Scoring multiple toxicological endpoints using a toxicogenomic database

As information regarding microarray data sets and toxicogenomic biomarkers grows rapidly, the process of analyzing data and interpreting the results is increasingly complicated. To facilitate data analysis, a simple expression ratio-based scoring method called the TGP1 score was previously proposed [Kiyosawa, N., Shiwaku, K., Hirode, M., Omura, K., Uehara, T., Shimizu, T., Mizukawa, Y., Miyagishima, T., Ono, A., Nagao, T., Urushidani, T., 2006. Utilization of a one-dimensional score for surveying chemical-induced changes in expression levels of multiple biomarker gene sets using a large-scale toxicogenomics database. J. Toxicol. Sci. 31, 433–448]. Although the TGP1 score has demonstrated its efficacy for rapid comprehension of large-scale toxicogenomic data sets, inclusion of low quality gene expression data in the biomarker gene set produced flaws in the calculated score. To overcome this shortcoming, we tested a new scoring method called the differentially expressed gene score (D-score), where Detection Call as well as signal log ratios generated by MAS5 algorithm on Affymetrix GeneChip data were considered for the calculation. Four prototypical toxicants, namely acetaminophen, phenobarbital, clofibrate and acetamidofluorene, were used for detailed analysis. A toxicogenomics database (TG-GATEs) was utilized as a reference data set. The D-score successfully alleviated the effects of low quality data on the score calculation, and captured the overall direction of expression changes as well as the magnitude of expression change level of a set of genes, highlighting the affected toxicological endpoints elicited by chemical treatment. The D-score will be useful for high-throughput toxicity screening using a toxicogenomic database and biomarkers.