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

创建阿尔茨海默病大脑区域特定的生物分子图谱

• 批准号: 10698158
• 负责人: LINGJUN LI
• 金额: $ 75.24万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698158
• 关键词: 3-Dimensional; APP-PS1; Address; Affect; Age-associated memory impairment; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid; Animal Model; Anterior; Applications Grants; Area; Atlases; Autopsy; Biochemical Process; Biological; Biological Models; Body Fluids; Brain; Ceramides; Chemistry; Classification; Communities; Coupled; Data; Defect; Development; Dimensions; Disease; Disease Progression; Endoglycosidase F; Event; Functional disorder; Funding; Glucose; Glycoproteins; Glycoside Hydrolases; Heterogeneity; Human; Imaging technology; Impaired cognition; Informatics; Isomerism; Knock-in; Knock-in Mouse; Knowledge; Late Onset Alzheimer Disease; Link; Lipids; Liquid Chromatography; Locales; Longevity; Machine Learning; Maps; Mass Spectrum Analysis; Membrane Glycoproteins; Membrane Lipids; Metabolic; Metabolism; Modeling; Molecular; Molecular Target; Mus; Nerve Degeneration; Occipital lobe; Organism; Paint; Parietal; Pathogenesis; Pathogenicity; Pathologic; Pathway interactions; Pattern; Peracetic Acid; Physiological; Play; Point Mutation; Polysaccharides; Population; Proteomics; Reporting; Research; Role; Spatial Distribution; Specificity; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Sphingolipids; Symptoms; Technology; Tissue Sample; Tissues; Unsaturated Fats; Validation; Wisconsin; Work; aging brain; biological systems; biomarker discovery; brain metabolism; brain tissue; cohort; diagnostic biomarker; glycosylation; human disease; humanized mouse; imaging approach; imaging platform; innovation; lipid metabolism; lipidome; machine learning algorithm; mass spectrometric imaging; metabolomics; mouse model; neuropathology; novel; pi bond; proteostasis; response; sialylation; tissue biomarkers; trafficking; translational potential

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.

项目摘要 包括阿尔茨海默病（AD）和AD相关性痴呆在内的许多衰老疾病都与阿尔茨海默病（AD）相关， 具有受不同分子类别调节的显著代谢变化。老化的节点-和 AD相关的代谢特征包括：（i）一般蛋白质组学谱，反映蛋白质组学谱的变化。 （ii）糖蛋白的糖基化，如反映 参与和运输的变化沿着分泌途径，以及“后交付”加工细胞， 表面糖蛋白;和（iii）生物膜脂质组成和一般生物活性脂质代谢。 几项研究表明，在整个AD进展过程中，脑代谢的变化并不均匀， 顶叶、后颞叶和前枕叶受影响最严重。因此，广泛 不包括空间信息的AD大脑分析的结论可能是绘画， AD发病机制不完整。由于大脑的复杂性，空间分布以及 上述节点的功能整合是理解大脑老化和AD的必要条件 病理生理学为了对区域特异性分子模式变化进行更全面的分析， AD脑，我们建议采用基质辅助激光解吸/电离质谱成像 （MALDI MSI）技术，以检查动物模型中几种分子类别的空间分布变化 以及迟发性AD（LOAD）患者的尸检脑组织。 这项研究的一般假设是，在不同的分子模式的改变， 生物学上相关的分子类别可以反映或影响AD的发作和进展。目标1将 在全脑组织切片中绘制区域特异性聚糖和糖蛋白表达模式的变化， AD小鼠模型和LOAD患者组织样品。我们将创建一个糖蛋白质组图谱， 阐明糖基化的变化可能是理解AD发病机制的关键。目标2将映射 脂质体和不饱和脂质异构体以及全脑组织切片中代谢特征的变化 AD小鼠模型和LOAD患者组织样品的平均值。使用创新的双键定位 化学将扩展我们目前对AD脂质组的理解，揭示不仅仅是脂质的分子图谱， 类，但AD脑内的特定脂质异构体。目标3将发展技术--和计算-- 用于生物分子验证、共定位和多维关联的驱动方法。我们提出的 机器学习算法将实现多个类别的同时和区域特定的相关性， 分子。我们的合作团队的正交研究重点和跨学科的专业知识将使我们能够 产生新的机制和翻译数据，将告知研究界的进展 衰老和AD机械部分有可能产生重要的知识， 以特定的生物分子为目标，并将我们的研究扩展到RFA之外。