Divergent airway microbiomes in lung transplant recipients with or without pulmonary infection

Abstract

Background: Lung transplant (LTx) recipients are at increased risk for airway infections, but the cause of infection is often difficult to establish with traditional culture-based techniques. The objectives of the study was to compare the airway microbiome in LTx patients with and without ongoing airway infection and identify differences in their microbiome composition.

Methods: LTx recipients were prospectively followed with bronchoalveolar lavage (BAL) during the first year after transplantation. The likelihood of airway infection at the time of sampling was graded based on clinical criteria and BAL cultures, and BAL fluid levels of the inflammatory markers heparin-binding protein (HBP), IL-1β and IL-8 were determined with ELISA. The bacterial microbiome of the samples were analysed with 16S rDNA sequencing and characterized based on richness and evenness. The distance in microbiome composition between samples were determined using Bray-Curtis and weighted and unweighted UniFrac.

Results: A total of 46 samples from 22 patients were included in the study. Samples collected during infection and samples with high levels of inflammation were characterized by loss of bacterial diversity and a significantly different species composition. Burkholderia, Corynebacterium and Staphylococcus were enriched during infection and inflammation, whereas anaerobes and normal oropharyngeal flora were less abundant. The most common findings in BAL cultures, including Pseudomonas aeruginosa, were not enriched during infection.

Conclusion: This study gives important insights into the dynamics of the airway microbiome of LTx recipients, and suggests that lung infections are associated with a disruption in the homeostasis of the microbiome.

Keywords: Airway infection; Cytokines; Inflammation; Lung transplant recipients; Microbiome.

Fig. 1

Microbiome composition of the study samples. The figure shows the microbiome of all included study samples from each patient. The patient number is indicated above the bars, and each bar shows the relative abundance of the 10 most common bacterial genera in this study. The clinical status of the patient at the time of sampling is indicated below each bar

Fig. 2

Alpha-diversity of BALF samples. The microbiome composition of each sample was assessed based on phylogenetic diversity (faith; left panels), species richness (number of observed ASVs; middle panels) and ASV diversity (shannon index; right panels). Samples were compared based on clinical signs of infection at the time of sampling (a) and high versus low BALF-levels of the inflammatory markers HBP (b), IL-1β (C) or IL-8 (D). *p < 0.05, **p < 0.01

Fig. 3

Beta-diversity of the BALF samples. Principal coordinate analysis (PCoA) plots demonstrating the distance in the microbiome composition between samples classified as infection (grey) versus no infection (brown), and between samples with high (circles) versus low (triangles) levels of HBP. a Shows differences in abundance calculated with Bray–Curtis distance, where PEMANOVA analyses demonstrated a significant difference in the distance between both infection vs no infection samples (p < 0.05) and high vs low levels of HBP (p < 0.05). b Shows the phylogenetic distance between samples calculated with weighted UniFrac analyses, demonstrating a significant difference between infection vs no infection samples (p < 0.05) as well as between high vs low levels of HBP (p < 0.05)

Fig. 4

Enrichment of microbes during infection. Differential abundance analysis of samples graded as infection versus no infection was used to identify enriched organisms during infection. The figure shows ASVs with adjusted p-values < 0.01. Each circle represents an ASV, and all ASVs with a log2FoldChange above zero are significantly enriched during infection. The different colours represent different phyla