Dataset Information

Coordinating Role of RXR? in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling.

ABSTRACT: During various inflammatory processes circulating cytokines including IL-6, IL-1?, and TNF? 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 RXR? playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXR? gene silencing versus IL-6 induced negative gene regulation (rs = 0.79; P<0.0001). These results concur with RXR? functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism.

SUBMITTER: Keller R

PROVIDER: S-EPMC4699813 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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

Keller Roland R Klein Marcus M Thomas Maria M Dräger Andreas A Metzger Ute U Templin Markus F MF Joos Thomas O TO Thasler Wolfgang E WE Zell Andreas A Zanger Ulrich M UM

PLoS computational biology 20160104 1

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 ...[more]

PMID: 26727233

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Project description:Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F- and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F- and deprotonation of the receptor by F- and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.

Dataset Information

Coordinating Role of RXR? in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling.

Publications

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

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