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Updated: Jan 23, 2026

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
Published on: August 25, 2018
Analysis of the effect of smoking on the buccal microbiome using next-generation sequencing technology
Sema Karabudak1, Oguz Ari1, Bengul Durmaz2
1Ankara Yildirim Beyazit University, Central Research and Application Center, Ankara, Turkey.
Purpose:
This study aimed to investigate the effect of smoking on the buccal microbiome and to analyse the descriptive ability of each of the seven hypervariable regions in their 16S rRNA genes.
Methodology:
Microbiome compositions of 40 buccal swab samples collected from smokers (n =20) and non-smokers (n =20) were determined using 16S rRNA sequencing. Seven different 16S rRNA hypervariable regions (V2, V3, V4, V6-7, V8 and V9) in each sample were amplified using the Ion Torrent 16S Metagenomics kit and were sequenced on the Ion S5 instrument.
Results:
Seven hypervariable regions in the 16S rRNA gene were successfully sequenced for all samples tested. The data obtained with the V2 region was found to be informative but the consensus data generated according to a number of operational taxonomic unit reads gathered from all seven hypervariable regions gave the most accurate result. At the phylum level, no statistically significant difference was found between smokers and non-smokers whereas relative abundances of Veillonella atypica, Streptococcus australis, Prevotella melaninogenica, Prevotella salivae and Rothia mucilaginosa showed significant increases in the smoker group (P-adj=0.05). Alpha diversity results did not show a significant difference between the two groups; however, beta diversity analysis indicated that samples of smoker and non-smoker groups had a tendency to be clustered within themselves.
Conclusion:
The results of the current study indicate that smoking is a factor influencing buccal microbiome composition. In addition, sequencing of all seven hypervariable regions yielded more accurate results than those with any of the single variable regions.
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