Biology subjects

Slaunwhite, C.

Publications and source records attributed to Slaunwhite, C..

3 recordsLinked to original sources

bioRxiv2020

A systems genomics approach uncovers molecular associates of RSV severity

Globally, RSV infection results in millions of hospitalizations and thousands of deaths each year. Specific factors that contribute to disease severity, such as premature birth and certain comorbidities, are well established. Additionally, genetic variants resulting in alterations in the adaptive and innate immune response appear to be associated with RSV severity. While previous studies focused on a single aspect of the disease, we jointly modeled the association of disparate data modalities with RSV severity. To investigate the host response to respiratory syncytial virus (RSV) infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. Specifically, we developed and employed a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation. Our novel approach enabled us to identify cell type specific and shared cellular pathways associated with RSV severity. Of particular interest was the association between RSV severity, activation of pathways controlling Th17 and acute phase response signaling, and inhibition of B cell receptor signaling, which were present in both airway and immune cells. These data identify specific aspects of dysregulation between the humoral and mucosal response to RSV that may play a critical role in determining illness severity.

genomics↗

bioRxiv2019

Insufficiency in airway interferon activation defines clinical severity to infant RSV infection

Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. Other than age at the time of infection, the causes and correlates of severe illness in infants lacking known risk factors are poorly defined. We recruited a cohort of confirmed RSV-infected infants and simultaneously assayed the presence of resident airway microbiota and the molecular status of their airways using a novel method. Rigorous statistical analyses identified a molecular airway gene expression signature of severe illness dominated by excessive chemokine expression. Global 16S rRNA sequencing confirmed an association between H. influenzae and clinical severity. Interestingly, adjusting for H. influenzae in our gene expression analysis revealed an association between severity and airway lymphocyte accumulation. Exploring the relationship between airway gene expression and the time of onset of clinical symptoms revealed a robust, acute activation of interferon (IFN) signaling, which was absent in subjects with severe illness. Finally, we explored the relationship between IFN activity, airway gene expression and productive RSV infection using a novel in vitro model of bona fide pediatric human airway epithelial cells. Interestingly, blocking IFN signaling, but not IFN ligand production, in these cells leads to increased viral infection. Our data reveal that acute airway interferon responses are physiologically relevant in the context of infant RSV infection and may be a target for therapeutic intervention. Additionally, the airway gene expression signature we define may be useful as a biomarker for efficacy of intervention responses.

genomics↗

bioRxiv2019

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

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 naive correlation ({rho}) of {rho}=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.

immunology↗