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

DNA methylation and its potential roles in common oral diseases

Oral diseases are among the most common diseases worldwide and are associated with systemic illnesses, and the rising occurrence of oral diseases significantly impacts the quality of life for many individuals. It is crucial to detect and treat these conditions early to prevent them from advancing. DNA methylation is a fundamental epigenetic process that contributes to a variety of diseases including various oral diseases. Taking advantage of its reversibility, DNA methylation becomes a viable therapeutic target by regulating various cellular processes. Understanding the potential role of this DNA alteration in oral diseases can provide significant advances and more opportunities for diagnosis and therapy. This article will review the biology of DNA methylation, and then mainly discuss the key findings on DNA methylation in oral cancer, periodontitis, endodontic disease, oral mucosal disease, and clefts of the lip and/or palate in the background of studies on global DNA methylation and gene-specific DNA methylation.

Methylome-wide analysis in systemic microbial-induced experimental periodontal disease in mice with different susceptibility

Objective

The study delved into the epigenetic factors associated with periodontal disease in two lineages of mice, namely C57bl/6 and Balb/c. Its primary objective was to elucidate alterations in the methylome of mice with distinct genetic backgrounds following systemic microbial challenge, employing high-throughput DNA methylation analysis as the investigative tool.

Methods

Porphyromonas gingivalis (Pg)was orally administered to induce periodontitis in both Balb/c and C57bl/6 lineage. After euthanasia, genomic DNA from both maxilla and blood were subjected to bisulfite conversion, PCR amplification and genome-wide DNA methylation analysis using the Ovation RRBS Methyl-Seq System coupled with the Illumina Infinium Mouse Methylation BeadChip.

Results

Of particular significance was the distinct methylation profile observed within the Pg-induced group of the Balb/c lineage, contrasting with both the control and Pg-induced groups of the C57bl/6 lineage. Utilizing rigorous filtering criteria, we successfully identified a substantial number of differentially methylated regions (DMRs) across various tissues and comparison groups, shedding light on the prevailing hypermethylation in non-induced cohorts and hypomethylation in induced groups. The comparison between blood and maxilla samples underscored the unique methylation patterns specific to the jaw tissue. Our comprehensive methylome analysis further unveiled statistically significant disparities, particularly within promoter regions, in several comparison groups.

Conclusion

The differential DNA methylation patterns observed between C57bl/6 and Balb/c mouse lines suggest that epigenetic factors contribute to the variations in disease susceptibility. The identified differentially methylated regions associated with immune regulation and inflammatory response provide potential targets for further investigation. These findings emphasize the importance of considering epigenetic mechanisms in the development and progression of periodontitis.

Association between biological ageing and periodontitis: Evidence from a cross-sectional survey and multi-omics Mendelian randomization analysis

AIM

To investigate the relationship and potential causality between biological ageing and periodontitis.

MATERIALS AND METHODS

We obtained the National Health and Nutrition Examination Survey (NHANES) and genome-wide association study (GWAS) summary statistics as well as single-cell sequencing data. Multivariate regression analysis based on cross-sectional data, Mendelian randomization (MR) and multi-omics integration analysis were employed to explore the causal association and potential molecular mechanisms between biological ageing and periodontitis. Additionally, two-step MR mediation analysis explored the risk factors in biological ageing-mediated periodontitis.

RESULTS

We analysed data from 3189 participants in the NHANES data and found that higher biological age was associated with increased risk of periodontitis. MR analyses revealed causal associations between biological age measures and periodontitis risk. Frailty (odds ratio [OR] = 2.08, 95% confidence interval [CI]: 1.04-4.18, p = .039) and GrimAge acceleration (OR = 1.16, 95% CI: 1.01-1.32, p = .033) were causally associated with periodontitis risk, and these results were validated in a large-scale meta-periodontitis GWAS dataset. Additionally, the risk effects of body mass index, waist circumference and lifetime smoking on periodontitis were partially mediated by frailty and GrimAge acceleration.

CONCLUSIONS

Evidence from cross-sectional survey and MR analysis suggests that biological ageing increases the risk of periodontitis. Additionally, improving the associated risk factors can help prevent both ageing and periodontitis.

