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

项目总结/摘要 早发性阿尔茨海默病（EOAD）定义为65岁以前发病，其特征为： 与晚发性阿尔茨海默病（LOAD）的神经病理学相同。有一个常见的误解，即EOAD 主要作为常染色体显性疾病发生，但APP、PSEN 1和PSEN 2中存在致病性变体 仅占所有EOAD病例的5%。只有一半或更少的EOAD患者携带强AD风险因素， APOE*E4。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。增加了进一步的复杂性，与典型的 LOAD的情节性记忆损害，EOAD经常表现为“非典型”临床症状（即，执行， 视觉空间或语言功能障碍），这是由于特定相关脑网络的神经变性。 虽然LOAD和EOAD由相同的神经病理学定义，但不同的潜在脑功能障碍的事实是， 网络受到影响表明EOAD由不同的潜在分子病因学引起。长期 这项工作的目的是阐明EOAD临床异质性的遗传驱动因素，以开发 个体化疾病风险的预测指标。在本研究中，900名EOAD，LOAD和健康年龄- 匹配的对照将通过全基因组测序进行研究，以寻找有助于 EOAD疾病风险。还将对一部分患者进行基于单细胞液滴的RNA测序 以鉴定区分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脑萎缩的基因组。 EOAD患者临床亚组的神经解剖脆弱性。这项研究的基本假设是 有不同的基因网络通过共同的生物学途径连接， 易受三种大脑网络中每一种的影响，这三种大脑网络在不同的EOAD病例中都是脆弱的。具体 本研究的目的是：（1）区分EOAD遗传风险和LOAD遗传风险;（2）识别基因 EOAD和LOAD之间的表达差异;（3）评估基因表达模式是否预测 EOAD萎缩模式。识别导致EOAD临床异质性的潜在遗传风险将 让我们对疾病生物学有了更深入的了解此外，提前预测哪个网络最 易受伤害的可能使我们能够确定在治疗过程中应该最密切监测的认知功能。 以患者特定的方式确定疾病的临床前阶段。拟议的横向研究将奠定 为将来评估使用基因组学、转录组学和成像谱在 联合用药预测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， 开发个性化基因组药物，用于临床干预期间的EOAD诊断和跟踪 审判

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.