Molecular differences between chronic and aggressive periodontitis

Wnt signaling in periodontitis

OBJECTIVE

This study aimed to evaluate the Wnt/β-catenin signaling pathway activity in gingival samples obtained from patients with periodontitis.

MATERIALS AND METHODS

Fifteen patients with stage III grade B (SIIIGB) and eleven with stage III grade C (SIIIGC) periodontitis were included and compared to 15 control subjects. β-Catenin, Wnt 3a, Wnt 5a, and Wnt 10b expressions were evaluated by Q-PCR. Topographic localization of tissue β-catenin, Wnt 5a, and Wnt 10b was measured by immunohistochemical analysis. TNF-α was used to assess the inflammatory state of the tissues, while Runx2 was used as a mediator of active destruction.

RESULTS

Wnt 3a, Wnt 5a, and Wnt 10b were significantly higher in gingival tissues in both grades of stage 3 periodontitis compared to the control group (p < 0.05). β-Catenin showed intranuclear staining in connective tissue in periodontitis, while it was confined to intracytoplasmic staining in epithelial tissue and the cell walls in the control group. Wnt5a protein expression was elevated in periodontitis, with the most intense staining observed in the connective tissue of SIIIGC samples. Wnt10b showed the highest density in the connective tissue of patients with periodontitis.

CONCLUSIONS

Our findings suggested that periodontal inflammation disrupts the Wnt/β-catenin signaling pathway.

CLINICAL RELEVANCE

Periodontitis disrupts Wnt signaling in periodontal tissues in parallel with tissue inflammation and changes in morphology. This change in Wnt-related signaling pathways that regulate tissue homeostasis in the immunoinflammatory response may shed light on host-induced tissue destruction in the pathogenesis of the periodontal disease.

Altered expression of MZB1 in periodontitis: A possible link to disease pathogenesis

BACKGROUND

Our previous study explored the molecular signatures of generalized aggressive periodontitis (GAgP) using gingival tissues through omics-based-whole-genome transcriptomic analysis. This continuation study aimed to investigate the whole protein profiling of these gingival samples through liquid chromatography-mass spectroscopy/mass spectroscopy (LC-MS/MS) analysis and to validate the identified proteins through immunohistochemistry to provide further evidence for the quality of the results.

METHODS

In previous study, gene expression patterns were identified in gingival tissues from 23 GAgP and 25 control individuals. In the current study, comparative proteomic analysis was performed on isolated proteins from the same study groups using LC-MS/MS analysis. The data from the transcriptomics study published before and the proteomics data were integrated to reveal any common genes and proteins. Additionally, immunohistochemical analysis was conducted to further investigate the findings.

RESULTS

The most upregulated proteins in patients compared to controls were ITGAM, AZU1, MMP9, BPI, UGGG1, MZB1, TRFL, PDIA6, PRDX4, and PLG. The top six pathways associated with these proteins were involved in innate immune system, post-translational protein phosphorylation, interleukin-4 and -13 signaling, toll-like receptors cascades, and extracellular matrix organization. Based on the integration and validation analysis of transcriptomics and proteomics data, as well as immunohistochemical analysis, MZB1 was identified as a shared gene and protein that were upregulated in the patients.

CONCLUSIONS

MZB1 is a protein that is involved in the development of B cells and the production of antibodies. Its upregulation in periodontitis suggests that there may be a dysregulation of the immune response in this condition, and MZB1 may be a potent biomarker for periodontitis.

Single-cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

OBJECTIVE

This narrative review illuminates on the application of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in periodontitis and highlights the probability of relating cell population and gene signatures to the pathogenesis of the disease for a better diagnosis.

METHODS

An electronic search of the literature in the PubMed database for the keywords, "single cell sequencing" OR "spatial transcriptomics" and "periodontitis" OR "gingiva" OR "oral mucosa" yielded 486 research articles and reviews. After filtering duplicates and careful curation, 22 papers conducted in humans were retained.

RESULTS

The molecular mechanisms underlying periodontitis are complex and involve the interaction of multiple cells and various gene expressions. Most residing cells in periodontal tissues participate in maintaining homeostasis and health, while in addition to infiltrating immune cells contribute to the fight against the bacterial insult.

CONCLUSION

scRNA-seq and ST have provided new insights into the cellular and molecular changes associated with periodontitis for a better diagnosis and clinical outcome. New functions of cells and genes are revealed with these techniques; however, no cells or gene signatures are attributed to periodontitis so far.

Sex and Age Effects on Healthy Gingival Transcriptomic Patterns

Many chronic inflammatory diseases demonstrate demographic associations such as sex, age, and race-ethnicity. Periodontitis has been found to be increased with age and in males. This study used nonhuman primates representing a human-like model for periodontitis and examined the gingival transcriptome stratified on sex and age. Thirty-six Macaca mulatta in 4 age groups-young (<3 y), adolescent (3-7 y), adult (12-15 y), and aged (>17 y)-with a healthy periodontium were used to characterize gene expression in healthy gingival tissues. Gene expression was compared to clinical measures of bleeding on probing (BOP) and probing pocket depth (PPD). The results demonstrated sex differences in number of up- and downregulated genes that increased with age. Female animals generally showed elevated expression of genes related to host immunoinflammatory responses, and males showed increased expression of tissue structural genes. Gene expression correlations with BOP and/or PPD showed minimal overlap between the sexes, while male animals demonstrated substantial overlap in genes that correlated with both BOP and PPD clinical features. A cluster analysis of genes significantly different between sexes showed a clear sex and age discrimination in the young and adolescent animals. In the older groups, the genes clustered predominately by sex, irrespective of age group. A pathway analysis identified that significant gene expression patterns were quite similar in adolescent and adult animals, while the young and aged samples were quite distinct. The results confirmed substantial sex related variations in gingival tissue biology that were affected by age and observed even in adolescent animals. This suggests that "programming" of the gingival tissues related to sex can occur rather early in life and presage variations in future risk for 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.

