral) database metaproteome

Project description:Metaproteomic data for Rodriguez-Ramos, et al. interrogating microbial and viral communities of hyporheic river sediments within the Columbia River. Samples were digested with trypsin, and analyzed by LC-MS/MS. Data was searched with MS-GF+ using PNNL's DMS Processing pipeline.

2021-04-28 | MSV000087330 | MassIVE

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