Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling

Abstract Background Thrombospondin-1 (TSP1) is a matricellular protein that functions to inhibit angiogenesis. An important pathway that contributes to this inhibitory effect is triggered by TSP1 binding to the CD36 receptor, inducing endothelial cell apoptosis. However, therapies that mimic this fu...

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Main Authors: Qianhui Wu, Stacey D. Finley
Format: Article
Language:English
Published: BMC 2017-12-01
Series:Cell Communication and Signaling
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12964-017-0207-9
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spelling doaj-4e3334bfceb44ed2a755740cbfd5b8ad2020-11-25T00:46:26ZengBMCCell Communication and Signaling1478-811X2017-12-0115111710.1186/s12964-017-0207-9Predictive model identifies strategies to enhance TSP1-mediated apoptosis signalingQianhui Wu0Stacey D. Finley1Department of Biomedical Engineering, University of Southern CaliforniaDepartment of Biomedical Engineering, University of Southern CaliforniaAbstract Background Thrombospondin-1 (TSP1) is a matricellular protein that functions to inhibit angiogenesis. An important pathway that contributes to this inhibitory effect is triggered by TSP1 binding to the CD36 receptor, inducing endothelial cell apoptosis. However, therapies that mimic this function have not demonstrated clear clinical efficacy. This study explores strategies to enhance TSP1-induced apoptosis in endothelial cells. In particular, we focus on establishing a computational model to describe the signaling pathway, and using this model to investigate the effects of several approaches to perturb the TSP1-CD36 signaling network. Methods We constructed a molecularly-detailed mathematical model of TSP1-mediated intracellular signaling via the CD36 receptor based on literature evidence. We employed systems biology tools to train and validate the model and further expanded the model by accounting for the heterogeneity within the cell population. The initial concentrations of signaling species or kinetic rates were altered to simulate the effects of perturbations to the signaling network. Results Model simulations predict the population-based response to strategies to enhance TSP1-mediated apoptosis, such as downregulating the apoptosis inhibitor XIAP and inhibiting phosphatase activity. The model also postulates a new mechanism of low dosage doxorubicin treatment in combination with TSP1 stimulation. Using computational analysis, we predict which cells will undergo apoptosis, based on the initial intracellular concentrations of particular signaling species. Conclusions This new mathematical model recapitulates the intracellular dynamics of the TSP1-induced apoptosis signaling pathway. Overall, the modeling framework predicts molecular strategies that increase TSP1-mediated apoptosis, which is useful in many disease settings.http://link.springer.com/article/10.1186/s12964-017-0207-9Thrombospondin-1Biochemical kineticsComputational modelingParameter estimationCell heterogeneity
collection DOAJ
language English
format Article
sources DOAJ
author Qianhui Wu
Stacey D. Finley
spellingShingle Qianhui Wu
Stacey D. Finley
Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling
Cell Communication and Signaling
Thrombospondin-1
Biochemical kinetics
Computational modeling
Parameter estimation
Cell heterogeneity
author_facet Qianhui Wu
Stacey D. Finley
author_sort Qianhui Wu
title Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling
title_short Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling
title_full Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling
title_fullStr Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling
title_full_unstemmed Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling
title_sort predictive model identifies strategies to enhance tsp1-mediated apoptosis signaling
publisher BMC
series Cell Communication and Signaling
issn 1478-811X
publishDate 2017-12-01
description Abstract Background Thrombospondin-1 (TSP1) is a matricellular protein that functions to inhibit angiogenesis. An important pathway that contributes to this inhibitory effect is triggered by TSP1 binding to the CD36 receptor, inducing endothelial cell apoptosis. However, therapies that mimic this function have not demonstrated clear clinical efficacy. This study explores strategies to enhance TSP1-induced apoptosis in endothelial cells. In particular, we focus on establishing a computational model to describe the signaling pathway, and using this model to investigate the effects of several approaches to perturb the TSP1-CD36 signaling network. Methods We constructed a molecularly-detailed mathematical model of TSP1-mediated intracellular signaling via the CD36 receptor based on literature evidence. We employed systems biology tools to train and validate the model and further expanded the model by accounting for the heterogeneity within the cell population. The initial concentrations of signaling species or kinetic rates were altered to simulate the effects of perturbations to the signaling network. Results Model simulations predict the population-based response to strategies to enhance TSP1-mediated apoptosis, such as downregulating the apoptosis inhibitor XIAP and inhibiting phosphatase activity. The model also postulates a new mechanism of low dosage doxorubicin treatment in combination with TSP1 stimulation. Using computational analysis, we predict which cells will undergo apoptosis, based on the initial intracellular concentrations of particular signaling species. Conclusions This new mathematical model recapitulates the intracellular dynamics of the TSP1-induced apoptosis signaling pathway. Overall, the modeling framework predicts molecular strategies that increase TSP1-mediated apoptosis, which is useful in many disease settings.
topic Thrombospondin-1
Biochemical kinetics
Computational modeling
Parameter estimation
Cell heterogeneity
url http://link.springer.com/article/10.1186/s12964-017-0207-9
work_keys_str_mv AT qianhuiwu predictivemodelidentifiesstrategiestoenhancetsp1mediatedapoptosissignaling
AT staceydfinley predictivemodelidentifiesstrategiestoenhancetsp1mediatedapoptosissignaling
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