ArticleAmerican journal of respiratory and critical care medicine2017
Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease.
Article in American journal of respiratory and critical care medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed, 157 citations in OpenAlex.
- AI-based prediction of aspirin-exacerbated respiratory disease using nasal epithelial mRNA expression profiles.The journal of allergy and clinical immunology. Global · 2026Article
- Aberrant mucin expression and keratinization distinguishing severe from mild asthma revealed by interpretable machine learning.JCI insight · 2026Article
- Sema3E-plexinD1 Axis Is a Modulator of Foxp3FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Clinical application of single-cell RNA sequencing in disease and therapy.Clinical and translational medicine · 2025Review
- Single-cell transcriptomic profiling of eosinophils and airway immune cells in childhood asthma.The Journal of allergy and clinical immunology · 2025Article
- Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records.The journal of allergy and clinical immunology. Global · 2025Article
- Robust self-supervised machine learning for single cell embeddings and annotations.bioRxiv : the preprint server for biology · 2025Article
- Biomarker-driven drug development for allergic diseases and asthma: An FDA public workshop.The Journal of allergy and clinical immunology · 2025Article
- Father's adolescent body silhouette is associated with offspring asthma, lung function and BMI through DNA methylation.Communications biology · 2025Article
- CD8+ TEMRAs in severe asthma associate with asthma symptom duration and escape proliferation arrest.JCI insight · 2025Article
- T1-T2 Interplay in the Complex Immune Landscape of Severe Asthma.Immunological reviews · 2025Review
- Article
- Development of an asthma health-care burden score as a measure of severity and predictor of remission in SARP III and U-BIOPRED: results from two major longitudinal asthma cohorts.The Lancet. Respiratory medicine · 2025Article
- Extreme temperatures modulate gene expression in the airway epithelium of the lungs in mice and asthma patients.Frontiers in medicine · 2025Article
- Nasal allergen and methacholine provocation tests influence co‑expression patterns of TGF‑β/SMAD and MAPK signaling pathway genes in patients with asthma.Experimental and therapeutic medicine · 2024Article
- Lasamide, a Potent Human Carbonic Anhydrase Inhibitor from the Market: Inhibition Profiling and Crystallographic Studies.ACS medicinal chemistry letters · 2024Article
- Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.The journal of allergy and clinical immunology. In practice · 2024Article
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- Nasal epithelial gene expression and total IgE in children and adolescents with asthma.The Journal of allergy and clinical immunology · 2024Article
- Presence of sputum IgG against eosinophilic inflammatory proteins in asthma.Frontiers in immunology · 2024Article
30 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors at 8 institutions in 1 country.
Funding
Abstract
rationaleSevere asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals.
objectivesIdentify networks of genes reflective of underlying biological processes that define SA.
methodsAirway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. MEASUREMENTS AND MAIN
resultsWeighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA.
conclusionsIn this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.