Gingival tissue transcriptomes in experimental gingivitis

Loricrin and Cytokeratin Disorganisation in Severe Forms of Periodontitis

OBJECTIVE

The aim of this research was to investigate the role of the cornified epithelium, the outermost layer of the oral mucosa, engineered to prevent water loss and microorganism invasion, in severe forms of periodontitis (stage III or IV, grade C).

METHODS

Porphyromonas gingivalis, a major periodontal disease pathogen, can affect cornified epithelial protein expression through chronic activation of signal transducer and activator of transcription 6 (Stat6). We used a mouse model, Stat6VT, that mimics this to determine the effects of barrier defect on P gingivalis-induced inflammation, bone loss, and cornified epithelial protein expression, and compared histologic and immunohistologic findings with tissues obtained from human controls and patients with stage III and IV, grade C disease. Alveolar bone loss in mice was assessed using micro-computerised tomography, and soft tissue morphology was qualitatively and semi-quantitatively assessed by histologic examination for several proteins, including loricrin, filaggrin, cytokeratin 1, cytokeratin 14, a proliferation marker, a pan-leukocyte marker, as well as morphologic signs of inflammation. Relative cytokine levels were measured in mouse plasma by cytokine array.

RESULTS

In the tissues from patients with periodontal disease, there were greater signs of inflammation (rete pegs, clear cells, inflammatory infiltrates) and a decrease and broadening of expression of loricrin and cytokeratin 1. Cytokeratin 14 expression was also broader and decreased in stage IV. P gingivalis-infected Stat6VT mice showed greater alveolar bone loss in 9 out of 16 examined sites, and similar patterns of disruption to human patients in expression of loricrin and cytokeratins 1 and 14. There were also increased numbers of leukocytes, decreased proliferation, and greater signs of inflammation compared with P gingivalis-infected control mice.

CONCLUSIONS

Our study provides evidence that changes in epithelial organisation can exacerbate the effects of P gingivalis infection, with similarities to the most severe forms of human periodontitis.

Association of rs142548867 (EEFSEC) and periodontitis Grade C in a young Brazilian population

BACKGROUND

Periodontitis Stage III-IV, Grade C (PerioC) is a severe form of Periodontitis. The individual genetic background has been shown to be an important etiopathogenic factor for the development of this disease in young, systemically healthy, and non-smokers patients. Recently, after exome sequencing of families with a history of the disease, PerioC was associated with three single nucleotide variations (SNVs) - rs142548867 (EEFSEC), rs574301770 (ZNF136), and rs72821893 (KRT25) - which were classified as deleterious or possibly harmful by prediction algorithms.

OBJECTIVE

Seeking to validate these findings in a cohort evaluation, this study aims to characterize the allele and genotypic frequency of the SNVs rs142548867, rs574301770, and rs72821893 in the Brazilian population with PerioC and who were periodontally healthy (PH).

METHODOLOGY

Thus, epithelial oral cells from 200 PerioC and 196 PH patients were harvested at three distinct centers at the Brazilian Southern region, their DNA were extracted, and the SNVs rs142548867, rs574301770, rs72821893 were genotyped using 5'-nuclease allelic discrimination assay. Differences in allele and genotype frequencies were analyzed using Fisher's Exact Test. Only the SNV rs142548867 (C > T) was associated with PerioC.

RESULTS

The CT genotype was detected more frequently in patients with PerioC when compared with PH subjects (6% and 0.5% respectively), being significantly associated with PerioC (odds ratio 11.76, p=0.02).

CONCLUSION

rs142548867 represents a potential risk for the occurrence of this disease in the Brazilian population.

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 Oral Health in the Acquisition and Severity of SARS-CoV-2: A Retrospective Chart Review

Objective

Studies have shown that gingival crevices may be a significant route for SARS-CoV-2 entry. However, the role of oral health in the acquisition and severity of COVID-19 is not known.

Design

A retrospective analysis was performed using electronic health record data from a large urban academic medical center between 12/1/2019 and 8/24/2020. A total of 387 COVID-19 positive cases were identified and matched 1:1 by age, sex, and race to 387 controls without COVID-19 diagnoses. Demographics, number of missing teeth and alveolar crestal height were determined from radiographs and medical/dental charts. In a subgroup of 107 cases and controls, we also examined the rate of change in alveolar crestal height. A conditional logistic regression model was utilized to assess association between alveolar crestal height and missing teeth with COVID-19 status and with hospitalization status among COVID-19 cases.

Results

Increased alveolar bone loss, OR = 4.302 (2.510 - 7.376), fewer missing teeth, OR = 0.897 (0.835-0.965) and lack of smoking history distinguished COVID-19 cases from controls. After adjusting for time between examinations, cases with COVID-19 had greater alveolar bone loss compared to controls (0.641 ± 0.613 mm vs 0.260 ± 0.631 mm, p < 0.01.) Among cases with COVID-19, increased number of missing teeth OR = 2.1871 (1.146- 4.174) was significantly associated with hospitalization.

