Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods

Abstract

BackgroundDespite of numerous studies on periodontitis, the mechanism underlying the progression of periodontitis still remains largely unknown. This study aimed to have an expression profiling comparison between periodontitis and normal control and to identify more candidate genes involved in periodontitis and to gain more insights into the molecular mechanisms of periodontitis progression.MethodsThe gene expression profile of GSE16134, comprising 241 gingival tissue specimens and 69 healthy samples as control which were obtained from 120 systemically healthy patients with periodontitis (65 with chronic and 55 with aggressive periodontitis), was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in periodontitis samples were screened using the limma package in R compared with control samples. Gene Ontology (GO) and pathway enrichment analysis upon the DEGs were carried out using Hypergeometric Distribution test. Protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape, followed by module selection from the PPI network using MCODE plugin. Moreover, transcription factors (TFs) of these DEGs were identified based on TRANSFAC database and then a regulatory network was constructed.ResultsTotally, 762 DEGs (507 up- and 255 down-regulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as immune system process, cell activation biological processes, cytokine-cytokine receptor interaction, and metabolic pathways. Cathepsin S (CTSS) and pleckstrin (PLEK) were the hub proteins in the PPI network and 3 significant modules were selected. Moreover, 19 TFs were identified including interferon regulatory factor 8 (IRF8), and FBJ murine osteosarcoma viral oncogene homolog B (FOSB).ConclusionThis study identified genes (CTSS, PLEK, IRF-8, PTGS2, and FOSB) that may be involved in the development and progression of periodontitis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12903-015-0086-7) contains supplementary material, which is available to authorized users.

Integrated analysis of long noncoding RNA‐associated competing endogenous RNA network in periodontitis

“…It has been found that gingival fibroblasts producing elevated levels of collagenase RNA amounts also express c‐FOS and EGR‐1, indicating a crucial role for both TFs in cellular proliferation and collagenase expression in gingival and periodontal tissue destruction in vivo . In a previous similar bioinformatics study, FOSB, 1 member of the FOB (FBJ Murine Osteosarcoma Viral Oncogene Homolog) gene family, was also predicted to be involved in the regulatory network of the DEG‐TFs of periodontitis. The expression of FOS was suggested to decrease in human periodontal ligament cells in response to the TLR4 ligand, lipopolysaccharide treatment .…”

“…Three pathways involved in periodontitis (ie, cytokine‐cytokine receptor, cell adhesion molecules (CAMs) and chemokine signaling pathway) were shown to have overlapping results, as compared to the pathway analysis results of previous similar bioinformatic studies about periodontitis . Cytokines are soluble proteins that bind to specific cytokine receptors on target cells and initiate intracellular signaling cascades.…”

“…The datasets categorized periodontitis samples as bleeding on probing, probing pocket depth ≧ 4 mm and clinical attachment loss ≧3 mm, and categorized healthy control gingival tissue samples as no bleeding on probing, probing pocket depth ≦4 mm and clinical attachment loss ≦2 mm. Given the fact that limited dissimilarities in gene expression profiles existed between established aggressive periodontitis and chronic periodontitis lesions, the datasets not only contained datasets with chronic periodontitis samples as experimental samples, but also datasets with chronic periodontitis samples and aggressive periodontitis samples together as experimental samples. To avoid prediction error, only datasets based on the same platform were screened.…”

“…Considering the great diversity of results in microarray studies, using bioinformatics techniques to perform integrated data analysis of all microarray datasets collected seems to be necessary. Recently, some studies applying bioinformatic techniques have identified hub genes, hub proteins and biological pathways associated with periodontitis. However, based on the established ceRNA hypothesis, there is still no research showing the functional significance of the lncRNA‐associated ceRNA network in the initiation and progression of periodontitis.…”

Er:YAG Laser and Cyclosporin A Effect on Cell Cycle Regulation of Human Gingival Fibroblast Cells

“…[14][15][16] Despite numerous investigations on periodontitis, the molecular mechanism of therapeutic modalities on HGFs still remains largely unknown. 17 HGFs are the most abundant cell type in human periodontal tissue which can influence promotion of gingival health damaged by periodontal diseases. 18 HGFs can be used as stem cells for following up therapeutic effects and periodontal tissue engineering.…”