MIR143HG promotes methylation of transcription factor HOXB7 promoter by recruiting methyltransferase DNMT1 to prevent the progression of colon cancer

In recent years, accumulating evidence has demonstrated the role of long noncoding RNAs (lncRNAs) in colon cancer. We aim to investigate the role of MIR143HG, also known as CARMN (Cardiac mesoderm enhancer-associated noncoding RNA) in colon cancer and explore the related mechanisms. An RNAseq data analysis was performed to screen differentially expressed lncRNAs associated with colon cancer. Next, MIR143HG expression was quantified in colon cancer cells. Moreover, the contributory roles of MIR143HG in the progression of colon cancer with the involvement of DNMT1 and HOXB7 (Homeobox B7) were evaluated after restored MIR143HG or depleted HOXB7. Finally, the effects of MIR143HG were investigated in vivo by measuring tumor formation in nude mice. High-throughput transcriptome sequencing was employed to validate the specific mechanisms by which MIR143HG and HOXB7 affect tumor growth in vivo. MIR143HG was found to be poorly expressed, while HOXB7 was highly expressed in colon cancer. MIR143HG could promote HOXB7 methylation by recruiting DNMT1 to reduce HOXB7 expression. Upregulation of MIR143HG or downregulation of HOXB7 inhibited cell proliferation, invasion and migration and facilitated apoptosis in colon cancer cells so as to delay the progression of colon cancer. The same trend was identified in vivo. Our study provides evidence that restoration of MIR143HG suppressed the progression of colon cancer via downregulation of HOXB7 through DNMT1-mediated HOXB7 promoter methylation. Thus, MIR143HG may be a potential candidate for the treatment of colon cancer.

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

Periodontitis is a prevalent and persistent inflammatory condition that impacts the supporting tissues of the teeth, including the gums and bone. Recent research indicates that mitochondrial dysfunction may be involved in the onset and advancement of periodontitis. The current work sought to reveal the interaction between mitochondrial dysfunction and the immune microenvironment in periodontitis. Public data were acquired from MitoCarta 3.0, Mitomap, and GEO databases. Hub markers were screened out by five integrated machine learning algorithms and verified by laboratory experiments. Single-cell sequencing data were utilized to unravel cell-type specific expression levels of hub genes. An artificial neural network model was constructed to discriminate periodontitis from healthy controls. An unsupervised consensus clustering algorithm revealed mitochondrial dysfunction-related periodontitis subtypes. The immune and mitochondrial characteristics were calculated using CIBERSORTx and ssGSEA algorithms. Two hub mitochondria-related markers (CYP24A1 and HINT3) were identified. Single-cell sequencing data revealed that HINT3 was primarily expressed in dendritic cells, while CYP24A1 was mainly expressed in monocytes. The hub genes based artificial neural network model showed robust diagnostic performance. The unsupervised consensus clustering algorithm revealed two distinct mitochondrial phenotypes. The hub genes exhibited a strong correlation with the immune cell infiltration and mitochondrial respiratory chain complexes. The study identified two hub markers that may serve as potential targets for immunotherapy and provided a novel reference for future investigations into the function of mitochondria in periodontitis.

Epigenetic regulation of osteogenic differentiation of periodontal ligament stem cells in periodontitis

Periodontitis is an inflammatory disease characterized by alveolar bone loss. Periodontal ligament stem cells (PDLSCs) have osteogenic differentiation potential, which can be influenced by epigenetics regulation in periodontitis. Therefore, this review aimed to shed light on the role of different epigenetic mechanisms in the osteogenic differentiation of PDLSCs and to consider the prospects of their possible therapeutic applications in periodontitis. Databases MEDLINE (through PubMed) and Web of Science were searched for the current knowledge of epigenetics in osteogenic differentiation of PDLSCs using the keywords "periodontal ligament stem cells", "epigenetic regulation", "epigenetics", "osteogenic differentiation", and "osteogenesis". All studies introducing epigenetic regulation and PDLSCs were retrieved. This review shows that epigenetic factors like DNMT, KDM6A, HDACi, some miRNAs, and lncRNAs can induce the osteogenic differentiation of PDLSCs in the noninflammatory microenvironment. However, the osteogenic differentiation of PDLSCs is inhibited in the inflammatory microenvironment through the upregulated DNA methylation of osteogenesis-related genes and specific changes in histone modification and noncoding RNA. Epigenetics of osteogenic differentiation of PDLSCs in inflammation exhibits the contrary effect compared with a noninflammatory environment. The application of epigenetic drugs to regulate the abnormal epigenetic status in periodontitis and focus on alveolar bone regeneration is promising.