Conclusions

Alveolar bone loss and missing teeth are positively associated with the acquisition and severity of COVID-19 disease, respectively.

Pleckstrin Levels Are Increased in Patients with Chronic Periodontitis and Regulated via the MAP Kinase-p38α Signaling Pathway in Gingival Fibroblasts

Chronic periodontitis (CP) is a bacteria-driven inflammatory disease characterized by the breakdown of gingival tissue, the periodontal ligament, and alveolar bone, leading ultimately to tooth loss. We previously reported the pleckstrin gene (PLEK) to be highly upregulated in gingival tissue of patients with CP and the only gene concurrently upregulated in other inflammatory diseases including rheumatoid arthritis and cardiovascular diseases. Using saliva from 169 individuals diagnosed with CP and healthy controls, we investigated whether pleckstrin could serve as a novel biomarker of periodontitis. Additionally, we explored signal pathways involved in the regulation of PLEK using human gingival fibroblasts (HGFs). Pleckstrin levels were significantly higher (p < 0.001) in the saliva samples of patients with CP compared to controls and closely associated with CP severity. Immunohistochemical analysis revealed the expression of pleckstrin in inflammatory cells and gingival fibroblasts of CP patients. To explore the signal pathways involved in pleckstrin regulation, we stimulated HGFs with either interleukin-1β (IL-1β) or lipopolysaccharides (LPS) alone, or in combination with inhibitors targeting c-Jun N-terminal kinase, tyrosine kinase, protein kinase C, or p38 MAP kinase. Results showed that IL-1β and LPS significantly increased PLEK mRNA and pleckstrin protein levels. VX-745, the p38 MAP kinase inhibitor significantly decreased IL-1β- and LPS-induced pleckstrin levels at both the mRNA and the protein level. Together, these findings show that pleckstrin could serve as a salivary biomarker for the chronic inflammatory disease periodontitis and a regulator of inflammation via the p38 MAP kinase pathway.

Gingival Displacement in the Vertical and Horizontal Dimension under the Condition of Mild Gingivitis-A Randomized Clinical Study

This randomized clinical study aimed at quantifying the gingival displacement performance in the vertical and horizontal directions of the 3M™ Astringent Retraction Paste (3M Oral Care, Seefeld, Germany) in comparison with the double-cord technique with aluminum chloride as an astringent. Afterward, any soft-tissue changes were assessed for 12 months. After inducing mild gingivitis, 18 probands received the intervention ‘cord’ and 22 probands received the intervention ‘paste’ at the palatal half of upper premolars prior to conventional impression making. The resulting plaster casts were digitized and analyzed for the vertical and horizontal gingival displacement, applying a newly developed computer-assisted methodology. The entire palatal half of the tooth was evaluated instead of only single sites. Under the condition of mild gingivitis, the gingival displacement performance was comparable for both techniques in the horizontal direction (width) and only somewhat better for the cord technique in the vertical direction (depth). The magnitude of displacement was in a similar range in both directions, with somewhat higher values in the vertical direction. The marginal gingiva height changes were of such low extent during the follow-up period of 12 months with only minimally higher values for the paste that they cannot be considered as clinically relevant recessions.

Influence of the Gingival Condition on the Performance of Different Gingival Displacement Methods-A Randomized Clinical Study

This randomized clinical study examined the influence of the gingival condition—healthy versus mild inflammation—on sulcus representation and possible gingival recession for two gingival displacement procedures prior to conventional impression making. The interventions double cord technique or a kaolin paste containing aluminum chloride were applied to 40 probands. The opposite quadrant served as intrapersonal reference (split-mouth design). Precision impressions were then made. Extraoral digitization of the plaster models resulting from the reference impression prior to gingival displacement, the intervention impression and control impressions were the basis for the computer-aided three-dimensional analysis. After six months, a mild artificial gingivitis was induced, and the contralateral quadrant (cross-over design) was examined for the intervention. The gingivitis deteriorated the sulcus representation for the double cord technique group but did not affect the paste technique group. The gingival condition had no influence on the marginal gingiva height changes. The minor extent of those changes, which were measured up to six months after intervention at the palatal study site, were not considered to be in the clinically relevant range for gingival recession. For healthy gingiva, the cord technique showed superior sulcus representation compared to the paste technique. This advantage was lost to a great extent under the conditions of mild gingivitis.

