Subgingival bacterial colonization profiles correlate with gingival tissue gene expression

Unveiling the Etiopathogenic Role of Epstein-Barr Virus in Periodontitis

Periodontitis, a prevalent and costly oral disease, remains incompletely understood in its etiopathogenesis. The conventional model attributes it to pathogenic bacteria, but emerging evidence suggests dysbiosis involving bacteria, herpesviruses, and an exaggerated host immune response. Among herpesviruses, Epstein-Barr virus (EBV) closely links to severe periodontitis, yet the mechanisms underlying EBV-related pathogenesis remain elusive. This study examined the presence, methylation patterns, and infection states of EBV in gingival tissues from healthy patients and those with periodontitis. It also assessed gene expression differences associated with EBV through whole-genome transcriptomic profiling in healthy and periodontitis-affected tissues. EBV DNA was found at similar frequencies in healthy and periodontitis tissues, suggesting common EBV infection even before disease manifestation. In healthy tissues, mostly unmethylated EBV genomes indicated lytic infection in gums, consistent with the literature on lytic EBV spread in epithelia and continual significant virus release in the saliva of healthy carriers. Conversely, EBV DNA in periodontitis tissues showed both methylated and unmethylated patterns, suggesting a mix of latent and lytic genomes. This indicates the coexistence of latent EBV in B-cells and lytic EBV in plasma cells (PCs), linking EBV presence with both cell types in periodontitis. Whole-genome transcriptomic analysis revealed distinct expression profiles in EBV-positive periodontitis tissues, with upregulated genes associated with inflammatory/immune responses and B-cell and PC markers, while downregulated genes were related to epithelial structure and organization. The EBV-positive periodontitis signature differed distinctly from that of EBV-positive healthy gums, eliciting only a typical viral-induced immune response. These findings provide new insights into EBV physiopathology in the gum, notably assigning a direct etiopathogenetic contribution to EBV in periodontitis. The results suggest a model where EBV can commonly, and apparently asymptomatically, spread in healthy gingiva but may also aggravate inflammation in the context of gum dysbiosis, involving infiltration of B-cells and PCs and loss of epithelial integrity.

Identification of ferroptosis-related genes in periodontitis through bioinformatics analysis and experimental validation

BACKGROUND

Periodontitis is a multifactorial chronic inflammatory disease of periodontal tissues. Ferroptosis is a form of regulated cell death, which is characterized by iron-dependent lipid peroxidation and involved in various inflammatory diseases. This study aims to identify ferroptosis-related genes associated with periodontitis and further validate their relevance through in vitro experiments.

METHODS

Iron accumulation and localization were detected using Prussian blue staining. Differentially expressed genes in periodontitis were identified from GSE16134 and GSE10334, and intersected with ferroptosis genes to obtain differentially expressed ferroptosis genes (FerDEGs). Functional enrichment analyses of FerDEGs were performed by GO and KEGG. Hub genes were screened through PPI network analysis. The expression of these hub genes in gingival tissues and in lipopolysaccharide (LPS)-stimulated human gingival fibroblasts (HGFs) with/without ferrostatin-1 (Fer-1) detected by qRT-PCR and Western Blot.

RESULTS

Ferroptosis was observed in gingival tissues affected by periodontitis. A total of 24 FerDEGs involved in periodontitis were identified. GO analysis and KEGG analysis highlighted the intrinsic apoptotic signaling pathway and ferroptosis as the top enriched pathways. PPI network analysis identified five hub genes. The mRNA expression levels of hub genes were significantly higher in inflammatory gingival tissues and HGFs stimulated with LPS (P < 0.05). The upregulated expression of PTGS2 and IL6 in HGFs were reversed by Fer-1 (P < 0.05).

CONCLUSION

This study highlights five ferroptosis-related genes as potential targets for future research into the pathogenesis of periodontitis.

Identification of Periodontal Disease Diagnostic Markers Via Data Cross-Validation

INTRODUCTION AND AIMS

Periodontitis is a globally prevalent disease that is clinically diagnosed when the periodontal tissues are pathologically affected. Therefore, it is vital to identify novel periodontitis-associated biomarkers that will aid in diagnosing or treating potential patients with periodontitis.

METHODS

The GSE16134 and GSE10334 datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes between periodontitis and healthy samples. Single-sample gene set enrichment analysis was performed to identify significantly involved signalling pathways. Weighted gene correlation network analysis was used to identify key molecular modules. Hub genes were screened using key genes to construct a diagnosis and prediction model of periodontitis. Microenvironment cell population-counter was used to analyse immune cell infiltration patterns in periodontal diseases.

RESULTS

Single-sample gene set enrichment analysis revealed that periodontitis involves the PI3K/AKT/mTOR signalling pathway and associated module genes (667 genes). Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the module genes revealed that periodontitis involves the type I interferon, rhythmic process, and response to type I interferon signalling pathways. GSEA identified 21 core genes associated with periodontitis and classified them into two clusters, A and B. Genomics of Drug Sensitivity in Cancer analysis revealed that AKT.inhibitor.VIII had high drug sensitivity in the cluster A subtype. Monocytes and myeloid dendritic cell infiltration were enriched in the cluster A subtype, whereas natural killer T cell infiltration was enriched in the cluster B subtype.

CONCLUSION

The pathway and gene modules identified in this study may help comprehensively diagnose periodontitis and provide a novel method for evaluating new treatments.

CLINICAL RELEVANCE

Our results are beneficial for classifying periodontitis subtypes and treatment using targeted medicine.

Multiomics of Aging and Aging-Related Diseases

Despite their astonishing biological diversity, surprisingly few shared traits connect all or nearly all living organisms. Aging, i.e., the progressive and irreversible decline in the function of multiple cells and tissues, is one of these fundamental features of all organisms, ranging from single-cell creatures to complex animals, alongside variability, adaptation, growth, healing, reproducibility, mobility, and, finally, death. Age is a key determinant for many pathologies, shaping the risks of incidence, severity, and treatment outcomes for cancer, neurodegeneration, heart failure, sarcopenia, atherosclerosis, osteoporosis, and many other diseases. In this review, we aim to systematically investigate the age-related features of the development of several diseases through the lens of multiomics: from genome instability and somatic mutations to pathway alterations and dysregulated metabolism.

Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

AIM

Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.

METHODS

Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach.

RESULTS

These three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity.

CONCLUSION

Predictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.

Differential expression of FSTL1 and its correlation with the pathological process of periodontitis

AIMS

This study aimed to elucidate the alterations in Follistatin-like protein 1 (FSTL1) and its association with the pathological process of periodontitis.

METHODS

This study included 48 patients with periodontitis and 42 healthy controls. The expression level of FSTL1 in the gingiva was determined by RT-qPCR, validated using the dataset GSE16134, and subsequently examined by western blotting. Bioinformatics analysis revealed a single-cell distribution of FSTL1, characteristic of angiogenesis and immune cell infiltration. The expression and distribution of FSTL1, vascular endothelial marker protein CD31 and myeloperoxidase (MPO), the indicator of neutrophil activity, were determined by immunohistochemistry (IHC). A series of correlation analyses was performed to determine the associations between FSTL1 and clinical parameters, including probing depth (PD) and clinical attachment loss (CAL), and their potential role in angiogenesis (CD31) and neutrophil infiltration (MPO).

RESULTS

FSTL1 was significantly upregulated in the gingiva of patients with periodontitis compared to their healthy counterparts. In addition, FSTL1 was positively correlated with the clinical parameters PD (r = .5971, p = .0005) and CAL (r = .6078, p = .0004). Bioinformatic analysis and IHC indicated that high FSTL1 expression was significantly correlated with angiogenesis and neutrophil infiltration in periodontitis. Moreover, receiver operating characteristic (ROC) analysis demonstrated that FSTL1 could serve as an independent indicator for evaluating the severity of periodontitis (area under the curve [AUC] = 0.9011, p < .0001).

