Periodontitis is a chronic and multifactorial inflammatory disease that can lead to tooth loss. At present, the diagnosis for periodontitis is primarily based on clinical examination and radiographic parameters. Detecting the periodontal pathogens at the subgingival plaque requires skilled professionals to collect samples. Periodontal pathogens are also detected on various mucous membranes in patients with periodontitis. In this study, we characterized the oral microbiome profiles from buccal mucosa and supragingival space in a total of 272 healthy subjects as a control group, and periodontitis patients as a disease group. We identified 13 phyla, 193 genera, and 527 species and determined periodontitis-associated taxa. Porphyromonas gingivalis, Tannerella forsythia, Treponema denticolar, Filifactor alocis, Porphyromonas endodontalis, Fretibacterium fastiosum and Peptostreptococcus species were significantly increased in both the buccal mucosa and the supragingival space in periodontitis patients. The identified eight periodontitis-associated bacterial species were clinically validated in an independent cohort. We generated the prediction model based on the oral microbiome profiles using five machine learning algorithms, and validated its capability in predicting the status of patients with periodontitis. The results showed that the oral microbiome profiles from buccal mucosa and supragingival space can represent the microbial composition of subgingival plaque and further be utilized to identify potential microbial biomarkers for the diagnosis of periodontitis. Besides, bacterial community interaction network analysis found distinct patterns associated with dysbiosis in periodontitis. In summary, we have identified oral bacterial species from buccal and supragingival sites which can predict subgingival bacterial composition and can be used for early diagnosis of periodontitis. Therefore, our study provides an important basis for developing easy and noninvasive methods to diagnose and monitor periodontitis.

Kim HJ, et al. J Periodontal Implant Sci. 2022 Aug;52:e27. https://doi.org/10.5051/jpis.2202080104

White spot lesions (WSL) that occur on teeth after orthodontic appliances have been attached are caused by bacterial demineralization of the enamel surface. This study investigated the anti-demineralization effect of orthodontic resins containing mesoporous bioactive glass nanoparticles (MBN) doped with gallium, which has antibacterial activity, as well as MBN with increased calcium and phosphate contents as these ions can remineralize enamel. Resins (CF, CharmFill Flow, Dentkist, Seoul, South Korea) containing 1%, 3%, and 5% Ga-doped MBN (GaMBN) were characterized using scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and isothermal tests, and their physical properties were measured in terms of Vickers microhardness, bracket retention force, and adhesive remnant index (ARI). Cell viability in the resins was confirmed by testing human dental pulp stem cells (hDPSCs), and ion release tests were performed after 1, 7, and 14 days to determine whether the resins released Ga³⁺, Ca²⁺, and PO₄^3–. After 14 days, antibacterial activity was determined using Streptococcus mutans (S. mutans)—the bacteria that causes tooth decay—and the chemical remineralization effect was investigated using a cycle of acid–base solutions. The microhardness of the resins increased with GaMBN concentration whereas their bracket retention force, ARI, and cell viability remained unchanged. The bacterial activity of the 5%-GaMBN resin decreased after 24 and 48 h; however, the change in activity was not statistically significant. Anti-demineralization testing demonstrated that the degree of enamel demineralization decreased as the GaMBN concentration increased, which indicates that resins containing 5%-GaMBN may be viable orthodontic adhesives for preventing WSLs.

Periodontitis is a chronic inflammatory disease induced by periodontopathogens such as Porphyromonas gingivalis (P. gingivalis). MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that regulate gene expression at the level of translation. MiRNAs have been reported to be involved in inflammatory processes. In this study, we examined the effects of P. gingivalis-induced inflammatory miRNAs expression on TNFα production in THP-1 derived macrophages. Porphyromonas gingivalis induced the expression of miR-132. P. gingivalis-induced miR-132 expression was significantly inhibited by TLR2/4 knock-down and NF-κB inhibitor. Additionally, miR-132 antagomir strongly repressed production of TNFα. The expression of NFE2L2 and NFAT5, the putative target genes of miR-132 involved in regulation of TNFα, decreased in response to P. gingivalis. Furthermore, miR-132 antagomir rescued P. gingivalis-induced suppression of NFE2L2 and NFAT5. These results suggest that the induction of miR-132 by P. gingivalis can modulate the pathogenesis of periodontitis induced via regulatory expression of TNFα.