The role of endoplasmic reticulum stress in the pathophysiology of periodontal disease

The endoplasmic reticulum (ER) is a principal organelle for folding, post-translational modifications and transport of secretory, luminal, and membrane proteins. ER stress is a condition induced by the accumulation of unfolded or misfolded proteins owing to a variety of physiological and pathological phenomena. To overcome the deleterious effects of ER stress, unfolded protein response (UPR) is initiated to translocate and remove the misfolded and accumulated proteins. Plenty of evidence shows the correlation between ER stress/UPR and the pathology of inflammatory disease. Periodontal disease is a chronic inflammatory disease characterized by the irreversible destruction of periodontal tissues, which associates with the onset and progress of several systemic diseases. Periodontopathic bacterium and pro-inflammatory mediators play a pivotal role in the progress of periodontal disease. Besides, cigarette smoke has long been associated with periodontal disease. As an inflammatory disorder of the periodontium, periodontal disease is highly related to ER stress. In this review, we provide an overview of the pathophysiological effect of ER stress on periodontal disease through five aspects as follow: ER stress and periodontal tissue remodeling, including both soft tissue and hard tissue; ER stress and the inflammation; ER stress and systematic effect during the periodontal disease; last but not least, ER stress and the autophagic apoptosis in cells.

Discovery, validation, and diagnostic ability of multiple protein-based biomarkers in saliva and gingival crevicular fluid to distinguish between health and periodontal diseases

Aim

To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque‐induced periodontal disease states.

Materials and Methods

One‐hundred and ninety participants were recruited from two centres (Birmingham and Newcastle upon Tyne, UK) comprising healthy, gingivitis, periodontitis, and edentulous donors. Samples from the Birmingham cohort were analysed by quantitative mass spectrometry proteomics for biomarker discovery. Shortlisted candidate proteins were then verified by enzyme‐linked immunosorbent assay in both cohorts. Leave‐one‐out cross validation logistic regression analysis was used to identify the best performing biomarker panels.

Results

Ninety‐five proteins were identified in both GCF and saliva samples, and 15 candidate proteins were selected based upon differences discovered between the donor groups. The best performing panels to distinguish between: health or gingivitis and periodontitis contained matrix metalloproteinase‐9 (MMP9), S100A8, alpha‐1‐acid glycoprotein (A1AGP), pyruvate kinase, and age (area under the curve [AUC] 0.970); health and gingivitis contained MMP9, S100A8, A1AGP, and pyruvate kinase, but not age (AUC 0.768); and mild to moderate and advanced periodontitis contained MMP9, S100A8, A1AGP, pyruvate kinase, and age (AUC 0.789).

Conclusions

Biomarker panels containing four proteins with and without age as a further parameter can distinguish between periodontal health and disease states.

IL10 promoter rs6667202 polymorphism is functional in health but not in grade c periodontitis patients: A pilot study

BACKGROUND AND OBJECTIVE

Previous studies have demonstrated an association between the IL10 promoter rs6667202 (C > A) single-nucleotide polymorphism (SNP) and grade C, stage 3 or 4 periodontitis (Perio4C) in the Brazilian population, where the altered A allele was detected more frequently in these patients. However, no functional analysis of this variation has yet been performed. Thus, the objective of this preliminary study was to evaluate the functionality of rs6667202 in gingival fibroblasts (GFs) of individuals with Perio4C and with periodontal health (PH) stimulated with Aggregatibacter actinomycetencomitans protein extract (AaPE).

METHODS

Patients with PH and Perio4C were segregated according to their genotype (AA, AC, or CC), and a biopsy was performed to establish the culture of the GFs. After GFs exposure to AaPE at 5 µg/ml for 1.5 h, RNA was extracted to analyze IL10 expression by qPCR. Aliquots of the cell's supernatant were subjected to immunoenzymatic analysis (MAGpix) to detect interleukin-10 (IL-10).

RESULTS

In PH, the genotypes AA and AC are related to less expression of IL10 (p = 0.027 and p < 0.0001) and less production of IL-10 (p = 0.002 and p = 0.001), when compared to CC. In Perio4C, there was no statistical difference between the genotypes (p > 0.05), although a lower IL-10 expression and release compared with PH CC was seen (p = 0.033 and p < 0.001).

CONCLUSION

The rs6667202 SNP is functional in PH, as it decreases the expression and production of IL-10. In Perio4C, other factors may be masking its action by altering the IL-10's response.

Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review

BACKGROUND

Machine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures.

AIM

The aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice.

RELEVANCE FOR PATIENTS

The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.

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.

Subgingival Microbiome and Specialized Pro-Resolving Lipid Mediator Pathway Profiles Are Correlated in Periodontal Inflammation

Failure of resolution pathways in periodontitis is reflected in levels of specialized pro-resolving lipid mediators (SPMs) and SPM pathway markers but their relationship with the subgingival microbiome is unclear. This study aimed to analyze and integrate lipid mediator level, SPM receptor gene expression and subgingival microbiome data in subjects with periodontitis vs. healthy controls. The study included 13 periodontally healthy and 15 periodontitis subjects that were evaluated prior to or after non-surgical periodontal therapy. Samples of gingival tissue and subgingival plaque were collected prior to and 8 weeks after non-surgical treatment; only once in the healthy group. Metabololipidomic analysis was performed to measure levels of SPMs and other relevant lipid mediators in gingiva. qRT-PCR assessed relative gene expression (2-ΔΔCT) of known SPM receptors. 16S rRNA sequencing evaluated the relative abundance of bacterial species in subgingival plaque. Correlations between lipid mediator levels, receptor gene expression and bacterial abundance were analyzed using the Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) and Sparse Partial Least Squares (SPLS) methods. Profiles of lipid mediators, receptor genes and the subgingival microbiome were distinct in the three groups. The strongest correlation existed between lipid mediator profile and subgingival microbiome profile. Multiple lipid mediators and bacterial species were highly correlated (correlation coefficient ≥0.6) in different periodontal conditions. Comparing individual correlated lipid mediators and bacterial species in periodontitis before treatment to healthy controls revealed that one bacterial species, Corynebacterium durum, and five lipid mediators, 5(S)6(R)-DiHETE, 15(S)-HEPE, 7-HDHA, 13-HDHA and 14-HDHA, were identified in both conditions. Comparing individual correlated lipid mediators and bacterial species in periodontitis before treatment to after treatment revealed that one bacterial species, Anaeroglobus geminatus, and four lipid mediators, 5(S)12(S)-DiHETE, RvD1, Maresin 1 and LTB4, were identified in both conditions. Four Selenomonas species were highly correlated with RvD1, RvE3, 5(S)12(S)-DiHETE and proinflammatory mediators in the periodontitis after treatment group. Profiles of lipid mediators, receptor gene and subgingival microbiome are associated with periodontal inflammation and correlated with each other, suggesting inflammation mediated by lipid mediators influences microbial composition in periodontitis. The role of correlated individual lipid mediators and bacterial species in periodontal inflammation have to be further studied.

