Multi‐scale Computational Model of Endothelial Cell‐Pericyte Coupling in Idiopathic Pulmonary Fibrosis
Authors: Leonard‐Duke Julie , Hung Claire , Sharma Anahita , Peirce Shayn M. 
Published: May 2022

Introduction

Microvascular stability is highly dependent on endothelial cell‐pericyte coupling. In fibrotic diseases, such as idiopathic pulmonary fibrosis (IPF), extracellular matrix stiffening and elevated concentrations of profibrotic factors, such as TGF‐β, can disrupt this cell‐cell communication. Treatment of IPF with the antifibrotic drug Nintedanib, which targets PDGF‐βR, FGF‐R, and VEGF‐R signaling, may counteract these effects to strengthen endothelial cell‐pericyte coupling and promote microvessel stability1,2. We have developed a multi‐scale computational model that simulates intracellular signaling in endothelial cells and pericytes, heterotypic cell‐cell communication, and the dynamic lung microenvironment to study how Nintedanib2 and other drugs affect microvascular network remodeling in IPF. We hypothesize that blocking PDGF‐βR and FGF‐R signaling with Nintedanib is sufficient to rescue TGF‐β‐induced endothelial cell‐pericyte decoupling.

Materials and Methods

The multi‐scale model is comprised of logic‐based ordinary differential equations representing intracellular signaling networks in both endothelial cells and pericytes integrated into an agent‐based model representing the lung environment wherein the simulated cells interact with one another, sensing and dynamically altering their microenvironment (Figure 1). The logic‐based network signaling models were developed using the Netflux3 toolkit in MATLAB. The agent‐based model of the spatiotemporal 2D lung environment was constructed in NetLogo4. The multi‐scale model was created by linking the logic‐based network models with the agent‐based model using the NL4Py5 package in Python.

Results and Discussion

Endothelial cell‐pericyte decoupling was signified by decreased N‐cadherin expression in pericytes, increased αSMA and Col1mRNA (indicating possible pericyte‐to‐myofibroblast transition), and increased physical separation and distances between endothelial cells and pericytes. The multi‐scale model predicted that increasing TGF‐β concentrations significantly elevated αSMA and Col1mRNA expression in simulated pericytes. N‐cadherin levels did not change in response to TGF‐β or Nintedanib treatment in the network model of the pericyte alone but were affected by the presence of endothelial cells in the agent‐based model, highlighting the importance of endothelial cell‐pericyte interactions in determining pericyte behaviors.

Conclusions

Our multi‐scale model demonstrates the importance of considering endothelial cell‐pericyte communication in predicting cellular responses to pro‐fibrotic environments. By representing intracellular biochemical signaling networks, heterotypic cell interactions, and the dynamic multi‐cell microenvironment, we were able to demonstrate a potential mechanism through which Nintedanib treatment affects endothelial cell‐pericyte coupling in IPF.

References

1) Hanumegowda, C., et al.; Chest. 2012 2) Wollin, L., et al.; Eur Respir J. 2015 3) Kraeutler, MJ., et al.; BMC Syst Biol. 2010 4) Sklar, E., Artif. Life 2007 5) Gunaratne, C.; 2018. NL4Py.