Relationship between periodontal inflammation and calcium channel blockers induced gingival overgrowth—a cross-sectional study in a Japanese population

Epithelial-to-mesenchymal transition, inflammation, subsequent collagen production, and reduced proteinase expression cooperatively contribute to cyclosporin-A-induced gingival overgrowth development

Drug-induced gingival overgrowth (DIGO), induced by certain immunosuppressive drugs, antihypertensive agents, and antiepileptic drugs, may contribute to the formation of deeper periodontal pockets and intractableness in periodontitis. To date, multiple factors such as enhanced matrix production, inflammation, and reduced matrix degradation might be involved in the pathogenesis of DIGO. We have previously reported that SPOCK-1, a heparan sulfate proteoglycan, could affect gingival thickening by promoting epithelial-to-mesenchymal transition (EMT) in gingival keratinocytes. However, few studies have investigated whether a combination of these factors enhances the DIGO phenotype in animal models. Therefore, we investigated whether SPOCK-1, periodontal inflammation, and cyclosporin-A (CsA) could cooperatively promote gingival overgrowth. We first confirmed that Spock-1 overexpressing (Spock1-Tg) mice showed significantly thicker gingiva and greater alveolar bone loss than WT mice in response to ligature-induced experimental periodontitis. DIGO was induced by the combination of CsA administration and experimental periodontitis was significantly enhanced in Spock1-Tg mice compared to that in WT mice. Ligature-induced alveolar bone loss in CsA-treated Spock1-Tg mice was also significantly greater than that in CsA-treated WT mice, while being accompanied by an increase in Rankl and Col1a1 levels and a reduction in matrix metalloprotease expression. Lastly, SPOCK-1 promoted RANKL-induced osteoclast differentiation in both human peripheral blood mononuclear cells and murine macrophages, while peritoneal macrophages from Spock1-Tg mice showed less TNFα and IL-1β secretion than WT mice in response to Escherichia coli lipopolysaccharide. These results suggest that EMT, periodontal inflammation, and subsequent enhanced collagen production and reduced proteinase production contribute to CsA-induced DIGO pathogenesis.

Impact of Immunosuppressive Drugs on Fibroblasts: An In Vitro Study

Background: The aim of this study was to compare the direct impact of different agents for immunosuppressive therapy on mouse fibroblasts as a possible cause of drug-induced gingival overgrowth (DIGO). Methods: 3T3 mouse fibroblasts were cultivated in cell-specific media (2 × 104 cells/mL) and treated for 6, 24, 48 and 72 h with one of three immunosuppressive drugs (IsDs): cyclosporin a (CsA), tacrolimus (TaC) and sirolimus (SiR). Different concentrations (10−750 ng/mL) were used to mimic serum levels under active immunosuppressive therapy conditions. Cell population characteristics (cell number, viability and morphology) were assessed using computer-assisted cell analysis. Expression of pro-collagen type I carboxy-terminal propeptide (PICP) was identified using an ELISA assay. Results: The influence of IsDs on the biological status of 3T3 fibroblasts was time- and dose-dependent. Comparing CsA and TaC, the total cell amount was enhanced using concentrations in the range of 10−150 ng/mL (p > 0.05). In contrast, treatment with SiR resulted in a decrease in the average cell number (p < 0.01). PICP and cell diameter of fibroblasts were not susceptible to IsD treatment (p > 0.05). Conclusions: Our results revealed time-dependent effects of IsDs, with distinct influences on cell number. The cell morphology and the PICP balance of the investigated fibroblast cell line remained unaffected. Hence, the potential role of IsDs is not a unilateral mechanism of action but rather a multifactorial process.

