miR-92a as a biomarker of diabetic retinopathy

miR-92a 作为糖尿病视网膜病变的生物标志物

• 批准号: 10677876
• 负责人: Ashay D Bhatwadekar
• 金额: $ 52.93万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10677876
• 关键词: Animal Model; Background Diabetic Retinopathy; Biological Markers; Biological Models; Biometry; Blindness; Blood Vessels; Blood capillaries; Bone Marrow; Bone Marrow Cells; CD14 gene; CD34 gene; Cell physiology; Cells; Chimera organism; Circulation; Clinical; Data; Defect; Development; Diabetes Mellitus; Diabetic Retinopathy; Diagnosis; Electroretinography; Endothelial Cells; Endothelium; Exhibits; Flow Cytometry; Foundations; Future; Genomics; Goals; Grant; Health; Healthcare; Human; ITGA5 gene; Impairment; Individual; Inflammation; Inflammatory; Inflammatory Response; Injury; Interleukin-1 beta; Lesion; Longitudinal Studies; Machine Learning; Maps; Messenger RNA; MicroRNAs; Modeling; Mus; Non-Insulin-Dependent Diabetes Mellitus; Pathologic; Pathology; Patients; Peripheral; Persons; Phenotype; Prognosis; Proliferating; Proto-Oncogene Protein c-kit; RNA; Reporter; Research; Research Design; Retina; Retinal Diseases; Rodent; Rodent Model; Role; Sampling; Series; Severities; Solid; Surface; System; Systems Biology; Testing; Toll-like receptors; Up-Regulation; Validity of Results; Vascular Diseases; Visual impairment; autocrine; biomarker discovery; candidate marker; cohort; db/db mouse; diabetic; diagnostic tool; differential expression; endothelial repair; feature selection; improved; inflammatory marker; insight; machine learning model; migration; non-diabetic; novel marker; overexpression; paracrine; potential biomarker; predictive tools; prognostic algorithm; proliferative diabetic retinopathy; repaired; reparative capacity; retinal damage; standard of care; transcriptome sequencing; transcriptomics; vascular injury

Diabetic retinopathy (DR) is a global problem due to an alarming increase in the number of individuals with diabetes. The lack of an early diagnostic tool predicting retinal vascular health is a critical barrier to progress. To this end, we have identified circulating angiogenic cells (CACs) as ideal reporter cells, which are most receptive to changes of diabetic milieu and retinopathy. Our preliminary studies suggest that CACs map a distinct RNA signature. We also found that a miRNA, miR-92a, is downregulated explicitly in CACs with DR, and its overexpression in CACs of people with diabetes decreased pro-inflammatory markers, such as IL-1β and CD14, emphasizing the importance of CACs for biomarker discovery of retinal vascular health. Our preliminary studies using machine learning models could accurately predict DR providing us a stronger rationale for developing this paradigm for our robust study design. Furthermore, using rodent models, we found a decrease in miR-92a in lineage-Sca1+c-kit+ (LSK; mouse equivalent of CACs) parallels with an increase in acellular capillaries, and miR- 92a inhibition in LSKs triggered a stronger inflammatory response. Therefore, this proposal's overarching goal is to study RNA (miRNA and mRNA) and inflammatory signature using a robust sequencing paradigm and differential modeling in individuals with different severities of DR and animal models to gain insights into retinal vascular health. Our central hypothesis is that RNA signature in CACs mirror DR severity, and miR-92a decrease within CACs serves as a candidate biomarker. We propose the following specific aims to test our hypothesis: Aim 1: To assess changes in RNA levels in CACs of patients with different severity of DR and validate miR-92a as a candidate biomarker. Aim 2: Determine whether the miR-92a loss in bone marrow cells is associated with the development of DR in rodents. We anticipate that our proposed studies will identify specific mRNA and miRNA changes of DR severity and validate miR-92a as a novel biomarker for retinal vascular health. Overall, our studies will help improve the standard of care of DR individuals by aiding in diagnosis, prognosis and providing mechanistic insights for future therapies.

糖尿病视网膜病变（DR）是一个全球性的问题，这是由于患有糖尿病视网膜病变的个体数量惊人地增加。 糖尿病缺乏预测视网膜血管健康的早期诊断工具是进展的关键障碍。到 为此，我们鉴定了循环血管生成细胞（CAC）作为理想的报告细胞， 糖尿病环境和视网膜病变的变化。我们的初步研究表明，CACs映射一个独特的RNA 签名.我们还发现，一种名为miR-92 a的miRNA在患有DR的CACs中明显下调， 糖尿病患者CAC中的过度表达降低了促炎标志物，如IL-1β和CD 14， 强调CAC对于视网膜血管健康的生物标志物发现的重要性。我们的初步研究 使用机器学习模型可以准确地预测DR，为我们提供了更有力的理由来开发这种方法。 我们稳健的研究设计的范例。此外，使用啮齿类动物模型，我们发现miR-92 a的减少， 谱系-Sca 1 +c-kit+（LSK; CAC的小鼠等效物）与无细胞毛细血管的增加平行，而miR- LSK中的92 a抑制引发了更强的炎症反应。因此，该提案的总体目标是 研究RNA（miRNA和mRNA）和炎症特征，使用强大的测序范式， 在具有不同DR严重程度的个体和动物模型中进行差分建模，以深入了解视网膜病变。 血管健康我们的中心假设是CAC中的RNA特征反映了DR的严重程度，而miR-92 a CAC内的减少作为候选生物标志物。我们提出以下具体目标，以测试我们的 假设：目的1：评估具有不同严重程度的DR的患者的CAC中RNA水平的变化， 验证miR-92 a作为候选生物标志物。目的2：确定骨髓细胞中miR-92 a是否丢失 与啮齿类动物DR的发生有关。我们预计，我们提出的研究将确定具体的 mRNA和miRNA的变化，并验证miR-92 a作为视网膜血管健康的新生物标志物。 总的来说，我们的研究将有助于提高DR个体的护理标准， 并为未来的治疗方法提供机制性的见解。