Harnessing Molecular Networks of Resilience for Therapeutic Discoveries in AD

尽管对阿尔茨海默病(AD)的预防和复原力构成了 了解阿尔茨海默病的病理生理学，这是一个相对较少研究的领域，也是弹性的分子基础 对AD来说，很大程度上是未知的。这是一个关键的知识鸿沟，因为揭示了弹性的生物学途径 可能导致发现治疗或预防阿尔茨海默病的新药物靶点。很明显，有几个 可能受到保护而不会患上AD病理的个人，尽管他们年事已高，且高度 阿尔茨海默病的遗传危险因素，如载脂蛋白Eε4等位基因。此外，还有一些人有认知能力 尽管有AD的病理或生物标志物变化，但仍未出现认知衰退的恢复力。当前 阿尔茨海默病的治疗发现工作主要集中在揭示 在这种情况下，这些途径中的关键分子，以及治疗化合物的鉴定 来停止和/或逆转这些扰动。这些努力是基于这样一种假设，即 提供关键生物功能的分子网络的扰动是许多 常见、复杂的疾病，包括阿尔茨海默病。虽然发现微扰分子是至关重要的 网络在公元后，这只是硬币的一面。我们假设网络生物学方法的应用 研究对阿尔茨海默病有保护和恢复能力的个体可以发现新的生物途径 这是应对AD的韧性的基础。除了揭示新的弹性途径外，对弹性个体的研究 对于扰动疾病网络的验证也是至关重要的，因为我们预计一些易感性和 复原力网络重叠，但作用方向相反。这样的验证工作对于 将这些发现转化为可行的药物靶点。在这项提案中，我们计划研究那些 通过利用来自尸检和活体人类队列的样本来保护和抵御AD，以利用 现有并生成新的分子数据，包括RNA序列(RNAseq)和全基因组序列 (WGS)，并应用分析方法来确定复原力网络。我们将利用单核RNAseq (SnRNAseq)识别大脑中弹性的细胞特异性分子变化，并使用IPSC- 基于模型。为了将这些知识转化为治疗靶点，我们将应用新的药物基因组学 工具。我们的建议响应RFA-AG-18-029，旨在：1)识别具有弹性的分子靶标 AD病理在尸检队列中的发展和传播；2)确定 在纵向评估的老年人中存在AD生物标志物时认知复原力的分子靶点 ApoEε34或ε44基因型阳性的临床正常受试者的队列；3) 在基于IPSC的模型中验证细胞特异性分子变化；4)破译AD复原力的可用药靶点 以及使用基于系统的药物基因组学工具的潜在治疗化合物。我们将整合我们的 与AMP-AD、M2OVE-AD、ADNI和ADSP的结果进行比较，并共享所有生成的数据和工具。

Although protection from and resilience to Alzheimer's disease (AD) constitute a fundamental aspect to understanding AD pathophysiology, this is a relatively understudied area and the molecular basis of resilience to AD is largely unknown. This is a critical knowledge gap, as uncovering biological pathways of resilience could lead to the discovery of novel therapeutic or prophylactic drug targets in AD. It is clear that there are individuals who may be protected from developing AD pathology, despite advanced age and having highly penetrant genetic risk factors for AD, such as APOE ε4 allele. Additionally, there are people with cognitive resilience who remain free of cognitive decline despite having pathologic or biomarker changes of AD. Current therapeutic discovery efforts in AD are largely focused on uncovering the biological pathways that are perturbed in this condition, the key molecules in these pathways, and identification of therapeutic compounds to stop and/or reverse these perturbations. These efforts are based on the hypothesis that concerted perturbations of molecular networks that serve key biological functions underlie the pathophysiology of many common, complex diseases including AD. While it is critically important to discover perturbed molecular networks in AD, this is only one side of the coin. We postulate that application of network biology approaches to investigate individuals who are protected from and resilient to AD can uncover novel biological pathways that underlie resilience to AD. In addition to revealing novel resilience pathways, study of resilient individuals is also critical for the validation of perturbed disease networks, as we expect some of the susceptibility and resilience networks to overlap but have opposite direction of effect. Such validation efforts are essential for the translation of these discoveries to viable drug targets. In this proposal, we plan to study individuals who are protected from and resilient to AD by leveraging samples from autopsied and living human cohorts, to utilize existing and generate new molecular data, including RNA sequence (RNAseq) and whole genome sequence (WGS), and to apply analytic approaches to identify resilience networks. We will utilize single-nucleus RNAseq (snRNAseq) to identify cell-specific molecular changes of resilience in brain and validate these using iPSC- based models. To translate this knowledge to therapeutic targets, we will apply novel pharmacogenomics tools. Our proposal is responsive to the RFA-AG-18-029 and aims to: 1) Identify molecular targets of resilience to development and propagation of AD pathology in an autopsy cohort enriched for older controls; 2) Identify molecular targets of cognitive resilience in the presence of AD biomarkers in a longitudinally assessed older cohort enriched for clinically normal individuals with positive amyloid PET and APOE ε34 or ε44 genotypes; 3) Validate cell-specific molecular changes in iPSC-based models; 4) Decipher druggable targets of AD resilience and potential therapeutic compounds using systems-based pharmacogenomics tools. We will integrate our findings with those from AMP-AD, M2OVE-AD, ADNI and ADSP and share all generated data and tools.