Using cell-specific late-phase asthma mRNA biomarkers to repurpose drugs that concurrently reverse disease signatures across multiple immune cell-types

BACKGROUND: The allergen-induced late-phase asthmatic response (LAR) is used to study the mechanisms and treatment of asthma. We hypothesized that gene-expression (mRNA) biomarkers of the LAR may predict asthma exacerbations and severity. METHODS: Individuals with mild allergic asthma underwent inhaled allergen challenge. We identified a baseline blood-based mRNA biomarker panel predictive of LAR development and combined it with previously reported LAR-associated biomarkers. These LAR-mRNA biomarkers were evaluated in multiple public gene-expression datasets spanning blood, airways, bronchoalveolar lavage fluid (BALF), and induced sputum to assess their ability to discriminate asthma exacerbations and severity. Cellular specificity and allergen responsiveness were examined using public single-cell RNA sequencing datasets from endobronchial biopsies obtained before and after segmental allergen challenge. To identify potential therapeutics, we performed drug-signature matching using a public single-cell perturbation dataset to identify compounds capable of reversing LAR-associated, cell-specific transcriptional changes. RESULTS: A combined set of 109 LAR-mRNA biomarkers was identified and shown to predict asthma exacerbations, with stronger performance in males than females. Biomarker-based prediction of asthma severity was most robust in BALF and induced sputum compared with blood. Highly predictive transcripts included the RNA-binding proteins GNAS and SF3B1 across multiple sample types. Single-cell analyses revealed that allergen challenge selectively altered the expression of LAR-mRNA biomarkers in T cell populations, including CD8 T cells (CLIC3+ and GZMK+), CD4 Th17 (RORA+) cells, CD4 (CD40LG+) T cells, and γδ (TRDC+) T cells, with minimal changes in allergic controls. Drug-signature matching identified corticosteroids such as mometasone furoate and prednisolone, as well as the heat shock protein 90 inhibitor AT13387, as compounds predicted to reverse these cell-specific transcriptional signatures. CONCLUSION: LAR-mRNA biomarkers reflect an asthma-specific inflammatory state that predisposes individuals to late-phase responses, exacerbations, and progression to more severe disease. These findings link systemic biomarkers to airway cellular mechanisms and nominate therapeutic strategies to modulate allergen-driven inflammation.

Authors

Institutions

Publication Details

Journal
PLoS Computational Biology
Published
2026-04-03
DOI
https://doi.org/10.1371/journal.pcbi.1014081
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using cell-specific late-phase asthma mRNA biomarkers to repurpose drugs that concurrently reverse disease signatures across multiple immune cell-types

Scott J. Tebbutt, Paul M. O’Byrne, Young Woong Kim, Mingming Zhang et al.
PLoS Computational Biology
Single-cell and spatial transcriptomics
article

Using cell-specific late-phase asthma mRNA biomarkers to repurpose drugs that concurrently reverse disease signatures across multiple immune cell-types

Scott J. Tebbutt, Paul M. O’Byrne, Young Woong Kim, Mingming Zhang, Gail M. Gauvreau, Louis-Philippe Boulet, Amrit Singh, Chen Xi Yang, J. Mark FitzGerald
article en

Abstract

BACKGROUND: The allergen-induced late-phase asthmatic response (LAR) is used to study the mechanisms and treatment of asthma. We hypothesized that gene-expression (mRNA) biomarkers of the LAR may predict asthma exacerbations and severity. METHODS: Individuals with mild allergic asthma underwent inhaled allergen challenge. We identified a baseline blood-based mRNA biomarker panel predictive of LAR development and combined it with previously reported LAR-associated biomarkers. These LAR-mRNA biomarkers were evaluated in multiple public gene-expression datasets spanning blood, airways, bronchoalveolar lavage fluid (BALF), and induced sputum to assess their ability to discriminate asthma exacerbations and severity. Cellular specificity and allergen responsiveness were examined using public single-cell RNA sequencing datasets from endobronchial biopsies obtained before and after segmental allergen challenge. To identify potential therapeutics, we performed drug-signature matching using a public single-cell perturbation dataset to identify compounds capable of reversing LAR-associated, cell-specific transcriptional changes. RESULTS: A combined set of 109 LAR-mRNA biomarkers was identified and shown to predict asthma exacerbations, with stronger performance in males than females. Biomarker-based prediction of asthma severity was most robust in BALF and induced sputum compared with blood. Highly predictive transcripts included the RNA-binding proteins GNAS and SF3B1 across multiple sample types. Single-cell analyses revealed that allergen challenge selectively altered the expression of LAR-mRNA biomarkers in T cell populations, including CD8 T cells (CLIC3+ and GZMK+), CD4 Th17 (RORA+) cells, CD4 (CD40LG+) T cells, and γδ (TRDC+) T cells, with minimal changes in allergic controls. Drug-signature matching identified corticosteroids such as mometasone furoate and prednisolone, as well as the heat shock protein 90 inhibitor AT13387, as compounds predicted to reverse these cell-specific transcriptional signatures. CONCLUSION: LAR-mRNA biomarkers reflect an asthma-specific inflammatory state that predisposes individuals to late-phase responses, exacerbations, and progression to more severe disease. These findings link systemic biomarkers to airway cellular mechanisms and nominate therapeutic strategies to modulate allergen-driven inflammation.

PLoS Computational BiologyVol. 22(4)
Providence Health Care (CA), University of British Columbia (CA), St. Paul's Hospital (CA), Prevention of Organ Failure (CA), Centre hospitalier de l'Université Laval (CA), Université Laval (CA), McMaster University (CA)
AllerGen
Reduced inequalities
Openalex Percentile: Top 33%
Single-cell and spatial transcriptomics
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.