Transcriptomic phases of periodontitis lesions using the nonhuman primate model

We used a nonhuman primate model of ligature-induced periodontitis to identify patterns of gingival transcriptomic after changes demarcating phases of periodontitis lesions (initiation, progression, resolution). A total of 18 adult Macaca mulatta (12-22 years) had ligatures placed (premolar, 1st molar teeth) in all 4 quadrants. Gingival tissue samples were obtained (baseline, 2 weeks, 1 and 3 months during periodontitis and at 5 months resolution). Gene expression was analyzed by microarray [Rhesus Gene 1.0 ST Array (Affymetrix)]. Compared to baseline, a large array of genes were significantly altered at initiation (n = 6049), early progression (n = 4893), and late progression (n = 5078) of disease, with the preponderance being up-regulated. Additionally, 1918 genes were altered in expression with disease resolution, skewed towards down-regulation. Assessment of the genes demonstrated specific profiles of epithelial, bone/connective tissue, apoptosis/autophagy, metabolism, regulatory, immune, and inflammatory responses that were related to health, stages of disease, and tissues with resolved lesions. Unique transcriptomic profiles occured during the kinetics of the periodontitis lesion exacerbation and remission. We delineated phase specific gene expression profiles of the disease lesion. Detection of these gene products in gingival crevicular fluid samples from human disease may contribute to a better understanding of the biological dynamics of the disease to improve patient management.

Gingival transcriptomics of follicular T cell footprints in progressing periodontitis

Follicular helper T cells (Tfh) cells have been identified in the circulation and in tertiary lymphoid structures in chronic inflammation. Gingival tissues with periodontitis reflect chronic inflammation, so genomic footprints of Tfh cells should occur in these tissues and may differ related to aging effects. Macaca mulatta were used in a ligature-induced periodontitis model [adult group (aged 12-23 years); young group (aged 3-7 years)]. Gingival tissue and subgingival microbiome samples were obtained at matched healthy ligature-induced disease and clinical resolution sites. Microarray analysis examined Tfh genes (n = 54) related to microbiome characteristics documented using 16S MiSeq. An increase in the major transcription factor of Tfh cells, BCL6, was found with disease in both adult and young animals, while master transcription markers of other T cell subsets were either decreased or showed minimal change. Multiple Tfh-related genes, including surface receptors and transcription factors, were also significantly increased during disease. Specific microbiome patterns were significantly associated with profiles indicative of an increased presence/function of Tfh cells. Importantly, unique microbial complexes showed distinctive patterns of interaction with Tfh genes differing in health and disease and with the age of the animals. An increase in Tfh cell responsiveness occurred in the progression of periodontitis, affected by age and related to specific microbial complexes in the oral microbiome. The capacity of gingival Tfh cells to contribute to localized B cell activation and active antibody responses, including affinity maturation, may be critical for controlling periodontal lesions and contributing to limiting and/or resolving the lesions.

Reliability of microarray analysis for studying periodontitis: low consistency in 2 periodontitis cohort data sets from different platforms and an integrative meta-analysis

PURPOSE

The aim of this study was to compare the characteristic expression patterns of advanced periodontitis in 2 cohort data sets analyzed using different microarray platforms, and to identify differentially expressed genes (DEGs) through a meta-analysis of both data sets.

METHODS

Twenty-two patients for cohort 1 and 40 patients for cohort 2 were recruited with the same inclusion criteria. The 2 cohort groups were analyzed using different platforms: Illumina and Agilent. A meta-analysis was performed to increase reliability by removing statistical differences between platforms. An integrative meta-analysis based on an empirical Bayesian methodology (ComBat) was conducted. DEGs for the integrated data sets were identified using the limma package to adjust for age, sex, and platform and compared with the results for cohorts 1 and 2. Clustering and pathway analyses were also performed.

RESULTS

This study detected 557 and 246 DEGs in cohorts 1 and 2, respectively, with 146 and 42 significantly enriched gene ontology (GO) terms. Overlapping between cohorts 1 and 2 was present in 59 DEGs and 18 GO terms. However, only 6 genes from the top 30 enriched DEGs overlapped, and there were no overlapping GO terms in the top 30 enriched pathways. The integrative meta-analysis detected 34 DEGs, of which 10 overlapped in all the integrated data sets of cohorts 1 and 2.

CONCLUSIONS

The characteristic expression pattern differed between periodontitis and the healthy periodontium, but the consistency between the data sets from different cohorts and metadata was too low to suggest specific biomarkers for identifying 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.

