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Gene expression profiles of murine fatty liver induced by the administration of valproic acid
- Gene expression profiles of murine fatty liver induced by the administration of valproic acid
- Lee M.-H.; Hong I.; Kim M.; Lee B.H.; Kim J.-H.; Kang K.-S.; Kim H.-L.; Yoon B.-I.; Chung H.; Kong G.; Lee M.-O.
- Ewha Authors
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- Toxicology and Applied Pharmacology
- Toxicology and Applied Pharmacology vol. 220, no. 1, pp. 45 - 59
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- Valproic acid (VPA) has been used as anticonvulsants, however, it induces hepatotoxicity such as microvesicular steatosis and necrosis in the liver. To explore the mechanisms of VPA-induced steatosis, we profiled the gene expression patterns of the mouse liver that were altered by treatment with VPA using microarray analysis. VPA was orally administered as a single dose of 100 mg/kg (low-dose) or 1000 mg/kg (high-dose) to ICR mice and the animals were killed at 6, 24, or 72 h after treatment. Serum alanine aminotransferase and aspartate aminotransferase levels were not significantly altered in the experimental animals. However, symptoms of steatosis were observed at 72 h with low-dose and at 24 h and 72 h with high-dose. After microarray data analysis, 1910 genes were selected by two-way ANOVA (P < 0.05) as VPA-responsive genes. Hierarchical clustering revealed that gene expression changes depended on the time rather than the dose of VPA treatment. Gene profiling data showed striking changes in the expression of genes associated with lipid, fatty acid, and steroid metabolism, oncogenesis, signal transduction, and development. Functional categorization of 1156 characteristically up- and down-regulated genes (cutoff > 1.5-fold) revealed that 60 genes were involved in lipid metabolism that was interconnected with biological pathways for biosynthesis of triglyceride and cholesterol, catabolism of fatty acid, and lipid transport. This gene expression profile may be associated with the known steatogenic hepatotoxicity of VPA and it may provide useful information for prediction of hepatotoxicity of unknown chemicals or new drug candidates through pattern recognition. © 2006 Elsevier Inc. All rights reserved.
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