Title

Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants

Department

Medicine

Document Type

Article

Publication Title

BMC Medical Genomics

Abstract

Background: A substantial number ofinfants infected withRSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severeillness.

Method: We definedairwaygeneexpression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthyRSV infected subjects during acute infection (day 1-10 ofillness) and convalescence stage (day 28 ofillness). All subjects were assigned a clinicalillnessseverity 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 stableexpression over time (NGSS2).

Results: NGSS1 is strongly correlated with thediseaseseverity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binaryclassifier (mild versus severe), NGSS1 correctlyclassifiesdiseaseseverity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 andclassification accuracy of 84.0%.

Conclusion: Airwaygeneexpression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate withdiseaseseverity in primaryRSV infection.

First Page

57

DOI

10.1186/s12920-021-00913-2.

Publication Date

2-25-2021

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