CONCLUSION

This study demonstrated FSTL1 upregulation in periodontitis and its potential contribution to the disease via angiogenesis and neutrophil infiltration.

Identification and Validation of Aging- and Endoplasmic Reticulum Stress-Related Genes in Periodontitis Using a Competing Endogenous RNA Network

Periodontitis is a multifactorial chronic inflammatory disease that destroy periodontium. Apart from microbial infection and host immune responses, emerging evidence shows aging and endoplasmic reticulum stress (ER stress) play a key role in periodontitis pathogenesis. The aim of this study is to identify aging-related genes (ARGs) and endoplasmic reticulum stress-related genes (ERGs) in periodontitis. Data were obtained from the Gene Expression Omnibus (GEO), Human Ageing Genomic Resources (HAGR) and GeneCards databases to identify differentially expressed mRNAs/miRNAs/lncRNAs (DEmRNAs/DEmiRNAs/DElncRNAs), ARGs and ERGs, respectively. We used the MultiMiR database for the reverse prediction of miRNAs and predicted miRNA-lncRNA interactions using the STARBase database. Afterwards, we constructed a mRNA-miRNA-lncRNA ceRNA network. A total of 10 hub genes, namely LCK, LYN, CXCL8, IL6, HCK, IL1B, BTK, CXCL12, GNAI1 and FCER1G, and 5 DEmRNAs-ARGs-ERGs were then discovered. Further, weighted gene co-expression network analysis (WGCNA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore co-expression modules and immune infiltration respectively. Finally, we used transmission electron microscope (TEM), inverted fluorescence microscopy, quantitative real-time polymerase chain reaction (qRT-PCR) and Western Blot to verify the bioinformatic results in periodontal ligament stem cells (PDLSCs) infected with Porphyromonas gingivalis (P. gingivalis). The experimental results broadly confirmed the accuracy of bioinformatic analysis. The present study established an aging- and ER stress-related ceRNA network in periodontitis, contributing to a deeper understanding of the pathogenesis of periodontitis.

CXCR4-mediated neutrophil dynamics in periodontitis

BACKGROUND AND OBJECTIVE

Periodontitis is a common oral disease closely related to immune response and this study is aimed to identify the key immune-related pathogenic genes and analyze the infiltration and function of immune cells in the disease using bioinformatics methods.

METHODS

Transcriptome datasets and single-cell RNA sequencing (scRNA-seq) datasets were downloaded from the GEO database. We utilized weighted correlation network analysis and least absolute selection and shrinkage operator, protein-protein interaction network construction to screen out key pathogenic genes as well as conducted the cell-type identification by estimating relative subsets of RNA transcripts algorithm to analyze and characterize immune cell types in periodontal tissues. In addition to bioinformatics validations, clinical and cell samples were collected and mouse periodontitis models were constructed to validate the important role of key genes in periodontitis.

RESULTS

Bioinformatics analysis pointed out the positive correlation between CXCR4 expression and periodontitis, and revealed the increased infiltration of neutrophils in periodontal inflammatory. Similar results were obtained from clinical samples and animal models. In addition, the clustering and functional enrichment results based on CXCR4 expression levels included activation of immune response and cell migration, implying the possible function of CXCR4 on regulating neutrophil dynamics, which might contribute to periodontitis. Subsequent validation experiments confirmed that the increased expression of CXCR4 in neutrophils under periodontitis, where cell migration-related pathways also were activated.

CONCLUSION

CXCR4 could be the key pathogenic gene of periodontitis and CXCR4/CXCL12 signal axial might contribute to the development of periodontitis by mediating neutrophil dynamics, suggesting that CXCR4 could be a potential target to help identify novel strategies for the clinical diagnosis and treatment of periodontitis.

Potential diagnostic markers and therapeutic targets for periodontitis and Alzheimer's disease based on bioinformatics analysis

BACKGROUND AND OBJECTIVE

As a chronic inflammatory disease, periodontitis threatens oral health and is a risk factor for Alzheimer's disease (AD). There is growing evidence that these two diseases are closely related. However, current research is still incomplete in understanding the common genes and common mechanisms between periodontitis and AD. In this study, we aimed to identify common genes in periodontitis and AD and analyze the relationship between crucial genes and immune cells to provide new therapeutic targets for clinical treatment.

MATERIALS AND METHODS

We evaluated differentially expressed genes (DEGs) specific to periodontitis and AD. Co-expressed genes were identified by obtaining gene expression profile data from the Gene Expression Omnibus (GEO) database. Using the STRING database, protein-protein interaction (PPI) networks were constructed, and essential genes were identified. We also used four algorithms to identify critical genes and constructed regulatory networks. The association of crucial genes with immune cells and potential therapeutic effects was also assessed.

RESULTS

PDGFRB, VCAN, TIMP1, CHL1, EFEMP2, and IGFBP5 were obtained as crucial common genes. Immune infiltration analysis showed that Natural killer cells and Myeloid-derived suppressor cells were significantly differentially expressed in patients with PD and AD compared with the normal group. FOXC1 and GATA2 are important TFs for PD and AD. MiR-23a, miR-23b, miR-23a, and miR-23b were associated with AD and PD. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecule and drug-target interactions.

CONCLUSION

This study reveals commonalities in common hub genes and immune infiltration between periodontitis and AD, and the analysis of six hub genes and immune cells may provide new insights into potential therapeutic directions for the pathogenesis of periodontitis complicated by AD.

CXCL8, MMP12, and MMP13 are common biomarkers of periodontitis and oral squamous cell carcinoma

OBJECTIVE

To analysis the relationship between periodontitis (PD) and oral squamous cell carcinoma (OSCC) by bioinformatic analysis.

MATERIALS AND METHODS

We analyzed the gene expression profiles of PD (GSE16134) from the Gene Expression Omnibus (GEO) database and OSCC samples from TCGA-HNSC (head and neck squamous cell carcinoma) and identified common differentially expressed genes (DEGs) in PD and OSCC. Then, functional annotation and signaling pathway enrichment, protein interaction network construction, and hub gene identification were performed. Subsequently, the function and signaling pathway enrichment of hub genes, miRNA interaction, and transcription factor interaction analyses were carried out. We analyzed GSE10334 and GSE30784 as validation datasets, and performed qRT-PCR experiments simultaneously for validation, and obtained 4 hub genes. Finally, immune infiltration analysis and clinical correlation analysis of 4 hub genes and related miRNAs were performed.

RESULTS

We identified 31 DEGs (16 up-regulated and 15 down-regulated). Four hub genes were obtained by qRT-PCR and validation dataset analysis, including IL-1β, CXCL8, MMP12, and MMP13. The expression levels of them were all significantly upregulated in both diseases. The functions of these genes focus on three areas: neutrophil chemotaxis, migration, and CXCR chemokine receptor binding. Key pathways include IL-17 signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interactions pathway. Immune infiltration analysis showed that the expressions of 4 hub genes were closely related to a variety of immune cells. ROC curve analysis indicated that AUCs of 4 hub genes are all greater than 0.7, among which MMP12 and MMP13 were greater than 0.9. Kaplan-Meier survival analysis indicated that worse OS was strongly correlated with CXCL8 and MMP13 high-expression groups. MMP12 low-expression group was strongly associated with worse OS. The results of multivariate Cox regression analysis showed that age, N stage, CXCL8, MMP12, and MMP13 were independent prognostic factors for OS. We also identified 3 miRNAs, including hsa-miR-19b-3p, hsa-miR-181b-2-3p, and hsa-miR-495-3p, that were closely related to 4 hub genes. Hsa-miR-495-3p is closely related to the diagnosis and prognosis of OSCC.

CONCLUSIONS

We identified 4 hub genes between PD and OSCC, including IL-1β, CXCL8, MMP12, and MMP13. These genes may mediate the co-morbid process of PD and OSCC through inflammation-related pathways such as the IL-17 signaling pathway. It is worth noting that CXCL8, MMP12, and MMP13 have great significance in the diagnosis and prognosis of OSCC.

