Literature DB >> 12628495

Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach.

Wilbert H M Heijne1, Rob H Stierum, Monique Slijper, Peter J van Bladeren, Ben van Ommen.   

Abstract

Toxicogenomics is a novel approach integrating the expression analysis of thousands of genes (transcriptomics) or proteins (proteomics) with classical methods in toxicology. Effects at the molecular level are related to pathophysiological changes of the organisms, enabling detailed comparison of mechanisms and early detection and prediction of toxicity. This report addresses the value of the combined use of transcriptomics and proteomics technologies in toxicology. Acute hepatotoxicity was induced in rats by bromobenzene administration resulting in depleted glutathione levels and reduced average body weights, 24hr after dosage. These physiological symptoms coincided with many changes of hepatic mRNA and protein content. Gene induction confirmed involvement of glutathione-S-transferase isozymes and epoxide hydrolase in bromobenzene metabolism and identified many genes possibly relevant in bromobenzene toxicity. Observed glutathione depletion coincided with induction of the key enzyme in glutathione biosynthesis, gamma-glutamylcysteine synthetase. Oxidative stress was apparent from strong upregulation of heme oxygenase, peroxiredoxin 1 and other genes. Bromobenzene-induced protein degradation was suggested from two-dimensional gel electrophoresis, upregulated mRNA levels for proteasome subunits and lysosomal cathepsin L, whereas also genes were upregulated with a role in protein synthesis. Both protein and gene expression profiles from treated rats were clearly distinct from controls as shown by principal component analysis, and several proteins found to significantly change upon bromobenzene treatment were identified by mass spectrometry. A modest overlap in results from proteomics and transcriptomics was found. This work indicates that transcriptomics and proteomics technologies are complementary to each other and provide new possibilities in molecular toxicology.

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Year:  2003        PMID: 12628495     DOI: 10.1016/s0006-2952(02)01613-1

Source DB:  PubMed          Journal:  Biochem Pharmacol        ISSN: 0006-2952            Impact factor:   5.858


  14 in total

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2.  Using protein abundance to indicate underlying mRNA expression levels in E.coli: an SEM modelling approach.

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3.  Comparison of multidimensional shotgun technologies targeting tissue proteomics.

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Journal:  Electrophoresis       Date:  2009-12       Impact factor: 3.535

Review 4.  Filling and mining the reactive metabolite target protein database.

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Journal:  Chem Biol Interact       Date:  2008-09-06       Impact factor: 5.192

5.  Inter-laboratory reproducibility of fast gas chromatography-electron impact-time of flight mass spectrometry (GC-EI-TOF/MS) based plant metabolomics.

Authors:  J William Allwood; Alexander Erban; Sjaak de Koning; Warwick B Dunn; Alexander Luedemann; Arjen Lommen; Lorraine Kay; Ralf Löscher; Joachim Kopka; Royston Goodacre
Journal:  Metabolomics       Date:  2009-07-24       Impact factor: 4.290

6.  Proteomic profiling of liver from Atlantic salmon (Salmo salar) fed genetically modified soy compared to the near-isogenic non-GM line.

Authors:  Nini H Sissener; Samuel A M Martin; Phillip Cash; Ernst M Hevrøy; Monica Sanden; Gro-Ingunn Hemre
Journal:  Mar Biotechnol (NY)       Date:  2009-07-17       Impact factor: 3.619

7.  Preliminary study of urine metabolism in type two diabetic patients based on GC-MS.

Authors:  Ning Zhang; Fang Geng; Zhong-Hua Hu; Bin Liu; Ye-Qiu Wang; Jun-Cen Liu; Yong-Hua Qi; Li-Jing Li
Journal:  Am J Transl Res       Date:  2016-07-15       Impact factor: 4.060

Review 8.  The role of toxicoproteomics in assessing organ specific toxicity.

Authors:  B Alex Merrick; Frank A Witzmann
Journal:  EXS       Date:  2009

9.  Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

Authors:  Rachel Cavill; Atanas Kamburov; James K Ellis; Toby J Athersuch; Marcus S C Blagrove; Ralf Herwig; Timothy M D Ebbels; Hector C Keun
Journal:  PLoS Comput Biol       Date:  2011-03-31       Impact factor: 4.475

10.  Functional assessment of time course microarray data.

Authors:  María José Nueda; Patricia Sebastián; Sonia Tarazona; Francisco García-García; Joaquín Dopazo; Alberto Ferrer; Ana Conesa
Journal:  BMC Bioinformatics       Date:  2009-06-16       Impact factor: 3.169

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