Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.
Keywords: children; dental caries; microbiome; next-generation sequencing.