Relationship between interleukin 1 (IL-1) genetic polymorphism and periimplantitis: systematic literature review and meta-analysis.

OBJECTIVE: Periimplantitis (PI) is a complex multifactorial chronic disease caused by interactions between bacteria, host immune-inflammatory responses, and genetic or environmental factors that modify buccal eutrophism. In daily clinical practice, an increase in the prevalence of PI (8%) determined the need to establish the PI causes and set optimal therapeutic strategies. The interleukin family (IL-1), a group of cytokines, triggers and perpetuates peri-implantitis. Therefore, they could be used as biomarkers for diagnosis and treatment. This systematic review aimed to analyze the correlation between IL-1 allelic polymorphism (IL-1A -889, IL-1ß -511, IL-1ß +3954) and the PI disease. MATERIALS AND METHODS: Selected databases were PubMed, Scopus, and Cochrane Library. The search strategy included the following terms: "dental implants"; "periimplantitis"; "interleukin-IL-1"; "polymorphism"; "perimplant bone loss". Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A meta-analysis was conducted on five of 40 review articles. p-values, confidence intervals (CI), and Odds ratios (OR) were assessed. In 4 articles, the p-value was lower than 0.05, confirming the statistical significance of the result. RESULTS: The prevalence of the selected studies reported the existence of a causal association between polymorphisms of IL-1 and the onset of peri-implantitis, especially for IL-1 allelic variants associated with further polymorphic genes encoding for IL-6, tumor necrosis factor-alpha (TNF-α), matrix metalloproteinases (MMP)-8, IL-1Na, IL-8, IL-18, osteopontin (OPN). In addition, the presence of the IL-1 polymorphism and PI is particularly higher in smokers, diabetes, and autoimmune disease patients. CONCLUSIONS: The detection of salivary biomarkers is, therefore, a diagnostic tool with a high potential to intercept the PI early and act with appropriate and non-invasive treatment. Due to the continued technological innovation in biomarkers and diagnostic sciences, further studies are needed to investigate the role of these biochemical mediators. The results of studies and the recent technological innovation in biomarkers and diagnostic sciences will allow further research to investigate the role of these biochemical mediators.

Genetic polymorphisms of inflammatory and bone metabolism related proteins in a population with dental implants of the Basque Country. A case-control study.

BACKGROUND: Peri-implantitis (PI) is a frequent inflammatory disorder characterised by progressive loss of the supporting bone. Not all patients with recognised risk factors develop PI. The aim of this study is to evaluate the presence of single nucleotide polymorphisms (SNP) of inflammatory and bone metabolism related proteins in a population treated with dental implants from the Basque Country (Spain). METHODS: We included 80 patients with diagnosis of PI and 81 patients without PI, 91 women and 70 men, with a mean age of 60.90 years. SNPs of BMP-4, BRINP3, CD14, FGF-3, FGF-10, GBP-1, IL-1α, IL-1ß, IL-10, LTF, OPG and RANKL proteins were selected. We performed a univariate and bivariate analysis using IBM SPSS® v.28 statistical software. RESULTS: Presence of SNPs GBP1 rs7911 (p = 0.041) and BRINP3 rs1935881 (p = 0.012) was significantly more common in patients with PI. Patients with PI who smoked (> 10 cig/day) showed a higher presence of OPG rs2073617 SNP (p = 0.034). Also, BMP-4 rs17563 (p = 0.018) and FGF-3 rs1893047 (p = 0.014) SNPs were more frequent in patients with PI and Type II diabetes mellitus. CONCLUSIONS: Our findings suggest that PI could be favoured by an alteration in the osseointegration of dental implants, based on an abnormal immunological response to peri-implant infection in patients from the Basque Country (Spain).

Bioinformatics investigation of adaptive immune-related genes in peri-implantitis and periodontitis: Characteristics and diagnostic values.

