Pathogen and Microbiome Temporal Changes During Resolution of HAP

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Project Summary Project 2: Pathogen and Microbiome Temporal Changes During Resolution of HAP Severe pneumonia is a dreaded complication among mechanically ventilated patients and is associated with high rates of mortality. To better understand these challenging infections, we propose to develop the Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center. The overall goal of SCRIPT Research Project 2 is to create a computational model based on microbial biosignatures that predicts clinical failure in patients with ventilator-associated pneumonia. Specific pathogens such as Pseudomonas aeruginosa and Acinetobacter baumannii are particularly problematic in ventilator-associated pneumonia and are associated with clinical failure rates as high as 50%, even in patients treated with appropriate antibiotic therapy. For this reason, we will focus on pneumonia caused by these pathogens. Work from our group and others has shown that strains of these bacteria differ dramatically in their ability to cause severe infections. Furthermore, emerging evidence indicates that alterations in the pulmonary microbiome induced by pathogens or by the antibiotics used to treat them may contribute to poor clinical outcomes. We hypothesize that specific genetic biosignatures of P. aeruginosa and Acinetobacter baumannii and other spp. and particular alterations to the pulmonary microbiome are associated with clinical failure in patients with HAP. To test this hypothesis, we will perform the following aims: Aim 1. We will identify genetic biosignatures of P. aeruginosa and A. baumannii strains associated with poor clinical responses in patients with severe pneumonia. Aim 2. We will identify pulmonary microbiome constituents (bacteria, viruses, and fungi) and longitudinal microbiome patterns associated with poor clinical responses in patients with severe pneumonia. Aim 3. Generate a computational model that integrates pathogen genome, pathogen transcriptome, and microbiome components to predict the clinical response in severe pneumonia caused by P. aeruginosa or A. baumannii. The data we generate will be used in an iterative manner to create and optimize a computational model that identifies patients at risk for clinical failure based upon the microbiology of their pneumonia. Highly discriminatory microbiological biosignatures for clinical failure will be further examined to determine whether they play a causal role in the progression of pneumonia.