Unknown,Transcriptomics,Genomics,Proteomics

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Inferring drug-induced gene regulatory relationships in primary human hepatocytes

ABSTRACT: Statins are widely used cholesterol-lowering drugs that inhibit HMG-CoA reductase, a key enzyme in cholesterol synthesis. In some cases, however, these drugs may cause a number of toxic side effects in hepatocytes and skeletal muscle tissue. Currently, the specific molecular mechanisms that cause these adverse effects are not sufficiently understood. In this work, genome-wide RNA expression changes in primary human hepatocytes of six individuals were measured at five time points upon atorvastatin treatment. A novel systems-level analysis workflow was applied to reconstruct regulatory mechanisms based on these drug-response data and available knowledge about transcription factor binding specificities, protein-protein interactions and protein-drug interactions. Several previously unknown transcription factors, regulatory cofactors and signaling molecules were found to be involved in atorvastatin-responsive gene expression. Some novel relationships, e.g., the regulatory influence of nuclear receptor NR2C2 on CYP3A4, were successfully validated in wet-lab experiments. Whole-genome Affymetrix U133 Plus 2.0 (Affymetrix, Santa Clara, CA) microarray measurements were conducted using samples of primary human hepatocytes cultured from six individuals (i.e., hh62, hh65, hh67, hh79, hh80 and hh81). Each sample was treated with atorvastatin and dimethylsulfoxide (DMSO), which was used as a control substance. Microarray measurements were performed at five time points (6 h, 12 h, 24 h, 48 h and 72 h) after the drug stimulus.

ORGANISM(S): Homo sapiens

SUBMITTER: Michael Bonin

PROVIDER: E-GEOD-29868 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:This is the model of atorvastatin metabolism in hepaitc cells described in the article: A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes Joachim Bucher , Stephan Riedmaier , Anke Schnabel , Katrin Marcus , Gabriele Vacun , Thomas S Weiss , Wolfgang E Thasler , Andreas K Nussler , Ulrich M Zanger and Matthias Reuss. BMC Systems Biology 2011, 5:66. DOI:10.1186/1752-0509-5-66 Abstract: Background: The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models. Results: In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated in primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n=150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics. Conclusions: A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology. The model is parameterized for patient 1 and reproduces the time courses in figure 2 of the article.

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Inferring drug-induced gene regulatory relationships in primary human hepatocytes

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