Personal network inference unveils heterogeneous immune response patterns to viral infection in children with acute wheezing

Laura A. Coleman, Siew Kim Khoo, Kimberley Franks, Franciska Prastanti, Peter Le Souëf, Yuliya V. Karpievitch, Ingrid A. Laing, Anthony Bosco

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Human rhinovirus (RV)-induced exacerbations of asthma and wheeze are a major cause of emergency room presentations and hospital admissions among children. Previous studies have shown that immune response patterns during these exacerbations are heterogeneous and are characterized by the presence or absence of robust interferon responses. Molecular phenotypes of asthma are usually identified by cluster analysis of gene expression levels. This approach however is limited, since genes do not exist in isolation, but rather work together in networks. Here, we employed personal network inference to characterize exacerbation response patterns and unveil molecular phenotypes based on variations in network structure. We found that personal gene network patterns were dominated by two major network structures, consisting of interferon-response versus FCER1G-associated networks. Cluster analysis of these structures divided children into subgroups, differing in the prevalence of atopy but not RV species. These network structures were also observed in an independent cohort of children with virus-induced asthma exacerbations sampled over a time course, where we showed that the FCER1G-associated networks were mainly observed at late time points (days four–six) during the acute illness. The ratio of interferon-and FCER1G-associated gene network responses was able to predict recurrence, with low interferon being associated with increased risk of readmission. These findings demonstrate the applicability of personal network inference for biomarker discovery and therapeutic target identification in the context of acute asthma which focuses on variations in network structure.

Original languageEnglish
Article number1293
JournalJournal of Personalized Medicine
Volume11
Issue number12
DOIs
Publication statusPublished - Dec 2021

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