Skip to main content

Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma



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


Therapeutic strategies for asthma have been established based on phenotypic features such as symptom severity and lung function [1]. However, conventional therapeutic approaches are often insufficient for adequate disease control, especially in severe cases due to the heterogeneous nature of asthma pathogenesis [2]. The asthma immune phenotypes have been used to explain the heterogeneity with various inflammatory and immunologic pathways involved in its pathogenesis [3]. For example, biomarkers of type 2 (T2) cell-mediated inflammation including eosinophils in sputum or blood cells, serum immunoglobulin E (IgE) levels, and fractional exhaled nitric oxide (FeNO) are used to identify potential candidates who may benefit from treatment with biologic agents targeting T2 inflammation. Recent clinical trials showed that anti-IL-4 or anti-IL-5 biologic agents successfully reduced the number of exacerbations and improved lung functions in T2 asthmatics [4, 5]. The conceptual classification of T2 and non-T2 asthma has led to a better understanding of the molecular mechanisms behind the heterogeneous phenotypes of asthma [6, 7]. Despite recent progress, the management of inhaled corticosteroid (ICS) refractory non-T2 asthma remains problematic, and the details of non-T2 asthma pathogenesis still need to be identified.

The transcriptome is a set of all the ribonucleic acid (RNA) transcripts for a specific biological condition. Transcriptome clustering has the potential to provide a deeper understanding of the biologic status [8]. Considering that over 80% of gene expressions are identified in peripheral blood mononuclear cells (PBMCs), transcriptomic analysis of peripheral blood samples is a relatively non-invasive, reproducible method of obtaining data. However, studies on transcriptomic cluster analyses using PBMCs are lacking in asthma patients. In addition, a recent study showed that transcriptome and microbiome profiles in the nasal epithelium of asthmatics were different from that of healthy controls, and these findings suggest that host-microbiome associations may exist in severe persistent asthma patients [9]. However, the association between the transcriptomic expressions of PBMCs and microbiome in the lung and gut has not been well evaluated in asthmatics.

In this study, we clustered asthmatics based on different blood transcriptomic expressions and investigated the clinical features, immune phenotypes, and microbiome compositions among clusters to gain a better understanding of asthma pathogenesis and to identify potential therapeutic targets.


Study design and eligibility criteria

In this cross-sectional study, we analyzed the medical records, transcriptome and microbiome data of the adult asthma cohort of asthmatics and non-asthmatic volunteers who visited thirteen medical centers across Korea from 2016 to 2019 [10]. All of the subjects in this cohort provided informed consent that was approved by the Institutional Review Board (IRB) of Seoul National University Hospital (IRB No. 1607-148-778). This study was conducted in compliance with the reporting of genetic association studies statement [11]. The transcriptome data extracted from PBMCs and microbiome data obtained from induced sputum and stool samples of all patients were included in the analysis.

Patients were diagnosed as asthmatics if they met the following criteria: (1) the presence of respiratory symptoms related to chronic airway inflammation including cough, sputum, dyspnea, and wheezing with (2) airway reversibility defined as a 12% and 200 mL or greater increase in forced expiratory volume in 1 s (FEV1) after the use of an inhaled short-acting bronchodilator or (3) airway hyperresponsiveness defined as a reduction of FEV1 20% or more after inhalation of less than 16 mg/mL of methacholine [12].

Patients were considered to have severe asthma if they had reduced lung functions (FEV1 < 80% of predicted value) and asthma control questionnaire (ACQ) scores of 1.5 or higher with a daily inhaled corticosteroid (ICS) requirement equivalent to 1000 μg of beclomethasone or greater [13]. Patients who used antibiotics or experienced an acute exacerbation event within three months prior to the study were excluded.

Assessment of clinical parameters

We obtained demographic and clinical information regarding age, sex, body mass index, smoking status, family history of asthma, pets, underlying conditions, results of lung function tests, and FeNO values. For asthmatics, we additionally obtained clinical information about asthma severity, levels of asthma control, ACQ-7 scores, asthma control test (ACT) scores, and doses of inhaled corticosteroids, oral steroids, long-acting beta2 agonists (LABA), long-acting muscarinic antagonists (LAMA), leukotriene receptor antagonists (LTRA), theophylline, omalizumab, and macrolides. To evaluate the immune phenotypes of asthmatics, sputum samples were counted for eosinophils (%), neutrophils (%), and macrophages (%). Blood samples were measured for total IgE, vitamin D, interferon (IFN)-γ, periostin, interleukin (IL)-4, IL-5, IL-13 and eosinophils (%).

