Identification of Chemical Modulators of the Constitutive Activated Receptor (CAR) in a Gene Expression Compendium

Abstract

The nuclear receptor family member constitutive activated receptor (CAR) is activated by structurally diverse drugs and environmentally-relevant chemicals leading to transcriptional regulation of genes involved in xenobiotic metabolism and transport. Chronic activation of CAR increases liver cancer incidence in rodents, whereas suppression of CAR can lead to steatosis and insulin insensitivity. Here, analytical methods were developed to screen for chemical treatments in a gene expression compendium that lead to alteration of CAR activity. A gene expression biomarker signature of 83 CAR-dependent genes was identified using microarray profiles from the livers of wild-type and CAR-null mice after exposure to three structurally-diverse CAR activators (CITCO, phenobarbital, TCPOBOP). A rank-based algorithm (Running Fisher’s algorithm (p-value ≤ 10-4)) was used to evaluate the similarity between the CAR biomarker signature and a test set of 28 and 32 comparisons positive or negative, respectively, for CAR activation; the test resulted in a balanced accuracy of 97%. The biomarker signature was used to identify chemicals that activate or suppress CAR in an annotated mouse liver/primary hepatocyte gene expression database of ~1850 comparisons. CAR was activated by 1) activators of the aryl hydrocarbon receptor (AhR) in wild-type but not AhR-null mice, 2) pregnane X receptor (PXR) activators in wild-type and to lesser extents in PXR-null mice, and 3) activators of PPARα in wild-type and PPARα-null mice. CAR was consistently activated by five conazole fungicides and four perfluorinated compounds. Comparison of effects in wild-type and CAR-null mice showed that the fungicide propiconazole increased liver weight and hepatocyte proliferation in a CAR-dependent manner, whereas the perfluorinated compound perfluorooctanoic acid (PFOA) increased these endpoints in a CAR-independent manner. A number of compounds suppressed CAR coincident with increases in markers of inflammation including acetaminophen, concanavalin A, lipopolysaccharide, and 300 nm silica particles. In conclusion, we have shown that a CAR biomarker signature coupled with a rank-based similarity method accurately predicts CAR activation. This analytical approach, when applied to a gene expression compendium, increased the universe of known chemicals that directly or indirectly activate CAR, highlighting the promiscuous nature of CAR activation and signaling through activation of other xenobiotic-activated receptors.

Using a gene expression biomarker to identify DNA damage‐inducing agents in microarray profiles

“…However, the high level of accuracy demonstrated the robustness of the computational procedures despite the fact that the biosets were derived from heterogeneous experiments with various exposure conditions carried out in multiple labs. These results are consistent with our past experience in accurately identifying chemical modulators of transcription factors in the mouse liver (Oshida et al, , , ) and MCF‐7 cells (Ryan et al, ). Our approach has a number of advantages compared to other methods used to identify DDI chemicals.…”

“…The strategy for comparison of a biomarker to collections of biosets has been described in previous studies (Oshida et al, 2015a(Oshida et al, , 2015b(Oshida et al, , 2015c. Using the Running Fisher algorithm, the biomarker was compared to each bioset in BSCE.…”

“…Transcriptional signatures have been developed to predict organ toxicities including liver cancer (Uehara et al, 2011;Doktorova et al, 2013;Eichner et al, 2013;Thomas et al, 2013;Yamada et al, 2013;Melis et al, 2014;Romer et al, 2014), renal tubular injury (Minowa et al, 2012), hepatocellular steatosis (Sahini et al, 2014), and immunotoxicity (Schmeits et al, 2015). Fewer examples exist in which transcriptional signatures have been built and validated for prediction of molecular initiating events (MIE) or downstream key events (KE) in adverse outcome pathways (AOP); examples include biomarkers predicting MIEs for rodent liver cancer and steatosis (Oshida et al, 2015a(Oshida et al, , 2015b(Oshida et al, , 2015c(Oshida et al, , 2016a(Oshida et al, , 2016b and human endocrine disruption (Ryan et al, 2016). Most of these derived signatures are not necessarily useful for screening human cells as the signatures are based on responses in rodent tissues and have not been extensively validated or applied across different testing platforms.…”

RNA-Seq reveals common and unique PXR- and CAR-target gene signatures in the mouse liver transcriptome

“…The CAR-mediated suppression of HNF4α may lead to a decrease in glucose and lipid metabolism pathways. There are apparent interactions between CAR and other receptors such as AhR, PXR, and PPARα, because the activators for these other receptors can also activate CAR-signaling (Oshida et al, 2015). The present study has shown that activation of CAR by TCPOBOP up-regulates the AhR-target genes Cyp1a as well as many PXR-target genes, however, it actually down-regulates the PPARα-target genes in the Cyp4a family.…”

Regulation of Drug Disposition Gene Expression in Pregnant Mice with Car Receptor Activation

“…As its name suggests, in either the absence, but particularly in the presence of an agonist, Car heterodimerizes with the retinoid X receptor alpha and binds to cognate response elements of target genes, thereby up-regulating their transcription [reviewed in 7]. Chemical activators of Car are structurally diverse and include drugs like phenobarbital, the fungicide propiconazole, as well as synthetic compounds such as 1,4-bis(2-(3,5-dichloropyridyloxy)) benzene (TCPOBOP) [8]. …”