Identification the Low Oxidative Stress Subtype of Periodontitis

OBJECTIVE

The aim of this research was to identify the low oxidative stress-related genes expression (L-OS) subtype in patients with periodontitis.

METHODS

Microarray data (MA) were retrieved from the Gene Expression Omnibus database. The different oxidative stress (OS) subtypes of periodontitis were identified by K-means clustering analysis and gene set variation analysis (GSVA). Differentially expressed genes (DEGs) (|Log fold change (FC)| >1, q < 0.05) amongst the OS subtypes and healthy controls (HCs) were identified by Limma R package. The genomic feature of L-OS subtype and corresponding medicines were evaluated and visualised with Drug-Gene Interaction Database and cytoscape-v3.7.2 software (Pearson correlation coefficient > 0.4). Finally, the LASSO-Logistic regression model was adopted to evaluate and predict patients' OS phenotype in routine clinical practice.

RESULTS

The 241 periodontitis samples and 69 HCs were included. Thirty-three DEGs between the L-OS and high oxidative stress-related genes expression (H-OS) subtypes and 96 DEGs, including 8 transcription factors, between L-OS subtype and HCs were identified, respectively. Then, the network of TFs-Genes-Drugs was constructed to determine genomic feature of L-OS subtype. Finally, a 4-gene signature formula and the cutoff value were identified by ML with LASSO model to predict patients' classification.

CONCLUSIONS

For the first time, we identified L-OS subtype of periodontitis and evaluated its genomic feature with MA.

Identification and Validation of Signature Genes and Potential Therapy Targets of Inflammatory Bowel Disease and Periodontitis

Background

Inflammatory bowel disease (IBD) and periodontitis (PD) are correlated, although the pathogenic mechanism behind their correlation has not been clarified. This study aims to explore the common signature genes and potential therapeutic targets of IBD and PD using transcriptomic analysis.

Methods

The GEO database was used to download datasets of IBD and PD, and differential expression analysis was used to identify DEGs. We then conducted GO and KEGG enrichment analyses of the shared genes. Next, we applied 4 machine learning (ML) algorithms (GLM, RF, GBM, and SVM) to select the best prediction model for diagnosing the disease and obtained the hub genes of IBD and PD. The diagnostic value of the signature genes was verified by a validation set and qRT‒PCR experiments. Subsequently, immune cell infiltration in IBD samples and PD samples was analyzed by ssGSEA. Finally, we investigated and validated the response of hub genes to infliximab therapy.

Results

We identified 43 upregulated genes as shared genes by intersecting the DEGs of IBD and PD. Functional enrichment analysis suggested that the shared genes were closely associated with immunity and inflammation. The ML algorithm and qRT‒PCR results indicated that IGKC and COL4A1 were the hub genes with the most diagnostic value for IBD and PD. Subsequently, through immune infiltration analysis, CD4 T cells, NK cells and neutrophils were identified to play crucial roles in the pathogenesis of IBD and PD. Finally, through in vivo and in vitro experiments, we found that IGKC and COL4A1 were significantly downregulated during the treatment of patients with IBD using infliximab.

Conclusion

We investigated the potential association between IBD and PD using transcriptomic analysis. The IGKC and COL4A1 genes were identified as characteristic genes and novel intervention targets for these two diseases. Infliximab may be used to treat or prevent IBD and PD.

Screening of feature genes related to immune and inflammatory responses in periodontitis

BACKGROUND

Immune and inflammatory responses are important in the occurrence and development of periodontitis. The aim of this study was to screen for immune-related genes and construct a disease diagnostic model to further investigate the underlying molecular mechanisms of periodontitis.

METHODS

GSE16134 and GSE10334 datasets were used in this study. Differentially expressed genes (DEGs) between the periodontitis and control groups were selected. Immune-related genes were identified, and functional analysis and construction of an interaction network were conducted. Immune characteristics were evaluated using gene set variation analysis GSVA. Immunity-related modules were analyzed using weighted gene co-expression network analysis (WGCNA). The LASSO algorithm was applied to optimize the module genes. Correlation between optimized immune-related DEGs and immune cells was analyzed.

RESULTS

A total of 324 immune-related DEGs enriched in immune- and inflammation-related functions and pathways were identified. Of which, 23 immune cells were significantly different between the periodontitis and control groups. Nine optimal immune-related genes were selected using the WGCNA and LASSO algorithms to construct a diagnostic model. Except for CXCL1, the other eight genes were significantly positively correlated with regulatory T cells, immature B cells, activated B cells, and myeloid-derived suppressor cells.

CONCLUSION

This study identified nine immune-related genes and developed a diagnostic model for periodontitis.

Immune infiltration and diagnostic value of immune-related genes in periodontitis using bioinformatics analysis

BACKGROUND AND OBJECTIVES

Periodontitis, which is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue, is the leading cause of tooth loss in adults. Many studies have found that inflammatory immune responses are involved in the risk of periodontal tissue damage. Therefore, we analyzed the association between immunity and periodontitis using bioinformatics methods to further understand this disease.

MATERIALS AND METHODS

First, the expression profiles of periodontitis and healthy samples were downloaded from the GEO database, including a training dataset GSE16134 and an external validation dataset GSE10334. Then, differentially expressed genes were identified using the limma package. Subsequently, immune cell infiltration was calculated by using the CIBERSORT algorithm. We further identified genes linking periodontitis and immunity from the ImmPort and DisGeNet databases. In addition, some of them were selected to construct a diagnostic model via a logistic stepwise regression analysis.

RESULTS AND CONCLUSIONS

Two hundred sixty differentially expressed genes were identified and found to be involved in responses to bacterial and immune-related processes. Subsequently, immune cell infiltration analysis demonstrates significant differences in the abundance of most immune cells between periodontitis and healthy samples, especially in plasma cells. These results suggested that immunity doses play a non-negligible role in periodontitis. Twenty-one genes linking periodontitis and immunity were further identified. And nine hub genes of them were identified that may be key genes involved in the development of periodontitis. Gene ontology analyses showed that these genes are involved in response to molecules of bacterial origin, cell chemotaxis, and response to chemokines. In addition, three genes of them were selected to construct a diagnostic model. And its good diagnostic performance was demonstrated by the receiver operating characteristic curves, with an area under the curve of 0.9424 for the training dataset and 0.9244 for the external validation dataset.

Infectious Spondylitis Caused by Streptococcus gordonii

Infectious spondylitis is a rare but severe disease of the spine caused by bacteria or other pathogens. Particularly in immunocompromised patients, a definitive source of infection often remains uncertain. Among many pathogens, Streptococcus gordonii, a normal oral flora, is a very rare pathogen in infectious spondylitis. Only a few articles have reported infectious spondylitis caused by Streptococcus gordonii. To the best of our knowledge, there have been no reports of surgically treated infectious spondylitis caused by Streptococcus gordonii. Hence, in the current report, we present the case of a 76-year-old woman with known type 2 diabetes who was transferred to our medical center due to infectious spondylitis caused by Streptococcus gordonii following an L1 compression fracture and underwent an operation for treatment.

Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source Tools

Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier "candidate" gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized -omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.A major issue when inferring biological information from high-throughput -omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences. Furthermore, machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Herein, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of -omics data using open-source tools. We outline a differential expression analysis pipeline that can be used for data from both arrays and sequencing experiments, and offers the possibility to account for random or fixed effects. Furthermore, we present an overview of the possibilities for a functional analysis of the obtained data including subsequent machine learning approaches in form of (i) supervised classification algorithms in class validation and (ii) unsupervised clustering in class discovery.

Genome-Wide Analysis of Periodontal and Peri-implant Cells and Tissues

-Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, microbiome, miRNome, and methylome of gingival and peri-implant tissues from human subjects and further studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens.Our data offer new insight on the pathophysiology underlying periodontal and peri-implant diseases, a possible route to a better and earlier diagnosis of these highly prevalent chronic inflammatory diseases and thus, to a personalized and efficient treatment approach.Herein, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.

