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

PLoS Comput Biol. 2016 Jan 4;12(1):e1004431. doi: 10.1371/journal.pcbi.1004431. eCollection 2016 Jan.


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 (rs = 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Computational Biology
  • Down-Regulation
  • Female
  • Fuzzy Logic
  • Hepatocytes / metabolism*
  • Humans
  • Inactivation, Metabolic / physiology*
  • Inflammation / metabolism*
  • Male
  • Middle Aged
  • Models, Biological*
  • Retinoid X Receptor alpha / metabolism*
  • Signal Transduction
  • Young Adult


  • Retinoid X Receptor alpha

Grant support

This work was funded by the Federal Ministry of Education and Research (BMBF, Germany) as part of the Virtual Liver Network (grant numbers 0315756 to AZ; 0315755 to UMZ; 0315742 to TJ and 0315759 to WET), by the Robert Bosch Foundation, Stuttgart, Germany, and by a Marie Curie International Outgoing Fellowship within the EU 7th Framework Program for Research and Technological Development (project AMBiCon, 332020, to AD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.