Samples and clinical parameters

We collected 2.5 mL of peripheral blood samples in the PAXgene Blood RNA Tube (BD Biosciences, San Jose, CA, USA) from each eligible patient. At least 2 mL of induced sputum samples were collected in the Falcon 50 mL conical centrifuge tube. We followed a standardized method to obtain induced sputum samples [14]. Sputum samples that contained squamous epithelial cells less than 20% of the total cell count were considered appropriate [15]. We collected at least 0.2 g of stool samples in a stool container (SPL Life Sciences, Pocheon-si, Korea). The collected blood, sputum, and stool samples were stored at − 70 °C.

RNA extraction and transcriptome sequencing

RNA extraction was performed with a PAXgene Blood RNA kit IVD (QIAGEN, Hilden, Germany). The RNA-seq library was prepared with a TruSeq Stranded mRNA Sample Preparation Kit (Illumina, San Diego, CA, USA). The messenger RNA libraries were sequenced by paired-end 100 cycles on the HiSeq 2500 (Illumina). Low-quality bases in the raw reads were filtered out by FastQC ver 0.11.5 [16] and potentially existing sequencing adapters were trimmed with Skewer ver 0.2.2 [17]. The high-quality reads were mapped to the human reference genome hg19 (downloaded from UCSC genome browser, by STAR ver 2.6 [18]. The gene expression level was quantified based on the aligned reads by Cufflinks package ver 2.2.1 [19]. The gene annotation of the reference genome was used as gene models and the expression values were calculated in Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) unit.

Clustering and transcriptome expression analysis

To identify any disease subsets within the asthmatic group, we explored the gene expression patterns using a hierarchical clustering method. The R-function “hclust” with the Euclidean distance and complete linkage option was used for the clustering analysis of gene expression profiles in FPKM values. The resultant clusters within the asthmatic group were designated as cluster 1, 2, and 3. The non-asthmatic group was not included in the clustering analysis and was regarded as an independent control cluster. The Principal Component Analysis (PCA) plots were generated using the R program. Heatmaps were plotted using the heatmap package and the Euclidean distance method. Different transcriptome expressions were analyzed, and comparisons were made among the three clusters as well as between asthmatics and non-asthmatics.

Microbiome analysis

Metagenomic deoxyribonucleic acid (DNA) was extracted from 1 mL of induced sputum and 0.2 g of stool using a FastDNA SPIN Kit (MP Biomedicals, Irvine, CA, USA). The bacterial 16S ribosomal RNA (rRNA) gene was amplified using the extracted DNAs as templates. For the polymerase chain reaction, primers 341F and 805R and 2X KAPA HiFi HotStart ReadyMix (KAPA Biosystems, Roche, Wilmington, MA, USA) were used [20]. The sequencing library was constructed according to the 16S metagenomic sequencing library preparation methods (Illumina) [21]. The amplicon was purified using a XSEP MagBead (Celemics Inc., Seoul, Korea) and 300 bp paired-end sequencing using the MiSeq v3 platform (Illumina) was performed.

The resultant sequences were analyzed using Quantitative Insights in Microbial Ecology software (QIIME) [22] and the EzBiocloud 16S rRNA gene sequence database [10]. The beta-diversity was calculated using the weighted normalized UniFrac distance. The Principal coordinate analysis (PCoA) and PERMANOVA implemented in the Vegan package of the R software were used to compare the microbiome structures.

Statistical analyses

The R statistical software, version 3.6.3 [R Core Team (2018), Vienna, Austria] was used for statistical analysis. A Log2 fold change > 2 or < -2 and a q-value < 0.05 were considered to be statistically significant. Categorical variables were analyzed with the Chi-square test and the Fisher’s exact test. Continuous variables were analyzed with the Kruskal–Wallis test. Differentially expressed genes (DEGs) were analyzed using the DESeq2 package [23]. The volcano plots for the expression-fold changes were drawn with the EnhancedVolcano package [24]. The difference of bacterial abundance was evaluated by Kruskal–Wallis test and Benjamini–Hochberg adjustment.


Differences in asthmatics and non-asthmatics

A total of 46 asthmatic patients and 21 non-asthmatics met the eligibility criteria, and their baseline characteristics are summarized in Additional file 1: Table S1. Asthmatics were older, had more asthma-related comorbidities with worse lung functions, and had more frequent family histories of asthma.

