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A highly efficient computational discrimination among Streptococcal species of periodontitis patients using 16S rRNA amplicons§
Korean J. Microbiol 2019;55(1):1-8
Published online March 31, 2019
© 2019 The Microbiological Society of Korea.

Nebras N. Al-Dabbagh1 , Hayder O. Hashim2 , and Mohammed Baqur S. Al-Shuhaib3*

1Department of Microbiology, College of Dentistry, University of Babylon, Babil 51001, Iraq, 2Department of Clinical Laboratory Sciences, College of Pharmacy, University of Babylon, Babil 51001, Iraq, 3Department of Animal Production, College of Agriculture, Al-Qasim Green University, Babil 51001, Iraq
Correspondence to: E-mail: mohammed79@agre.uoqasim.edu.iq; Tel.: +964-7707115693
Received December 17, 2018; Revised February 15, 2019; Accepted March 15, 2019.
Abstract
Due to the major role played by several species of Streptococcus in the etiology of periodontitis, it is important to assess the pattern of Streptococcus pathogenic pathways within the infected subgingival pockets using a bacterial specific 16S rRNA fragment. From the total of 50 patients with periodontitis included in the study, only 23 Streptococcal isolates were considered for further analyses, in which their 16S rRNA fragments were amplified and sequenced. Then, a comprehensive phylogenetic tree was constructed and in silico prediction was performed for the observed Streptococcal species. The phylogenetic analysis of the subgingival Streptococcal species revealed a high discrimination power of the 16S rRNA fragment to accurately identify three groups of Streptococcus on the species level, including S. salivarius (14 isolates), S. anginosus (5 isolates), and S. gordonii (4 isolates). The employment of state-of-art in silico tools indicated that each Streptococcal species group was characterized with particular transcription factors that bound exclusively with a different 16S rRNA-based secondary structure. In conclusion, the observed data of the present study provided in-depth insights into the mechanism of each Streptococcal species in its pathogenesis, which differ in each observed group, according to the differences in the 16S rRNA secondary structure it takes, and the consequent binding with its corresponding transcription factors. This study paves the way for further interventions of the in silico prediction, with the main conventional in vitro microbiota identification to present an interesting insight in terms of the gene expression pattern and the signaling pathway that each pathogenic species follows in the infected subgingival site.
Keywords : Streptococcus anginosus, Streptococcus gordonii, Streptococcus salivarius, in silico, periodontitis


March 2019, 55 (1)
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