BackgroundWe aimed to investigate the potential genetic relationships between the polymorphisms of gene rs5498 ICAM-1 and rs1041163 VCAM-1 and chronic periodontitis in a Chinese population within Heilongjiang.Material/MethodsGenomic DNA was extracted from oral mucosa cells of 584 periodontal patients and 182 healthy individuals. Genotyping of the rs5498 ICAM-1 and rs1041163 VCAM-1 gene polymorphisms was performed with the Multiplex SNaPshot technique.ResultsStatistically significant associations were identified between the chronic periodontal patients and the controls in the gene polymorphisms of rs5498 ICAM-1 (P=0.007) and rs1041163 VCAM-1 (P=0.029). The distribution of rs5498 (P=0.029) and rs1041163 (P=0.049) differed significantly across the mild, moderate, and severe groups of periodontitis.ConclusionsOur findings indicate that ICAM-1 rs5498 and VCAM-1 rs1041163 polymorphisms contribute to chronic periodontitis, and ICAM-1 rs5498 and VCAM-1 rs1041163 gene polymorphisms might be associated with periodontitis severity in the Heilongjiang Chinese population. Further studies should be conducted to determine whether these polymorphisms could be used as biomarkers of periodontitis.
Background. Oxidative stress is implicated in the progression of many neurological diseases, which could be induced by various chemicals, such as hydrogen peroxide (H2O2) and acrylamide. Triphala is a well-recognized Ayurvedic medicine that possesses different therapeutic properties (e.g., antihistamine, antioxidant, anticancer, anti-inflammatory, antibacterial, and anticariogenic effects). However, little information is available regarding the neuroprotective effect of Triphala on oxidative stress. Materials and Methods. An in vitro H2O2-induced SH-SY5Y cell model and an in vivo acrylamide-induced zebrafish model were established. Cell viability, apoptosis, and proliferation were examined by MTT assay, ELISA, and flow cytometric analysis, respectively. The molecular mechanism underlying the antioxidant activity of Triphala against H2O2 was investigated dose dependently by Western blotting. The in vivo neuroprotective effect of Triphala on acrylamide-induced oxidative injury in Danio rerio was determined using immunofluorescence staining. Results. The results indicated that Triphala plays a neuroprotective role against H2O2 toxicity in inhibiting cell apoptosis and promoting cell proliferation. Furthermore, Triphala pretreatment suppressed the phosphorylation of the mitogen-activated protein kinase (MARK) signal pathway (p-Erk1/2, p-JNK1/2, and p-p38), whereas it restored the activities of antioxidant enzymes (superoxide dismutase 1 (SOD1) and catalase) in the H2O2-treated SH-SY5Y cells. Consistently, similar protective effects of Triphala were observed in declining neuroapoptosis and scavenging free radicals in the zebrafish central neural system, possessing a critical neuroprotective property against acrylamide-induced oxidative stress. Conclusion. In summary, Triphala is a promising neuroprotective agent against oxidative stress in SH-SY5Y cells and zebrafishes with significant antiapoptosis and antioxidant activities.
BackgroundPeriodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.MethodsTwo periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.ResultsWe produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.ConclusionThis study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.
AimThis study aims to identify pyroptosis-related genes (PRGs), their functional immune characteristics, and distinct pyroptosis-related clusters in periodontitis.MethodsDifferentially expressed (DE)-PRGs were determined by merging the expression profiles of GSE10334, GSE16134, and PRGs obtained from previous literatures and Molecular Signatures Database (MSigDB). Least absolute shrinkage and selection operator (LASSO) regression was applied to screen the prognostic PRGs and develop a prognostic model. Consensus clustering was applied to determine the pyroptosis-related clusters. Functional analysis and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological characteristics and immune activities of the clusters. The hub pyroptosis-related modules were defined using weighted correlation network analysis (WGCNA).ResultsOf the 26 periodontitis-related DE-PRGs, the highest positive relevance was for High-Mobility Group Box 1 (HMGB1) and SR-Related CTD Associated Factor 11 (SCAF11). A 14-PRG-based signature was developed through the LASSO model. In addition, three pyroptosis-related clusters were obtained based on the 14 prognostic PRGs. Caspase 3 (CASP3), Granzyme B (GZMB), Interleukin 1 Alpha (IL1A), IL1Beta (B), IL6, Phospholipase C Gamma 1 (PLCG1) and PYD And CARD Domain Containing (PYCARD) were dysregulated in the three clusters. Distinct biological functions and immune activities, including human leukocyte antigen (HLA) gene expression, immune cell infiltration, and immune pathway activities, were identified in the three pyroptosis-related clusters of periodontitis. Furthermore, the pink module associated with endoplasmic stress-related functions was found to be correlated with cluster 2 and was suggested as the hub pyroptosis-related module.ConclusionThe study identified 14 key pyroptosis-related genes, three distinct pyroptosis-related clusters, and one pyroptosis-related gene module describing several molecular aspects of pyroptosis in the pathogenesis and immune micro-environment regulation of periodontitis and also highlighted functional heterogeneity in pyroptosis-related mechanisms.
ObjectiveTo identify the genetic linkage mechanisms underlying Parkinson’s disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases.MethodsThe gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated.ResultsOverall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD.ConclusionFive genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.
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