Creating a region- specific biomolecular atlas of the brain of Alzheimer’s disease

项目总结 许多老年病，包括阿尔茨海默病(AD)和AD相关的痴呆症，已经被联系在一起 具有显著的新陈代谢变化，受不同的分子类别调节。老龄化的节点-和 与AD相关的代谢特征包括：(I)反映蛋白质变化的一般蛋白质组学特征 动态平衡和/或正在进行的神经变性事件；(Ii)糖蛋白的糖基化，如反映 分泌途径中参与和运输的变化，以及“产后”处理细胞-- 表面糖蛋白；以及(Iii)生物膜脂成分和一般生物活性脂类代谢。 几项研究表明，在整个AD进展过程中，大脑代谢的变化并不是统一的， 顶叶、后颞叶和枕前叶受影响最严重。正因为如此，布罗德 在不包括空间信息的分析后得出的关于AD大脑的结论可能正在描绘一个 阿尔茨海默病的发病机制尚不完整。由于大脑的复杂性，空间分布以及 以上节点的功能整合是理解大脑老化和AD的必要保证 病理生理学。要对区域特定的分子模式变化进行更全面的分析 对于AD脑，我们建议使用基质辅助激光解吸/电离质谱学成像 (MALDI MSI)技术检测动物模型中几个分子类的空间分布变化 以及迟发性AD(LOAD)患者的死后脑组织。 这项研究的一般假设是各种不同分子模式的变化 生物学上相关的分子类可以反映或影响AD的发生和发展。目标1将 MAP全脑组织切片中区域特异性糖链和糖蛋白表达模式的变化 AD小鼠模型和加载患者组织样本。我们将创建糖蛋白质组的图谱，并 阐明糖基化的变化可能是理解AD发病机制的关键。Aim 2将绘制地图 全脑组织切片中脂体和不饱和脂异构体与代谢特征的变化 制作AD小鼠模型并加载患者组织样本。创新的双键局域化的使用 化学将扩展我们目前对AD脂体的理解，揭示出不仅仅是脂类的分子图谱 类，但在AD大脑中有特定的类脂异构体。目标3将开发技术--在计算方面-- 生物分子验证、共定位和多维关联的驱动方法。我们的建议 机器学习算法将实现多个类别的同时和特定于区域的关联 分子。我们协作团队的正交研究重点和跨学科专业知识将使我们能够 生成新的机械性和翻译性数据，向研究社区通报进展情况 衰老和阿尔茨海默病。机械部分有可能产生大量可使用的知识 以特定的生物分子为目标，并将我们的研究扩展到RFA之外。

PROJECT SUMMARY Many diseases of aging, including Alzheimer’s disease (AD) and AD related dementia, have been linked with significant metabolic changes that are regulated by diverse molecular classes. Nodes of the aging- and AD-associated metabolic signature include: (i) General proteomic profile as reflecting changes in protein homeostasis and/or an ongoing neurodegenerative event; (ii) Glycosylation of glycoproteins, as reflecting changes in engagement and trafficking along the secretory pathway, as well as “post-delivery” processing cell- surface glycoproteins; and (iii) Biological membrane lipid composition and general bioactive lipid metabolism. Several studies have shown changes in brain metabolism are not uniform throughout AD progression, with parietal, posterior temporal, and anterior occipital lobes most severely affected. Because of this, broad conclusions about the AD brain following analysis that does not include spatial information may be painting an incomplete picture of AD pathogenesis. Due to the complexity of the brain, spatial distribution as well as functional integrations of the above nodes are warranted to understand both aging of the brain and AD pathophysiology. To perform a more comprehensive analysis of region-specific molecular pattern changes in the AD brain, we propose to employ matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) technology to examine spatial distribution changes of several molecular classes in animal models of AD as well as postmortem brain tissue of late-onset AD (LOAD) patients. The GENERAL HYPOTHESIS of this research is that alteration in the molecular pattern of various biologically relevant molecular classes can reflect or influence the onset and progression of AD. Aim 1 will map region-specific glycan and glycoprotein expression pattern changes in the whole brain tissue sections of AD mouse models and LOAD patient tissue samples. We will create an atlas of the glycoproteome and illuminating changes in glycosylation that could be key in understanding AD pathogenesis. Aim 2 will map lipidome and unsaturated lipid isomers and changes in metabolic signature in the whole brain tissue sections of AD mouse models and LOAD patient tissue samples. The use of innovative double-bond localization chemistry will expand our current understanding of the AD lipidome, revealing a molecular map of not just lipid classes, but specific lipid isomers within the AD brain. Aim 3 will develop technology- and computationally- driven approaches for biomolecule validation, co-localization, and multidimensional correlation. Our proposed machine learning algorithms will enable simultaneous and region-specific correlation of multiple classes of molecules. Our collaborative team’s orthogonal research foci and interdisciplinary expertise will enable us to generate novel mechanistic and translational data that will inform the research community on the progression of aging and AD. The mechanistic component has the potential to yield significant knowledge that can be used to target specific biomolecules and expand our research beyond this RFA.