Independent and combined effects of obesity and traumatic joint injury to the structure and composition of rat knee cartilage.

Osteoarthritis (OA) is a multifactorial joint disease characterized by articular cartilage degradation. Risk factors for OA include joint trauma, obesity, and inflammation, each of which can affect joint health independently, but their interaction and the associated consequences of such interaction were largely unexplored. Here, we studied compositional and structural alterations in knee joint cartilages of Sprague-Dawley rats exposed to two OA risk factors: joint injury and diet-induced obesity. Joint injury was imposed by surgical transection of anterior cruciate ligaments (ACLx), and obesity was induced by a high fat/high sucrose diet. Depth-dependent proteoglycan (PG) content and collagen structural network of cartilage were measured from histological sections collected previously in Collins et al.. (2015). We found that ACLx primarily affected the superficial cartilages. Compositionally, ACLx led to reduced PG content in lean animals, but increased PG content in obese rats. Structurally, ACLx caused disorganization of collagenous network in both lean and obese animals through increased collagen orientation in the superficial tissues and a change in the degree of fibrous alignment. However, the cartilage degradation attributed to joint injury and obesity was not necessarily additive when the two risk factors were present simultaneously, particularly for PG content and collagen orientation in the superficial tissues. Interestingly, sham surgeries caused a through-thickness disorganization of collagen network in lean and obese animals. We conclude that the interactions of multiple OA risk factors are complex and their combined effects cannot be understood by superposition principle. Further research is required to elucidate the interactive mechanism between OA subtypes.

Correction: A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait.

[This corrects the article DOI: 10.1371/journal.pcbi.1009398.].

A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait.

Abnormal loading of the knee due to injuries or obesity is thought to contribute to the development of osteoarthritis (OA). Small animal models have been used for studying OA progression mechanisms. However, numerical models to study cartilage responses under dynamic loading in preclinical animal models have not been developed. Here we present a musculoskeletal finite element model of a rat knee joint to evaluate cartilage biomechanical responses during a gait cycle. The rat knee joint geometries were obtained from a 3-D MRI dataset and the boundary conditions regarding loading in the joint were extracted from a musculoskeletal model of the rat hindlimb. The fibril-reinforced poroelastic (FRPE) properties of the rat cartilage were derived from data of mechanical indentation tests. Our numerical results showed the relevance of simulating anatomical and locomotion characteristics in the rat knee joint for estimating tissue responses such as contact pressures, stresses, strains, and fluid pressures. We found that the contact pressure and maximum principal strain were virtually constant in the medial compartment whereas they showed the highest values at the beginning of the gait cycle in the lateral compartment. Furthermore, we found that the maximum principal stress increased during the stance phase of gait, with the greatest values at midstance. We anticipate that our approach serves as a first step towards investigating the effects of gait abnormalities on the adaptation and degeneration of rat knee joint tissues and could be used to evaluate biomechanically-driven mechanisms of the progression of OA as a consequence of joint injury or obesity.

Effect of cells on spatial quantification of proteoglycans in articular cartilage of small animals.

OBJECTIVE: Histochemical characterization of proteoglycan content in articular cartilage is important for the understanding of osteoarthritis pathogenesis. However, cartilage cells may interfere with the measurement of matrix proteoglycan content in small animal models (e.g. mice and rats) due to the high cell volume fraction (38%) in mice compared to human tissue (~1%). We investigated whether excluding the cells from image analysis affects the histochemically measured proteoglycan content of rat knee joint cartilage and assessed the effectiveness of a deep learning algorithm-based tool named U-Net in cell segmentation. DESIGN: Histological sections were stained with Safranin-O, after which optical densities were measured using digital densitometry to estimate proteoglycan content. U-Net was trained with 600 annotated Safranin-O cartilage images for exclusion of cells from the cartilage extracellular matrix. Optical densities of the ECM with and without cells were compared as a function of normalized tissue depth. RESULTS: U-Net cell segmentation was accurate, with the measured cell area fraction following largely that of ground-truth images (average difference: 4.3%). Cell area fraction varied as a function of tissue depth and took up 8-21% of the tissue area. The exclusion of cells from the analysis led to an increase in the analyzed depth-dependent optical density of cartilage by approximately 0.6-1.8% (p < 0.01). CONCLUSIONS: Although the effect of cells on the analyzed proteoglycan content is small, it should be considered for improved sensitivity, especially at the onset of the disease during which cells may proliferate in small animals.