Integrated Machine Learning and Bioinformatic Analyses Constructed a Network Between Mitochondrial Dysfunction and Immune Microenvironment of Periodontitis

Influence of METTL3 knockdown on PDLSC osteogenesis in E. coli LPS-induced inflammation

OBJECTIVE

This study aimed to investigate the effect of METTL3 knockdown on osteogenic differentiation of human periodontal ligament stem cells (PDLSCs) in the weak inflammation microenvironments, as well as the underlying mechanisms.

MATERIALS AND METHODS

PDLSCs were stimulated by lipopolysaccharide from Escherichia coli (E. coli LPS), followed by quantification of METTL3. METTL3 expression was assessed using RT-qPCR and Western blot analysis in periodontitis. METTL3 knockdown PDLSCs were stimulated with or without E. coli LPS. The evaluation included proinflammatory cytokines, osteogenic markers, ALP activity, and mineralized nodules. Bioinformatics analysis and Western blot determined the association between METTL3 and the PI3K/Akt pathway.

RESULTS

METTL3 was overexpressed in periodontitis. METTL3 knockdown in PDLSCs reduced proinflammatory cytokines, osteogenic markers, ALP activity, and mineralized nodules in both environments. Bioinformatics analysis suggested a link between METTL3 and the PI3K/Akt pathway. METTL3 knockdown inhibited PI3K/Akt signaling pathway activation.

CONCLUSION

METTL3 knockdown might inhibit osteogenesis in PDLSCs through the inactivation of PI3K/Akt signaling pathway. Concomitant findings might shed novel light on the roles and potential mechanisms of METTL3 in the LPS-stimulated inflammatory microenvironments of PDLSCs.

Leveraging genetics to investigate causal effects of immune cell phenotypes in periodontitis: a mendelian randomization study

Introduction

Immune cells are dynamic in the inflammatory environment and play a key role in eradicating periodontal pathogens, modulating immune responses, and instigating tissue destruction. Identifying specific immune cell phenotypes associated with periodontitis risk is essential for targeted immunotherapeutic interventions. However, the role of certain specific immune cell phenotypes in the development of periodontitis is unknown. Mendelian randomization offers a novel approach to reveal causality and address potential confounding factors through genetic instruments.

Methods

This two-sample Mendelian randomization study assessed the causal relationship between 731 immune cell phenotypes and periodontitis using the inverse variance weighting method with the GWAS catalog genetic database. Methodological robustness was ensured through Cochran's Q test, MR-Egger regression, MR-PRESSO, and Leave-One-Out analysis.

Results

14 immune cell phenotypes showed potential positive causal associations with periodontitis risk (p < 0.05), suggesting an increased risk, while 11 immune cell phenotypes exhibited potential negative causal associations (p < 0.05), indicating a reduced risk. No significant heterogeneity or pleiotropy was observed.

Conclusion

This study underscores certain immune cell types as potential periodontitis risk biomarkers, laying a theoretical foundation for future individualized treatment and precision medicine development.

Identification of biomarkers associated with ferroptosis in diabetic retinopathy based on WGCNA and machine learning

Objective: Diabetic retinopathy (DR) is a chronic progressive eye disease that affects millions of diabetic patients worldwide, and ferroptosis may contribute to the underlying mechanisms of DR. The main objective of this work is to explore key genes associated with ferroptosis in DR and to determine their feasibility as diagnostic markers. Methods: WGCNA identify the most relevant signature modules in DR. Machine learning methods were used to de-screen the feature genes. ssGSEA calculated the scoring of immune cells in the DR versus control samples and compared the associations with the core genes by Spearman correlation. Results: We identified 2,897 differential genes in DR versus normal samples. WGCNA found tan module to have the highest correlation with DR patients. Finally, 20 intersecting genes were obtained from differential genes, tan module and iron death genes, which were screened by LASSO and SVM-RFE method, and together identified 6 genes as potential diagnostic markers. qPCR verified the expression and ROC curves confirmed the diagnostic accuracy of the 6 genes. In addition, our ssGSEA scoring identified these 6 core genes as closely associated with immune infiltrating cells. Conclusion: In conclusion, we analyzed for the first time the potential link of iron death in the pathogenesis of DR. This has important implications for future studies of iron death-mediated pro-inflammatory immune mechanisms.

Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods

BACKGROUND

Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis.

METHODS

Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method.

RESULTS

A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes.

CONCLUSION

The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings.