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: 10516443
• 负责人: LINGJUN LI
• 金额: $ 75.4万
• 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/10516443
• 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; Brain; Ceramides; Chemistry; Classification; Communities; Coupled; Data; Defect; Development; 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; Liquid substance; Locales; Longevity; Machine Learning; Maps; Mass Spectrum Analysis; Membrane Glycoproteins; Membrane Lipids; Metabolic; Metabolism; Modeling; Molecular; Molecular Target; Mus; Nerve Degeneration; Occipital lobe; Organism; Parietal; Pathogenesis; Pathogenicity; Pathologic; Pathway interactions; Patients; 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; base; 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.

项目总结