Causal relationships of infection with Helicobacter pylori and herpesvirus on periodontitis: A Mendelian randomization study.

Background: To explore the causal association between Helicobacter pylori (H. pylori) infection, herpesvirus infection and periodontitis (PD) from a genetic perspective using Mendelian randomization (MR). Methods: The PD data were derived from genome-wide association study (GWAS) from the Dental Endpoints (GLIDE) consortium, and the FinnGen Biobank provided data on H. pylori and herpesvirus infections. In addition, we examined GWAS data for subtypes of H. pylori and herpesvirus infection. Inverse variance weighting (IVW) was selected as a major analysis technique, and weighted median (WM), weighted model, simple model, and MR-Egger regression were added as supplementary methods. To verify the findings, the effects of pleiotropy and heterogeneity were assessed. Results: Genetically predicted H. pylori infection (OR = 0.914, 95%CI = 0.693-1.205, P = 0.523), anti-H. pylori VacA (OR = 0.973, 95%CI = 0.895-1.057, P = 0.515), anti-H. pylori CagA (OR = 1.072, 95%CI = 0.986-1.164; P = 0.102), Epstein-Barr virus (EBV) infection (OR = 1.026, 95%CI = 0.940-1.120, P = 0.567), Herpes simplex virus (HSV) infection (OR = 0.962, 95%CI = 0.883-1.048, P = 0.372), cytomegalovirus (CMV) infection (OR = 1.025, 95%CI = 0.967-1.088, P = 0.415), EBV nuclear antigen-1 (EBNA1) (OR = 1.061, 95%CI = 0.930-1.209, P = 0.378), EBV virus capsid antigen (VCA) (OR = 1.043, 95CI% = 0.890-1.222, P = 0.603), HSV-1 (OR = 1.251, 95%CI = 0.782-2.001, P = 0.351), HSV-2 (OR = 1.020, 95%CI = 0.950-1.096, P = 0.585), CMV IgG (OR = 0.990, 95CI% = 0.882-1.111, P = 0.861) were not associated with PD, indicated that H. pylori and herpesvirus infection had no causal relationship to PD. Reverse studies also found no cause effect of PD on H. pylori or herpesvirus infection. The results of the sensitivity analysis suggested the robustness of the MR results. Conclusion: This study offered preliminary proof that H. pylori and herpesvirus infections were not causally linked to PD, and vice versa. However, more robust instrumental variables (IVs) and larger samples of GWAS data were necessary for further MR analysis.

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis.

BACKGROUND: Although periodontitis has previously been reported to be linked with multiple sclerosis (MS), but the molecular mechanisms and pathological interactions between the two remain unclear. This study aims to explore potential crosstalk genes and pathways between periodontitis and MS. METHODS: Periodontitis and MS data were obtained from the Gene Expression Omnibus (GEO) database. Shared genes were identified by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Then, enrichment analysis for the shared genes was carried out by multiple methods. The least absolute shrinkage and selection operator (LASSO) regression was used to obtain potential shared diagnostic genes. Furthermore, the expression profile of 28 immune cells in periodontitis and MS was examined using single-sample GSEA (ssGSEA). Finally, real-time quantitative fluorescent PCR (qRT-PCR) and immune histochemical staining were employed to validate Hub gene expressions in periodontitis and MS samples. RESULTS: FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ genes were the shared genes between periodontitis, and MS. GO analysis revealed that the shared genes exhibited the greatest enrichment in response to molecules of bacterial origin. LASSO analysis indicated that CFI, DDIT4L, and FAM46C were the most effective shared diagnostic biomarkers for periodontitis and MS, which were further validated by qPCR and immunohistochemical staining. ssGSEA analysis revealed that T and B cells significantly influence the development of MS and periodontitis. CONCLUSIONS: FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ were the most important crosstalk genes between periodontitis, and MS. Further studies found that CFI, DDIT4L, and FAM46C were potential biomarkers in periodontitis and MS.

The role of non-coding RNAs in diabetes-induced osteoporosis.

Diabetes mellitus (DM) and osteoporosis are two major health care problems worldwide. Emerging evidence suggests that DM poses a risk for osteoporosis and can contribute to the development of diabetes-induced osteoporosis (DOP). Interestingly, some epidemiological studies suggest that DOP may be at least partially distinct from those skeletal abnormalities associated with old age or postmenopausal osteoporosis. The increasing number of DM patients who also have DOP calls for a discussion of the pathogenesis of DOP and the investigation of drugs to treat DOP. Recently, non-coding RNAs (ncRNAs) have received more attention due to their significant role in cellular functions and bone formation. It is worth noting that ncRNAs have also been demonstrated to participate in the progression of DOP. Meanwhile, nano-delivery systems are considered a promising strategy to treat DOP because of their cellular targeting, sustained release, and controlled release characteristics. Additionally, the utilization of novel technologies such as the CRISPR system has expanded the scope of available options for treating DOP. Hence, this paper explores the functions and regulatory mechanisms of ncRNAs in DOP and highlights the advantages of employing nanoparticle-based drug delivery techniques to treat DOP. Finally, this paper also explores the potential of ncRNAs as diagnostic DOP biomarkers.