Coordinating Role of RXRα in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling

Abstract

During various inflammatory processes circulating cytokines including IL-6, IL-1β, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (r _s = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism.

Author Summary

During inflammation, circulating proinflammatory cytokines such as TNFα, IL-1ß, and IL-6, which are produced by, e.g., Kupffer cells, macrophages, or tumor cells, play important roles in hepatocellular signaling pathways and in the regulation of cellular homeostasis. In particular, these cytokines are responsible for the acute phase response (APR) but also for a dramatic reduction of drug detoxification capacity due to impaired expression of numerous genes coding for drug metabolic enzymes and transporters. Here we used high-throughput (phospho-)proteomic and gene expression data to investigate the impact of canonical signaling pathways in mediating IL-6-induced downregulation of drug metabolism related genes. We performed chemical inhibition perturbations to show that most of the IL-6 effects on gene expression are mediated through the MAPK and PI3K/AKT pathways. We constructed a prior knowledge network as basis for a fuzzy logic model that was trained with extensive gene expression data to identify critical regulatory nodes. Our results suggest that the nuclear receptor RXRα plays a central role, which was convincingly validated by RXRα gene silencing experiments. This work shows how computational modeling can support identifying decisive regulatory events from large-scale experimental data.