TY - JOUR
T1 - An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis
AU - Lesage, Raphaëlle
AU - Ferrao Blanco, Mauricio N.
AU - Narcisi, Roberto
AU - Welting, Tim
AU - van Osch, Gerjo J.V.M.
AU - Geris, Liesbet
PY - 2022
Y1 - 2022
N2 - 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. Graphical Abstract: [Figure not available: see fulltext.]
AB - 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. Graphical Abstract: [Figure not available: see fulltext.]
KW - Chondrocyte hypertrophy
KW - Computational modeling
KW - Drug targets
KW - In vitro validation
KW - Network of signal transduction
KW - Osteoarthritis
KW - Regulatory network inference
KW - Virtual cell
UR - http://www.scopus.com/inward/record.url?scp=85141471537&partnerID=8YFLogxK
U2 - 10.1186/s12915-022-01451-8
DO - 10.1186/s12915-022-01451-8
M3 - Article
C2 - 36352408
AN - SCOPUS:85141471537
SN - 1741-7007
VL - 20
JO - BMC Biology
JF - BMC Biology
IS - 1
M1 - 253
ER -