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.

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.

Identification of Key Pyroptosis-Related Genes and Distinct Pyroptosis-Related Clusters in Periodontitis

Aim

This study aims to identify pyroptosis-related genes (PRGs), their functional immune characteristics, and distinct pyroptosis-related clusters in periodontitis.

Methods

Differentially 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).

Results

Of 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.

Conclusion

The 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.

Identification of microRNA-mRNA-TF regulatory networks in periodontitis by bioinformatics analysis

BACKGROUND

Periodontitis is a complex infectious disease with various causes and contributing factors. The aim of this study was to identify key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct a miRNA-mRNA-TF regulatory networks to investigate the underlying molecular mechanism in periodontitis.

METHODS

The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using the "limma" package in R. The intersection of the target genes of candidate DEMis and DEMs were considered significant DEMs in the regulatory network. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Subsequently, DEMs were uploaded to the STRING database, a protein-protein interaction (PPI) network was established, and the cytoHubba and MCODE plugins were used to screen out key hub mRNAs and significant modules. Ultimately, to investigate the regulatory network underlying periodontitis, a global triple network including miRNAs, mRNAs, and TFs was constructed using Cytoscape software.

RESULTS

8 DEMis and 121 DEMs were found between the periodontal and control groups. GO analysis showed that mRNAs were most significantly enriched in positive regulation of the cell cycle, and KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. A PPI network was constructed including 81 nodes and 414 edges. Furthermore, 12 hub genes ranked by the top 10% genes with high degree connectivity and five TFs, including SRF, CNOT4, SIX6, SRRM3, NELFA, and ONECUT3, were identified and might play crucial roles in the molecular pathogenesis of periodontitis. Additionally, a miRNA-mRNA-TF coregulatory network was established.

CONCLUSION

In this study, we performed an integrated analysis based on public databases to identify specific TFs, miRNAs, and mRNAs that may play a pivotal role in periodontitis. On this basis, a TF-miRNA-mRNA network was established to provide a comprehensive perspective of the regulatory mechanism networks of periodontitis.

Autophagy-Related Genes Predict the Progression of Periodontitis Through the ceRNA Network

Purpose

The goal of this study was to identify the crucial autophagy-related genes (ARGs) in periodontitis and construct mRNA-miRNA-lncRNA networks to further understand the pathogenesis of periodontitis.

Methods

We used the Gene Expression Omnibus (GEO) database and Human Autophagy Database (HADb) to identify differentially expressed mRNAs, miRNAs, and ARGs. These ARGs were subjected to Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, and PPI (protein–protein interaction) network analysis. Two databases (miRDB and StarBase v2.0) were used to reverse-predict miRNAs while the miRNA-lncRNA interaction was predicted using the StarBase v2.0 and LncBase Predicted v.2 databases. After excluding the lncRNAs only present in the nucleus, a competing endogenous RNA (ceRNA) network was built. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the levels of mRNA expression in the ceRNA network.

Results

The differential expression analysis revealed 10 upregulated and 10 downregulated differentially expressed ARGs. After intersecting the reverse-predicted miRNAs with the differentially expressed miRNAs, a ceRNA network consisting of 4 mRNAs (LAMP2, NFE2L2, NCKAP1, and EGFR), 3 miRNAs (hsa-miR-140-3p, hsa-miR-142-5p, and hsa-miR-671-5p), and 30 lncRNAs was constructed. In addition, qRT-PCR results revealed that EGFR expression was downregulated in diseased gingival tissue of periodontitis patients.

Conclusion

Four autophagy-related genes, especially EGFR, may play a key role in periodontitis progression. The novel ceRNA network may aid in elucidating the role and the mechanism of autophagy in periodontitis, which could be important in developing new therapeutic options.

Identification of hub genes related to immune cell infiltration in periodontitis using integrated bioinformatic analysis

BACKGROUND AND OBJECTIVE

Periodontitis is an inflammatory disease of the periodontium. However, the hub genes in periodontitis and their correlation with immune cells are not clear. This study aimed to identify hub genes and immune infiltration properties in periodontitis and to explore the correlation between hub genes and immune cells.

MATERIAL AND METHODS

Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed both on GSE10334 and GSE173078 datasets. Hub genes were identified via WGCNA and DEGs. The proportions of infiltrating immune cells were calculated by CIBERSORT algorithm, and single-cell RNA-sequencing dataset GSE164241 was used to explore cell-type-specific expression profiles of hub genes.

RESULTS

Eight hub genes (DERL3, FKBP11, LAX1, CD27, SPAG4, ST6GAL1, MZB1, and SEL1L3) were selected via WGCNA and DEGs by combining GSE10334 and GSE173078 datasets. CIBERSORT analysis showed a significant difference in the proportion of B cells, dendritic cells resting, and neutrophils in the gingival tissues between healthy and periodontitis patients, and expressions of these genes were highly correlated with the infiltration of B cells in periodontitis. Furthermore, real-time quantitative PCR results further confirmed the overexpression of hub genes. Analysis of GSE164241dataset further identified that most of hub genes were mainly expressed in B cells.

CONCLUSIONS

By integrating WGCNA, DEGs, and CIBERSORT analysis, eight genes were identified to be the hub genes of periodontitis and most of them were mainly expressed in B cells encouraging further researches on B cells in periodontitis pathogenesis.

Identification of Cross-Talk and Pyroptosis-Related Genes Linking Periodontitis and Rheumatoid Arthritis Revealed by Transcriptomic Analysis

BACKGROUND

The current study is aimed at identifying the cross-talk genes between periodontitis (PD) and rheumatoid arthritis (RA), as well as the potential relationship between cross-talk genes and pyroptosis-related genes.

METHODS

Datasets for the PD (GSE106090, GSE10334, GSE16134) and RA (GSE55235, GSE55457, GSE77298, and GSE1919) were downloaded from the GEO database. After batch correction and normalization of datasets, differential expression analysis was performed to identify the differentially expressed genes (DEGs). The cross-talk genes linking PD and RA were obtained by overlapping the DEGs dysregulated in PD and DEGs dysregulated in RA. Genes involved in pyroptosis were summarized by reviewing literatures, and the correlation between pyroptosis genes and cross-talk genes was investigated by Pearson correlation coefficient. Furthermore, the weighted gene coexpression network analysis (WGCNA) was carried out to identify the significant modules which contained both cross-talk genes and pyroptosis genes in both PD data and RA data. Thus, the core cross-talk genes were identified from the significant modules. Receiver-operating characteristic (ROC) curve analysis was performed to identify the predictive accuracy of these core cross-talk genes in diagnosing PD and RA. Based on the core cross-talk genes, the experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed.

RESULTS

A total of 40 cross-talk genes were obtained. Most of the pyroptosis genes were not differentially expressed in disease and normal samples. By selecting the modules containing both cross-talk genes or pyroptosis genes, the blue module was identified to be significant module. Three genes, i.e., cross-talk genes (TIMP1, LGALS1) and pyroptosis gene-GPX4, existed in the blue module of PD network, while two genes (i.e., cross-talk gene-VOPP1 and pyroptosis gene-AIM2) existed in the blue module of RA network. ROC curve analysis showed that three genes (TIMP1, VOPP1, and AIM2) had better predictive accuracy in diagnosing disease compared with the other two genes (LGALS1 and GPX4).

CONCLUSIONS

This study revealed shared mechanisms between RA and PD based on cross-talk and pyroptosis genes, supporting the relationship between the two diseases. Thereby, five modular genes (TIMP1, LGALS1, GPX4, VOPP1, and AIM2) could be of relevance and might serve as potential biomarkers. These findings are a basis for future research in the field.

Identification of hub genes and transcription factors involved in periodontitis on the basis of multiple microarray analysis

OBJECTIVES

To identify the differentially expressed genes (DEGs) during the pathogenesis of periodontitis by bioinformatics analysis.