BACKGROUND: Peri-implantitis and periodontitis have similar immunological bioprocesses and inflammatory phenotypes. In the inflammatory process, the adaptive immune cells can drive the development of disease. This research investigated the differences and diagnostic significance of peri-implantitis and periodontitis in adaptive immune responses. METHODS: We acquired four GEO datasets of gene expressions in surrounding tissues in healthy person, healthy implant, periodontitis, and peri-implantitis patients. The structural characteristics and enrichment analyses of differential expression genes were examined. The adaptive immune landscapes in peri-implantitis and periodontitis were then evaluated using single sample gene set enrichment analysis. The STRING database and Cytoscape were used to identify adaptive hub genes, and the ROC curve was used to verify them. Finally, qRT-PCR method was used to verify the expression level of Hub gene in activated T cells on the titanium-containing or titanium-free culture plates. RESULTS: At the transcriptome level, the data of healthy implant, peri-implantitis and periodontitis were highly dissimilar. The peri-implantitis and periodontitis both exhibited adaptive immune response. Except for the activated CD4+T cells, there was no significant difference in other adaptive immune cells between peri-implantitis and periodontitis. In addition, correlation analysis showed that CD53, CYBB, and PLEK were significantly positively linked with activated CD4+T cells in the immune microenvironment of peri-implantitis, making them effective biomarkers to differentiate it from periodontitis. CONCLUSIONS: Peri-implantitis has a uniquely immunogenomic landscape that differs from periodontitis. This study provides new insights and ideas into the activated CD4+T cells and hub genes that underpin the immunological bioprocess of peri-implantitis.

Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis.

OBJECTIVES: This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest (RF) and artificial neural network (ANN). METHODS: GSE-33774, GSE106090, and GSE57631 datasets were obtained from the GEO database. The GSE33774 and GSE106090 datasets were analyzed for differential expression and functional enrichment. The protein-protein interaction networks (PPI) and RF screened vital genes. A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets. A transcription factor-gene interaction network and a transcription factor-micro-RNA (miRNA) regulatory network were also established. RESULTS: A total of 124 differentially expressed genes (DEGs) involved in the regulation of peri-implantitis were screened. Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration. The PPI and RF screened six essential genes, namely, CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8. The receiver operating characteristic curve (ROC) indicated that the ANN model had an excellent diagnostic performance. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 may be a key miRNA. CONCLUSIONS: The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence, and CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8 are potential diagnostic markers. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 plays a vital role as a critical miRNA.

Deciphering peri-implantitis: Unraveling signature genes and immune cell associations through bioinformatics and machine learning.

Early diagnosis of peri-implantitis (PI) is crucial to understand its pathological progression and prevention. This study is committed to investigating the signature genes, relevant signaling pathways and their associations with immune cells in PI. We analyzed differentially expressed genes (DEGs) from a PI dataset in the gene expression omnibus database. Functional enrichment analysis was conducted for these DEGs. Weighted Gene Co-expression Network Analysis was used to identify specific modules. Least absolute shrinkage and selection operator and support vector machine recursive feature elimination were ultimately applied to identify the signature genes. These genes were subsequently validated in an external dataset. And the immune cells infiltration was classified using CIBERSORT. A total of 180 DEGs were screened from GSE33774. Weighted Gene Co-expression Network Analysis revealed a significant association between the MEturquoise module and PI (cor = 0.6, P < .0001). Least absolute shrinkage and selection operator and support vector machine recursive feature elimination algorithms were applied to select the signature genes, containing myeloid-epithelial-reproductive tyrosine kinase, microfibrillar-associated protein 5, membrane-spanning 4A 4A, tribbles homolog 1. In the validation on the external dataset GSE106090, all these genes achieved area under curve values exceeding 0.95. GSEA analysis showed that these genes were correlated with the NOD-like receptor signaling pathway, metabolism of xenobiotics by cytochrome P450, and arachidonic acid metabolism. CIBERSORT revealed elevated levels of macrophage M2 and activated mast cells in PI. This study provides novel insights into understanding the molecular mechanisms of PI and contributes to advancements in its early diagnosis and prevention.

Association analysis between CD14 gene polymorphisms and peri-implantitis susceptibility in a Chinese population.

