Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea.
Interdisciplinary Program in Precision Public Health, Korea University, Seoul, Korea.
Respir Res. 2022 Sep 8;23(1):237. doi: 10.1186/s12931-022-02156-w.
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.
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.
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.
Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered.
转录组分析已被用于阐明异质疾病的复杂发病机制，通过描绘枢纽基因，也可能有助于确定潜在的治疗靶点。本研究旨在探讨血液转录组聚类是否可以区分哮喘患者的临床和免疫表型，以及哮喘患者的微生物组。
使用 RNA 测序测量 47 例哮喘患者和 21 例非哮喘患者外周血单个核细胞（PBMCs）的转录组表达。使用层次聚类算法对哮喘患者进行分类。评估每个转录组簇的差异表达基因、临床表型、免疫表型和微生物组。
在哮喘患者中，确定了三个具有大量不同转录组表达的不同转录组簇。严重哮喘患者的比例在第 3 组中最高，为 73.3%，其次是第 2 组（45.5%）和第 1 组（28.6%）。第 1 组代表临床上非严重 T2 哮喘，而第 3 组倾向于包括严重非 T2 哮喘。第 2 组具有 T2 和非 T2 哮喘的特征，其特点是血清 IgE 水平最高和中性粒细胞占主导地位的痰细胞群。与非哮喘患者相比，第 1 组显示出更高的 CCL23 和 IL1RL1 表达，而第 3 组的 TREML4 表达受到抑制。CTSD 和 ALDH2 在三个簇中表现出显著的正线性关系，从第 1 簇到第 3 簇。哮喘患者和非哮喘患者的转录组簇之间的肺部和肠道微生物组多样性没有显著差异。然而，我们的研究存在一些局限性，即分析了小样本量的数据，存在未测量的混杂因素，并且没有验证因果关系或功能途径。
基于血液转录组的遗传聚类可能提供新的免疫学见解，可以作为哮喘免疫表型的生物标志物。
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