Identification of Neuropeptides as Potential Crosstalks Linking Down Syndrome and Periodontitis Revealed by Transcriptomic Analyses

Association of Down syndrome with periodontal diseases: Systematic review and meta-analysis

BACKGROUND

Down syndrome (DS) is distinguished by cognitive disability, a concave profile, and systemic complications. Oral diseases have been reported to be common in DS patients.

OBJECTIVE

To investigate the association between DS and periodontal diseases.

METHODS

Two independent reviewers searched six bibliographic databases up to January 2023 and used additional search methods to identify published studies on gingivitis or periodontitis in people with and without DS. Meta-analysis, risk of bias, sensibility analysis, publication bias, and evidence grading were all carried out.

RESULTS

Twenty-six studies were included for analysis. There was a tendency for increased plaque accumulation, periodontal probing, periodontal attachment level, bleeding on probing and indices in DS individuals. Meta-analysis of 11 studies showed a significant association between DS and periodontitis (OR 3.93; 95% CI 1.81-8.53). Probing depth was significantly high in individuals with DS as compared to controls (mean difference 0.40 mm; 95% CI 0.09-0.70). Gingivitis was significantly associated (OR 1.93; 95% CI 1.09-3.41) with DS in four studies. The evidence was classified as 'moderate certainty'.

CONCLUSION

Medium/low-quality studies demonstrate that Down syndrome is strongly associated with periodontitis and moderately associated with gingivitis.

Network pharmacology-based prediction and validation of the active ingredients and potential mechanisms of the Huangxiong formula for treating ischemic stroke

ETHNOPHARMACOLOGICAL RELEVANCE

Huangxiong Formula (HXF) is composed of four herbs: Rheum palmatum L., Ligusticum striatum DC., Curcuma aromatica Salisb., and Acorus gramineus Aiton. HXF is clinically used for the treatment of ischemic stroke (IS). However, its molecular mechanism remains unclear.

AIM OF THE STUDY

A network pharmacology-based strategy combined with experimental study in vivo and in vitro to were used to investigate the bioactive components, potential targets, and molecular mechanisms of HXF in the treatment of IS.

MATERIALS AND METHODS

The components of HXF were detected by ultra-performance liquid chromatography (UPLC). The potential active ingredients of HXF were acquired from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and literature, and corresponding targets were discerned through the Swiss TargetPrediction database. IS-related targets were obtained from Genecards, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), and DisGeNET. The intersection of ingredient and disease targets was screened, and a herbal-compound-target network was constructed. A protein-protein interaction (PPI) network was created, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Based on these analyses, we established a compound-target-pathway (C-T-P) network. A cerebral ischemia-reperfusion (I/R) animal model was established, and the cerebral protective effect of HXF was assessed. The accuracy of the predicted targets was verified by real-time quantitative polymerase chain reaction (RT-qPCR). Hippocampal neuronal injury cell model induced by oxygen-glucose deprivation and reperfusion (OGD/R) was used to evaluate the protective effect of α-Asarone. Furthermore, molecular docking, drug affinity responsive target stability (DARTS) assay, and cellular thermal shift assay (CETSA) were performed to verify whether α-Asarone can bind to PI3K.

RESULTS

A total of 44 active ingredients and 795 gene targets were identified through network pharmacology. Network analysis showed that naringenin, eupatin, kaempferol, and α-Asarone were possible drug candidates. SRC, AKT1, TP53, MAPK3, STAT3, HRAS, CTNNB1, EGFR, VEGFA, PIK3R1 could serve as potential drug targets. KEGG analysis implied that the PI3K/AKT signaling pathway might play an important role in treating IS by HXF. Moreover, HXF significantly reduced neurological impairment, cerebral infarct volume, brain index, and brain histopathological damage in I/R rats. The mRNA expression of the top 10 potential targets was verified in the brain tissue. The C-T-P network and UPLC analysis suggested that α-Asarone might be an important component of HXF and can inhibit oxidative stress and apoptosis in HT22 cells by activating the PI3K/AKT signaling pathway. Molecular docking, DARTS, and CETSA assay analysis confirmed that there were direct interactions between α-Asarone and PI3K.

CONCLUSION

HXF had a therapeutic effect in IS with multi-component, multi-target, and multi-approach features. α-Asarone, identified as one of the major active components of HXF, could alleviate oxidative stress and apoptosis by targeting PI3K/AKT pathway.

Discovering genetic linkage between periodontitis and type 1 diabetes: A bioinformatics study

Background: Relationship between periodontitis (PD) and type 1 diabetes (T1D) has been reported, but the detailed pathogenesis requires further elucidation. This study aimed to reveal the genetic linkage between PD and T1D through bioinformatics analysis, thereby providing novel insights into scientific research and clinical treatment of the two diseases.

Methods: PD-related datasets (GSE10334, GSE16134, GSE23586) and T1D-related datasets(GSE162689)were downloaded from NCBI Gene Expression Omnibus (GEO). Following batch correction and merging of PD-related datasets as one cohort, differential expression analysis was performed (adjusted p-value <0.05 and ∣log2  fold change| > 0.5), and common differentially expressed genes (DEGs) between PD and T1D were extracted. Functional enrichment analysis was conducted via Metascape website. The protein-protein interaction (PPI) network of common DEGs was generated in The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Hub genes were selected by Cytoscape software and validated by receiver operating characteristic (ROC) curve analysis.

Results: 59 common DEGs of PD and T1D were identified. Among these DEGs, 23 genes were commonly upregulated, and 36 genes were commonly downregulated in both PD- and T1D-related cohorts. Functional enrichment analysis indicated that common DEGs were mainly enriched in tube morphogenesis, supramolecular fiber organization, 9 + 0 non-motile cilium, plasma membrane bounded cell projection assembly, glomerulus development, enzyme-linked receptor protein signaling pathway, endochondral bone morphogenesis, positive regulation of kinase activity, cell projection membrane and regulation of lipid metabolic process. After PPI construction and modules selection, 6 hub genes (CD34, EGR1, BBS7, FMOD, IGF2, TXN) were screened out and expected to be critical in linking PD and T1D. ROC analysis showed that the AUC values of hub genes were all greater than 70% in PD-related cohort and greater than 60% in T1D-related datasets.

Conclusion: Shared molecular mechanisms between PD and T1D were revealed in this study, and 6 hub genes were identified as potential targets in treating PD and T1D.

Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses

Objective

To 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.

Methods

The 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.

Results

Overall, 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.

Conclusion

Five 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.

Exploration of cross-talk and pyroptosis-related gene signatures and molecular mechanisms between periodontitis and diabetes mellitus via peripheral blood mononuclear cell microarray data analysis

OBJECTIVES

This bioinformatics study is aimed at identifying cross-talk genes, pyroptosis-related genes, and related pathways between periodontitis (PD) and diabetes mellitus (DM), which includes type 1 diabetes (T1DM) and type 2 diabetes (T2DM).

METHODS

GEO datasets containing peripheral blood mononuclear cell (PBMC) data of PD and DM were acquired. After batch correction and normalization, differential expression analysis was performed to identify the differentially expressed genes (DEGs). And cross-talk genes in the PD-T1DM pair and the PD-T2DM pair were identified by overlapping DEGs with the same trend in each pair. The weighted gene coexpression network analysis (WGCNA) algorithm helped locate the pyroptosis-related genes that are related to cross-talk genes. Receiver-operating characteristic (ROC) curve analysis confirmed the predictive accuracy of these hub genes in diagnosing PD and DM. The correlation between hub genes and the immune microenvironment of PBMC in these diseases was investigated by Spearman correlation analysis. The experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Subnetwork analysis helped identify the key pathway connecting DM and PD.

RESULTS

Hub genes in the PD-T1DM pair (HBD, NLRC4, AIM2, NLRP2) and in the PD-T2DM pair (HBD, IL-1Β, AIM2, NLRP2) were identified. The similarity and difference in the immunocytes infiltration levels and immune pathway scores of PD and DM were observed. ROC analysis showed that AIM2 and HBD exhibited pleasant discrimination ability in all diseases, and the subnetwork of these genes indicated that the NOD-like receptor signaling pathway is the most potentially relevant pathway linking PD and DM.

CONCLUSION

HBD and AIM2 could be the most relevant potential cross-talk and pyroptosis-related genes, and the NOD-like receptor signaling pathway could be the top candidate molecular mechanism linking PD and DM, supporting a potential pathophysiological relationship between PD and DM.

Unveiling the m6A Methylation Regulator Links between Prostate Cancer and Periodontitis by Transcriptomic Analysis

Objective

To identify the N6-methyladenosine (m6A) methylation regulator genes linking prostate adenocarcinoma (PRAD) and periodontitis (PD).

Materials and Methods

PD and TCGA-PRAD GEO datasets were downloaded and analyzed through differential expression analysis to determine the differentially expressed genes (DEGs) deregulated in both conditions. Twenty-three m6A RNA methylation-related genes were downloaded in total. The m6A-related genes that overlapped between PRAD and PD were identified as crosstalk genes. Survival analysis was performed on these genes to determine their prognostic values in the overall survival outcomes of prostate cancer. The KEGG pathways were the most significantly enriched by m6A-related crosstalk genes. We also performed lasso regression analysis and univariate survival analysis to identify the most important m6A-related crosstalk genes, and a protein-protein interaction (PPI) network was built from these genes.

Results

Twenty-three m6A methylation-related regulator genes were differentially expressed and deregulated in PRAD and PD. Among these, seven (i.e., ALKBH5, FMR1, IGFBP3, RBM15B, YTHDF1, YTHDF2, and ZC3H13) were identified as m6A-related cross-talk genes. Survival analysis showed that only the FMR1 gene was a prognostic indicator for PRAD. All other genes had no significant influence on the overall survival of patients with PRAD. Lasso regression analysis and univariate survival analysis identified four m6A-related cross-talk genes (i.e., ALKBH5, IGFBP3, RBM15B, and FMR1) that influenced risk levels. A PPI network was constructed from these genes, and 183 genes from this network were significantly enriched in pathogenic Escherichia coli infection, p53 signaling pathway, nucleocytoplasmic transport, and ubiquitin-mediated proteolysis.

Conclusion

Seven m6A methylation-related genes (ALKBH5, FMR1, IGFBP3, RBM15B, YTHDF1, YTHDF2, and ZC3H13) were identified as cross-talk genes between prostate cancer and PD.