OBJECTIVE: The goal of the study was to examine the genetic correlation of cluster of differentiation 14 (CD14) gene polymorphisms with peri-implantitis (PI) predisposition in a Chinese Han population. METHODS: In the case-control study, blood samples were collected from PI patients and healthy individuals (n = 120/group), who were admitted to the Affiliated Hospital of Yangzhou University from 2021 to 2023. One-way analysis of variance (ANOVA) was applied to compare differences of continuous variables among different groups. Genotype and allele distributions of CD14 gene rs2569190 and rs2915863 polymorphisms were analyzed between groups via χ2 test. RESULTS: A high percentage of rs2569190 GG genotype or G allele carriers were identified in PI group compared with control group (p < .01). Rs2569190 GG genotype carriers had high risk to develop PI (odds ratio: 2.545, 95% confidence interval: 1.257-5.156, p = .009). The rs2569190 AA genotype carriers had the lowest values of gingival index, plaque index, calculus index, peri-implant pocket depth, and clinical attachment level, which were the highest in cases with GG genotype. CONCLUSION: Rs2569190 polymorphism of CD14 gene was significantly associated with PI predisposition in the Chinese Han population, and the GG genotype and G allele were risk factors for the development of PI.

Dental implant material related changes in molecular signatures in peri-implantitis - A systematic review of omics in-vivo studies.

OBJECTIVE: Titanium particles have been shown in in-vitro studies to lead to the activation of specific pathways, this work aims to systematically review in- vivo studies examining peri-implant and periodontal tissues at the transcriptome, proteome, epigenome and genome level to reveal implant material-related processes favoring peri-implantitis development investigated in animal and human trials. METHODS: Inquiring three literature databases (Medline, Embase, Cochrane) a systematic search based on a priori defined PICOs was conducted: '-omics' studies comparing molecular signatures in healthy and infected peri-implant sites and/or healthy and periodontitis-affected teeth in animals/humans. After risk of bias assessments, lists of differentially expressed genes and results of functional enrichment analyses were compiled whenever possible. RESULTS: Out of 2187 screened articles 9 publications were deemed eligible. Both healthy and inflamed peri-implant tissues showed distinct gene expression patterns compared to healthy/diseased periodontal tissues in animal (n = 4) or human studies (n = 5), with immune response, bone metabolism and oxidative stress being affected the most. Due to the lack of available re-analyzable data and inconsistency in methodology of the eligible studies, integrative analyses on differential gene expression were not applicable CONCLUSION: The differences of transcriptomic signatures in between peri-implant lesions compared to periodontal tissue might be related to titanium particles arising from dental implants and are in line with the in-vitro data recently published by our group. Nevertheless, limitations emerge from small sample sizes of included studies and insufficient publication of re-analyzable data.

Evaluation of IL-4, MIP-1α, and MMP-9 gene expression levels in peri-implant tissues in peri-implantitis.

This case-control study evaluated the gene expression levels of interleukin (IL)-4, macrophage inflammatory protein type 1 alpha (MIP-1α), and metalloproteinase (MMP)-9, factors involved in the formation of giant cells in healthy peri-implant tissue and peri-implantitis. Thirty-five subjects (15 healthy and 20 with peri-implantitis), who met the inclusion and exclusion criteria, were included in this study. The peri-implant tissue biopsies were subjected to total RNA extraction, DNAse treatment, and cDNA synthesis. Subsequently, the reaction of real-time PCR was performed to evaluate the gene expression levels of IL-4, MIP-1α, and MMP-9 concerning the reference gene. IL-4 gene expression showed higher (18-fold) values in the Peri-Implantitis Group of Patients when compared with the Healthy (Control) Group (p<0.0001). Although MIP- 1α and MMP-9 gene expression levels were higher in diseased implants, they showed no significant differences (p=0.06 and p=0.2337), respectively. Within the limitations of this study, the results showed that in tissues affected by peri-implantitis, only levels of Il-4 were increased when compared with tissues in the control group.

Dysregulated m6A methylation modification is associated with human peri-implantitis - A pilot study.

