Shotgun Lipidomics and Alterations in Sphingolipidomes in Alzheimer's Diseases

描述(申请人提供)：脂质组学，对细胞脂质的途径和网络的大规模研究，是一个新兴的和迅速扩大的研究领域。通过使用PI最近开发的一项技术-鸟枪脂质组学对脑脂类的分析，我们已经表明，在阿尔茨海默病(即非常轻微的阿尔茨海默病)临床可识别的最早阶段，个体中存在大量硫脂(一类特殊的髓鞘鞘脂)和显著的神经酰胺(一类与细胞死亡有关的神经鞘脂代谢产物)的质量损失。硫脂丢失和神经酰胺升高是AD发病机制中的早期事件，可能与神经变性、突触丢失和AD病理的发展有关。然而，导致这些变化的原因(S)仍然未知。此外，目前尚不清楚其他鞘脂类物质水平的变化是否也发生在非常轻微的AD中，哪些途径发生了变化导致这些变化，以及这些脂质变化是否为AD早期诊断的潜在生物标志物。为了确定AD中硫脂丢失和神经酰胺增加的原因(S)并解决上述问题，我们将进一步发展鸟枪脂组学来分析所有感兴趣的脂类，特别是许多轻微的鞘磷脂类。将开发一种生物信息学方法来实现对复杂脂质组学数据的自动化、高通量处理，以识别由疾病状态引起的脂类分子物种的变化，并构建脂类代谢网络图。开发的平台的结构应该适合于识别任何疾病状态引起的脂代谢改变的途径。然而，我们建议的研究将集中在识别极轻度AD患者神经鞘脂体网络改变途径背后的生化机制(S)，并利用开发的平台通过测定极轻度AD受试者脑组织和脑脊液中脂质的变化来发现潜在的AD早期诊断的脂质生物标志物。总而言之，在这一应用中，我们将进一步发展鸟枪式脂质组学和综合生物信息学计划，并将这一开发的平台应用于AD研究。这些结果将揭示AD硫脂丢失的生化机制，为AD的早期诊断寻找新的脂质生物标志物，并为AD的发病机制提供深入的认识。

DESCRIPTION (provided by applicant): Lipidomics, the large-scale study of the pathways and networks of cellular lipids, is an emerging and rapidly expanding research field. Through the analyses of brain lipids using shotgun lipidomics, a technology recently developed by the PI, we have shown that a substantial mass loss of sulfatide (a class of specialized myelin sphingolipids) and a significant mass increase in ceramide (a class of sphingolipid metabolites associated with cell death) are present in individuals at the earliest clinically-recognizable stage of Alzheimer's disease (i.e., very mild AD). Sulfatide loss and ceramide elevation represent early events in AD pathogenesis and may contribute to neurodegeneration, synapse loss, and the development of AD pathology. However, the cause(s) leading to these changes still remain unknown. Moreover, it is unclear whether alterations in the mass levels of other sphingolipid classes also occur in very mild AD, which pathways are changed leading to these alterations, and whether these lipid alterations are potential biomarkers for the early diagnosis of AD. To identify the cause(s) of sulfatide loss and ceramide increase in AD and to address the above questions, we will further develop shotgun lipidomics to analyze all lipid classes of interest, specifically many minor sphingolipid classes. A bioinformatics approach will be developed to yield automated, high-throughput processing of complex lipidomics data, to identify the altered lipid molecular species induced by a disease state, and to construct a lipid metabolic network map. The structure of the developed platform should be suitable to identify altered pathways of lipid metabolism induced by any disease state. However, we will focus our proposed studies on the identification of the biochemical mechanism(s) underlying the altered pathways of the sphingolipidome networks present in very mild AD and the discovery of potential lipid biomarkers for the early diagnosis of AD through determination of the altered lipid profiles of brain tissue and cerebrospinal fluid from subjects with very mild AD using the developed platform. Collectively, in this application, we will further develop shotgun lipidomics and an integrated bioinformatics program and will apply this developed platform for AD studies. The obtained results will reveal the biochemical mechanisms underlying sulfatide loss in AD, identify novel lipid biomarkers for the early diagnosis of AD, and provide insight into AD pathogenesis.