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

Several studies have demonstrated that nuclear magnetic resonance (NMR) metabolic profiles can differentiate patients with caries from healthy individuals; however, these studies only identified individual metabolites. The present study aimed to identify a salivary metabolite biomarker panel for the diagnosis of early childhood caries (ECC). Saliva samples from children with and without caries were analyzed using NMR spectroscopy. Multivariate and univariate analyses were performed to identify the discriminating metabolites. Selected metabolites were further evaluated and used to detect ECC. The saliva samples of children with ECC were characterized based on the increased levels of formate, glycerophosphocholine, and lactate and reduced levels of alanine, glycine, isoleucine, lysine, proline, and tyrosine. The levels of these metabolites were significantly different from those in the control in the ECC subgroup according to caries severity and correlated with the number of decayed and filled teeth or surfaces. Subsequently, an optimal salivary metabolite biomarker panel comprising formate, lactate, proline, and glycine was developed. This panel exhibited a better diagnostic performance for ECC than a single metabolite. These results demonstrate that salivary metabolic signatures can reflect oral conditions associated with dental caries, thereby emphasizing the importance of distinct salivary metabolic profiles as potential biomarkers of ECC.

Porphyromonase gingivalis (P. gingivalis) is a type of bacteria that causes periodontitis, which is strongly correlated with systemic diseases such as diabetes. However, the effect of hyperglycemia on periodontitis are unclear. The present study examined the effects of high glucose levels on the response to P. gingivalis infection. The expression of P. gingivalis-induced interleukin-1β (IL-1β) and inflammasomes increased as the glucose concentration increased. High glucose conditions suppressed P. gingivalis–induced autophagy in human acute monocytic leukemia cell line (THP-1) macrophages. Zingerone increased autophagy and alleviated P. gingivalis-induced inflammatory response in THP-1 macrophages under high glucose conditions. In addition, P. gingivalis- induced inflammation in bone marrow-derived macrophages of diabetic mice was higher than in wild-type mice, but a zingerone treatment decreased the levels. Alveolar bone loss due to a P. gingivalis infection was significantly higher in diabetic mice than in wild-type mice. High-glucose conditions aggravated the inflammatory response to P. gingivalis infection by suppressing of autophagy, suggesting that autophagy induction could potentially to treat periodontitis in diabetes. Zingerone has potential use as a treatment for periodontal inflammation induced by P. gingivalis in diabetes patients.

(1) Background: Peptides are appealing as pharmacological materials because they are easily produced, safe, and tolerable. Despite increasing gum-care awareness, periodontitis is still prevalent and is influenced by factors like high sugar consumption, smoking, and aging. Porphyromonas gingivalis is considered a major etiologic agent of periodontitis and activates the NLR family pyrin domain containing 3 (NLRP3) but is absent in melanoma 2 (AIM2) inflammasomes, resulting in pro-inflammatory cytokine release. (2) Methods: We examined the anti-inflammatory effects of 18 peptides derived from human stromal cell-derived factor-1 (SDF-1) on THP-1 macrophages. Inflammation was induced by P. gingivalis, and the anti-inflammatory effects were analyzed using molecular biological techniques. In a mouse periodontitis model, alveolar bone resorption was assessed using micro-CT. (3) Results: Of the 18 SDF-1-derived peptides, S10 notably reduced IL-1β and TNF-α secretion. S10 also diminished the P. gingivalis-induced expression of NLRP3, AIM2, ASC (apoptosis-associated speck-like protein), caspase-1, and IL-1β. Furthermore, S10 attenuated the enhanced TLR (toll-like receptor) signaling pathway and decreased the phosphorylation of nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs). In addition, S10 mitigated alveolar bone loss in our P. gingivalis-induced mouse model of periodontitis. (4) Conclusions: S10 suppressed TLR/NF-κB/NLRP3 inflammasome signaling and the AIM2 inflammasome in our P. gingivalis-induced murine periodontitis model, which suggests that it has potential use as a therapeutic treatment for periodontitis.

Son M, et al. J Periodontal Implant Sci. 2023 Jun;53:e20. https://doi.org/10.5051/jpis.2300100005