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: 10477379
• 负责人: Ashay D Bhatwadekar
• 金额: $ 50.43万
• 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/10477379
• 关键词: Animal Model; Background Diabetic Retinopathy; Biological Markers; Biological Models; Biometry; Blindness; Blood Circulation; Blood Vessels; Blood capillaries; Bone Marrow; Bone Marrow Cells; CD14 gene; CD34 gene; Cell physiology; Cells; Chimera organism; 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; 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; TLR6 gene; Testing; 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)是一个全球性问题，原因是患有糖尿病的人数惊人地增加 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。至 为此，我们已经确定循环中的血管生成细胞(Ca)是理想的报告细胞，它是最具接受性的。 对糖尿病环境和视网膜病变的影响。我们的初步研究表明，CAC映射了一个不同的RNA 签名。我们还发现，在伴有DR的CAC中，miR-92a明显下调，并且其 糖尿病患者CACs的过度表达降低了促炎标志物，如IL-1β和CD14， 强调CACs对于发现视网膜血管健康的生物标记物的重要性。我们的初步研究 使用机器学习模型可以准确地预测DR，为我们开发这一模型提供了更有力的理由 我们健壮的研究设计的范例。此外，使用啮齿动物模型，我们发现miR-92a在 Lineage-Sca1+c-kit+(LSK；相当于小鼠的CACs)与脱细胞毛细血管的增加平行，miR- 抑制LSKs的92a可引起更强的炎症反应。因此，这项提案的首要目标是 是使用强大的测序范例来研究RNA(miRNA和mRNA)和炎症信号，并 糖尿病视网膜病变严重程度不同的个体和动物模型的差异建模以获得对视网膜的洞察力 血管健康。我们的中心假设是，CAC中的RNA签名反映了DR的严重程度，而miR-92a CACs内的降低可作为候选生物标志物。我们提出以下具体目标来测试我们的 假设：目标1：评估不同严重程度的DR和DR患者CACs中RNA水平的变化 验证miR-92a作为候选生物标志物。目的2：确定miR-92a在骨髓细胞中的丢失 与啮齿动物糖尿病视网膜病变的发生有关。我们预计，我们提议的研究将确定具体的 视网膜病变严重程度的mRNA和miRNA的变化，并验证miR-92a作为视网膜血管健康的新的生物标志物。 总体而言，我们的研究将有助于提高糖尿病视网膜病变患者的护理水平，有助于诊断、预后 并为未来的治疗提供机械性的见解。