Project 1: Dynamic Host Responses During Resolution of HAP

项目 1：解决 HAP 期间的动态主机响应

• 批准号: 10097983
• 负责人: RICHARD G WUNDERINK
• 金额: $ 45.98万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-17 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10097983
• 关键词: Acinetobacter; Alveolar; Alveolar Macrophages; Alveolus; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Biological Markers; Cause of Death; Cells; Clinical; Clinical Data; Complex; Computer Models; Critical Illness; Data; Data Analyses; Data Set; Development; Dose; Ecosystem; Electronic Health Record; Equilibrium; Failure; Flow Cytometry; Funding Opportunities; Gene Expression Profile; Goals; Hospitals; Human; Immune; Immune response; Infection; Inflammation; Infrastructure; Institution; Instruction; Irrigation; Liquid substance; Lung Lavage Fluid; Lymphocyte; Lymphocyte Subset; Mechanical ventilation; Metagenomics; Modeling; Nosocomial Infections; Nosocomial pneumonia; Pathway interactions; Patients; Phenotype; Pneumonia; Population; Protocols documentation; Pseudomonas; Pseudomonas aeruginosa; Regimen; Regulatory T-Lymphocyte; Research; Research Project Grants; Resolution; Sampling; Secondary to; Shotguns; Structure; Systems Biology; Testing; Time; Tissue-Specific Gene Expression; Virulence Factors; advanced system; base; biosignature; clinical predictors; cohort; epigenomics; genomic data; humanized mouse; macrophage; microbial; microbiome; microbiome analysis; mortality; mouse model; novel; novel strategies; outcome prediction; pathogen; peripheral blood; phenomics; pneumonia treatment; predict clinical outcome; predictive marker; predictive tools; prospective; recruit; response; success; therapeutic target; transcriptomics

Project Summary Project 1: Dynamic Host Responses During Resolution of HAP   Hospital-acquired pneumonia (HAP) is one of the leading causes of death from nosocomial infections, with high rates of associated mortality. HAP due to Pseudomonas aeruginosa and Acinetobacter spp. is substantially more difficult to treat than other pathogens, with clinical failure rates as high as 50%. These rates persist despite optimization of antibiotic regimens, suggesting factors beyond antibiotic resistance contribute. In this Project, we will examine the host response to pneumonia. We hypothesize that persistent inflammation in the alveolus after appropriate antibiotic treatment contributes to clinical failure in patients with P. aeruginosa or Acinetobacter spp. pneumonia. We will test this hypothesis in three interrelated Specific Aims. Aim 1. To determine whether pathogen-associated changes in the transcriptomic signatures of alveolar macrophages and lymphocyte subsets over time predict outcome of severe pneumonia. Aim 2. To prospectively validate predictive host responses identified using an ecosystem-based modeling approach in a separate cohort of patients with severe pneumonia. Aim 3. To determine whether validated host responses predictive of clinical outcome are related to pneumonia endpoints in murine models. We will combine clinical data from the Electronic Health Record and integrate these clinical data with transcriptomic and epigenomic data obtained from flow sorted alveolar macrophages and Treg cells isolated from serial samples of NBBAL over the course of the patient's illness. Through the development of these predictive tools, the SCRIPT Systems Biology Center offers the potential to discover novel pathways that drive pneumonia pathobiology for therapeutic targeting, and the ability to revise understanding of pneumonia as a complex interaction between the host, pathogen, and microbiome.

项目1:HAP解析过程中的动态主机响应