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Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthmaopen access

Authors
Lee, Hyun WooBaek, Min-GyungChoi, SungmiAhn, Yoon HaeBang, Ji-YoungSohn, Kyoung-HeeKang, Min-GyuJung, Jae-WooChoi, Jeong-HeeCho, Sang-HeonYi, HanaKang, Hye-Ryun
Issue Date
8-Sep-2022
Publisher
BMC
Keywords
Asthma; Cluster analysis; Microbiome; RNA-Seq; Transcriptome
Citation
RESPIRATORY RESEARCH, v.23, no.1
Indexed
SCIE
SCOPUS
Journal Title
RESPIRATORY RESEARCH
Volume
23
Number
1
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/145758
DOI
10.1186/s12931-022-02156-w
ISSN
1465-9921
1465-993X
Abstract
Background Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics. Methods Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed. Results In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified. Conclusions Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered
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