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

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

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2021.732917

DOI=10.3389/fbioe.2021.732917

ISSN=2296-4185

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 provide opportunities for treatment of cartilage disease and tissue engineering strategies. 
To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. The visualization of a signaling network stays as close as possible to biological traditions and it provides a good match with the level of detail found in most experimental data. ANIMO can predict biological responses, both by manually testing hypotheses, as well as by using the model-checking capabilities offered by the underlying mathematical language of Timed Automata. In ANIMO, we generated an activity network integrating 8 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We called this model ECHO, for executable chondrocyte. Recently, 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. The model was validated using literature not used for model building. In addition, we show how model checking was used to prioritize combination treatments that are validated in the wet-lab. This allowed us to validate combinations of treatments that were predicted to induce the switch from SOX9 to RUNX2 or vice versa. We found that simultaneous inhibition of ERK and activation of the IGF pathway prevented bone formation while enhancing cartilage formation in rat metatarsal explants. In contrast, simultaneous activation of DLX5 and inhibition of IGF via GLI2 prevented cartilage formation and enhanced hypertrophic differentiation.