Factors associated with alveolar bone depth mesial to the mandibular third molars after orthodontic protraction.

INTRODUCTION: The objective of this research was to study the factors associated with the alveolar bone depth mesial to the mandibular third molars (M8) after the mandibular second (M7) and third molars were protracted into the space of the mandibular first molars (M6), which were newly extracted for orthodontic treatment or extracted more than 1 year before treatment. METHODS: This retrospective study included 57 adult patients (mean age 23.40 ± 4.40 years) in whom M6 were newly extracted for orthodontic treatment or extracted more than 1 year before treatment. The alveolar bone depth mesial to M8 was measured on posttreatment panoramic radiographs. The vertical, horizontal, and angular changes of M8 were measured on both pre- and posttreatment panoramic radiographs. Linear correlation and regression analyses were conducted to explore the factors associated with the alveolar bone depth mesial to M8. RESULTS: The alveolar bone conditions of M6 (R= -0.391, P <0.001) and the vertical movement directions of M8 (R= -0.433, P <0.001) were significant factors associated with the alveolar bone depth mesial to M8 after orthodontic protraction. CONCLUSIONS: Without considering the pretreatment periodontal status of M8, patients with M6 extracted exceeding 1 year before treatment and with M8 extruded after orthodontic protraction may exhibit deeper alveolar bone depth mesial to M8.

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.

LncRNA-mediated ceRNA network was identified as a crucial determinant of differential effects in periodontitis and periimplantitis by high-throughput sequencing.

BACKGROUND AND OBJECTIVE: Although periimplantitis and periodontitis share similar features, particularly clinical features, they are two different diseases and should be analyzed separately. Thus far, few omics-level differences in periimplantitis and periodontitis have been reported. This study was aimed at exploring the differential effects of expression mRNAs, lncRNAs, and miRNAs in periodontitis and periimplantitis by high-throughput sequencing and competitive endogenous RNA (ceRNA) analysis. METHODS: Gingival tissues of healthy individuals (HI) and periimplantitis (PI) and periodontitis (P) patients were collected and used for genome-wide sequencing. The differentially expressed genes (DEGs) were screened and visualized by R software. The functions and pathways of DEGs were analyzed using Metascape, and the ceRNA network was constructed using the Cytoscape software. Finally, gene set enrichment analysis (GSEA) was used to predict the function of key nodes in ceRNA. RESULTS AND CONCLUSION: By constructing the regulated ceRNA network, six genes (FAM126B, SORL1, PRLR, CPEB2, RAP2C, and YOD1) and 16 miRNAs (hsa-miR-338-5p, hsa-miR-650, hsa-miR-9-5p, hsa-miR-1290, hsa-miR-544a, hsa-miR-3179, hsa-miR-1269a, hsa-miR-3679-5p, hsa-miR-149-5p, hsa-miR-615-3p, hsa-miR-33b-5p, hsa-miR-31-5p, hsa-miR-4639-5p, hsa-miR-204-5p, hsa-miR-5588-5p, and hsa-mir-196a-5p) were detected. Five long non-coding RNAs (lnc-CORO2B-1, lnc-MBL2-7, lnc-TRIM45-1, lnc-CHST10-2, and lnc-TNP1-6) were found to target these miRNAs in this ceRNA network. The ceRNA network based on transcriptome data revealed that FAM126B, SORL1, PRLR, CPEB2, RAP2C, and YOD1 were crucial proteins of differential effects in periodontitis and periimplantitis. The lncRNA-miRNA-mRNA interaction involved the regulation of the Hippo signaling pathway, Wnt signaling pathway, Toll-like receptor signaling pathway, NOD signaling pathway, oxidative stress, and innate immune process. These regulated pathways and biological processes may be factors contributing to the pathogenesis of periimplantitis being distinct from that of periodontitis.