BACKGROUND: N6-methyladenosine (m6A) RNA modification and its regulatory enzymes play important roles in the modulation of inflammation by regulating inflammation-related gene expression. Dysregulation of m6A has been associated with inflammatory diseases, including periodontitis. This study aimed to investigate the potential role of m6A modification and its master regulatory enzyme METTL3 in patients with peri­implantitis. MATERIALS AND METHODS: Peri-implant soft tissues from 20 subjects (10 healthy controls and 10 patients with peri­implantitis) were enrolled in this study. Quantitative reverse transcription PCR (RT-qPCR) was used to detect METTL3 gene expression and western blotting was used to detect METTL3 protein expression. The m6A mRNA levels were measured using an m6A-RNA methylation quantification kit. Protein-protein interaction networks and in silico functional analyses were conducted using various bioinformatics tools. RESULTS: m6A mRNA levels significantly increased in the peri­implantitis group. Higher METTL3 mRNA and protein levels were observed in the peri­implantitis group. High METTL3 expression might influence elevated levels of m6A RNA methylation. In addition, in silico functional analysis indicated that the METTL3 gene and protein were associated with inflammatory pathways. CONCLUSIONS: Our data provide evidence, for the first time, that dysregulation of m6A modification is associated with peri­implantitis and may represent a strong risk factor for this inflammatory disease.

Transcriptome analysis of human peri-implant soft tissue and periodontal gingiva: a paired design study.

OBJECTIVES: Limited information is available about the biological characterization of peri-implant soft tissue at the transcriptional level. The aim of this study was to investigate the effect of dental implant on the soft tissue in vivo by using paired samples and compare the differences between peri-implant soft tissue and periodontal gingiva at the transcriptional level. METHODS: Paired peri-implant soft tissue and periodontal gingiva tissue from 6 patients were obtained, and the pooled RNAs were analyzed by deep sequencing. Venn diagram was used to further screen out differentially expressed genes in every pair of samples. Annotation and enrichment analysis was performed. Further verification was done by quantitative real-time PCR. RESULTS: Totally 3549 differentially expressed genes (DEGs) were found between peri-implant and periodontal groups. The Venn diagram further identified 185 DEGs in every pair of samples, of which the enrichment analysis identified significant enrichment for cellular component was associated with external side of plasma membrane, for molecular function was protein binding, for biological process was immune system process, and for KEGG pathway was cytokine-cytokine receptor interaction. Among the DEGs, CST1, SPP1, AQP9, and SFRP2 were verified to be upregulated in peri-implant soft tissue. CONCLUSIONS: Peri-implant soft tissue showed altered expressions of several genes related to the cell-ECM interaction compared to periodontal gingiva. CLINICAL RELEVANCE: Compared to periodontal gingiva, altered cell-ECM interactions in peri-implant may contribute to the susceptibility of peri-implant diseases. At the transcriptional level, periodontal gingiva is generally considered the appropriate control for peri-implantitis, except regarding the cell-ECM interactions.

Identification of immunological bioprocesses involved in peri-implantitis using weighted gene co-expression network analysis.

BACKGROUND: Peri-implantitis is an irreversible infectious disease that occurs with high incidence. Exploring the immune responses of peri-implantitis is key to developing targeted treatment strategies. However, there is limited research on the immune response of peri-implantitis. METHODS: This study performed a weighted gene co-expression network analysis to identify the peri-implantitis related gene network and conducted a functional enrichment analysis of the gene network. Thereafter, the candidate hub genes were selected by constructing a protein-protein interaction network and drawing an upset plot. The hub genes were identified through their significant associations with disease condition and validated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. Using the gene set variation analysis, the hub genes were further used to explore infiltrating immunocytes and immune factors in peri-implantitis. Finally, the immunocytes and immune factor related hub genes were intersected to obtain the therapeutic target, which was validated using histological staining. RESULTS: The peri-implantitis related gene network was enriched in innate and adaptive immune response. Subsequently, interleukin (IL)1B, IL10, ITGAM, ITGB1, STAT3, and TLR4 were identified as hub genes. Plasmacytoid dendritic cells, macrophages, myeloid-derived suppressor cells, natural killer T cells, and immature B cells were positively and significantly related to the hub genes IL1B, TLR4, ITGAM, and ITGB1 (correlation coefficient > 0.80). While immune factors CXCL10, IL6, and CXCL12 and hub genes IL10 and IL1B held the highest degree in the immune factors network. IL1B may be a promising therapeutic target. CONCLUSION: This study provides new insights into the hub genes, immunocytes, and immune factors underlying peri-implantitis immunological bioprocess.