Transcriptomic clusters and their phenotypes in asthmatics

A total of 19,841 transcriptomic expressions was observed in the blood samples. Although most transcriptomic expressions in PBMCs were similar in both asthmatics and non-asthmatics (Fig. 1A), VSTM4 and CCL23 were expressed significantly higher in asthmatics (VSTM4, log2 fold change = 5.14, q-value = 1.41 × 10–5; CCL23, log2 fold change = 2.41, q-value = 1.41 × 10–5) (Additional file 1: Table S2).

Fig. 1
figure 1

Distinctive patterns of transcriptomic expression of three clusters in asthmatics and non-asthmatics

Among asthmatics, three distinct clusters were obtained based on the blood transcriptomic expressions: 21, 11, and 15 patients were grouped into three clusters, which were visualized through PCA (Fig. 1B). The heatmap for transcriptomic expression showed distinctive pattern expressions among the three clusters (Fig. 1C).

Phenotypic features of each cluster are described in Table 1. Phenotype analyses showed that the proportion of severe asthmatics was lowest at 28.6% in cluster 1 and highest in cluster 3 at 73.3%. Accordingly, cluster 3 involved more patients with an ACT score < 20 and had the lowest FEV1 and forced expiratory flow between 25 and 75% (FEF25–75%). The use of LAMA was found only in cluster 3. Cluster 1 had the highest levels of IL-5 and IL-13 while cluster 3 had the lowest levels of IL-5 and IL-13. There were no differences of serum IL-4 and IFN-γ levels among the three clusters. The blood IgE level and the sputum neutrophil count was highest in cluster 2.

Table 1 Baseline characteristics and clinical features of the asthmatics

Differential expression of transcriptome in asthmatic clusters compared to non-asthmatics

The 20 most differentially expressed transcriptomes between each asthmatic cluster and non-asthmatics were selected based on the absolute value of log2 fold changes, and these results are summarized in Table 2. Across the three clusters, the expression of VSTM4 was consistently enhanced in each asthmatic cluster compared to non-asthmatics. In contrast, PWP2 was commonly suppressed in all asthmatic clusters. Cluster-specifically, cluster 1 showed at least a twofold increase of CCL23 and IL1RL1 expression while the expression of TREML4 was significantly suppressed in cluster 3.

Table 2 Top 20 transcriptomes differentially expressed between each cluster of asthmatics and non-asthmatics

The differential expression of transcriptomes among the three asthmatic clusters are summarized in Fig. 2. The 20 most differentially expressed transcriptomes based on the absolute value of log2 fold changes are described in Additional file 1: Table S3. Pre-specified genes not included in the top 20 genes of each cluster were compared among three clusters of asthmatics (Additional file 1: Table S4).

Fig. 2
figure 2

Volcano plots for differential expression of transcriptome among three clusters in asthmatics. FC fold change

CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3 in the linear regression model after Bonferroni correction (Fig. 3 and Additional file 1: Table S5).

Fig. 3
figure 3

Linear regression analysis for transcriptome expression among three clusters in asthmatics and non-asthmatics

Lung and gut microbiomes in asthmatic clusters

Analyses of the alpha-diversity in the lung and the gut microbiomes did not show significant differences among three asthmatic clusters and non-asthmatics (Fig. 4). Analyses of the beta-diversity in the lung and gut microbiomes using the UniFrac distance showed no significant differences among three asthmatic clusters and non-asthmatics. Among lung clusters, Streptococcus, Leptotrichia, Gemella, Alloprevotella, Granulicatella, Aggregatibacter showed a difference in abundance compared to the non-asthmatics; however, these differences were attenuated after the Benjamini–Hochberg adjustment with the exception of Alloprevotella which revealed a significantly reduced abundance in cluster 3 (adjusted p-value = 0.049, Additional file 1: Table S6). The gut microbiome composition did not significantly differ among the three clusters and non-asthmatics.

Fig. 4
figure 4

Alpha-/beta-diversity and phylum composition of lung and gut microbiomes in three clusters in asthmatics and non-asthmatics. a Alpha-diversity of lung microbiome, b Alpha-diversity of gut microbiome, c Beta-diversity of lung microbiome, d Beta-diversity of gut microbiome, e Phylum composition of lung and gut microbiome


Transcriptome analysis of PBMCs from asthmatics uncovered three distinct clusters through hierarchical clustering with different distributions in the PCA plot. The three clusters with different transcriptome expression profiles showed distinctive immunophenotypic features: cluster 1 was representative of non-severe T2 asthma, cluster 3 consisted of severe non-T2 asthmatics, and cluster 2 showed in-between clinical features with highest blood IgE and sputum neutrophil levels. These differences among clusters show that transcriptomic expressions of PBMCs may reflect the immunophenotypic features of asthmatics. However, we were unable to identify significant differences in the diversity of lung or gut microbiome among the three clusters in asthmatics. Therefore, transcriptomic expression of PBMCs may be largely unaffected by the diversity of lung and gut microbiomes.

