Nasal epithelial gene expression data improves the prediction of asthma exacerbations

Asthma exacerbations contribute to the morbidity and healthcare burden associated with asthma. Accurate prediction of exacerbations remains a challenge. This study investigated if nasal brush transcriptome may be used as a low-invasive tool to improve prediction of asthma exacerbations. Demographic and clinical data (44±13 years old, 41% male, 1.1±2.4 pack years, FEV<sub>1</sub> of 81±16.8pp, FEF50 of 65±28pp, blood eos of 0.3±0.2 10<sup>9</sup>/L, FeNO of 34±29ppb), as well as RNA extracted from nasal epithelial brushes were collected from 303 patients included in the ATLANTIS study. Differential gene expression analysis was used to identify genes associated with moderate to severe exacerbation during 1-year follow-up. Gene set variation analysis (GSVA) was used to compute a composite score for exacerbation-related genes. This study included 62 exacerbators (≥1 exacerbation) and 241 non-exacerbators. Exacerbators were older, more symptomatic, and had lower lung function than non-exacerbators. We identified a set of 25 up- and 1 down-regulated genes associated with exacerbations. The top 3 higher expressed genes were the antisense ACSL-1, MTND4LP23, and MTCYBP23, whereas the lower expressed gene was KIAA1549L. The identified genes are involved in the signalling pathways of hyaluronic acid, calcium, and fibronectin binding. The clinical-based prediction model of exacerbations (age, sex, blood eos, smoking, ACQ-6, FEV1pp, and exacerbation history) achieved an AUC of 0.77 and a sensitivity of 26%. The addition of the GSVA scores improved the AUC to 0.8 and sensitivity to 39%. We identified a nasal gene signature that includes transcriptomic biomarkers, improving exacerbation prediction compared to a clinical model.

Authors

Institutions

Publication Details

Published
2024-09-14
DOI
https://doi.org/10.1183/13993003.congress-2024.pa820
Primary Topic
Asthma and respiratory diseases
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nasal epithelial gene expression data improves the prediction of asthma exacerbations

Maarten van den Berge, Gabriela Moura, Salman Siddiqui, Alen Faiz et al.
Asthma and respiratory diseases
article

Nasal epithelial gene expression data improves the prediction of asthma exacerbations

Maarten van den Berge, Gabriela Moura, Salman Siddiqui, Alen Faiz, Judith M. Vonk, Tessa Kole, Martijn C. Nawijn, Klaus F. Rabe, Alberto Papi, Rui Marçalo, Pankaj Kumar Bhavsar, Kian Fan Chung, Tessa E. Gillett, Christopher Edward Brightling, Alda Sofia Pires De Dias Marques, Victor Guryev, Bianca Beghé, Nazanin Zounemat Kermani, Ian Michael Adcock, Dave Singh, Janwillem Kocks, Tatiana Karp, Corry‐Anke Brandsma, Thys van der Molen, Monica Kraft
article en

Abstract

Asthma exacerbations contribute to the morbidity and healthcare burden associated with asthma. Accurate prediction of exacerbations remains a challenge. This study investigated if nasal brush transcriptome may be used as a low-invasive tool to improve prediction of asthma exacerbations. Demographic and clinical data (44±13 years old, 41% male, 1.1±2.4 pack years, FEV<sub>1</sub> of 81±16.8pp, FEF50 of 65±28pp, blood eos of 0.3±0.2 10<sup>9</sup>/L, FeNO of 34±29ppb), as well as RNA extracted from nasal epithelial brushes were collected from 303 patients included in the ATLANTIS study. Differential gene expression analysis was used to identify genes associated with moderate to severe exacerbation during 1-year follow-up. Gene set variation analysis (GSVA) was used to compute a composite score for exacerbation-related genes. This study included 62 exacerbators (≥1 exacerbation) and 241 non-exacerbators. Exacerbators were older, more symptomatic, and had lower lung function than non-exacerbators. We identified a set of 25 up- and 1 down-regulated genes associated with exacerbations. The top 3 higher expressed genes were the antisense ACSL-1, MTND4LP23, and MTCYBP23, whereas the lower expressed gene was KIAA1549L. The identified genes are involved in the signalling pathways of hyaluronic acid, calcium, and fibronectin binding. The clinical-based prediction model of exacerbations (age, sex, blood eos, smoking, ACQ-6, FEV1pp, and exacerbation history) achieved an AUC of 0.77 and a sensitivity of 26%. The addition of the GSVA scores improved the AUC to 0.8 and sensitivity to 39%. We identified a nasal gene signature that includes transcriptomic biomarkers, improving exacerbation prediction compared to a clinical model.

University of Technology Sydney (AU), University of Modena and Reggio Emilia (IT), University Medical Center Groningen (NL), University of Ferrara (IT), Christian-Albrechts-Universität zu Kiel (DE), Manchester University NHS Foundation Trust (GB), Mount Sinai Medical Center (US), Lung Institute (US), Center for Translational Neurophysiology of Speech and Communication (IT), NIHR Leicester Biomedical Research Centre (GB), University of Aveiro (PT), Icahn School of Medicine at Mount Sinai (US)
Zero hunger
Openalex Percentile: Top 72%
Asthma and respiratory diseases
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.