MicroRNAs: Harbingers and shapers of periodontal inflammation

Periodontal disease is an inflammatory reaction of the periodontal tissues to oral pathogens. In the present review we discuss the intricate effects of a regulatory network of gene expression modulators, microRNAs (miRNAs), as they affect periodontal morphology, function and gene expression during periodontal disease. These miRNAs are small RNAs involved in RNA silencing and post-transcriptional regulation and affect all stages of periodontal disease, from the earliest signs of gingivitis to the regulation of periodontal homeostasis and immunity and to the involvement in periodontal tissue destruction. MiRNAs coordinate periodontal disease progression not only directly but also through long non-coding RNAs (lncRNAs), which have been demonstrated to act as endogenous sponges or decoys that regulate the expression and function of miRNAs, and which in turn suppress the targeting of mRNAs involved in the inflammatory response, cell proliferation, migration and differentiation. While the integrity of miRNA function is essential for periodontal health and immunity, miRNA sequence variations (genetic polymorphisms) contribute toward an enhanced risk for periodontal disease progression and severity. Several polymorphisms in miRNA genes have been linked to an increased risk of periodontitis, and among those, miR-146a, miR-196, and miR-499 polymorphisms have been identified as risk factors for periodontal disease. The role of miRNAs in periodontal disease progression is not limited to the host tissues but also extends to the viruses that reside in periodontal lesions, such as herpesviruses (human herpesvirus, HHV). In advanced periodontal lesions, HHV infections result in the release of cytokines from periodontal tissues and impair antibacterial immune mechanisms that promote bacterial overgrowth. In turn, controlling the exacerbation of periodontal disease by minimizing the effect of periodontal HHV in periodontal lesions may provide novel avenues for therapeutic intervention. In summary, this review highlights multiple levels of miRNA-mediated control of periodontal disease progression, (i) through their role in periodontal inflammation and the dysregulation of homeostasis, (ii) as a regulatory target of lncRNAs, (iii) by contributing toward periodontal disease susceptibility through miRNA polymorphism, and (iv) as periodontal microflora modulators via viral miRNAs.

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.

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.

Do periodontal defects affect periodontal inflammation and destruction? Histological/microbiological changes and gene expression profiles of a pilot study in beagle dogs

BACKGROUND

The present study focused on the inflammatory disease progress after periodontal defect induction and aimed to specifically determine periodontal tissue responses following dental plaque accumulation by ligatures on a site with/without standardized periodontal defect induction.

METHODS

After 1 month from extraction of the adjacent teeth, semi-circumferential defects were surgically created in the unilateral second and fourth premolars (test group), whereas no defects were being induced at the contralateral sites (control group). One week later, silk was used to ligate the tooth cervix at both sites to encourage the accumulation of dental plaque. Four weeks later, the tissue samples were collected for histological/histomorphometric and microarray analysis. Microbiological analysis was performed before defect induction and at ligatures, and after 4 weeks of dental plaque accumulation.

RESULTS

Remarkable inflammation was clinically and histologically observed in both groups after plaque accumulation, and the intrabony type of periodontal defect exaggerated inflammatory cell infiltration into the connective tissue layer. Expression of genes related to inflammation such as IL-1 was highly up-regulated in test sites. However, these inflammatory infiltrations did not invade to a boundary of periodontal ligament and connective tissue attachment in both groups, and histomorphometric results corresponds to these observational results. Bacterial findings also showed no significant differences in detected microbiome compositions between control and test groups at three-time points.

CONCLUSION

Intrabony defect might exaggerate the plaque-induced inflammation in the aspect of inflammatory cell infiltration and the related gene expression, but both dental plaque and the pre-existing periodontal defect negligibly disrupt periodontal attachment and the underlying alveolar bone.

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.

Salivary proteotypes of gingivitis tolerance and resilience

Aim

This study aimed to characterize the salivary proteome during the induction and resolution of gingival inflammation in the course of human experimental gingivitis (EG), and to cluster the proteomic profiles based on the clinically defined “slow” and “fast” response patterns.

Materials and Methods

A total of 50 unstimulated whole saliva were obtained from the EG model which was induced over 21 days (days 0, 7, 14 and 21), followed by a two‐week resolution phase (day 35). Label‐free quantitative proteomics using liquid chromatography–tandem mass spectrometry was applied. Regulated proteins were subject to Gene Ontology enrichment analysis.

Results

A total of 804 human proteins were quantified by ≥ 2 peptides. Principal component analysis depicted significant differences between “fast” and “slow” responders. Despite gingival and plaque scores being similar at baseline among the two groups, “fast” responders presented with 48 proteins that were at > 4‐fold higher levels than “slow” responders. These up‐regulated proteins showed enrichment in “antigen presentation” and “proteolysis.”

