Network Protein Interaction in Parkinson's Disease and Periodontitis Interplay: A Preliminary Bioinformatic Analysis

Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach

Introduction

Peri-implantitis, a destructive inflammatory condition affecting the tissues surrounding dental implants, shares pathological similarities with periodontitis, a chronic inflammatory disease that impacts the supporting structures of natural teeth. This study utilizes a network-based approach to classify interactome hub genes associated with peri-implantitis and periodontitis, aiming to improve understanding of disease mechanisms and identify potential therapeutic targets.

Methods

We employed gradient boosting and Weighted Gene Co-expression Network Analysis (WGCNA) to predict and classify these interactome hub genes. Gene expression data related to these diseases were sourced from the NCBI GEO dataset GSE223924, and differential gene expression analysis was conducted using the NCBI GEO R tool. Through WGCNA, we constructed a co-expression network to identify key hub genes, while gradient boosting was used to predict these hub genes.

Results

Our analysis revealed a co-expression network comprising 216 genes, including prominent hub genes such as IL17RC, CCN2, BMP7, TPM1, and TIMP1, which are implicated in periodontal disease. The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis.

Discussion

These identified genes play roles in inflammation, osteoclast genesis, angiogenesis, and immune response regulation. This study highlights essential hub genes and molecular pathways associated with peri-implantitis and periodontitis, suggesting potential therapeutic targets for developing innovative treatment strategies.

Elucidation of common molecular diagnostic biomarkers between chronic periodontitis and Parkinson's disease via bioinformatics analyses

BACKGROUND AND OBJECTIVES

Parkinson's disease (PD) and chronic periodontitis (CP) are both inflammatory diseases; a correlation between the two diseases has been reported, but the underlying mechanisms of this association have not been investigated. We investigated the common molecular mechanisms between PD and CP and the role of immune cells in the pathogenesis of them using bioinformatics analyses to elucidate the association between the two diseases.

METHODS

We obtained gene expression data from the Gene Expression Omnibus (GEO) database: GSE10334, GSE16134, and GSE23586 for CP gingival samples and GSE20146 for PD brain samples. Subsequently, we conducted an enrichment analysis of the differentially expressed genes (DEGs) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Moreover, all DEGs were analysed for protein-transcription factor interactions and protein-immune cell co-expression. We constructed protein-transcription factor, protein-protein interaction (PPI), and protein-immune cell co-expression networks using the Cytoscape software. Moreover, we identified the hub genes and investigated them for potential diagnostic value.

RESULTS AND CONCLUSION

We identified 99 DEGs in the three CP datasets, 520 DEGs in the PD dataset and found five common DEGs in the CP and PD datasets, namely CXCR4, CXCL8, CD19, RPTN, and SLC16A9. These common DEGs identified in our study may have a potential impact on disease pathogenesis through the involvement of CXCR4-CXCL8-CD19 protein-complexes in dendritic cells. Therefore, CD19, LCP2, CXCR4, and LYN could be used as target molecules for the clinical diagnosis of both diseases.

Parkinson's disease alters the composition of subgingival microbiome

Aim

The current study aimed to test the hypothesis that Parkinson's disease exacerbates periodontitis by altering its microbiome.

Materials and Methods

Clinical periodontal parameters were recorded. Subgingival samples from healthy controls, periodontitis patients (PD), and Parkinson's patients with periodontitis (PA+PD) were analyzed using the checkerboard DNA-DNA hybridization technique for targeting 40 bacterial species typically associated with periodontal disease and health. Next-generation sequencing (NGS) of the 16S ribosomal RNA gene (V1-V3 regions) was performed to analyze the microbiome comprehensively.

Results

Parkinson's patients had mild-to-moderate motor dysfunctions. Bleeding on probing was significantly increased in the PA+PD group compared to PD (p < 0.05). With checkerboard analysis, PA was associated with increased Treponema socranskii (p = 0.0062), Peptostreptococcaceae_[G-6] [Eubacterium]_nodatum (p = 0.0439), Parvimona micra (p < 0.0001), Prevotella melaninogenica (p = 0.0002), Lachnoanaerobaculum saburreum (p < 0.0001), and Streptococcus anginosus (p = 0.0020). Streptococcus intermedia (p = 0.0042), P.nodatum (p = 0.0022), P. micra (p = 0.0002), Treponema denticola (p = 0.0045), L.saburreum (p = 0.0267), P.melaninogenica (p = 0.0017), Campylobacter rectus (p = 0.0020), and T.socranskii (p = 0.0002) were higher; Aggregatibacter actinomycetemcomitans (p = 0.0072) was lower in deep pockets in the PA+PD compared to PD. Schaalia odontolytica (p = 0.0351) and A.actinomycetemcomitans (p = 0.002) were lower; C.rectus (p = 0.0002), P. micra (p = 0065), Streptococcus constellatus (p = 0.0151), T.denticola (p = 0.0141), P.melaninogenica (p = 0.0057), and T.socranskii (p = 0.0316) were higher in shallow pockets in the PA+PD. Diversity decreased in PD (p = 0.001) and PA+PD (p = 0.026) compared to control, with minimal differences in alpha and beta diversities among PD and PA+PD based on NGS results.

