Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Microarray analysis of CL-20 reversible neurotoxicity in the earthworm Eisenia fetida

ABSTRACT: The earthworm Eisenia fetida is one of the most used species in standardized soil ecotoxicity tests. Endpoints such as survival, growth and reproduction are ecologically relevant but provide little mechanistic insight into the toxicity pathways, especially at the molecular level. To better understand toxicological modes of action and to facilitate the development of molecular biomarkers, we have obtained 30,245 unique EST sequences from E. fetida and have designed a novel microarray with 15,119 60-mer oligonucleotide probes. These probes target the unique non-redundant EST sequences identified in E. fetida. Using this array we have profiled gene expression of E. fetida after exposure to CL-20, a cage cyclic nitramine previously found exhibiting reversible neurotoxicity to worms. Worms were exposed for 6 days to CL-20. Half of the exposed worms were allowed to recover in a clean environment for 7 days. Electrophysiological analysis showed that the conduction velocity of worm medial giant nerve fiber was significantly decreased after 6-d exposure to CL-20, and that giant nerve fiber function was restored at the end of the 7-d recovery period. Total RNA samples isolated from four treatment groups (6 replicates per group), i.e., 6-d control, 6-d exposed, 13-d control and 6-d exposed with 7-d recovery, were analyzed using the new 15K oligo array. Bioinformatics and statistical analyses have identified specific neurological pathways affected by CL-20 and recovery of these pathways after CL-20 removal. These results provide significant insights on the CL-20 toxic mode of action and how earthworms can recover from chemical stressors. Adult earthworms (E. fetida) were exposed on filter paper to CL-20 (0.2 ug/cm2) for 6 days with or without 7-day recovery (4 treatment groups in total). Each treatment group had 9 replicate worms, six of which were used for gene expression analysis. Worms were measured for their medial giant nerve fiber conduction velocity using a non-invasive electrophysiological technique immediately before takedown. At the termination of the 6-d or 13-d experiment, worms were snap-frozen and fixed in RNAlater-ICE. Total RNA was isolated from the fixed worms. A total of 24 worm RNA samples were hybridized to three 8x15K custom-designed Agilent oligo arrays 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. After hybridization and scanning, gene expression data were acquired using GenePix Pro 6.0.

ORGANISM(S): Eisenia fetida

SUBMITTER: Xin Guan

PROVIDER: E-GEOD-18536 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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. Adult earthworms (Eisenia fetida) were exposed in soil spiked with HMX (0, 8, 16, 32, 64, or 128 mg/kg) for 4, 14 or 28 days. Each treatment originally had 10 replicate worms with all 10 worms survived at the end of exposure. Total RNA was isolated from the surviving worms. A total of 120 worm RNA samples were hybridized to a custom-designed oligo array using AgilentM-bM-^@M-^Ys 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. After hybridization and scanning, gene expression data were acquired using AgilentM-bM-^@M-^Ys Feature Extraction Software (v.9.1.3). The 120-array dataset consists of three exposure groups (4 day, 14 day and 28 day) with each group having the following 8 treatments: day 0 control, blank control, solvent control, 8, 16, 32, 64, and 128 mg HMX/g soil. Each treatment had 5 biological replicates (i.e., five individual worms).

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.

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Microarray analysis of CL-20 reversible neurotoxicity in the earthworm Eisenia fetida

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