Project description:Deregulation of the transforming growth factor-? (TGF?) signaling pathway in epithelial ovarian cancer has been reported, but the precise mechanism underlying disrupted TGF? signaling in the disease remains unclear. We performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate genome-wide screening of TGF?-induced SMAD4 binding in epithelial ovarian cancer. Following TGF? stimulation of the A2780 epithelial ovarian cancer cell line, we identified 2,362 SMAD4 binding loci and 318 differentially expressed SMAD4 target genes. Comprehensive examination of SMAD4-bound loci, revealed four distinct binding patterns: 1) Basal; 2) Shift; 3) Stimulated Only; 4) Unstimulated Only. SMAD4-bound loci were primarily classified as either Stimulated only (74%) or Shift (25%), indicating that TGF?-stimulation alters SMAD4 binding patterns in epithelial ovarian cancer cells compared to normal epithelial cells. Furthermore, based on gene regulatory network analysis, we determined that the TGF?-induced SMAD4-dependent regulatory network was strikingly different in ovarian cancer compared to normal cells. Importantly, the TGF?/SMAD4 target genes identified in the A2780 epithelial ovarian cancer cell line were predictive of patient survival, based on in silico mining of publically available patient data bases. In conclusion, our data highlight the utility of next generation sequencing technology to identify genome-wide SMAD4 target genes in epithelial ovarian cancer. The results link aberrant TGF?/SMAD signaling to ovarian tumorigenesis. Furthermore, the identified SMAD4 binding loci, combined with gene expression profiling and in silico data mining of patient cohorts, may provide a powerful approach to determine potential gene signatures with biological and future translational research in ovarian and other cancers. ChIP-Seq: 1 control lane. 4 unstimulated lanes 4 stimulated lanes Gene expression: 3 technical replicates each of SMAD4 stimulated and SMAD4 unstimulated cells

Project description:This model is from the article: Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics. Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 , Abstract: Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination. Note: Developer of the model: Zhike Zi Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011 1. The global parameter that set the type of stimulation (a) for sustained TGF-beta stimulation: set stimulation_type = 1. (b) for single pulse of TGF-beta stimulation: set stimulation_type = 2. parameter "single_pulse_duration" is for the duration of stimulation, for example, single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation. *Note: make sure that the time course cover the time point when the event is triggered. (c) for single pulse of TGF-beta stimulation in COPASI change the trigger of event "single_pulse_TGF_beta_washout" from "and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT) to "and(eq(stimulation_type, 2), gt(time, single_pulse_duration))" (for COPASI) 2. Notes for TGF-beta dose in terms of molecules per cell (a) The following equation applies for conversion of TGF-beta dose in molecules per cell TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23 (b) for standard experimental setup 1e6 cells in 2 mL medium 0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell 0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell (c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed. initial Vmed = 1e-8 (1e6 cells in 10 mL medium) Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium) 3. Please note that this model contains events and the medium compartment size is varied. 4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.

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Homo sapiens

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