The airway microbiome in patients with severe asthma: Associations with disease features and severity

Abstract

Background: Asthma is heterogeneous, and airway dysbiosis is associated with clinical features in patients with mild-to-moderate asthma. Whether similar relationships exist among patients with severe asthma is unknown.

Objective: We sought to evaluate relationships between the bronchial microbiome and features of severe asthma.

Methods: Bronchial brushings from 40 participants in the Bronchoscopic Exploratory Research Study of Biomarkers in Corticosteroid-refractory Asthma (BOBCAT) study were evaluated by using 16S ribosomal RNA-based methods. Relationships to clinical and inflammatory features were analyzed among microbiome-profiled subjects. Secondarily, bacterial compositional profiles were compared between patients with severe asthma and previously studied healthy control subjects (n = 7) and patients with mild-to-moderate asthma (n = 41).

Results: In patients with severe asthma, bronchial bacterial composition was associated with several disease-related features, including body mass index (P < .05, Bray-Curtis distance-based permutational multivariate analysis of variance; PERMANOVA), changes in Asthma Control Questionnaire (ACQ) scores (P < .01), sputum total leukocyte values (P = .06), and bronchial biopsy eosinophil values (per square millimeter, P = .07). Bacterial communities associated with worsening ACQ scores and sputum total leukocyte values (predominantly Proteobacteria) differed markedly from those associated with body mass index (Bacteroidetes/Firmicutes). In contrast, improving/stable ACQ scores and bronchial epithelial gene expression of FK506 binding protein (FKBP5), an indicator of steroid responsiveness, correlated with Actinobacteria. Mostly negative correlations were observed between biopsy eosinophil values and Proteobacteria. No taxa were associated with a TH2-related epithelial gene expression signature, but expression of TH17-related genes was associated with Proteobacteria. Patients with severe asthma compared with healthy control subjects or patients with mild-to-moderate asthma were significantly enriched in Actinobacteria, although the largest differences observed involved a Klebsiella genus member (7.8-fold increase in patients with severe asthma, adjusted P < .001).

Conclusions: Specific microbiota are associated with and may modulate inflammatory processes in patients with severe asthma and related phenotypes. Airway dysbiosis in patients with severe asthma appears to differ from that observed in those with milder asthma in the setting of inhaled corticosteroid use.

Keywords: 16S ribosomal RNA; Microbiota; T(H)2; asthma control; body mass index; inflammation; lung; steroids.

Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1

Non-metric multidimensional scaling analysis (NMDS) based on Bray-Curtis distances showing differences in bronchial bacterial composition among 30 severe asthma subjects. Each dot represents the overall bacterial community in each subject. Vectors indicate linear regression-based fits for variables found by independent distance-based PERMANOVA testing to be associated with distinct bacterial compositions. Vector directions indicate that the specific communities associated with differences in ACQ score (ACQdiff) and sputum total leukocytes (SputLeuk) are similar to each other, and differ from specific communities associated with body-mass index (BMI) and with biopsy eosinophils (BxEos).

Figure 2

A. Relative abundance plotted against BMI for the 98 taxa found to be significantly correlated with BMI among the severe asthma subjects studied (R = 0.5 – 0.8, Benjamini-Hochberg adjusted p < 0.05). The distribution of bacterial phyla representing these taxa are shown, which are mainly Bacteroidetes and Firmicutes. B. Differences in the relative abundance of all detected taxa between obese (BMI ≥ 30, n=10) and non-obese (n=19) severe asthma subjects. Taxa significantly enriched among obese subjects (≥ 2-fold, adjusted p < 0.10) include members of the Bacteroidetes and Firmicutes, e.g. Prevotella spp. Dashed lines indicate, respectively, 2-fold difference (log2 = 1.0) and FDR significance level of 10%. Asterisks indicate taxa most significantly greater in abundance among obese subjects (adjusted p < 0.05), also represented by larger circles in the plot.

Figure 2

A. Relative abundance plotted against BMI for the 98 taxa found to be significantly correlated with BMI among the severe asthma subjects studied (R = 0.5 – 0.8, Benjamini-Hochberg adjusted p < 0.05). The distribution of bacterial phyla representing these taxa are shown, which are mainly Bacteroidetes and Firmicutes. B. Differences in the relative abundance of all detected taxa between obese (BMI ≥ 30, n=10) and non-obese (n=19) severe asthma subjects. Taxa significantly enriched among obese subjects (≥ 2-fold, adjusted p < 0.10) include members of the Bacteroidetes and Firmicutes, e.g. Prevotella spp. Dashed lines indicate, respectively, 2-fold difference (log2 = 1.0) and FDR significance level of 10%. Asterisks indicate taxa most significantly greater in abundance among obese subjects (adjusted p < 0.05), also represented by larger circles in the plot.

Figure 3

A. Relative abundance plotted against changes in ACQ score for the bacterial taxa (n=448) found to be positively correlated with this parameter (R = 0.5 – 0.7, Benjamini-Hochberg adjusted p < 0.05). Increasing values of the change in ACQ are associated with greater relative abundance predominantly of Proteobacteria phylum members. B. Relative abundance plotted against change in ACQ score for the bacterial taxa (n=362) found to be negatively correlated with changes in ACQ score (R = −0.5 to −0.8, Benjamini-Hochberg adjusted p < 0.05). Decreasing values of the change in ACQ are associated with greater relative abundance of predominantly members of the Actinobacteria followed by Firmicutes phyla. C. Total 16S rRNA copy numbers, a proxy for bacterial burden, are inversely correlated with absolute values of the change in ACQ (Spearman ρ = −0.54; p < 0.01).