Dental implant material related changes in molecular signatures in peri-implantitis - A systematic review and integrative analysis of omics in-vitro studies.

OBJECTIVE: Since peri-implantitis differs clinically and histopathologically from periodontitis, implant wear debris is considered to play a role in the destructive processes. This work aims to systematically review if titanium particles affect oral-related cells through changes in molecular signatures (e.g., transcriptome, proteome, epigenome), thereby promoting peri-implantitis. METHODS: Leveraging three literature databases (Medline, Embase, Cochrane) a systematic search based on a priori defined PICOs was conducted: '-omics' studies examining titanium exposure in oral-related cells. After risk of bias assessments, lists of differentially expressed genes, proteins, and results of functional enrichment analyses were compiled. The significance of overlapping genes across multiple studies was assessed via Monte Carlo simulation and their ranking was verified using rank aggregation. RESULTS: Out of 2104 screened articles we found 12 eligible publications. A significant overlap of gene expression in oral-related cells exposed to titanium particles was found in four studies. Furthermore, changes in biological processes like immune/inflammatory or stress response as well as toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling pathways were linked to titanium in transcriptome and proteome studies. Epigenetic changes caused by titanium were detected but inconsistent. CONCLUSION: An influence of titanium implant wear debris on the development and progression of peri-implantitis is plausible but needs to be proven in further studies. Limitations arise from small sample sizes of included studies and insufficient publication of re-analyzable data.

Association between Osteoprotegerin rs2073618 polymorphism and peri-implantitis susceptibility: a meta-analysis.

OBJECTIVES: Peri-implantitis was an inflammatory progress on the tissue around the implant. The Osteoprotegerin G1181C (rs2073618) polymorphism was reported to be related to the increased risk of the peri-implantitis, whereas another found no relationship. The present study was conducted to research the relationship between Osteoprotegerin rs2073618 polymorphism and peri-implantitis susceptibility. MATERIALS AND METHODS: The meta-analysis was performed according to the Preferred Reporting Items for Systematic reviews. Electronic databases including PubMed, Web of science, Springer Link and Embase (updated to April 15, 2022) were retrieved. The cohort study, case-control study or cross-sectional study focusing on the Osteoprotegerin rs2073618 polymorphism and peri-implantitis were retrieved. The data included basic information of each study and the genotype and allele frequencies of the cases and controls. RESULTS: Three studies were finally included, including 160 cases and 271 controls. Allelic model, homozygote model, recessive model, dominant model, and heterozygous model were established to assess the relationship between OPG rs2073618 polymorphism and peri-implantitis susceptibility. The Osteoprotegerin rs2073618 polymorphism was significantly associated with peri-implantitis in Recessive model and Homozygote model. CONCLUSION: OPG rs2073618 polymorphism in Recessive model and Homozygote model was highly likely related to the risk of peri-implantitis. PROSPERO registration number: CRD42022320812.

The study of genetic predisposition on periodontitis and peri-implantitis.