Conclusion

These data demonstrated that Parkinson's disease modifies PD-associated subgingival microbiome.

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.

Bioinformatics analysis of gene expression profile and functional analysis in periodontitis and Parkinson's disease

Periodontitis is a chronic inflammatory disease inextricably linked to both the innate and acquired immune systems of the body. Parkinson's disease (PD) is a neurodegenerative disease caused by immune system dysfunction. Although recent studies suggest that a clinical relationship exists between PD and periodontitis, the pathogenesis of this relationship is unclear. Therefore, in the present study, we obtained datasets of periodontitis and PD from the Gene Expression Omnibus (GEO) database and extracted 785 differentially expressed genes (DEGs), including 15 common upregulated genes and four common downregulated genes. We performed enrichment analyses of these DEGs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. We found that the genes were mainly enriched in keratinocyte differentiation, neuronal cell bodies, and structural constituents of epidermis terms, and pathways such as immune response and synaptic pathways. In addition, we screened matching hub genes by constructing a protein-protein interaction (PPI) network map and a Molecular Complex Detection (MCODE) map using the Cytoscape software. The hub genes were then subjected to GO enrichment analysis, which revealed that the dopamine biosynthetic process, dopaminergic synapse and dopamine-binding terms, and dopaminergic synapse and serotonergic synapse pathways were primarily where they were expressed. Finally, we selected four of these genes for validation in the periodontitis and PD datasets, and we confirmed that these hub genes were highly sensitive and specific for diagnosing and monitoring PD and periodontitis. In conclusion, the above experimental results indicate that periodontitis is a high-risk factor for PD, and the association between these two conditions is mainly manifested in immune and dopamine-related pathways. Hub genes, such as the CDSN, TH, DDC, and SLC6A3 genes, may serve as potential biomarkers for diagnosing or detecting PD.

Research progress on the relationship between chronic periodontitis and Parkinson's disease

Chronic periodontitis is an infectious disease, which has a reciprocal relationship with a variety of systemic disorders. Parkinson's disease is a prevalent neurodegenerative disease in which inflammation plays an important role for its progression. A vast number of studies suggest that there is a potential connection between chronic periodontitis and neurodegenerative diseases such as Parkinson's disease. Individuals with Parkinson's disease usually have poor periodontal health, and their oral flora composition differs from that of healthy people; at the same time, patients with chronic periodontitis have a higher risk of Parkinson's disease, which can be reduced with regular periodontal treatment. In fact, the mechanism of interaction between chronic periodontitis and Parkinson's disease is not clear. According to several studies, the clinical symptoms of Parkinson's disease prevent patients to maintain oral hygiene effectively, increasing the risk of periodontitis. Neuroinflammation mediated by microglia may be the key to the influence of chronic periodontitis on Parkinson's disease. Periodontal pathogens and inflammatory mediators may enter the brain and activate microglia in various ways, and ultimately leading to occurrence and development of Parkinson's disease. This article reviews the recent research progress on the association between chronic periodontitis and Parkinson's disease, and its potential mechanism to provide information for further research.

Network Protein Interaction in the Link between Stroke and Periodontitis Interplay: A Pilot Bioinformatic Analysis

The clinical interaction between stroke and periodontitis has been consistently studied and confirmed. Hence, exploring potentially new protein interactions in this association using bioinformatic strategies presents potential interest. In this exploratory study, we conducted a protein-protein network interaction (PPI) search with documented encoded proteins for both stroke and periodontitis. Genes of interest were collected via GWAS database. The STRING database was used to predict the PPI networks, first in a sensitivity purpose (confidence cut-off of 0.7), and then with a highest confidence cut-off (0.9). Genes over-representation was inspected in the final network. As a result, we foresee a prospective protein network of interaction between stroke and periodontitis. Inflammation, pro-coagulant/pro-thrombotic state and, ultimately, atheroma plaque rupture is the main biological mechanism derived from the network. These pilot results may pave the way to future molecular and therapeutic studies to further comprehend the mechanisms between these two conditions.