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, 10 (10), 2435-46

Cigarette Smoking and the Oral Microbiome in a Large Study of American Adults

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Cigarette Smoking and the Oral Microbiome in a Large Study of American Adults

Jing Wu et al. ISME J.

Abstract

Oral microbiome dysbiosis is associated with oral disease and potentially with systemic diseases; however, the determinants of these microbial imbalances are largely unknown. In a study of 1204 US adults, we assessed the relationship of cigarette smoking with the oral microbiome. 16S rRNA gene sequencing was performed on DNA from oral wash samples, sequences were clustered into operational taxonomic units (OTUs) using QIIME and metagenomic content was inferred using PICRUSt. Overall oral microbiome composition differed between current and non-current (former and never) smokers (P<0.001). Current smokers had lower relative abundance of the phylum Proteobacteria (4.6%) compared with never smokers (11.7%) (false discovery rate q=5.2 × 10(-7)), with no difference between former and never smokers; the depletion of Proteobacteria in current smokers was also observed at class, genus and OTU levels. Taxa not belonging to Proteobacteria were also associated with smoking: the genera Capnocytophaga, Peptostreptococcus and Leptotrichia were depleted, while Atopobium and Streptococcus were enriched, in current compared with never smokers. Functional analysis from inferred metagenomes showed that bacterial genera depleted by smoking were related to carbohydrate and energy metabolism, and to xenobiotic metabolism. Our findings demonstrate that smoking alters the oral microbiome, potentially leading to shifts in functional pathways with implications for smoking-related diseases.

Figures

Figure 1
Figure 1
Overall oral microbiome composition according to smoking status (current, former and never) in 1204 individuals. The principal coordinate analysis was conducted based on the weighted UniFrac distance. Sixty-eight percent confidence ellipses were drawn using the panel.ellipse function (Lattice, R), and centroids represent the coordinate mean of the first and second axes. (a) Adjusting for data set, age and sex, there was a significant difference in composition according to smoking status (P=0.001). (b) When combining former and never smokers, there was a significant difference in composition between current and non-current smokers (P=0.001). Comparison of within (c) and between (d) group distances for all smoking categories indicated that never and former smokers are more alike than are the current smokers.
Figure 2
Figure 2
Cladogram representation of oral microbiome OTUs associated with smoking status. A red branch indicates a taxon or OTU enriched in current smokers and a green branch indicates a taxon or OTU depleted in current smokers, as detected in the DESeq2 analysis. The bars represent log2 fold changes of counts in current compared with never smokers; red bars indicate positive fold change and green bars indicate negative fold change. A total of 1158 OTUs are included in the cladogram, representing OTUs with at least two sequences in at least 30 subjects in the five major phyla; only OTUs with q<0.05 are colored. Cladogram was created using EvolView (Zhang et al., 2012).
Figure 3
Figure 3
Median relative abundance of selected taxa according to the number of cigarettes smoked per day and number of years since quitting. Plot (a) includes current and never smokers, while plot (b) includes former and current smokers. False discovery rate adjusted q-values were calculated based on meta-analysis P-values of correlations between relative abundance of taxa and number of cigarettes smoked per day or number of years since quitting.
Figure 4
Figure 4
Bacterial taxa associated with smoking status are related to several gene functional pathways. Bacterial gene functions were predicted from 16S rRNA gene-based microbial compositions using the PICRUSt algorithm to make inferences from KEGG annotated databases. Genus and KEGG pathway counts were normalized for DESeq2 size factors and adjusted for data set using the 'removeBatchEffect' function (limma). Spearman's correlation coefficients were estimated for each pairwise comparison of genus counts and KEGG pathway counts, adjusting for age and sex. Only KEGG pathways relating to carbohydrate, energy, xenobiotic and glycan metabolism and selected genera of interest are included in the heatmap; full lists of genera and KEGG pathways associated with smoking can be found in Supplementary Tables S4, S5 and S7.

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