Abstract
During OA the homeostasis of healthy articular chondrocytes is dysregulated, which leads to a phenotypical transition of the cells, further influenced by external stimuli. Chondrocytes sense those stimuli, integrate them at the intracellular level and respond by modifying their secretory and molecular state. This process is controlled by a complex interplay of intracellular factors. Each factor is influenced by a myriad of feedback mechanisms, making the prediction of what will happen in case of external perturbation challenging. Hampering the hypertrophic phenotype has emerged as a potential therapeutic strategy to help OA patients (Ripmeester et al. 2018). Therefore, we developed a computational model of the chondrocyte's underlying regulatory network (RN) to identify key regulators as potential drug targets
A mechanistic mathematical model of articular chondrocyte differentiation was implemented with a semi-quantitative formalism. It is composed of a protein RN and a gene RN(GRN) and developed by combining two strategies. First, we established a mechanistic network based on accumulation of decades of biological knowledge. Second, we combined that mechanistic network with data-driven modelling by inferring an OA-GRN using an ensemble of machine learning methods. This required a large gene expression dataset, provided by distinct public microarrays merged through an in-house pipeline for cross-platform integration.
We successfully merged various micro-array experiments into one single dataset where the biological variance was predominant over the batch effect from the different technical platforms. The gain of information provided by this merge enabled us to reconstruct an OA-GRN which subsequently served to complete our mechanistic model. With this model, we studied the system's multi-stability, equating the model's stable states to chondrocyte phenotypes. The network structure explained the occurrence of two biologically relevant phenotypes: a hypertrophic-like and a healthy-like phenotype, recognized based on known cell state markers. Second, we tested several hypotheses that could trigger the onset of OA to validate the model with relevant biological phenomena. For instance, forced inflammation pushed the chondrocyte towards hypertrophy but this was partly rescued by higher levels of TGF-β. However, we could annihilate this rescue by concomitantly mimicking an increase in the ALK1/ALK5 balance. Finally, we performed a screening of in-silico (combinatorial) perturbations (inhibitions and/or over-activations) to identify key molecular factors involved in the stability of the chondrocyte state. More precisely, we looked for the most potent conditions for decreasing hypertrophy. Preliminary validation experiments have confirmed that PKA activation could decrease the hypertrophic phenotype in primary chondrocytes. Importantly the in-silico results highlighted that targeting two factors at the same time would greatly help reducing hypertrophic changes.
A priori testing of conditions with in-silico models may cut time and cost of experiments via target prioritization and opens new routes for OA combinatorial therapies.