Conclusions

Together, these findings highlight the utility of integrative systems‐level quantitative proteomic approaches to unravel the molecular basis of “salivary proteotypes” associated with gingivitis dubbed as “fast” and “slow” responders. Hence, these differential responses may help prognosticate individual susceptibility to gingival inflammation.

An Evidence-Based Update on the Molecular Mechanisms Underlying Periodontal Diseases

Several investigators have reported about the intricate molecular mechanism underlying periodontal diseases (PD). Nevertheless, the role of specific genes, cells, or cellular mechanisms involved in the pathogenesis of periodontitis are still unclear. Although periodontitis is one of the most prevalent oral diseases globally, there are no pre-diagnostic markers or therapeutic targets available for such inflammatory lesions. A pivotal role is played by pro- and anti-inflammatory markers in modulating pathophysiological and physiological processes in repairing damaged tissues. In addition, effects on osteoimmunology is ever evolving due to the ongoing research in understanding the molecular mechanism lying beneath periodontal diseases. The aim of the current review is to deliver an evidence-based update on the molecular mechanism of periodontitis with a particular focus on recent developments. Reports regarding the molecular mechanism of these diseases have revealed unforeseen results indicative of the fact that significant advances have been made to the periodontal medicine over the past decade. There is integrated hypothesis-driven research going on. Although a wide picture of association of periodontal diseases with immune response has been further clarified with present ongoing research, small parts of the puzzle remain a mystery and require further investigations.

Periodontal inflammation: Integrating genes and dysbiosis

Biofilm bacteria co‐evolve and reach a symbiosis with the host on the gingival surface. The disruption of the homeostatic relationship between plaque bacteria and the host can initiate and promote periodontal disease progression. Recent advances in sequencing technologies allow researchers to profile disease‐associated microbial communities and quantify microbial metabolic activities and host transcriptional responses. In addition to confirming the findings from previous studies, new putative pathogens and novel genes that have not previously been associated with periodontitis, emerge. For example, multiple studies have reported that Synergistetes bacteria are associated with periodontitis. Genes involved in epithelial barrier defense were downregulated in periodontitis, while excessive expression of interleukin‐17 was associated with a hyperinflammatory response in periodontitis and with a unique microbial community. Bioinformatics‐enabled gene ontology pathway analyses provide a panoramic view of the bacterial and host activities as they shift from periodontal health to disease. Additionally, host innate factors, such as genetic variants identified by either a candidate‐gene approach or genome‐wide association analyses, have an impact on subgingival bacterial colonization. Transgenic mice carrying candidate genetic variants, or with the deletion of candidate genes mimicking the deleterious loss‐of‐function variant effect, provide experimental evidence validating the biologic relevance of the novel markers associated with the microbial phenotype identified through a statistical approach. Further refinement in bioinformatics, data management approaches, or statistical tools, are required to gain insight into host‐microbe interactions by harmonizing the multidimensional “big” data at the genomic, transcriptional, and proteomic levels.

Dental plaque-induced gingival conditions

OBJECTIVE

This review proposes revisions to the current classification system for gingival diseases and provides a rationale for how it differs from the 1999 classification system.

IMPORTANCE

Gingival inflammation in response to bacterial plaque accumulation (microbial biofilms) is considered the key risk factor for the onset of periodontitis. Thus, control of gingival inflammation is essential for the primary prevention of periodontitis.

FINDINGS

The clinical characteristics common to dental plaque-induced inflammatory gingival conditions include: a) clinical signs and symptoms of inflammation that are confined to the gingiva: b) reversibility of the inflammation by removing or disrupting the biofilm; c) the presence of a high bacterial plaque burden to initiate the inflammation; d) systemic modifying factors (e.g., hormones, systemic disorders, drugs) which can alter the severity of the plaque-induced inflammation and; e) stable (i.e., non-changing) attachment levels on a periodontium which may or may not have experienced a loss of attachment or alveolar bone. The simplified taxonomy of gingival conditions includes: 1) introduction of the term "incipient gingivitis;" 2) a description of the extent and severity of gingival inflammation; 3) a description of the extent and severity of gingival enlargement and; 4) a reduction of categories in the dental plaque-induced gingival disease taxonomy.

CONCLUSIONS

Dental plaque-induced gingival inflammation is modified by various systemic and oral factors. The appropriate intervention is crucial for the prevention of periodontitis.

Regulation of PGLYRP1 and TREM-1 during Progression and Resolution of Gingival Inflammation

INTRODUCTION

The triggering receptor expressed on myeloid cells 1 (TREM-1) signaling pathway is stimulated by bacteria and, together with its putative ligand peptidoglycan recognition protein 1 (PGLYRP1), propagates proinflammatory responses.

OBJECTIVES

We aimed to evaluate the TREM-1/PGLYRP1/interleukin (IL)-1β regulation in response to biofilm accumulation and removal in an experimental human gingivitis model.