The role of nuclear receptor 4A1 (NR4A1) in drug-induced gingival overgrowth

Drug-induced gingival overgrowth (DIGO) is a side effect of cyclosporine A (CsA), nifedipine (NIF), and phenytoin (PHT). Nuclear receptor 4A1 (NR4A1) plays a role in fibrosis in multiple organs. However, the relationship between NR4A1 and DIGO remains unclear. We herein investigated the involvement of NR4A1 in DIGO. In the DIGO mouse model, CsA inhibited the up-regulation of Nr4a1 expression induced by periodontal disease (PD) in gingival tissue, but not that of Col1a1 and Pai1. We detected gingival overgrowth (GO) in Nr4a1 knock out (KO) mice with PD. A NR4A1 agonist inhibited the development of GO in DIGO model mice. TGF-β increased Col1a1 and Pai1 expression levels in KO mouse gingival fibroblasts (mGF) than in wild-type mice, while the overexpression of NR4A1 in KO mGF suppressed the levels. NR4A1 expression levels in gingival tissue were significantly lower in DIGO patients than in PD patients. We also investigated the relationship between nuclear factor of activated T cells (NFAT) and NR4A1. NFATc3 siRNA suppressed the TGF-β-induced up-regulation of NR4A1 mRNA expression in human gingival fibroblasts (hGF). CsA suppressed the TGF-β-induced translocation of NFATc3 into the nuclei of hGF. Furthermore, NIF and PHT also decreased NR4A1 mRNA expression levels and suppressed the translocation of NFATc3 in hGF. We confirmed that CsA, NIF, and PHT reduced cytosolic calcium levels increased by TGF-β, while CaCl₂ enhanced the TGF-β-up-regulated NR4A1 expression. We propose that the suppression of the calcium-NFATc3-NR4A1 cascade by these three drugs plays a role in the development of DIGO.

Prospective Longitudinal Changes in the Periodontal Inflamed Surface Area Following Active Periodontal Treatment for Chronic Periodontitis

Periodontal disease is a chronic inflammatory disease of the periodontal tissue. The periodontal inflamed surface area (PISA) is a proposed index for quantifying the inflammatory burden resulting from periodontitis lesions. This study aimed to investigate longitudinal changes in the periodontal status as evaluated by the PISA following the active periodontal treatment. To elucidate the prognostic factors of PISA, mixed-effect modeling was performed for clinical parameters, tooth-type, and levels of periodontal pathogens as independent variables. One-hundred-twenty-five patients with chronic periodontitis who completed the active periodontal treatment were followed-up for 24 months, with evaluations conducted at 6-month intervals. Five-times repeated measures of mean PISA values were 130+/−173, 161+/−276, 184+/−320, 175+/−417, and 209+/−469 mm². Changes in clinical parameters and salivary and subgingival periodontal pathogens were analyzed by mixed-effect modeling. Plaque index, clinical attachment level, and salivary levels of Porphyromonas gingivalis were associated with changes in PISA at the patient- and tooth-level. Subgingival levels of P. gingivalis and Prevotella intermedia were associated with changes in PISA at the sample site. For most patients, changes in PISA were within 10% of baseline during the 24-month follow-up. However, an increase in the number of bleeding sites in a tooth with a deep periodontal pocket increased the PISA value exponentially.

Estimation of the Periodontal Inflamed Surface Area by Simple Oral Examination

The periodontal inflamed surface area (PISA) is a useful index for clinical and epidemiological assessments, since it can represent the inflammation status of patients in one contentious variable. However, calculation of the PISA is difficult, requiring six point probing depth measurements with or without bleeding on probing on 28 teeth, followed by data input in a calculation program. More simple methods are essential for screening periodontal disease or in epidemiological studies. In this study, we tried to establish a convenient partial examination method to estimate PISA. Cross-sectional data of 254 subjects who completed active periodontal therapy were analyzed. Teeth that represent the PISA value were selected by an item response theory approach. The maxillary second molar, first premolar, and lateral incisor and the mandibular second molar and lateral incisor were selected. The sum of the PISAs of these teeth was significantly correlated with the patient’s PISA (R² = 0.938). More simply, the sum of the maximum values of probing pocket depth with bleeding for these teeth were also significantly correlated with the patient’s PISA (R² = 0.6457). The simple model presented in this study may be useful to estimate PISA.