Harnessing Molecular Networks of Resilience for Therapeutic Discoveries in AD

虽然对阿尔茨海默病（AD）的保护和恢复力构成了阿尔茨海默病的一个基本方面， 了解AD的病理生理学，这是一个相对欠研究的领域和恢复力的分子基础 AD在很大程度上未知。这是一个关键的知识差距，因为揭示了恢复力的生物途径， 可能会导致发现新的治疗或预防药物的目标，在AD。显然有 可以保护免于发展AD病理的个体，尽管高龄并且具有高度的 AD的显性遗传危险因素，如APOE ε4等位基因。此外，还有一些人的认知 尽管具有AD的病理学或生物标志物变化，但仍然没有认知能力下降的恢复力。电流 AD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 来阻止和/或逆转这些扰动。这些努力是基于这样一种假设， 提供关键生物功能的分子网络的扰动是许多疾病的病理生理学基础。 常见的复杂疾病，包括AD。虽然发现扰动分子是至关重要的， 在AD网络中，这只是硬币的一面。我们假设网络生物学的应用 研究那些受到保护并对AD有弹性的个体可以发现新的生物学途径 这是抗AD的基础。除了揭示新的恢复力途径外，对恢复力个体的研究还 这对扰动疾病网络的验证也至关重要，因为我们预计一些易感性和 弹性网络重叠，但有相反的方向的影响。这种验证工作对于 将这些发现转化为可行的药物靶点。在这个建议中，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， 现有的和产生新的分子数据，包括RNA序列（RNAseq）和全基因组序列 (WGS)，并应用分析方法来识别弹性网络。我们将利用单核RNAseq （snRNAseq）来鉴定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.我们的提案是对RFA-AG-18-029的响应，旨在：1）确定弹性的分子靶点 AD病理学的发展和传播在尸检队列中富集为老年对照; 2）识别 在一个纵向评估的老年人中，存在AD生物标志物的认知弹性的分子靶点 富集具有阳性淀粉样蛋白PET和APOE ε34或ε44基因型的临床正常个体的队列; 3） 基于iPSC的模型中的胰岛细胞特异性分子变化; 4）破译AD恢复力的药物靶点 和潜在的治疗化合物使用系统为基础的药物基因组学工具。我们将整合我们的 与来自AMP-AD、M2 OVE-AD、ADNI和ADSP的研究结果进行比较，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.