METHODS

The study (n = 42 participants, mean age: 23.8 ± 3.7 y) comprised a recruitment step (day -14) followed by experimentally induced biofilm formation (induction [I] phase, day 0 to +21) and a 2-wk resolution (R) phase (day +21 to +35). Plaque was recorded by the Modified Quigley and Hein Plaque Index (TQHPI), while records of gingival inflammation were based on the Modified Gingival Index (MGI). Unstimulated whole saliva supernatants (n = 210, 5 time points) were tested for TREM-1, PGLYRP1, and IL-1β by enzyme-linked immunosorbent assay.

RESULTS

During the I-phase, concentrations of all analytes showed a tendency for downregulation at day +7 compared to day 0. TREM-1 (P = 0.019) and PGLYRP1 (P = 0.007) increased significantly between day +7 and day +21. Although all analyte levels decreased during the R-phase, the difference was not significant except TREM-1 being at borderline significance (P = 0.058). Moreover, TREM-1, PGLYRP1, and IL-1β showed significant positive correlations (P < 0.0001) with each other. The study participants were grouped into "fast" and "slow" responders based on clinical gingival inflammation scores. At each time point, fast responders showed significantly higher concentrations of TREM-1 (P < 0.025), PGLYRP1 (P < 0.007), and IL-1β (P < 0.025) compared to slow responders. Mixed-effects multilevel regression analyses revealed that PGLYRP1 (P = 0.047) and IL-1β (P = 0.005) showed a significant positive association with the MGI scores.

CONCLUSION

The study demonstrated that TREM-1 and PGLYRP1 are regulated in response to biofilm accumulation and removal, and fast responders demonstrated higher levels of these analytes compared to slow responders.

KNOWLEDGE TRANSFER STATEMENT

The results of this study demonstrated the suitability of salivary TREM-1 and PGLYRP1 to reflect biofilm accumulation and removal and PGLYRP1 to monitor the progression and resolution of inflammation in gingivitis-susceptible individuals (fast responders). Combined with conventional risk factors, the molecular toolbox proposed here should be further validated in future studies to confirm whether it can be used for population-based monitoring and prevention of gingivitis.

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.

The Human Salivary Antimicrobial Peptide Profile according to the Oral Microbiota in Health, Periodontitis and Smoking

Antimicrobial peptides (AMPs) are a diverse family of peptides that defend the mucosal surfaces of the oral cavity and other locations. Many AMPs have multiple functions and properties that influence aspects of innate defense and colonization by microorganisms. The human oral cavity is home to the second-most diverse microbiome, and the health of the mouth is influenced by the presence of these bacteria as well as by extrinsic factors such as periodontitis and smoking. This study hypothesized that the AMP profile is different in the presence of extrinsic factors and that this would also be reflected in the bacteria present. The AMP profile was analyzed by quantitative selected-reaction-monitoring mass spectrometry analysis and 40 bacterial species were quantified by DNA-DNA hybridization in saliva donated by 41 individuals. Periodontal status was assessed through dental examination and smoking status through medical charting. Periodontal health (in nonsmokers) was associated with a higher abundance of ribonuclease 7, protachykinin 1, β-defensin 128, lipocalin 1, bactericidal permeability-increasing protein fold-containing family B member 3, and bone-marrow proteoglycan. Nonsmoking periodontal disease was associated with an abundance of neutrophil defensin 1 and cathelicidin. However, 7 AMPs were overabundant in periodontal disease in smokers: adrenomedullin, eosinophil peroxidase, 3 different histones, myeloperoxidase, and neutrophil defensin 1. There were no differentially abundant AMPs in smokers versus nonsmokers with periodontal health. Correlation network inference of healthy nonsmokers, healthy smokers, nonsmoking periodontitis, or smoking periodontitis donors demonstrated very different networks growing in complexity with increasing numbers of stressors. The study highlights the importance of the interaction between the oral cavity and its resident microbiota and how this may be influenced by periodontal disease and smoking.

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.

Gene Regulation, Two Component Regulatory Systems, and Adaptive Responses in Treponema Denticola

The oral microbiome consists of a remarkably diverse group of 500-700 bacterial species. The microbial etiology of periodontal disease is similarly complex. Of the ~400 bacterial species identified in subgingival plaque, at least 50 belong to the genus Treponema. As periodontal disease develops and progresses, T. denticola transitions from a low to high abundance species in the subgingival crevice. Changes in the overall composition of the bacterial population trigger significant changes in the local physical, immunological and physiochemical conditions. For T. denticola to thrive in periodontal pockets, it must be nimble and adapt to rapidly changing environmental conditions. The purpose of this chapter is to review the current understanding of the molecular basis of these essential adaptive responses, with a focus on the role of two component regulatory systems with global regulatory potential.

