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Cell signaling model for arterial mechanobiology

Linda Irons, Jay D. Humphrey

Abstract

Arterial growth and remodeling at the tissue level is driven by mechanobiological processes at cellular and sub-cellular levels. Although it is widely accepted that cells seek to promote tissue homeostasis in response to biochemical and biomechanical cues—such as increased wall stress in hypertension—the ways by which these cues translate into tissue maintenance, adaptation, or maladaptation are far from understood. In this paper, we present a logic-based computational model for cell signaling within the arterial wall, aiming to predict changes in extracellular matrix turnover and cell phenotype in response to pressure-induced wall stress, flow-induced wall shear stress, and exogenous sources of angiotensin II, with particular interest in mouse models of hypertension. We simulate a number of experiments from the literature at both the cell and tissue level, involving single or combined inputs, and achieve high qualitative agreement in most cases. Additionally, we demonstrate the utility of this modeling approach for simulating alterations (in this case knockdowns) of individual nodes within the signaling network. Continued modeling of cellular signaling will enable improved mechanistic understanding of arterial growth and remodeling in health and disease, and will be crucial when considering potential pharmacological interventions.

Introduction

Central arteries actively maintain their geometry, composition, properties, and function over long periods under normal conditions. Moreover, they often adapt well to altered mechanical loading via the turnover of cells and extracellular matrix (ECM) in evolving configurations. Both of these observations are consistent with mechanical homeostasis, which exists across scales from sub-cellular to cellular to tissue levels [1]. Because of the complexity of such growth and remodeling (G&R) processes, computational models have proven useful in quantifying and comparing responses across both normal adaptations and disease conditions, often at the tissue-level [2–5]. Although tissue-level models increase our understanding of the time-course of certain homeostatic mechanisms, and their loss in cases of disease, and enable clinically relevant predictions, they are yet limited because of the lack of consideration of the underlying cell signaling pathways. There is, therefore, a pressing need for cell signaling models that affect responses at the tissue level.

Methods

We use a graph representation to describe signaling events within the arterial wall, where nodes correspond to species of interest and edges depict relationships between them such as activation and inhibition. To implement a graph-based model, we must understand (i) the components involved and (ii) the way they interact (i.e. activation vs inhibition). We constructed a network (Fig 1) from an extensive curation of the literature, and formulated the relations as a set of logic statements (S1 Appendix).

Discussion

Many different models focusing on different conditions have been proposed to study arterial growth and remodeling [65–70]. Among others, we have found phenomenological models to be useful in generating and testing diverse hypotheses fundamental to arterial adaptations [5, 22], in studying arterial disease progression [71, 72], and in the design of tissue engineered constructs and their clinical usage [73, 74]. Nevertheless, tissue-level manifestations arise from molecular and cellular level changes [75–78]. There is, therefore, a pressing need for models that enable one to examine changes in cell phenotype and ECM turnover in terms of cell signaling pathways. As noted above, both kinetic and logic-based models offer considerable promise in this regard. Some models coupling tissue mechanics to cell signaling have been developed using kinetic formulations [4, 79], and provide illustrative examples through parameter studies. Biochemical species—primarily growth factors and proteases—were modeled using either a system of ODEs [4, 80] or reaction–diffusion PDEs [79]. Yet, with time-course data for these species lacking, parameterization and quantitative verification remains a challenge, particularly if more detailed signaling is to be considered in the future.

Citation: Irons L, Humphrey JD (2020) Cell signaling model for arterial mechanobiology. PLoS Comput Biol 16(8): e1008161. https://doi.org/10.1371/journal.pcbi.1008161

Editor: Jeffrey J. Saucerman, University of Virginia, UNITED STATES

Received: February 13, 2020; Accepted: July 17, 2020; Published: August 24, 2020

Copyright: © 2020 Irons, Humphrey. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: MATLAB files are available at: https://github.com/irons-l/arterialsignaling.

Funding: This work was supported, in part, by grants awarded to JDH from the US National Institutes of Health (NIH): R01 HL105297, P01 HL134605, U01 HL142518, and R01 HL146723. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

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