The Molecular Toxicity Identification Evaluation (mTIE) Approach Predicts Chemical Exposure in Daphnia magna
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ABSTRACT:
ORGANISM(S): Daphnia magna
PROVIDER: GSE43564 | GEO | 2013/01/17
SECONDARY ACCESSION(S): PRJNA186710
REPOSITORIES: GEO
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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:Toxic chemical contaminants have variety of detrimental effects on various species and the impact of pollutants on ecosystems has become an urgent issue. However, very limited species have been examined to date and those studies are mainly limited to vertebrates. In this study, we aimed to establish an ecotoxicogenomic bases for Daphnia magna. Based on a daphnia EST database, we made oligonucleotide-based DNA microarray that has high reproducibility. The DNA microarray was applied to evaluate gene expression profiles of daphnid exposed to chemicals. Characteristic gene expression patterns depending on chemicals indicate that the Daphnia microarray can be used for mechanistic understanding of chemical toxicity. Although acute toxicity test or reproductive toxicity test can provide hazardous concentrations of chemicals, they give no information about mode of action. Our study can be a breakthrough for the evaluation of chemical toxicity on environmental organisms. Keywords: Chemical response
2005-12-31 | GSE3445 | GEO
Project description:Background: Toxicogenomics provides new opportunities for innovative and proactive approaches to chemical screening, risk assessment, and predictive toxicology. If applied to ecotoxicology, genomics tools could greatly enhance the ability to detect toxicants and understand the modes of toxicity in an environmental setting. However, few studies have yet to illustrate the potential of genomic techniques in ecotoxicology. Objective: Therefore, our objective was to demonstrate the potential utility of gene expression profiling in ecotoxicology using Daphnia magna, a standard aquatic ecotoxicity test organism. Methods: D. magna were exposed to copper, cadmium, and zinc at the 1/10 LC50 for 24 hours. Following each exposure, RNA was isolated, reverse transcribed, and the cDNA was hybridized to a 5000 clone cDNA microarray for D. magna. Differentially expressed cDNAs were sequenced and homology searches revealed each gene product's potential function. Real time PCR was used to verify the differential expression of several genes, and enzyme assays were used to assess the significance of these changes. Results: We identified distinct expression profiles in response to acute copper, cadmium, and zinc exposures and discovered specific biomarkers of exposure including two probable metallothioneins, and a ferritin mRNA with a functional IRE. The gene expression patterns support known mechanisms of metal toxicity and reveal novel modes of action including zinc inhibition of chitinase activity. Conclusions: Using a cDNA microarray for traditional ecotoxicology organism, D. magna, we have identified novel biomarkers of exposure and revealed possible modes of toxicity, providing experimental support for the utility of ecotoxicogenomics. Keywords: comparative toxicant exposure
2006-12-22 | GSE4759 | GEO
Project description:Today, many contaminants of emerging concern can be measured in waters across the United States, including the tributaries of the Great Lakes. However, just because the chemicals can be measured does not mean that they necessarily result in harm to fish and other aquatic species. Complicating risk assessment in these waters is the fact that aquatic species are encountering the chemicals as mixtures, which may have additive or synergistic risks that cannot be calculated using single chemical hazard and concentration-response information. We developed an in vitro effects-based screening approach to help us predict potential liver toxicity and cancer in aquatic organisms using water from specific Great Lakes tributaries: St. Louis River (MN), Bad River (WI), Fox River (WI), Manitowoc River (WI), Milwaukee River (WI), Indiana Harbor Canal (IN), St. Joseph River (MI), Grand River (MI), Clinton River (MI), River Rouge (MI), Maumee River (OH), Vermilion River (OH), Cuyahoga River (OH), Genesee River (NY), and Oswego River (NY). We exposed HepG2 cells for 48hrs to medium spiked with either field collected water (final concentration of environmental samples in the exposure medium were 75% of the field-collected water samples) or purified water. Using a deep neural network we clustered our collection sites from each tributary based on water chemistry. We also performed high throughput transcriptomics on the RNA obtained from the HepG2 cells. We used the transcriptomics data with our Bayesian Inferene for Sustance and Chemical Toxicity (BISCT) Bayesian Network for Steatosis to predict the probability of the field samples yielding a gene expression pattern consistent with predicting steatosis as an outcome. Surprisingly, we found that the probability of steatosis did not correspond to the surface water chemistry clustering. Our analysis suggests that chemical signatures are not informative in predicting biological effects. Furthermore, recent reports published after we obtained our samples, suggest that chemical levels in the sediment may be more relevant for predicting potential biological effects in the fish species developing tumors in the Great Lakes basin.
2022-12-01 | GSE146903 | GEO
Project description:Daphnia magna has been used extensively to evaluate organism- and population-level responses to pollutants in acute toxicity and reproductive toxicity tests. We have previously reported that exposure to juvenile hormone (JH) agonists results in a reduction of reproductive function and production of male offspring in a cyclic parthenogenesis, D. magna. Recent advances in molecular techniques have provided tools to better understand the responses to pollutants in aquatic organisms including D. magna. DNA microarray was used to evaluate gene expression profiles of neonatal daphnids exposed to JH agonists; methoprene (125, 250 and 500 ppb), fenoxycarb (0.5, 1 and 2 ppb), and epofenonane (50, 100 and 200 ppb). Exposure to these JH analogs resulted in chemical specific patterns of gene expression. The heat map analyses based on hierarchical clustering revealed a similar pattern between treatments with a high dose of methoprene and with epofenonane. In contrast, treatment with low to middle doses of methoprene resulted in similar profiles to fenoxycarb treatments. Hemoglobin and JH epoxide hydrolase genes clustered to be JH-responsive genes. These data suggest that fenoxycarb has high activity as a JH agonist, methoprene shows high toxicity and epofenonane works through a different mechanism compared with other JH analogs, agreeing with data of previously reported toxicity tests. In conclusion, D. magna DNA microarray is useful for classification of JH analogs and identification of JH-responsive genes. Juvenile hormone agonists induced gene expression in daphnids neonates was measured at 48 hours after exposure to methoprene (125, 250, 500 ppb), fenoxycarb (0.5, 1, 2 ppb), epofenonane (50, 100, 200) and DMF as a control. Three independent experiments were performed at each chemicals.
2013-12-31 | E-GEOD-47990 | biostudies-arrayexpress
Project description:The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development are overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days, 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points-of-departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with 8 additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment.
2013-04-22 | GSE45892 | GEO
Project description:MOA categorization of chemicals with narcotic mechanism by combining gene transcription profiling and physiological effect assessment after EC10 exposure in systems biology approach in Daphnia magna.
2015-11-14 | GSE52663 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29867 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29862 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29864 | GEO