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.

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.

Analysis of protein expression in periodontal pocket tissue: a preliminary study

Background

The periodontal disease is caused by a set of inflammatory disorders characterized by periodontal pocket formation that lead to tooth loss if untreated. The proteomic profile and related molecular conditions of pocket tissue in periodontally-affected patients are not reported in literature. To characterize the proteomic profile of periodontally-affected patients, their interproximal periodontal pocket tissue was compared with that of periodontally-healthy patients. Pocket-associated and healthy tissue samples, harvested during surgical therapy, were treated to extract the protein content. Tissues were always collected at sites where no periodontal-pathogenic bacteria were detectable. Proteins were separated using two-dimensional gel electrophoresis and identified by liquid chromatography/mass spectrometry. After identification, four proteins were selected for subsequent Western Blot quantitation both in pathological and healty tissues.

Results

A significant unbalance in protein expression between healthy and pathological sites was recorded. Thirty-two protein spots were overall identified, and four proteins (S100A9, HSPB1, LEG7 and 14-3-3) were selected for Western blot analysis of both periodontally-affected and healthy patients. The four selected proteins resulted over-expressed in periodontal pocket tissue when compared with the corresponding tissue of periodontally-healthy patients. The results of Western blot analysis are congruent with the defensive and the regenerative reaction of injured periodontal tissues.

Conclusions

The proteomic analysis was performed for the first time directly on periodontal pocket tissue. The proteomic network highlighted in this study enhances the understanding of periodontal disease pathogenesis necessary for specific therapeutic strategies setting.

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.

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.

Beyond microbial community composition: functional activities of the oral microbiome in health and disease

The oral microbiome plays a relevant role in the health status of the host and is a key element in a variety of oral and non-oral diseases. Despite advances in our knowledge of changes in microbial composition associated with different health conditions the functional aspects of the oral microbiome that lead to dysbiosis remain for the most part unknown. In this review, we discuss the progress made towards understanding the functional role of the oral microbiome in health and disease and how novel technologies are expanding our knowledge on this subject.

Macrophage subsets and osteoimmunology: tuning of the immunological recognition and effector systems that maintain alveolar bone

Chronic and aggressive periodontal diseases are characterized by the failure to resolve local inflammation against periodontopathogenic bacteria in the subgingival biofilm. Alveolar bone resorption is associated with altered innate and adaptive immune responses to periodontal pathogens. Macrophage-derived cytokines, chemokines and growth factors, present in both destructive and reparative phases of periodontitis, are elevated in numerous animal and human studies. Macrophage polarization to either a predominantly pro-inflammatory or anti-inflammatory phenotype may be a critical target for monitoring disease activity, modulating immune responses to subgingival biofilms in patients at risk and reducing alveolar bone loss.

Beyond microbial community composition: functional activities of the oral microbiome in health and disease

The oral microbiome plays a relevant role in the health status of the host and is a key element in a variety of oral and non-oral diseases. Despite advances in our knowledge of changes in microbial composition associated with different health conditions the functional aspects of the oral microbiome that lead to dysbiosis remain for the most part unknown. In this review, we discuss the progress made towards understanding the functional role of the oral microbiome in health and disease and how novel technologies are expanding our knowledge on this subject.

Aging and contribution of MyD88 and TRIF to expression of TLR pathway-associated genes following stimulation with Porphyromonas gingivalis

BACKGROUND AND OBJECTIVE

Periodontal disease is a highly complex chronic inflammatory disease of the oral cavity. Multiple factors influence periodontal disease, including socio-economic status, genetics and age; however, inflammation elicited by the presence of specific bacteria in the subgingival space is thought to drive the majority of soft- and hard-tissue destruction. Porphyromonas gingivalis is closely associated with periodontal disease. Toll-like receptors (TLRs) and their intracellular signaling pathways play roles in the host response to P. gingivalis. The focus of the current study was to use microarray analysis to define the contributions of the TLR adaptor molecules myeloid differentiation factor 88 (MyD88) and Toll/interleukin-1 receptor domain-containing adaptor inducing interferon-beta (TRIF), and aging, on the expression of TLR pathway-associated mRNAs in response to P. gingivalis.

MATERIAL AND METHODS

Bone marrow-derived macrophages (BMØ) from wild-type (Wt), MyD88 knockout (MyD88-KO) and Trif(Lps2) [i.e. containing a point mutation in the lipopolysaccharide 2 (Lps2) gene rendering the Toll/interleukin (IL)-1 receptor domain-containing adaptor inducing interferon-beta (TRIF) protein nonfunctional] mice, at 2-and 12-mo of age, were cultured with P. gingivalis. Expression of genes in BMØ cultured with P. gingivalis was determined in comparison with expression of genes in BMØ cultured in medium only.

