Abstract
BackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.MethodWe 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).ResultsNGSS1 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%.ConclusionAirway 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.
Highlights
A substantial number of infants infected with Respiratory syncytial virus (RSV) develop severe symptoms requiring hospitalization
As part of the AsPIRES study [21] we recently reported on the feasibility of measuring gene expression of airway cells collected by nasal swab in healthy infants in order to study RSV disease pathogenesis [22]
In this manuscript, we describe the use of this gene expression data during RSV infection to develop two airway gene expression-based classifiers that are highly correlated with clinical disease severity
Summary
A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We and others have reported a relationship between disease severity and host gene expression in peripheral blood cells and nasal swab samples during infection [17,18,19,20] These results suggest such an approach may allow development of biomarkers to accurately categorize RSV disease severity. As part of the AsPIRES study [21] we recently reported on the feasibility of measuring gene expression of airway cells collected by nasal swab in healthy infants in order to study RSV disease pathogenesis [22] In this manuscript, we describe the use of this gene expression data during RSV infection to develop two airway gene expression-based classifiers that are highly correlated with clinical disease severity. This represents a first step in developing a biomarker using gene expression responses capable of accurately classifying clinical severity in primary RSV-infection that could be used in conjunction with clinical evaluation
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