Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA355198 | ENA |
REPOSITORIES: ENA
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Project description:Hazard Assessment of High-Nitrogen Compounds: Coupling Transcriptomics with Multi-Cellular Co-Culture Assays Provides Accurate Systemic Toxicity Screening
| PRJNA355187 | ENA
Project description:Hazard Assessment of High-Nitrogen Compounds: Coupling Transcriptomics with Multi-Cellular Co-Culture Assays Provides Accurate Systemic Toxicity Screening [DNAN]
| PRJNA355199 | ENA
Project description:Abstract - Current in vitro hazard assessment techniques lack the systems context necessary to holistically describe in vivo toxicity. To remedy this shortcoming, we have employed the Integrated discrete Multiple Organ Co-Culture (IdMOC) system in conjunction with global-transcriptomic expression assessment to provide a systems context of interactive organ and toxicity pathway responses. Specifically, IdMOC exposures containing hµMan cells representative of five organ types (kidney, liver, lung, vascular endotheliµM and heart muscle) were compared to mono-culture exposures, to evaluate toxic effects in a well-studied legacy munition, 2,4,6-trinitrotoluene (TNT) compared to a structurally similar insensitive munition, 2,4-dinitroanisole (DNAN) having sparsely described toxicity. DNAN toxicity, based on cell viability, was significantly lower than TNT for three cell lines: liver, vascular endotheliµM, and heart. Toxicity pathways enriched in transcriptional analysis of kidney cells exposed to TNT and DNAN through the IdMOC system reflected the literature-based in vivo mammalian toxicity of parent compounds and metabolites where multiple screening values matched chronic dosing levels within 1 order of magnitude. Enriched pathways were primarily unique to each chemical where TNT exposures affected xenobiotic metabolism, oxidative stress, and cell cycle pathways in addition to pathways providing potential screening for hematotoxicity, renal toxicity and urinary cancer. Ten pathway-level responses from IdMOC suggested that DNAN elicited toxicity representative of the primary metabolite, 2,4-dinitrophenol (2,4-DNP). Relative to monoculture, IdMOC results provided a higher nµMber and more robust enrichment of pathways involved in known in vivo toxicity mechanisms for TNT and a profile indicative of in vivo systemic toxicity of DNAN and metabolite 2,4-DNP.
2018-12-01 | GSE90618 | GEO
Project description:Abstract - Current in vitro hazard assessment techniques lack the systems context necessary to holistically describe in vivo toxicity. To remedy this shortcoming, we have employed the Integrated discrete Multiple Organ Co-Culture (IdMOC) system in conjunction with global-transcriptomic expression assessment to provide a systems context of interactive organ and toxicity pathway responses. Specifically, IdMOC exposures containing human cells representative of five organ types (kidney, liver, lung, vascular endothelium and heart muscle) were compared to mono-culture exposures, to evaluate toxic effects in a well-studied legacy munition, 2,4,6-trinitrotoluene (TNT) compared to a structurally similar insensitive munition, 2,4-dinitroanisole (DNAN) having sparsely described toxicity. DNAN toxicity, based on cell viability, was significantly lower than TNT for three cell lines: liver, vascular endothelium, and heart. Toxicity pathways enriched in transcriptional analysis of kidney cells exposed to TNT and DNAN through the IdMOC system reflected the literature-based in vivo mammalian toxicity of parent compounds and metabolites where multiple screening values matched chronic dosing levels within 1 order of magnitude. Enriched pathways were primarily unique to each chemical where TNT exposures affected xenobiotic metabolism, oxidative stress, and cell cycle pathways in addition to pathways providing potential screening for hematotoxicity, renal toxicity and urinary cancer. Ten pathway-level responses from IdMOC suggested that DNAN elicited toxicity representative of the primary metabolite, 2,4-dinitrophenol (2,4-DNP). Relative to monoculture, IdMOC results provided a higher number and more robust enrichment of pathways involved in known in vivo toxicity mechanisms for TNT and a profile indicative of in vivo systemic toxicity of DNAN and metabolite 2,4-DNP.