Epigenome-wide association study using peripheral blood leukocytes identifies genomic regions associated with periodontal disease and edentulism in the Atherosclerosis Risk in Communities study

AIM

To investigate individual susceptibility to periodontitis by conducting an epigenome-wide association study using peripheral blood.

MATERIALS AND METHODS

We included 1077 African American and 457 European American participants of the Atherosclerosis Risk in Communities (ARIC) study who had completed a dental examination or reported being edentulous at Visit 4 and had available data on DNA methylation from Visit 2 or 3. DNA methylation levels were compared by periodontal disease severity and edentulism through discovery analyses and subsequent testing of individual CpGs.

RESULTS

Our discovery analysis replicated findings from a previous study reporting a region in gene ZFP57 (6p22.1) that was significantly hypomethylated in severe periodontal disease compared with no/mild periodontal disease in European American participants. Higher methylation levels in a separate region in an unknown gene (located in Chr10: 743,992-744,958) was associated with significantly higher odds of edentulism compared with no/mild periodontal disease in African American participants. In subsequent CpG testing, four CpGs in a region previously associated with periodontitis located within HOXA4 were significantly hypermethylated in severe periodontal disease compared with no/mild periodontal disease in African American participants (odds ratio per 1 SD increase in methylation level: cg11015251: 1.28 (1.02, 1.61); cg14359292: 1.24 (1.01, 1.54); cg07317062: 1.30 (1.05, 1.61); cg08657492: 1.25 (1.01, 1.55)).

CONCLUSIONS

Our study highlights epigenetic variations in ZPF57 and HOXA4 that are significantly and reproducibly associated with periodontitis. Future studies should evaluate gene regulatory mechanisms in the candidate regions of these loci.

Interleukin 6 and Interleukin 1β hypomethylation and overexpression are common features of apical periodontitis: a case-control study with gingival tissue as control

OBJECTIVES

Apical periodontitis is a periradicular tissue disorder that usually arises from infection by microorganisms in the root canal system resulting in local bone resorption. This usually involves the dysregulation of inflammatory mediators, which can be mediated by epigenetic mechanisms. Thus, the objective of this study was to evaluate Interleukin 6 (IL6) and Interleukin 1β (IL1β) and DNA methylation and gene expression levels in apical periodontitis.

METHODS

Gene expression was analyzed in 60 participants using quantitative polymerase chain reaction, while the methylation levels of IL6 and IL1β promoters were analyzed in 72 patients using pyrosequencing. All statistical analyzes were performed using the GraphPad Prism software version 8.0. The p value was considered statistically significant when < 0.05.

RESULTS

A significantly higher IL6 and IL1β expression levels were observed in cases relative to controls (fold-changes of 27.4 and 11.43, respectively, and p < 0.0001). By comparing the same groups, lower promoter methylation levels were observed for both genes in cases (methylation percentage delta relative to controls of -24.57% and -16.02%, respectively, and p < 0.0001). A significant inverse correlation between gene expression and promoter methylation was observed for both IL6 (p = 0.0002) and IL1β (p = 0.001). Neither IL6 expression nor promoter methylation were significantly associated with cases' age, smoking history, alcohol consumption history or sex. For IL1β, alcoholic cases showed lower methylation level relative to non-alcoholic cases (p = 0.01), while females showed higher methylation levels relative to males (p = 0.03).

CONCLUSIONS

Our data suggest a role for DNA methylation in IL6 and IL1β upregulation in apical periodontitis.

