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NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASESNIH · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASESNIH

Systems Biology Modeling of Severe Hospital-Acquired Pneumonia

Hauser, Alan R (Contact)·NORTHWESTERN UNIVERSITY AT CHICAGO, IL·2018–2027·ACTIVE
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INSTITUTION

NORTHWESTERN UNIVERSITY AT CHICAGO, IL

PRINCIPAL INVESTIGATOR

Hauser, Alan R (Contact)

FUNDING

$548K

YEAR

2018

MOONBASE SCORE

Not commercializable

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Abstract

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.

NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASESU19ZAI1-LG-Munderstandingintegratemechanicalsystemsappropriatemicrobiomeresponsepredictssavedpneumoniasummarysfdbettergeneticpatternsrecentmanagementcomprehensivepulmonaryscriptcountlesscomputational modelingSystems biologymechanical ventilationventilator-associated pneumoniamulti-omic approacheshost inflammatory responsepathogenic strainpulmonary microbiomebronchoalveolar lavageclinical outcomes prediction

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