title: 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.
authors.affiliation: Department of Microbiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
authors.forename: Emily S
authors.initials: ES
authors.lastname: Charlson
authors.forename: Jun
authors.initials: J
authors.lastname: Chen
authors.forename: Rebecca
authors.initials: R
authors.lastname: Custers-Allen
authors.forename: Kyle
authors.initials: K
authors.lastname: Bittinger
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li
authors.forename: Rohini
authors.initials: R
authors.lastname: Sinha
authors.forename: Jennifer
authors.initials: J
authors.lastname: Hwang
authors.forename: Frederic D
authors.initials: FD
authors.lastname: Bushman
authors.forename: Ronald G
authors.initials: RG
authors.lastname: Collman
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D015373:Bacterial Typing Techniques; D004269:DNA, Bacterial / Q000235:genetics; D005260:Female; D006801:Humans; D008280:Magnetics; D008297:Male; D008875:Middle Aged; D016133:Polymerase Chain Reaction / Q000379:methods; D012336:RNA, Ribosomal, 16S / Q000378:metabolism*; D012137:Respiratory System / Q000382:microbiology*; D017422:Sequence Analysis, DNA / Q000379:methods; D012907:Smoking / Q000009:adverse effects*
chemical_list: D004269:DNA, Bacterial; D012336:RNA, Ribosomal, 16S