Multiscale Computational Modeling of Chondrocyte Clustering Reveals Mechanoregulation of Cartilage Phenotype and Matrix Homeostasis
Year: 2026
Type: article
Abstract: Chondrocyte clustering represents a fundamental but incompletely understood aspect of cartilage remodeling that determines whether tissue regenerates or degenerates. This study integrates computational modeling, molecular pathway analysis, and biomechanical simulation to elucidate how clustering regulates chondrocyte phenotype, metabolism, and matrix functionality. A multiscale systems-biology framework was established to simulate cell–cell aggregation, extracellular-matrix feedback, and intracellular signaling dynamics involving TGF-β/SMAD, BMP/SMAD1/5, Wnt/β-catenin, YAP/TAZ, and HIF-1α pathways. The results revealed that clustered chondrocytes exhibit strong activation of anabolic signaling (TGF-β/SMAD, BMP/SMAD) and HIF-1α-mediated metabolic adaptation, leading to enhanced SOX9, COL2A1, and ACAN expression, increased sulfated glycosaminoglycan and collagen deposition, and approximately twofold higher mechanical stiffness compared with non-clustered constructs. In contrast, non-clustered cells displayed dominant Wnt/β-catenin and YAP/TAZ activation with elevated COL10A1, MMP-13, and ADAMTS-5, correlating with matrix degradation and oxidative imbalance. Integrated correlation and regression analyses demonstrated that TGF-β/SMAD and HIF-1α contributed most to functional tissue performance, while Wnt and YAP pathways exerted negative effects, defining a “regenerative” versus “degenerative” network polarity. Collectively, these findings identify chondrocyte clustering as a mechanobiological integrator linking molecular signaling, energy metabolism, and mechanical function. The proposed framework provides quantitative and conceptual insight into cartilage homeostasis and offers a theoretical foundation for designing cluster-guided strategies in cartilage tissue engineering and regenerative medicine.
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7161273338
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