Project 1: Discovery of proteins with altered abundance and stability

项目 1：发现丰度和稳定性发生改变的蛋白质

基本信息

批准号：
10573256
负责人：
Michael MacCoss
金额：
$ 48.48万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-15 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10573256
关键词：
Adopted Alzheimer&apos s disease pathology Alzheimer&apos s disease related dementia Amyloid Amyloid Fibrils Amyloid beta-42 Apolipoprotein E Biological Assay Biological Markers Brain Categories Cells Cholesterol Homeostasis Clinical Data Detection Development Differential Diagnosis Digestion Disease Enzyme-Linked Immunosorbent Assay Extracellular Fluid Failure Functional disorder Human Hybrids Individual Interneurons Lipoproteins Mass Spectrum Analysis Measures Methods Modification Molecular Conformation Molecular Weight Monitor Nerve Degeneration Neurobiology Neurodegenerative Disorders Pathogenesis Pathogenicity Pathology Peptide Hydrolases Peptides Performance Predisposition Process Protein Isoforms Protein Truncation Proteins Proteome Proteomics RNA Splicing Reaction Reproducibility Research Role Sampling Source Symptoms Technology Temperature Trypsin Validation Variant brain cell candidate marker clinical diagnosis clinical practice cohort comorbidity exosome extracellular vesicles falls improved inhibitor misfolded protein nervous system disorder neuroinflammation next generation particle programs protein folding protein misfolding recruit sulfated glycoprotein 2 tau Proteins tau-1 thermal stress translational proteomics

项目摘要

Abstract Despite the association between the levels of CSF Aβ42, tau, phosphorylated tau and underlying AD pathology, measures of biomarker accuracy for clinical diagnosis vary widely between studies. Given that other neurodegenerative conditions can present with AD-like clinical symptoms, and individuals with AD frequently have comorbid pathologies, additional markers are needed that can aid in differential diagnosis and identify mixed pathologies. Over the last decade, many candidate biomarkers have been identified, reflecting a range of pathophysiological processes including cholesterol metabolism, neuroinflammation and amyloid processing. However, few, have been adopted in clinical practice or been validated in large independent cohorts1. The MacCoss lab and others have been pioneering the development of next generation proteomics methods as an alternative to the classic stochastic mass spectrometry-based methods. These new methods offer a hybrid between a targeted and global proteomics strategy. While mass spectrometry data is collected in an unbiased way, the data is analyzed in a targeted strategy where specific peptides, albeit 1000s are analyzed using prior information. Thus, the reproducible targeting, throughput, and confident MS/MS-based quantification of parallel reaction monitoring (PRM) can be combined with classical discovery methods' ability to qualitatively detect thousands of proteins. These new methods based on systematically collected mass spectrometry data can offer similar quantitative figures of merit and can be validated in analogous fashion to clinical assays. Despite advances in proteomics technologies, most attempts at discovering new CSF markers have either used 1) stochastic sampling methods (e.g. data dependent acquisition) with poorly characterized quantitative performance, 2) small sample cohorts, 3) focused entirely on total CSF, 4) ignored protein processing, and 5) did not consider protein misfolding or stability. The purpose of this project within the cooperative research program is to take our methods to another level – apply true quantitative methods to large well characterized cohorts and extend them to functionally relevant subpopulations. We have a CSF assay that can measure >1050 proteins from completely unfractionated material with figures of merit of within/between day precision, linearity, LOD/LOQ, etc... Furthermore, we can use this assay on subsets of the CSF proteome to assess functionally relevant aspects of the neurobiology including quantity as part of solution or CSF particles, intact protein MW, and protein stability. Finally, we have enough throughput to apply these analyses on a scale sufficient to eliminate the chance that an observation is due to an aberrant subpopulation.

抽象的尽管CSFAβ42，tau，磷酸化的tau和基础AD的水平之间有关联病理学，研究临床诊断的生物标志物准确性的测量在研究之间差异很大。鉴于其他神经退行性疾病可能会出现类似AD的临床症状，并且患有AD的个体经常有合并症的病理，需要其他标记，以帮助进行鉴别诊断和识别混合病理。在过去的十年中，已经确定了许多候选生物标志物，反映了病理生理过程包括胆固醇代谢，神经炎症和淀粉样蛋白加工。但是，很少有人在临床实践中采用或在大型独立中得到验证同伴1。 MacCoss实验室和其他实验室一直在开发下一代蛋白质组学方法的发展作为经典随机质谱法的替代方法。这些新方法提供了靶向和全球蛋白质组学策略之间的混合。质谱数据收集到公正的方式，在针对性的策略中分析了数据，尽管有1000次分析了特定的胡椒粉，但该数据是在该策略中分析的使用先前的信息。这是可再现的定位，吞吐量和自信的基于MS/MS的自信平行反应监测（PRM）的数量可以与经典发现方法的能力相结合定性检测数千种蛋白质。这些基于系统收集质量的新方法光谱数据数据可以提供相似的绩效数字，并且可以以类似方式验证临床测定。尽管蛋白质组学技术取得了进步，但大多数发现新的CSF标记的尝试都具有使用的1）随机抽样方法（例如数据依赖性获取）的定量表征较差性能，2）小样本队列，3）完全专注于总CSF，4）忽略蛋白质加工和5）没有考虑蛋白质错误折叠或稳定性。合作研究中该项目的目的程序是将我们的方法提高到另一个级别 - 将真实的定量方法应用于大井表征人群并将其扩展到功能相关的亚群。我们有一个CSF测定法，可以用数字测量完全未分流的材料的1050蛋白在日内/日之间的优点，线性，lod/loq等...此外，我们可以在上使用此测定法 CSF蛋白质组的子集评估神经生物学的功能相关方面，包括数量溶液或CSF颗粒的一部分，完整的蛋白质MW和蛋白质稳定性。最后，我们有足够的吞吐量在足以消除检查的机会上，应用这些分析的量表亚种群。