Blood transcriptome differentiates clinical clusters for asthma

Jin An; Seungpil Jeong; Kyungtaek Park; Heejin Jin; Jaehyun Park; Eunsoon Shin; Ji-Hyang Lee; Woo-Jung Song; Hyouk-Soo Kwon; You Sook Cho; Jong Eun Lee; Sungho Won; Tae-Bum Kim

doi:10.1016/j.waojou.2024.100871

Blood transcriptome differentiates clinical clusters for asthma

World Allergy Organ J. 2024 Jan 26;17(2):100871. doi: 10.1016/j.waojou.2024.100871. eCollection 2024 Feb.

Authors

Jin An¹, Seungpil Jeong², Kyungtaek Park³, Heejin Jin³, Jaehyun Park⁴, Eunsoon Shin⁵, Ji-Hyang Lee⁶, Woo-Jung Song⁶, Hyouk-Soo Kwon⁶, You Sook Cho⁶, Jong Eun Lee⁵, Sungho Won^{2

3

7}, Tae-Bum Kim⁶

Affiliations

¹ Department of Pulmonary, Allergy and Critical Care Medicine, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea.
² Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea.
³ Institute of Health and Environment, Seoul National University, Seoul, South Korea.
⁴ Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, South Korea.
⁵ DNA Link, Seoul, South Korea.
⁶ Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
⁷ Interdisciplinary Program of Bioinformatics, College of Natural Science, Seoul National University, Seoul, South Korea.

Abstract

Background: In previous studies, several asthma phenotypes were identified using clinical and demographic parameters. Transcriptional phenotypes were mainly identified using sputum and bronchial cells.

Objective: We aimed to investigate asthma phenotypes via clustering analysis using clinical variables and compare the transcription levels among clusters using gene expression profiling of the blood.

Methods: Clustering analysis was performed using 6 parameters: age of asthma onset, body mass index, pack-years of smoking, forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity, and blood eosinophil counts. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood samples and RNA was extracted from selected PBMCs. Transcriptional profiles were generated (Illumina NovaSeq 6000) and analyzed using the reference genome and gene annotation files (hg19.refGene.gft). Pathway enrichment analysis was conducted using GO, KEGG, and REACTOME databases.

Results: In total, 355 patients with asthma were included in the analysis, of whom 72 (20.3%) had severe asthma. Clustering of the 6 parameters revealed 4 distinct subtypes. Cluster 1 (n = 63) had lower predicted FEV1 % and higher pack-years of smoking and neutrophils in sputum. Cluster 2 (n = 43) had a higher proportion and number of eosinophils in sputum and blood, and severe airflow limitation. Cluster 3 (n = 110) consisted of younger subjects with atopic features. Cluster 4 (n = 139) included features of late-onset mild asthma. Differentially expressed genes between clusters 1 and 2 were related to inflammatory responses and cell activation. Th17 cell differentiation and interferon gamma-mediated signaling pathways were related to neutrophilic inflammation in asthma.

Conclusion: Four clinical clusters were differentiated based on clinical parameters and blood eosinophils in adult patients with asthma form the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA) cohort. Gene expression profiling and molecular pathways are novel means of classifying asthma phenotypes.

Keywords: Asthma; Cluster; Pathway; Transcriptome.