Omics-Based Mathematical Modeling Unveils Pathogenesis of Periodontitis in an Experimental Murine Model

Area of Science:

• Oral biology
• Immunology
• Systems biology

Background:

• Periodontitis is a complex, multifactorial disease influenced by bacterial and host genetic factors.
• While omics analyses have identified numerous risk factors, their individual impact on periodontitis pathogenesis remains unclear.

Purpose of the Study:

• To identify key factors in periodontitis pathogenesis.
• To quantitatively predict the influence of these factors on alveolar bone resorption using omics and mathematical modeling.

Main Methods:

• Periodontitis was induced in mice via tooth ligation.
• Alveolar bone resorption, gene expression (RNA sequencing), and gingival microbiome (16S rRNA sequencing) were assessed.
• A mathematical model using ordinary differential equations was constructed to simulate disease dynamics.

Main Results:

• Omics data identified critical bacterial and molecular players in periodontitis.
• The mathematical model, parameterized with experimental data, predicted that monocyte recruitment via bacteria-induced Toll-like receptor activation is the primary driver of alveolar bone resorption.
• Osteoblast-mediated osteoclast differentiation showed a lesser impact on bone integrity during periodontitis.

Conclusions:

• Bacterial-mediated Toll-like receptor activation and subsequent monocyte recruitment are the principal regulators of alveolar bone resorption in periodontitis.
• This study provides a quantitative framework for understanding periodontitis pathogenesis and identifying therapeutic targets.