Matrix-embedded cytokines to simulate osteoarthritis-like cartilage microenvironments

基质嵌入细胞因子模拟骨关节炎样软骨微环境

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作者:Sumit Murab, Shibu Chameettachal, Maumita Bhattacharjee, Sanskrita Das, David L Kaplan, Sourabh Ghosh

Abstract

In vivo, cytokines noncovalently bind to the extracellular matrix (ECM), to facilitate intimate interactions with cellular receptors and potentiate biological activity. Development of a biomaterial that simulates this type of physiological binding and function is an exciting proposition for designing controlled advanced delivery systems for simulating in vivo conditions in vitro. We have decorated silk protein with sulfonated moieties through diazonium coupling reactions to noncovalently immobilize pro-inflammatory cytokines interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α) in such a biomimetic manner. After adsorption of the cytokines to the diazonium-modified silk matrix, constant release of cytokines up to at least 3 days was demonstrated, as an initial step to simulate an osteoarthritic (OA) microenvironment in vitro. Matrix-embedded cytokines induced the formation of multiple elongated processes in chondrocytes in vitro, akin to what is seen in OA cartilage in vivo. Gene expression profiles with this in vitro tissue model of OA showed significant similarities to profiles from explanted OA cartilage tissues collected from patients who underwent total knee replacement surgery. The common markers of OA, including COL, MMP, TIMP, ADAMTS, and metallothioneins, were upregulated at least 35-fold in the in vitro model when compared to the control-non-OA in vitro generated tissue-engineered cartilage. The microarray data were validated by reverse transcriptase-polymerase chain reaction. Mechanistically, protein interaction studies indicated that TNF-α and IL-1β synergistically controlled the equilibrium between MMPs and their inhibitors, TIMPs, resulting in ECM degradation through the MAPK pathway. This study offers a promising initial step toward establishing a relevant in vitro OA disease model, which can be further modified to assess signaling mechanisms, responses to cell or drug treatments and patient-specific features.

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