Humans; Mice; Animals; Cartilage, Articular/metabolism; Osteoarthritis/drug therapy/genetics/metabolism; Chondrocytes/metabolism; Hypertrophy/metabolism; Signal Transduction; Chondrocyte hypertrophy; Computational modeling; Drug targets; In vitro validation; Network of signal transduction; Osteoarthritis; Regulatory network inference; Virtual cell
Abstract :
[en] BACKGROUND: Without the availability of disease-modifying drugs, there is an unmet therapeutic need for osteoarthritic patients. During osteoarthritis, the homeostasis of articular chondrocytes is dysregulated and a phenotypical transition called hypertrophy occurs, leading to cartilage degeneration. Targeting this phenotypic transition has emerged as a potential therapeutic strategy. Chondrocyte phenotype maintenance and switch are controlled by an intricate network of intracellular factors, each influenced by a myriad of feedback mechanisms, making it challenging to intuitively predict treatment outcomes, while in silico modeling can help unravel that complexity. In this study, we aim to develop a virtual articular chondrocyte to guide experiments in order to rationalize the identification of potential drug targets via screening of combination therapies through computational modeling and simulations. RESULTS: We developed a signal transduction network model using knowledge-based and data-driven (machine learning) modeling technologies. The in silico high-throughput screening of (pairwise) perturbations operated with that network model highlighted conditions potentially affecting the hypertrophic switch. A selection of promising combinations was further tested in a murine cell line and primary human chondrocytes, which notably highlighted a previously unreported synergistic effect between the protein kinase A and the fibroblast growth factor receptor 1. CONCLUSIONS: Here, we provide a virtual articular chondrocyte in the form of a signal transduction interactive knowledge base and of an executable computational model. Our in silico-in vitro strategy opens new routes for developing osteoarthritis targeting therapies by refining the early stages of drug target discovery.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Lesage, Raphaëlle ; Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Biomechanics Section, KU Leuven, Leuven, Belgium.
Ferrao Blanco, Mauricio N ; Department of Orthopaedics and Sports Medicine, Erasmus MC, University Medical
Narcisi, Roberto ; Department of Orthopaedics and Sports Medicine, Erasmus MC, University Medical
Welting, Tim ; Orthopedic Surgery Department, UMC+, Maastricht, the Netherlands.
van Osch, Gerjo J V M ; Department of Orthopaedics and Sports Medicine, Erasmus MC, University Medical ; Department of Otorhinolaryngology, Erasmus MC, University Medical Center, ; Department of Biomechanical Engineering, Delft University of Technology, Delft
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique ; Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Biomechanics Section, KU Leuven, Leuven, Belgium.
Language :
English
Title :
An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis.
Publication date :
09 November 2022
Journal title :
BMC Biology
eISSN :
1741-7007
Publisher :
BioMed Central, Gb
Volume :
20
Issue :
1
Pages :
253
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 772418 - INSITE - Development and use of an integrated in silico-in vitro mesofluidics system for tissue engineering
Funders :
ERC - European Research Council [BE] Union Européenne [BE]
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