MicroRNAs as Diagnostic and Prognostic Biomarker of Alzheimer's Disease

摘要 迟发性阿尔茨海默病(AD)的预防和治疗策略依赖于获得可靠的 用于早期检测的生物标记物，理想的微创和廉价的更具侵入性和昂贵的生物标记物 保留用于后续诊断。尽管血液生物标记物的开发在检测AD方面取得了进展- 相关的β-淀粉样蛋白或Tau病理，挑战仍然存在，因为AD的特征是调节失调 由遗传和环境风险因素组合引发的神经基因表达。基因组-环境 相互作用由表观遗传过程协调，其中包括microRNAs(miRNAs，miRs)的功能 这是为了调节基因表达和蛋白平衡。当重复观察到改变的microRNA表达时 在AD生物样品中，小样本量和缺乏机制维持了对microRNAs的认识差距 可作为AD的诊断和预后的生物标志物。史无前例的样品组合 阿尔茨海默病神经成像倡议(ADNI)、弗雷明翰心脏研究(FHS)和德国的认知 这项应用中的损伤和痴呆症研究(DELCODE)为我们提供了弥合这一差距的机会。 在Aim 1we中，我们测试了以下假设：循环血浆microRNA水平的不同模式在 认知正常(CN)、轻度认知障碍(MCI)和痴呆症患者(AD)会议 AD的临床诊断标准。从847个样品中获得用于microRNA-Seq分析的横截面血浆样品 ADNI-1/GO/-2的参与者和DELCODE的585名参与者。我们将使用互补计算 比较AD与CN、MCI与CN和AD与MCI样本并识别MCI的microRNA特征的方法 和AD。结果将与FHS队列的数据整合在一起，该队列包括48名有诊断的参与者 确认死后和3个按年份分隔的纵向血浆样本；患上阿尔茨海默病的参与者 提供了临床诊断前的2例和诊断后的1例。我们将使用统计和机器学习 生成基于miRNA的分类器的方法。目标2将确定循环中的差异表达 区分AD和CN受试者的候选生物标志物miRs可以在脆弱的细胞中观察到，激光- 从AIM 1 FHS参与者的死后皮质进行显微解剖。在这些单元格中，我们将量化该表达式 MiR-181a-5p、miR-148-3p和miR-146a-5p，这是一种3-miR血浆签名，揭示了面临 在我们的探索性研究中，从MCI向AD的转变。我们还将检查至少3个TOP的表达式 MicroRNA AD生物标记物候选在AIM 1分析中得到验证。在目标3中，我们将揭示 通过操纵候选生物标记物的水平在体外和体内建立候选生物标记物microRNAs 它们在人诱导的多能干细胞来源的皮质神经元和神经胶质细胞以及NEXT中的功能读数 一代AD小鼠模型。我们全面的跨学科分析将把观察性和 确定MCI和AD相关的microRNA签名如何反映AD相关的实验数据 作为AD发生的生物标志物。

ABSTRACT Preventative and therapeutic strategies for late onset Alzheimer’s Disease (AD) depend on access to reliable biomarkers for early detection, ideally minimally invasive and inexpensive with more invasive and costly ones reserved for subsequent diagnostics. Despite the progress in the development of blood biomarkers to detect AD- associated beta-amyloid or Tau pathology, the challenges remain because AD is characterized by dysregulated neural gene expression triggered by combinations of genetic and environmental risk factors. Genome-environment interactions are orchestrated by epigenetic processes that include the function of microRNAs (miRNAs, miRs) which is to regulate gene-expression and proteostasis. While altered microRNA expression is repeatedly observed in AD biospecimens, small sample size and lack of mechanism maintain the gap of knowledge whethermicroRNAs could serve as diagnostic and prognostic AD biomarker. The unprecedented combination of samples from Alzheimer’s Disease Neuroimaging Initiative (ADNI), Framingham Heart Study (FHS), and Germany’s cognitive impairment and dementia study (DELCODE) in this application provides us with the opportunity to bridge that gap. In Aim 1we test the hypothesis that distinct patterns of circulating plasma microRNA levels differentiate among cognitively normal (CN), individuals with mild cognitive impairment (MCI), and dementia patients (AD) meeting clinical criteria for AD. Cross-sectional plasma samples for microRNA-Seq analysis were obtained from 847 participants in ADNI-1/GO/-2 and from 585 participants in DELCODE. We will use complementary computational approaches to compare: AD vs CN, MCI vs CN, and AD vs MCI samples and identify microRNA signatures of MCI and AD. The results will be integrated with the data from the FHS cohort comprising 48 participants with diagnoses confirmed postmortem and 3 longitudinal plasma samples separated by years; the participants who developed AD provided 2 samples before and 1 after their clinical AD diagnosis. We will use statistical and machine learning approaches to generate miRNA-based classifiers. Aim 2 will determine if differential expression of circulating candidate biomarker miRs that discriminate between AD and CN subjects can be observed in vulnerable cells, laser- microdissected from postmortem cortices of Aim 1 FHS participants. In these cells we will quantify the expression of miR-181a-5p, miR-148-3p and miR-146a-5p, a 3-miR plasma signature that uncovered patients at risk for converting from MCI to AD in our exploratory study in press. We will also examine the expression of at least 3 top microRNA AD biomarker candidates validated in Aim 1 analyses. In Aim 3 we will uncover the functions of candidate biomarker microRNAs in vitro and in vivo by manipulating levels of biomarker candidates to establish their functional readouts in human induced pluripotent stem cell- derived cortical neurons and glia and in next generation AD mouse models. Our comprehensive interdisciplinary analyses will integrate observational and experimental data to determine how MCI- and AD-associated microRNA signatures reflect AD-associated pathology and serve as a biomarker for developing AD.