METHODS

GEO2R was used to screen DEGs in GSE10334 and GSE16134. Then, the overlapped DEGs were used for further analysis. g:Profiler was used to perform Gene Ontology analysis and pathway analysis for upregulated and downregulated DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network, which was further visua-lized and analyzed by Cytoscape software. Hub genes and key modules were identified by cytoHubba and MCODE plug-ins, respectively. Finally, transcription factors were predicted via iRegulon plug-in.

RESULTS

A total of 196 DEGs were identified, including 139 upregulated and 57 downregulated DEGs. Functional enrichment analysis showed that the upregulated DEGs were mainly enriched in immune-related pathways including immune system, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and chemokine receptors bind chemokines. On the contrary, the downregulated DEGs were mainly related to the formation of the cornified envelope and keratinization. The identified hub genes in the PPI network were CXCL8, CXCL1, CXCR4, SEL, CD19, and IKZF1. The top three modules were involved in chemokine response, B cell receptor signaling pathway, and interleukin response, respectively. iRegulon analysis revealed that IRF4 scored the highest.

CONCLUSIONS

The pathogenesis of periodontitis was closely associated with the expression levels of the identified hub genes including CXCL8, CXCL1, CXCR4, SELL, CD19, and IKZF1. IRF4, the predicted transcription factor, might serve as a dominant upstream regulator.

Differential DNA methylation and mRNA transcription in gingival tissues in periodontal health and disease

AIM

We investigated differential DNA methylation in gingival tissues in periodontal health, gingivitis, and periodontitis, and its association with differential mRNA expression.

MATERIALS AND METHODS

Gingival tissues were harvested from individuals and sites with clinically healthy and intact periodontium, gingivitis, and periodontitis. Samples were processed for differential DNA methylation and mRNA expression using the IlluminaEPIC (850 K) and the IlluminaHiSeq2000 platforms, respectively. Across the three phenotypes, we identified differentially methylated CpG sites and regions, differentially expressed genes (DEGs), and genes with concomitant differential methylation at their promoters and expression were identified. The findings were validated using our earlier databases using HG-U133Plus2.0Affymetrix microarrays and Illumina (450 K) methylation arrays.

RESULTS

We observed 43,631 differentially methylated positions (DMPs) between periodontitis and health, and 536 DMPs between gingivitis and health (FDR < 0.05). On the mRNA level, statistically significant DEGs were observed only between periodontitis and health (n = 126). Twelve DEGs between periodontitis and health (DCC, KCNA3, KCNA2, RIMS2, HOXB7, PNOC, IRX1, JSRP1, TBX1, OPCML, CECR1, SCN4B) were also differentially methylated between the two phenotypes. Spearman correlations between methylation and expression in the EPIC/mRNAseq dataset were largely replicated in the 450 K/Affymetrix datasets.

CONCLUSIONS

Concomitant study of DNA methylation and gene expression patterns may identify genes whose expression is epigenetically regulated in periodontitis.

Cell Type-Specific Decomposition of Gingival Tissue Transcriptomes

Genome-wide transcriptomic analyses in whole tissues reflect the aggregate gene expression in heterogeneous cell populations comprising resident and migratory cells, and they are unable to identify cell type-specific information. We used a computational method (population-specific expression analysis [PSEA]) to decompose gene expression in gingival tissues into cell type-specific signatures for 8 cell types (epithelial cells, fibroblasts, endothelial cells, neutrophils, monocytes/macrophages, plasma cells, T cells, and B cells). We used a gene expression data set generated using microarrays from 120 persons (310 tissue samples; 241 periodontitis affected and 69 healthy). Decomposition of the whole-tissue transcriptomes identified differentially expressed genes in each of the cell types, which mapped to biologically relevant pathways, including dysregulation of Th17 cell differentiation, AGE-RAGE signaling, and epithelial-mesenchymal transition in epithelial cells. We validated selected PSEA-predicted, differentially expressed genes in purified gingival epithelial cells and B cells from an unrelated cohort (n = 15 persons), each of whom contributed with 1 periodontitis-affected and 1 healthy gingival tissue sample. Differential expression of these genes by quantitative reverse transcription polymerase chain reaction corroborated the PSEA predictions and pointed to dysregulation of biologically important pathways in periodontitis. Collectively, our results demonstrate the robustness of the PSEA in the decomposition of gingival tissue transcriptomes and its ability to identify differentially regulated transcripts in particular cellular constituents. These genes may serve as candidates for further investigation with respect to their roles in the pathogenesis of periodontitis.

Treponema denticola stimulates Oncostatin M cytokine release and de novo synthesis in neutrophils and macrophages

Oncostatin M (OSM) is a pleiotropic cytokine elevated in a number of inflammatory conditions including periodontal disease. OSM is produced by a variety of immune cells and has diverse functionality such as regulation of metabolic processes, cell differentiation, and the inflammatory response to bacterial pathogens. The oral cavity is under constant immune surveillance including complementary neutrophil and macrophage populations, due to a persistent symbiotic bacterial presence. Periodontal disease is characterized by a dysbiotic bacterial community, with an abundance of Treponema denticola. Despite strong associations with severe periodontal disease, the source and mechanism of the release of OSM have not been defined in the oral cavity. We show that OSM protein is elevated in the gingival epithelium and immune cell infiltrate during periodontal disease. Furthermore, salivary and oral neutrophil OSM is elevated in correlation with the presence of T. denticola. In an air pouch infection model, T. denticola stimulated higher levels of OSM than the oral pathogen Porphorymonas gingivalis, despite differential recruitment of innate immune cells suggesting T. denticola has distinct properties to elevate OSM levels. OSM release and transcription were increased in isolated human blood, oral neutrophils, or macrophages exposed to T. denticola in vitro as measured by ELISA, qPCR, and microscopy. Using transcription, translation, and actin polymerization inhibition, we found that T. denticola stimulates both OSM release through degranulation and de novo synthesis in neutrophils and also OSM release and synthesis in macrophages. Differential induction of OSM by T. denticola may promote clinical periodontal disease.

Differential immune cell infiltrations between healthy periodontal and chronic periodontitis tissues

Background

Host immunity plays an important role against oral microorganisms in periodontitis.

Methods

This study assessed the infiltrating immune cell subtypes in 133 healthy periodontal and 210 chronic periodontitis tissues from Gene Expression Omnibus (GEO) datasets using the CIBERSORT gene signature files.

Results

Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues, when compared to those in healthy controls. In contrast, memory B cells, resting dendritic, mast cells and CD4 memory cells, as well as activated mast cells, M1 and M2 macrophages, and follicular helper T cells, were mainly present in healthy periodontal tissues. Furthermore, these periodontitis tissues generally contained a higher proportion of activated CD4 memory T cells, while the other subtypes of T cells, including resting CD4 memory T cells, CD8 T cells, follicular helper T cells (T_FH) and regulatory T cells (Tregs), were relatively lower in periodontitis tissues, when compared to healthy tissues. The ratio of dendritic and mast cells and macrophages was lower in periodontitis tissues, when compared to healthy tissues. In addition, there was a significant negative association of plasma cells with most of the other immune cells, such as plasma cells vs. memory B cells (γ = − 0.84), plasma cells vs. resting dendritic cells (γ = − 0.64), plasma cells vs. resting CD4 memory T cells (γ = 0.50), plasma cells versus activated dendritic cells (γ = − 0.46), plasma cells versus T_FH (γ = − 0.46), plasma cells versus macrophage M2 cells (γ = − 0.43), or plasma cells versus macrophage M1 cells (γ = − 0.40), between healthy control and periodontitis tissues.

Conclusion

Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues. The infiltration of different immune cell subtypes in the periodontitis site could lead the host immunity against periodontitis.