Development of a classification model and an immune-related network based on ferroptosis in periodontitis

BACKGROUND AND OBJECTIVES

Periodontitis is an immunoinflammatory disease characterized by irreversible periodontal attachment loss and bone destruction. Ferroptosis is a kind of immunogenic cell death that depends on the participation of iron ions and is involved in various inflammatory and immune processes. However, information regarding the relationship between ferroptosis and immunomodulation processes in periodontitis is extremely limited. The purpose of this study was to investigate the correlation between ferroptosis and immune responses in periodontitis.

METHODS

Gene expression profiles of gingivae were collected from the Gene Expression Omnibus data portal. After detecting differentially expressed ferroptosis-related genes (FRGs), we used univariate logistic regression analysis followed by logistic least absolute shrinkage and selection operator (LASSO) regression to establish a ferroptosis-related classification model in an attempt to accurately distinguish periodontitis gingival tissues from healthy samples. The infiltration level of immunocytes in periodontitis was then assessed through single-sample gene-set enrichment analysis. Subsequently, we screened out immune-related genes by weighted correlation network analysis and protein-protein interaction (PPI) analysis and constructed an immune-related network based on FRGs and immune-related genes.

RESULTS

A total of 24 differentially expressed FRGs were detected, and an 8-FRG combined signature constituted the classification model. The established model showed outstanding discriminating ability according to the results of receiver operating characteristic (ROC) curve analysis. In addition, the periodontitis samples had a higher degree of immunocyte infiltration. Activated B cells had the strongest positive correlation while macrophages had a strong negative correlation with certain FRGs, and we found that XBP1, ALOX5 and their interacting genes might be crucial genes in the immune-related network.

CONCLUSIONS

The FRG-based classification model had a satisfactory determination ability, which could bring new insights into the pathogenesis of periodontitis. Those genes in the immune-related network, especially hub genes along with XBP1 and ALOX5, would have the potential to serve as promising targets of immunomodulatory treatments for periodontitis.

Transcriptomic Network Regulation of Rat Tooth Germ from Bell Differentiation Stage to Secretory Stage: MAPK Signaling Pathway Is Crucial to Extracellular Matrix Remodeling

Hard tissues make up the vast majority of teeth and are mineralized from the surrounding matrix. If the development of tooth germ is affected during mineralization, hypoplasia of the tooth tissue can occur. To better understand the mechanisms mediating hypoplasia, we need to first study normal development. Using a rodent model, we highlight the transcriptomic changes that occur from the differentiation to secretion stages of mandibular molar germs. The tooth germ was dissected from rats at postnatal day 1.5 or 3.5 for high-throughput sequencing. Combining transcriptome analysis and DNA methylation, we identified 590 differentially expressed genes (436 upregulated and 154 downregulated) and 551 differentially expressed lncRNAs (long noncoding RNA; 369 upregulated and 182 downregulated) which were linked to the biological processes of odontogenesis, amelogenesis, tooth mineralization, and the alteration of extracellular matrix (ECM), especially matrix metalloproteinases (MMPs) and elastin. We found DNA methylation changes in 32 selected fragments involved in 5 chromosomes, 26 targets, and 2 haplotypes. Finally, three novel genes were identified: MMP20, Tgfb3, and Dusp1. Further analysis revealed that MMP20 has a role in odontogenesis and amelogenesis by influencing Slc24a4 and DSPP; Tgfb3 is involved in epithelial cell proliferation, cellular component disassembly process, ECM cellular component, and decomposition of cell components. But lncRNA expression could affect DNA methylation and mRNA expression. Moreover, the degree of DNA methylation could also affect the transcriptome level. Thus, Tgfb3 had no difference in DNA methylation, and Dusp1 conferred no difference at the transcriptome level. These three genes were all enriched in the MAPK pathway and played an important role in ECM remodeling. These data suggest that during the period of the bell differentiation stage to the secretory stage, along with enamel/dentin matrix secretion and hard tissue occurrence, the ECM is remodeled via MAPK signaling.