RESULTS

Using, as criteria, a twofold increase or decrease in mRNA expression, differential expression of 32 genes was observed when Wt BMØ from 2-mo-old mice were cultured with P. gingivalis compared with the medium-only control. When compared with 2-mo-old Wt mice, 21 and 12 genes were differentially expressed (p < 0.05) as a result of the mutations in MyD88 or TRIF, respectively. The expression of five genes was significantly (p < 0.05) reduced in Wt BMØ from 12-mo-old mice compared with those from 2-mo-old mice following culture with P. gingivalis. Age also influenced the expression of genes in MyD88-KO and Trif(Lps2) mice challenged with P. gingivalis.

CONCLUSIONS

Our results indicate that P. gingivalis induces differential expression of TLR pathway-associated genes, and both MyD88 and TRIF play roles in the expression of these genes. Age also played a role in the expression of TLR-associated genes following stimulation of BMØ with P. gingivalis.

Effect of probiotic lozenges on inflammatory reactions and oral biofilm during experimental gingivitis

AIM

Probiotic bacteria have been introduced for prevention and treatment of periodontal diseases. The aim was to assess if daily oral administration of probiotic bacteria could influence the inflammatory response and the composition of supragingival plaque in an experimental gingivitis model.

MATERIALS AND METHODS

Eighteen healthy female adults volunteered after informed consent. A double-blind randomized placebo-controlled cross-over design was used. The buccal surface of first molars was used as experimental sites. A mouth-guard covering the first premolar to second molar was used when brushing, preventing accidental cleaning during 3 weeks of plaque accumulation. Lozenges containing L. reuteri (ATCC55730 and ATCC PTA5289) or placebo were taken twice a day. During the run-in and washout periods, professional tooth cleaning was performed 5 days/week. At baseline and follow-up, plaque index, gingival index and bleeding on probing were recorded. Samples of gingival crevicular fluid (GCF) were analysed for concentration of seven inflammatory mediators. Bacterial samples were processed with checkerboard DNA/DNA-hybridization.

RESULTS

All subjects presented a local plaque accumulation and developed manifest gingivitis at the test sites during the intervention periods. The volume of GCF increased in both groups but was statistically significant only in the placebo group (p < 0.05). The concentrations of IL1-β and IL-18 increased significantly (p < 0.05), while IL-8 and MIP1-β decreased (p < 0.05). No differences were displayed between test and placebo. Likewise, the microbial composition did not differ between the groups.

CONCLUSION

Daily intake of probiotic lozenges did not seem to significantly affect the plaque accumulation, inflammatory reaction or the composition of the biofilm during experimental gingivitis.

Non-bacterial protein expression in periodontal pockets by proteome analysis

OBJECTIVES

To compare the proteomic profile of inter-proximal pocket tissues with inter-proximal healthy tissues in the same subject to reveal proteins associated with periodontal disease in sites where periodontopathogenic bacteria were not detectable.

METHODS

Twenty-five healthy patients, with moderate-to-advanced chronic periodontitis and presenting with at least one intra-bony defect next to a healthy inter-proximal site were enrolled. The periodontal defects were treated with osseous resective surgery, and the flap design included both the periodontal pockets and the neighbouring inter-proximal healthy sites. Pocket-associated and healthy tissues were harvested for proteomic analyses.

RESULTS

Fifteen proteins were differently expressed between pathological and healthy tissues. In particular, annexin A2, actin cytoplasmic 1, carbonic anhydrase 1 & 2; Ig kappa chain C region (two spots) and flavinreductase were overexpressed, whereas 14-3-3 protein sigma and zeta/delta, heat-shock protein beta -1 (two spots), triosephosphateisomerase, peroxiredoxin-1, fatty acid-binding protein-epidermal, and galectin-7 were underexpressed in pathological tissue.

CONCLUSIONS

The unbalanced functional network of proteins involved could hinder adequate tissue response to pathogenic noxa. The study of periodontal pocket tissue proteomic profile would be crucial to better understand the pathogenesis of and the therapeutic strategies for periodontitis.

