Project description:Rationale: 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. Objective: This study assesses the capacity of the host gene expression response to infection to predict lower respiratory pathogens without reliance on culture data. Methods: Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or PBS 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. Measurements and Main Results: We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. We were able to develop a predictive model that used a limited number of specifically regulated transcripts to discriminate perfectly among infecting pathogens in the training data. Validation trials using 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. Keywords: pathogen- and time-dependent host lung gene expression changes in pneumonia Mouse lungs were removed 6 h after challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or sham (PBS), RNA was collected from leukoreduced lung homogenates, then cRNA was amplified and hybridized to Illumina Sentrix mouse-6 gene expression arrays v1.1. Gene expression data was analyzed to determine if there were distinct patterns of host trasnscription that corresponded with a specific infection.

Project description:Rationale: 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. Objective: This study assesses the capacity of the host gene expression response to infection to predict lower respiratory pathogens without reliance on culture data. Methods: Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or PBS 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. Measurements and Main Results: We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. We were able to develop a predictive model that used a limited number of specifically regulated transcripts to discriminate perfectly among infecting pathogens in the training data. Validation trials using 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. Keywords: pathogen- and time-dependent host lung gene expression changes in pneumonia

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HMDM infection with different bacteria

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