Proteome and Microbiome Mapping of Human Gingival Tissue in Health and Disease

Efforts to map gingival tissue proteomes and microbiomes have been hampered by lack of sufficient tissue extraction methods. The pressure cycling technology (PCT) is an emerging platform for reproducible tissue homogenisation and improved sequence retrieval coverage. Therefore, we employed PCT to characterise the proteome and microbiome profiles in healthy and diseased gingival tissue. Healthy and diseased contralateral gingival tissue samples (total n = 10) were collected from five systemically healthy individuals (51.6 ± 4.3 years) with generalised chronic periodontitis. The tissues were then lysed and digested using a Barocycler, proteins were prepared and submitted for mass spectrometric analysis and microbiome DNA for 16S rRNA profiling analysis. Overall, 1,366 human proteins were quantified (false discovery rate 0.22%), of which 69 proteins were differentially expressed (≥2 peptides and p < 0.05, 62 up, 7 down) in periodontally diseased sites, compared to healthy sites. These were primarily extracellular or vesicle-associated proteins, with functions in molecular transport. On the microbiome level, 362 species-level operational taxonomic units were identified. Of those, 14 predominant species accounted for >80% of the total relative abundance, whereas 11 proved to be significantly different between healthy and diseased sites. Among them, Treponema sp. HMT253 and Fusobacterium naviforme and were associated with disease sites and strongly interacted (r > 0.7) with 30 and 6 up-regulated proteins, respectively. Healthy-site associated strains Streptococcus vestibularis, Veillonella dispar, Selenomonas sp. HMT478 and Leptotrichia sp. HMT417 showed strong negative interactions (r < −0.7) with 31, 21, 9, and 18 up-regulated proteins, respectively. In contrast the down-regulated proteins did not show strong interactions with the regulated bacteria. The present study identified the proteomic and intra-tissue microbiome profile of human gingiva by employing a PCT-assisted workflow. This is the first report demonstrating the feasibility to analyse full proteome profiles of gingival tissues in both healthy and disease sites, while deciphering the tissue site-specific microbiome signatures.

Expression profile of lipoxygenases in gingival tissues of human periodontitis

OBJECTIVES

This study aimed to clarify the expression profile and significance of lipoxygenases in periodontitis.

MATERIALS AND METHODS

The mRNA levels of lipoxygenases in gingival tissues from 14 patients with periodontitis and 14 healthy individuals were determined by real-time PCR, and validated in datasets, GSE16134 and GSE10334, and by Western blotting. Correlation of differentially expressed lipoxygenases with clinical parameters and expression of tumor necrosis factor-α (TNF-α), interleukin-1β, matrix metalloproteinase (MMP)-8, MMP-9, and receptor activator of nuclear factor-κB ligand (RANKL) was investigated in patients with periodontitis by Spearman's correlation analysis.

RESULTS

The expression of ALOX5 (2.1-fold, p < .05), ALOX12B (2.9-fold, p < .001), and ALOX15B (9.4-fold, p < .001) was upregulated in gingival tissues from patients with periodontitis, which was validated by dataset analysis and Western blotting. Positive correlations were observed between ALOX5 and probing depth, and ALOX15B and probing depth and clinical attachment loss. Furthermore, ALOX5 expression was positively correlated with TNF-α, MMP-8, MMP-9, and RANKL expression, and ALOX15B was positively correlated with MMP-8 and RANKL.

CONCLUSIONS

Our findings indicated the upregulation of ALOX5 and ALOX15B in periodontitis and suggested that ALOX5 and ALOX15B may be involved in periodontitis pathogenesis, including inflammation, connective tissue destruction, and abnormal bone metabolism.

Subgingival microbiome and clinical periodontal status in an elderly cohort: The WHICAP ancillary study of oral health

BACKGROUND

There is a sparsity of data describing the periodontal microbiome in elderly individuals. We analyzed the association of subgingival bacterial profiles and clinical periodontal status in a cohort of participants in the Washington Heights-Inwood Columbia Aging Project (WHICAP).

METHODS

Dentate individuals underwent a full-mouth periodontal examination at six sites/tooth. Up to four subgingival plaque samples per person, each obtained from the mesio-lingual site of the most posterior tooth in each quadrant, were harvested and pooled. Periodontal status was classified according to the Centers for Disease Control/American Academy of Periodontology (CDC/AAP) criteria as well as based on the percentage of teeth/person with pockets ≥4 mm deep. Bacterial DNA was isolated and was processed and analyzed using Human Oral Microbe Identification using Next Generation Sequencing (HOMINGS). Differential abundance across the periodontal phenotypes was calculated using the R package DESeq2. α- and β-diversity metrics were calculated using DADA2-based clustering.

RESULTS

The mean age of the 739 participants was 74.5 years, and 32% were male. Several taxa including Sneathia amnii-like sp., Peptoniphilaceae [G-1] bacterium HMT 113, Porphyromonas gingivalis, Fretibacterium fastidiosum, Filifactor alocis, and Saccharibacteria (TM7) [G-1] bacterium HMT 346 were more abundant with increasing severity of periodontitis. In contrast, species such as Veillonella parvula, Veillonella dispar, Rothia dentocariosa, and Lautropia mirabilis were more abundant in health. Microbial diversity increased in parallel with the severity and extent of periodontitis.

CONCLUSIONS

The observed subgingival bacterial patterns in these elderly individuals corroborated corresponding findings in younger cohorts and were consistent with the concept that periodontitis is associated with perturbations in the resident microbiome.

The association between rheumatoid arthritis and periodontal disease in a population-based cross-sectional case-control study

Background

The association between rheumatoid arthritis (RA) and periodontitis remains unclear.

Methods

We studied oral health and periodontitis in a population-based case-control study of individuals with ≥10 remaining teeth ≥61 years of age and either with, or without a diagnosis of RA. 126 dentate individuals with RA were recruited together with age-matched control individuals without RA. The control individuals were recruited from the general population from the same city (n = 249). A dental examination including a panoramic radiograph was performed on all participants. All individuals with RA were examined and medical records were reviewed by a rheumatologist. In the control group, none of the participants presented with symptoms of RA and their medical records were also negative.

Results

The RA group included more women (66.7% vs. 55.8%) (p < 0.01). Individuals in the RA group had a higher body mass index (BMI) (p < 0.001). A diagnosis of periodontitis was more common in the RA group (61.1%) than in the control group (33.7%) (p = 0.001). Binary logistic regression analysis identified that a BMI > 25 (OR 6.2, 95% CI 3.6, 10.5, p = 0.000), periodontitis (OR 2.5 95% CI 1.5, 4.2 p = 0.000), and female gender (OR 2.3, 95% CI 1.3–4.0, p = 0.003) were associated with RA.

Conclusion

RA was associated a diagnosis of periodontitis.

Granulocyte colony stimulating factor (G-CSF) regulates neutrophils infiltration and periodontal tissue destruction in an experimental periodontitis

Although granulocyte colony-stimulating factor(G-CSF) has pathogenic roles in several immune inflammatory diseases, its role in periodontitis has not been investigated. Here we detected local expression of G-CSF using public datasets in the Gene Expression Omnibus (GEO) database, and immune cell infiltration into gingival tissue was estimated based on single-sample gene set enrichment analysis (ssGSEA). G-CSF expression and neutrophil infiltration were also confirmed by human gingival biopsies analysis. Moreover, anti-G-CSF neutralizing antibody was locally administrated to investigate the effects of G-CSF neutralization on neutrophils infiltration and periodontal tissue destruction in periodontitis mice model. Two public datasets (GSE10334 and GSE16134), which included 424 patients with periodontitis and 133 health controls, were used in the analysis. Markedly increased immune cell infiltration and G-CSF expression in gingival tissues were found in the periodontitis group as compared to the control group. The higher expression of G-CSF was correlated with higher infiltration of immune cells, especially with neutrophil infiltration. Analysis of gingival biopsies further confirmed high neutrophil infiltration and G-CSF expression. In addition, anti-G-CSF antibody-treated mice with periodontitis showed significantly reduced alveolar bone resorption and neutrophil infiltration when compared with periodontitis mice treated with isotype control antibody. Also, anti-G-CSF antibody treatment significantly reduced mRNA expression of CXC chemokines (CXCL1, CXCL2 and CXCL3), interleukin 1β (IL-1β), IL-6, matrix metalloproteinases 9, receptor activator of nuclear factor κB ligand/osteoprotegerin (RANKL/OPG) ratio and osteoclasts number in periodontal tissues. In summary, neutrophil infiltration and G-CSF expression levels were significantly increased in inflamed gingival tissues. G-CSF neutralization in periodontal inflammation could alleviate neutrophil infiltration and periodontal tissue destruction in experimental periodontitis.

