Metabolic Network Analysis of Biochemical Trajectories in Alzheimer's Disease

阿尔茨海默病生化运动轨迹的代谢网络分析 摘要 尽管取得了进展，但临床试验尚未产生预防或减缓阿尔茨海默氏症进展的疗法 疾病(AD)最近的失败突显了我们对疾病机制的不完全了解。积累 有证据表明，阿尔茨海默病的突触失败与多个网络的调节失调有关，而且 广告不是一种单一的情况，而可能是需要系统方法的改变的网络的组合。 AD的易感性可能受到许多不同的常见和罕见的基因组变异的影响。 数以百计的基因。这种遗传异质性给定义疾病机制带来了巨大的挑战 以及药物开发的方法。越来越多的证据支持AD是一种代谢性疾病 糖尿病合并发病和疾病早期发生的一系列代谢紊乱。ApoE4是 阿尔茨海默病最强的遗传危险因素。载脂蛋白E在脂代谢中的作用及载脂蛋白4变异体的存在 与血液中较高的胆固醇水平相关，再次表明代谢在阿尔茨海默病中的重要作用。 此外，最近发现的与阿尔茨海默病有关的大多数基因都表明与脂质加工有关， 免疫功能调节和吞噬作用都与代谢功能有关。然而，一份详细的 在AD中失败的相互连接的代谢网络的映射没有被定义。代谢组学允许 同时测定100‘S至1000’S代谢物的扰动到代谢的映射 网络，使人们能够以系统的方式研究AD。新陈代谢组捕捉到了 基因组、肠道微生物群和环境与阿尔茨海默病代谢组学数据提供了对 基因变异对新陈代谢的影响，将遗传学的复杂性降低到对代谢途径的共同影响 与疾病有牵连。作为NIA大型计划AMP-AD和MOVE-AD的一部分，我们建立了 AD代谢组学联盟(ADMC)旨在为AD建立一个全面的代谢组学数据库 用于询问疾病中的全球代谢故障，并定义与 认知能力下降。通过利用NIH在AD神经成像计划(ADNI)、ADMC、AMP- AD和MOVE-AD，通过增加代谢重建和建模方面的专家，我们将制定 AD进展的代谢基础。在目标1中，我们建议从ADNI队列中提取纵向样本 存在深入的表型数据，使我们能够跟踪与两种脑脊液相关的早期生化变化 淀粉样β蛋白和tau病理以及整个疾病的发展轨迹。通过成像数据，我们将把 外周代谢变化到大脑的变化。在目标2中，我们建议创建一种完整的分子 AD的地图集。AD社区的这一资源将连接基因类型和代谢型，提供 阿尔茨海默病相关基因变异的功能读数。在目标3中，我们建议建立第一个基因组规模 阿尔茨海默病进展的代谢变化模型。总而言之，这一努力将提供一种生化 对AD进行有效早期干预的路线图。

METABOLIC NETWORK ANALYSIS OF BIOCHEMCIAL TRAJECTORIES IN ALZHEIMER'S DISEASE ABSTRACT Despite advances, clinical trials have not yielded therapies to prevent or slow progression of Alzheimer's Disease (AD) with recent failures highlighting our incomplete knowledge of disease mechanisms. Accumulating evidence suggests the synaptic failure in AD is associated with dysregulation in multiple networks and that AD is not a singular condition but may be a combination of altered networks calling for a systems approach. AD susceptibility is likely influenced by many different common and rare genomic variants spread across hundreds of genes. Such genetic heterogeneity poses enormous challenges in defining disease mechanisms and approaches for drug development. Increasing evidence supports that AD is a metabolic disease with diabetes co-morbidity and a range of metabolic perturbations occurring early in disease. APOE4 is the strongest genetic risk factor for AD. APOE functions in lipid metabolism and presence of the APOE4 variant is correlated with higher cholesterol levels in the blood, suggesting again an important role for metabolism in AD. In addition, most of the genes that have recently been implicated in AD suggest a role for lipid processing, immune function regulation and phagocytosis that are all related to metabolic functions. Yet, a detailed mapping of interconnected metabolic networks that fail in AD are not defined. Metabolomics allows simultaneous measurement of 100's to 1000's of metabolites for mapping perturbations into metabolic networks, enabling a systems approach to the study of AD. The metabolome captures net influences of the genome, gut microbiome and environment in AD. Metabolomics data provides a functional readout of effects of genetic variants on metabolism, reducing the complexity of genetics to common effects on metabolic pathways that are implicated in disease. As part of NIA's large initiatives AMP-AD and MOVE-AD, we have established the AD Metabolomics Consortium (ADMC) aiming at building a comprehensive metabolomics database for AD for interrogation of global metabolic failures in disease and to define metabolic pathways that are implicated in cognitive decline. By leveraging large NIH investments in the AD Neuroimaging Initiative (ADNI), ADMC, AMP- AD and MOVE-AD and by adding experts in metabolic reconstruction and modeling we will frame the metabolic basis for AD progression. In Aim 1, we propose to profile longitudinal samples from ADNI cohorts where deep phenotype data exists that enables us to track early biochemical changes related to both CSF amyloid-beta and tau pathology and across the trajectory of disease. Through imaging data we will link peripheral metabolic changes to changes in the brain. In Aim 2, we propose to create an integrated molecular Atlas for AD. This resource for the AD community will connect genotypes and metabotypes, providing a functional readout of genetic variants implicated in AD. In Aim 3, we propose to build the first genome-scale model of metabolic changes underlying AD progression. Together, this effort will provide a biochemical roadmap towards effective early interventions for AD.