Time-dependent antagonist-agonist switching in receptor tyrosine kinase-mediated signaling

BackgroundErbB4/HER4 is a unique member of the ErbB family of receptor tyrosine kinases concerning its activation of anti-proliferative JAK2-STAT5 pathway when stimulated by ligand Neuregulin (NRG). Activation of this pathway leads to expression of genes like β-casein which promote cell differentiation. Recent experimental studies on mouse HC11 mammary epithelial cells stimulated by ligand Neuregulin (NRG) showed a time-dependent switching behavior in the β-casein expression. This behavior cannot be explained using currently available mechanistic models of the JAK-STAT pathway. We constructed an improved mechanistic model which introduces two crucial modifications to the canonical HER4-JAK2-STAT5 pathway based on literature findings. These modifications include competitive HER4 heterodimerization with other members of the ErbB family and a slower JAK2 independent activation STAT5 through HER4. We also performed global sensitivity analysis on the model to test the robustness of the predictions and parameter combinations that are sensitive to the outcome.ResultsOur model was able to reproduce the time-dependent switching behavior of β-casein and also establish that the modifications mentioned above to the canonical JAK-STAT pathway are necessary to reproduce this behavior. The sensitivity studies show that the competitive HER4 heterodimerization reactions have a profound impact on the sensitivity of the pathway to NRG stimulation, while the slower JAK2-independent pathway is necessary for the late stage promotion of β-casein mRNA transcription. The difference in the time scales of the JAK-dependent and JAK-independent pathways was found to be the main contributing factor to the time-dependent switch. The transport rates controlling activated STAT5 dimer nuclear import and β-casein mRNA export to cytoplasm affected the time delay between NRG stimulation and peak β-casein mRNA activity.ConclusionThis study highlights the effect of competitive and parallel reaction pathways on both short and long-term dynamics of receptor-mediated signaling. It provides robust and testable predictions of the dynamical behavior of the HER4 mediated JAK-STAT pathway which could be useful in designing treatments for various cancers where this pathway is activated/altered.

[1]  D. Riese,et al.  secErbB4-26/549 antagonizes ligand-induced ErbB4 tyrosine phosphorylation. , 2004, Oncology research.

[2]  A. Robling,et al.  Amphiregulin-EGFR signaling regulates PTHrP gene expression in breast cancer cells , 2008, Breast Cancer Research and Treatment.

[3]  D. Lauffenburger,et al.  Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data , 2009, Molecular systems biology.

[4]  P. Sorger,et al.  Sequential Application of Anticancer Drugs Enhances Cell Death by Rewiring Apoptotic Signaling Networks , 2012, Cell.

[5]  William Arbuthnot Sir Lane,et al.  ERBB4/HER4 Potentiates STAT5A Transcriptional Activity by Regulating Novel STAT5A Serine Phosphorylation Events*[boxs] , 2005, Journal of Biological Chemistry.

[6]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[7]  D. Edwards,et al.  Lactogenic Hormonal Induction of Long Distance Interactions between β-Casein Gene Regulatory Elements* , 2009, The Journal of Biological Chemistry.

[8]  L. Hennighausen,et al.  Interpretation of cytokine signaling through the transcription factors STAT5A and STAT5B. , 2008, Genes & development.

[9]  Frank E. Jones,et al.  The ERBB4/HER4 receptor tyrosine kinase regulates gene expression by functioning as a STAT5A nuclear chaperone , 2004, The Journal of cell biology.

[10]  V. Grantcharova,et al.  Therapeutically Targeting ErbB3: A Key Node in Ligand-Induced Activation of the ErbB Receptor–PI3K Axis , 2009, Science Signaling.

[11]  Will Usher,et al.  SALib: An open-source Python library for Sensitivity Analysis , 2017, J. Open Source Softw..

[12]  Satoru Shiono,et al.  Control mechanism of JAK/STAT signal transduction pathway , 2003, FEBS letters.

[13]  Arul Jayaraman,et al.  Identification of a time‐varying intracellular signalling model through data clustering and parameter selection: application to NF‐ κ B signalling pathway induced by LPS in the presence of BFA , 2019, IET systems biology.

[14]  B. Kholodenko,et al.  The dynamic control of signal transduction networks in cancer cells , 2015, Nature Reviews Cancer.

[15]  I. Sobol.  Uniformly distributed sequences with an additional uniform property , 1976 .

[16]  S. Ethier,et al.  Altered EGFR localization and degradation in human breast cancer cells with an amphiregulin/EGFR autocrine loop. , 2009, Cellular signalling.

[17]  William C Hahn,et al.  Oncogenic Transformation by Inhibitor-Sensitive and -Resistant EGFR Mutants , 2005, PLoS medicine.

[18]  Z Zi,et al.  Sensitivity analysis approaches applied to systems biology models. , 2011, IET systems biology.

[19]  J. Timmer,et al.  Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  A. Citri,et al.  EGF–ERBB signalling: towards the systems level , 2006, Nature Reviews Molecular Cell Biology.

[21]  D. Riese.  Ligand-based receptor tyrosine kinase partial agonists: new paradigm for cancer drug discovery? , 2011, Expert opinion on drug discovery.

[22]  A. Thor,et al.  Subcellular localization of the HER4 intracellular domain, 4ICD, identifies distinct prognostic outcomes for breast cancer patients. , 2009, The American journal of pathology.

[23]  S. Telesco.  Multiscale modeling of the ErbB receptor tyrosine kinase signaling network through theory and experiment , 2011 .

[24]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[25]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[26]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[27]  S. L. Brunton,et al.  Model selection for hybrid dynamical systems via sparse regression , 2018, Proceedings of the Royal Society A.

[28]  Grand canonical Monte Carlo simulations of elastic membranes with fluidity , 2003 .

[29]  M. Rijnkels,et al.  Integration of prolactin and glucocorticoid signaling at the beta-casein promoter and enhancer by ordered recruitment of specific transcription factors and chromatin modifiers. , 2006, Molecular endocrinology.

[30]  Anna Gambin,et al.  Computational models of the JAK1/2-STAT1 signaling , 2013, JAK-STAT.

[31]  Melissa A. Sandahl,et al.  Prolactin and ErbB4/HER4 signaling interact via Janus kinase 2 to induce mammary epithelial cell gene expression differentiation. , 2008, Molecular endocrinology.

[32]  Mudita Singhal,et al.  COPASI - a COmplex PAthway SImulator , 2006, Bioinform..

[33]  Melissa A. Sandahl,et al.  The intracellular domain of ErbB4 induces differentiation of mammary epithelial cells. , 2006, Molecular biology of the cell.

[34]  Colas Schretter,et al.  Monte Carlo and Quasi-Monte Carlo Methods , 2016 .