ObjectiveMicroorganisms play a key role in the initiation and progression of periodontal disease. Research studies have focused on seeking specific microorganisms for diagnosing and monitoring the outcome of periodontitis treatment. Large samples may help to discover novel potential biomarkers and capture the common characteristics among different periodontitis patients. This study examines how to screen and merge high-quality periodontitis-related sequence datasets from several similar projects to analyze and mine the potential information comprehensively.MethodsIn all, 943 subgingival samples from nine publications were included based on predetermined screening criteria. A uniform pipeline (QIIME2) was applied to clean the raw sequence datasets and merge them together. Microbial structure, biomarkers, and correlation network were explored between periodontitis and healthy individuals. The microbiota patterns at different periodontal pocket depths were described. Additionally, potential microbial functions and metabolic pathways were predicted using PICRUSt to assess the differences between health and periodontitis.ResultsThe subgingival microbial communities and functions in subjects with periodontitis were significantly different from those in healthy subjects. Treponema, TG5, Desulfobulbus, Catonella, Bacteroides, Aggregatibacter, Peptostreptococcus, and Eikenella were periodontitis biomarkers, while Veillonella, Corynebacterium, Neisseria, Rothia, Paludibacter, Capnocytophaga, and Kingella were signature of healthy periodontium. With the variation of pocket depth from shallow to deep pocket, the proportion of Spirochaetes, Bacteroidetes, TM7, and Fusobacteria increased, whereas that of Proteobacteria and Actinobacteria decreased. Synergistic relationships were observed among different pathobionts and negative relationships were noted between periodontal pathobionts and healthy microbiota.ConclusionThis study shows significant differences in the oral microbial community and potential metabolic pathways between the periodontitis and healthy groups. Our integrated analysis provides potential biomarkers and directions for in-depth research. Moreover, a new method for integrating similar sequence data is shown here that can be applied to other microbial-related areas.

The metabolomics analysis by gas chromatography-mass spectrometry is an effective and minimally non-invasive way to differentiate the metabolites characteristic of patients with GAgP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GAgP and healthy controls. These metabolic profiles have great potential in detecting GAgP and helping to understand its underlying mechanisms.