The respiratory tract microbial biogeography in alcohol use disorder

Abstract

Individuals with alcohol use disorders (AUDs) are at an increased risk of pneumonia and acute respiratory distress syndrome. Data of the lung microbiome in the setting of AUDs are lacking. The objective of this study was to determine the microbial biogeography of the upper and lower respiratory tract in individuals with AUDs compared with non-AUD subjects. Gargle, protected bronchial brush, and bronchoalveolar lavage specimens were collected during research bronchoscopies. Bacterial 16S gene sequencing and phylogenetic analysis was performed, and the alterations to the respiratory tract microbiota and changes in microbial biogeography were determined. The microbial structure of the upper and lower respiratory tract was significantly altered in subjects with AUDs compared with controls. Subjects with AUD have greater microbial diversity [ P < 0.0001, effect size = 16 ± 1.7 observed taxa] and changes in microbial species relative abundances. Furthermore, microbial communities in the upper and lower respiratory tract displayed greater similarity in subjects with AUDs. Alcohol use is associated with an altered composition of the respiratory tract microbiota. Subjects with AUDs demonstrate convergence of the microbial phylogeny and taxonomic communities between distinct biogeographical sites within the respiratory tract. These results support a mechanistic pathway potentially explaining the increased incidence of pneumonia and lung diseases in patients with AUDs.

Keywords: alcohol; biogeography; lung; microbiome; respiratory.

Fig. 1.

Chronic alcohol use is associated with increased α-diversity of the respiratory tract microbiota. The observed operational taxonomic unit (OUT) α-diversity of the respiratory tract microbial communities was compared between healthy controls and alcohol use disorder (AUD) cohorts at each distinct biogeographical site. We evaluated the effects of alcohol use on the respiratory tract microbiota at all three distinct sites combined (A), the upper airway (gargle) (B), the middle airway (brush) (C), and the lower airway (BAL) (D). Boxplots denote top quartile and bottom quartile. Whiskers are plotted by Tukey’s method, *P < 0.05 for healthy controls vs. AUD cohorts (AUD subjects, n = 16; control subjects, n = 23) via Mann-Whitney U-test. Data are representative of two sequencing runs, which make up the full cohort; n = 78.

Fig. 2.

Chronic alcohol use is associated with changes in the community composition of the respiratory tract microbiota. Unweighted β-diversity of the respiratory tract microbial communities was compared between healthy controls and AUD cohorts at each distinct biogeographical site. Unweighted UniFrac distances were plotted using nonmetric multidimensional scaling (NMDS) ordination. We evaluated the effects of alcohol use on the respiratory tract microbiota at the upper airway (gargle) (A), the middle airway (brush) (B), and the lower airway (BAL) (C). Points represent individual subject samples. Circles outline the 95% confidence intervals around the sample group centroid indicated by X. P values indicate significant clustering of healthy control and AUD cohorts (AUD, n = 16; control, n = 23) as calculated by ADONIS multivariate permutational analysis of variance. Data are representative of two sequencing runs, which makeup the full cohort; n = 78.

Fig. 3.

Chronic alcohol use alters individual OTU relative abundances of upper and lower airway microbiota. Taxa with significantly different relative abundance in AUD cohorts compared with controls at each distinct biogeographical site [gargle (A), brush (B), and BAL (C)] were determined using DESeq2. Bars represent the mean log2 fold change in relative abundance ± SE for AUD subjects compared with healthy controls as determined by DESeq2 analysis. All taxa are significantly differentially abundant following correction for multiple comparisons (Q < 0.05). Repeated taxonomic classifications represent different OTUs.

Fig. 4.

Chronic alcohol use is associated with increases in the relative read counts of genus-agglomerated OTUs classified to Gram-negative organisms. The number of sequencing reads of OTUs that map to known Gram-negative genera was significantly increased in AUD populations compared with controls in the gargle (A), brush (B), and BAL samples (C). Boxplot whiskers 95% confidence interval of the mean ± SE number of Gram-negative OTUs in the different sites for AUD and healthy controls; *P < 0.05 for healthy controls vs. AUD cohorts (AUD, n = 41; control, n = 37). D: differentially abundant, genus-agglomerated Gram-negative OTUs in each biogeographical site in AUD subjects over healthy controls.

Fig. 5.

Core microbiome of controls and AUD cohorts at each distinct sampling site. Core taxa present in 100% of subjects within a cohort were compared. Taxa in light gray were found in the full cohort of samples, taxa in dark gray were only found in AUD subject samples, and taxa in gray were only found in healthy control samples. Taxa found in each section are shown in Supplemental Table S1.

Fig. 6.

Chronic alcohol use is associated with a disrupted airway microbial biogeography. Mean unweighted UniFrac (phylogenetic) distances were computed for AUD and control subjects between each sample site. Between-site correlations in taxon relative abundance were estimated using Spearman and Kendall correlation coefficients. Gargle vs. brush (A, B, and C) sample shifts in community similarity between healthy control and AUD are plotted alongside brush vs. BAL (D, E, and F) and gargle vs. BAL (G, H, and I). Boxplots denote first and third quartiles. Whiskers are plotted by Tukey’s method. *P < 0.05 for healthy controls vs. AUD cohorts via Mann-Whitney U-test. Bars represent the median correlation magnitude of coabundant taxa between different sites for AUD and healthy controls. *P < 0.05 by Mann-Whitney U-test between healthy controls and AUD cohorts (AUD, n = 41; control, n = 37).

Fig. 7.

Core microbiome between biogeographical sites of controls and AUD cohorts. Core taxa present in 100% of subjects for each treatment condition were compared. Taxa in gray mesh were found in all of the samples, whereas taxa found in gray hatches were found in both the gargle and the brush, taxa in gray dashes were found in the brush and the BAL, and taxa in gray dots were found in the BAL and gargle. Similarly, taxa in dark gray were only found in gargle, taxa in gray were only found in BAL, and taxa in light gray were only found in brush samples. Taxa found in each section are shown in Supplemental Table S2.