Influence of cigarette smoking on the human duodenal mucosa-associated microbiota

Background: Cigarette smoking is a known risk factor in a number of gastrointestinal (GI) diseases in which the microbiota is implicated, including duodenal ulcer and Crohn's disease. Smoking has the potential to alter the microbiota; however, to date, the impact of smoking on the mucosa-associated microbiota (MAM), and particularly that of the upper GI tract, remains very poorly characterised. Thus, we investigated the impact of smoking on the upper small intestinal MAM. A total of 102 patients undergoing upper GI endoscopy for the assessment of GI symptoms, iron deficiency, or Crohn's disease, but without identifiable lesions in the duodenum, were recruited. Smoking status was determined during clinical assessment and patients classified as current (n = 21), previous smokers (n = 40), or having never smoked (n = 41). The duodenal (D2) MAM was profiled via 16S rRNA gene amplicon sequencing. Results: Smoking, both current and previous, is associated with significantly reduced bacterial diversity in the upper small intestinal mucosa, as compared to patients who had never smoked. This was accompanied by higher relative abundance of Firmicutes, specifically Streptococcus and Veillonella spp. The relative abundance of the genus Rothia was also observed to be greater in current smokers; while in contrast, levels of Prevotella and Neisseria were lower. The MAM profiles and diversity of previous smokers were observed to be intermediate between current and never smokers. Smoking did not impact the total density of bacteria present on the mucosa. Conclusions: These data indicate the duodenal MAM of current smokers is characterised by reduced bacterial diversity, which is partially but not completely restored in previous smokers. While the precise mechanisms remain to be elucidated, these microbiota changes may in some part explain the adverse effects of smoking on mucosa-associated diseases of the GI tract. Smoking status requires consideration when interpreting MAM data.