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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

Differential gene expression 18 hours after infectious challenge. (A) A heatmap shows the expression patterns of 367 DEGs after inhalational challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). By unsupervised clustering, the samples all correctly segregate themselves by condition. (B) A Venn diagram indicates the striking specificity of these expression patterns, with <10% of DEGs induced or repressed by more than one condition.
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Figure 4: Differential gene expression 18 hours after infectious challenge. (A) A heatmap shows the expression patterns of 367 DEGs after inhalational challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). By unsupervised clustering, the samples all correctly segregate themselves by condition. (B) A Venn diagram indicates the striking specificity of these expression patterns, with <10% of DEGs induced or repressed by more than one condition.

Mentions: By 18 h after challenge, unsupervised clustering resulted in all of the specimens correctly segregating themselves by pathogen (Figure 4A). After identifying patterns associated with each infectious condition, we focused on individual transcripts with each condition. The 367 DEGs at 18 h were sorted according to pathogen specificity (Figure 4B). Not surprisingly, the two conditions that caused mortality induced more gene expression changes than did A. fumigatus. However, each condition induced unique changes, and by lessening the FDR requirements, these numbers further increase.


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)

Differential gene expression 18 hours after infectious challenge. (A) A heatmap shows the expression patterns of 367 DEGs after inhalational challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). By unsupervised clustering, the samples all correctly segregate themselves by condition. (B) A Venn diagram indicates the striking specificity of these expression patterns, with <10% of DEGs induced or repressed by more than one condition.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Differential gene expression 18 hours after infectious challenge. (A) A heatmap shows the expression patterns of 367 DEGs after inhalational challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham). By unsupervised clustering, the samples all correctly segregate themselves by condition. (B) A Venn diagram indicates the striking specificity of these expression patterns, with <10% of DEGs induced or repressed by more than one condition.
Mentions: By 18 h after challenge, unsupervised clustering resulted in all of the specimens correctly segregating themselves by pathogen (Figure 4A). After identifying patterns associated with each infectious condition, we focused on individual transcripts with each condition. The 367 DEGs at 18 h were sorted according to pathogen specificity (Figure 4B). Not surprisingly, the two conditions that caused mortality induced more gene expression changes than did A. fumigatus. However, each condition induced unique changes, and by lessening the FDR requirements, these numbers further increase.

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