Background: Peri-implant mucositis and peri-implantitis cases increase in number with the increase of implant applications. Peri-implant mucositis and peri-implantitis are defined as inflammatory diseases with inflammation and loss in soft and hard tissue, similar to the other periodontal diseases. As observed in many diseases, genetic predisposition factors also affect the progress of periodontitis and peri-implantitis. Aim: This study examines if there is any solid genetic predisposition causing periodontitis and peri-implantitis formation in Turkish patients. Patients & Methods: In order to evaluate single nucleotide polymorphism (SNP), Interleukin-8 (IL-8) and N-formyl-L-methionyl-L-leucyl-phenylalanine (fMLP), playing a role in the chemotaxis of neutrophils, and Fc Gamma Receptor IIA (FcγRIIA) and Fc Gamma Receptor IIIA (FcγRIIIA), playing a role in the antigen-antibody complexes and phagocytosis, were selected. Thirty-two Turkish non-smoking subjects, having periodontitis, thirty-three Turkish non-smoking subjects, having peri-implantitis and thirty-three Turkish non-smoking healthy subjects were selected. In total 98 adults participated in our study. Collected saliva samples from the participants were used for DNA isolation. SNPs were determined in these subgroups of the study by means of genotype-specific polymerase chain reactions. Results: When IL-8 A-251T, FcγRIIa -H131 and FcγRIIIa -V158 polymorphism were evaluated, no significant difference was found between periodontitis, peri-implantitis and healthy groups. However, this study observed that fMLP Receptor (FPR1) gene polymorphism creates a significant difference in individuals at higher risk of periodontitis or peri-implantitis. Conclusion: Results show that individuals with the G genotype have a higher risk of periodontitis, while individuals with G / C genotype have higher risk of peri-implantitis.

Ageing-Associated Transcriptomic Alterations in Peri-Implantitis Pathology: A Bioinformatic Study.

Background: Ageing is associated with increased incidence of peri-implantitis but the roles of ageing-associated biological mechanisms in the occurrence of peri-implantitis are not known. This study is aimed at performing integrative bioinformatic analysis of publically available datasets to uncover molecular mechanisms related to ageing and peri-implantitis. Methods: Gene expression datasets related to ageing and peri-implantitis (PI) were sought, and differentially expressed genes (DEGs) were analysed. Ageing-related genes were also identified from the "Aging Atlas" database. Using intersection analysis, an age-related-PI gene set was identified. Functional enrichment analysis for enriched GO biological process and KEGG pathways, protein-protein interaction (PPI) network analysis, correlation analysis, and immune cell infiltration analysis to determine high-abundance immune cells were performed. Least absolute shrinkage and selection operator (LASSO) logistic regression identified key age-related-PI genes. Transcription factor-gene and drug-gene interactions and enriched KEGG pathways for the key age-related-PI genes were determined. Results: A total of 52 genes were identified as age-related-PI genes and found enriched in several inflammation-associated processes including myeloid leukocyte activation, acute inflammatory response, mononuclear cell differentiation, B cell activation, NF-kappa B signalling, IL-17 signalling, and TNF signalling. LYN, CDKN2A, MAPT, BTK, and PRKCB were hub genes in the PPI network. Immune cell infiltration analysis showed activated dendritic cells, central memory CD4 T cells, immature dendritic cells, and plasmacytoid dendritic cells were highly abundant in PI and ageing. 7 key age-related PI genes including ALOX5AP, EAF2, FAM46C, GZMK, MAPT, RGS1, and SOSTDC1 were identified using LASSO with high predictive values and found to be enriched in multiple neurodegeneration-associated pathways, MAPK signalling, and Fc epsilon RI signalling. MAPT and ALOX5AP were associated with multiple drugs and transcription factors and interacted with other age-related genes to regulate multiple biological pathways. Conclusion: A suite of bioinformatics analysis identified a 7-signature gene set highly relevant to cooccurrence of ageing and peri-implantitis and highlighted the role of neurodegeneration, autoimmune, and inflammation related pathways. MAPT and ALOX5AP were identified as key candidate target genes for clinical translation.

Weighted Gene Coexpression Network Analysis Identified IL2/STAT5 Signaling Pathway as an Important Determinant of Peri-Implantitis.

