An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation.

Sakshi Khurana,Jacqueline R M Plass,Liesbet Geris,Stefano Schivo,Rom Langerak,Jaco Van De Pol,Janine N Post,Johan Kerkhofs,Leilei Zhong,Marcel Karperien,Jetse Scholma,Nikolas Mersinis

doi:10.3389/fbioe.2021.732917

Abstract

A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.

Highlights

Proper development of cartilage is important for the length of our long bones by anatomical movement of growth plate cartilage and supple joint movement through formation of articular cartilage
In order to get a complete and precise description of how Analysis of Networks with Interactive Modeling (ANIMO) models are translated into Timed Automata (TA) and how those models approximate Ordinary Differential Equations (ODEs), we refer the interested reader to our previous work (Schivo et al, 2012; Schivo et al, 2014a; Schivo et al, 2014b; Scholma et al, 2014; Schivo and Langerak, 2017), where we show how nodes and interactions are represented, and how TA are used to update the activities along the course of a model simulation
The complexity of the signaling network that controls the activities of SOX9 or RUNX2 prevents a thorough understanding of the mechanisms that regulate formation of transient or permanent cartilage

Summary

Introduction

Proper development of cartilage is important for the length of our long bones by anatomical movement of growth plate cartilage and supple joint movement through formation of articular cartilage. Cartilage dysregulation occurs in a variety of diseases, including growth disorders, rheumatic diseases, osteoarthritis, as well as in chondrosarcomas. To understand cartilage disorders and identify new biomarkers or therapies, insight into the dynamics of the cellular networks that control chondrogenesis is necessary. Cartilage formation is under control of the transcription factor SOX9, that regulates expression of genes important for the cartilage phenotype, including collagen 2 and aggrecan (Goldring, 2012). Loss of SOX9 activity and subsequent decrease in target gene expression is observed in osteoarthritis (Kim et al, 2013) and is correlated to osteoarthritis progression (Zhong et al, 2016a)

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