As PBMCs are easily accessible through blood sampling and can reflect the presence of systemic inflammation, previous studies have evaluated the association between gene expression patterns in PBMCs and immune phenotypes in asthmatic patients. In one study, transcriptomic clustering analysis of PBMCs revealed systemic changes such as activation of innate immunity and antigen-independent T cell activation during acute exacerbation of asthma [25]. A clustering analysis based on transcriptomic profiles of PBMCs in asthmatic children identified a distinct cluster with a gene expression pattern associated with Th1 or Th17 inflammation and poor treatment outcomes [26]. In another study, severe asthmatics showed enhanced inflammatory mechanisms related to myeloid cell trafficking while lymphoid cell development in the same patients was attenuated [27]. Complementary to the findings of these previous studies, our study also showed that transcriptomic expression among the three clusters in adult asthmatics had distinct features related to T2 or non-T2 asthma. Therefore, transcriptome expression in PBMCs may predominantly show the immune phenotype of asthma while not including lung structural changes associated with asthma.

The expression of VSTM4, a B7-like protein that downregulates T cell activation [28], was higher in asthmatics compared to non-asthmatics regardless of cluster subtypes. Given that VSTM4 reduces IFN-γ and IL-2 produced by T cells and inhibits naïve CD4+T cell differentiation into Th1 cells [28], VSTM4 may contribute to a relative predominance of Th2 inflammation over Th1 inflammation in asthma. There may be a correlation between respiratory syncytial virus (RSV) infection and VSTM4 in children. RSV showed a relationship with higher serum IgE levels and elevated Th2 inflammatory responses [29]. The causal relationship between expression of VSTM4 and RSV infection is a topic that warrants further investigation.

CCL23, an eosinophilic-derived cytokine belonging to the CC chemokine family, has high chemotactic activity for resting T cells associated with eosinophilic airway inflammation [30, 31]. Although CCL23 expression was increased in asthmatics compared to non-asthmatics, a higher transcriptomic expression of CCL23 may be more closely related to T2 inflammatory features in cluster 1. As CCL23 is produced and released from eosinophils, its expression is associated with increased eosinophilic inflammation [32, 33]. In nasal epithelial tissue and blood, higher expressions of CCL23 were also reported in chronic eosinophilic rhinosinusitis [30, 34]. In our study, the expression of CCL23 was higher in the PBMCs of cluster 1 compared to that of non-asthmatics. Similarly, the expression of IL1RL1 was also higher in cluster 1 compared to non-asthmatics. IL1RL1, also known as ST2 which binds to IL-33, is associated with T2 inflammatory pathways and are upregulated in atopic asthma [35]. In bronchial epithelial cells of asthmatic patients, a higher expression of IL1RL1 correlated with Th2 inflammation [36]. These results show that cluster 1 may be related to systemically enhanced Th2 inflammation caused by the activation of the IL-33/ST2 axis.

In our study, the severity of asthma increased with a linear trend of cathepsin D (CTSD) and aldehyde dehydrogenase 2 (ALDH2) expression from non-asthmatics to cluster 3. CTSD, a protease that breaks down abnormal or denatured proteins in airway, was more expressed with lung inflammation, especially in cystic fibrosis [37]. Considering that CTSD showed a potential role in the degradation of tumstatin that inhibits airway remodeling and airway hyperresponsiveness [38, 39], increased CTSD expression may be associated with severe asthma predominance in cluster 3. Deficiency in ALDH2, an enzyme that catalyzes the transformation from acetaldehyde to acetic acid, results in increased blood acetaldehyde levels and is related to the pathogenesis of alcohol-induced asthma [40]. In addition, ALDH2 plays a protective role in the inflammatory damage caused by reactive oxygen species [41], which is known to be related with aggravated airway damage in asthma [42]. Therefore, a higher expression of ALDH2 may be a protective response in asthmatics with high oxygen stress.