Oral microbiome interactions with gingival gene expression patterns for apoptosis, autophagy and hypoxia pathways in progressing periodontitis

Oral mucosal tissues must react with and respond to microbes comprising the oral microbiome ecology. This study examined the interaction of the microbiome with transcriptomic footprints of apoptosis, autophagy and hypoxia pathways during periodontitis. Adult Macaca mulatta (n = 18; 12-23 years of age) exhibiting a healthy periodontium at baseline were used to induce progressing periodontitis through ligature placement around premolar/molar teeth. Gingival tissue samples collected at baseline, 0·5, 1 and 3 months of disease and at 5 months for disease resolution were analysed via microarray. Bacterial samples were collected at identical sites to the host tissues and analysed using MiSeq. Significant changes in apoptosis and hypoxia gene expression occurred with initiation of disease, while autophagy gene changes generally emerged later in disease progression samples. These interlinked pathways contributing to cellular homeostasis showed significant correlations between altered gene expression profiles in apoptosis, autophagy and hypoxia with groups of genes correlated in different directions across health and disease samples. Bacterial complexes were identified that correlated significantly with profiles of host genes in health, disease and resolution for each pathway. These relationships were more robust in health and resolution samples, with less bacterial complex diversity during disease. Using these pathways as cellular responses to stress in the local periodontal environment, the data are consistent with the concept of dysbiosis at the functional genomics level. It appears that the same bacteria in a healthy microbiome may be interfacing with host cells differently than in a disease lesion site and contributing to the tissue destructive processes.

Definitions used for a healthy periodontium-A systematic review

OBJECTIVE

To investigate the explicitness and variability of the definition of periodontal health in the current scientific literature.

MATERIAL AND METHODS

The authors conducted a systematic literature review using PubMed and CENTRAL (2013-01/2019-05) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the guidelines of the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) statement.

RESULTS

A total of 51 papers met the predefined inclusion criteria. Of these, 13 papers did not report any explicit definitions of periodontal health. Out of the 38 remaining articles, half of them used a reference to support their definition and half of them not. The studies published in periodontics-related journals or those that scored a low risk of bias for the methodical quality presented more explicit and valid definitions. Probing pocket depth was the most frequently used individual parameter for defining periodontal health. However, there were substantial variations in the methods of measurement and cut-off values.

CONCLUSIONS

Given the diversity of periodontal health definitions, a cross-study comparison is difficult. The results of this review may be useful in making others aware of the significance of standardizing the definition of a healthy periodontium.

Transcriptomic profiles and their correlations in saliva and gingival tissue biopsy samples from periodontitis and healthy patients

Purpose

This study was conducted to analyze specific RNA expression profiles in gingival tissue and saliva samples in periodontitis patients and healthy individuals, and to determine their correlations in light of the potential use of microarray-based analyses of saliva samples as a periodontal monitoring tool.

Methods

Gingival tissue biopsies and saliva samples from 22 patients (12 with severe periodontitis and 10 with a healthy periodontium) were analyzed using transcriptomic microarray analysis. Differential gene expression was assessed, and pathway and clustering analyses were conducted for the samples. The correlations between the results for the gingival tissue and saliva samples were analyzed at both the gene and pathway levels.

Results

There were 621 differentially expressed genes (DEGs; 320 upregulated and 301 downregulated) in the gingival tissue samples of the periodontitis group, and 154 DEGs (44 upregulated and 110 downregulated) in the saliva samples. Nine of these genes overlapped between the sample types. The periodontitis patients formed a distinct cluster group based on gene expression profiles for both the tissue and saliva samples. Database for Annotation, Visualization and Integrated Discovery analysis revealed 159 enriched pathways from the tissue samples of the periodontitis patients, as well as 110 enriched pathways In the saliva samples. Thirty-four pathways overlapped between the sample types.

Conclusions

The present results indicate the possibility of using the salivary transcriptome to distinguish periodontitis patients from healthy individuals. Further work is required to enhance the extraction of available RNA from saliva samples.

Evidence-based, personalised and minimally invasive treatment for periodontitis patients - the new EFP S3-level clinical treatment guidelines

The 2017 international classification system for periodontal diseases characterises periodontitis patients based upon disease extent, severity and past experience, and in stage IV includes a treatment complexity component. In addition, taking into account well-established risk factors, it aims to estimate the risk for future attachment loss. This classification system draws upon current understanding of disease pathobiology and reflects the complex, multifactorial nature of the periodontitis. It also acknowledges individual patients' risk profiles.Classification and diagnosis are distinct but linked entities, and the inclusion of established risk factors in the system helps signpost the clinician and patient towards a more personalised approach to care provision. The European Federation of Periodontology (EFP) has recently developed an S3-level clinical treatment guideline for stages I to III periodontitis, based upon a rigorous standardised process involving 15 systematic reviews of current evidence and their synthesis by a representative group of experts and stakeholders. The aim is to guide the practitioner through the complex plethora of periodontal management options and to facilitate pragmatic decision-making in full knowledge of the evidence base.Here, we present the structured, stepwise treatment protocols developed by the EFP guideline group and highlight selected specific clinical recommendations. The adapted guidelines for the UK healthcare system will be published in early 2021.

Effect of photobiomodulation therapy as an adjunct to scaling and root planing in a rat model of ligature-induced periodontitis: a histological and radiographic study

This study aimed to histologically and radiographically evaluate the effectiveness of low-intensity laser irradiation of different wavelengths (660 or 808 nm) as an adjunct to scaling and root planing in the treatment of experimental periodontitis in rats. Periodontitis was induced by placing a ligature around the mandibular first molar of the rats. In total, 40 Wistar rats were randomly divided into five groups (n = 8 each): control (CG), periodontal disease (PD), scaling and root planing (SRP), SRP + 660 nm laser (GL660) and SRP + 808 nm laser (GL808). Groups with laser use received radiation at 6 points in the first molar. The animals were euthanized at baseline and at 7 and 14 days after the interventions. Mandibles were surgically removed for histomorphometric and radiographic assessment of periodontal tissues. The GL660 group showed lesser bone loss than the PD group (P < 0.05) and greater alveolar bone margin after 14 days, indicating a better long-term treatment response (P < 0.05). These findings suggest that SRP with the 660 nm laser as an adjunct results in more favorable radiographic and histological responses than the 808 nm laser.

