Exploring the sepsis-delirium connection through glycoproteomics

摘要 我们的建议旨在创建和应用新的糖蛋白质组学程序，允许公正地发现糖蛋白质组学。 蛋白质糖基化的改变。这包括创建组装糖蛋白形式的算法 网络从多维质谱（MS）数据集，预测潜在的聚糖 直接来自完整糖蛋白MS光谱信息的信息，以及独特的统计评分 糖蛋白信息学工具。所提出的技术的一个创新方面是， 当糖基化发生在更高温度时，允许评估糖蛋白和预测聚糖水平信息， 这是自上而下质谱领域中公认的瓶颈。我们的目标 还包括算法的应用，以实现糖蛋白形式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患者的血浆/血清， DECODE-脓毒症和BRAIN-ICU计划，旨在发现与以下疾病相关的糖蛋白形式： 在败血症疾病谱中的特定内型或临床症状，包括长期- 术语认知功能障碍。特别是，我们寻求建立独特的数据集，可用于告知 败血症与不同的解剖区域有关，或与败血症和炎症相关的复杂机制有关。 （内皮功能障碍和炎症反应）和脓毒症相关（即免疫抑制）事件。 脓毒症是危及生命的，由于宿主对感染的反应失调而导致器官功能障碍， 这是一个重要的全球健康问题，每年造成1100万人死亡，数百万人致残。在 美国疾病控制和预防中心报告说，87%的败血症或感染引起的败血症开始在医院外。 这些指标突出了对能够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。我们有针对性的信息学工作流程将能够编译大量的 患者数据，以提供有意义的见解，非模板驱动的糖基化调节引起的 特异性基因表达，提供了新的亚表型和内型知识，这是缺乏经典的 非糖蛋白质组学发现。如果我们的项目目标是成功的，我们不仅将开发创新的 糖组学和糖蛋白质组学的工具，而且还建立了临床蛋白质组学程序， 和基于糖蛋白的生物标志物的开发。

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.