Gene expression patterns of airway structural cells are different to that of PBMCs. Among 90 DEGs in nasal epithelial cells and 4 DEGs in airway smooth muscle cells discovered in previous studies [43, 44], none were consistent with our findings using PBMCs. A recent study found 5 hub genes (SERPINB2, SERPINB4, LTF, MUC5B, and CST4) related to the pathogenesis of asthma in bronchial and nasal cells [45]. SERPINB2 and LTF expressions in PBMCs were higher in cluster 3 compared to non-asthmatics and clusters 1 and 2. SERPINB2 protects macrophages from apoptosis and regulates chemokines such as CCL2 associated with monocyte and macrophage influx. As SERPINB2 is upregulated during immune reactions to lipopolysaccharide, its high expression in non-T2 cluster 3 is not unexpected. Lactoferrin encoded by LTF is an immunomodulator with antimicrobial activity [46], and its increased expression may reflect the pathology of non-T2 neutrophilic asthma [47].

The relationship between transcriptomic profiles and the host microbiome is currently a topic of interest. Different IL1A expressions in airway epithelium of asthma patients were identified according to various microbiome profiles [48]. Transcriptomic profiles of PBMCs may provide a glimpse of systemic inflammatory features partly reflecting the inflammatory features in the lower respiratory tract in asthma [49]. We hypothesized that a relationship between the PBMC transcriptome and lung or gut microbiomes exists. However, we were unable to find a link between transcriptomic profiles in the peripheral blood and the diversity and taxonomy of microbiomes in the lung and gut. In fact, there have been studies that reported no significant differences in microbiome according to the phenotypes of asthma. The diversity and composition of lung and gut microbiome were broadly similar between asthmatics and non-asthmatics [50, 51]. In a comparison between patients with obstructive lung disease and control subjects, the diversity and composition of microbiome in BAL fluid were similar, when the samples were obtained using the same method [52]. The lack of such a relationship suggests that the immune response induced by the microbiome may be limited to the local environment.

This study has several limitations. First, the small sample size may have hindered the detection of important but small differences in immune phenotypes and microbiomes among clusters. Although the sputum eosinophil count, blood eosinophil count, and IL-4 levels seemed to differ among the three clusters, we could not find statistical significance behind these differences due to the large standard deviations. Second, due to the cross-sectional design of this study, we were unable to identify the causal relationship between transcriptomic profiles and clinical phenotypes or immune phenotypes. To prove the pathogenic causality, a longitudinal cohort study is needed. Third, confounding factors were not fully controlled. The results of our study should be interpreted with caution because different treatment regimens were used across the three clusters. Further studies with better control over confounding factors are needed. Fourth, although numerously different transcriptomic expressions were discovered among three clusters, we still do not know which inflammatory or functional pathways are responsible for the phenotypic differences among the clusters. Finally, caution is needed in generalizing our results, because validation analysis was not performed in a different cohort. Further external validation is necessary in a larger cohort of asthmatics.


Transcriptomic profiles of PBMCs successfully differentiated asthmatics into three groups with different clinical and immune phenotypes, and differentially expressed genes may be potentially used as biomarkers representing asthma immune phenotypes.

Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.



Asthma control questionnaire


Asthma control test


Differentially expressed genes


Deoxyribonucleic acid

FEF25–75 :

Forced expiratory flow between 25 and 75%


Fractional exhaled nitric oxide


Forced expiratory volume in 1 s


Fragments per kilobase of transcript per million fragments mapped


Genome-wide association study


Inhaled corticosteroid


Institutional review board


Acting muscarinic antagonist


Leukotriene receptor antagonists


Peripheral blood mononuclear cells


Principal component analysis


Principal coordinate analysis


Ribonucleic acid


Type 2


  1. Global Initiative for Asthma. Global strategy for asthma management and prevention, Available at [Accessed 2021.5.10]. 2020.

  2. Chung KF, Wenzel SE, Brozek JL, Bush A, Castro M, Sterk PJ, Adcock IM, Bateman ED, Bel EH, Bleecker ER, et al. International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma. Eur Respir J. 2014;43:343–73.

    Article  CAS  PubMed  Google Scholar 

  3. Bousquet J, Mantzouranis E, Cruz AA, Aït-Khaled N, Baena-Cagnani CE, Bleecker ER, Brightling CE, Burney P, Bush A, Busse WW, et al. Uniform definition of asthma severity, control, and exacerbations: document presented for the World Health Organization Consultation on Severe Asthma. J Allergy Clin Immunol. 2010;126:926–38.

    Article  PubMed  Google Scholar 

  4. Wenzel S, Castro M, Corren J, Maspero J, Wang L, Zhang B, Pirozzi G, Sutherland ER, Evans RR, Joish VN, et al. Dupilumab efficacy and safety in adults with uncontrolled persistent asthma despite use of medium-to-high-dose inhaled corticosteroids plus a long-acting β2 agonist: a randomised double-blind placebo-controlled pivotal phase 2b dose-ranging trial. Lancet. 2016;388:31–44.