Label-free quantitative proteomics reveals differentially regulated proteins in experimental gingivitis

We investigated the sequential protein expression in gingival crevicular fluid samples during the induction (I) and resolution (R) of experimental gingivitis. Periodontally and systemically healthy volunteers (n = 20) participated in a three-week experimental gingivitis protocol, followed by debridement and two weeks of regular plaque control. Gingival crevicular fluid (GCF) samples were collected at baseline, Day 7, 14, and 21 (induction; I-phase), and at Day 21, 25, 30, and 35 (resolution; R-phase). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) for label-free quantitative proteomics was applied. A total of 287 proteins were identified including 254 human, 14 bacterial, 12 fungal, and 7 yeast proteins. Ontology analysis revealed proteins primarily involved in cytoskeletal rearrangements, immune response, antimicrobial function, protein degradation, and DNA binding. There was considerable variation in the number of proteins identified, both among subjects and within subjects across time points. After pooling of samples between subjects at each time point, the levels of 59 proteins in the I-phase and 73 proteins in the R-phase were quantified longitudinally. Our data demonstrate that LC-MS/MS label-free quantitative proteomics is valuable in the assessment of the protein content of the GCF and can facilitate a better understanding of the molecular mechanisms involved in the induction and resolution of plaque-induced gingival inflammation in humans.

Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization

AIM

To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets.

MATERIALS AND METHODS

GCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased.

RESULTS

A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples.

CONCLUSIONS

The proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy.

Genome-wide approaches (GWA) in oral and craniofacial diseases research

Oral Diseases (2012) Underlying molecular genetic mechanisms of diseases can be deciphered with unbiased strategies using recently developed technologies enabling genome-wide scale investigations. These technologies have been applied in scanning for genetic variations, gene expression profiles, and epigenetic changes for oral and craniofacial diseases. However, these approaches as applied to oral and craniofacial conditions are in the initial stages, and challenges remain to be overcome, including analysis of high throughput data and their interpretation. Here, we review methodology and studies using genome-wide approaches in oral and craniofacial diseases and suggest future directions.

Er:YAG laser in the treatment of periodontal sites with recurring chronic inflammation: a 12-month randomized, controlled clinical trial

AIM

The objective of this randomized, controlled clinical trial was to compare the clinical and microbiological effects of pocket debridement using erbium-doped: yttrium, aluminium and garnet (Er:YAG) laser with conventional debridement in maintenance patients.

MATERIAL & METHODS

Fifteen patients, all smokers, having at least four teeth with residual probing depth (PD) ≥ 5 mm were recruited. Two pockets in two jaw quadrants were randomly assigned to subgingival debridement using an Er:YAG laser (test) or ultrasonic scaler/curette (control) at 3-month intervals. Relative attachment level (RAL), PD, bleeding on probing and dental plaque were recorded at baseline and at 6 and 12 months. Microbiological subgingival samples were taken at the same time points and analysed using a checkerboard DNA-DNA hybridization technique.

RESULTS

A significant decrease in PD took place in both treatments from baseline to 12 months (p < 0.01). In the control, the mean initial PD decreased from 5.4 to 4.0 mm at 12 months. For the test, a similar decrease occurred. No significant between-treatment differences were shown at any time point. The mean RAL showed no overall significant inter- or intra-treatment differences (p > 0.05). No significant between-treatment differences were observed in subgingival microbiological composition or total pathogens.

CONCLUSION

The results failed to support that an Er:YAG laser may be superior to conventional debridement in the treatment of smokers with recurring chronic inflammation. This appears to be the first time that repeated Er-YAG laser instrumentation has been compared with mechanical instrumentation of periodontal sites with recurring chronic inflammation over a clinically relevant time period.

Polymicrobial periodontal pathogen transcriptomes in calvarial bone and soft tissue

Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia are consistently associated with adult periodontitis. This study sought to document the host transcriptome to a P. gingivalis, T. denticola, and T.forsythia challenge as a polymicrobial infection using a murine calvarial model of acute inflammation and bone resorption. Mice were infected with P. gingivalis, T. denticola, and T. forsythia over the calvaria, after which the soft tissues and calvarial bones were excised. A Murine GeneChip(®) array analysis of transcript profiles showed that 6997 genes were differentially expressed in calvarial bones (P < 0.05) and 1544 genes were differentially transcribed in the inflamed tissues after the polymicrobial infection. Of these genes, 4476 and 1035 genes in the infected bone and tissues were differentially expressed by upregulation. Biological pathways significantly impacted by the polymicrobial infection in calvarial bone included leukocyte transendothelial migration (LTM), cell adhesion molecules, adherens junction, major histocompatibility complex antigen, extracellular matrix-receptor interaction, and antigen processing and presentation resulting in inflammatory/cytokine/chemokine transcripts stimulation in bone and soft tissue. Intense inflammation and increased activated osteoclasts were observed in calvarias compared with sham-infected controls. Quantitative real-time RT-PCR analysis confirmed that the mRNA level of selected genes corresponded with the microarray expression. The polymicrobial infection regulated several LTM and extracellular membrane pathway genes in a manner distinct from mono-infection with P. gingivalis, T. denticola, or T. forsythia. To our knowledge, this is the first definition of the polymicrobially induced transcriptome in calvarial bone and soft tissue in response to periodontal pathogens.