Local IL-10 level as a predictive factor in generalized aggressive periodontitis treatment response

Generalized aggressive periodontitis (GAgP) presents a reduced response to non-surgical therapy. However, it is not clear if the initial clinical, microbiological or immunological characteristics are impacting the worse response to treatment. This study aimed to identify the predictive value of clinical, microbiological and immunological patterns on the clinical response to therapy in GAgP patients. Twenty-four GAgP patients were selected, and gingival crevicular fluid (GCF) and subgingival biofilm were collected. Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Tannerella forsythia levels were evaluated by qPCR, and IL-1β and IL-10 concentration by ELISA. Twelve patients were treated with SRP (scaling and root planning), and twelve with SRP plus 375 mg amoxicillin and 250 mg metronidazole (8/8 hours, 7 days) (SRP + AM). The clinical changes (Probing Pocket Depth [PPD] reduction and Clinical Attachment Level [CAL] gain) 6 months post-treatment were correlated to the initial clinical, inflammatory and microbiological variables using stepwise logistic regression (α = 5%). CAL gain at 6 months was 1.16 ± 0.77 for SRP and 1.74 ± 0.57 mm for SRP + AM (P > .05). PPD reduction was 1.96 ± 0.82 for SRP and 2.45 ± 0.77 mm for SRP + AM (P < .05). In the SRP group, IL-10 showed a predictive value for clinical response. The higher the IL-10 concentration at baseline, the higher the reduction in PPD at 6 months (P = .01, r = .68). However, when antimicrobials were administered, no significant influence was detected (P > .05). It can be concluded that the IL-10 levels in GFC act as a predictor of clinical response to GAgP. Moreover, the intake of antimicrobials appears to overlap the influence of the inflammatory response on clinical response to treatment. Clinical trial registration number: NCT03933501.

Targeting epigenetic mechanisms in periodontal diseases

Epigenetic factors are heritable genome modifications that potentially impact gene transcription, contributing to disease states. Epigenetic marks play an important role in chronic inflammatory conditions, as observed in periodontal diseases, by allowing microbial persistence or by permitting microbial insult to play a role in the so-called 'hit-and-run' infectious mechanism, leading to lasting pathogen interference with the host genome. Epigenetics also affects the health sciences by providing a dynamic mechanistic framework to explain the way in which environmental and behavioral factors interact with the genome to alter disease risk. In this article we review current knowledge of epigenome regulation in light of the multifactorial nature of periodontal diseases. We discuss epigenetic tagging in identified genes, and consider the potential implications of epigenetic changes on host-microbiome dynamics in chronic inflammatory states and in response to environmental stressors. The most recent advances in genomic technologies have placed us in a position to analyze interaction effects (eg, between periodontal disease and type 2 diabetes mellitus), which can be investigated through epigenome-wide association analysis. Finally, because of the individualized traits of epigenetic biomarkers, pharmacoepigenomic perspectives are also considered as potentially novel therapeutic approaches for improving periodontal disease status.

Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO)

BACKGROUND

Chronic periodontitis (CP) is a multifactorial inflammatory disease. For the diagnosis of CP, it is necessary to investigate molecular biomarkers and the biological pathway of CP. Although analysis of mRNA expression profiling with microarray is useful to elucidate pathological mechanisms of multifactorial diseases, it is expensive. Therefore, we utilized pooled microarray gene expression data on the basis of data sharing to reduce hybridization costs and compensate for insufficient mRNA sampling. The aim of the present study was to identify molecular biomarker candidates and biological pathways of CP using pooled datasets in the Gene Expression Omnibus (GEO) database.

METHODS

Three pooled transcriptomic datasets (GSE10334, GSE16134, and GSE23586) of gingival tissue with CP in the GEO database were analyzed for differentially expressed genes (DEGs) using GEO2R, functional analysis and biological pathways with the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction (PPI) network and hub gene with the Search Tool for the Retrieval of Interaction Genes database, and biomarker candidates for diagnosis and prognosis and upstream regulators of dominant biomarker candidates with the Ingenuity Pathway Analysis database.

RESULTS

We shared pooled microarray datasets in the GEO database. One hundred and twenty-three common DEGs were found in gingival tissue with CP, including 81 upregulated genes and 42 downregulated genes. Upregulated genes in Gene Ontology were significantly enriched in immune responses, and those in the Kyoto Encyclopedia of Genes and Genomes pathway were significantly enriched in the cytokine-cytokine receptor interaction pathway, cell adhesion molecules, and hematopoietic cell lineage. From the PPI network, the 12 nodes with the highest degree were screened as hub genes. Additionally, six biomarker candidates for CP diagnosis and prognosis were screened.

CONCLUSIONS

We identified several potential biomarkers for CP diagnosis and prognosis (e.g., CSF3, CXCL12, IL1B, MS4A1, PECAM1, and TAGLN) and upstream regulators of biomarker candidates for CP diagnosis (TNF and TGF2). We also confirmed key genes of CP pathogenesis such as CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA. To our knowledge, this is the first report to reveal associations of CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP.

Associations of the Oral Microbiota with Obesity and Menarche in Inner City Girls

Objective

Alterations of the oral microbiome have been associated with obesity, possibly based on inflammatory processes mediated by bacteria. Specific bacterial strains have been associated with obesity and periodontal disease. Little is known about the oral microbiome in children. Understanding the relationship between oral health and childhood growth could help identify preventable factors contributing to obesity and related conditions, including onset of menarche which is associated with obesity.

Methods

In this pilot study, we investigated the saliva microbiome among 25 girls 7-15 years old (mean 11.1) and their mothers in an inner city dental clinic in New York City. The main outcome measures were body size, presence or absence of menarche and dental practices. We examined associations of microbiome richness, diversity, and relative abundance with pubertal and demographic factors and oral health.

Results

Girls had good dental health and a typical rich oral microbiome, based on the Shannon Index of all species detected. Older girls flossed more often and younger girls had more frequent dental check-ups. Microbiome richness among girls was similar to their mothers', but diversity was greater among mothers than girls. Richness was reduced among mothers with gum bleeding, flossing and increased teeth brushing. Overweight girls had greater diversity and less richness than normal weight girls. Certain bacterial species differed in abundance with respect to whether girls had reached menarche (Flavobacteria, Actinobacteria), overweight (Megasphaera, Lactorbacillales, Lactobacillus) and gingivitis in the girls (Scardovia, Bifidobacteriales, Gemellaceae).

Conclusions

Differences found in specific bacteria in the oral microbiome were related to body size and menarche. With increasing interest on studying microbiome variability related to the multifactorial etiology of obesity in children, saliva is capable of providing clinically informative markers of this and related conditions.

Molecular signatures of chronic periodontitis in gingiva: A genomic and proteomic analysis

BACKGROUND

To elucidate molecular signatures of chronic periodontitis (CP) using gingival tissue samples through omics-based whole-genome transcriptomic and whole protein profiling.

METHODS

Gingival tissues from 18 CP and 25 controls were analyzed using gene expression microarrays to identify gene expression patterns and the proteins isolated from these samples were subjected to comparative proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data from transcriptomics and proteomics were integrated to reveal common shared genes and proteins.