    Article  CAS  PubMed  Google Scholar 

  5. Pavord ID, Korn S, Howarth P, Bleecker ER, Buhl R, Keene ON, Ortega H, Chanez P. Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial. Lancet. 2012;380:651–9.

    Article  CAS  PubMed  Google Scholar 

  6. Fahy JV. Type 2 inflammation in asthma–present in most, absent in many. Nat Rev Immunol. 2015;15:57–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Robinson D, Humbert M, Buhl R, Cruz AA, Inoue H, Korom S, Hanania NA, Nair P. Revisiting Type 2-high and Type 2-low airway inflammation in asthma: current knowledge and therapeutic implications. Clin Exp Allergy. 2017;47:161–75.

    Article  CAS  PubMed  Google Scholar 

  8. Park HW, Weiss ST. Understanding the molecular mechanisms of asthma through transcriptomics. Allergy Asthma Immunol Res. 2020;12:399–411.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chun Y, Do A, Grishina G, Grishin A, Fang G, Rose S, Spencer C, Vicencio A, Schadt E, Bunyavanich S. Integrative study of the upper and lower airway microbiome and transcriptome in asthma. JCI Insight 2020; 5.

  10. Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, Chun J. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, von Elm E, Khoury MJ, Cohen B, Davey-Smith G, Grimshaw J, et al. STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med. 2009;6: e22.

    Article  PubMed  Google Scholar 

  12. Expert Panel Report 3 (EPR-3). Guidelines for the diagnosis and management of asthma-summary report 2007. J Allergy Clin Immunol 2007, 120:S94–138.

  13. Huang YJ, Nariya S, Harris JM, Lynch SV, Choy DF, Arron JR, Boushey H. The airway microbiome in patients with severe asthma: associations with disease features and severity. J Allergy Clin Immunol. 2015;136:874–84.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Paggiaro PL, Chanez P, Holz O, Ind PW, Djukanović R, Maestrelli P, Sterk PJ. Sputum induction. Eur Respir J Suppl. 2002;37:3s–8s.

    CAS  PubMed  Google Scholar 

  15. Jung JW, Kang HR, Lee HS, Park HW, Cho SH, Min KU, Sohn SW. Expression levels of eosinophil granule protein mRNAs in induced sputum reflect airway hyperresponsiveness and airflow limitation. Tohoku J Exp Med. 2014;233:49–56.

    Article  CAS  PubMed  Google Scholar 

  16. Andrew SJFQ. A quality control tool for high throughput sequence data. 2010; 532:1.

  17. Jiang H, Lei R, Ding SW, Zhu S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics. 2014;15:182.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  CAS  PubMed  Google Scholar 

  19. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31:46–53.

    Article  CAS  PubMed  Google Scholar 

  20. Choi S, Sohn KH, Jung JW, Kang MG, Yang MS, Kim S, Choi JH, Cho SH, Kang HR, Yi H. Lung virome: new potential biomarkers for asthma severity and exacerbation. J Allergy Clin Immunol. 2021;148:1007.

    Article  CAS  PubMed  Google Scholar 

  21. Illumina. 16S metagenomic sequencing library preparation. Available at: Accessed March 6, 2017.

  22. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Blighe K, RS LM. EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. R package version 1.10. 0. 2021.

  25. Bjornsdottir US, Holgate ST, Reddy PS, Hill AA, McKee CM, Csimma CI, Weaver AA, Legault HM, Small CG, Ramsey RC, et al. Pathways activated during human asthma exacerbation as revealed by gene expression patterns in blood. PLoS ONE. 2011;6: e21902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yeh YL, Su MW, Chiang BL, Yang YH, Tsai CH, Lee YL. Genetic profiles of transcriptomic clusters of childhood asthma determine specific severe subtype. Clin Exp Allergy. 2018;48:1164–72.

    Article  CAS  PubMed  Google Scholar 

  27. Bigler J, Boedigheimer M, Schofield JPR, Skipp PJ, Corfield J, Rowe A, Sousa AR, Timour M, Twehues L, Hu X, et al. A severe asthma disease signature from gene expression profiling of peripheral blood from U-BIOPRED cohorts. Am J Respir Crit Care Med. 2017;195:1311–20.

    Article  CAS  PubMed  Google Scholar 

  28. Gérard A, Ghiotto M, Fos C, Guittard G, Compagno D, Galy A, Lemay S, Olive D, Nunès JA. Dok-4 is a novel negative regulator of T cell activation. J Immunol. 2009;182:7681–9.

