Osteocyte-dependent mechanisms of bone cartilage crosstalk in osteoarthritis

骨关节炎中骨软骨串扰的骨细胞依赖性机制

• 批准号: 10727267
• 负责人: Tamara N Alliston
• 金额: $ 40.87万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10727267
• 关键词: Academia; Aging; Arthralgia; Arthritis; Automobile Driving; Big Data; Biological; Biological Markers; Biology; Candidate Disease Gene; Cartilage; Chondrocytes; Clinical; Clinical Research; Collaborations; Data; Data Science; Defect; Degenerative polyarthritis; Development; Diagnosis; Diagnostic; Dimensions; Disease; Early Diagnosis; Face; Gene set enrichment analysis; Genes; Genetic; Genetic Markers; Genetic Risk; Genetic study; Genomics; Goals; Heterogeneity; Hip Osteoarthritis; Hip region structure; Homeostasis; Human; Human Genetics; Human Genome; Image; In Vitro; Individual; Industry; Joints; Knee; Knee Osteoarthritis; MEPE gene; Magnetic Resonance Imaging; Molecular; Mus; Osteocytes; Outcome; Pain; Patients; Peptides; Persons; Pharmaceutical Preparations; Phase; Phase II Clinical Trials; Process; Publishing; Regulation; Research; Risk; Role; Shapes; Signal Transduction; Subgroup; TGFB1 gene; Testing; Therapeutic; Therapeutic Intervention; Variant; arthropathies; biobank; bone; cartilage degradation; clinical predictors; clinically relevant; cohort; deep learning; deep learning algorithm; differential expression; drug development; gain of function; genetic association; genetic variant; genome wide association study; high risk; human imaging; imaging biomarker; improved; innovation; interest; joint function; joint injury; loss of function; magnetic resonance imaging biomarker; mouse model; novel; novel strategies; predictive marker; prevent; programs; skeletal; subchondral bone; targeted agent; targeted treatment; trait; transcriptome; transcriptome sequencing

SUMMARY Cartilage and subchondral bone cooperate to support healthy joint function, and damage to either contributes to osteoarthritis and pain. Nonetheless, the mechanisms by which this cooperation between cartilage and bone occurs remain unclear. Preliminary and published data support the diagnostic and clinical importance of subchondral bone shape in osteoarthritis (OA) progression and pain. Bone shape features, identified by deep learning algorithms, are among the strongest predictive biomarkers for OA. However, a major gap in understanding remains identification of the cellar and molecular mechanisms controlling joint shape. Defining these mechanisms could reveal preventative or therapeutic strategies to protect joints from OA. This team described a new and causal role for osteocytes in OA, such that loss of subchondral bone osteocyte function causes cartilage degeneration and joint shape change. Therefore, with expertise in osteocyte biology, deep learning, and statistical genetics, this team takes an innovative, multi-dimensional approach to identify these mechanisms, as well as genetic and imaging biomarkers that can be used to diagnose early-stage OA when the disease can still be therapeutically modified. This project will test the hypothesis that MRI and genetic markers of joint shape can identify individuals at high risk of OA, and that agents targeting osteocytes can prevent joint shape changes to mitigate OA. Aim 1 will extract genetic factors associated with joint shape traits that predict OA progression and joint pain in the human Osteoarthritis Initiative (OAI) cohort. The function of these genetic factors, including a candidate osteocyte-derived factor that has therapeutic potential in clinical studies, will be examined in Aim 2. This project will impact the identification of genetic correlates to imaging traits that predict clinically relevant OA outcomes in early OA, suggest biological mechanisms driving joint shape change, and highlight these mechanisms as potential targets for OA diagnostics and therapies. Therefore, successful completion of this project could fill a major clinical gap by developing imaging and genetic biomarkers and therapies that can precisely identify and treat subgroups of people at high risk of OA due to joint shape change early enough to prevent severe joint disease.

概括 软骨和软骨下骨共同支持健康的关节功能，任何一方的损伤都会导致 骨关节炎和疼痛。尽管如此，软骨和骨之间的这种合作机制 发生情况仍不清楚。初步和已发表的数据支持诊断和临床的重要性 软骨下骨形状在骨关节炎（OA）进展和疼痛中的作用。骨骼形状特征，通过深度识别 学习算法是 OA 最强的预测生物标志物之一。然而，存在重大差距 理解仍然是控制关节形状的地窖和分子机制的识别。定义 这些机制可以揭示保护关节免受骨关节炎的预防或治疗策略。这支球队 描述了骨细胞在 OA 中的新的因果作用，例如软骨下骨骨细胞功能的丧失 导致软骨退化和关节形状改变。因此，凭借骨细胞生物学方面的专业知识，深入 学习和统计遗传学，该团队采用创新的多维方法来识别这些 机制以及遗传和影像生物标志物，可用于诊断早期 OA 疾病仍然可以通过治疗方法进行改变。该项目将检验 MRI 和遗传标记的假设 关节形状的研究可以识别骨关节炎高危人群，而针对骨细胞的药物可以预防骨关节炎 改变形状以减轻 OA。目标 1 将提取与预测关节形状特征相关的遗传因素 人类骨关节炎倡议 (OAI) 队列中的 OA 进展和关节疼痛。这些基因的功能 因子，包括在临床研究中具有治疗潜力的候选骨细胞衍生因子，将被 在目标 2 中进行了检查。该项目将影响与预测的成像特征的遗传相关性的识别 早期 OA 的临床相关 OA 结果，表明驱动关节形状变化的生物机制，以及 强调这些机制作为 OA 诊断和治疗的潜在目标。因此，成功 该项目的完成可以通过开发成像和遗传生物标志物来填补主要的临床空白 能够精确识别和治疗因关节形状变化而导致骨关节炎高风险的亚群的疗法 尽早预防严重的关节疾病。