Aggregatibacter actinomycetemcomitans Biofilm Reduces Gingival Epithelial Cell Keratin Expression in an Organotypic Gingival Tissue Culture Model

Epithelial cells express keratins, which are essential for the structural integrity and mechanical strength of the cells. In the junctional epithelium (JE) of the tooth, keratins such as K16, K18, and K19, are expressed, which is typical for non-differentiated and rapidly dividing cells. The expression of K17, K4, and K13 keratins can be induced by injury, bacterial irritation, smoking, and inflammation. In addition, these keratins can be found in the sulcular epithelium and in the JE. Our aim was to estimate the changes in K4, K13, K17, and K19 expression in gingival epithelial cells exposed to Aggregatibacter actinomycetemcomitans. An organotypic gingival mucosa and biofilm co-culture was used as a model system. The effect of the biofilm after 24 h was assessed using immunohistochemistry. The structure of the epithelium was also studied with transmission electron microscopy (TEM). The expression of K17 and K19, as well as total keratin expression, decreased in the suprabasal layers of epithelium, which were in close contact with the A. actinomycetemcomitans biofilm. The effect on keratin expression was biofilm specific. The expression of K4 and K13 was low in all of the tested conditions. When stimulated with the A. actinomycetemcomitans biofilm, the epithelial contact site displayed a thick necrotic layer on the top of the epithelium. The A. actinomycetemcomitans biofilm released vesicles, which were found in close contact with the epithelium. After A. actinomycetemcomitans irritation, gingival epithelial cells may lose their resistance and become more vulnerable to bacterial infection.

Hidden noise in immunologic parameters might explain rapid progression in early-onset periodontitis

To investigate in datasets of immunologic parameters from early-onset and late-onset periodontitis patients (EOP and LOP), the existence of hidden random fluctuations (anomalies or noise), which may be the source for increased frequencies and longer periods of exacerbation, resulting in rapid progression in EOP. Principal component analysis (PCA) was applied on a dataset of 28 immunologic parameters and serum IgG titers against periodontal pathogens derived from 68 EOP and 43 LOP patients. After excluding the PCA parameters that explain the majority of variance in the datasets, i.e. the overall aberrant immune function, the remaining parameters of the residual subspace were analyzed by computing their sample entropy to detect possible anomalies. The performance of entropy anomaly detection was tested by using unsupervised clustering based on a log-likelihood distance yielding parameters with anomalies. An aggregate local outlier factor score (LOF) was used for a supervised classification of EOP and LOP. Entropy values on data for neutrophil chemotaxis, CD4, CD8, CD20 counts and serum IgG titer against Aggregatibacter actinomycetemcomitans indicated the existence of possible anomalies. Unsupervised clustering confirmed that the above parameters are possible sources of anomalies. LOF presented 94% sensitivity and 83% specificity in identifying EOP (87% sensitivity and 83% specificity in 10-fold cross-validation). Any generalization of the result should be performed with caution due to a relatively high false positive rate (17%). Random fluctuations in immunologic parameters from a sample of EOP and LOP patients were detected, suggesting that their existence may cause more frequently periods of disease activity, where the aberrant immune response in EOP patients result in the phenotype "rapid progression".

RNA sequencing for ligature induced periodontitis in mice revealed important role of S100A8 and S100A9 for periodontal destruction

Periodontitis is an inflammatory disease caused by pathogenic oral microorganisms that induce the destruction of periodontal tissue. We sought to identify the relevant differentially expressed genes (DEGs) and clarify the mechanism underlying the rapid alveolar bone loss by using ligature-induced periodontitis in mice. A silk ligature was tied around the maxillary left second molar in 9-week-old C57BL/6 J male mice. In-vivo micro-CT analysis revealed that ligation induced severe bone loss. RNA-sequencing analysis, to examine host responses at 3 days post-ligation, detected 12,853 genes with fragments per kilobase of exon per million mapped reads ≥ 1, and 78 DEGs. Gene ontology term enrichment analysis revealed the expression profiles related to neutrophil chemotaxis and inflammatory responses were significantly enriched in the ligated gingiva. The expression levels of innate immune response-related genes, including S100a8 and S100a9, were significantly higher in the ligated side. S100A8 was strongly detected by immunohistochemistry at the attached epithelium in ligated sites. Inhibition of S100A8 and S100A9 expression revealed that they regulated IL1B and CTSK expression in Ca9-22 cells. Thus, innate immune response-related molecules might be associated with the burst-destruction of periodontal tissue in ligature-induced periodontitis. Especially, S100A8 and S100A9 may play an important role in alveolar bone resorption.

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.

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.

Age and Periodontal Health - Immunological View

PURPOSE OF THE REVIEW

Aging clearly impacts a wide array of systems, in particular the breadth of the immune system leading to immunosenescence, altered immunoactivation, and coincident inflammaging processes. The net result of these changes leads to increased susceptibility to infections, increased neoplastic occurrences, and elevated frequency of autoimmune diseases with aging. However, as the bacteria in the oral microbiome that contribute to the chronic infection of periodontitis is acquired earlier in life, the characteristics of the innate and adaptive immune systems to regulate these members of the autochthonous microbiota across the lifespan remains ill defined.

RECENT FINDINGS

Clear data demonstrate that both cells and molecules of the innate and adaptive immune response are adversely impacted by aging, including in the oral cavity, yielding a reasonable tenet that the increased periodontitis noted in aging populations is reflective of the age-associated immune dysregulation. Additionally, this facet of host-microbe interactions and disease needs to accommodate the population variation in disease onset and progression, which may also reflect an accumulation of environmental stressors and/or decreased protective nutrients that could function at the gene level (ie. epigenetic) or translational level for production and secretion of immune system molecules.

SUMMARY

Finally, the majority of studies of aging and periodontitis have emphasized the increased prevalence/severity of disease with aging, all based upon chronological age. However, evolving areas of study focusing on "biological aging" to help account for population variation in disease expression, may suggest that chronic periodontitis represents a co-morbidity that contributes to "gerovulnerability" within the population.

