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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

Early development of infection-specific transcription profiles. (A) Six hours after challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham), lungs were removed and submitted to microarray analysis, and a heatmap was generated with green indicating decreased gene expression and red indicating increased gene expression. At this time, 4,274 genes were highly differentially expressed (FDR< 1 × 10-7), and by unsupervised clustering, most samples self-segregated by challenge. (N = 6 sham infected mice, 8 mice for each infection.) (B) The 30 genes that were most strongly differentially expressed at 18 h after infection were examined at earlier time points, demonstrating the increasing clarity of the differential pattern. (N = 6 sham infected mice, 4 mice for each infection.)
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Figure 3: Early development of infection-specific transcription profiles. (A) Six hours after challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham), lungs were removed and submitted to microarray analysis, and a heatmap was generated with green indicating decreased gene expression and red indicating increased gene expression. At this time, 4,274 genes were highly differentially expressed (FDR< 1 × 10-7), and by unsupervised clustering, most samples self-segregated by challenge. (N = 6 sham infected mice, 8 mice for each infection.) (B) The 30 genes that were most strongly differentially expressed at 18 h after infection were examined at earlier time points, demonstrating the increasing clarity of the differential pattern. (N = 6 sham infected mice, 4 mice for each infection.)

Mentions: Since the protein-level cytokine response only differentiated P. aeruginosa from the other conditions, we interrogated the transcriptional response of differently infected lungs. Gene expression differences emerged very early after infection. Using an extremely rigorous false discovery rate (FDR) < 1 × 10-7, we identified 20 differentially expressed genes (DEGs) at our earliest investigated time point, 2 h after challenge. By 6 h after challenge, this number had increased to 4,274 DEGs, nearly 10% of the 45,992 oligonucleotides probed. By unsupervised clustering, the samples tended to assemble themselves into condition-specific groups even at this early time point, with grossly recognizable patterns already developing (Figure 3B).


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)

Early development of infection-specific transcription profiles. (A) Six hours after challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham), lungs were removed and submitted to microarray analysis, and a heatmap was generated with green indicating decreased gene expression and red indicating increased gene expression. At this time, 4,274 genes were highly differentially expressed (FDR< 1 × 10-7), and by unsupervised clustering, most samples self-segregated by challenge. (N = 6 sham infected mice, 8 mice for each infection.) (B) The 30 genes that were most strongly differentially expressed at 18 h after infection were examined at earlier time points, demonstrating the increasing clarity of the differential pattern. (N = 6 sham infected mice, 4 mice for each infection.)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Early development of infection-specific transcription profiles. (A) Six hours after challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or PBS (sham), lungs were removed and submitted to microarray analysis, and a heatmap was generated with green indicating decreased gene expression and red indicating increased gene expression. At this time, 4,274 genes were highly differentially expressed (FDR< 1 × 10-7), and by unsupervised clustering, most samples self-segregated by challenge. (N = 6 sham infected mice, 8 mice for each infection.) (B) The 30 genes that were most strongly differentially expressed at 18 h after infection were examined at earlier time points, demonstrating the increasing clarity of the differential pattern. (N = 6 sham infected mice, 4 mice for each infection.)
Mentions: Since the protein-level cytokine response only differentiated P. aeruginosa from the other conditions, we interrogated the transcriptional response of differently infected lungs. Gene expression differences emerged very early after infection. Using an extremely rigorous false discovery rate (FDR) < 1 × 10-7, we identified 20 differentially expressed genes (DEGs) at our earliest investigated time point, 2 h after challenge. By 6 h after challenge, this number had increased to 4,274 DEGs, nearly 10% of the 45,992 oligonucleotides probed. By unsupervised clustering, the samples tended to assemble themselves into condition-specific groups even at this early time point, with grossly recognizable patterns already developing (Figure 3B).

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