Molecular differences between chronic and aggressive periodontitis

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.

MicroRNAs Regulate Cytokine Responses in Gingival Epithelial Cells

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

Transcriptome Analysis of B Cell Immune Functions in Periodontitis: Mucosal Tissue Responses to the Oral Microbiome in Aging

Evidence has shown activation of T and B cells in gingival tissues in experimental models and in humans diagnosed with periodontitis. The results of this adaptive immune response are noted both locally and systemically with antigenic specificity for an array of oral bacteria, including periodontopathic species, e.g., Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. It has been recognized through epidemiological studies and clinical observations that the prevalence of periodontitis increases with age. This report describes our studies evaluating gingival tissue transcriptomes in humans and specifically exploiting the use of a non-human primate model of naturally occurring periodontitis to delineate gingival mucosal tissue gene expression profiles focusing on cells/genes critical for the development of humoral adaptive immune responses. Patterns of B cell and plasmacyte genes were altered in aging healthy gingival tissues. Substantial increases in a large number of genes reflecting antigen-dependent activation, B cell activation, B cell proliferation, and B cell differentiation/maturation were observed in periodontitis in adults and aged animals. Finally, evaluation of the relationship of these gene expression patterns with those of various tissue destructive molecules (MMP2, MMP9, CTSK, TNFα, and RANKL) showed a greater frequency of positive correlations in healthy tissues versus periodontitis tissues, with only MMP9 correlations similar between the two tissue types. These results are consistent with B cell response activities in healthy tissues potentially contributing to muting the effects of the tissue destructive biomolecules, whereas with periodontitis this relationship is adversely affected and enabling a progression of tissue destructive events.

Identification of Master Regulator Genes in Human Periodontitis

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

Reosseointegration Following Regenerative Therapy of Tissue-Engineered Bone in a Canine Model of Experimental Peri-Implantitis

BACKGROUND

Due to the existence of inflammation and limited osteogenesis on the precontaminated implant surface, reosseointegration is difficult to realize by current therapies. Tissue-engineering strategy has been proved quite effective in intractable bone defect situation.

PURPOSE

This study was designed to see whether the adoption of tissue-engineered bone complex of adipose-derived stem cells (ASCs) and bone morphogenetic protein-2 (BMP-2) gene delivery would work efficiently in the correction of experimental peri-implantitis.

METHODS

All premolars in both side of mandibular were removed from six beagle canines three months before implant placement. Typical peri-implantitis were then induced by three month ligature placement. After the implementation of identical anti-bacterial and mechanical debridement therapy, the shaped peri-implant defect were stuffed with four groups of constructs, as A: beta tricalcium phosphate (β-TCP); B: β-TCP with ASCs; C: β-TCP with enhanced green fluorescent protein gene transduced ASCs (AdGFP-ASCs); and D: β-TCP with bone morphogenetic protein-2 gene-modified ASCs (AdBMP-2-ASCs). Systematic radiographic, micro-CT, and histomorphometrical assessments were performed.

RESULTS

After six months of healing, more bone formation and reosseointegration was found around the implant of groups B and C than group A. And group D further promoted the new bone height and reosseointegration percentage. Moreover, sequential fluorescence labeling tells that group D exhibited the quickest and strongest bone formation on the cleaned implant surface during the entire observation period as compared to the other three groups.

CONCLUSIONS

These data demonstrated that tissue engineered bone of ASCs, BMP-2 gene delivery, and β-TCP could exert powerful therapeutic effect on peri-implantitis as expected, which may suggest a feasible way to maintain the stability and masticatory function of dental implant.

Aggressive Periodontitis: microbes and host response, who to blame?

A paradigm shift several decades ago elucidated that aggressive periodontitis (AgP) was not a degenerative disorder but a rapid progressive form of plaque-induced inflammatory periodontal disease. Ensuing years of research have led to linkage analysis identification of specific genetic defects responsible for AgP in some families and to the finding that subgingival detection of A. actinomycet-emcomitans JP2 clone is a predictive factor for disease onset and progression. However, rather disappointingly, these ‘proven’ risk factors are only detected in a small subset of AgP cases. Recent advances are leading to a new paradigm shift, with the realization that genetically-driven dysbiotic changes in the subgingival microbiota may predispose to a cascade of events leading to the rapid periodontal tissue destruction seen in AgP. This review tries to dissect the existing literature on the host response-microbial axis of AgP and to propose possible pathogenic pathways in line with current theories.

