Algorithms for Glyco-Proteoform Detection

糖蛋白型检测算法

• 批准号: 9628069
• 负责人: Steven Matthew Patrie
• 金额: $ 33.09万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9628069
• 关键词: Algorithms; Glyco; Proteoform; Detection

 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，允许无偏见地发现蛋白质糖基化的改变。这包括创建从多维质谱（MS）数据集组装糖蛋白形式网络的算法，应用互相关分析将糖蛋白形式网络与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形式信息学工具进行统计评分。所提出的技术的一个创新方面是，它们旨在允许评估在多于一个氨基酸残基处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，使无监督的“发现”的糖蛋白生物标志物的生物流体。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）谱系障碍相关的病理或临床症状的发展相关的生物标志物。特别是，我们试图建立啮齿动物模型，以确定生命早期的脑代谢功能障碍是否有助于脑血管和脑膜（MG）中的淀粉样β（Aβ）肽沉积，这是痴呆患者中常见的现象。Aβ沉积在老年人中零星发生，但在AD和唐氏综合征患者中患病率较高。这种沉积物很难在不对大脑取样的情况下诊断，并且通常直到发生多个脑损伤和认知障碍发作后才被发现。在此，我们将确定BIR是否诱导啮齿动物模型脑血管和脑膜中的Aβ沉积，并将变化与CSF中的新型蛋白质相关联。预期这些实验将提供用于在CSF人类受试者中进行评价的候选标志物列表。如果我们的项目目标是成功的，我们不仅为糖科学家开发了创新的基础科学工具，而且还为发现和开发基于糖蛋白的生物标志物建立了创新的临床蛋白质组学程序。