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Published on: December 10, 2013
Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants
Lu Wang1, Chin-Yi Chu2, Matthew N McCall1
1Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA.
Insights
New biomarkers for respiratory syncytial virus (RSV) infection severity were identified using airway gene expression. These gene expression profiles can potentially predict severe illness in infants, aiding clinical decisions.
Area of Science:
- Respiratory viral infections
- Genomics and transcriptomics
- Biomarker discovery
Background:
- Respiratory syncytial virus (RSV) causes severe illness and hospitalization in many infants.
- Current diagnostic methods lack accurate biomarkers for predicting RSV disease severity.
- There is a critical need for reliable indicators of severe RSV infection in infants.
Purpose of the Study:
- To identify airway gene expression profiles associated with respiratory syncytial virus (RSV) disease severity.
- To develop potential biomarkers for predicting severe RSV infection in infants.
- To correlate gene expression patterns with clinical illness severity scores.
Main Methods:
- RNA sequencing was performed on nasal brush samples from 106 infants with RSV infection.
- Gene expression profiles were analyzed during acute illness and convalescence.
- Statistical models (NGSS1 and NGSS2) were developed to correlate gene expression with clinical severity scores (GRSS).
Main Results:
- A 41-gene signature (NGSS1) strongly correlated with RSV disease severity (cross-validated correlation of 0.813).
- NGSS1 achieved 89.6% accuracy in classifying mild versus severe RSV infection.
- A 13-gene signature (NGSS2) showed comparable accuracy (84.0% classification accuracy).
Conclusions:
- Airway gene expression patterns can serve as potential biomarkers for RSV disease severity.
- Minimally-invasive sampling allows for the development of clinically useful biomarkers.
- These findings may lead to improved prediction and management of severe RSV infections in infants.
Background:
A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.
Method:
We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).
Results:
NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%.
Conclusion:
Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.
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