Full-length 16S rRNA sequencing reveals microbial characteristics in supragingival plaque of periodontitis patients

Background/Purpose

Periodontitis is a chronic inflammatory disease that disrupts oral microbial homeostasis and contributes to systemic inflammation. While previous studies have focused on subgingival microbiota, the role of supragingival plaque as an early microbial reservoir remains underexplored. Most previous studies have relied on short-read 16S rRNA sequencing but limited detailed classification. This study uses Third-Generation Sequencing (TGS) with full-length 16S rRNA sequencing and DADA2-based error correction to better characterize the supragingival microbiome in periodontitis.

Materials and methods

A total of 30 participants (15 periodontitis patients and 15 healthy controls) were recruited. Supragingival plaque samples were collected, and full-length 16S rRNA sequencing was performed using a TGS platform. Sequencing data were processed with DADA2 for error correction and taxonomic classification using the Human Oral Microbiome Database (HOMD), including diversity indices and Principal Component Analysis (PCA), which were conducted to compare microbial compositions between groups.

Results

The periodontitis group exhibited significantly higher microbial diversity (P < 0.05) and enrichment of key periodontal pathogens, including Porphyromonas, Prevotella, Fusobacterium, and Treponema. On the other hand, commensal bacteria such as Streptococcus and Neisseria were more abundant in the healthy group. PCA demonstrated distinct clustering patterns, indicating supragingival microbial shifts associated with disease progression.

Conclusion

This study provides the high-resolution microbial profiling of supragingival plaque in periodontitis using full-length 16S rRNA sequencing. The findings suggest that supragingival plaque may serve as an early-stage reservoir for periodontal pathogens, which leads to subgingival colonization and disease progression. It highlights the potential of microbial biomarkers in early diagnosis and suggests that targeted microbiome-based therapies could be developed to restore microbial balance in periodontal disease. This method offers greater precision in bacterial identification and future translational potential for personalized periodontal care.