LOXL2 deletion in post-traumatic injury dysregulates knee joint homeostasis and promotes pain sensitivity, inflammation, and osteoarthritis

Traumatic knee injuries lead to cartilage degeneration and osteoarthritis (OA). Cartilage has limited potential for self-regeneration, and any damage can lead to structural, molecular, and functional aberrations in the knee joint. Early changes in extracellular matrix (ECM) affecting cartilage are primarily asymptomatic and progress towards knee joint dysfunction, pre-OA, and finally OA. This study aimed to elucidate the mechanism of lysyl oxidase-like 2 (LOXL2) in maintaining healthy knee joint articular cartilage, its regeneration, and potential therapeutic applications. LOXL2 loss-of-function was evaluated using Acan promoter-specific inducible Loxl2 knockout, followed by immunohistochemistry, RNA-seq, transcriptional analysis, and knee joint functional and pain analysis. Our results showed that LOXL2 deletion increases the severity of destabilized medial meniscus (DMM) -induced cartilage damage. LOXL2-overexpressing mice were protected against degenerative cartilage changes in the knee joint compared with their wild-type littermates. Interestingly, Intra-articular injection of adenovirus-delivered LOXL2 protected knee joint function, alleviated cartilage degeneration, restored treadmill running capability, and reduced allodynia. Overall, LOXL2 loss initiates cartilage damage, inflammation, and pain, leading to OA. The gain of LOXL2 protects against progressive cartilage damage and relieves pain and inflammation. Thus, we identified a novel function for LOXL2 in OA-related local pain. Overall design: A total of 8 mice knee joint samples from Acan-Cre;Loxl2^fl/fl mice (Jax #019148) were subjected to RNA sequencing using the Illumina NovaSeq 6000 platform. Among these, 4 samples were vehicle-treated controls, and 4 were LOXL2 conditional knockouts. Raw fastq files were processed by aligning the reads to the Mus musculus mm10 genome assembly. Gene expression levels were quantified using featureCounts to generate a count matrix. Differential gene expression analysis was performed using the DESeq2 package in R, where standard normalization and filtering were applied to identify significant changes in gene expression between the control and knockout groups.