Unveiling potential driver taxa in subgingival plaque and their roles in mediating periodontitis progression

Background: Periodontitis progression is accompanied by a succession of the oral microbiome. However, the dynamic microbial transitions that link different disease stages and contribute to disease progression remain incompletely understood. Objective: This study aims to identify microbial taxa that may serve as potential drivers underlying increasing severity of periodontitis. Design: Subgingival sample 16S rRNA gene sequencing data were reprocessed for quality control and taxonomic annotation. MaAsLin2 was used to identify microbial differences between groups, and co-occurrence networks were built based on the differential taxa. NetMoss algorithm was applied to identify key microbes driving the transition from health to periodontitis. Correlation and mediation analyses were used to assess the associations between these driver taxa and periodontal clinical indicators. Results: ) had higher relative abundance in the health group. The microbial co-occurrence networks in the periodontitis groups were progressively disrupted, characterized by reduced network robustness and heightened vulnerability in the Stage III and IV groups. The driver taxa probably influenced the severity of periodontitis through modulation of periodontal clinical indicators, with positive or negative correlations observed between these taxa and periodontal clinical indicators. Conclusions: are proposed as key driver taxa across different periodontal health conditions, and exhibited significant correlations with periodontal clinical indicators.