Association between circulating levels of heat-shock protein 27 and aggressive periodontitis

Heat-shock protein (Hsp) 27 is a major intracellular molecular chaperone and controller of intracellular responses to inflammatory signals. In the extracellular space, recombinant Hsp27 has been described to exert anti-inflammatory activities. The aim of this study was to assess the association between circulating levels of Hsp27 and different types of periodontitis. Pro- and anti-inflammatory cytokines and the stress proteins Hsp27 and Hsp60 with proposed anti- and pro-inflammatory properties, respectively, were measured by two-site ELISA in the serum of patients with aggressive periodontitis (AgP, n = 30), chronic periodontitis (CP, n = 29) and periodontally healthy controls (H, n = 28). Furthermore, Hsp27 and Hsp60 levels were also measured longitudinally in 12 AgP patients at 6 time points up to 3 months after treatment. AgP patients had lower levels of Hsp27 compared to CP patients and healthy subjects (adjusted one-way ANOVA, p < 0.001, followed by post hoc Tukey HSD comparisons), while no differences in levels of Hsp60 or cytokines between the three groups were detected. In CP patients and H subjects, the systemic Hsp27 levels correlated with Hsp60 (r = 0.43, p < 0.001; r = 0.59, p < 0.001, respectively) and with pro-inflammatory cytokines TNF-α (r = 0.48, p < 0.001; r = 0.55, p < 0.001, respectively) and IL-6 (r = 0.44, p < 0.01). However, no such correlations were detected in AgP cases. No consistent temporal patterns of changes of Hsp27 concentration were detected across AgP patients following periodontal treatment. This study provides the first evidence that Hsp27 may be differentially expressed and regulated in AgP patients as compared with CP patients and healthy individuals.

Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics

BACKGROUND

Recent findings about the differential gene expression signature of periodontal lesions have raised the hypothesis of distinctive biological phenotypes expressed by generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients. Therefore, this cross-sectional investigation was planned, primarily, to determine the ability of nuclear magnetic resonance (NMR) spectroscopic analysis of unstimulated whole saliva to discriminate GCP and GAgP disease-specific metabolomic fingerprint and, secondarily, to assess potential metabolites discriminating periodontitis patients from periodontally healthy individuals (HI).

METHODS

NMR-metabolomics spectra were acquired from salivary samples of patients with a clinical diagnosis of GCP (n = 33) or GAgP (n = 28) and from HI (n = 39). The clustering of HI, GCP, and GAgP patients was achieved by using a combination of the Principal Component Analysis and Canonical Correlation Analysis on the NMR profiles.

RESULTS

These analyses revealed a significant predictive accuracy discriminating HI from GCP, and discriminating HI from GAgP patients (both 81%). In contrast, the GAgP and GCP saliva samples seem to belong to the same metabolic space (60% predictive accuracy). Significantly lower levels (P < 0.05) of pyruvate, N-acetyl groups and lactate and higher levels (P < 0.05) of proline, phenylalanine, and tyrosine were found in GCP and GAgP patients compared with HI.

CONCLUSIONS

Within the limitations of this study, CGP and GAgP metabolomic profiles were not unequivocally discriminated through a NMR-based spectroscopic analysis of saliva.

Interleukin-1β rs1143627 polymorphism with susceptibility to periodontal disease

Association between interleukin-1 beta (IL-1β) rs1143627 polymorphism and periodontal disease susceptibility was inconsistent; hence we performed this meta-analysis to explore the precise correlation between them. The degree of association was appraised through calculating pooled odds ratio (OR) and its 95% confidence interval (CI). The databases known as PubMed, Embase, and Chinese National Knowledge Infrastructure were searched up to October 26, 2016. A total of 8 eligible case-control studies were finally included, which involved 229 aggressive periodontitis patients, 382 chronic periodontitis patients, and 555 healthy controls. All the five genetic models revealed a non-significant association between IL-1β rs1143627 polymorphism and periodontal disease susceptibility (TT vs. CC: OR = 1.22, 95% CI = 0.80-1.87; CT+TT vs. CC: OR = 0.66, 95% CI = 0.44-1.01; TT vs. CT + CC: OR = 1.19, 95% CI = 0.81-1.74; T vs. C: OR = 0.92, 95% CI = 0.81-1.12; CT vs. CC: OR = 0.92, 95% CI = 0.69-1.23). Sensitivity analyses indicated that the results were robust and the subgroup analyses reached similar conclusions. IL-1β rs1143627 polymorphism is not related to periodontal disease susceptibility in the overall population based on the current evidence, but further studies are required in more large scale sample size with risk factor adjusted.

Integrated analysis of long noncoding RNA-associated competing endogenous RNA network in periodontitis

BACKGROUND AND OBJECTIVES

Long noncoding RNAs (lncRNAs) play critical and complex roles in regulating various biological processes of periodontitis. This bioinformatic study aims to construct a putative competing endogenous RNA (ceRNA) network by integrating lncRNA, miRNA and mRNA expression, based on high-throughput RNA sequencing and microarray data about periodontitis.

MATERIAL AND METHODS

Data from 1 miRNA and 3 mRNA expression profiles were obtained to construct the lncRNA-associated ceRNA network. Gene Ontology enrichment analysis and pathway analysis were performed using the Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes. A protein-protein interaction network was constructed based on the Search Tool for the retrieval of Interacting Genes/Proteins. Transcription factors (TFs) of differentially expressed genes were identified based on TRANSFAC database and then a regulatory network was constructed.

RESULTS

Through constructing the dysregulated ceRNA network, 6 genes (HSPA4L, PANK3, YOD1, CTNNBIP1, EVI2B, ITGAL) and 3 miRNAs (miR-125a-3p, miR-200a, miR-142-3p) were detected. Three lncRNAs (MALAT1, TUG1, FGD5-AS1) were found to target both miR-125a-3p and miR-142-3p in this ceRNA network. Protein-protein interaction network analysis identified several hub genes, including VCAM1, ITGA4, UBC, LYN and SSX2IP. Three pathways (cytokine-cytokine receptor, cell adhesion molecules, chemokine signaling pathway) were identified to be overlapping results with the previous bioinformatics studies in periodontitis. Moreover, 2 TFs including FOS and EGR were identified to be involved in the regulatory network of the differentially expressed genes-TFs in periodontitis.

