The fermentation product 2,3-butanediol alters P. aeruginosa clearance, cytokine response and the lung microbiome

Abstract

Diseases that favor colonization of the respiratory tract with Pseudomonas aeruginosa are characterized by an altered airway microbiome. Virulence of P. aeruginosa respiratory tract infection is likely influenced by interactions with other lung microbiota or their products. The bacterial fermentation product 2,3-butanediol enhances virulence and biofilm formation of P. aeruginosa in vitro. This study assessed the effects of 2,3-butanediol on P. aeruginosa persistence, inflammatory response, and the lung microbiome in vivo. Here, P. aeruginosa grown in the presence of 2,3-butanediol and encapsulated in agar beads persisted longer in the murine respiratory tract, induced enhanced TNF-α and IL-6 responses and resulted in increased colonization in the lung tissue by environmental microbes. These results led to the following hypothesis that now needs to be tested with a larger study: fermentation products from the lung microbiota not only have a role in P. aeruginosa virulence and abundance, but also on the increased colonization of the respiratory tract with environmental microbes, resulting in dynamic shifts in microbiota diversity and disease susceptibility.

Figure 1

2,3-butanediol enhances virulence and persistence of P. aeruginosa in the respiratory tract. C57BL/6 mice were infected with agar-encapsulated PA14 grown in the presence of 2,3-butanediol or glucose and with beads with just these substrates. (a) Body weight following infection. (b) Lung weight. (c) P. aeruginosa counts from lung homogenates plated on MacConkey agar plates. (d) Bronchioalveolar lavage cellular composition. (e–f) HE-stained lung section two days following PA14 grown in 2,3-butanediol (e) and glucose (f); bar=200 μm. (g–i) Inflammatory cytokine expression in lung. Expression of mRNA of TNF-α (g), IL-1β (h), and IL-6 (i) was quantified by TaqMan Real-Time RT-PCR and normalized to GAPDH RNA. Data are shown as means±s.e.m. of five mice per group. ND=none detected. *P<0.05; **P<0.001.

Figure 2

Microbiome of the murine lung. (a) Taxonomic summary of lung microbiota. 16S rRNA gene sequences of lung bacterial community of 15 mice 3 days after infection with varying agar beads treatments, indicated by colored bars above the sample ID: PA14 with 2,3-butanediol (2,3-Bd+PA14, green); PA14 with glucose (Gluc+PA14, purple); 2,3-butanediol medium only (2,3-Bd, red); and glucose medium only (Gluc, blue). Each column represents a sample from one mouse. Bacteria are presented at the phyla level. (b) Principal Coordinates Analysis biplot of murine lung microbiomes. Beta diversity Principal Coordinates Analysis of a weighted UniFrac distance matrix from taxonomic composition within these samples. Only the first two axes are shown. Each circle represents one mouse, which is colored by agar beads treatment: PA14 with 2,3-butanediol (green; n=4); PA14 with glucose (purple; n=3); 2,3-butanediol medium only (red; n=4); and glucose medium only (blue; n=4). Gray circles represent taxa coordinates that are calculated based on mean relative abundance (size of circle) for each taxon in all 15 lung samples (1-Pseudomonas spp.; 2-Acinetobacter spp.; 3-Escherichia spp.; 4-Turicibacter spp.; 5-Staphylococcus spp.). The percentage of distribution described by each axis is as indicated. (c) Machine-learning analysis of murine lung microbiomes. Machine-learning analysis of lung samples binned into two groups based on agar treatment: Group A (2,3-butanediol with PA14; green; n=4) and Group B (glucose medium with PA14, 2,3-butanediol medium, and glucose medium; brown; n=11). Shown are the 15 highest correlated taxa (of the 593 taxa used to train the algorithm); the overall machine-learning error rate was 0.19 via nearest shrunken centroid method. (d). The average relative abundances of the 15 most predictive taxa from machine-learning analysis, and colored as in c.