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Open Access Research

Host lung gene expression patterns predict infectious etiology in a mouse model of pneumonia

Scott E Evans12*, Michael J Tuvim12, Jiexin Zhang3, Derek T Larson1, Cesar D García4, Sylvia Martinez Pro4, Kevin R Coombes3 and Burton F Dickey12

Author Affiliations

1 Department of Pulmonary Medicine, University of Texas - M. D. Anderson Cancer Center, Houston, Texas, USA

2 Center for Lung Inflammation and Infection, Texas A&M Institute for Biosciences and Technology, Houston, Texas, USA

3 Department of Bioinformatics and Computational Biology, University of Texas - M. D. Anderson Cancer Center, Houston, Texas, USA

4 Tecnológico de Monterrey School of Medicine, Monterrey, Nuevo León, Mexico

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Respiratory Research 2010, 11:101  doi:10.1186/1465-9921-11-101

Published: 23 July 2010

Abstract

Background

Lower respiratory tract infections continue to exact unacceptable worldwide mortality, often because the infecting pathogen cannot be identified. The respiratory epithelia provide protection from pneumonias through organism-specific generation of antimicrobial products, offering potential insight into the identity of infecting pathogens. This study assesses the capacity of the host gene expression response to infection to predict the presence and identity of lower respiratory pathogens without reliance on culture data.

Methods

Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or saline prior to whole genome gene expression microarray analysis of their pulmonary parenchyma. Characteristic gene expression patterns for each condition were identified, allowing the derivation of prediction rules for each pathogen. After confirming the predictive capacity of gene expression data in blinded challenges, a computerized algorithm was devised to predict the infectious conditions of subsequent subjects.

Results

We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. Use of an algorithmic decision tree revealed 94.4% diagnostic accuracy when discerning the presence of bacterial infection. The model subsequently differentiated between bacterial pathogens with 71.4% accuracy and between non-bacterial conditions with 70.0% accuracy, both far exceeding the expected diagnostic yield of standard culture-based bronchoscopy with bronchoalveolar lavage.

Conclusions

These data substantiate the specificity of the pulmonary innate immune response and support the feasibility of a gene expression-based clinical tool for pneumonia diagnosis.