Triggering Receptor Expressed On Myeloid cells-2 Stimulates Osteoclast Differentiation and Bone Loss in Periodontitis

The detection of Gper1 as an important gene promoting jawbone regeneration in the context of estrogen deficiency

Numerous studies have demonstrated that estrogen deficiency inhibit the proliferation and differentiation of pre-osteoblasts in skeleton by affecting osteogenic signaling, lead to decreased bone mass and impaired regeneration. To explore the mechanisms maintaining bone regeneration under estrogen deficiency, we randomly selected 1102 clinical cases, in which female patients aged between 18 and 75 have underwent tooth extraction in Stomatological Hospital of Tongji University, there is little difference in the healing effect of extraction defects, suggesting that to some extent, the regeneration of jawbone is insensitive to the decreased estrogen level. To illuminate the mechanisms promoting jawbone regeneration under estrogen deficiency, a tooth extraction defect model was established in the maxilla of female rats who underwent ovariectomy (OVX) or sham surgery, and jawbone marrow stromal cells (BMSCs) were isolated for single-cell sequencing. Further quantitative PCR, RNA interference, alizarin red staining, immunohistochemistry and western blotting experiments demonstrated that in the context of ovariectomy, maxillary defects promoted G protein-coupled estrogen receptor 1 (Gper1) expression, stimulate downstream cAMP/PKA/pCREB signaling, and facilitate cell proliferation, and thus provided sufficient progenitors for osteogenesis and enhanced the regeneration capacity of the jawbone. Correspondingly, the heterozygous deletion of the Gper1 gene attenuated the phosphorylation of CREB, led to decreased cell proliferation, and impaired the restoration of maxillary defects. This study demonstrates the importance of Gper1 in maintaining jawbone regeneration, especially in the context of estrogen deficiency.

Interactions of Fibroblast Subtypes Influence Osteoclastogenesis and Alveolar Bone Destruction in Periodontitis

Background

To analyze the fibroblasts subtypes in the gingival tissues of healthy controls, gingivitis and periodontitis patients, as well as the effects of interaction between subtypes on alveolar bone destruction.

Methods

Gingival tissues were divided into three groups according to clinical and radiographic examination, and the immunostaining of EDA+FN was assessed. Fibroblasts from gingiva developed colony formation units (CFUs) and induced Trap+MNCs. The expression of osteoclastogenesis-related genes was assessed by real-time PCR. Variances in the gene profiles of CFUs were identified by principal component analysis, and cluster analysis divided CFUs into subtypes. The induction of Trap+MNCs and gene expression were compared among individual or cocultured subtypes. The fibroblast subtypes exerted critical effect on Trap+MNCs formation were selected and edited by CRISPR/Cas to investigate the influence on osteoclastogenesis in the periodontitis in mice.

Results

Most periodontitis samples exhibited intensive EDA+FN staining (P < 0.05), and these fibroblasts also induced most Trap+MNCs among three groups; consistently, fibroblasts from periodontitis highly expressed genes facilitating osteoclastogenesis. According to gene profiles and osteoclastogenic induction, four clusters of CFUs were identified. The proportion of clusters was significantly different (P < 0.05) among three groups, and their interaction influenced osteoclastogenic induction. Although Cluster 4 induced less osteoclasts, it enhanced the effects of Clusters 1 and 3 on Trap+MNCs formation (P < 0.05). EDA knockout in Cluster 4 abrogated this promotion (P < 0.05), and decreased osteoclasts and alveolar bone destruction in experimental periodontitis (P < 0.05).

Conclusion

Heterogeneous fibroblast subtypes affect the switch or development of periodontitis. A subtype (Cluster 4) played important role during alveolar bone destruction, by regulating other subtypes via EDA+FN paracrine.

Transcriptomic Analysis Reveals Genetic Cross-Talk between Periodontitis and Hypothyroidism

Background. Aim of this bioinformatics study based on transcriptomic analysis was to reveal the cross-talk between periodontitis (PD) and hypothyroidism (HT). Methods. The gene expression datasets GSE18152 and GSE176153 of HT and GSE10334, GSE16134, and GSE173078 of PD were downloaded through the Gene Expression Omnibus (GEO) database. Differential Expression Genes (DEG) between cases and controls in each microarray were assessed by using the “limma” (linear models for microarray data) R package (|log2 fold change (FC)| >0 and $P$ -value <0.05). To analyze the cross-talk effect between HT and PD, the intersection of DEG of HT and PD was selected. To investigate the biological function of cross-talk genes, the gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied. Protein-Protein Interaction (PPI) network was constructed using Cytoscape software. Top 10 cross-talk genes were screened, and the expression values of these 10 genes were extracted. ROC analysis was performed by using the pROC package and GGplot2 package of R language to predict the classification accuracy. Results. The overlapping DEG between HT and PD were 107 cross-talk genes. The results revealed that developmental process ( $P$ -value =1.06E-21) was the most significantly enriched biological process, followed by cell differentiation ( $P$ -value =8.49E-18) and immune system process ( $P$ -value =6.78E-11). KEGG analysis showed that Complement and coagulation cascades ( $P$ -value =2.29E-05), Hematopoietic cell lineage ( $P$ -value =2.66E-05), Phospholipase D signaling pathway ( $P$ -value =0.034367878) and Chemokine signaling pathway ( $P$ -value =0.04946333) were significantly enriched. The top 10 genes with most connections were LCE1B, LCE2B, LCE2A, LCE2C, LCE1C, LCE1F, ITGAM, C1QB, TREM2, and CD19. The AUC values of the two datasets of HT were both greater than 65% (GSE18152 = 81.42%, GSE176153 = 68.75%). AUC values of three datasets of PD were all greater than 60% (GSE10334 = 69.23%, GSE16134 = 73.72%, GSE173078 = 81.6%). Conclusions. A genetic cross-talk between HT and PD was detected, whereby LCE family genes appeared to play the most important role.