Improving Sepsis Care with Deep RNA Sequencing Data

项目总结/摘要 脓毒症是造成全世界五分之一死亡的原因。因此，有必要更好地了解 更好地诊断和治疗败血症。我们建议使用败血症全血的深度RNA测序， 患者更好地识别致病病原体，分流适当的资源，预测 结果，并确定新的治疗靶点。利用新的计算分析方法， 我们将评估微生物种群、RNA生物学（特别是RNA剪接熵） 和RNA变体）并鉴定新的治疗靶标/鉴定可能受益的患者。一些RNA测序 与其来自的物种（人类）不匹配的数据通常被丢弃。我们将以典型的 丢弃微生物（细菌和病毒）种群的数据，以提高诊断能力。评估宿主 我们将研究RNA生物学。RNA剪接是一种直接发生在所有细胞中的基本分子功能 在RNA转录之后，但在蛋白质翻译之前，其中内含子被去除，外显子被连接 一起超过90%的具有多个外显子的人类基因具有选择性剪接事件。我们希望评估， RNA剪接熵可能是脓毒症的潜在生物标志物。内含子通常在 然而，在剪接过程中去除这些变体可能意味着RNA代谢功能障碍， 我们会将其与结果联系起来。RNA测序数据还将允许应用新的干预措施， 如PD-1抗体，最有可能受益的患者;基本上是将精准医学应用于严重的 生病的病人。我们将使用来自脓毒症患者的全血，并与对照患者进行比较， 没有脓毒症。将在重症监护室期间连续采集样本。 我们假设在脓毒症期间获得的深度RNA测序数据可以量化并改善 在乎

Project Summary/Abstract Sepsis is responsible for one out of every five deaths worldwide. It is therefore essential to better understand better diagnose and treat sepsis. We propose to use deep RNA sequencing of the whole blood of sepsis patients to better identify the pathogen causing the disease, triage the appropriate resources, predict outcomes, and identify novel therapeutic targets. Utilizing novel methods of computational analysis of deep RNA sequencing data we will assess microbial populations, RNA biology (specifically RNA splicing entropy and RNA lariats) and identify novel treatment targets/identify patients likely to benefit. Some RNA sequencing data that does not match the species it came from (human) is typically discarded. We will look at this typically discarded data for microbial (bacteria and viruses) populations to improve diagnostic ability. To assess the host response we will study RNA biology. RNA splicing is a basic molecular function that occurs in all cells directly after RNA transcription, but before protein translation in which introns are removed and exons are joined together. Over 90% of human genes with multiple exons have alternative splicing events. We hope to assess if RNA splicing entropy could be a potential biomarker in sepsis. Introns are typically degraded rapidly after removal during splicing, however, the presence of these lariats could signify RNA metabolism dysfunction and we will correlate this to outcomes. RNA sequencing data will also allow for application of novel interventions, such as PD-1 antibodies, to patients most likely to benefit; essentially applying precision medicine to a critically ill patient. We will utilize whole blood from humans with sepsis and compare to control patients in the intensive care unit without sepsis. Samples will be collected serially over the course of the stay in the intensive care unit. We hypothesize that data from deep RNA sequencing obtained during sepsis can be quantified and improve care.