Systems Biology Modeling of Severe Hospital-Acquired Pneumonia

严重医院获得性肺炎的系统生物学模型

基本信息

批准号：
10551467
负责人：
ALAN R HAUSER
金额：
$ 43.35万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-17 至 2027-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10551467
关键词：
Age Antibiotics Bacteria Biological Assay Bronchoalveolar Lavage Bronchoalveolar Lavage Fluid Clinical Communities Complication Computer Models Data Dedications Deterioration Development Disease Disease Outcome Enrollment Failure Flow Cytometry Funding Future Genetic Genome Goals Hospitalization Hospitals Immune response Immunosuppression Infection Inflammatory Response Intervention Intervention Studies Klebsiella Knowledge Lung Mechanical ventilation Metadata Modeling Morbidity - disease rate Multi-Drug Resistance Multiomic Data Nosocomial pneumonia Outcome Pathogenesis Pathogenicity Patient-Focused Outcomes Patients Pattern Pneumonia Pseudomonas aeruginosa Research Research Project Grants Resources Respiratory Failure Ribosomal RNA Sampling Shotguns Staphylococcus aureus Systems Biology Techniques Testing Therapeutic Intervention Unfavorable Clinical Outcome Validation Work biobank biomarker identification biosignature clinical predictors cohort community acquired pneumonia cytokine humanized mouse improved insight lung microbiome metagenomic sequencing microbiome microbiota mortality mouse model multiple omics novel novel therapeutic intervention outcome prediction pathogen pathogenic bacteria pneumonia model pneumonia treatment predict clinical outcome predictive modeling predictive test response single-cell RNA sequencing transcriptome transcriptomics ventilator-associated pneumonia

项目摘要

PROJECT SUMMARY/ABSTRACT – Project 2 Mechanical ventilation has saved the lives of countless patients with respiratory failure but may lead to the dreaded complication of ventilator-associated pneumonia (VAP), a form of hospital-acquired pneumonia (HAP). HAP/VAP are associated with particularly high mortality rates despite the administration of appropriate and highly potent antibiotics. Recent work suggests that the host inflammatory response, the specific pathogenic strain causing the infection, and the pulmonary microbiome all contribute to the pathogenesis of HAP/VAP. However, to our knowledge these three contributing factors have not been comprehensively examined together. To better understand HAP/VAP, we established the Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center to enroll pneumonia patients, collect bronchoalveolar lavage samples from them, and apply multi-omic approaches to these samples. We hypothesize that these approaches will identify specific host response patterns, pathogen genetic biosignatures, and pulmonary microbiome consortia that define and predict the clinical trajectory of patients with HAP/VAP and define the pathogenesis of these infections. To test this hypothesis, we will perform the following aims: (1) Identify host response biosignatures, (2) features of bacterial pathogens, and (3) patterns of pulmonary microbiome consortia that inform pathogenesis and are associated with clinical improvement or deterioration in HAP/VAP. (4) Integrate host response, pathogen, and microbiome features with clinical metadata to generate a comprehensive computational model that predicts clinical outcomes and favorable/unfavorable transitions in HAP/VAP. The data we generate will be useful clinically in identifying those patients who require aggressive management and scientifically in providing a deeper understanding of the pathogenesis of HAP/VAP.

项目摘要/摘要 – 项目 2 机械通气挽救了无数呼吸衰竭患者的生命，但也可能导致呼吸机相关性肺炎 (VAP) 是医院获得性肺炎 (HAP) 的一种形式，是一种可怕的并发症。尽管给予适当和高度的治疗，HAP/VAP 仍与特别高的死亡率相关。最近的研究表明，宿主的炎症反应与特定的致病菌株有关。然而，引起感染的细菌和肺部微生物组都有助于 HAP/VAP 的发病机制。据我们所知，这三个影响因素尚未得到更好的综合研究。 HAP/VAP，我们建立了肺炎治疗的成功临床反应（SCRIPT）系统生物学中心招募肺炎患者，收集他们的支气管肺泡灌洗样本，并我们发现这些方法将识别特定的宿主。定义和预测的反应模式、病原体遗传生物特征和肺部微生物组联盟 HAP/VAP 患者的临床轨迹并确定这些感染的发病机制以测试这一点。假设，我们将实现以下目标：（1）识别宿主反应生物特征，（2）细菌特征病原体，以及（3）肺部微生物群的模式，其告知发病机制并与之相关 (4) 整合宿主反应、病原体和微生物组具有临床元数据的特征，可生成预测临床结果的综合计算模型我们生成的数据将有助于临床识别。那些需要积极管理和科学地提供更深入了解的患者 HAP/VAP 的发病机制。