Roles of Pyroptosis-Related Genes in the Diagnosis and Subtype Classification of Periodontitis

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1β, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis.

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.

Identification of abnormally methylated differentially expressed genes in chronic periodontitis by integrated bioinformatics analysis

BACKGROUND

DNA methylation plays a vital role as an epigenetic change that contributes to chronic periodontitis.

OBJECTIVE

This study aimed to integrate two methylation datasets (GSE173081 and GSE59962) and two gene expression datasets (GSE10334 and GES16134) to identify abnormally methylated differentially expressed genes related to chronic periodontitis.

METHODS

Differentially methylated genes were obtained. Functional enrichment analysis of DMGs was performed. The protein-protein interaction (PPI) network was constructed using STRING and Cytoscape software. Finally, the hub genes were selected from the PPI network by using CytoHubba.

RESULTS

In total, 122 hypomethylated and highly expressed genes were enriched in the biological mechanisms that are involved in the differentiation of extracellular matrix organization, extracellular structure organization, and cell chemotaxis. The three selected hub genes of the PPI network were IL1B, KDR, and MMP9. A total of 122 hypermethylated and lowly expressed genes were identified, and biological processes, such as cornification, epidermis development, skin development, and keratinocyte differentiation were enriched. CDSN DSG1, and KRT2 were identified as the top 3 hub genes of the PPI network.

CONCLUSION

Based on the comprehensive bioinformatics analysis, six hub genes (IL1B, KDR, MMP9, CDSN DSG1, and KRT2) were associated with chronic periodontitis. Our findings provide novel insights into the mechanisms underlying epigenetic changes in chronic periodontitis.

Identification and validation of a classifier based on hub aging-related genes and aging subtypes correlation with immune microenvironment for periodontitis

BACKGROUND

Periodontitis (PD), an age-related disease, is characterized by inflammatory periodontal tissue loss, and with the general aging of the global population, the burden of PD is becoming a major health concern. Nevertheless, the mechanism underlying this phenomenon remains indistinct. We aimed to develop a classification model for PD and explore the relationship between aging subtypes and the immune microenvironment for PD based on bioinformatics analysis.

MATERIALS AND METHODS

The PD-related datasets were acquired from the Gene Expression Omnibus (GEO) database, and aging-related genes (ARGs) were obtained from the Human Aging Genomic Resources (HAGR). Four machine learning algorithms were applied to screen out the hub ARGs. Then, an artificial neural network (ANN) model was constructed and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Consensus clustering was employed to determine the aging expression subtypes. A series of bioinformatics analyses were performed to explore the PD immune microenvironment and its subtypes. The hub aging-related modules were defined using weighted correlation network analysis (WGCNA).

RESULTS

Twenty-seven differentially expressed ARGs were dysregulated and a classifier based on four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) was constructed to diagnose PD with excellent accuracy. Subsequently, the mRNA levels of the hub ARGs were validated by quantitative real-time PCR (qRT-PCR). Based on differentially expressed ARGs, two aging-related subtypes were identified. Distinct biological functions and immune characteristics including infiltrating immunocytes, immunological reaction gene sets, the human leukocyte antigen (HLA) gene, and immune checkpoints were revealed between the subtypes. Additionally, the black module correlated with subtype-1 was manifested as the hub aging-related module and its latent functions were identified.

CONCLUSION

Our findings highlight the critical implications of aging-related genes in modulating the immune microenvironment. Four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) formed a classification model, and accompanied findings revealed the essential role of aging in the immune microenvironment for PD, providing fresh inspiration for PD etiopathogenesis and potential immunotherapy.

