Distinctive microbiota distribution from healthy oral to post-treatment apical periodontitis

Abstract

Post-treatment apical periodontitis (PoAP) occurs when root canal treatment has not adequately eliminated bacterial invasion and infection. Yet little is known about the bacterial composition and changes related to the etiology and pathogenesis of PoAP. In this study, clinical samples classified as root apex (HARD) and periapical granulation tissues (SOFT) were separately collected from 10 patients with PoAP. The microbiota of each sample was characterized by 16S rRNA gene sequencing, and the obtained dataset was coanalyzed with 20 NCBI sequence read archive (SRA) datasets of healthy oral (HO) and primary apical periodontitis (PAP). We observed 2522 operational taxonomic units (OTUs) belonging to 29 phyla, and all samples shared 86.5% of the sequence reads. The OTUs affiliated with Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and Actinobacteria, were identified as core microbiota, which accounted for nearly 90% of 16S rRNA sequences in all samples. However, the principal coordinates analysis (PCoA) of the beta diversity demonstrated that the three periapical statuses have distinct microbial compositions. Compared with HO and PoAP, Actinomyces has a significantly increased abundance in PAP. The microbial diversities in PoAP were significantly lower than those in the HO and PAP (p<0.05). The relative abundance of most bacterial taxa was decreasing, except that Clostridia and Synergistia were increased. Furthermore, we explored the potential metabolic differences of the microbial communities by KEGG pathway prediction. We revealed that the microbiota of PoAP might have a more active metabolic capacity, including carbohydrate metabolism, energy metabolism, and enzyme cofactor/carrier biosynthesis (p<0.05). Our study revealed that invasion of opportunistic pathogens such as Clostridia and Synergistia might play a significant role in PoAP, thus guiding the further study of complex microbial-host interactions and the development of more effective diagnostic and therapeutic methods.

Keywords: bacteria; bioinformation; metabolic pathway prediction; microbiota; next-generation sequencing; post-treatment apical periodontitis.