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Relationship between hepatic gene expression profiles and hepatotoxicity in five typical hepatotoxicant-administered

Keiichi Minami1, Toshiro Saito, Masatoshi Narahara

  • 1Drug Metabolism and Toxicology, Division of Pharmaceutical Sciences, Kanazawa University, Kanazawa, Japan.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|June 25, 2005
PubMed

Insights

This study reveals that gene expression profiles from DNA microarray analysis can predict liver toxicity in rats. Researchers identified 17 potential toxicity markers that correlate with biochemical markers of liver damage, aiding drug safety evaluation.

Area of Science:

  • Toxicogenomics
  • Gene Expression Analysis
  • Drug Development

Background:

  • DNA microarray technology is crucial for toxicogenomics, aiding in predicting adverse drug effects and evaluating safety.
  • Investigating the link between hepatotoxic phenotypes and gene expression profiles is essential for understanding chemical-induced liver injury.

Purpose of the Study:

  • To explore the relationship between hepatotoxic phenotypes and gene expression profiles of hepatotoxic chemicals using DNA microarray analysis.
  • To identify potential gene expression markers for hepatotoxicity and assess their correlation with biochemical indicators of liver damage.

Main Methods:

  • Administered five hepatotoxicants (acetaminophen, bromobenzene, carbon tetrachloride, dimethylnitrosamine, thioacetamide) to Sprague-Dawley rats.
  • Measured serum biochemical markers to determine maximal toxic time for each chemical.
  • Analyzed hepatic mRNA gene expression profiles using a DNA microarray with 1,097 drug response genes.

Main Results:

  • Each chemical induced specific gene expression patterns, with acetaminophen clustering separately.
  • Identified 10 up-regulated and 10 down-regulated genes as potential hepatotoxicity markers.
  • Quality-Threshold (QT) clustering revealed major expression patterns correlated with biochemical markers of liver toxicity, irrespective of toxicity level.

Conclusions:

  • Identified 17 potential toxicity markers whose expression profiles can estimate maximal toxic time independently of hepatotoxicity levels.
  • Gene expression profiling serves as a valuable tool for evaluating potential hepatotoxicants during drug development.
  • This approach enhances the assessment of drug safety and risk evaluation processes.

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