CONCLUSION

These findings suggest that 6 mRNAs (HSPA4L, PANK3, YOD1, CTNNBIP1, EVI2B, ITGAL), 3 miRNAs (hsa-miR-125a-3p, hsa-miR-200a, hsa-miR-142-3p) and 3 lncRNAs (MALAT1, TUG1, FGD5-AS1) might be involved in the lncRNA-associated ceRNA network of periodontitis. This study sought to illuminate further the genetic and epigenetic mechanisms of periodontitis through constructing an lncRNA-associated ceRNA network.

Amelotin gene expression is temporarily being upregulated at the initiation of apoptosis induced by TGFβ1 in mouse gingival epithelial cells

Amelotin (AMTN) is expressed and secreted by ameloblasts in the maturation stage of amelogenesis and persist with low levels in the junctional epithelium (JE) of erupted teeth. The purpose of this study is to investigate the transcriptional regulation of the AMTN gene by transforming growth factor beta1 (TGFβ1) in gingival epithelial (GE1) cells in the apoptosis phase. Apoptosis was evaluated by the fragmentation of chromosomal DNA and TUNEL staining. A real-time PCR was carried out to examine the AMTN mRNA levels induced by TGFβ1 and Smad3 overexpression. Transient transfection analyses were completed using the various lengths of mouse AMTN gene promoter constructs with or without TGFβ1. Chromatin immunoprecipitation (ChIP) assays were performed to investigate the Smad3 bindings to the AMTN gene promoter by TGFβ1. TGFβ1-induced apoptosis in GE1 cells were detected at 24 and 48 h by DNA fragmentation and TUNEL staining. AMTN mRNA levels increased at 6 h and reached maximum at 24 h in GE1 cells. Luciferase activities of the mouse AMTN gene promoter constructs were induced by TGFβ1. The results of the ChIP assays showed that there was an increase in Smad3 binding to Smad-binding element (SBE)#1 and SBE#2 after stimulation by TGFβ1. Immunohistochemical localization of AMTN was detected in the JE, and the AMTN protein levels in Smad3-deficient mice were decreased compared with wild-type mice. AMTN mRNA levels were induced at the initiation of apoptosis by TGFβ1, which mediated through the Smad3 bindings to SBEs in the mouse AMTN gene promoter.

CCR5Δ32 (rs333) polymorphism is associated with decreased risk of chronic and aggressive periodontitis: A case-control analysis based in disease resistance and susceptibility phenotypes

Chronic and aggressive periodontitis are infectious diseases characterized by the irreversible destruction of periodontal tissues, which is mediated by the host inflammatory immune response triggered by periodontal infection. The chemokine receptor CCR5 play an important role in disease pathogenesis, contributing to pro-inflammatory response and osteoclastogenesis. CCR5Δ32 (rs333) is a loss-of-function mutation in the CCR5 gene, which can potentially modulate the host response and, consequently periodontitis outcome. Thus, we investigated the effect of the CCR5Δ32 mutation over the risk to suffer periodontitis in a cohort of Brazilian patients (total N=699), representative of disease susceptibility (chronic periodontitis, N=197; and aggressive periodontitis, N=91) or resistance (chronic gingivitis, N=193) phenotypes, and healthy subjects (N=218). Additionally, we assayed the influence of CCR5Δ32 in the expression of the biomarkers TNFα, IL-1β, IL-10, IL-6, IFN-γ and T-bet, and key periodontal pathogens P. gingivalis, T. forsythia, and T. denticola. In the association analysis of resistant versus susceptible subjects, CCR5Δ32 mutant allele-carriers proved significantly protected against chronic (OR 0.49; 95% CI 0.29-0.83; p-value 0.01) and aggressive (OR 0.46; 95% CI 0.22-0.94; p-value 0.03) periodontitis. Further, heterozygous subjects exhibited significantly decreased expression of TNFα in periodontal tissues, pointing to a functional effect of the mutation in periodontal tissues during the progression of the disease. Conversely, no significant changes were observed in the presence or quantity of the periodontal pathogens P. gingivalis, T. forsythia, and T. denticola in the subgingival biofilm that could be attributable to the mutant genotype.

Oral Health: The Need for Both Conventional Microbial and Molecular Characterization

This study aims to consider the microbial distribution in oral disease, as well as gene analysis and expression, in elucidating: 1, the fundamental underpinnings of oral disease, and 2, the potential relationship between oral diseases and systemic health. A key focus is identifying the microbiota associated with oral disease manifestations characterized by both conventional microbiological and molecular methods. Variations in the observed microbial populations characterized by conventional and molecular approaches have been identified for caries, periodontitis, peri-implantitis, and stomatitis. The discovery of therapeutic approaches for oral disease will require comprehensive microbial and genomic analysis. This study evaluated the current state of the relevant microbial and genomic information for several prevalent oral diseases.

The Role of Apoptotic Factors in Assessing Progression of Periodontal Disease

Background

The mechanisms responsible for periodontal disease progression remain unclear. However, recent studies suggest that apoptosis may be one mechanism underlying the pathophysiology of periodontal disease progression. This pilot study is the 3 month follow-up of our published baseline study on the presence of apoptotic factors in serum, saliva, and gingival crevicular fluid (GCF) and their association with periodontal disease severity and activity.

Methods

GCF samples were obtained from 37 adult patients with chronic periodontitis (CP) and 7 healthy controls. Clinical measurements, including probing depth (PD), clinical attachment level (CAL), and radiographs, were used to evaluate data by sites and to classify patients into healthy, mild, and moderate/severe CP groups. Enzyme-linked immunosorbent assays were used to measure apoptosis or DNA fragmentation levels in GCF. Western immunoblotting was used to detect several apoptotic proteins, Fas, FasL, sFasL, and caspase-3 expression and its cleavage products in GCF.

Results

At the patient level clinical and apoptotic measurements change minimally over time. At the site level, DNA fragmentation levels increase with increasing PDs at 3 months and baseline. Apoptotic protein expression exhibits increasing trends with increasing PDs at baseline and 3 months. FasL and Active FasL show a high specificity and PPV; low sensitivity and NPV. Caspase-3 products (ProCas35K and Active Cas) show a high PPV with moderate to high specificity; low sensitivity and NPV. ProCas70K shows a high PPV with moderate to high sensitivity; low specificity and NPV.

