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Hyperelastic characterization reveals proteoglycans drive the nanoscale strain-stiffening response in hyaline cartilage.

Journal of biomechanics(2022)

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摘要
Degenerative diseases such as osteoarthritis (OA) result in deterioration of cartilage extracellular matrix (ECM) components, significantly compromising tissue function. For measurement of mechanical properties at micron resolution, atomic force microscopy (AFM) is a leading technique in biomaterials research, including in the study of OA. It is common practice to determine material properties by applying classical Hertzian contact theory to AFM data. However, errors are consequential because the application of a linear elastic contact model to tissue ignores the fact that soft materials exhibit nonlinear properties even at small strains, influencing the biological conclusions of clinically-relevant studies. Additionally, nonlinear material properties are not well characterized, limiting physiological relevance of Young's modulus. Here, we probe the ECM of hyaline cartilage with AFM and explore the application of Hertzian theory in comparison to five hyperelastic models: NeoHookean, Mooney-Rivlin, Arruda-Boyce, Fung, and Ogden. The Fung and Ogden models achieved the best fits of the data, but the Fung model demonstrated robust sensitivity during model validation, demonstrating its ideal application to cartilage ECM and potentially other connective tissues. To develop a biological understanding of the Fung nonlinear parameter, we selectively degraded ECM components to target collagens (purified collagenase), hyaluronan (bacterial hyaluronidase), and glycosaminoglycans (chondroitinase ABC). We found significant differences in both Fung parameters in response to enzymatic treatment, indicating that proteoglycans drive the nonlinear response of cartilage ECM, and validating biological relevance of these phenomenological parameters. Our findings add value to the biomechanics community of using two-parameter material models for microindentation of soft biomaterials.
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