Epigenetics in susceptibility, progression, and diagnosis of periodontitis

Periodontitis is characterized by irreversible destruction of periodontal tissue. At present, the accepted etiology of periodontitis is based on a three-factor theory including pathogenic bacteria, host factors, and acquired factors. Periodontitis development usually takes a decade or longer and is therefore called chronic periodontitis (CP). To search for genetic factors associated with CP, several genome-wide association study (GWAS) analyses were conducted; however, polymorphisms associated with CP have not been identified. Epigenetics, on the other hand, involves acquired transcriptional regulatory mechanisms due to reversibly altered chromatin accessibility. Epigenetic status is a condition specific to each tissue and cell, mostly determined by the responses of host cells to stimulations by local factors, like bacterial inflammation, and systemic factors such as nutrition status, metabolic diseases, and health conditions. Significantly, epigenetic status has been linked with the onset and progression of several acquired diseases. Thus, epigenetic factors in periodontal tissues are attractive targets for periodontitis diagnosis and treatments. In this review, we introduce accumulating evidence to reveal the epigenetic background effects related to periodontitis caused by genetic factors, systemic diseases, and local environmental factors, such as smoking, and clarify the underlying mechanisms by which epigenetic alteration influences the susceptibility of periodontitis.

Next-Generation Examination, Diagnosis, and Personalized Medicine in Periodontal Disease

Periodontal disease, a major cause of tooth loss, is an infectious disease caused by bacteria with the additional aspect of being a noncommunicable disease closely related to lifestyle. Tissue destruction based on chronic inflammation is influenced by host and environmental factors. The treatment of periodontal disease varies according to the condition of each individual patient. Although guidelines provide standardized treatment, optimization is difficult because of the wide range of treatment options and variations in the ideas and skills of the treating practitioner. The new medical concepts of “precision medicine” and “personalized medicine” can provide more predictive treatment than conventional methods by stratifying patients in detail and prescribing treatment methods accordingly. This requires a new diagnostic system that integrates information on individual patient backgrounds (biomarkers, genetics, environment, and lifestyle) with conventional medical examination information. Currently, various biomarkers and other new examination indices are being investigated, and studies on periodontal disease-related genes and the complexity of oral bacteria are underway. This review discusses the possibilities and future challenges of precision periodontics and describes the new generation of laboratory methods and advanced periodontal disease treatment approaches as the basis for this new field.

Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics

Recently, there are many researches on signature molecules of periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential protein profiles, functional enrichment analysis, and protein–protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and periodontitis patients. DEPs correlating with periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658 proteins and 115 DEPs, and the 115 DEPs are closely related to inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of periodontitis and are potential signature molecules involved in periodontitis.

Pyroptosis may play a crucial role in modifications of the immune microenvironment in periodontitis

BACKGROUND AND OBJECTIVE

Published studies proved that both pyroptosis and periodontitis owned a substantial relationship with immunity, and recent research revealed a solid correlation between periodontitis and pyroptosis. While abundant findings have confirmed pyroptosis has a strong impact on the tumor microenvironment, the function of pyroptosis in influencing the periodontitis immune microenvironment remains poorly understood. Thus, we aimed to identify pyroptosis-related genes whose expression signature can well discriminate periodontitis from healthy controls and to comprehend the role of pyroptosis in the periodontitis immune microenvironment.

MATERIALS AND METHODS

The periodontitis-related datasets were acquired from the Gene Expression Omnibus (GEO) database. A series of bioinformatics analyses were conducted to investigate the underlying mechanism of pyroptosis in the periodontitis immune microenvironment. Infiltrating immunocytes, immunological reaction gene sets, and the human leukocyte antigen (HLA) gene were all investigated as potential linkages between periodontitis immune microenvironment and pyroptosis.

RESULTS

Twenty-one pyroptosis-related genes were dysregulated. A four-mRNA combined classification model was constructed, and the receiver operating characteristic (ROC) curve analysis demonstrated its prominent classification capabilities. Subsequently, the mRNA levels of the four hub markers (CYCS, CASP3, NOD2, CHMP4B) were validated by quantitative real-time PCR (qRT-PCR). The correlation coefficients between each hub gene and immune characteristics were calculated, and CASP3 exhibited the most significant correlations with the immune characteristics. Furthermore, distinct pyroptosis-related expression patterns were revealed, along with immunological features of each pattern. Afterward, we discovered 1868 pyroptosis phenotype-related genes, 134 of which were related to immunity. According to the functional enrichment analysis, these 134 genes were closely related to cytokine signaling in immune system, and defense response. Finally, a co-expression network was constructed via the 1868 gene expression profiles.