Conclusion

Factors associated with apoptosis show minimal changes in expression in periodontitis groups in comparison to a healthy group over a short time interval (3 months). However, at the site level, apoptotic factors (DNA fragmentation and apoptotic proteins) exhibit significant increases or increasing trends with increasing PDs at any time point examined (baseline or 3 months). Several of these apoptotic factors also exhibit a high sensitivity and high positive predictive value. Thus, apoptotic molecules may be helpful biomarkers of disease status at any point in time.

Gingival crevicular fluid as a source of biomarkers for periodontitis

In evaluating the pathogenesis of periodontal diseases, the diagnostic potential of gingival crevicular fluid has been extensively explored during the last twenty years, from initially just confirming health and disease states to more recently investigating it as a potential prognostic tool. As host susceptibility is a critical determinant in periodontal disease pathogenesis, the inflammatory mediator levels present in gingival crevicular fluid represent relevant risk indicators for disease activity. Considerable work has been carried out to identify the many different cytokine inflammatory pathways and microbial stimuli that are associated with periodontal disease pathogenesis. Now, 'omics' approaches aim to summarize how these pathways interact and probably converge to create critical inflammatory networks. More recently, gingival crevicular fluid metabolomics appears promising as an additional diagnostic method. Biofilm structure and the host inflammatory response to the microbial challenge may induce specific inflammatory signatures. Host genetics and epigenetics may also modulate microbial colonization, adding to the multiplicity of potential causal pathways. Omics analyses of gingival crevicular fluid, measuring microbial and host interactions in association with the onset and progression of periodontal diseases, still show the potential to expand the landscape for the discovery of diagnostic, prognostic and therapeutic markers.

Genetic Factors and the Risk of Periodontitis Development: Findings from a Systematic Review Composed of 13 Studies of Meta-Analysis with 71,531 Participants

Purpose. This work aimed to synthesize the results of recent meta-analysis focusing on polymorphism in inflammatory mediators and its relation with the risk of periodontitis development. Materials and Methods. A systematic search was conducted using databases for publications prior to October 2016. Three examiners extracted data from articles with a clear association between polymorphisms in the inflammatory mediator gene and the development of periodontitis through meta-analysis using the fixed or randomized statistical models to calculate the Odds Ratio with values of P < 0.05 considered significant. Results. A total of 13 meta-analysis articles with 25 polymorphisms in seven interleukins (IL-1A, IL-1B, IL-4, IL-6, IL-8, IL-10, and IL-18), three cellular receptors (Fcγ receptors: FCGR2A, FCGR3A, and FCGR3B), and five inflammatory mediators (COX-2, MMP-2, MMP-3, MMP-8, and MMP-9), with a total of 71,531 participants, approaching different classifications of the disease. Conclusion. The study demonstrated that polymorphisms in the IL-1A, IL-1B, IL-6, IL-10, MMP-3 (chronic form), and MMP-9 (chronic form) polymorphisms were significantly associated with the risk of developing periodontitis, whereas other polymorphisms in the IL-4, IL-8, IL-18, Fcγ, COX-2, MMP-2, MMP-3 (aggressive), MMP-8, and MMP-9 (aggressive) polymorphisms had no significant association with risk of developing periodontitis.

Integrated biomarker profiling of smokers with periodontitis

BACKGROUND

In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups.

AIM

The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers).

MATERIAL & METHODS

The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups.

RESULTS

Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups.

CONCLUSIONS

Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient-specific risk profiles and tailored interventions.

Molecular aspects of the pathogenesis of periodontitis

The past decade of basic research in periodontology has driven radical changes in our understanding and perceptions of the pathogenic processes that drive periodontal tissue destruction. The core elements of the classical model of disease pathogenesis, developed by Page & Kornman in 1997, remain pertinent today; however, our understanding of the dynamic interactions between the various microbial and host factors has changed significantly. The molecular era has unraveled aspects of genetics, epigenetics, lifestyle and environmental factors that, in combination, influence biofilm composition and the host's inflammatory immune response, creating a heterogenic biological phenotype that we label as 'periodontitis'. In this volume of Periodontology 2000, experts in their respective fields discuss these emerging concepts, such as a health-promoting biofilm being essential for periodontal stability, involving a true symbiosis between resident microbial species and each other and also with the host response to that biofilm. Rather like the gut microbiome, changes in the local environment, which may include inflammatory response mediators or viruses, conspire to drive dysbiosis and create a biofilm that supports pathogenic species capable of propagating disease. The host response is now recognized as the major contributor to periodontal tissue damage in what becomes a dysfunctional, poorly targeted and nonresolving inflammation that only serves to nourish and sustain the dysbiosis. The role of epithelial cells in signaling to the immune system is becoming clearer, as is the role of dendritic cells as transporters of periodontal pathogens to distant sites within the body, namely metastatic infection. The involvement of nontraditional immune cells, such as natural killer cells, is being recognized, and the simple balance between T-helper 1- and T-helper 2-type T-cell populations has become less clear with the emergence of T-regulatory cells, T-helper 17 cells and follicular helper cells. The dominance of the neutrophil has emerged, not only as a potential destructor when poorly regulated but as an equally unpredictable effector cell for specific B-cell immunity. The latter has emerged, in part, from the realization that neutrophils live for 5.4 days in the circulation, rather than for 24 h, and are also schizophrenic in nature, being powerful synthesizers of proinflammatory cytokines but also responding to prostaglandin signals to trigger a switch to a pro-resolving phenotype that appears capable of regenerating the structure and function of healthy tissue. Key to these outcomes are the molecular signaling pathways that dominate at any one time, but even these are influenced by microRNAs capable of 'silencing' certain inflammatory genes. This volume of Periodontology 2000 tries to draw these complex new learnings into a contemporary model of disease pathogenesis, in which inflammation and dysbiosis impact upon whether the outcome is driven toward acute resolution and stability, chronic resolution and repair, or failed resolution and ongoing periodontal tissue destruction.

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.

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques

Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of -omics data.