Abstract
BACKGROUND: Smokeless tobacco (ST) products are widely used throughout the world and contribute to morbidity and mortality in users through an increased risk of cancers and oral diseases. Bacterial populations in ST contribute to taste, but their presence can also create carcinogenic, Tobacco-Specific N-nitrosamines (TSNAs). Previous studies of microbial communities in tobacco products lacked chemistry data (e.g. nicotine, TSNAs) to characterize the products and identify associations between carcinogen levels and taxonomic groups. This study uses statistical analysis to identify potential associations between microbial and chemical constituents in moist snuff products. METHODS: We quantitatively analyzed 38 smokeless tobacco products for TSNAs using liquid chromatography with tandem mass spectrometry (LC-MS/MS), and nicotine using gas chromatography with mass spectrometry (GC-MS). Moisture content determinations (by weight loss on drying), and pH measurements were also performed. We used 16S rRNA gene sequencing to characterize the microbial composition, and additionally measured total 16S bacterial counts using a quantitative PCR assay. RESULTS: Our findings link chemical constituents to their associated bacterial populations. We found core taxonomic groups often varied between manufacturers. When manufacturer and flavor were controlled for as confounding variables, the genus Lactobacillus was found to be positively associated with TSNAs. while the genera Enteractinococcus and Brevibacterium were negatively associated. Three genera (Corynebacterium, Brachybacterium, and Xanthomonas) were found to be negatively associated with nicotine concentrations. Associations were also investigated separately for products from each manufacturer. Products from one manufacturer had a positive association between TSNAs and bacteria in the genus Marinilactibacillus. Additionally, we found that TSNA levels in many products were lower compared with previously published chemical surveys. Finally, we observed consistent results when either relative or absolute abundance data were analyzed, while results from analyses of log-ratio-transformed abundances were divergent.