Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center

项目摘要/摘要--总体 肺炎，包括社区获得性肺炎(CAP)和医院获得性/呼吸机相关肺炎 肺炎(HAP/VAP)在美国几乎80%的感染死亡是由肺炎引起的。正如正在进行的 在新冠肺炎大流行期间，重症肺炎是发病率和死亡率的重要原因。我们的U19 系统生物学中心，肺炎治疗的成功临床反应(脚本)，我们将其重新命名 超级脚本(SCRIPT2)用于此次更新，利用了支气管肺泡灌洗(BAL)、鼻部刮除和血液 采样与尖端的多组学技术相结合-包括多参数流式细胞仪，单参数- 细胞RNA测序、病原体深度测序、DNA元基因组学和深度临床表型分析 建立肺炎发病机制模型。在脚本中，我们生成了一个详细的系统生物学模型 SARS-CoV-2病理生物学支持一种治疗严重SARS-CoV-2肺炎的新方法 在II期临床试验中有效。在SCRIPT2中，我们将确定是否使用高- 描述寄主反应、病原体、微生物组和临床表型的纵向维度数据 重度CAP(项目1)和HAP/VAP(项目2)的患者可以预测临床有利或不利 在肺炎发作过程中的过渡。我们检验了这样一种假设，即多重经济学投入将 创建可操作的模型，以确定对两者有利和不利的临床过渡/结果 重度CAP和重度HAP/VAP。 目标1/项目1：建立可操作的模型以改善重症患者的临床病程 CAP对来自远端肺和鼻咽的纵向样本进行多组学分析。 目标2/项目2：建立可操作的模型以改善重症患者的临床病程 HAP/VAP对来自远端肺的纵向样本进行多组学分析。 有三个科学核心支持这些项目。技术核心发展前沿的多元组学 作为临床护理的一部分收集的支气管肺泡灌洗样本的分析方法。数据 管理和生物信息学核心将从我们自己的电子病历和从 其他中心。建模核心与项目和核心无缝协作，以集成、验证和 反复改进疾病的潜在空间模型。为患者提供直接护理的临床学者 肺炎导致或促进了每个项目和核心，增强了我们研究结果的翻译影响。

PROJECT SUMMARY/ABSTRACT – Overall Pneumonia, including both community-acquired pneumonia (CAP) and hospital-acquired/ventilator-associated pneumonia (HAP/VAP), causes almost 80% of deaths from infections in the US. As evidenced by the ongoing COVID-19 pandemic, severe pneumonia represents an important cause of morbidity and mortality. Our U19 Systems Biology Center, Successful Clinical Response In Pneumonia Therapy (SCRIPT), which we rename Super-SCRIPT (SCRIPT2) for this renewal, leverages bronchoalveolar lavage (BAL), nasal curettage, and blood sampling paired with cutting-edge multi-omics technologies—including multiparameter flow cytometry, single- cell RNA-sequencing, deep pathogen sequencing, DNA metagenomics, and deep clinical phenotyping—to develop models of pneumonia pathogenesis. In SCRIPT, we generated a detailed systems biology model of SARS-CoV-2 pathobiology that supported a novel therapy for severe SARS-CoV-2 pneumonia that was efficacious in a phase II clinical trial. In SCRIPT2, we will determine whether models generated using high- dimension longitudinal data describing the host response, pathogen, microbiome, and clinical phenome in patients with severe CAP (Project 1) and HAP/VAP (Project 2) can predict favorable or unfavorable clinical transitions over the course of a pneumonia episode. We test the hypothesis that multi-omics inputs will create actionable models that identify favorable and unfavorable clinical transitions/outcomes for both severe CAP and severe HAP/VAP. Aim 1/Project 1. To generate actionable models to improve the clinical course of patients with severe CAP using multi-omics analysis of longitudinal samples from the distal lung and nasopharynx. Aim 2/Project 2. To generate actionable models to improve the clinical course of patients with severe HAP/VAP using a multi-omics analysis of longitudinal samples from the distal lung. Three scientific cores support the projects. The Technology Core developed cutting edge multi-omics approaches for the analysis of bronchoalveolar lavage samples collected as part of clinical care. The Data Management and Bioinformatics Core weaves together clinical data extracted from our own EHR and those from other centers. The Modeling Core works seamlessly with the Projects and Cores to integrate, validate and iteratively improve latent space models of disease. Clinician scholars who provide direct care to patients with pneumonia lead or contribute to each project and core, enhancing the translational impact of our findings.