Periodontitis is a chronic inflammatory disease induced by bacteria. Exposure of the host to periodontal pathogens and their virulence factors induces a state of hyporesponsiveness to subsequent stimulations, which is termed endotoxin tolerance. The role and mechanism of lipopolysaccharide (LPS)–tolerized monocytes in inflammatory responses in neutrophils are currently unclear. Here, conditioned supernatants were collected from THP-1 cells treated with or without repeated 1 μg/ml Porphyromonas gingivalis (P.gingivalis) LPS. The chemotactic response of freshly isolated neutrophils recruited by supernatants was determined by a transwell migration assay, which demonstrated a reduced migration of neutrophils stimulated with supernatants from tolerized THP-1 cells in comparison to non-tolerized THP-1 cells. In addition, there was a marked increase in reactive oxygen species (ROS) generation and a significant decrease in Caspase 3 activities in neutrophils treated with supernatants from THP-1 cells that were treated repeatedly with P.gingivalis LPS in comparison to single treatment. A cytokine antibody array was then used to assess cytokine expression patterns in THP-1 cells. In tolerized THP-1 cells, 43 cytokine (43/170) expression levels were decreased, including chemokine ligand 23 (CCL23) and IFN-γ, while 11 cytokine (11/170) expression levels were increased, such as death receptor 6 (DR6). Furthermore, there was decreased production of IFN-γ and epithelial neutrophil activating peptide-78 (ENA-78) in THP-1 cells after stimulation with repeated P. gingivalis LPS in comparison to single challenge, which was confirmed by ELISA. Therefore, P.gingivalis LPS- tolerized THP-1 cells were able to depress neutrophil chemotaxis and apoptosis, and contribute to respiratory burst, which might be related to the changes in cytokine expression patterns in THP-1 cells.

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1β, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis.