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，但可能导致呼吸机相关肺炎（VAP）的可怕并发症，这是一种医院获得的肺炎（HAP）。 HAP/VAP与尤其高的死亡率目的地有关潜在的抗生素。最近的工作表明宿主炎症反应，特定的致病菌株引起感染，肺微生物组均导致HAP/VAP的发病机理。然而，据我们所知，这三个促成因素尚未得到全面研究。更好了解HAP/VAP，我们建立了肺炎治疗（脚本）的成功临床反应系统生物学中心注册肺炎患者，从中收集支气管肺泡样品，然后对这些样品采用多词方法。我们假设这些方法将确定特定的主机反应模式，病原体遗传生物签名和定义和预测的肺微生物组伴侣 HAP/VAP患者的临床轨迹并定义了这些感染的发病机理。测试这个假设，我们将执行以下目的：（1）确定宿主反应生物签名，（2）细菌的特征病原体和（3）肺微生物组伴侣的模式，这些模式为发病机理提供了信息 HAP/VAP中的临床改进或确定。（4）整合宿主反应，病原体和微生物组具有临床元数据的功能，生成一个预测临床结果的综合计算模型 HAP/VAP中有利/不利的过渡。我们生成的数据将在临床上有用那些需要积极管理并科学地提供更深入了解的患者 HAP/VAP的发病机理。