Elucidating clinical heterogeneity in early-onset AD via genomics, transcriptomics, and neuroimaging

通过基因组学、转录组学和神经影像学阐明早发 AD 的临床异质性

基本信息

批准号：
10655368
负责人：
Jennifer S Yokoyama
金额：
$ 80.74万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
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/10655368
关键词：
Affect Age Age of Onset Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer&apos s disease risk Atrophic Biological Biological Markers Biology Brain Cells Characteristics Clinical Complex Cross-Sectional Studies Data Diagnosis Disease Early Onset Alzheimer Disease Early Onset Familial Alzheimer&apos s Disease Etiology Exclusion Family Study Foundations Functional disorder Future Gene Expression Gene Expression Profile Genes Genetic Genetic Risk Genetic Variation Genomics Goals HLA Antigens Heritability Image Immune Individual Intervention Intervention Trial Language Late Onset Alzheimer Disease Link Longitudinal cohort Measures Methods Molecular Monitor Nerve Degeneration Neuroanatomy Oncology Onset of illness Outcome Study Pathogenicity Pathway interactions Patients Pattern Peripheral Prognosis Public Health Research Risk Risk Factors Subgroup Symptoms Syndrome Techniques Testing Therapeutic Intervention Variant Visuospatial Work anticancer research apolipoprotein E-4 autosome cerebral atrophy clinical heterogeneity clinical predictors clinical subtypes cognitive function disease prognosis disorder risk early onset episodic memory impairment gene network genetic variant genome sequencing improved inclusion criteria innovation multimodality neuroimaging neuropathology new therapeutic target novel patient subsets personalized genomic medicine pre-clinical presenilin-1 presenilin-2 risk prediction transcriptome sequencing transcriptomics whole genome

项目摘要

Project Summary/Abstract Early-onset Alzheimer's disease (EOAD) is defined by an onset before age 65 and is characterized by the same neuropathology of late-onset Alzheimer's disease (LOAD). There is a common misconception that EOAD occurs primarily as an autosomal dominant disease, yet pathogenic variants in APP, PSEN1, and PSEN2 account for merely 5% of all EOAD cases. Only half or fewer EOAD patients carry the strong AD risk factor, APOE*E4. Nevertheless, family studies indicate that EOAD has a stronger heritable component than LOAD, suggesting a large portion of genetic risk remains unknown. Adding further complexity, unlike the typical episodic memory impairment of LOAD, EOAD often presents with “atypical” clinical symptoms (i.e., executive, visuospatial, or language dysfunction) due to neurodegeneration of specific associated brain networks. Although LOAD and EOAD are defined by the same neuropathology, the fact that different underlying brain networks are affected suggests that EOAD results from a distinct underlying molecular etiology. The long-term goal of this work is to elucidate the genetic drivers of clinical heterogeneity in EOAD in order to develop predictive measures of individualized disease risk. In the present study, 900 EOAD, LOAD, and healthy age- matched controls will be studied through whole genome sequencing for novel genetic variation contributing to EOAD disease risk. Single-cell droplet-based RNA-sequencing will also be performed on a subset of patients to identify signatures of peripheral gene expression that distinguish EOAD patients. Finally, changes in gene expression will be related to patterns of brain atrophy to identify the group of genes contributing to selective neuroanatomical vulnerability in clinical subgroups of EOAD patients. The underlying hypothesis of this study is that there are different networks of genes linked by common biological pathways that drive selective vulnerability to each of the three brain networks that are vulnerable in different cases of EOAD. The specific aims of this project are: (1) Distinguish between EOAD genetic risk and LOAD genetic risk; (2) Identify gene expression differences between EOAD and LOAD; (3) Evaluate whether gene expression patterns predict EOAD atrophy patterns. Identifying underlying genetic risk contributing to EOAD clinical heterogeneity will inform our understanding of disease biology. In addition, predicting in advance which network is most vulnerable may enable us to identify the cognitive functions that should be monitored most closely during preclinical stages of disease in a patient-specific manner. The proposed cross-sectional study will lay the foundation for future work assessing the clinical value of using genomic, transcriptomic, and imaging profiles in combination to predict disease presentation and clinical progression in longitudinal cohorts of EOAD patients. This work will contribute to our biological understanding of variability in AD and may inform future efforts to develop personalized genomic medicine for EOAD prognostication and tracking during clinical intervention trials.

项目概要/摘要早发性阿尔茨海默病 (EOAD) 是指 65 岁之前发病，其特征是与迟发性阿尔茨海默病（LOAD）相同的神经病理学有一个常见的误解，即 EOAD。主要作为常染色体显性遗传疾病发生，但 APP、PSEN1 和 PSEN2 存在致病性变异仅占所有 EOAD 病例的 5%，只有一半或更少的 EOAD 患者携带强 AD 危险因素，然而，家族研究表明 EOAD 比 LOAD 具有更强的遗传成分，这表明很大一部分遗传风险仍然未知，这进一步增加了复杂性，这与典型的风险不同。 LOAD、EOAD 的情景记忆障碍通常表现为“非典型”临床症状（即执行、由于特定相关大脑网络的神经变性而导致的视觉空间或语言功能障碍。尽管 LOAD 和 EOAD 是由相同的神经病理学定义的，但事实上不同的底层大脑网络受到影响表明 EOAD 是由不同的潜在分子病因引起的。这项工作的目标是阐明 EOAD 临床异质性的遗传驱动因素，以便开发个体化疾病风险的预测措施在本研究中，900 EOAD、LOAD 和健康年龄。将通过全基因组测序来研究匹配的对照，以寻找新的遗传变异，从而有助于还将对一部分患者进行基于单细胞液滴的 RNA 测序。识别区分 EOAD 患者的外周基因表达特征最后，基因的变化。表达将与脑萎缩的模式相关，以确定有助于选择性的基因组 EOAD 患者临床亚组的神经解剖学脆弱性本研究的基本假设是。存在由共同的生物途径连接的不同基因网络，这些基因驱动选择性在不同 EOAD 情况下容易受到攻击的三个大脑网络的脆弱性。该项目的目标是：（1）区分EOAD遗传风险和LOAD遗传风险；（2）识别基因； (3) 评估基因表达模式是否预测识别导致 EOAD 临床异质性的潜在遗传风险将。此外，提前预测哪个网络最重要。脆弱性可能使我们能够识别在学习过程中应该最密切监测的认知功能。拟议的横断面研究将以患者特异性的方式确定疾病的临床前阶段。为未来评估使用基因组、转录组和成像特征的临床价值的工作奠定了基础结合预测 EOAD 患者纵向队列的疾病表现和临床进展。这项工作将有助于我们对 AD 变异性的生物学理解，并可能为未来的努力提供信息开发个性化基因组医学，用于临床干预期间的 EOAD 预测和跟踪试验。