Literature DB >> 16917068

Gene expression profiles in fathead minnow exposed to 2,4-DNT: correlation with toxicity in mammals.

Henri Wintz1, Leslie J Yoo, Alex Loguinov, Ying-Ying Wu, Jeffrey A Steevens, Ricky D Holland, Richard D Beger, Edward J Perkins, Owen Hughes, Chris D Vulpe.   

Abstract

Toxicogenomics, the genome-wide analysis of gene expression to study the effect of toxicants, has great potential for use in environmental toxicology. Applied to standard test organisms, it has possible applications in aquatic toxicology as a sensitive monitoring tool to detect the presence of contaminants while providing information on the mechanisms of action of these pollutants. We describe the use of a complementary DNA (cDNA) microarray of the fathead minnow (Pimephales promelas) a standard sentinel organism in aquatic toxicology, to better understand the mechanisms of toxicity of 2,4-dinitrotoluene (2,4-DNT) which is released in the environment through military and industrial use. We have constructed a fathead minnow microarray containing 5000 randomly picked anonymous cDNAs from a whole fish cDNA library. Expression profiles were analyzed in fish exposed to 2,4-DNT for 10 days at three concentrations (11, 22, and 44 microM, respectively) below the measured median lethal concentration (58 microM). Sequence analysis of cDNAs corresponding to differentially expressed genes affected by exposure revealed that lipid metabolism and oxygen transport genes were prominently affected in a dose-specific manner. We measured liver lipids and demonstrate that lipid metabolism is indeed perturbed following exposure. These observations correlate well with available toxicological data on 2,4-DNT. We present possible modes of action of 2,4-DNT toxicity and suggest that fathead minnow cDNA microarrays can be useful to identify mechanisms of toxicity in fish and as a predictive tool for toxicity in mammals.

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Year:  2006        PMID: 16917068     DOI: 10.1093/toxsci/kfl080

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  11 in total

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3.  A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.

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4.  In vitro gene regulatory networks predict in vivo function of liver.

Authors:  Youping Deng; David R Johnson; Xin Guan; Choo Y Ang; Junmei Ai; Edward J Perkins
Journal:  BMC Syst Biol       Date:  2010-11-12

5.  Validation of a genomics-based hypothetical adverse outcome pathway: 2,4-dinitrotoluene perturbs PPAR signaling thus impairing energy metabolism and exercise endurance.

Authors:  Mitchell S Wilbanks; Kurt A Gust; Sahar Atwa; Imran Sunesara; David Johnson; Choo Yaw Ang; Sharon A Meyer; Edward J Perkins
Journal:  Toxicol Sci       Date:  2014-06-03       Impact factor: 4.849

6.  Identification of copper-responsive genes in an early life stage of the fathead minnow Pimephales promelas.

Authors:  Solange S Lewis; Stephen J Keller
Journal:  Ecotoxicology       Date:  2008-11-20       Impact factor: 2.823

7.  Analysis of common and specific mechanisms of liver function affected by nitrotoluene compounds.

Authors:  Youping Deng; Sharon A Meyer; Xin Guan; Barbara Lynn Escalon; Junmei Ai; Mitchell S Wilbanks; Ruth Welti; Natàlia Garcia-Reyero; Edward J Perkins
Journal:  PLoS One       Date:  2011-02-08       Impact factor: 3.240

8.  A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT.

Authors:  Sudhir Chowbina; Youping Deng; Junmei Ai; Xiaogang Wu; Xin Guan; Mitchell S Wilbanks; Barbara Lynn Escalon; Sharon A Meyer; Edward J Perkins; Jake Y Chen
Journal:  BMC Genomics       Date:  2010-12-01       Impact factor: 3.969

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10.  Systems toxicology identifies mechanistic impacts of 2-amino-4,6-dinitrotoluene (2A-DNT) exposure in Northern Bobwhite.

Authors:  Kurt A Gust; Bindu Nanduri; Arun Rawat; Mitchell S Wilbanks; Choo Yaw Ang; David R Johnson; Ken Pendarvis; Xianfeng Chen; Michael J Quinn; Mark S Johnson; Shane C Burgess; Edward J Perkins
Journal:  BMC Genomics       Date:  2015-08-07       Impact factor: 3.969

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