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

Evans SE, Tuvim MJ, Zhang J, Larson DT, García CD, Martinez-Pro S, Coombes KR, Dickey BF - Respir. Res. (2010)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pulmonary Medicine, University of Texas-M D, Anderson Cancer Center, Houston, Texas, USA. seevans@mdanderson.org

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.

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Related in: MedlinePlus

Proteomic analysis of post-challenge BAL fluid. Mice were challenged with aerosolized P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). 24 h later, BAL was performed and concentrations of 16 cytokines and chemokines were measured by ELISA. In all cases, P. aeruginosa induced the highest level of cytokine or chemokine expression, with no test identifying any other infectious condition. Shown are representative examples: (A) Interferon-γ, (B) tumor necrosis factor (C) Interleukin-6 and (D) CCL17. (mean ± SD, N = 5 mice/group, *p < 0.005 compared to all other conditions.
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Figure 2: Proteomic analysis of post-challenge BAL fluid. Mice were challenged with aerosolized P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). 24 h later, BAL was performed and concentrations of 16 cytokines and chemokines were measured by ELISA. In all cases, P. aeruginosa induced the highest level of cytokine or chemokine expression, with no test identifying any other infectious condition. Shown are representative examples: (A) Interferon-γ, (B) tumor necrosis factor (C) Interleukin-6 and (D) CCL17. (mean ± SD, N = 5 mice/group, *p < 0.005 compared to all other conditions.

Mentions: We initially suspected that lung cytokine responses to different pathogens might be diagnostically predictive. To test this, we compared the BAL concentration of 16 inflammatory cytokines by ELISA (Additional File 3) to determine whether this approach would be allow discernment of the conditions. Representative examples of IFN-γ, TNF-α, IL-6, and CCL-17 are shown in Figure 2 to be strongly induced by P. aeruginosa infection, with lesser induction by other infections. No cytokines were uniquely induced by any other pathogen.


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

Evans SE, Tuvim MJ, Zhang J, Larson DT, García CD, Martinez-Pro S, Coombes KR, Dickey BF - Respir. Res. (2010)

Proteomic analysis of post-challenge BAL fluid. Mice were challenged with aerosolized P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). 24 h later, BAL was performed and concentrations of 16 cytokines and chemokines were measured by ELISA. In all cases, P. aeruginosa induced the highest level of cytokine or chemokine expression, with no test identifying any other infectious condition. Shown are representative examples: (A) Interferon-γ, (B) tumor necrosis factor (C) Interleukin-6 and (D) CCL17. (mean ± SD, N = 5 mice/group, *p < 0.005 compared to all other conditions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914038&req=5

Figure 2: Proteomic analysis of post-challenge BAL fluid. Mice were challenged with aerosolized P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). 24 h later, BAL was performed and concentrations of 16 cytokines and chemokines were measured by ELISA. In all cases, P. aeruginosa induced the highest level of cytokine or chemokine expression, with no test identifying any other infectious condition. Shown are representative examples: (A) Interferon-γ, (B) tumor necrosis factor (C) Interleukin-6 and (D) CCL17. (mean ± SD, N = 5 mice/group, *p < 0.005 compared to all other conditions.
Mentions: We initially suspected that lung cytokine responses to different pathogens might be diagnostically predictive. To test this, we compared the BAL concentration of 16 inflammatory cytokines by ELISA (Additional File 3) to determine whether this approach would be allow discernment of the conditions. Representative examples of IFN-γ, TNF-α, IL-6, and CCL-17 are shown in Figure 2 to be strongly induced by P. aeruginosa infection, with lesser induction by other infections. No cytokines were uniquely induced by any other pathogen.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pulmonary Medicine, University of Texas-M D, Anderson Cancer Center, Houston, Texas, USA. seevans@mdanderson.org

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.

Show MeSH
Related in: MedlinePlus