Chung Hsing Li1, Salomon Amar. 1. Department of Periodontology and Oral Biology, Goldman School of Dental Medicine, Boston University, Boston, MA, USA.
Abstract
BACKGROUND: Porphyromonas gingivalis is recognized as one of the major periodontal pathogens in chronic periodontitis, a common infectious disease characterized by inflammation and destruction of periodontal tissues. Several animal models with P. gingivalis have been used in periodontitis studies. Additionally, multiple approaches have also been applied to measuring alveolar bone loss in periodontitis models, including histomorphometry, morphometry, and radiography. The aims of this study were to assess periodontal inflammatory lesions after P. gingivalis-induced periodontitis and use this model to compare three approaches for assessing alveolar bone loss. METHODS: Twelve-week-old male C57BL/6 mice were divided into two groups: 48 P. gingivalis-infected and 52 untreated control mice. Periodontitis was induced by wrapping P. gingivalis-soaked ligatures around the left maxillary second molar and changing the ligatures every other day. Mice were euthanized on days 0, 3, 7, and 10 after ligature placement, for a total of 12 experimental and 13 control mice per time point. Epithelial downgrowth, inflammation, and osteoclast activity were evaluated; alveolar bone loss was determined by histomorphometry, morphometry, and microcomputed tomography. RESULTS: The P. gingivalis-infected group showed significantly increased epithelial downgrowth (P <0.05), inflammation (P <0.05), alveolar bone loss (P <0.05), and osteoclast activity (P <0.05) throughout the experimental period compared to the controls. All three methods yielded efficient evaluation of alveolar bone loss. CONCLUSIONS: Our results show evidence that the P. gingivalis-soaked ligature-induced murine model mounts an adequate inflammatory response and exhibits periodontal tissue breakdown compatible with other models of periodontal disease. In addition, alveolar bone loss can accurately be quantified using any of the three alveolar bone analyses presented in this article.
BACKGROUND:Porphyromonas gingivalis is recognized as one of the major periodontal pathogens in chronic periodontitis, a common infectious disease characterized by inflammation and destruction of periodontal tissues. Several animal models with P. gingivalis have been used in periodontitis studies. Additionally, multiple approaches have also been applied to measuring alveolar bone loss in periodontitis models, including histomorphometry, morphometry, and radiography. The aims of this study were to assess periodontal inflammatory lesions after P. gingivalis-induced periodontitis and use this model to compare three approaches for assessing alveolar bone loss. METHODS: Twelve-week-old male C57BL/6 mice were divided into two groups: 48 P. gingivalis-infected and 52 untreated control mice. Periodontitis was induced by wrapping P. gingivalis-soaked ligatures around the left maxillary second molar and changing the ligatures every other day. Mice were euthanized on days 0, 3, 7, and 10 after ligature placement, for a total of 12 experimental and 13 control mice per time point. Epithelial downgrowth, inflammation, and osteoclast activity were evaluated; alveolar bone loss was determined by histomorphometry, morphometry, and microcomputed tomography. RESULTS: The P. gingivalis-infected group showed significantly increased epithelial downgrowth (P <0.05), inflammation (P <0.05), alveolar bone loss (P <0.05), and osteoclast activity (P <0.05) throughout the experimental period compared to the controls. All three methods yielded efficient evaluation of alveolar bone loss. CONCLUSIONS: Our results show evidence that the P. gingivalis-soaked ligature-induced murine model mounts an adequate inflammatory response and exhibits periodontal tissue breakdown compatible with other models of periodontal disease. In addition, alveolar bone loss can accurately be quantified using any of the three alveolar bone analyses presented in this article.
Authors: Jonathan G Messer; Stephanie La; Deborah E Kipp; Evelyn J Castillo; Joshua F Yarrow; Marda Jorgensen; Russell D Wnek; Donald B Kimmel; José Ignacio Aguirre Journal: Comp Med Date: 2019-10-01 Impact factor: 0.982