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Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles

BACKGROUND: The Fibroblast Growth Factor (FGF) pathway is driving various aspects of cellular responses in both normal and malignant cells. One interesting characteristic of this pathway is the biphasic nature of the cellular response to some FGF ligands like FGF2. Specifically, it has been shown th...

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Autores principales: Kanodia, Jitendra, Chai, Diana, Vollmer, Jannik, Kim, Jaeyeon, Raue, Andreas, Finn, Greg, Schoeberl, Birgit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4036111/
https://www.ncbi.nlm.nih.gov/pubmed/24885272
http://dx.doi.org/10.1186/1478-811X-12-34
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author Kanodia, Jitendra
Chai, Diana
Vollmer, Jannik
Kim, Jaeyeon
Raue, Andreas
Finn, Greg
Schoeberl, Birgit
author_facet Kanodia, Jitendra
Chai, Diana
Vollmer, Jannik
Kim, Jaeyeon
Raue, Andreas
Finn, Greg
Schoeberl, Birgit
author_sort Kanodia, Jitendra
collection PubMed
description BACKGROUND: The Fibroblast Growth Factor (FGF) pathway is driving various aspects of cellular responses in both normal and malignant cells. One interesting characteristic of this pathway is the biphasic nature of the cellular response to some FGF ligands like FGF2. Specifically, it has been shown that phenotypic behaviors controlled by FGF signaling, like migration and growth, reach maximal levels in response to intermediate concentrations, while high levels of FGF2 elicit weak responses. The mechanisms leading to the observed biphasic response remains unexplained. RESULTS: A combination of experiments and computational modeling was used to understand the mechanism behind the observed biphasic signaling responses. FGF signaling involves a tertiary surface interaction that we captured with a computational model based on Ordinary Differential Equations (ODEs). It accounts for FGF2 binding to FGF receptors (FGFRs) and heparan sulfate glycosaminoglycans (HSGAGs), followed by receptor-phosphorylation, activation of the FRS2 adapter protein and the Ras-Raf signaling cascade. Quantitative protein assays were used to measure the dynamics of phosphorylated ERK (pERK) in response to a wide range of FGF2 ligand concentrations on a fine-grained time scale for the squamous cell lung cancer cell line H1703. We developed a novel approach combining Particle Swarm Optimization (PSO) and feature-based constraints in the objective function to calibrate the computational model to the experimental data. The model is validated using a series of extracellular and intracellular perturbation experiments. We demonstrate that in silico model predictions are in accordance with the observed in vitro results. CONCLUSIONS: Using a combined approach of computational modeling and experiments we found that competition between binding of the ligand FGF2 to HSGAG and FGF receptor leads to the biphasic response. At low to intermediate concentrations of FGF2 there are sufficient free FGF receptors available for the FGF2-HSGAG complex to enable the formation of the trimeric signaling unit. At high ligand concentrations the ligand binding sites of the receptor become saturated and the trimeric signaling unit cannot be formed. This insight into the pathway is an important consideration for the pharmacological inhibition of this pathway.
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spelling pubmed-40361112014-06-11 Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles Kanodia, Jitendra Chai, Diana Vollmer, Jannik Kim, Jaeyeon Raue, Andreas Finn, Greg Schoeberl, Birgit Cell Commun Signal Research BACKGROUND: The Fibroblast Growth Factor (FGF) pathway is driving various aspects of cellular responses in both normal and malignant cells. One interesting characteristic of this pathway is the biphasic nature of the cellular response to some FGF ligands like FGF2. Specifically, it has been shown that phenotypic behaviors controlled by FGF signaling, like migration and growth, reach maximal levels in response to intermediate concentrations, while high levels of FGF2 elicit weak responses. The mechanisms leading to the observed biphasic response remains unexplained. RESULTS: A combination of experiments and computational modeling was used to understand the mechanism behind the observed biphasic signaling responses. FGF signaling involves a tertiary surface interaction that we captured with a computational model based on Ordinary Differential Equations (ODEs). It accounts for FGF2 binding to FGF receptors (FGFRs) and heparan sulfate glycosaminoglycans (HSGAGs), followed by receptor-phosphorylation, activation of the FRS2 adapter protein and the Ras-Raf signaling cascade. Quantitative protein assays were used to measure the dynamics of phosphorylated ERK (pERK) in response to a wide range of FGF2 ligand concentrations on a fine-grained time scale for the squamous cell lung cancer cell line H1703. We developed a novel approach combining Particle Swarm Optimization (PSO) and feature-based constraints in the objective function to calibrate the computational model to the experimental data. The model is validated using a series of extracellular and intracellular perturbation experiments. We demonstrate that in silico model predictions are in accordance with the observed in vitro results. CONCLUSIONS: Using a combined approach of computational modeling and experiments we found that competition between binding of the ligand FGF2 to HSGAG and FGF receptor leads to the biphasic response. At low to intermediate concentrations of FGF2 there are sufficient free FGF receptors available for the FGF2-HSGAG complex to enable the formation of the trimeric signaling unit. At high ligand concentrations the ligand binding sites of the receptor become saturated and the trimeric signaling unit cannot be formed. This insight into the pathway is an important consideration for the pharmacological inhibition of this pathway. BioMed Central 2014-05-15 /pmc/articles/PMC4036111/ /pubmed/24885272 http://dx.doi.org/10.1186/1478-811X-12-34 Text en Copyright © 2014 Kanodia et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Kanodia, Jitendra
Chai, Diana
Vollmer, Jannik
Kim, Jaeyeon
Raue, Andreas
Finn, Greg
Schoeberl, Birgit
Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles
title Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles
title_full Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles
title_fullStr Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles
title_full_unstemmed Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles
title_short Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles
title_sort deciphering the mechanism behind fibroblast growth factor (fgf) induced biphasic signal-response profiles
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4036111/
https://www.ncbi.nlm.nih.gov/pubmed/24885272
http://dx.doi.org/10.1186/1478-811X-12-34
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