    Article  PubMed  CAS  Google Scholar 

  29. Becker Y. Respiratory syncytial virus (RSV) evades the human adaptive immune system by skewing the Th1/Th2 cytokine balance toward increased levels of Th2 cytokines and IgE, markers of allergy–a review. Virus Genes. 2006;33:235–52.

    Article  CAS  PubMed  Google Scholar 

  30. Poposki JA, Uzzaman A, Nagarkar DR, Chustz RT, Peters AT, Suh LA, Carter R, Norton J, Harris KE, Grammer LC, et al. Increased expression of the chemokine CCL23 in eosinophilic chronic rhinosinusitis with nasal polyps. J Allergy Clin Immunol. 2011;128:73-81.e74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Du X, Li F, Zhang C, Li N, Huang H, Shao Z, Zhang M, Zhan X, He Y, Ju Z, et al. Eosinophil-derived chemokine (hCCL15/23, mCCL6) interacts with CCR1 to promote eosinophilic airway inflammation. Signal Transduct Target Ther. 2021;6:91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Novak H, Müller A, Harrer N, Günther C, Carballido JM, Woisetschläger M. CCL23 expression is induced by IL-4 in a STAT6-dependent fashion. J Immunol. 2007;178:4335–41.

    Article  CAS  PubMed  Google Scholar 

  33. Virkud YV, Kelly RS, Croteau-Chonka DC, Celedón JC, Dahlin A, Avila L, Raby BA, Weiss ST, Lasky-Su JA. Novel eosinophilic gene expression networks associated with IgE in two distinct asthma populations. Clin Exp Allergy. 2018;48:1654–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Matsumoto K, Fukuda S, Hashimoto N, Saito H. Human eosinophils produce and release a novel chemokine, CCL23, in vitro. Int Arch Allergy Immunol. 2011;155(Suppl 1):34–9.

    Article  CAS  PubMed  Google Scholar 

  35. Jiang Y, Gruzieva O, Wang T, Forno E, Boutaoui N, Sun T, Merid SK, Acosta-Pérez E, Kull I, Canino G, et al. Transcriptomics of atopy and atopic asthma in white blood cells from children and adolescents. Eur Respir J. 2019;53:1900102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Traister RS, Uvalle CE, Hawkins GA, Meyers DA, Bleecker ER, Wenzel SE. Phenotypic and genotypic association of epithelial IL1RL1 to human TH2-like asthma. J Allergy Clin Immunol. 2015;135:92–9.

    Article  CAS  PubMed  Google Scholar 

  37. Kasper M, Lackie P, Haase M, Schuh D, Müller M. Immunolocalization of cathepsin D in pneumocytes of normal human lung and in pulmonary fibrosis. Virchows Arch. 1996;428:207–15.

    CAS  PubMed  Google Scholar 

  38. Faiz A, Tjin G, Harkness L, Weckmann M, Bao S, Black JL, Oliver BG, Burgess JK. The expression and activity of cathepsins D, H and K in asthmatic airways. PLoS ONE. 2013;8: e57245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Burgess JK, Boustany S, Moir LM, Weckmann M, Lau JY, Grafton K, Baraket M, Hansbro PM, Hansbro NG, Foster PS, et al. Reduction of tumstatin in asthmatic airways contributes to angiogenesis, inflammation, and hyperresponsiveness. Am J Respir Crit Care Med. 2010;181:106–15.

    Article  CAS  PubMed  Google Scholar 

  40. Takao A, Shimoda T, Kohno S, Asai S, Harda S. Correlation between alcohol-induced asthma and acetaldehyde dehydrogenase-2 genotype. J Allergy Clin Immunol. 1998;101:576–80.

    Article  CAS  PubMed  Google Scholar 

  41. Hu XY, Fang Q, Wang JS, Xie JQ, Chai BS, Li FQ, Cui X, Yang Y. Over-expression of aldehyde dehydrogenase-2 protects against H2O2-induced oxidative damage and apoptosis in peripheral blood mononuclear cells. Acta Pharmacol Sin. 2011;32:245–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sahiner UM, Birben E, Erzurum S, Sackesen C, Kalayci O. Oxidative stress in asthma. World Allergy Organ J. 2011;4:151–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Pandey G, Pandey OP, Rogers AJ, Ahsen ME, Hoffman GE, Raby BA, Weiss ST, Schadt EE, Bunyavanich S. A nasal brush-based classifier of asthma identified by machine learning analysis of nasal RNA sequence data. Sci Rep. 2018;8:8826.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Yick CY, Zwinderman AH, Kunst PW, Grünberg K, Mauad T, Chowdhury S, Bel EH, Baas F, Lutter R, Sterk PJ. Gene expression profiling of laser microdissected airway smooth muscle tissue in asthma and atopy. Allergy. 2014;69:1233–40.

