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。这包括创造组装糖蛋白形式的算法 从多维质谱(MS)数据集的网络中预测潜在的多糖 信息直接从完整的糖蛋白MS波谱信息中提取，并统计评分具有唯一性 糖蛋白信息学工具。拟议技术的一个创新方面是它们的目的是 当糖基化发生时，允许评估糖蛋白和预测糖蛋白水平信息 超过一个氨基酸残基，这在自上而下的质谱学领域是公认的瓶颈。我们的目标 还包括算法的应用，使糖蛋白形式生物标记物能够在无监督的情况下“发现” 在生物体液中。我们将使用这些新工具来监测患者的血浆/血清 解码-败血症和脑-ICU计划，目的是发现与以下因素相关的糖蛋白 脓毒症疾病谱的特定内型或临床症状，包括预测长期... 认知功能障碍术语。特别是，我们寻求建立独特的数据集，可用于通知 与不同的解剖区域相关或与两种败血症相关的复杂机制有关的脓毒症 (内皮功能障碍和炎症反应)和脓毒症邻近(即免疫抑制)事件。 脓毒症危及生命，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，每年导致1100万人死亡，数百万人残疾。在 美国疾病控制与预防中心报告称，87%的脓毒症或导致脓毒症的感染始于医院外。 这些指标突显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。我们的目标信息学工作流程将能够汇编大量 患者数据为非模板驱动的糖基化调节提供有意义的见解 特定的基因表达，提供经典基因所不具备的新亚型和内型知识 非糖蛋白组学的发现。如果我们的项目目标成功，我们不仅将开发出创新的 糖组学和糖蛋白组学的工具，但也建立了临床蛋白质组学程序的发现 以及基于糖蛋白的生物标记物的开发。

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.