Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease

了解导致阿尔茨海默病神经精神症状的分子机制

基本信息

批准号：
10651757
负责人：
STEVEN M FINKBEINER
金额：
$ 183.32万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10651757
关键词：
Acceleration Adverse effects Agitation Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Autopsy Biological Biological Assay Biological Markers Biological Models Bipolar Disorder Brain Caregiver Burden Cell Nucleus Cells Clinical Clinical Trials Data Data Set Delusions Dementia Development Diagnosis Disease Disease Progression Electronic Health Record Elements Enhancers Epigenetic Process Epitopes Factor Analysis Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Enhancer Element Genetic Markers Genetic Variation Genomics Genotype Goals Hallucinations Human Impaired cognition Institutionalization Intervention Link Machine Learning Major Depressive Disorder Measurement Medicine Mental Depression Methods Modeling Molecular Nerve Degeneration Nuclear Outcome Output Pathology Pathway interactions Patients Pattern Persons Phenotype Prefrontal Cortex Prognosis Quality of life Regulatory Element Research Resolution Sampling Schizophrenia Severities Slide Structure Symptoms Testing Therapeutic Time Tissues Variant Weight associated symptom biobank brain tissue caregiving cell type clinical application clinical diagnosis clinical predictors cohort cost daily functioning deep learning epigenome epigenomic profiling epigenomics functional genomics genetic risk factor genetic variant genomic data high dimensionality indexing innovation interest learning strategy mild cognitive impairment molecular marker neuropathology neuropsychiatric symptom neuropsychiatry novel novel therapeutics patient stratification phenomics phenotypic biomarker potential biomarker predictive modeling prognostic model severe mental illness therapeutically effective trait transcriptome transcriptomics translational potential

项目摘要

PROJECT SUMMARY Neuropsychiatric symptoms (NPS) are core features of Alzheimer's disease (AD) and related dementias that are associated with major adverse effects on daily function and quality of life, and accelerate time to institutionalization. Of all the NPS, depression is the most frequently observed symptom in people with mild cognitive impairment and early AD. As the disease progresses, agitation, delusions and hallucinations become more common, whereas apathy is the most persistent and frequent NPS throughout all the stages of AD. AD-NPS share some clinical features with serious mental illnesses (SMIs), such as schizophrenia, bipolar disorder and major depressive disorder, but whether these conditions share similar aethiopathies is unclear. Given that reliable treatments for NPS in the context of AD and other dementias do not exist, a better understanding of the molecular mechanisms and pathways underlying NPS in AD and other neuropsychiatric illnesses is a critical next step to identify reliable biomarkers that could lead to novel therapeutics. There are two overarching goals of this proposal. First, we will identify the molecular mechanisms and neuropathological changes that are associated with the presence of NPS in patients with AD. Second, we will examine if the mechanisms of pathology associated with NPS are shared or distinct among AD and SMIs. More specifically, we propose to build multi-scale integrative models using phenomics and genomics data from 1,264 autopsy cases derived from a single brain bank. The bank includes detailed phenomics data such as well characterized NPS, clinical diagnosis (AD and other neurodegenerative or neuropsychiatric traits), severity of cognitive decline and neuropathology for each patient sample. From each case, we will apply innovative approaches that reduce the cost and technical biases associated with conventional methods, and capture gene expression signatures and epigenetic regulatory elements at the single-cell level. Novel deep-learning methods will be applied for the multi-scale integration of neuropathologic changes with genetic markers and functional genomic changes (such as changes in gene expression and enhancer sequences) within specific cell types, to predict various NPS in AD and other neuropsychiatric traits; we refer to these integrative models as genotype- marker-phenotype models. We expect that these models will enable us to assign genotypes and molecular markers to specific NPS within AD and other neuropsychiatric traits at the single-cell level, an unprecedented level of resolution. In addition, we will test the translational potential of the genotype-marker-phenotype models to predict AD-NPS using independent large-scale biobank datasets, in which genotypes and electronic health records are available. Successful completion of the proposed studies will have immediate utility by generating potential biomarkers for NPS diagnosis and prognosis and by providing predictive models for patient stratification in clinical trials. In the longer term, our models will help us create a blueprint for therapeutic strategies and interventions to treat NPS in AD.

项目概要神经精神症状 (NPS) 是阿尔茨海默病 (AD) 及相关疾病的核心特征痴呆症与日常功能和生活质量的重大不利影响有关，并加速制度化的时间。在所有 NPS 中，抑郁症是患有以下疾病的人中最常见的症状：轻度认知障碍和早期 AD。随着病情的进展，会出现烦躁、妄想和幻觉变得更加普遍，而冷漠是在所有阶段中最持久和最常见的NPS 广告。 AD-NPS 与严重精神疾病 (SMI) 具有一些共同的临床特征，例如精神分裂症、双向情感障碍障碍和重度抑郁症，但这些疾病是否具有相似的疾病尚不清楚。鉴于 AD 和其他痴呆症中不存在 NPS 的可靠治疗方法，因此更好的方法了解 AD 和其他神经精神疾病中 NPS 的分子机制和途径疾病是识别可靠生物标志物的关键下一步，这些生物标志物可能会带来新的治疗方法。该提案有两个总体目标。首先，我们将确定分子机制以及与 AD 患者中 NPS 的存在相关的神经病理学变化。第二，我们将检查与 NPS 相关的病理机制在 AD 和 SMI 之间是否相同或不同。更具体地说，我们建议使用表型组学和基因组学数据构建多尺度综合模型来自单一脑库的 1,264 例尸检病例。该银行包含详细的表型组学数据，例如明确的 NPS、临床诊断（AD 和其他神经退行性或神经精神特征）、严重程度每个患者样本的认知能力下降和神经病理学。针对每一个案例，我们都会应用创新减少与传统方法相关的成本和技术偏差并捕获基因的方法单细胞水平的表达特征和表观遗传调控元件。新颖的深度学习方法将应用于神经病理学变化与遗传标记和功能的多尺度整合特定细胞类型内的基因组变化（例如基因表达和增强子序列的变化），预测 AD 中的各种 NPS 和其他神经精神特征；我们将这些综合模型称为基因型- 标记表型模型。我们期望这些模型将使我们能够分配基因型和分子在单细胞水平上对 AD 内的特定 NPS 和其他神经精神特征进行标记，这是前所未有的分辨率级别。此外，我们将测试基因型-标记-表型模型的转化潜力使用独立的大规模生物库数据集来预测 AD-NPS，其中基因型和电子健康记录可用。成功完成拟议的研究将立即产生效用 NPS 诊断和预后的潜在生物标志物，并为患者提供预测模型临床试验中的分层。从长远来看，我们的模型将帮助我们创建治疗蓝图治疗 AD 中 NPS 的策略和干预措施。