    Article  CAS  PubMed  Google Scholar 

  45. Singh P, Sharma A, Jha R, Arora S, Ahmad R, Rahmani AH, Almatroodi SA, Dohare R, Syed MA. Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma. Sci Rep. 2020;10:13354.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Actor JK, Hwang SA, Kruzel ML. Lactoferrin as a natural immune modulator. Curr Pharm Des. 2009;15:1956–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kruzel ML, Zimecki M, Actor JK. Lactoferrin in a context of inflammation-induced pathology. Front Immunol. 2017;8:1438.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Pérez-Losada M, Castro-Nallar E, Bendall ML, Freishtat RJ, Crandall KA. Dual transcriptomic profiling of host and microbiota during health and disease in pediatric asthma. PLoS ONE. 2015;10: e0131819.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Hoshino M, Ohtawa J, Akitsu K. Increased C-reactive protein is associated with airway wall thickness in steroid-naive asthma. Ann Allergy Asthma Immunol. 2014;113:37–41.

    Article  CAS  PubMed  Google Scholar 

  50. Jung JW, Choi JC, Shin JW, Kim JY, Park IW, Choi BW, Park HW, Cho SH, Kim K, Kang HR. Lung microbiome analysis in Steroid-Naїve asthma patients by using whole sputum. Tuberc Respir Dis (Seoul). 2016;79:165–78.

    Article  Google Scholar 

  51. Kullberg RFJ, Haak BW, Abdel-Aziz MI, Davids M, Hugenholtz F, Nieuwdorp M, Galenkamp H, Prins M, Maitland-van der Zee AH, Wiersinga WJ. Gut microbiota of adults with asthma is broadly similar to non-asthmatics in a large population with varied ethnic origins. Gut Microbes. 2021;13:1995279.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Grønseth R, Drengenes C, Wiker HG, Tangedal S, Xue Y, Husebø GR, Svanes Ø, Lehmann S, Aardal M, Hoang T, et al. Protected sampling is preferable in bronchoscopic studies of the airway microbiome. ERJ Open Res 2017; 3.

Download references


Not applicable.


This work was supported by the Research of Korea Centers for Disease Control and Prevention (Grant numbers: 2017ER670100, 2017ER670101, and 2017ER670103).

Author information

Authors and Affiliations



HRK is the guarantor of the article. HWL, MGB, HY and HRK were responsible for the development of the design and protocol of the study; HY and HRK acquired data; HWL and MGB performed the statistical analysis and wrote the paper; HY provided methodological guidance; SC, YHA, JYB, KHS, MGB, JWJ, JHC, and SHC were involved in interpreting the results and editing the manuscript for important intellectual content; HY and HRK supervised the process of this study; HY and HRK provided critical revision of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Hana Yi or Hye-Ryun Kang.

Ethics declarations

Ethics approval and consent to participate

Our study was conducted according to the ethical guidelines of the Declaration of Helsinki in 1975. The Institutional Review Board (IRB) Committee of the Seoul National University Hospital approved the study protocol and waived the requirement for informed consent from the study participants for access to the electronic medical records (IRB No. 1607-148-778).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Baseline characteristics of the included asthmatics and non-asthmatics. BMI, body mass index; FeNO, fractional exhaled nitric oxide; FEF25-75, forced expiratory flow between 25–75%; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; PY, pack years; SD, standard deviation. Table S2. Difference in transcriptome expression between asthmatic patients and non-asthmatic subjects. If fold changes are upper than 1 (each cluster of asthmatics > non-asthmatics), then log2 fold change becomes positive. Table S3. Top 20 transcriptomes differentially expressed among three clusters in asthmatics. If fold changes are upper than 1 (preceding cluster > following cluster), then log2 fold change becomes positive. Table S4. Pre-specified genes associated with asthma pathogenesis among three clusters in asthmatics. If fold changes are upper than 1 (preceding cluster > following cluster), then log2 fold change becomes positive. Table S5. Linear regression analysis for transcriptome expression among three clusters in asthmatics and non-asthmatics. a Bonferroni correction was conducted. Table S6. Lung and gut microbiome abundance in asthma patients classified by different three clusters. B-H, Benjamini-Hochberg.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, H.W., Baek, Mg., Choi, S. et al. Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma. Respir Res 23, 237 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: