Identification of Master Regulator Genes in Human Periodontitis

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

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

BACKGROUND AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

20 abnormal metabolites of Stage IV Grade C periodontitis was discovered by CPSI-MS

Saliva is a noninvasive biofluid that contains the metabolic signature of severe periodontitis (SP, Stage IV and Grade C). Conductive polymer spray ionization mass spectrometry (CPSI-MS) was used to record a wide range of metabolites within a few seconds, making this technique a promising point-of-care method for the early detection of SP (Stage IV and Grade C). Saliva samples from 31 volunteers, consisting of 16 healthy controls (HC) and 15 patients with SP (Stage IV and Grade C), were collected to identify dysregulated metabolites. Twenty metabolites were screened out, including seven amino acids. Moreover, the results showed that amino acid metabolism is closely related to the development of periodontitis. The present study further confirmed that salivary metabolites in the oral cavity were significantly altered after plaque removal. These results suggest that the combination of CPSI-MS is a feasible tool for preclinical screening of SP (Stage IV and Grade C).

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

Genomic Medicine in Periodontal Disease: Old Issue, New Insights

Genetic variability is the main cause of phenotypic variation. Some variants may be associated with several diseases and can be used as risk biomarkers, identifying animals with higher susceptibility to develop the pathology. Genomic medicine uses this genetic information for risk calculation, clinical diagnosis and prognosis, allowing the implementation of more effective preventive strategies and/or personalized therapies. Periodontal disease (PD) is the inflammation of the periodontium induced mainly by bacterial plaque and is the leading cause of tooth loss. Microbial factors are responsible for the PD initiation; however, several studies support the genetic influence on the PD progression. The main purpose of the present publication is to highlight the main steps involved in the genomic medicine applied to veterinary patients, describing the flowchart from the characterization of the genetic variants to the identification of potential associations with specific clinical data. After investigating which genes might potentially be implicated in canine PD, the RANK gene, involved in the regulation of osteoclastogenesis, was selected to illustrate this approach. A case-control study was performed using DNA samples from a population of 90 dogs – 50 being healthy and 40 with PD. This analysis allowed for the discovery of four new intronic variations that were banked in GenBank (g.85A>G, g.151G>T, g.268A>G and g.492T>C). The results of this study are not intended to be applied exclusively to PD. On the contrary, this genetic information is intended to be used by other researchers as a foundation for the development of multiple applications in the veterinary clinical field.

Comparative Analysis of Gene Expression Patterns for Oral Epithelial Cell Functions in Periodontitis

The structure and function of epithelial cells are critical for the construction and maintenance of intact epithelial surfaces throughout the body. Beyond the mechanical barrier functions, epithelial cells have been identified as active participants in providing warning signals to the host immune and inflammatory cells and in communicating various detailed information on the noxious challenge to help drive specificity in the characteristics of the host response related to health or pathologic inflammation. Rhesus monkeys were used in these studies to evaluate the gingival transcriptome for naturally occurring disease samples (GeneChip® Rhesus Macaque Genome Array) or for ligature-induced disease (GeneChip® Rhesus Gene 1.0 ST Array) to explore up to 452 annotated genes related to epithelial cell structure and functions. Animals were distributed by age into four groups: ≤ 3 years (young), 3–7 years (adolescent), 12–16 years (adult), and 18–23 years (aged). For naturally occurring disease, adult and aged periodontitis animals were used, which comprised 34 animals (14 females and 20 males). Groups of nine animals in similar age groups were included in a ligature-induced periodontitis experiment. A buccal gingival sample from either healthy or periodontitis-affected tissues were collected, and microarray analysis performed. The overall results of this investigation suggested a substantial alteration in epithelial cell functions that occurs rapidly with disease initiation. Many of these changes were prolonged throughout disease progression and generally reflect a disruption of normal cellular functions that would presage the resulting tissue destruction and clinical disease measures. Finally, clinical resolution may not signify biological resolution and represent a continued risk for disease that may require considerations for additional biologically specific interventions to best manage further disease.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

Gene Regulation Analysis Reveals Perturbations of Autism Spectrum Disorder during Neural System Development

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that impedes patients' cognition, social, speech and communication skills. ASD is highly heterogeneous with a variety of etiologies and clinical manifestations. The prevalence rate of ASD increased steadily in recent years. Presently, molecular mechanisms underlying ASD occurrence and development remain to be elucidated. Here, we integrated multi-layer genomics data to investigate the transcriptome and pathway dysregulations in ASD development. The RNA sequencing (RNA-seq) expression profiles of induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs) and neuron cells from ASD and normal samples were compared in our study. We found that substantially more genes were differentially expressed in the NPCs than the iPSCs. Consistently, gene set variation analysis revealed that the activity of the known ASD pathways in NPCs and neural cells were significantly different from the iPSCs, suggesting that ASD occurred at the early stage of neural system development. We further constructed comprehensive brain- and neural-specific regulatory networks by incorporating transcription factor (TF) and gene interactions with long 5 non-coding RNA(lncRNA) and protein interactions. We then overlaid the transcriptomes of different cell types on the regulatory networks to infer the regulatory cascades. The variations of the regulatory cascades between ASD and normal samples uncovered a set of novel disease-associated genes and gene interactions, particularly highlighting the functional roles of ELF3 and the interaction between STAT1 and lncRNA ELF3-AS 1 in the disease development. These new findings extend our understanding of ASD and offer putative new therapeutic targets for further studies.

Transcriptomic phases of periodontitis lesions using the nonhuman primate model

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

Gingival transcriptomics of follicular T cell footprints in progressing periodontitis

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Cell Type-Specific Decomposition of Gingival Tissue Transcriptomes

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

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

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

Biosensor and Lab-on-a-chip Biomarker-identifying Technologies for Oral and Periodontal Diseases

Periodontitis is a complex multifactorial disease that can lead to destruction of tooth supporting tissues and subsequent tooth loss. The most recent global burden of disease studies highlight that severe periodontitis is one of the most prevalent chronic inflammatory conditions affecting humans. Periodontitis risk is attributed to genetics, host-microbiome and environmental factors. Empirical diagnostic and prognostic systems have yet to be validated in the field of periodontics. Early diagnosis and intervention prevents periodontitis progression in most patients. Increased susceptibility and suboptimal control of modifiable risk factors can result in poor response to therapy, and relapse. The chronic immune-inflammatory response to microbial biofilms at the tooth or dental implant surface is associated with systemic conditions such as cardiovascular disease, diabetes or gastrointestinal diseases. Oral fluid-based biomarkers have demonstrated easy accessibility and potential as diagnostics for oral and systemic diseases, including the identification of SARS-CoV-2 in saliva. Advances in biotechnology have led to innovations in lab-on-a-chip and biosensors to interface with oral-based biomarker assessment. This review highlights new developments in oral biomarker discovery and their validation for clinical application to advance precision oral medicine through improved diagnosis, prognosis and patient stratification. Their potential to improve clinical outcomes of periodontitis and associated chronic conditions will benefit the dental and overall public health.

Master regulator genes and their impact on major diseases

Master regulator genes (MRGs) have become a hot topic in recent decades. They not only affect the development of tissue and organ systems but also play a role in other signal pathways by regulating additional MRGs. Because a MRG can regulate the concurrent expression of several genes, its mutation often leads to major diseases. Moreover, the occurrence of many tumors and cardiovascular and nervous system diseases are closely related to MRG changes. With the development in omics technology, an increasing amount of investigations will be directed toward MRGs because their regulation involves all aspects of an organism’s development. This review focuses on the definition and classification of MRGs as well as their influence on disease regulation.

Long non-coding RNA TUG1 participates in LPS-induced periodontitis by regulating miR-498/RORA pathway

AIM

This study was aimed to investigate the role of TUG1 in LPS-stimulated hPDLCs and to evaluate the potential functions of TUG1 in the pathogenesis of periodontitis.

METHODS

LPS-stimulated hPDLCs were established as the cell model. CCK-8 assay was performed to assess cell proliferation ability. Flow cytometry was performed to detect cell cycle distribution, and quantitative RT-PCR and Western blotting were conducted to measure gene expressions. ELISA kits were used to evaluate the production of inflammatory cytokines. The putative binding site between TUG1 and miR-498 was verified using luciferase reporter and RNA immunoprecipitation assays.

RESULTS

TUG1 was downregulated upon LPS stimulation in hPDLCs. TUG1 overexpression promoted cell proliferation through regulating the cell cycle distribution, along with the decreased expression of p21 and increased expression of CDK2 and cyclin D1. Besides, TUG1 overexpression decreased the production of inflammatory cytokines. The effects were opposite upon TUG1 knockdown. TUG1 negatively regulated its target miR-498, and influenced the expression of RORA, the direct target of miR-498. Simultaneous TUG1 overexpression and miR-498 reversed the effect of TUG1 overexpression alone on alleviating LPS-induced cell injury and inhibition of Wnt/β-catenin signaling, which was further changeover after co-overexpression with RORA.

CONCLUSION

Therefore, TUG1 could protect against periodontitis via regulating miR-498/RORA mediated Wnt/β-catenin signaling.

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.

Genomics of periodontal disease and tooth morbidity

In this review we critically summarize the evidence base and the progress to date regarding the genomic basis of periodontal disease and tooth morbidity (ie, dental caries and tooth loss), and discuss future applications and research directions in the context of precision oral health and care. Evidence for these oral/dental traits from genome-wide association studies first emerged less than a decade ago. Basic and translational research activities in this domain are now under way by multiple groups around the world. Key departure points in the oral health genomics discourse are: (a) some heritable variation exists for periodontal and dental diseases; (b) the environmental component (eg, social determinants of health and behavioral risk factors) has a major influence on the population distribution but probably interacts with factors of innate susceptibility at the person-level; (c) sizeable, multi-ethnic, well-characterized samples or cohorts with high-quality measures on oral health outcomes and genomics information are required to make decisive discoveries; (d) challenges remain in the measurement of oral health and disease, with current periodontitis and dental caries traits capturing only a part of the health-disease continuum, and are little or not informed by the underlying biology; (e) the substantial individual heterogeneity that exists in the clinical presentation and lifetime trajectory of oral disease can be identified and leveraged in a precision medicine framework or, if unappreciated, can hamper translational efforts. In this review we discuss how composite or biologically informed traits may offer improvements over clinically defined ones for the genomic interrogation of oral diseases. We demonstrate the utility of the results of genome-wide association studies for the development and testing of a genetic risk score for severe periodontitis. We conclude that exciting opportunities lie ahead for improvements in the oral health of individual patients and populations via advances in our understanding of the genomic basis of oral health and disease. The pace of new discoveries and their equitable translation to practice will largely depend on investments in the education and training of the oral health care workforce, basic and population research, and sustained collaborative efforts..

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

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

Toll-like receptor 9 gene in Periodontal Disease - A promising biomarker

Periodontal Disease is an infectious and inflammatory disorder triggered mainly by periodontopathogenic bacteria, however, as a multifactorial disease, several factors modulate its progression, namely, genetic factors. Toll-like receptors (TLR) recognize molecular patterns present in pathogens and trigger an immune response against them. Thus, sequences variants in TLR genes seem to have the potential to modify the predisposition to Periodontal Disease and its progression. Based on this fact, TLR9 gene were analysed in a case-control study. DNA was obtained from 90 dogs (50 control and 40 cases) and a fragment of TLR9 gene was amplified by PCR and sequenced. The variants were identified by comparison with the dog wild type sequences. Our results suggest that rs375556098 and rs201959275 polymorphisms in the TLR9 gene are good candidates to become biomarkers of the canine predisposition to Periodontal Disease. It's important to notice that these polymorphic sites exist in Human exactly in the same codon. Since the dog is the best animal model to replicate the pathophysiological mechanisms of human Periodontal Disease, these results can potentially be extrapolated to humans.

Environmental lead effects on gene expression in oral epithelial cells

BACKGROUND AND OBJECTIVE

Host responses in periodontitis span a range of local and emigrating cell types and biomolecules. Accumulating evidence regarding the expression of this disease across the population suggests some component of genetic variation that controls onset and severity of disease, in concert with the qualitative and quantitative parameters of the oral microbiome at sites of disease. However, there remains little information regarding the capacity of accruing environmental stressors or modifiers over a lifespan at both the host genetic and microbial ecology levels to understand fully the population variation in disease. This study evaluated the impact of environmental lead exposure on the responses of oral epithelial cells to challenge with a model pathogenic oral biofilm.

METHODS AND RESULTS

Using NanoString technology to quantify gene expression profiles of an array of 511 host response-associated genes in the epithelial cells, we identified an interesting primary panel of basal responses of the cells with numerous genes not previously considered as major response markers for epithelial cells, eg, interleukin (IL)-32, CTNNB1, CD59, MIF, CD44 and CD99. Even high levels of environment lead had little effect on these constitutive responses. Challenge of the cells with the biofilms (Streptococcus gordonii/Fusobacterium nucleatum/Porphyromonas gingivalis) resulted in significant increases in an array of host immune-related genes (134 of 511). The greatest magnitude in differential expression was observed with many genes not previously described as major response genes in epithelial cells, including IL-32, CD44, NFKBIA, CTSC, TNFAIP3, IL-1A, IL-1B, IL-8 and CCL20. The effects of environmental lead on responses to the biofilms were mixed, although levels of IL-8, CCL20 and CD70 were significantly decreased at lead concentrations of 1 and/or 5 μmol/L.

CONCLUSION

The results provided new information on a portfolio of genes expressed by oral epithelial cells, targeted substantial increases in an array of immune-related genes post-biofilm challenge, and a focused impact of environmental lead on these induced responses.

Analysis of metabolic profiles of generalized aggressive periodontitis

BACKGROUND AND OBJECTIVE

The specific pathogenesis of generalized aggressive periodontitis (GAgP) has not yet been clarified, and few studies have focused on the association between GAgP and metabolomics. To elucidate the roles of metabolic profiles in the status of GAgP, this study aimed to identify the differential metabolic profiles between patients with GAgP and healthy controls using an untargeted metabolomic profiling method.

MATERIAL AND METHODS

Serum and gingival crevicular fluid samples were collected from healthy controls (n = 20) and patients with GAgP (n = 20) in this cross-sectional study. The relative levels of biomarkers in the samples were measured by gas chromatography-mass spectrometry. Principal components analysis and orthogonal partial least-squares discriminant analysis were used for statistical analysis. Metabolites were analysed qualitatively using the FiehnLib and NIST databases. Full-mouth probing depth and clinical attachment loss were recorded as indexes of periodontal disease.

RESULTS

A total of 349 metabolites were qualitatively detected in the gingival crevicular fluid samples, and 200 metabolites were detected in the serum samples. Compared with healthy controls, patients with GAgP showed significant increases in serum urea and allo-inositol levels. In contrast, glutathione, 2,5-dihydroxybenzaldehyde, adipic acid and 2-deoxyguanosine levels were decreased in patients with GAgP. In the gingival crevicular fluid samples, noradrenaline, uridine, α-tocopherol, dehydroascorbic acid, xanthine, galactose, glucose-1-phosphate and ribulose-5-phosphate levels were increased in patients with GAgP, while thymidine, glutathione and ribose-5-phosphate levels were decreased.

CONCLUSION

The metabolomics analysis by gas chromatography-mass spectrometry is an effective and minimally non-invasive way to differentiate the metabolites characteristic of patients with GAgP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GAgP and healthy controls. These metabolic profiles have great potential in detecting GAgP and helping to understand its underlying mechanisms.

Transcription Factor Binding Site Enrichment Analysis in Co-Expression Modules in Celiac Disease

The aim of this study was to construct celiac co-expression patterns at a whole genome level and to identify transcription factors (TFs) that could drive the gliadin-related changes in coordination of gene expression observed in celiac disease (CD). Differential co-expression modules were identified in the acute and chronic responses to gliadin using expression data from a previous microarray study in duodenal biopsies. Transcription factor binding site (TFBS) and Gene Ontology (GO) annotation enrichment analyses were performed in differentially co-expressed genes (DCGs) and selection of candidate regulators was performed. Expression of candidates was measured in clinical samples and the activation of the TFs was further characterized in C2BBe1 cells upon gliadin challenge. Enrichment analyses of the DCGs identified 10 TFs and five were selected for further investigation. Expression changes related to active CD were detected in four TFs, as well as in several of their in silico predicted targets. The activation of TFs was further characterized in C2BBe1 cells upon gliadin challenge, and an increase in nuclear translocation of CAMP Responsive Element Binding Protein 1 (CREB1) and IFN regulatory factor-1 (IRF1) in response to gliadin was observed. Using transcriptome-wide co-expression analyses we are able to propose novel genes involved in CD pathogenesis that respond upon gliadin stimulation, also in non-celiac models.

A novel statistical approach for identification of the master regulator transcription factor

BACKGROUND

Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.

METHODS

We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one.

RESULTS

In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software .

CONCLUSION

We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.