Multicenter study of acetaminophen hepatotoxicity reveals the importance of biological endpoints in genomic analyses.

Gene expression profiling is a widely used technique with data from the majority of published microarray studies being publicly available. These data are being used for meta-analyses and in silico discovery; however, the comparability of toxicogenomic data generated in multiple laboratories has not been critically evaluated. Using the power of prospective multilaboratory investigations, seven centers individually conducted a common toxicogenomics experiment designed to advance understanding of molecular pathways perturbed in liver by an acute toxic dose of N-acetyl-p-aminophenol (APAP) and to uncover reproducible genomic signatures of APAP-induced toxicity. The nonhepatotoxic APAP isomer N-acetyl-m-aminophenol was used to identify gene expression changes unique to APAP. Our data show that c-Myc is induced by APAP and that c-Myc-centered interactomes are the most significant networks of proteins associated with liver injury. Furthermore, sources of error and data variability among Centers and methods to accommodate this variability were identified by coupling gene expression with extensive toxicological evaluation of the toxic responses. We show that phenotypic anchoring of gene expression data is required for biologically meaningful analysis of toxicogenomic experiments.

Gene expression profiling of rat livers reveals indicators of potential adverse effects.

This study tested the hypothesis that gene expression profiling can reveal indicators of subtle injury to the liver induced by a low dose of a substance that does not cause overt toxicity as defined by conventional criteria of toxicology (e.g., abnormal clinical chemistry and histopathology). For the purpose of this study we defined this low dose as subtoxic, i.e., a dose that elicits effects which are below the detection of conventional toxicological parameters. Acetaminophen (APAP) was selected as a model hepatotoxicant because (1) considerable information exists concerning the mechanism of APAP hepatotoxicity that can occur following high doses, (2) intoxication with APAP is the leading cause of emergency room visits involving acute liver failure within the United States, and (3) conventional clinical markers have poor predictive value. Rats treated with a single dose of 0, 50, 150, or 1500 mg/kg APAP were examined at 6, 24, or 48 h after exposure for conventional toxicological parameters and for gene expression alterations. Patterns of gene expression were found which indicated cellular energy loss as a consequence of APAP toxicity. Elements of these patterns were apparent even after exposure to subtoxic doses. With increasing dose, the magnitude of changes increased and additional members of the same biological pathways were differentially expressed. The energy loss suggested by gene expression changes was confirmed at the 1500 mg/kg dose exposure by measuring ATP levels. Only by ultrastructural examination could any indication of toxicity be identified after exposure to a subtoxic dose of APAP and that was occasional mitochondrial damage. In conclusion, this study provides evidence that supports the hypothesis that gene expression profiling may be a sensitive means of identifying indicators of potential adverse effects in the absence of the occurrence of overt toxicity.

Standardizing global gene expression analysis between laboratories and across platforms.

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories increased markedly when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Reproducibility was highest when analysis was based on biological themes defined by enriched Gene Ontology (GO) categories. These findings indicate that microarray results can be comparable across multiple laboratories, especially when a common platform and set of procedures are used.