RESULTS

The most upregulated genes in CP compared with controls were found as MZB1, BMS1P20, IGLL1/IGLL5, TNFRSF17, ALDH1A1, KIAA0125, MMP7, PRL, MGC16025, ADAM11, and the most upregulated proteins in CP compared with controls were BPI, ITGAM, CAP37, PCM1, MMP-9, MZB1, UGTT1, PLG, RAB1B, HSP90B1. Functions of the identified genes were involved cell death/survival, DNA replication, recombination/repair, gene expression, organismal development, cell-to-cell signaling/interaction, cellular development, cellular growth/proliferation, cellular assembly/organization, cellular function/maintenance, cellular movement, B-cell development, and identified proteins were involved in protein folding, response to stress, single-organism catabolic process, regulation of peptidase activity, and negative regulation of cell death. The integration and validation analysis of the transcriptomics and proteomics data revealed two common shared genes and proteins, MZB1 and ECH1.

CONCLUSION

Integrative data from transcriptomics and proteomics revealed MZB1 as a potent candidate for chronic periodontitis.

Genome-Wide Analysis of Periodontal and Peri-Implant Cells and Tissues

Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, miRNome and methylome of gingival tissues from subjects with different diagnostic entities of periodontal disease, and studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens. Our data further our understanding of the pathobiology of periodontal diseases and indicate that the gingival -omes translate into discernible phenotypic characteristics and possibly support an alternative, "molecular" classification of periodontitis.Here, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.

Differential Expression and Functional Analysis of High-Throughput -Omics Data Using Open Source Tools

Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier "candidate" gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized -omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.A major issue when inferring biological information from high-throughput -omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences.In this chapter, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of -omics data generated using microarrays or next-generation sequencing technology using open-source tools. Starting with quality control measures and necessary preprocessing steps for data originating from different -omics technologies, we next outline a differential expression analysis pipeline that can be used for data from both microarray and sequencing experiments, and offers the possibility to account for random or fixed effects. Finally, we present an overview of the possibilities for a functional analysis of the obtained data.

Identification of the Disrupted Pathways Associated with Periodontitis Based on Human Pathway Network

Objective: The objective of this work is to search for a novel method to explore the disrupted pathways associated with periodontitis (PD) based on the network level.

Methods: Firstly, the differential expression genes (DEGs) between PD patients and cognitively normal subjects were inferred based on LIMMA package. Then, the protein-protein interactions (PPI) in each pathway were explored by Empirical Bayesian (EB) co-expression program. Specifically, we determined the 100th weight value as the threshold value of the disrupted pathways of PPI by constructing the randomly model and confirmed the weight value of each pathway. Meanwhile, we dissected the disrupted pathways under the weight value > the threshold value. Pathways enrichment analyses of DEGs were carried out based on Expression Analysis Systematic Explored (EASE) test. Finally, the better method was selected based on the more rich and significant obtained pathways by comparing the two methods.

Results: After the calculation of LIMMA package, we estimated 524 DEGs in all. Then we determined 0.115222 as the threshold value of the disrupted pathways of PPI. When the weight value>0.115222, there were 258 disrupted pathways of PPI enriched in. Additionally, we observed those 524 DEGs that were enriched in 4 pathways under EASE=0.1.

Conclusion: We proposed a novel network method inferring the disrupted pathway for PD. The disrupted pathways might be underlying biomarkers for treatment associated with PD.

MicroRNAs Regulate Cytokine Responses in Gingival Epithelial Cells

MicroRNAs (miRNAs) have been established as key regulators of various biological processes with possible involvement in the pathobiology of periodontal disease. Expanding our earlier observations of substantial differential expression of specific miRNAs between clinically healthy and periodontitis-affected gingival tissues, we used miRNA inhibitors (sponges) in loss-of-function experiments to investigate the involvement of specific miRNAs in the response of pocket epithelium-derived, telomerase-immortalized human gingival keratinocytes (TIGKs) to microbial infection. We constructed stable knockdown (KD) cell lines for five epithelium-expressed miRNAs (miR-126, miR-141, miR-155, miR-210, and miR-1246) and assessed their response to infection with periodontal pathogens using microarray analysis, quantitative PCR (qPCR), enzyme-linked immunosorbent assay (ELISA), and Western blot assay. miR-126 KD cells showed lower expression of interleukin 8 (IL-8) and CXCL1, both on the mRNA and protein levels, than did controls upon stimulation by heat-killed wild-type Porphyromonas gingivalis, live P. gingivalis protease-deficient mutant KDP128, and live Aggregatibacter actinomycetemcomitans In contrast, infection of miR-155 KD and miR-210 KD cells with the same organisms resulted in higher IL-8 and CXCL1 mRNA and protein expression. These effects appeared to be regulated by NF-κB, as suggested by altered transcription and/or phosphorylation status of components of the NF-κB system. Reduced neutrophil-like HL-60 cell chemotactic activity was observed in response to infection of miR-126 KD cells, indicating that miR-126 plays an important role in immune responses. Our findings indicate that specific miRNAs regulate the expression of inflammatory cytokines in human gingival epithelial cells in response to microbial infection.

Identification of Master Regulator Genes in Human Periodontitis

Analytic approaches confined to fold-change comparisons of gene expression patterns between states of health and disease are unable to distinguish between primary causal disease drivers and secondary noncausal events. Genome-wide reverse engineering approaches can facilitate the identification of candidate genes that may distinguish between causal and associative interactions and may account for the emergence or maintenance of pathologic phenotypes. In this work, we used the algorithm for the reconstruction of accurate cellular networks (ARACNE) to analyze a large gene expression profile data set (313 gingival tissue samples from a cross-sectional study of 120 periodontitis patients) obtained from clinically healthy (n = 70) or periodontitis-affected (n = 243) gingival sites. The generated transcriptional regulatory network of the gingival interactome was subsequently interrogated with the master regulator inference algorithm (MARINA) and gene expression signature data from healthy and periodontitis-affected gingiva. Our analyses identified 41 consensus master regulator genes (MRs), the regulons of which comprised between 25 and 833 genes. Regulons of 7 MRs (HCLS1, ZNF823, XBP1, ZNF750, RORA, TFAP2C, and ZNF57) included >500 genes each. Gene set enrichment analysis indicated differential expression of these regulons in gingival health versus disease with a type 1 error between 2% and 0.5% and with >80% of the regulon genes in the leading edge. Ingenuity pathway analysis showed significant enrichment of 36 regulons for several pathways, while 6 regulons (those of MRs HCLS1, IKZF3, ETS1, NHLH2, POU2F2, and VAV1) were enriched for >10 pathways. Pathways related to immune system signaling and development were the ones most frequently enriched across all regulons. The unbiased analysis of genome-wide regulatory networks can enhance our understanding of the pathobiology of human periodontitis and, after appropriate validation, ultimately identify target molecules of diagnostic, prognostic, or therapeutic value.

Mini but mighty: microRNAs in the pathobiology of periodontal disease

MicroRNAs (miRNAs) are a family of small, noncoding RNA molecules that negatively regulate protein expression either by inhibiting initiation of the translation of mRNA or by inducing the degradation of mRNA molecules. Accumulating evidence suggests that miRNA-mediated repression of protein expression is of paramount importance in a broad range of physiologic and pathologic conditions. In particular, miRNA-induced dysregulation of molecular processes involved in inflammatory pathways has been shown to contribute to the development of chronic inflammatory diseases. In this review, first of all we provide an overview of miRNA biogenesis, the main mechanisms of action and the miRNA profiling tools currently available. Then, we summarize the available evidence supporting a specific role for miRNAs in the pathobiology of periodontitis. Based on a review of available data on the differential expression of miRNAs in gingival tissues in states of periodontal health and disease, we address specific roles for miRNAs in molecular and cellular pathways causally linked to periodontitis. Our review points to several lines of evidence suggesting the involvement of miRNAs in periodontal tissue homeostasis and pathology. Although the intricate regulatory networks affected by miRNA function are still incompletely mapped, further utilization of systems biology tools is expected to enhance our understanding of the pathobiology of periodontitis.