Identification of novel blood-based biomarkers of Alzheimer's Disease by pseudotime analysis

通过伪时间分析鉴定阿尔茨海默病的新型血液生物标志物

• 批准号: 10431743
• 负责人: Ignazio Stefano Piras
• 金额: $ 19.2万
• 依托单位: TRANSLATIONAL GENOMICS RESEARCH INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431743
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Alzheimer’s disease biomarker; Appearance; Autopsy; Biological Markers; Blood; Brain; Cerebrospinal Fluid; Clinical; Collection; Consumption; Correlation Studies; Cross-Sectional Studies; Data; Data Set; Dementia; Development; Diagnosis; Diet; Disease; Disease Progression; Exercise; Exhibits; Expression Profiling; Future; Gene Expression; Gene Expression Profiling; Generations; Genes; Genetic Transcription; Goals; Gold; Intervention; Light; Machine Learning; Medicine; Methods; Molecular; Monitor; Neurodegenerative Disorders; Onset of illness; Pathologic; Pathway Analysis; Pathway interactions; Pharmaceutical Preparations; Pharmacological Treatment; Phase; Physical Examination; Positron-Emission Tomography; Process; Publishing; RNA; Research; Sampling; Senile Plaques; Specimen; Staging; Symptoms; Time; Tissues; Translating; Treatment Factor; United States; Validation; Whole Blood; base; blood-based biomarker; brain tissue; candidate marker; clinical practice; cost; density; differential expression; effective therapy; follow-up; lifestyle factors; machine learning algorithm; machine learning method; minimally invasive; neuroimaging; non-demented; novel; pre-clinical; rural area; single cell sequencing; symptomatic improvement; transcriptome sequencing

PROJECT SUMMARY Alzheimer's Disease (AD) is the most prevalent neurodegenerative disease in United States. Current medications are only effective at improving the symptoms for a short period of time and blood-based biomarkers for the disease are only recently beginning to emerge in research and clinical practice. In this proposal we aim to apply pseudotime analysis on publicly available RNA profiling data to detect both novel molecular processes in brain tissue and blood-based RNA biomarkers associated with AD progression. Pseudotime algorithms are machine learning approaches capable of extracting latent temporal information to order samples along a pseudotemporal progression. These approaches utilize cross-sectional data without the need of disease stage information or longitudinal specimen sampling making them uniquely well suited to the large collection of cross-sectional gene expression data currently available for AD. In Aim 1 we will focus on post-mortem brain gene expression analysis, using RNA sequencing data from bulk sampled brain tissue as well as single cell sequencing studies (e.g., Mount Sinai, ROSMAP) that include clinical and neuropathological variables related to AD staging. After extracting the pseudotime trajectories with the phenoPath method, we will prioritize genes according to their statistical correlation with pseudotime. Molecular processes associated with disease onset and progression will be inferred by Weighted Gene Coexpression Network Analysis (WGCNA). In Aim 2 we will focus on RNA expression profiling data from whole blood. Pseudotime trajectories will be determined from existing AD patient blood-based gene expression data as in aim 1, and genes will be prioritized according to their correlation with pseudotime. Then, we will retain genes highly correlated with pseudotime that simultaneously exhibit significant differential expression when compared to control samples, with the goal of finding genes that demonstrate a gradient of expression change from a non-pathological to a pathological stage that are also correlated with disease progression. Finally, we will validate the findings obtained from whole blood in post-mortem brain data from Aim 1, to assess the correlation with the gold- standard neuropathological-based staging. The findings from this proposal will allow us to identify targets for new AD treatments and identify potential candidate blood-based biomarkers of AD progression.

项目摘要 阿尔茨海默病（Alzheimer's Disease，AD）是美国最常见的神经退行性疾病。电流 药物只能在短时间内有效改善症状， 该疾病的生物标志物只是最近才开始出现在研究和临床实践中。 在这项提案中，我们的目标是对公开可用的RNA分析数据应用伪时间分析，以检测 脑组织中的新分子过程和与AD进展相关的血液RNA生物标志物。 伪时间算法是能够提取潜在时间信息以 沿着伪时间进程对样本进行沿着。这些方法利用横截面数据， 需要疾病阶段信息或纵向样本取样，使其特别适合于 目前可用于AD的大量横截面基因表达数据的收集。 在目标1中，我们将重点关注死后大脑基因表达分析，使用来自 大量取样的脑组织以及单细胞测序研究（例如，西奈山，ROSMAP），其中包括 与AD分期相关的临床和神经病理学变量。在提取伪时间轨迹后， 在phenoPath方法中，我们将根据基因与伪时间的统计相关性对基因进行优先级排序。 与疾病发生和进展相关的分子过程将通过加权基因推断 共表达网络分析（WGCNA）。 在目标2中，我们将重点关注来自全血的RNA表达谱数据。伪时间轨道将是 如目的1所述，从现有的AD患者基于血液的基因表达数据中确定基因，并且将 根据它们与伪时间的相关性进行优先级排序。然后，我们将保留与以下高度相关的基因： 当与对照样品相比时同时表现出显著差异表达的伪时间， 目的是寻找表现出从非病理性到病理性表达梯度变化的基因。 病理阶段也与疾病进展相关。最后，我们将验证研究结果 从Aim 1的死后脑数据中的全血中获得，以评估与金- 标准的神经病理学分期这项建议的结果将使我们能够确定目标， 新的AD治疗方法，并确定AD进展的潜在候选血液生物标志物。