Objective: Peri-implantitis (PI) is one of the main reasons for dental implant failure. Until now, the etiology and pathogenesis of PI remain unclear. Methods: In this study, we used differentially expressed genes (DEGs) analysis and gene function enrichment analysis to assess the expression profile of peri-implant bone tissue and gingiva in PI public data from the Gene Expression Omnibus (GEO) database. Then, we used gingival tissues from patients with PI and healthy individual to construct gene coexpression networks to reveal the biological functions of the genes in PI using RNA sequencing data. Afterward, key gene modules were selected to reveal the critical biological process or signaling pathway using Hallmark's gene enrichment and expression analysis of the related pathway members in PI. Results: DEGs were enriched in the formation of cellular responses to external stimuli in bone tissue. Cytokine production, lymphocyte activation, immune response-regulating signaling pathway, and blood vessel development were the top GO biology process or pathways of the DEGs in gingival tissue. Weighted gene coexpression network analysis (WGCNA) of RNA-seq data was used to assess the results of correlation analysis between modules and traits and correlation analysis between modules and functions. kMEpurple, kMEgreen, and kMEred modules were selected as the key gene modules. Signaling pathways and gene expression analysis were performed on selected modules, such as IL2/STAT5 signaling pathway, TNFα signaling pathway via NFκB, and angiogenesis were enriched in kMEpurple module. Hedgehog signaling pathway, Wnt ß-catenin signaling pathway, and IL2/STAT5 signaling pathway were enriched in kMEgreen module. Peroxisome, IL2/STAT5 signaling pathway, and epithelial-mesenchymal transformation process were enriched in kMEred module. All the enrichment results of key modules contained IL2/STAT5 signaling pathway. Conclusion. Differential gene and enrichment analysis based on public data showed differences in gene expression patterns and biological process between bone and gingival tissues in PI. This spatial-temporal heterogeneity is reflected in the formation of cellular responses to external stimuli, which was enriched in bone tissue, but cytokine production, lymphocyte activation, immune response regulating signaling pathway, and blood vessel development were enriched in gingival tissue. WGCNA and Hallmark gene sets enrichment analysis of the gingival tissue expression profile and showed that IL2-mediated activation of immune cells could be a critical mechanism in PI. As a new clinical treatment alternative, we suggest that IL2/STAT5 pathway blockers could be helpful in the treatment of PI.

Association of HMGBP1 expression with clinical periimplant parameters among smokers and never-smokers with and without peri-implantitis.

OBJECTIVE: With our study we aimed at investigating the levels of high mobility group box chromosomal protein-1 (HMGB-1), tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-1ß in periimplant crevicular fluid (PICF) of smokers and never-smokers, with and without periimplantitis, and correlate these levels with the clinical and radiographic periimplant parameters. SUBJECTS AND METHODS: Sixty participants (n=15/group) were recruited and divided into 4 groups: cigarette smokers with periimplantitis (CSPI); cigarette smokers without periimplantitis (CSNPI); never-smokers with periimplantitis (NSPI); and never-smokers without periimplantitis (NSNPI). Clinical and radiographic periimplant parameters, including plaque scores (PS), bleeding on probing (BOP), probing depth (PD) and crestal bone level (CBL), were assessed. Crevicular levels of HMGB-1, TNF-α, and IL-1ß were quantified using human enzyme linked immunosorbent assay. p-values were generated using Kruskal-Wallis' test for comparison between the study groups, while correlations between HMGB-1, TNF-α, IL-1ß levels and clinical variables were analyzed using Spearman rank correlation coefficient analysis. RESULTS: Bleeding on probing was least in NSNPI and CSNPI followed by CSPI and NSPI (p<0.05). The highest PD and CBL was recorded for CSPI and NSPI groups, while the least PD and CBL were recorded among non-periimplantitis groups. HMGB-1 and IL-1ß were found to be significantly highest in CSPI groups followed by NSPI and CSNPI groups with no statistically significant difference between CSPI and NSPI groups (p<0.05). CSPI groups reported the highest TNF-α levels in the PICF in comparison to other groups (p<0.05). A significant negative correlation was observed between plaque scores (p=0.0187) and CBL (p=0.0049) in NSNPI and CSPI groups with HMGB-1, respectively. A significant positive correlation was seen for HMGB-1 in groups CSPI (p=0.0023) and NSPI (p=0.0018) for BOP. In CSPI group, a significant positive correlation was observed between TNF-α and PD (p=0.0443). On correlating IL-1ß, a significant positive correlation was observed for CBL in CSPI (p=0.0006) and NSPI (p=0.0275) groups, respectively. CONCLUSIONS: HMGB-1 could play a significant role in periimplant inflammatory response and inflammation. Higher crevicular fluid HMGB-1 levels are indicative of a possible surrogate biomarker for peri-implantitis.

