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Algorithms for Glyco-Proteoform Detection

糖蛋白型检测算法

基本信息

批准号：
9899257
负责人：
Steven Matthew Patrie
金额：
$ 35.55万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9899257
关键词：
Address Albumins Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease model Amino Acids Amyloid beta-Protein Animal Model Basic Science Biological Markers Biology Blood Vessels Brain Central Nervous System Diseases Cerebral hemisphere hemorrhage Cerebrospinal Fluid Cerebrovascular system Chemistry Clinical Clinical Research Communities Complement Complex Computing Methodologies Data Data Set Dementia Deposition Detection Development Diagnosis Disease Down Syndrome Elderly Environment Eukaryota Evaluation Event Family member Feedback Fourier Transform Gel Genes Glycobiology Glycoproteins Goals Heterogeneity High Prevalence Hour Impaired cognition Individual Insulin Resistance Investigation Isoelectric Focusing Laboratories Letters Life Link Liquid Chromatography Mass Spectrum Analysis Meninges Metabolic dysfunction Metabolic syndrome Methods Modeling Molecular Monitor Monoclonal Antibodies Nerve Degeneration Pathology Pathway Analysis Pathway interactions Patients Peptides Phenotype Physiology Positioning Attribute Procedures Process Protein Glycosylation Proteins Proteome Proteomics Protocols documentation Rattus Reproducibility Research Ribonucleases Rodent Model Sampling Scheme Senile Plaques Stimulus Study Subject Technology Time Translating Visualization Work abeta deposition amyloid formation associated symptom base biomarker evaluation candidate marker chemical reaction computing resources experimental study glycoproteomics glycosylation human subject improved informatics tool innovation insight instrumentation man multidimensional data multidisciplinary next generation novel pre-clinical public health relevance response tool

项目摘要

DESCRIPTION (provided by applicant): Our proposal seeks to create and apply new top-down glyco-proteomics procedures that permit unbiased discovery of alterations in protein glycosylation. This includes the creation of algorithms that assemble glycoproteoform networks from multi-dimensional mass spectrometry (MS) datasets, the application of cross-correlation analysis to link glycoproteoform networks with MS spectral information and the creation of an evidence feedback strategy to permit statistical scoring with unique glycoproteoform informatics tools. An innovative aspect of the proposed technologies is that they are intended to permit evaluation of glycoproteins that present with glycosylation at more than one amino acid residue, a well-recognized bottleneck in the top-down field. Our aims also include the application of the top-down algorithms to enable unsupervised "discovery" of glycoprotein biomarkers in biofluids. We will use these new tools to monitor the cerebrospinal fluid (CSF) of brain insulin resistance (BIR) rodent models with the intent to discover biomarkers that correlate with development of pathologies or clinical symptoms that are associated with Alzheimer's disease (AD) spectrum disorders. In particular, we seek to establish rodent models to determine if brain metabolic dysfunction early in life contributes to amyloid-beta (Aβ) peptide deposition in cerebral vasculature and meninges (MG), a common occurrence in dementia patients. Aβ deposits occur sporadically in the elderly but have a high prevalence in AD and Down syndrome patients. Such deposits are difficult to diagnose without sampling of the brain, and are often not caught until after the occurrence of multiple cerebral hemorrhages and onset of cognitive impairment. Here, we will determine if BIR induces Aβ deposition in cerebral vasculature and meninges of the rodent models and correlate changes to novel proteins in CSF. These experiments are expected to provide a list of candidate markers for evaluation in CSF human subjects. If our project aims are successful, we will not only have developed innovative new basic science tools for glycoscientists, but also, established innovative clinical proteomics procedures for the discovery and development of glycoprotein-based biomarkers.

描述（由申请人提供）：我们的提案旨在创建和应用新的自上而下的糖蛋白质组学程序，允许公正地发现蛋白质糖基化的改变。这包括创建从多维质谱 (MS) 数据集组装糖蛋白型网络的算法、应用互相关分析将糖蛋白型网络与 MS 光谱信息联系起来，以及创建证据反馈策略以允许使用独特的糖蛋白型信息学工具进行统计评分。所提出技术的一个创新方面是，它们旨在允许评估在多个氨基酸残基上存在糖基化的糖蛋白，这是自上而下领域中公认的瓶颈。我们的目标还包括应用自上而下的算法，以实现生物体液中糖蛋白生物标志物的无监督“发现”。我们将使用这些新工具来监测脑胰岛素抵抗（BIR）啮齿动物模型的脑脊液（CSF），目的是发现与阿尔茨海默病（AD）谱系疾病相关的病理或临床症状的发展相关的生物标志物。特别是，我们寻求建立啮齿动物模型，以确定生命早期的大脑代谢功能障碍是否会导致淀粉样β（Aβ）肽沉积在脑血管和脑膜（MG）中，这在痴呆患者中很常见。 Aβ 沉积偶发于老年人，但在 AD 和唐氏综合症患者中发病率较高。如果不进行大脑取样，此类沉积物很难诊断，并且通常要等到发生多发性脑出血和认知障碍发作后才能发现。在这里，我们将确定 BIR 是否会诱导啮齿动物模型的脑血管和脑膜中 Aβ 沉积，并将变化与脑脊液中的新蛋白质相关联。这些实验预计将提供一系列用于评估脑脊液人类受试者的候选标记物。如果我们的项目目标成功，我们不仅将为糖科学家开发出创新的基础科学工具，而且还将为发现和开发基于糖蛋白的生物标志物建立创新的临床蛋白质组学程序。