Lung Microbiota Contribute to Pulmonary Inflammation and Disease Progression in Pulmonary Fibrosis

Abstract

Rationale: Idiopathic pulmonary fibrosis (IPF) causes considerable global morbidity and mortality, and its mechanisms of disease progression are poorly understood. Recent observational studies have reported associations between lung dysbiosis, mortality, and altered host defense gene expression, supporting a role for lung microbiota in IPF. However, the causal significance of altered lung microbiota in disease progression is undetermined. Objectives: To examine the effect of microbiota on local alveolar inflammation and disease progression using both animal models and human subjects with IPF. Methods: For human studies, we characterized lung microbiota in BAL fluid from 68 patients with IPF. For animal modeling, we used a murine model of pulmonary fibrosis in conventional and germ-free mice. Lung bacteria were characterized using 16S rRNA gene sequencing with novel techniques optimized for low-biomass sample load. Microbiota were correlated with alveolar inflammation, measures of pulmonary fibrosis, and disease progression. Measurements and Main Results: Disruption of the lung microbiome predicts disease progression, correlates with local host inflammation, and participates in disease progression. In patients with IPF, lung bacterial burden predicts fibrosis progression, and microbiota diversity and composition correlate with increased alveolar profibrotic cytokines. In murine models of fibrosis, lung dysbiosis precedes peak lung injury and is persistent. In germ-free animals, the absence of a microbiome protects against mortality. Conclusions: Our results demonstrate that lung microbiota contribute to the progression of IPF. We provide biological plausibility for the hypothesis that lung dysbiosis promotes alveolar inflammation and aberrant repair. Manipulation of lung microbiota may represent a novel target for the treatment of IPF.

Keywords: bleomycin; germ free; idiopathic pulmonary fibrosis; inflammation; lung microbiota.

Figure 1.

Alveolar bacterial burden predicts disease progression in idiopathic pulmonary fibrosis. (A) BAL was performed in 68 patients with idiopathic pulmonary fibrosis, and bacterial burden was quantified using droplet digital PCR of the 16S rRNA gene. Disease progression was defined by death, acute exacerbation, decline in lung function, or lung transplant. A Kaplan-Meier curve was generated by using a Cox proportional hazards model stratified by bacterial burden (displayed as tertile ranges; lowest tertile range, 9.76e+03–4.64e+04 16S copies; middle tertile range, 4.68E+04–1.31E+05 16S copies; highest tertile range, 1.31E+05–1.14E+07 16S copies). Log-rank P test value is reported. (B and C) Higher bacterial burden is associated with altered lung bacterial community structure or dysbiosis. (B) Redundancy analyses (RDA1, RDA2) of bacterial communities in all three BAL 16S tertile groups, with statistical testing by permutational multivariate ANOVA (PERMANOVA). (C) Increased bacterial burden is associated with a lower α-diversity (Shannon diversity index), as determined by Mann-Whitney U test with Dunn’s multiple comparisons.

Figure 2.

