Feng Xinran, Peng Da, Qiu Yunjing, Guo Qian, Zhang Xiaoyu, Li Zhixuan, Pan Chunling
State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Preventive Dentistry, School of Stomatology, The Fourth Military Medical University, Xi'an, 710032, China.
School and Hospital of Stomatology, China Medical University, Shenyang, 110002, China.
Inflammation. 2024 Aug 13. doi: 10.1007/s10753-024-02124-0.
Periodontitis is a multifactorial chronic inflammatory disease that destroy periodontium. Apart from microbial infection and host immune responses, emerging evidence shows aging and endoplasmic reticulum stress (ER stress) play a key role in periodontitis pathogenesis. The aim of this study is to identify aging-related genes (ARGs) and endoplasmic reticulum stress-related genes (ERGs) in periodontitis. Data were obtained from the Gene Expression Omnibus (GEO), Human Ageing Genomic Resources (HAGR) and GeneCards databases to identify differentially expressed mRNAs/miRNAs/lncRNAs (DEmRNAs/DEmiRNAs/DElncRNAs), ARGs and ERGs, respectively. We used the MultiMiR database for the reverse prediction of miRNAs and predicted miRNA-lncRNA interactions using the STARBase database. Afterwards, we constructed a mRNA-miRNA-lncRNA ceRNA network. A total of 10 hub genes, namely LCK, LYN, CXCL8, IL6, HCK, IL1B, BTK, CXCL12, GNAI1 and FCER1G, and 5 DEmRNAs-ARGs-ERGs were then discovered. Further, weighted gene co-expression network analysis (WGCNA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore co-expression modules and immune infiltration respectively. Finally, we used transmission electron microscope (TEM), inverted fluorescence microscopy, quantitative real-time polymerase chain reaction (qRT-PCR) and Western Blot to verify the bioinformatic results in periodontal ligament stem cells (PDLSCs) infected with Porphyromonas gingivalis (P. gingivalis). The experimental results broadly confirmed the accuracy of bioinformatic analysis. The present study established an aging- and ER stress-related ceRNA network in periodontitis, contributing to a deeper understanding of the pathogenesis of periodontitis.
牙周炎是一种破坏牙周组织的多因素慢性炎症性疾病。除了微生物感染和宿主免疫反应外，新出现的证据表明衰老和内质网应激（ER应激）在牙周炎发病机制中起关键作用。本研究的目的是鉴定牙周炎中与衰老相关的基因（ARGs）和内质网应激相关的基因（ERGs）。分别从基因表达综合数据库（GEO）、人类衰老基因组资源数据库（HAGR）和基因卡片数据库中获取数据，以鉴定差异表达的mRNA/miRNA/lncRNA（DEmRNAs/DEmiRNAs/DElncRNAs）、ARGs和ERGs。我们使用MultiMiR数据库对miRNA进行反向预测，并使用STARBase数据库预测miRNA-lncRNA相互作用。随后，我们构建了一个mRNA-miRNA-lncRNA ceRNA网络。然后共发现了10个枢纽基因，即LCK、LYN、CXCL8、IL6、HCK、IL1B、BTK、CXCL12、GNAI1和FCER1G，以及5个DEmRNAs-ARGs-ERGs。此外，进行了加权基因共表达网络分析（WGCNA）和单样本基因集富集分析（ssGSEA），分别用于探索共表达模块和免疫浸润。最后，我们使用透射电子显微镜（TEM）、倒置荧光显微镜、定量实时聚合酶链反应（qRT-PCR）和蛋白质免疫印迹法，在感染牙龈卟啉单胞菌（P. gingivalis）的牙周膜干细胞（PDLSCs）中验证生物信息学结果。实验结果大致证实了生物信息学分析的准确性。本研究在牙周炎中建立了一个与衰老和内质网应激相关的ceRNA网络，有助于更深入地了解牙周炎的发病机制。
