Transcriptome of Healthy Gingival Tissue from Edentulous Sites in Patients with a History of Aggressive Periodontitis

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

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

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.

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.

Host-microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

Periodontal studies using transcriptomics, proteomics, and metabolomics encompass the collection of mRNA transcripts, proteins, and small-molecule chemicals in the context of periodontal health and disease. The number of studies using these approaches has significantly increased in the last decade and they have provided new insight into the pathogenesis and host-microbe interactions that define periodontal diseases. This review provides an overview of current molecular findings using -omic approaches that underlie periodontal disease, including modulation of the host immune response, tissue homeostasis, and complex metabolic processes of the host and the oral microbiome. Integration of these -omic approaches will broaden our perspective of the molecular mechanisms involved in periodontal disease, advancing and improving the diagnosis and treatment of various stages and forms of periodontal disease.

Integration of genome-wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

AIM

To identify risk variants associated with gene expression in peripheral blood and to identify genes whose expression change may contribute to the susceptibility to periodontitis.

MATERIAL AND METHODS

We systematically integrated the genetic associations from a recent large-scale periodontitis GWAS and blood expression quantitative trait loci (eQTL) data using Sherlock, a Bayesian statistical framework. We then validated the potential causal genes in independent gene expression data sets. Gene co-expression analysis was used to explore the functional relationship for the identified causal genes.

RESULTS

Sherlock analysis identified 10 genes (rs7403881 for MT1L, rs12459542 for SIGLEC5, rs12459542 for SIGLEC14, rs6680386 for S100A12, rs10489524 for TRIM33, rs11962642 for HIST1H3E, rs2814770 for AIM2, rs7593959 for FASTKD2, rs10416904 for PKN1, and rs10508204 for WDR37) whose expression may influence periodontitis. Among these genes, AIM2 was consistent significantly upregulated in periodontium of periodontitis patients across four data sets. The cis-eQTL (rs2814770, ~16 kb upstream of AIM2) showed significant association with AIM2 (p = 6.63 × 10^-6 ) and suggestive association with periodontitis (p = 7.52 × 10^-4 ). We also validated the significant association between rs2814770 and AIM2 expression in independent expression data set. Pathway analysis revealed that genes co-expressed with AIM2 were significantly enriched in immune- and inflammation-related pathways.

CONCLUSION

Our findings implicate that AIM2 is a susceptibility gene, expression of which in gingiva may influence periodontitis risk. Further functional investigation of AIM2 may provide new insight for periodontitis pathogenesis.

Long non-coding RNA and mRNA expression profiles in peri-implantitis vs periodontitis

BACKGROUND AND OBJECTIVE

Peri-implantitis is a biofilm-mediated infectious disease that results in progressive loss of implant-supporting bone. As compared to its analogue periodontitis, peri-implantitis is generally known to be more aggressive, with comparatively rapid progression and less predictable treatment outcomes, especially when advanced. An understanding of molecular mechanisms underpinning the similarities and differences between peri-implantitis and periodontitis is essential to develop novel management strategies. This study aimed to compare long non-coding RNAs (lncRNAs) and messenger RNA (mRNA) expression profiles between peri-implantitis and periodontitis.

METHODS

Inflamed soft tissue from peri-implantitis and periodontitis lesions, and healthy gingival tissue controls were analyzed by microarray. Cluster graphs, gene ontology (GO) analysis, and pathway analysis were performed. Quantitative real-time PCR was employed to verify microarray results. The expression levels of RANKL and OPG in the three tissue types were also evaluated, using qRT-PCR. Coding non-coding (CNC) network analyses were performed.

RESULTS

Microarray analyses revealed 1079 lncRNAs and 1003 mRNAs as differentially expressed in peri-implantitis when compared to periodontitis. The cyclooxygenase-2 pathway was the most up-regulated biological process in peri-implantitis as compared to periodontitis, whereas hemidesmosome assembly was the most down-regulated pathway. Osteoclast differentiation was relatively up-regulated, and RANKL/OPG ratio was higher in peri-implantitis than in periodontitis.

CONCLUSIONS

The study demonstrated that peri-implantitis and periodontitis exhibit significantly different lncRNA and mRNA expression profiles, suggesting that osteoclast differentiation-related pathways are comparatively more active in peri-implantitis. These data highlight potential molecular targets for periodontitis and peri-implantitis therapy development.

Local IL-10 level as a predictive factor in generalized aggressive periodontitis treatment response

Generalized aggressive periodontitis (GAgP) presents a reduced response to non-surgical therapy. However, it is not clear if the initial clinical, microbiological or immunological characteristics are impacting the worse response to treatment. This study aimed to identify the predictive value of clinical, microbiological and immunological patterns on the clinical response to therapy in GAgP patients. Twenty-four GAgP patients were selected, and gingival crevicular fluid (GCF) and subgingival biofilm were collected. Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Tannerella forsythia levels were evaluated by qPCR, and IL-1β and IL-10 concentration by ELISA. Twelve patients were treated with SRP (scaling and root planning), and twelve with SRP plus 375 mg amoxicillin and 250 mg metronidazole (8/8 hours, 7 days) (SRP + AM). The clinical changes (Probing Pocket Depth [PPD] reduction and Clinical Attachment Level [CAL] gain) 6 months post-treatment were correlated to the initial clinical, inflammatory and microbiological variables using stepwise logistic regression (α = 5%). CAL gain at 6 months was 1.16 ± 0.77 for SRP and 1.74 ± 0.57 mm for SRP + AM (P > .05). PPD reduction was 1.96 ± 0.82 for SRP and 2.45 ± 0.77 mm for SRP + AM (P < .05). In the SRP group, IL-10 showed a predictive value for clinical response. The higher the IL-10 concentration at baseline, the higher the reduction in PPD at 6 months (P = .01, r = .68). However, when antimicrobials were administered, no significant influence was detected (P > .05). It can be concluded that the IL-10 levels in GFC act as a predictor of clinical response to GAgP. Moreover, the intake of antimicrobials appears to overlap the influence of the inflammatory response on clinical response to treatment. Clinical trial registration number: NCT03933501.

SOD2 is upregulated in periodontitis to reduce further inflammation progression

OBJECTIVES

Periodontitis is a highly prevalent chronic inflammatory disease that results in destruction of tooth-supporting structures followed by tooth-loss. Until now, periodontitis has been regarded to be initiated by bacterial infection followed by aberrant host response. Although increasing evidence suggests a strong association between oxidative stress and periodontitis, precise molecular mechanism has been left unanswered. In this study, we investigated roles of SOD2, the main antioxidant enzyme maintaining reactive oxygen species (ROS) homeostasis, under inflammatory conditions.

METHODS

We computationally analyzed SOD2 expression in periodontitis. To confirm this data, immunoblot assay was performed with samples from periodontitis patients. The cellular mechanism of change in SOD2 expression was identified through immunoblot assay and immunofluorescence. To evaluate the molecular function of SOD2, we generated SOD2-deficient cells by utilizing the CRISPR/Cas9 system.

RESULTS

We first determined that SOD2 expression was significantly increased in periodontitis. We also confirmed that SOD2 expression was upregulated through the NF-κB pathway when the inflammatory signal was stronger and extended. Gene manipulation against SOD2 through the CRISPR/Cas9 system showed that the absence of SOD2 increased production of NLRP3 inflammasome components.

CONCLUSIONS

Our study demonstrates that intracellular SOD2 has a protective role by suppressing NLRP inflammasome-caspase-1-IL-1β axis under inflammatory conditions.