Alveolar inflammatory and fibrotic cytokines are disordered in idiopathic pulmonary fibrosis (IPF) and correlated with lung microbiota. Alveolar immunity was characterized using a multiplex ELISA of BAL fluid from 39 patients with IPF and 5 healthy volunteers. Lung bacteria were characterized using 16S rRNA gene sequencing from this subset of 39 patients with IPF. (A) As visualized via principal component analysis and tested using permutational multivariate ANOVA, alveolar cytokine concentrations were altered in patients with IPF compared with healthy control subjects (P = 0.009, permutational multivariate ANOVA). (B and C) These differences in alveolar immunity were driven by increased IL-1Ra in IPF (P < 0.0001; adjusted P = 0.04) and by decreased IL-15 in IPF (P < 0.0001; adjusted P = 0.0035). (D–I) Among patients with IPF, decreased diversity of lung bacteria was correlated with increased alveolar concentration of IL-1Ra (D) (P = 0.0015; adjusted P = 0.0058), IL-1β (E) (P < 0.0001; adjusted P = 0.0004), CXCL8 (C-X-C motif chemokine ligand 8)/IL-8 (F) (P = 0.0049; adjusted P = 0.015), MIP-1α (macrophage inflammatory protein-1α) (G) (P = 0.001; adjusted P = 0.0009), granulocyte colony–stimulating factor (G-CSF) (H) (P = 0.01; adjusted P = 0.02), and EGF (epidermal growth factor) (I) (P = 0.0081; adjusted P = 0.04). (J and K) Alveolar concentrations of IL-6 were positively correlated with relative abundance of the lung Firmicutes phyla (J) (P = 0.02; adjusted P = 0.001), whereas alveolar IL-12p70 was negatively correlated with relative abundance of lung Proteobacteria phylum (K) (P = 0.0056; adjusted P = 0.0014). Statistical significance was determined using univariate and multivariable logistic regression modeling of log-transformed cytokine data, adjusted for age and sex in healthy versus IPF model and univariate/multivariate linear regression models adjusted for age, sex, baseline pulmonary function, and smoking status in the IPF model. Benjamini-Hochberg correction was applied to account for multiple comparisons with a false discovery rate of 0.1 when applicable. Continuous variables were examined using an unpaired t test or Mann-Whitney U test when applicable.

Figure 3.

After bleomycin exposure, lung dysbiosis precedes peak lung injury and persists until the development of fibrosis. Adult (8–10 wk old) mice (C57BL/6) were challenged with bleomycin, after which their lung microbiota were quantified using droplet digital PCR and characterized using 16S rRNA gene sequencing. (A–C) After bleomycin instillation, acute inflammation peaks within 1 day were reflected in increased alveolar concentrations of inflammatory cytokines TNF-α (A) and IL-17 (B). Peak lung injury does not occur until 7 days after instillation (C). (D) Total bacterial burden in BAL fluid is unchanged in the week after bleomycin exposure. (E) Diversity of lung bacterial communities, as measured by community richness, increases after bleomycin and peaks before peak lung injury. (F and G) The community composition of lung bacteria is altered acutely after bleomycin instillation, reflected in the relative enrichment by the Firmicutes phylum (F) and principal component analysis (G). (H–M) At later time points, alterations in lung microbiota persist until the establishment of pulmonary fibrosis at 21 days. (H and I) Bacterial DNA burden (H) and community richness (I) did not differ significantly at 21 days. (J and K) After bleomycin, lung communities contained increased relative abundance of the Firmicutes phylum (J), with a nonsignificant but consistent decline in Bacteroidetes (K). (L and M) Lung communities of bleomycin-treated mice were significantly distinct from those of untreated mice and remained altered until the establishment of pulmonary fibrosis at 21 days. Significance was determined via ANOVA with the Holm-Sidak multiple comparisons test and the Kruskal-Wallis test with Dunn’s multiple comparisons. OTUs = operational taxonomic units; TNF-α = tumor necrosis factor-α. *P < 0.05, ***P < 0.001, and ****P < 0.0001.

Figure 4.

After bleomycin-induced lung injury, germ-free (GF) mice are protected from mortality and have altered lung immunity. Genetically identical (C57BL/6) GF and conventional mice were challenged with oropharyngeal bleomycin and compared in terms of mortality, fibrosis, and pulmonary inflammation. (A) Compared with conventional mice, GF mice were protected from mortality after bleomycin. The survival difference emerged between Days 10 and 21 after bleomycin exposure. (B and C) Despite this difference in mortality, GF mice had similar severity of pulmonary fibrosis as characterized histologically (B) and by lung concentration of hydroxyproline (C). (D) Pulmonary cytokines measured by multiplex ELISA differed according to microbiome across all four experimental groups. (E) After bleomycin exposure, GF and conventional mice differed in their cellular immunity. Lungs of GF mice contained increased numbers of FOXP3⁺ regulatory T cells (T-regs) with decreased numbers of T-helper type 1 (Th1) cells. Significance was determined by Kaplan-Meier analysis and batch effect testing, unpaired t test, permutational multivariate ANOVA or Mann-Whitney U test when applicable. *P < 0.05 and ***P < 0.001. Scale bar, 100 μm. Ctrl = control.