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: 10633132
• 负责人: Matlock Jeffries
• 金额: $ 38.46万
• 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/10633132
• 关键词: Affect; Age Years; Algorithms; Big Data; Biological Markers; Blood; Blood Cells; Blood specimen; Cartilage; Chronic; Classification; Clinical; Clinical Data; Creativeness; DNA; DNA Methylation; DNA methylation profiling; Data; Degenerative polyarthritis; Development; Diagnosis; Disease; Early Diagnosis; Environment; Epigenetic Process; FDA approved; Fostering; Foundations; Future; Genes; Genetic Transcription; Genomic Segment; 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; Tissues; Translating; Translations; United States National Institutes of Health; Urine; Validation; Work; algorithm development; biobank; biomarker development; bisulfite; bisulfite sequencing; clinical application; clinically relevant; cohort; deep sequencing; disability; economic impact; epigenetic marker; epigenome; genome-wide; genomic locus; 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; timeline

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.

项目总结/摘要 这项研究的目的是为了更好地了解外周血表观遗传模式是如何 与膝关节骨关节炎（OA）有关。大量的工作已经得到了广泛的展示 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。我们的第一个目标是评估外周血 从患者基线血样中的细胞DNA表观遗传模式， 和/或在随后的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）研究平行。DNA甲基化 将使用下一代亚硫酸氢盐测序方法（methylSeq）和开发的算法进行评估 使用尖端的机器学习技术。我们将把我们的发现转化为更高的- 通过开发和验证靶向捕获， 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-， OA事件前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Aim 1类似的技术，并将其转化为靶向捕获测序系统。这种独特 纵向的方法，这将使我们不仅能够确定是否以及何时表观遗传模式发展 在OA发展之前，还跟踪OA发展时的纵向表观遗传变化。拟议 工作是重要的，因为没有FDA批准的生物标志物用于OA诊断或预后。我们的工作是 在“大数据”表观遗传分析和尖端机器学习的结合方面， 应用于特定临床问题的技术，以及在OA中PBMC表观遗传学的检查中， 其尚未被描述。此外，我们通过以下方式解决大数据研究的翻译问题： 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， 可访问的形式。我们提案的成功将产生两种算法模型，对临床有直接影响， 预测未来OA的发展和进展，以及扩大我们对表观遗传变化的理解， OA患者的外周血细胞。