Background RSV infection is common in infants, with a majority of those affected displaying mild clinical symptoms. However, a substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack sensitive and specific predictors to identify a majority of those who require hospitalization.Method We used our previously described methods to define comprehensive airway gene expression profiles from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness; 106 samples), and during the convalescence stage (day 14-28 of illness; 69 samples). All subjects were assigned a cumulative clinical illness severity score (GRSS). High throughput RNA sequencing (RNAseq) defined airway gene expression patterns in RSV infected subjects. Using AIC-based model selection, we built a sparse linear predictor of GRSS based on the expression of 41 genes (NGSS1). Using a similar statistical approach, we built an alternate predictor based upon 13 genes displaying stable expression over time (NGSS2). We evaluated predictive performance of both models using leave-one-out cross-validation analyses.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 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, prediction accuracy with a cross-validated correlation of 0.741 and cross-validated classification accuracy of 84.0%.Conclusion Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility as prognostic biomarkers for severe infant RSV infections.

Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants