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...
Main Authors: | , |
---|---|
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 |
id |
doaj-4e3334bfceb44ed2a755740cbfd5b8ad |
---|---|
record_format |
Article |
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 |
_version_ |
1725265526494593024 |