Abstract
Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has focused on identifying subgroups of patients with similar pathobiological underpinnings. Previous studies of the Severe Asthma Research Program (SARP) cohort linked gene expression changes to specific clinical and physiologic characteristics. While invaluable for hypothesis generation, these data include extensive candidate gene lists that complicate target identification and validation. In this analysis, we performed unsupervised clustering of the SARP cohort using bronchial epithelial cell gene expression data, identifying a transcriptional signature for participants suffering exacerbation-prone asthma with impaired lung function. Clinically, participants in this asthma cluster exhibited a mixed inflammatory process and bore transcriptional hallmarks of NF-κB and activator protein 1 (AP-1) activation, despite high corticosteroid exposure. Using supervised machine learning, we found a set of 31 genes that classified patients with high accuracy and could reconstitute clinical and transcriptional hallmarks of our patient clustering in an external cohort. Of these genes, IL18R1 (IL-18 Receptor 1) negatively associated with lung function and was highly expressed in the most severe patient cluster. We validated IL18R1 protein expression in lung tissue and identified downstream NF-κB and AP-1 activity, supporting IL-18 signaling in severe asthma pathogenesis and highlighting this approach for gene and pathway discovery.
Highlights
The understanding that asthma is a heterogeneous disease has long been appreciated[1]
As a starting point for our analysis, we identified genes that varied between asthma clinical disease severity classes and healthy control participants after controlling for biologic sex and corticosteroid use
Using a combination of machine learning tools for reanalysis of the Severe Asthma Research Program (SARP) 1&2 cohort, we identified the targetable IL-18 pathway from over 18,000 thousand genes expressed by airway epithelial cells
Summary
The understanding that asthma is a heterogeneous disease has long been appreciated[1]. We understand that depending on disease severity, 4070% of asthmatics exhibit heightened Type-2 (T2) inflammation which clinically improves with IL-4 and IL-5 targeted therapies[3, 4]. Response to these therapeutics is not uniform, even in patients with biomarker evidence of Type-2 inflammation. Others have utilized asthma-relevant pathway expression from bronchial epithelial cell (BECs) brushings [13] or gene signatures associated with clinically validated biomarkers [14] to cluster patients Though thought provoking, these analyses often return large lists of genes for further hypothesis testing without prioritization or validation
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