CONCLUSION

Four hub mRNAs (CYCS, CASP3, NOD2, and CHMP4B) formed a classification model and concomitant results revealed the crucial role of pyroptosis in the periodontitis immune microenvironment, providing fresh insights into the etiopathogenesis of periodontitis and potential immunotherapy.

Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality

Background

The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach.

Results

We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%).

Conclusions

In this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably  corrected statistically in well-designed experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04775-y.

Dissecting B/Plasma Cells in Periodontitis at Single-Cell/Bulk Resolution

In recent decades, our understanding of periodontitis has evolved from that based on a gross/histologic level to one on a cellular/molecular level. Previous landscape studies have explored molecular subtyping, diagnosis, and gingival tissue cell decomposition in periodontitis, and meaningful results have been obtained at a transcriptomic level. However, current periodontitis transcriptomic studies lack a finer dissection of the intercommunication between immune cells and the biological processes of specific immune cell subtypes. In this study, we classified 15 immune cell types in periodontitis at a single-cell level and conducted a cell communication analysis based on a multicenter integrated single-cell transcriptome profile, in which plasma cell-generated macrophage migration inhibitory factor can communicate with most other immune cells in periodontitis. A pseudotime analysis focusing on B/plasma cell infiltration in periodontitis revealed 2 distinct cell fates (CFs) for B/plasma cells. In addition, at a bulk tissue level, a single-sample gene set enrichment analysis showed a similar immune cell infiltration trend, and a weighted gene coexpression network analysis identified an immune-related gene module. Combined with the above findings, we used machine learning methods to further narrow down potential gene candidates for developing and validating molecular diagnostic models of periodontitis. Multivariable logistic regression of a large public cohort (68 healthy vs. 235 periodontitis) and an independent validation cohort (12 healthy vs. 7 periodontitis) showed the CF1 signature provides a good discrimination and calibration performance with clinical benefits at a proper threshold probability. Furthermore, quantitative real-time polymerase chain reaction validation of the gene candidates was performed in both snap-frozen gingival tissues and gingival crevicular fluids. Our transcriptomic landscape analysis at both single-cell and bulk tissue resolutions thereby illustrates the B/plasma cell infiltration process in periodontitis and reveals a gene signature that may assist in molecular diagnosis of the disease.

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.

Epigenetic adaptations of the masticatory mucosa to periodontal inflammation

Background

In mucosal barrier interfaces, flexible responses of gene expression to long-term environmental changes allow adaptation and fine-tuning for the balance of host defense and uncontrolled not-resolving inflammation. Epigenetic modifications of the chromatin confer plasticity to the genetic information and give insight into how tissues use the genetic information to adapt to environmental factors. The oral mucosa is particularly exposed to environmental stressors such as a variable microbiota. Likewise, persistent oral inflammation is the most important intrinsic risk factor for the oral inflammatory disease periodontitis and has strong potential to alter DNA-methylation patterns. The aim of the current study was to identify epigenetic changes of the oral masticatory mucosa in response to long-term inflammation that resulted in periodontitis.

Methods and results

Genome-wide CpG methylation of both inflamed and clinically uninflamed solid gingival tissue biopsies of 60 periodontitis cases was analyzed using the Infinium MethylationEPIC BeadChip. We validated and performed cell-type deconvolution for infiltrated immune cells using the EpiDish algorithm. Effect sizes of DMPs in gingival epithelial and fibroblast cells were estimated and adjusted for confounding factors using our recently developed “intercept-method”. In the current EWAS, we identified various genes that showed significantly different methylation between periodontitis-inflamed and uninflamed oral mucosa in periodontitis patients. The strongest differences were observed for genes with roles in wound healing (ROBO2, PTP4A3), cell adhesion (LPXN) and innate immune response (CCL26, DNAJC1, BPI). Enrichment analyses implied a role of epigenetic changes for vesicle trafficking gene sets.

Conclusions

Our results imply specific adaptations of the oral mucosa to a persistent inflammatory environment that involve wound repair, barrier integrity, and innate immune defense.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01190-7.