Regulation of p53 under hypoxic and inflammatory conditions in periodontium

OBJECTIVES

Different studies suggest that inflammation as well as hypoxia leads to an increase of p53 protein levels. However, the implication of p53 during oral inflammatory processes is still unknown. The aim of this study was therefore to investigate the effect of hypoxia and inflammation on p53 regulation in human periodontium in vitro and in vivo.

MATERIALS AND METHODS

Under hypoxic and normoxic conditions, human primary periodontal ligament (PDL) fibroblasts (n = 9) were stimulated with lipopolysaccharides (LPS) from Porphyromonas gingivalis (P.g.), a periodontal pathogenic bacterium. After different time points, cell viability was tested; p53 gene expression, protein synthesis, and activation were measured using quantitative RT-PCR, immunoblotting, and immunofluorescence. Moreover, healthy and inflamed periodontal tissues were obtained from 12 donors to analyze p53 protein in oral inflammatory diseases by immunohistochemistry.

RESULTS

LPS-P.g. and hypoxia initially induced a significant upregulation of p53 mRNA expression and p53 protein levels. Nuclear translocation of p53 after inflammatory stimulation supported these findings. Hypoxia first enhanced p53 levels, but after 24 h of incubation, protein levels decreased, which was accompanied by an improvement of PDL cell viability. Immunohistochemistry revealed an elevation of p53 immunoreactivity in accordance to the progression of periodontal inflammation.

CONCLUSIONS

Our data indicate that p53 plays a pivotal role in PDL cell homeostasis and seems to be upregulated in oral inflammatory diseases.

CLINICAL RELEVANCE

Upregulation of p53 may promote the destruction of periodontal integrity. A possible relationship with carcinogenesis may be discussed.

Genome-wide transcriptome induced by Porphyromonas gingivalis LPS supports the notion of host-derived periodontal destruction and its association with systemic diseases

Chronic periodontitis (CP) is a prevalent pathogen-associated inflammatory disorder characterized by the destruction of tooth-supporting tissues, and linked to several systemic diseases. Both the periodontopathogen Porphyromonas gingivalis (Pg), and the genetically determined host immune response, are hypothesized to play a crucial role in this association. To identify new target genes for CP and its associated systemic diseases, we investigated the transcriptome induced by Pg in human monocytes using a genome-wide approach. Monocytes were isolated from healthy male volunteers of European origin and challenged with the Pg virulence factor LPS. Array-based gene expression analysis comprising >47,000 transcripts was performed followed by pathway analyses. Transcriptional data were validated by protein and cell surface markers. LPS Pg challenge led to the significant induction of 902 transcripts. Besides known periodontitis-associated targets, several new candidates were identified (CCL23↑, INDO↑, GBP 1/4↑, CFB↑, ISG20↑, MIR155HG↑, DHRS9↓). Moreover, various transcripts correspond to the host immune response, and have been linked to cancer, atherosclerosis and arthritis, thus highlighting the systemic impact of CP. Protein data of immunological markers validated our results. The present findings expand understanding of Pg elicited immune responses, and indicate new target genes and pathways of relevance to diagnostic and therapeutic strategies.

Mini but mighty: microRNAs in the pathobiology of periodontal disease

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

Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis

This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM). Our findings demonstrate that performance measures (sensitivity, specificity and accuracy) of traditional classification as single classifier were comparable to that of the SVA counterparts using clinical labels of the samples as ground truth. However, unlike traditional single classifier approaches, the normalized ensemble vote-counts from SVA revealed varying proclivity of the subjects for each of the disease groups. More importantly, the SVA identified a subset of gingivitis and periodontitis samples that demonstrated a biological proclivity commensurate with the other clinical group. This subset was confirmed across SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM. Heatmap visualization of their ensemble sets revealed lack of consensus between these subsets and the rest of the samples within the respective disease groups indicating the unique nature of the patients in these subsets. While the source of variation is not known, the results presented clearly elucidate the need for novel approaches that accommodate inherent heterogeneity and personalized variations within disease groups in diagnostic characterization. The proposed approach falls within the scope of P4 medicine (predictive, preventive, personalized, and participatory) with the ability to identify unique patient profiles that may predict specific disease trajectories and targeted disease management.

Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods

BACKGROUND

Despite of numerous studies on periodontitis, the mechanism underlying the progression of periodontitis still remains largely unknown. This study aimed to have an expression profiling comparison between periodontitis and normal control and to identify more candidate genes involved in periodontitis and to gain more insights into the molecular mechanisms of periodontitis progression.

METHODS

The gene expression profile of GSE16134, comprising 241 gingival tissue specimens and 69 healthy samples as control which were obtained from 120 systemically healthy patients with periodontitis (65 with chronic and 55 with aggressive periodontitis), was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in periodontitis samples were screened using the limma package in R compared with control samples. Gene Ontology (GO) and pathway enrichment analysis upon the DEGs were carried out using Hypergeometric Distribution test. Protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape, followed by module selection from the PPI network using MCODE plugin. Moreover, transcription factors (TFs) of these DEGs were identified based on TRANSFAC database and then a regulatory network was constructed.

RESULTS

Totally, 762 DEGs (507 up- and 255 down-regulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as immune system process, cell activation biological processes, cytokine-cytokine receptor interaction, and metabolic pathways. Cathepsin S (CTSS) and pleckstrin (PLEK) were the hub proteins in the PPI network and 3 significant modules were selected. Moreover, 19 TFs were identified including interferon regulatory factor 8 (IRF8), and FBJ murine osteosarcoma viral oncogene homolog B (FOSB).

CONCLUSION

This study identified genes (CTSS, PLEK, IRF-8, PTGS2, and FOSB) that may be involved in the development and progression of periodontitis.

Targeted salivary biomarkers for discrimination of periodontal health and disease(s)

Generally, clinical parameters are used in dental practice for periodontal disease, yet several drawbacks exist with the clinical standards for addressing the needs of the public at large in determining the current status/progression of the disease, and requiring a significant amount of damage before these parameters can document disease. Therefore, a quick, easy and reliable method of assessing and monitoring periodontal disease should provide important diagnostic information that improves and speeds treatment decisions and moves the field closer to individualized point-of-care diagnostics.

OBJECTIVE

This report provides results for a saliva-based diagnostic approach for periodontal health and disease based upon the abundance of salivary analytes coincident with disease, and the significant progress already made in the identification of discriminatory salivary biomarkers of periodontitis.

METHODS

We evaluated biomarkers representing various phases of periodontitis initiation and progression (IL-1ß, IL-6, MMP-8, MIP-1α) in whole saliva from 209 subjects categorized with periodontal health, gingivitis, and periodontitis.

RESULTS

Evaluation of the salivary analytes demonstrated utility for individual biomarkers to differentiate periodontitis from health. Inclusion of gingivitis patients into the analyses provided a more robust basis to estimate the value of each of these analytes. Various clinical and statistical approaches showed that pairs or panels of the analytes were able to increase the sensitivity and specificity for the identification of disease.

CONCLUSIONS

Salivary concentrations of IL-1ß, IL-6, MMP-8, MIP-1α alone and in combination are able to distinguish health from gingivitis and periodontitis. The data clearly demonstrated a heterogeneity in response profiles of these analytes that supports the need for refinement of the standard clinical classifications if we are to move toward precision/personalized dentistry for the twenty-first century.

[Precision stomatology: current status and challenges]

The completion of human genome project and the progress in medical practice have inevitably lead to the development of precision medicine, which is a medical model that proposes the customization of medical care including medical decisions, practices, and/or medical products with patient's genetic background, environmental factors and life behavior being taken into account. The current work proposed precision stomatology for the first time, and by integrating data reported in recent literature, we described the current practice of precision stomatology in multiple disciplines in modem dentistry. The clinical significance of precision stomatology and its future challenges have also been discussed.