Gene Correlation Network Analysis to Identify Biomarkers of Peri-Implantitis.

Background and Objectives: The histopathological and clinical conditions for transforming peri-implant mucositis into peri-implantitis (PI) are not fully clarified. We aim to uncover molecular mechanisms and new potential biomarkers of PI. Materials and Methods: Raw GSE33774 and GSE57631 datasets were obtained from the Gene Expression Omnibus (GEO) database. The linear models for microarray data (LIMMA) package in R software completes differentially expressed genes (DEGs). We conducted a weighted gene co-expression network analysis (WGCNA) on the top 25% of altered genes and identified the key modules associated with the clinical features of PI. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the R software. We constructed a protein-protein interaction (PPI) network through the STRING database. After that we used Cytohubba plug-ins of Cytoscape to screen out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in another dataset, GSE178351, and revalidation of genes through the DisGeNET database. Results: We discovered 632 DEGs (570 upregulated genes and 62 downregulated genes). A total of eight modules were screened by WGCNA, among which the turquoise module was most correlated with PI. The Cytohubba plug-ins were used for filtering hub genes, which are highly linked with PI development, from the candidate genes in the protein-protein interaction (PPI) network. Conclusions: We found five key genes from PI using WGCNA. Among them, ICAM1, CXCL1, and JUN are worthy of further study of new target genes, providing the theoretical basis for further exploration of the occurrence and development mechanism of PI.

MicroRNA sequence and function analysis in peri-implantitis and periodontitis: An animal study.

OBJECTIVE: To compare miRNA expression levels and predict relevant target genes and signaling pathways in peri-implantitis and periodontitis. BACKGROUND: There are many differences between periodontitis and peri-implantitis. An understanding of the similarities and differences in the transcriptional patterns of these diseases, as well as the molecular mechanisms, is beneficial for the development of management strategies. MATERIALS AND METHODS: Rat models of periodontitis (PD, n = 6) and peri-implantitis (PI, n = 5) were established by ligation. Implantation without ligation (PIC, n = 5) and normal rats (PDC, n = 6) were used as controls. Micro-CT was used to confirm the successful establishment of the model. Gingiva was harvested for miRNA transcriptome sequencing, and the results were confirmed by qRT-PCR. miRNA target genes were predicted with miRTarBase. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. RESULTS: Sixty-nine miRNAs were differentially expressed in PI vs. PD, 105 were differentially expressed in PI vs. PIC, and 70 were differentially expressed in PD vs. PDC (log2 FC ≥1 and padj <0.05). The upregulated genes in all three comparisons were mostly involved in the biological process response to stimulus, whereas most of the downregulated genes were involved in nervous system development (p < .01). The upregulated genes in PI vs. PD and PI vs. PIC were involved in Toll-like receptor signaling and RIG-I-like signaling. The upregulated genes in PI vs. PD were involved in T- and B-cell receptor signaling, apoptosis, and osteoclast differentiation. Focal adhesion was downregulated in all three comparisons, and adherens junction was downregulated in PI vs. PD and PD vs. PDC (p < .1). CONCLUSION: This study showed differences in the miRNA expression profiles between peri-implantitis and periodontitis and annotated the possible target genes and molecular mechanisms; this study could lay a foundation for the development of management strategies.

Influence of epigenetics on periodontitis and peri-implantitis pathogenesis.

Periodontitis is a disease characterized by tooth-associated microbial biofilms that drive chronic inflammation and destruction of periodontal-supporting tissues. In some individuals, disease progression can lead to tooth loss. A similar condition can occur around dental implants in the form of peri-implantitis. The immune response to bacterial challenges is not only influenced by genetic factors, but also by environmental factors. Epigenetics involves the study of gene function independent of changes to the DNA sequence and its associated proteins, and represents a critical link between genetic and environmental factors. Epigenetic modifications have been shown to contribute to the progression of several diseases, including chronic inflammatory diseases like periodontitis and peri-implantitis. This review aims to present the latest findings on epigenetic influences on periodontitis and to discuss potential mechanisms that may influence peri-implantitis, given the paucity of information currently available.