The IL1RL1 (ST2) gene locus is robustly associated with asthma; however, the contribution of single nucleotide polymorphisms (SNPs) in this locus to specific asthma subtypes and the functional mechanisms underlying these associations remain to be defined. We tested for association between IL1RL1 region SNPs and characteristics of asthma as defined by clinical and immunological measures and addressed functional effects of these genetic variants in lung tissue and airway epithelium. Utilizing 4 independent cohorts (Lifelines, Dutch Asthma GWAS [DAG], Genetics of Asthma Severity and Phenotypes [GASP], and Manchester Asthma and Allergy Study [MAAS]) and resequencing data, we identified 3 key signals associated with asthma features. Investigations in lung tissue and primary bronchial epithelial cells identified context-dependent relationships between the signals and IL1RL1 mRNA and soluble protein expression. This was also observed for asthma-associated IL1RL1 nonsynonymous coding TIR domain SNPs. Bronchial epithelial cell cultures from asthma patients, exposed to exacerbation-relevant stimulations, revealed modulatory effects for all 4 signals on IL1RL1 mRNA and/or protein expression, suggesting SNP-environment interactions. The IL1RL1 TIR signaling domain haplotype affected IL-33–driven NF-κB signaling, while not interfering with TLR signaling. In summary, we identify that IL1RL1 genetic signals potentially contribute to severe and eosinophilic phenotypes in asthma, as well as provide initial mechanistic insight, including genetic regulation of IL1RL1 isoform expression and receptor signaling.
Michael A. Portelli, F. Nicole Dijk, Maria E. Ketelaar, Nick Shrine, Jenny Hankinson, Sangita Bhaker, Néomi S. Grotenboer, Ma’en Obeidat, Amanda P. Henry, Charlotte K. Billington, Dominick Shaw, Simon R. Johnson, Zara E.K. Pogson, Andrew Fogarty, Tricia M. McKeever, David C. Nickle, Yohan Bossé, Maarten van den Berge, Alen Faiz, Sharon Brouwer, Judith M. Vonk, Paul de Vos, Corry-Anke Brandsma, Cornelis J. Vermeulen, Amisha Singapuri, Liam G. Heaney, Adel H. Mansur, Rekha Chaudhuri, Neil C. Thomson, John W. Holloway, Gabrielle A. Lockett, Peter H. Howarth, Robert Niven, Angela Simpson, John D. Blakey, Martin D. Tobin, Dirkje S. Postma, Ian P. Hall, Louise V. Wain, Martijn C. Nawijn, Christopher E. Brightling, Gerard H. Koppelman, Ian Sayers
Usage data is cumulative from June 2022 through June 2023.
Usage | JCI | PMC |
---|---|---|
Text version | 897 | 239 |
81 | 64 | |
Figure | 158 | 4 |
Table | 53 | 0 |
Supplemental data | 32 | 6 |
Citation downloads | 29 | 0 |
Totals | 1,250 | 313 |
Total Views | 1,563 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.