Difference in oral microbial composition between chronic periodontitis patients with and without diabetic nephropathy

Prediction of delayed graft function by early salivary microbiota following kidney transplantation

Delayed graft function (DGF) is a frequently observed complication following kidney transplantation (KT). Our prior research revealed dynamic shifts in salivary microbiota post-KT with immediate graft function (IGF), yet its behavior during DGF remains unexplored. Five recipients with DGF and 35 recipients with IGF were enrolled. Saliva samples were collected during the perioperative period, and 16S rRNA gene sequencing was performed. The salivary microbiota of IGFs changed significantly and gradually stabilized with the recovery of renal function. The salivary microbiota composition of DGFs was significantly different from that of IGFs, although the trend of variation appeared to be similar to that of IGFs. Salivary microbiota that differed significantly between patients with DGF and IGF at 1 day after transplantation were able to accurately distinguish the two groups in the randomForest algorithm (accuracy = 0.8333, sensitivity = 0.7778, specificity = 1, and area under curve = 0.85), with Selenomonas playing an important role. Bacteroidales (Spearman's r =  - 0.4872 and p = 0.0293) and Veillonella (Spearmen's r =  - 0.5474 and p = 0.0125) were significantly associated with the serum creatinine in DGF patients. Moreover, the significant differences in overall salivary microbiota structure between DGF and IGF patients disappeared upon long-term follow-up. This is the first study to investigate the dynamic changes in salivary microbiota in DGFs. Our findings suggested that salivary microbiota was able to predict DGF in the early stages after kidney transplantation, which might help the perioperative clinical management and early-stage intervention of kidney transplant recipients. KEY POINTS: • Salivary microbiota on the first day after KT could predict DGF. • Alterations in salivary taxa after KT are related to recovery of renal function.

Deciphering the molecular nexus of BTG2 in periodontitis and diabetic kidney disease

OBJECTIVE

To investigate the role of BTG2 in periodontitis and diabetic kidney disease (DKD) and its potential underlying mechanism.

METHODS

Gene expression data for periodontitis and DKD were acquired from the Gene Expression Omnibus (GEO) database. Differential expression analysis identified co-expressed genes between these conditions. The Nephroseq V5 online nephropathy database validated the role of these genes in DKD. Pearson correlation analysis identified genes associated with our target gene. We employed Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks to elucidate potential mechanisms. Expression levels of BTG2 mRNA were examined using quantitative polymerase Chain Reaction (qPCR) and immunofluorescence assays. Western blotting quantified proteins involved in epithelial-to-mesenchymal transition (EMT), apoptosis, mTORC1 signaling, and autophagy. Additionally, wound healing and flow cytometric apoptosis assays evaluated podocyte migration and apoptosis, respectively.

RESULTS

Analysis of GEO database data revealed BTG2 as a commonly differentially expressed gene in both DKD and periodontitis. BTG2 expression was reduced in DKD compared to normal conditions and correlated with proteinuria. GSEA indicated enrichment of BTG2 in the EMT and mTORC1 signaling pathways. The PPI network highlighted BTG2's relevance to S100A9, S100A12, and FPR1. Immunofluorescence assays demonstrated significantly lower BTG2 expression in podocytes under high glucose (HG) conditions. Reduced BTG2 expression in HG-treated podocytes led to increased levels of EMT markers (α-SMA, vimentin) and the apoptotic protein Bim, alongside a decrease in nephrin. Lower BTG2 levels were associated with increased podocyte mobility and apoptosis, as well as elevated RPS6KB1 and mTOR levels, but reduced autophagy marker LC3.

CONCLUSION

Our findings suggest that BTG2 is a crucial intermediary gene linking DKD and periodontitis. Modulating autophagy via inhibition of the mTORC1 signaling pathway, and consequently suppressing EMT, may be pivotal in the interplay between periodontitis and DKD.

Current scientific evidence for why periodontitis should be included in diabetes management

This Perspective provides a brief summary of the scientific evidence for the two-way links between periodontal diseases and hyperglycemia (diabetes mellitus [DM] and pre-DM). It delivers in a nutshell current scientific evidence for manifestations of hyperglycemia on periodontal health status and effects of periodontal diseases on blood glucose levels and in turn incidence, progression, and complications of diabetes. Of outmost importance is presentation of scientific evidence for the potential of routine periodontal treatment to lower blood glucose levels, providing a novel, economical tool in DM management. Non-surgical periodontal treatment ("deep cleaning") can be provided by dental hygienists or dentists in general dental offices, although severe cases should be referred to specialists. Such therapy can decrease the costs of DM care and other health care costs for people with DM. The great importance of a healthy oral cavity free of infection and subsequent inflammation - especially periodontitis that if untreated will cause loosening and eventually loss of affected teeth - has largely gone unnoticed by the medical community as the health care curricula are largely void of content regarding the bi-directional links between oral health and systemic health, despite elevation of blood glucose levels being an integral part of the general systemic inflammation response. The importance of keeping disease-free, natural teeth for proper biting and chewing, smiling, self-esteem, and pain avoidance cannot be overestimated. Medical and dental professionals are strongly encouraged to collaborate in patient-centered care for their mutual patients with - or at risk for - hyperglycemia.

Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets

Background

2019 Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has already had a serious influence on human existence, causing a huge public health concern for countries all around the world. Because SARS-CoV-2 infection can be spread by contact with the oral cavity, the link between oral illness and COVID-19 is gaining traction. Through bioinformatics approaches, we explored the possible molecular mechanisms linking the COVID-19 and periodontitis to provide the basis and direction for future research.

Methods

Transcriptomic data from blood samples of patients with COVID-19 and periodontitis was downloaded from the Gene Expression Omnibus database. The shared differentially expressed genes were identified. The analysis of Gene Ontology, Kyoto Encyclopedia of Genesand Genomes pathway, and protein–protein interaction network was conducted for the shared differentially expressed genes. Top 5 hub genes were selected through Maximal Clique Centrality algorithm. Then mRNA-miRNA network of the hub genes was established based on miRDB database, miRTarbase database and Targetscan database. The Least absolute shrinkage and selection operator regression analysis was used to discover possible biomarkers, which were then investigated in relation to immune-related genes.

Results

Fifty-six shared genes were identified through differential expression analysis in COVID-19 and periodontitis. The function of these genes was enriched in regulation of hormone secretion, regulation of secretion by cell. Myozenin 2 was identified through Least absolute shrinkage and selection operator regression Analysis, which was down-regulated in both COVID-19 and periodontitis. There was a positive correlation between Myozenin 2 and the biomarker of activated B cell, memory B cell, effector memory CD4 T cell, Type 17 helper cell, T follicular helper cell and Type 2 helper cell.

Conclusion

By bioinformatics analysis, Myozenin 2 is predicted to correlate to the pathogenesis and immune infiltrating of COVID-19 and periodontitis. However, more clinical and experimental researches are needed to validate the function of Myozenin 2.