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Sobotta S, Raue A, Huang X, Vanlier J, Jünger A, Bohl S, Albrecht U, Hahnel MJ, Wolf S, Mueller NS, D'Alessandro LA, Mueller-Bohl S, Boehm ME, Lucarelli P, Bonefas S, Damm G, Seehofer D, Lehmann WD, Rose-John S, van der Hoeven F, Gretz N, Theis FJ, Ehlting C, Bode JG, Timmer J, Schilling M, Klingmüller U. Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib. Front Physiol 2017; 8:775. [PMID: 29062282 PMCID: PMC5640784 DOI: 10.3389/fphys.2017.00775] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 09/22/2017] [Indexed: 12/12/2022] Open
Abstract
IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.
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Affiliation(s)
- Svantje Sobotta
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Andreas Raue
- Discovery Division, Merrimack Pharmaceuticals, Cambridge, MA, United States
| | - Xiaoyun Huang
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Joep Vanlier
- Institute of Physics, Albert Ludwigs University of Freiburg, Freiburg, Germany.,BIOSS Centre for Biological Signalling Studies, Albert Ludwigs University of Freiburg, Freiburg, Germany
| | - Anja Jünger
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Sebastian Bohl
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Ute Albrecht
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Maximilian J Hahnel
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Stephanie Wolf
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Nikola S Mueller
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Lorenza A D'Alessandro
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Stephanie Mueller-Bohl
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Martin E Boehm
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Philippe Lucarelli
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Sandra Bonefas
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Georg Damm
- Department of Hepatobiliary Surgery and Visceral Transplantation, Leipzig University, Leipzig, Germany
| | - Daniel Seehofer
- Department of Hepatobiliary Surgery and Visceral Transplantation, Leipzig University, Leipzig, Germany
| | - Wolf D Lehmann
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | | | - Frank van der Hoeven
- Transgenic Service, Center for Preclinical Research, German Cancer Research Center, Heidelberg, Germany
| | - Norbert Gretz
- Medical Research Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Christian Ehlting
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Johannes G Bode
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Jens Timmer
- Institute of Physics, Albert Ludwigs University of Freiburg, Freiburg, Germany.,BIOSS Centre for Biological Signalling Studies, Albert Ludwigs University of Freiburg, Freiburg, Germany
| | - Marcel Schilling
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
| | - Ursula Klingmüller
- Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany
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D’Alessandro LA, Samaga R, Maiwald T, Rho SH, Bonefas S, Raue A, Iwamoto N, Kienast A, Waldow K, Meyer R, Schilling M, Timmer J, Klamt S, Klingmüller U. Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling. PLoS Comput Biol 2015; 11:e1004192. [PMID: 25905717 PMCID: PMC4427303 DOI: 10.1371/journal.pcbi.1004192] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 02/12/2015] [Indexed: 01/25/2023] Open
Abstract
Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.
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Affiliation(s)
- Lorenza A. D’Alessandro
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Regina Samaga
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Tim Maiwald
- Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Seong-Hwan Rho
- Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Sandra Bonefas
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Andreas Raue
- Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
- Merrimack Pharmaceuticals, Inc., Cambridge, Massachusetts, United States of America
| | - Nao Iwamoto
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Alexandra Kienast
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Katharina Waldow
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Rene Meyer
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Marcel Schilling
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
| | - Jens Timmer
- Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
- * E-mail: (JT); (SK); (UK)
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- * E-mail: (JT); (SK); (UK)
| | - Ursula Klingmüller
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany
- * E-mail: (JT); (SK); (UK)
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