Figure 3

A. Relative abundance plotted against changes in ACQ score for the bacterial taxa (n=448) found to be positively correlated with this parameter (R = 0.5 – 0.7, Benjamini-Hochberg adjusted p < 0.05). Increasing values of the change in ACQ are associated with greater relative abundance predominantly of Proteobacteria phylum members. B. Relative abundance plotted against change in ACQ score for the bacterial taxa (n=362) found to be negatively correlated with changes in ACQ score (R = −0.5 to −0.8, Benjamini-Hochberg adjusted p < 0.05). Decreasing values of the change in ACQ are associated with greater relative abundance of predominantly members of the Actinobacteria followed by Firmicutes phyla. C. Total 16S rRNA copy numbers, a proxy for bacterial burden, are inversely correlated with absolute values of the change in ACQ (Spearman ρ = −0.54; p < 0.01).

Figure 3

A. Relative abundance plotted against changes in ACQ score for the bacterial taxa (n=448) found to be positively correlated with this parameter (R = 0.5 – 0.7, Benjamini-Hochberg adjusted p < 0.05). Increasing values of the change in ACQ are associated with greater relative abundance predominantly of Proteobacteria phylum members. B. Relative abundance plotted against change in ACQ score for the bacterial taxa (n=362) found to be negatively correlated with changes in ACQ score (R = −0.5 to −0.8, Benjamini-Hochberg adjusted p < 0.05). Decreasing values of the change in ACQ are associated with greater relative abundance of predominantly members of the Actinobacteria followed by Firmicutes phyla. C. Total 16S rRNA copy numbers, a proxy for bacterial burden, are inversely correlated with absolute values of the change in ACQ (Spearman ρ = −0.54; p < 0.01).

Figure 4

A. Total 16S rRNA copy numbers, a proxy for bacterial burden, are inversely correlated with biopsy eosinophil cells numbers (R = − 0.50, p < 0.01). Data shown represent 29/40 subjects, including the subjects in whom array-based bacterial community profiling could not be performed due to low bacterial content. B. The relative abundance of bacterial taxa (n=83; q < 0.10) associated with FKBP5 expression plotted against the relative expression values for this gene. The phylum-level classification of these correlated communities are shown. C. Strongly significant correlations are seen between FKBP5 expression and two different measures of bacterial community diversity, Faith’s phylogenetic diversity and inverse Simpson diversity index. The former weights phylogenetic relationships among communities in the diversity determination, while the latter reflects primarily community richness and evenness.

Figure 4

A. Total 16S rRNA copy numbers, a proxy for bacterial burden, are inversely correlated with biopsy eosinophil cells numbers (R = − 0.50, p < 0.01). Data shown represent 29/40 subjects, including the subjects in whom array-based bacterial community profiling could not be performed due to low bacterial content. B. The relative abundance of bacterial taxa (n=83; q < 0.10) associated with FKBP5 expression plotted against the relative expression values for this gene. The phylum-level classification of these correlated communities are shown. C. Strongly significant correlations are seen between FKBP5 expression and two different measures of bacterial community diversity, Faith’s phylogenetic diversity and inverse Simpson diversity index. The former weights phylogenetic relationships among communities in the diversity determination, while the latter reflects primarily community richness and evenness.

Figure 4

A. Total 16S rRNA copy numbers, a proxy for bacterial burden, are inversely correlated with biopsy eosinophil cells numbers (R = − 0.50, p < 0.01). Data shown represent 29/40 subjects, including the subjects in whom array-based bacterial community profiling could not be performed due to low bacterial content. B. The relative abundance of bacterial taxa (n=83; q < 0.10) associated with FKBP5 expression plotted against the relative expression values for this gene. The phylum-level classification of these correlated communities are shown. C. Strongly significant correlations are seen between FKBP5 expression and two different measures of bacterial community diversity, Faith’s phylogenetic diversity and inverse Simpson diversity index. The former weights phylogenetic relationships among communities in the diversity determination, while the latter reflects primarily community richness and evenness.

Figure 5

Compositionally distinct bacterial taxa are positively associated with FKBP5 expression (green Fs; n=83; 58% Actinobacteria) and with the Th17-related gene expression pattern (red Ts; n=110; 72% Proteobacteria). The NMDS ordination is based on a Bray-Curtis distance matrix for all associated taxa in both expression patterns. Ellipses represent the 95% CI for the standard deviation of the distances for each group.

Figure 6

A. Heatmap of the ten taxa found significantly enriched among severe asthmatics (n=30) compared to healthy controls (n=7) (Benjamini-Hochberg padj < 0.15; R package limma) B. Heatmap of the 95 total taxa found to significantly differ in relative abundance between severe asthma (n=30) and mild-moderate asthma subjects (n=41) (≥ 2-fold-difference; Benjamini-Hochberg padj < 0.01; R package limma). Relative to the other group, severe asthmatics were enriched in 53 taxa, while mild-moderate asthmatics were conversely enriched in 42 taxa.

Figure 6

A. Heatmap of the ten taxa found significantly enriched among severe asthmatics (n=30) compared to healthy controls (n=7) (Benjamini-Hochberg padj < 0.15; R package limma) B. Heatmap of the 95 total taxa found to significantly differ in relative abundance between severe asthma (n=30) and mild-moderate asthma subjects (n=41) (≥ 2-fold-difference; Benjamini-Hochberg padj < 0.01; R package limma). Relative to the other group, severe asthmatics were enriched in 53 taxa, while mild-moderate asthmatics were conversely enriched in 42 taxa.