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. 2017 Jul 13;12(7):e0180859.
doi: 10.1371/journal.pone.0180859. eCollection 2017.

Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome

Affiliations

Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome

Marion Engel et al. PLoS One. .

Abstract

Background: Changes in microbial community composition in the lung of patients suffering from moderate to severe COPD have been well documented. However, knowledge about specific microbiome structures in the human lung associated with CT defined abnormalities is limited.

Methods: Bacterial community composition derived from brush samples from lungs of 16 patients suffering from different CT defined subtypes of COPD and 9 healthy subjects was analyzed using a cultivation independent barcoding approach applying 454-pyrosequencing of 16S rRNA gene fragment amplicons.

Results: We could show that bacterial community composition in patients with changes in CT (either airway or emphysema type changes, designated as severe subtypes) was different from community composition in lungs of patients without visible changes in CT as well as from healthy subjects (designated as mild COPD subtype and control group) (PC1, Padj = 0.002). Higher abundance of Prevotella in samples from patients with mild COPD subtype and from controls and of Streptococcus in the severe subtype cases mainly contributed to the separation of bacterial communities of subjects. No significant effects of treatment with inhaled glucocorticoids on bacterial community composition were detected within COPD cases with and without abnormalities in CT in PCoA. Co-occurrence analysis suggests the presence of networks of co-occurring bacteria. Four communities of positively correlated bacteria were revealed. The microbial communities can clearly be distinguished by their associations with the CT defined disease phenotype.

Conclusion: Our findings indicate that CT detectable structural changes in the lung of COPD patients, which we termed severe subtypes, are associated with alterations in bacterial communities, which may induce further changes in the interaction between microbes and host cells. This might result in a changed interplay with the host immune system.

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Conflict of interest statement

Competing Interests: HH is an employee of Synlab MVZ Gauting & IML red GmbH and received salaries from Synlab MVZ Gauting & IML red GmbH. This does not alter his adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Genus level community composition for COPD patients with and without abnormalities in CT and controls based on PCoA.
(a) Lung derived microbial community composition in severe subtype COPD patients, mild subtype cases and controls were compared based on principal coordinate 1 and 2. (b) Boxplot comparing principal coordinate 1 between case and control samples. (c) Boxplot comparing principal coordinate 1 for severe subtype and mild subtype COPD patients and control samples. P-values were computed based on two-sided Wilcoxon-Mann-Whitney tests after correction for confounding effects from samples processed during winter or summer, respectively.
Fig 2
Fig 2. Diversity of microbial communities derived from lungs of severe subtype cases and mild subtype and control individuals.
(a) Pielou’s evenness and (b) Chao richness of microbial communities on genus level for severe vs mild/control samples. P-values were computed based on two-sided Wilcoxon-Mann-Whitney tests after correction for confounding effects from samples processed during winter or summer, respectively. (c) Phylogenetic community composition based on 16S rRNA gene fragment sequences in bronchial brushing samples. Bars represent the relative abundance of the most abundant families (>5% in one sample). The remaining families are subsumed as others.
Fig 3
Fig 3. Heatmap of genera showing significantly different abundances between severe versus mild subtype cases and controls.
Genera showing significantly increased (a) or decreased (b) abundances in severe vs mild subtype cases and controls. Colors at the top represent COPD subtypes and controls. For each genus, samples were colored based on quartiles of non-zero abundances from light red to dark red. All Samples for which a genus was not present were colored in white.
Fig 4
Fig 4. Co-occurrence analysis of bacterial genera.
(a) Microbial correlation network of bacterial genera as a surrogate for bacterial interaction. Each node represents one microbial genus and shading colors (blue, red, green, yellow) highlight microbial communities. Coloring of nodes indicates differences between severe subtype versus mild subtype cases and controls (red colors: higher average abundance in severe subtype cases; blue colors: higher average abundance in mild subtype cases and controls). Communities containing ≤2 genera were not shown. (b) Heatmap showing spearman correlation coefficients between bacterial genera from the four microbial communities. Colors at the upper and left side of the figure indicate the community affiliation of respective genera.

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