The microbiome of the lung and its extracellular vesicles in nonsmokers, healthy smokers and COPD patients View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-04

AUTHORS

Hyun Jung Kim, You-Sun Kim, Kang-Hyun Kim, Jun-Pyo Choi, Yoon-Keun Kim, Sunmi Yun, Lokesh Sharma, Charles S Dela Cruz, Jae Seung Lee, Yeon-Mok Oh, Sang-Do Lee, Sei Won Lee

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease, and bacterial infection plays a role in its pathogenesis. Bacteria secrete nanometer-sized extracellular vesicles (EVs), which may induce more immune dysfunction and inflammation than the bacteria themselves. We hypothesized that the microbiome of lung EVs might have distinct characteristics depending on the presence of COPD and smoking status. We analyzed and compared the microbiomes of 13 nonsmokers with normal spirometry, 13 smokers with normal spirometry (healthy smokers) and 13 patients with COPD by using 16S ribosomal RNA gene sequencing of surgical lung tissue and lung EVs. Subjects were matched for age and sex in all groups and for smoking levels in the COPD and healthy smoker groups. Each group included 12 men and 1 woman with the same mean age of 65.5 years. In all groups, EVs consistently showed more operational taxonomic units (OTUs) than lung tissue. In the healthy smoker and COPD groups, EVs had a higher Shannon index and a lower Simpson index than lung tissue and this trend was more prominent in the COPD group. Principal component analysis (PCA) showed clusters based on sample type rather than participants' clinical characteristics. Stenotrophomonas, Propionibacterium and Alicyclobacillus were the most commonly found genera. Firmicutes were highly present in the EVs of the COPD group compared with other samples or groups. Our analysis of the lung microbiome revealed that the bacterial communities present in the EVs and in the COPD group possessed distinct characteristics with differences in the OTUs, diversity indexes and PCA clustering. More... »

PAGES

e316

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/emm.2017.7

DOI

http://dx.doi.org/10.1038/emm.2017.7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084849832

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28408748


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