Project description:Aquatic organisms are exposed to many toxic chemicals and interpreting the cause and effect relationships between occurrence and impairment is difficult. Toxicity Identification Evaluation (TIE) provides a systematic approach for identifying responsible toxicants. TIE relies on relatively uninformative and potentially insensitive toxicological endpoints. Gene expression analysis may provide needed sensitivity and specificity aiding in the identification of primary toxicants. The current work aims to determine the added benefit of integrating gene expression endpoints into the TIE process. A cDNA library and a custom microarray were constructed for the marine amphipod Ampelisca abdita. Phase 1 TIEs were conducted using 10% and 40% dilutions of acutely toxic sediment. Gene expression was monitored in survivors and controls. An expression-based classifier was developed and evaluated against control organisms, organisms exposed to low or medium toxicity diluted sediment, and chemically selective manipulations of highly toxic sediment. The expression-based classifier correctly identified organisms exposed to toxic sediment even when little mortality was observed, suggesting enhanced sensitivity of the TIE process. The ability of the expression-based endpoint to correctly identify toxic sediment was lost concomitantly with acute toxicity when organic contaminants were removed. Taken together, this suggests that gene expression enhances the performance of the TIE process.
Project description:Flounder fish were exposed in mesocosms for seven months to a contaminated estuarine sediment made by mixing material from the Forth (high organics) and Tyne (high metals and tributyltin) estuaries (FT) or control sediment from the Ythan estuary (Y). Their gene expression profiles were compatred by cDNA microarrays.
Project description:Aquatic organisms are exposed to many toxic chemicals and interpreting the cause and effect relationships between occurrence and impairment is difficult. Toxicity Identification Evaluation (TIE) provides a systematic approach for identifying responsible toxicants. TIE relies on relatively uninformative and potentially insensitive toxicological endpoints. Gene expression analysis may provide needed sensitivity and specificity aiding in the identification of primary toxicants. The current work aims to determine the added benefit of integrating gene expression endpoints into the TIE process. A cDNA library and a custom microarray were constructed for the marine amphipod Ampelisca abdita. Phase 1 TIEs were conducted using 10% and 40% dilutions of acutely toxic sediment. Gene expression was monitored in survivors and controls. An expression-based classifier was developed and evaluated against control organisms, organisms exposed to low or medium toxicity diluted sediment, and chemically selective manipulations of highly toxic sediment. The expression-based classifier correctly identified organisms exposed to toxic sediment even when little mortality was observed, suggesting enhanced sensitivity of the TIE process. The ability of the expression-based endpoint to correctly identify toxic sediment was lost concomitantly with acute toxicity when organic contaminants were removed. Taken together, this suggests that gene expression enhances the performance of the TIE process. Wild-collected Ampelisca abdita were exposed to either control (from sites in Long Island Sound, labeled LIS) sediment, toxic (from site on Elizabeth River, labeled ER) sediment, a series of mixtures of LIS and ER sediment, sediments manipulated to alter toxin bioavailability, or toxicant amended sediments. Lethality was scored, and survivors were subjected to mRNA expression analysis via oligo microarray.
Project description:Nitrate-reducing iron(II)-oxidizing bacteria are widespread in the environment contribute to nitrate removal and influence the fate of the greenhouse gases nitrous oxide and carbon dioxide. The autotrophic growth of nitrate-reducing iron(II)-oxidizing bacteria is rarely investigated and poorly understood. The most prominent model system for this type of studies is enrichment culture KS, which originates from a freshwater sediment in Bremen, Germany. To gain insights in the metabolism of nitrate reduction coupled to iron(II) oxidation under in the absence of organic carbon and oxygen limited conditions, we performed metagenomic, metatranscriptomic and metaproteomic analyses of culture KS. Raw sequencing data of 16S rRNA amplicon sequencing, shotgun metagenomics (short reads: Illumina; long reads: Oxford Nanopore Technologies), metagenome assembly, raw sequencing data of shotgun metatranscriptomes (2 conditions, triplicates) can be found at SRA in https://www.ncbi.nlm.nih.gov/bioproject/PRJNA682552. This dataset contains proteomics data for 2 conditions (heterotrophic and autotrophic growth conditions) in triplicates.
Project description:16s RNA gene sequencing data from seawater, bed sediment and steel corrosion samples from Shoreham Harbour, UK, collected to allow bacterial species comparisons between microbially influenced corrosion, the surrounding seawater, and the sea bed sediment at the seafloor and 50cm depth below seafloor.
Project description:This study aimed to evaluate gene expression patterns in urinary sediment samples of children with steroid-resistant nephrotic syndrome (SRNS).
Project description:Metagenome data from soil samples were collected at 0 to 10cm deep from 2 avocado orchards in Channybearup, Western Australia, in 2024. Amplicon sequence variant (ASV) tables were constructed based on the DADA2 pipeline with default parameters.
Project description:Primary outcome(s): Aspiration volume by continuous aspiration catheter and mass of sediment extracted from endoscopically collected intestinal fluid over time.
Project description:Metagenome sequencing All specimens were collected and immediately stored in a -80 freezer. All BALF samples were subjected to MS. DNA was extracted from BALF using the TIANamp Micro DNA kit (DP316, Tiangen Biotech). DNA libraries were constructed with the end-repair method and then sequenced on the BGI Sequencer platform (BGI Genomics, Shenzhen, China). Bioinformatic pipeline analysis Low-quality and short (<35 bp) reads were removed from raw data using fastp [10]. Remaining reads were mapped to the human reference genome (hg19) using the Burrows-Wheeler method to remove sequences of human origin. Filtered reads were classified with RefSeq, downloaded from NCBI (ftp://ftp.ncbi.nlm.nih.gov/genomes/).