Metagenomic investigation of bacteria associated with dental lesions: a cross-sectional study

Dental caries is considered as one of the most significant global health problem over the world. Dental caries initiates from bacterial shifts within the supragingival biofilm, then a polymicrobial biofilm is formed on the surface of tooth, and finally various bacterial species aggregate in a complex-organized manner. The exploiting variability in 16S rRNA gene sequence has been considered as a cost-efficient high-throughput characterization approach in human oral microbiome investigations. The aim of this study is to characterize bacterial species associated with superficial dental biofilm, underlying carious dentine and root caries lesion by16S rRNA gene-based metagenomic analysis. Herein, the bacterial communities in carious dentin lesion, biofilm and root canal samples of 30 subjects (aged 4-76 years) admitted to a clinic in Tehran during 2017 were investigated using a culture independent approach. Total genomic DNA of each tissue was subjected to metagenomic identification of bacteria using a nested PCR assay and 16S rRNA library construction method. 31 samples collected from 30 consenting patients (29 samples from 29 patients ant two biofilm samples from one patient). Bioinformatics analyses of a-800bp sequences of the second step of Nested-PCR revealed presence of 156 bacterial isolates in carious (n=45), biofilm (n=81) and root canal (n=30) specimens. Prevotella spp., Lactobacillus vaginalis, and streptococcus spp. showed higher prevalence in carious dentin, root and biofilm samples, respectively. Exploring the dental microbiota and comparing them in health or diseased conditions is critical step in the determination of human general health. The method applied in this study could identify bacteria related to the three dental lesions. However, due to lack of data for comparison in Genbank or because of the sequence similarity lower than 98% for most identified bacteria, the use of more powerful approaches like NGS platforms or typing of multiple loci (MLST) in future studies is recommended.