Peripheral blood mononuclear cell epigenetic associations in and biomarkers for knee osteoarthritis development and progression

膝骨关节炎发生和进展的外周血单核细胞表观遗传关联及其生物标志物

• 批准号: 10426357
• 负责人: Matlock Jeffries
• 金额: $ 38.07万
• 依托单位: OKLAHOMA MEDICAL RESEARCH FOUNDATION
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10426357
• 关键词: Affect; Age-Years; Algorithms; Big Data; Biological Markers; Blood; Blood Cells; Blood specimen; Cartilage; Chronic; Classification; Clinical; Clinical Data; DNA; DNA Methylation; DNA methylation profiling; Data; Degenerative polyarthritis; Development; Diagnosis; Disease; Environment; Epigenetic Process; FDA approved; Fostering; Foundations; Future; Genes; Genetic Transcription; Genomic Segment; Genomics; Health; High-Throughput Nucleotide Sequencing; Hip Osteoarthritis; Immunologics; Individual; Inflammation Mediators; Inflammatory; Joints; Knee Osteoarthritis; Libraries; Location; Machine Learning; Methods; Methylation; Mission; Modeling; Modification; Morbidity - disease rate; Musculoskeletal Diseases; Pain; Pathogenesis; Pathogenicity; Pathway interactions; Patients; Pattern; Performance; Peripheral Blood Mononuclear Cell; Pharmaceutical Preparations; Phenotype; Physicians; Pilot Projects; Prognosis; Public Health; Research; Research Personnel; Sampling; Serum; System; Techniques; Testing; Time; TimeLine; Tissues; Translating; Translations; United States National Institutes of Health; Urine; Validation; Work; algorithm development; base; biobank; biomarker development; bisulfite; bisulfite sequencing; clinical application; clinically relevant; cohort; deep sequencing; disability; economic impact; epigenetic marker; epigenome; genome-wide analysis; improved; innovation; insight; joint function; machine learning model; methylation pattern; model development; mortality; next generation; next generation sequencing; novel; peripheral blood; protein biomarkers; radiological imaging; sodium bisulfite; subchondral bone; success; systemic inflammatory response; targeted sequencing

Project Summary / Abstract The objective of the proposed research is to better understand how peripheral blood epigenetic patterns are associated with knee osteoarthritis (OA). A great deal of work has already been demonstrated widespread epigenetic changes within articular tissues in both knee and hip OA. Others have described serum and urine protein biomarkers as predictors of future knee OA progression. Our first Aim is to evaluate peripheral blood cell DNA epigenetic patterns in baseline blood samples from patients who will go on to have rapid radiographc and/or pain progression in the subsequent 24 months. We will then use these data to develop develop and evaluate the performance of epigenetic algorithmic models to discriminate these groups. Patient samples will parallel the National Institutes of Health OA Biomarkers Consortium (OABC-FNIH) study. DNA methylation will be evaluated using a next-generation bisulfite sequencing approach (methylSeq), and algorithms developed using cutting-edge machine learning techniques. We will then translate our findings into a more high- throughput, inexpensive, and clinically relevant form by developing and validating a targeted capture sequencing system to interrogate these specific epigenetic locations. Our second Aim is to evaluate the peripheral blood DNA methylation patterns that precede the development of OA, using samples from 48-, 24-, 12-, and 0-months before incident OA. We will again develop algorithms to predict future OA development using similar techniques as Aim 1 and translate this to a targeted capture sequencing system. This unique longitudinal approach which will allow us not only to determine whether and when epigenetic patterns develop preceding OA development, but also track longitudinal epigenetic changes as OA develops. The proposed work is important, as there are no FDA approved biomarkers for OA diagnosis or prognosis. Our work is quite innovative both in its combination of "big data" epigenetic analysis and cutting-edge machine learning techniques applied to a specific clinical problem, as well as in its examination of PBMC epigenetics in OA, which has not yet been described. Moreover, we tackle the problem of translation of big-data research by aiming specifically to develop high-throughput methods to translate our findings into a clinically-relevant and accessible form. Success in our proposal will produce both algorithmic models with direct clinical impact to predict future OA development and progression, as well as broaden our understanding of epigenetic changes in peripheral blood cells from OA patients.

项目概要/摘要 拟议研究的目的是更好地了解外周血表观遗传模式如何 与膝骨关节炎（OA）有关。大量工作已经得到广泛证明 膝关节和髋关节 OA 关节组织内的表观遗传变化。其他人描述了血清和尿液 蛋白质生物标志物作为未来膝关节 OA 进展的预测因子。我们的首要目标是评估外周血 将继续进行快速放射线检查的患者基线血液样本中的细胞 DNA 表观遗传模式 和/或随后 24 个月内疼痛进展。然后我们将使用这些数据来开发和 评估表观遗传算法模型的性能以区分这些群体。患者样本将 与美国国立卫生研究院 OA 生物标志物联盟 (OABC-FNIH) 的研究并行。 DNA甲基化 将使用下一代亚硫酸氢盐测序方法（methylSeq）进行评估，并开发算法 使用尖端的机器学习技术。然后我们将把我们的发现转化为更高的 通过开发和验证目标捕获，实现高通量、廉价且临床相关的形式 测序系统来询问这些特定的表观遗传位置。我们的第二个目标是评估 使用来自 48-、24-、 事件 OA 发生前 12 个月和 0 个月。我们将再次开发算法来预测未来OA的发展 使用与目标 1 类似的技术并将其转化为目标捕获测序系统。这种独特的 纵向方法不仅使我们能够确定表观遗传模式是否以及何时发展 OA 发展之前，还跟踪 OA 发展过程中的纵向表观遗传变化。拟议的 工作很重要，因为 FDA 还没有批准用于 OA 诊断或预后的生物标志物。我们的工作是 “大数据”表观遗传分析与前沿机器学习的结合颇具创新性 应用于特定临床问题的技术，以及 OA 中 PBMC 表观遗传学的检查， 尚未描述。此外，我们通过以下方式解决大数据研究的转化问题： 专门致力于开发高通量方法，将我们的发现转化为临床相关的和 可访问的形式。我们提案的成功将产生两种对临床有直接影响的算法模型 预测未来 OA 的发展和进展，并拓宽我们对表观遗传变化的理解 OA 患者的外周血细胞。