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NRS 13, e002 | Figure 1

Oshida K, Vasani N, Jones C, Moore T, Hester S, Nesnow S, Auerbach S, Geter D, Aleksunes L, Thomas R, Applegate D, Klaassen D and Corton J (2015). Identification of chemical modulators of the constitutive activated receptor (CAR) in a gene expression compendium. Nucl Recept Signal 13, e002. doi:10.1621/nrs.13002



Figure 1. CAR biomarker signature development/characterization and screening of a mouse liver gene expression compendium. Left, biomarker signature development and characterization. Wild-type and CAR-null mice were treated with CITCO, phenobarbital (PB) or TCPOBOP (Chua and Moore, 2005) and microarray analysis was carried out on the livers. Rosetta Resolver was used to identify differentially expressed genes (DEGs), as indicated. Biomarker signature genes were identified from the DEGs after applying a number of filtering steps described in the Methods. Genes in the biomarker signature were evaluated by the Comparative Toxicogenomics Database (CTD) to evaluate literature evidence for consistent regulation of biomarker signature genes by CAR activators and by Ingenuity Pathway Analysis (IPA) for canonical pathway enrichment and potential transcription factor (TF) regulators. Right, biomarker signature testing and screening. The CAR biomarker signature was imported into the NextBio environment. Internal protocols rank ordered the genes based on their fold-change. A pair-wise rank-based enrichment analysis (the Running Fisher’s algorithm) was used to compare the CAR biomarker signature to each bioset in the NextBio database, resulting in the direction of correlation and p-value of the comparison for each bioset in the compendium. All comparison information was exported and used to populate a master table containing bioset experimental details. An accuracy test of the biomarker signature predictions was carried out with treatments that are known positives and negatives for CAR activation. A number of predictions were tested in independent studies based on screening “hits”. An external gene expression database of experiments using Affymetrix gene chips was used for the machine learning classification analysis by BRB Array Tools. The database was also used to assess the relationship between the Running Fisher’s algorithm p-value and behavior of the CAR biomarker signature genes. Parts of the figure were adapted from a figure in Kupershmidt et al. (2010) and Oshida et al. (2015).