Exploring the sepsis-delirium connection through glycoproteomics

通过糖蛋白质组学探索脓毒症与谵妄之间的联系

• 批准号: 10511841
• 负责人: Steven Matthew Patrie
• 金额: $ 23.64万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10511841
• 关键词: Acute-Phase Proteins; Age; Algorithms; Amino Acids; Anabolism; Anatomy; Biological Markers; Blood; Blood Screening; Brain; Centers for Disease Control and Prevention (U.S.); Chemicals; Chemistry; Clinical; Clinical Data; Collection; Communities; Complex; Critical Illness; DNA; Data; Data Set; Delirium; Detection; Development; Diagnosis; Disease; Disease Progression; Environment; Enzymatic Biochemistry; Evaluation; Event; Functional disorder; Gene Expression; Glycopeptides; Glycoproteins; Goals; Health; Heterogeneity; Hospitals; Human; Immune response; Immunosuppression; Impaired cognition; Infection; Inflammatory Response; Informatics; Investigation; Knowledge; Life; Mass Spectrum Analysis; Measures; Memory; Meta-Analysis; Methods; Molecular; Monitor; Organ; Parents; Pathway Analysis; Pathway interactions; Patients; Persons; Phase; Phenotype; Physiology; Plasma; Polysaccharides; Procedures; Protein Glycosylation; Proteins; Proteome; Proteomics; RNA; Reaction; Regulation; Reporting; Reproducibility; Research; Resolution; Resources; Sampling; Science; Sepsis; Septic Shock; Severities; Shapes; Signal Transduction; Site; Staging; Symptoms; Technology; Testing; Time; United States; Work; base; brain health; data mining; data streams; endothelial dysfunction; experience; global health; glycoproteomics; glycosylation; human subject; individual patient; informatics tool; innovation; insight; knowledgebase; molecular marker; next generation; novel; programs; septic patients; temporal measurement; tool

ABSTRACT Our proposal seeks to create and apply new glycoproteomics 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 prediction of underlying glycan information directly from intact glycoprotein MS spectral information, and statistical scoring with unique glycoproteoform informatics tools. An innovative aspect of the proposed technologies is that they are intended to permit evaluation of glycoproteins and prediction of glycan level information when glycosylation occurs at more than one amino acid residue, a well-recognized bottleneck in the top-down mass spectrometry field. Our aims also include the application of the algorithms to enable unsupervised "discovery" of glycoproteoforms biomarkers in biofluids. We will use these new tools to monitor the blood-plasma/serum of patients that derive from the DECODE-Sepsis and BRAIN-ICU programs with the intent to discover glycoproteoforms that correlate with specific endotypes or clinical symptoms across the spectrum of sepsis disorders, including prediction of long- term cognitive dysfunction. In particular, we seek to establish unique datasets that can be used to inform upon sepsis that is tied to different anatomical regions or tied to complex mechanisms involved in both sepsis (endothelial dysfunction and inflammatory responses) and sepsis-adjacent (i.e. immunosuppression) events. Sepsis is life-threatening, leading to organ dysfunction due to a dysregulated host response to infection and is an important global health problem that kills 11 million people each year and disables millions more. In the United States, the CDC reports that 87% of sepsis or the infection causing sepsis starts outside the hospital. These metrics highlight the urgent need for resources that can rapidly detect and stratify stages and mechanisms associated with individual patients. Our targeted informatics workflow will be able to compile large volumes of patient data to provide meaningful insight into the non-template driven regulation of glycosylation caused by specific gene expression, providing both novel sub-phenotype and endotype knowledge that is absent in classic non-glycoproteomics discovery. If our project aims are successful, we will not only have developed innovative tools for glycomics and glycoproteomics, but also established clinical proteomics procedures for the discovery and development of glycoprotein-based biomarkers.

抽象的 我们的建议旨在创建和应用新的糖蛋白质组学程序，以无偏见的发现 蛋白质糖基化的改变。这包括创建组装糖蛋白酶的算法 来自多维质谱（MS）数据集的网络，基础聚糖的预测 直接来自完整糖蛋白MS光谱信息的信息，并以独特的统计评分 糖蛋白酶试剂信息学工具。拟议技术的创新方面是它们是旨在的 允许评估糖蛋白和聚糖水平信息的预测，当时发生糖基化。 比一个氨基酸残基，这是自上而下的质谱场中公认的瓶颈。我们的目标 还包括应用算法以实现糖蛋白酶生物标志物的无监督“发现” 在生物流体中。我们将使用这些新工具来监测源自源自的患者的血浆/血清 解码性和脑ICU程序的目的是发现与 败血症频谱的特定内型或临床症状，包括长期预测 术语认知功能障碍。特别是，我们试图建立可用于告知的独特数据集 败血症与不同的解剖区域相关或与涉及两个败血症的复杂机制相关 （内皮功能障碍和炎症反应）和败血症（即免疫抑制）事件。 败血症正在威胁生命 一个重要的全球健康问题，每年造成1100万人丧生，并使数百万次致命。在 美国疾病预防控制中心（CDC）报告说，败血症或导致败血症的感染的87％开始在医院外。 这些指标强调了可以快速检测和分层阶段和机制的资源的迫切需求 与个别患者有关。我们的目标信息学工作流将能够编译大量 患者数据可提供有意义的洞察力，以了解由非板驱动的糖基化调节 特定的基因表达，提供经典中不存在的新型亚表测和内型知识 非糖蛋白质组学发现。如果我们的项目目标成功，我们不仅会发展创新 糖基因和糖蛋白质组学的工具，但也建立了发现的临床蛋白质组学程序 以及基于糖蛋白的生物标志物的开发。