Disordered microbial communities in the upper respiratory tract of cigarette smokers

Abstract

Cigarette smokers have an increased risk of infectious diseases involving the respiratory tract. Some effects of smoking on specific respiratory tract bacteria have been described, but the consequences for global airway microbial community composition have not been determined. Here, we used culture-independent high-density sequencing to analyze the microbiota from the right and left nasopharynx and oropharynx of 29 smoking and 33 nonsmoking healthy asymptomatic adults to assess microbial composition and effects of cigarette smoking. Bacterial communities were profiled using 454 pyrosequencing of 16S sequence tags (803,391 total reads), aligned to 16S rRNA databases, and communities compared using the UniFrac distance metric. A Random Forest machine-learning algorithm was used to predict smoking status and identify taxa that best distinguished between smokers and nonsmokers. Community composition was primarily determined by airway site, with individuals exhibiting minimal side-of-body or temporal variation. Within airway habitats, microbiota from smokers were significantly more diverse than nonsmokers and clustered separately. The distributions of several genera were systematically altered by smoking in both the oro- and nasopharynx, and there was an enrichment of anaerobic lineages associated with periodontal disease in the oropharynx. These results indicate that distinct regions of the human upper respiratory tract contain characteristic microbial communities that exhibit disordered patterns in cigarette smokers, both in individual components and global structure, which may contribute to the prevalence of respiratory tract complications in this population.

Figure 1. Comparison of bacterial community composition reveals that the upper airway microbiota is primarily structured by body habitat.

Unweighted UniFrac was used to generated distances between oropharynx (red), nasopharynx (pink) and fecal (blue) microbiome samples, then scatterplots were generated using Principal Coordinate Analysis. The percentage of variation explained by each PCoA is indicated on the axes. The differences among communities from different body sites was significant with p<0.001 (t-test with permutation). Fecal microbial communities were from .

Figure 2. Analysis of abundances of bacterial lineages demonstrates that oro- and nasopharyngeal bacterial communities cluster based on smoking status.

The relative abundance of each genus (rows) is shown by the key to the left of the figure. Communities are clustered by hierarchical clustering using complete linkage of Euclidean distance matrices. The number of times each split in the tree is seen in 1,000 bootstrapped samples is indicated at each node. The tree to the left of the heatmap groups genera together based on similarity of abundance profiles (i.e. if two genera are close in the tree, their abundance profiles across each airway site are similar).

Figure 3. Partitioning airway microbial communities by smoking status using Random Forrest.

Bacterial communities from each airway site were sorted by smoking status using the Random Forests trained algorithm and compared to guessing. Misclassification frequencies are plotted by airway site and side of body. RF = Random Forrest machine. Guess = guessing alone. The lower- and upper-most bars designate the lowest and highest value excluding outliers (defined as >1.5*IQR). The bottom and top of the green boxes denote the lower and upper hinge (close to 25% and 75% quantiles). The heavy black line designates the median misclassification frequency. The distribution of misclassification errors is significantly different between the two algorithms (P – value<2.2E-16 for all airway sites, Friedman Rank Sum test) and in all airway sites, Random Forests performs better than guessing (95% Confidence Interval: oropharynx right (−0.15–−0.13), oropharynx left (−0.20–−0.18); nasopharynx right (−0.23–−0.22), nasopharynx left (−0.22–−0.20).