Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of human MCF7 cells treated with ErbB receptor ligands EGF and heregulin at multiple doses and sampled at multiple time points reveals signal transduction pathways devotedly convey quantitative properties of the dose-dependent activation of ErbB receptor to early transcription

ABSTRACT: ErbB receptor ligands, epidermal growth factor (EGF) and heregulin (HRG), induce dose-dependent transient and sustained intracellular signaling, proliferation and differentiation of MCF-7 breast cancer cells, respectively. In an effort to delineate the ligand-specific cell determination mechanism, we investigated time-course gene expressions induced by EGF and HRG that induce distinct cellular phenotypes in MCF-7 cells. To analyze the effects of ligand dosage and time for the gene expression independently, we developed a statistical method for decomposing the expression profiles into the two effects. Our results indicated that signal transduction pathways devotedly convey quantitative properties of the dose-dependent activation of ErbB receptor to early transcription. The results also implied that moderate changes in the expression levels of numbers of genes, not the predominant regulation of a few specific genes, might cooperatively work at the early stage of the transcription for determining the cell fate. However, the EGF- and HRG-induced distinct signal durations resulted in the ligand-oriented biphasic induction of proteins after 20 min. The selected gene list and HRG-induced prolonged signaling suggested that transcriptional feedback to the intracellular signaling results in a graded to biphasic response in the cell determination process, and that each ErbB receptor is inextricably responsible for the control of amplitude and duration of cellular biochemical reactions. MCF-7 human breast cancer cells were incubated from 5min to 90min after administration of different concentrations of the growth hormones (epidermal growth factor (EGF) and heregulin (HRG)). Control was set as growth hormone non-treated cells. For each growth hormone, one control was used.

ORGANISM(S): Homo sapiens

SUBMITTER: Mariko Hatakeyama

PROVIDER: E-GEOD-6462 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Quantitative transcriptional control of ErbB receptor signaling undergoes graded to biphasic response for cell differentiation.

Nagashima Takeshi T Shimodaira Hidetoshi H Ide Kaori K Nakakuki Takashi T Tani Yukitaka Y Takahashi Kaoru K Yumoto Noriko N Hatakeyama Mariko M

The Journal of biological chemistry 20061201 6

ErbB receptor ligands, epidermal growth factor (EGF) and heregulin (HRG), induce dose-dependent transient and sustained intracellular signaling, proliferation, and differentiation of MCF-7 breast cancer cells, respectively. In an effort to delineate the ligand-specific cell determination mechanism, we investigated time course gene expressions induced by EGF and HRG that induce distinct cellular phenotypes in MCF-7 cells. To analyze independently the effects of ligand dosage and time for gene exp ...[more]

PMID: 17142811

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Project description:This is A431 IERMv1.0 model described in the article Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. William W Chen, Birgit Schoeberl, Paul J Jasper, Mario Niepel, Ulrik B Nielsen, Douglas A Lauffenburger and Peter K Sorger. Molecular Systems Biology 2009; 5:239. PMID: 19156131 , DOI: 10.1038/msb.2008.74 Abstract: The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation. The sbml model is available as supplemental material to the article and at http://www.cdpcenter.org/resources/models/chen-et-al-2008/ . It was slightly changed to make it valid SBML and to incorporate the step functions, described in the readme file and needed for inhibitor preincubation. the equilibration processes end at 1800 sec, so to reproduce the dynamics shown in the publication and supplemental material, only the time points after 1800 need to be considered. The parameter set is the hand fitted one used for Sfigure 3 in the supplemental materials. All species are in molecules, apart from HRG, EGF and Inh, which are in M. The results shown in SFigure 3 can be calculated dividing the parameters ERK_PP , AKT_PP and ERB_B1_P_tot by ERK_t , AKT_t and EGFR_t , respectively. Somehow we did not find the right scaleing factor for the phosphorylated ErbB1 receptor. Therefore the model does only qualitatively reproduces the timecourses shown in the first row of Sfigure 3. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team. For more information see the terms of use . To cite BioModels Database, please use Le Nov��re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Project description:This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. The model comprises lumped reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. The parameter set implemented here corresponds to that used for generating Figs. 4 B,C,D (red curves for 10nM HRG) of the below article in Cell (2010). Moreover, we found that the same model described well the dynamics in different cell types (MCF-7 and PC-12), of different ligands (EGF and HRG) and at different doses (0.1nM, 1nM, 10nM) for a unique set of parameter values (as implemented here and reported in Table SD4_1 of the article) except for four parameters characterising the input, cytoplasmic ppERK. These four parameters K1, K2, tau1 and tau2 are used in the two equations involving species x1 and x2. These two equations define a phenomenological input module to describe the ligand-, dose- and cell type-dependent dynamics of ppERKc which are not modelled in mechanistic detail here. The four parameter values can be adjusted to model a specific ligand, dose and cell type. 8 parameter sets for different experiments are given in Table SD4_2 of the article. This SBML file, however, carries just one such parameter set. We have chosen that of MCF-7 cells stimulated by 10nM of HRG. To reproduce all simulations from the article, please replace the parameter values for K1, K2, tau1, tau2 as needed. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010, doi:10.1016/j.cell.2010.03.054 (1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan (2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland (3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA (4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA (5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan (6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Quantitative transcriptional control of ErbB receptor signaling undergoes graded to biphasic response for cell differentiation.

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