The expression of NLRP3, NLRP1 and AIM2 in the gingival tissue of periodontitis patients: RT-PCR study and immunohistochemistry

OBJECTIVE

Detecting the distribution and intensity of NLRP3, NLRP1 and AIM2 expression in different types of periodontitis gingival tissues.

MATERIALS AND METHODS

A total of 65 gingival tissues were collected from clinic and been divided into three groups: patients with chronic periodontitis (CP), patients with generalized aggressive periodontitis (G-AgP) and healthy control subjects. Real-time polymerase chain reaction (RT-PCR) was performed to detect mRNA expression of NLRP3, NLRP1 and AIM2 in full-thickness tissue. In the meanwhile, immunohistochemistry was used to detect distribution of NLRP3, NLRP1 and AIM2 in the periodontal epithelium and in the connective tissue cells.

RESULTS

The overall intensity of NLRP3 expression was significantly higher in CP or G-AgP than healthy tissue. A more significant difference was observed in the periodontal epithelium layer. NLRP1 was barely expressed in the healthy and periodontitis gingival tissues, whereas AIM2 was expressed at a higher level in the chronic periodontitis group than others.

CONCLUSION

The NLRP3, NLRP1 and AIM2 proteins were differentially expressed in gingival tissues from patients with CP and G-AgP and may play vital roles in the progression of periodontal inflammation to different degrees. Our studies may provide a new direction for personalized periodontal treatment.

Interleukin-1β rs1143634 polymorphism and aggressive periodontitis susceptibility: a meta-analysis

Multiple studies had focused on the association between interleukin-1 (IL-1) rs1143634 polymorphism and aggressive periodontitis (AgP) susceptibility, but the results remained inconclusive. Therefore, this meta-analysis was conducted to explore its role in the development of AgP. PubMed and Embase databases were searched up to April 15, 2014. After study selection and data extraction form eligible studies, meta-analysis was performed. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the association. All the analysis was performed using Comprehensive Meta-Analysis software. Finally a total of 25 case-control studies were included. The pooled results showed non-association between AgP susceptibility and IL-1 rs1143634 polymorphism [for T vs. C: OR = 0.99, 95% CI = 0.79-1.23; for TT vs. CC: OR = 1.14, 95% CI = 0.78-1.66; for CT vs. CC: OR = 0.97, 95% CI = 0.70-1.36; for (CT + TT) vs. CC: OR = 1.02, 95% CI = 0.76-1.37; for TT vs. (CT + CC): OR = 1.22, 95% CI = 0.85-1.75]. Subgroup analyses remain did not find any association. No publication bias was detected. Hence, our meta-analysis showed that IL-1β rs1143634 polymorphism is not linked to AgP susceptibility, regardless of ethnicity.

Gingival tissue transcriptomes identify distinct periodontitis phenotypes

The currently recognized principal forms of periodontitis-chronic and aggressive-lack an unequivocal, pathobiology-based foundation. We explored whether gingival tissue transcriptomes can serve as the basis for an alternative classification of periodontitis. We used cross-sectional whole-genome gene expression data from 241 gingival tissue biopsies obtained from sites with periodontal pathology in 120 systemically healthy nonsmokers with periodontitis, with available data on clinical periodontal status, subgingival microbial profiles, and serum IgG antibodies to periodontal microbiota. Adjusted model-based clustering of transcriptomic data using finite mixtures generated two distinct clusters of patients that did not align with the current classification of chronic and aggressive periodontitis. Differential expression profiles primarily related to cell proliferation in cluster 1 and to lymphocyte activation and unfolded protein responses in cluster 2. Patients in the two clusters did not differ with respect to age but presented with distinct phenotypes (statistically significantly different whole-mouth clinical measures of extent/severity, subgingival microbial burden by several species, and selected serum antibody responses). Patients in cluster 2 showed more extensive/severe disease and were more often male. The findings suggest that distinct gene expression signatures in pathologic gingival tissues translate into phenotypic differences and can provide a basis for a novel classification.

Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters

There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP) from chronic periodontitis (CP) patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs) to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC). ANNs were trained by cross entropy (CE) values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE). The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-γ and TNF-α level from monocytes, antibody levels against A. actinomycetemcomitans (A.a.) and P.gingivalis (P.g.). ANNs gave 90%-98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.