Daphnia magna
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA143811 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, Daphnia magna were exposed for 24 hours to five different munitions constituents 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), trinitrobenzene (TNB), dinitrobenzene (DNB), or 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) as well as to 8 different munitions mixtures and ground water contaminated with munitions constituents. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. While mixtures and ground water exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the non-additivity of chemical effects in these mixtures.
2011-06-23 | GSE30163 | GEO
Project description:Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, Daphnia magna were exposed for 24 hours to five different munitions constituents 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), trinitrobenzene (TNB), dinitrobenzene (DNB), or 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) as well as to 8 different munitions mixtures and ground water contaminated with munitions constituents. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. While mixtures and ground water exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the non-additivity of chemical effects in these mixtures. The study consisted of three different experiments: (1) exposure to a concentration corresponding to 70% of 1/10th of the LC50 value of six individual MCs (TNT, 2,4-DNT, 2,6-DNT, DNB, TNB, RDX) and a control; (2) exposure to eight different laboratory mixtures of the previously mentioned MCs. Different combinations of MCs including four mixtures (Mixtures 5, 6, 7 and 8) representative of field collected groundwater from LAAP (Louisiana Army Ammunition Plant) were created; and (3) exposure to MC-contaminated ground water field-collected from 3 different wells (85, 108, and 141) at the LAAP. All exposures were conducted for 24h.
2011-06-22 | E-GEOD-30163 | biostudies-arrayexpress
Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.
2013-01-17 | GSE43564 | GEO
Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability. D. magna were exposed to 36 Chemicals and 5 control series in quadruplicate.
2013-01-17 | E-GEOD-43564 | biostudies-arrayexpress
Project description:This experiment was conducted to study the short-term (12h) transcriptional responses in Daphnia magna after exposure to the anti-sea lice chemical emamectin benzoate (EMB). The microarray results were further vefiried using qPCR. The gene exression responses were linked to adverse effects after 48h exposure, in order to supply knowledge for environmental hazard assessment of this chemical in non-target crustaceans. Neonatal (<24h) Daphnia magna were exposed to 7.8-2000 pM waterborne emamectin benzoate for 12h. Microarray analysis was performed using pooled whole-organism D. magna (8 individuals) and 4 biological replicates were analyzed for each treatment group.
2016-06-30 | E-GEOD-83859 | biostudies-arrayexpress
Project description:This experiment was conducted to study the short-term (12h) transcriptional responses in Daphnia magna after exposure to the anti-sea lice chemical emamectin benzoate (EMB). The microarray results were further vefiried using qPCR. The gene exression responses were linked to adverse effects after 48h exposure, in order to supply knowledge for environmental hazard assessment of this chemical in non-target crustaceans.
2016-06-30 | GSE83859 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE29854: Daphnia magna exposed to narcotics and polar narcotics - aniline GSE29856: Daphnia magna exposed to narcotics and polar narcotics - 4-chloroaniline GSE29857: Daphnia magna exposed to narcotics and polar narcotics - 3,5-dichloroaniline GSE29858: Daphnia magna exposed to narcotics and polar narcotics - 2,3,4-trichloroaniline GSE29862: Daphnia magna exposed to narcotics and polar narcotics - ethanol GSE29864: Daphnia magna exposed to narcotics and polar narcotics - isopropanol GSE29867: Daphnia magna exposed to narcotics and polar narcotics - methanol Refer to individual Series
2012-02-01 | E-GEOD-29993 | biostudies-arrayexpress
Project description:Investigation of mRNA expression (using HiSeq 2500) in response to treatment of Daphnia magna to pyriproxyfen, wetland water, or stormwater samples.
2022-07-26 | GSE208591 | GEO
Project description:Nickel and binary metal mixtures in Daphnia magna after 96h
| PRJNA110467 | ENA
Project description:Transcriptomics studies are more likely to achieve predictive results when they rely on tissue- and cell-specific transcriptional data. Identification of cell types in novel model organisms by their transcriptional profiles is difficult without data on transcriptional differences among major tissues and anatomical features. Here we report the first dataset on tissue- and organ-specific transcriptomics in freshwater plankton crustacean Daphnia magna, reporting markers of eggs, hemocytes, gut, carapace, antennae and head (as well as the remaining carcass).
2024-04-19 | GSE264123 | GEO