2018-12-01 | GSE90617 | GEO
Project description:To understand molecular mechanisms of the joint effects of 2,4,6-trinitrotoluene (TNT) and hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), both widely used ordnance compounds, we constructed a microarray consisting of 4,032 cDNA isolated from the earthworm Eisenia fetida using the suppressive subtractive hybridization technique. Worms were exposed to TNT-, RDX-, or TNT+RDX-spiked soil for 28 days (TNT 50 mg/kg, RDX 30 mg/kg). Keywords: Combined toxicity of TNT and RDX to earthworm (Eisenia fetida)
2008-08-28 | GSE8909 | GEO
Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes.
2010-10-08 | GSE18495 | GEO
Project description:To understand molecular mechanisms of the joint effects of 2,4,6-trinitrotoluene (TNT) and hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), both widely used ordnance compounds, we constructed a microarray consisting of 4,032 cDNA isolated from the earthworm Eisenia fetida using the suppressive subtractive hybridization technique. Worms were exposed to TNT-, RDX-, or TNT+RDX-spiked soil for 28 days (TNT 50 mg/kg, RDX 30 mg/kg). Keywords: Combined toxicity of TNT and RDX to earthworm (Eisenia fetida) We analyzed 40 arrays for 4 treatments (control, TNT 50ppm, RDX 30ppm, TNT 50ppm + RDX 30ppm) with 5 biological replicates per treatment using an interwoven loop design.
2008-08-28 | E-GEOD-8909 | biostudies-arrayexpress
Project description:The use of a systems biology approach to analyze common and specific mechanisms of liver toxicity induced by munitions compounds TNT, 2,6-DNT, 2,4-DNT, 4A-DNT, and 2A-DNT The munitions compound 2,4,6-trinitrotoluene (TNT), its environmental degradation products 2-amino-4,6-dinitrotoluene (2A-DNT) and 4-amino-2,6-dinitrotoulene (4A-DNT), and two other munitions, 2,4-dinitrotoluene (2,4-DNT) and 2,4-dinitrotoluene (2,6-DNT) contaminate contaminate land, water and retired ammunitions plants. The release of these compounds to the environment is due to military activities and a series of manufacturing processes. Although toxicity has been characterized for these compounds, little is known of their mechanism of action. Here we describe to use an integrative systems biology approach including toxicology, pathology, transcriptomics, metabolomics, gene function classification, pathway analysis and gene network modeling to try to understand the mechanisms of toxicity of these compounds.
2009-12-25 | GSE19628 | GEO
Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes. Adult earthworms (E. fetida) were exposed in soil spiked with TNT (0, 6, 12, 24, 48, or 96 mg/kg) or RDX (8, 16, 32, 64, or 128 mg/kg) for 0, 4 or 14 days. The 4-day treatment was repeated with RDX concentration being 2, 4, 8, 16 or 32 mg/kg soil. Each treatment originally had 10 replicate worms with 8-10 survivors at the end of exposure. Total RNA was isolated from the surviving worms. A total of 248 worm RNA samples were hybridized to a custom-designed oligo array using Agilentâs one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript (Gong et al. 2009). After hybridization and scanning, gene expression data were acquired using Agilentâs Feature Extraction Software (v.9.1.3). The 248-array dataset consists of three worm groups: 32 untreated controls, 96 TNT-treated, and 120 RDX-treated.
2010-10-08 | E-GEOD-18495 | biostudies-arrayexpress
Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT, RDX and HMX. One important goal of microarray experiments is to discover novel biomarker genes for quantitative phenotypic prediction. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can be used to quantitatively predict earthworm tissue residues of the explosives compounds that they were exposed to and took in from the HMX-spiked soil. Results: We collected a large microarray gene expression and earthworm tissue residue dataset. First, differentially expressed genes were identified for each exposure duration (4, 14 and 28 days). These genes were used in multivariate regression modeling for HMX residue prediction. Eighteen different regression models were tested and compared. The best performing model was able to achieve very high prediction accuracies with R2 values of 0.715, 0.728 and 0.822 for 4 days, 14 days and 28 days exposures, separately. Conclusions: This study demonstrated that multivariate regression coupled with high throughput microarray gene expression was a promising approach to quantitative phenotypic prediction.
2013-08-10 | GSE42866 | GEO