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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos 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

项目摘要

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.

项目概要许多衰老疾病，包括阿尔茨海默病 (AD) 和 AD 相关痴呆症，都与衰老相关具有受不同分子类别调节的显着代谢变化。 AD 相关代谢特征包括： (i) 反映蛋白质变化的一般蛋白质组学特征 (ii) 糖蛋白的糖基化，反映了体内平衡和/或正在进行的神经退行性事件；沿着分泌途径的参与和运输的变化，以及“交付后”处理细胞表面糖蛋白；和 (iii) 生物膜脂质成分和一般生物活性脂质代谢。多项研究表明，在 AD 进展过程中，大脑代谢的变化并不均匀，因此，顶叶、后颞叶和前枕叶受影响最严重。根据不包括空间信息的分析得出的有关 AD 大脑的结论可能是错误的由于大脑的复杂性、空间分布以及AD发病机制的不完整。上述节点的功能整合有助于理解大脑衰老和 AD 病理生理学对区域特异性分子模式变化进行更全面的分析。对于 AD 大脑，我们建议采用基质辅助激光解吸/电离质谱成像 (MALDI MSI) 技术，用于检查动物模型中几种分子类别的空间分布变化 AD 以及晚发 AD (LOAD) 患者死后脑组织。这项研究的一般假设是，各种分子模式的改变生物学相关的分子类别可以反映或影响 AD 的发生和进展。绘制整个脑组织切片中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 患者组织样本我们将创建糖蛋白组图谱并进行分析。阐明糖基化的变化可能是理解 AD 发病机制的关键，目标 2 将绘制图谱。脂质组和不饱和脂质异构体以及整个脑组织切片中代谢特征的变化 AD 小鼠模型和 LOAD 患者组织样本的使用创新的双键定位。化学将扩大我们目前对 AD 脂质组的理解，揭示不仅仅是脂质的分子图类，但 AD 大脑内的特定脂质异构体将开发技术和计算。我们提出的生物分子验证、共定位和多维关联的驱动方法。机器学习算法将实现多个类别的同时和特定区域的关联我们的合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颖的机制和转化数据，为研究界通报进展情况衰老和 AD 的机械部分有可能产生可用的重要知识。针对特定生物分子并将我们的研究扩展到 RFA 之外。