In-Depth Multi-Approach Analysis of WGS Metagenomics Data Reveals Signatures Potentially Explaining Features in...

Background: Periodontitis is a chronic inflammatory disease mostly associated with Porphyromonas gingivalis infection and characterized by progressive destruction of the supporting structures of the tooth, including the gingiva, periodontal ligament and alveolar bone. However, the impact of other members of the periodontal microbiome on stage of the severity of the periodontitis remains largely uncharacterized. Methods: This exploratory study employs whole-genome shotgun (WGS) metagenomics to characterize the periodontal microbiome in patients suffering from mild and severe periodontitis, aiming to identify microbial signatures linked to disease severity via analysis of taxonomic composition, predicted metabolic pathways and metagenome-assembled genomes (MAGs). After initial selection, 28 adult patients with a computer tomography (CT)-confirmed diagnosis of mild and severe stage of periodontitis from 2 clinics were included in the research project. Results: Taxonomic analysis confirms the presence of various commensal and pathogenic bacteria detectable at the species level, especially belonging to so-called "red, orange and green periodontal complexes"-P. gingivalis, T. forsythia, C. rectus, and Capnocytophaga spp. that may contribute to disease heterogeneity. The conducted investigation suggests that non-microbial factors such as cardiovascular diseases and antibiotic usage in the last 6 months prior to the hospital admission could explain variance of disease progression and impact on severity. Analysis of microbial functional composition revealed metabolic traits showing positive correlations with severe stage of periodontitis. Robust network analysis suggested interactions between pathogenic bacteria of the red complex and other members of the periodontal microbiome. Conclusions: These findings underscore the multifactorial nature of periodontitis pathogenesis, highlighting the need for integrated approaches combining microbial, host, and environmental data to unravel drivers of disease progression. The study provides a foundation for future large-scale investigations into personalized diagnostic or therapeutic strategies.