A systems genomics approach uncovers molecular associates of RSV severity

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to 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. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.
Author summary This paper presents a novel approach to understanding the localized molecular responses to respiratory syncytial virus (RSV) and the system-level correlates of clinical outcomes. To do this, we developed a novel statistical method able to integrate high dimensional molecular data characterizing the host airway microbota and immune and nasal gene expression. We show that this integrative approach facilitates superior performance in estimating clinical outcome as opposed to any single data type. Using this approach, we identified both cell type-specific and shared biomarkers and regulatory pathways associated with RSV severity. Specifically, we identified an association between RSV severity, activation of pathways controlling Th17, and inhibition of B cell receptor signaling, which were present in both the site of infection airway and in peripheral immune cells. These results can guide future efforts to identify biomarkers for identifying or predicting illness severity following infant RSV infection. They may also be useful as biomarkers to inform the efficacy of future interventions (e.g., therapies) or preventative measures to suppress the rate of severe disease (e.g., vaccines).