Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BackgroundOsteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development.
Methodology/Principal FindingsIn this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential “interactome” network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes.
Conclusions/SignificanceOur findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets.

研究背景：骨关节炎(Osteoarthritis)是一种多因素疾病，其特征为关节软骨破坏，由遗传、机械及环境因素共同介导，全球受累人群超1亿。尽管该疾病患病率居高不下，但大规模分子研究的匮乏限制了我们对其分子病理生物学的理解，同时阻碍了药物开发靶点的筛选。
材料与方法/主要研究结果：本研究整合遗传学、生物信息学与蛋白质组学手段，旨在鉴定参与骨关节炎发病机制的新基因及其协同调控网络。对患者来源的骨关节炎软骨与正常软骨开展微RNA(microRNA)表达谱分析，筛选得到包含16个微RNA的骨关节炎特征基因集。在相同组织中采用反相蛋白质阵列(reverse-phase protein arrays)技术，检测到骨关节炎软骨细胞与正常软骨细胞间存在76个差异表达蛋白，其中包括SOX11、FGF23、KLF6、WWOX及GDF15等此前未被报道与骨关节炎发生相关的蛋白。将微RNA与蛋白质组学数据结合微RNA基因靶标预测算法，构建了由11个微RNA、58个蛋白及414条潜在功能关联构成的潜在“相互作用组(interactome)”网络。对分子与临床数据进行关联分析后发现，特定微RNA（miR-22、miR-103）与蛋白（PPARA、BMP7、IL1B）的表达水平与体质量指数(BMI, Body Mass Index)显著相关。实验验证结果表明，miR-22可调控PPARA与BMP7的表达，抑制miR-22可阻断骨关节炎软骨细胞的炎症反应与分解代谢变化。
结论与意义：本研究结果显示，肥胖与炎症与骨关节炎密切相关，该疾病属于伴随微RNA表达失调的代谢性疾病。基因网络分析方法为阐明骨关节炎等复杂多因素疾病的发病机制提供了全新视角。整合微RNA、蛋白质组学与临床数据，可清晰揭示疾病相关网络状态的调控规律，同时为新型治疗策略的开发提供依据。该研究策略有助于加深对骨关节炎等多因素疾病发病机制的理解，并为潜在新型治疗靶点的发掘提供指导方向。