Orthodontic Department of Affiliated Stomatological Hospital of Nanchang University, Nanchang, China.
Pingxiang People's Hospital, Pingxiang, China.
Arch Oral Biol. 2023 Sep;153:105720. doi: 10.1016/j.archoralbio.2023.105720. Epub 2023 May 8.
This study aimed to explore the key genes, metabolites, and pathways that influence periodontitis pathogenesis by integrating transcriptomic and metabolomic studies.
Gingival crevicular fluid samples from periodontitis patients and healthy controls were collected for liquid chromatography/tandem mass-based metabolomics. RNA-seq data for periodontitis and control samples were obtained from the GSE16134 dataset. Differential metabolites and differentially expressed genes (DEGs) between the two groups were then compared. Based on the protein-protein interaction (PPI) network module analysis, key module genes were selected from immune-related DEGs. Correlation and pathway enrichment analyses were performed for differential metabolites and key module genes. A multi-omics integrative analysis was performed using bioinformatic methods to construct a gene-metabolite-pathway network.
From the metabolomics study, 146 differential metabolites were identified, which were mainly enriched in the pathways of purine metabolism and Adenosine triphosphate binding cassette transporters (ABC transporters). The GSE16134 dataset revealed 102 immune-related DEGs (458 upregulated and 264 downregulated genes), 33 of which may play core roles in the key modules of the PPI network and are involved in cytokine-related regulatory pathways. Through a multi-omics integrative analysis, a gene-metabolite-pathway network was constructed, including 28 genes (such as platelet derived growth factor D (PDGFD), neurturin (NRTN), and interleukin 2 receptor, gamma (IL2RG)); 47 metabolites (such as deoxyinosine); and 8 pathways (such as ABC transporters).
PDGFD, NRTN, and IL2RG may be potential biomarkers of periodontitis and may affect disease progression by regulating deoxyinosine to participate in the ABC transporter pathway.
本研究旨在通过整合转录组学和代谢组学研究，探讨影响牙周炎发病机制的关键基因、代谢物和途径。
收集牙周炎患者和健康对照者的龈沟液样本进行基于液相色谱/串联质谱的代谢组学分析。从 GSE16134 数据集获得牙周炎和对照样本的 RNA-seq 数据。然后比较两组之间的差异代谢物和差异表达基因（DEGs）。基于蛋白质-蛋白质相互作用（PPI）网络模块分析，从免疫相关 DEGs 中选择关键模块基因。对差异代谢物和关键模块基因进行相关性和通路富集分析。使用生物信息学方法进行多组学整合分析，构建基因-代谢物-通路网络。
从代谢组学研究中鉴定出 146 个差异代谢物，主要富集在嘌呤代谢和三磷酸腺苷结合盒转运体（ABC 转运体）途径中。GSE16134 数据集显示 102 个免疫相关 DEGs（458 个上调和 264 个下调基因），其中 33 个可能在 PPI 网络的关键模块中发挥核心作用，参与细胞因子相关调控途径。通过多组学整合分析，构建了一个基因-代谢物-通路网络，包括 28 个基因（如血小板衍生生长因子 D（PDGFD）、神经生长因子（NRTN）和白细胞介素 2 受体，γ（IL2RG））；47 种代谢物（如脱氧肌苷）；和 8 条通路（如 ABC 转运体）。
PDGFD、NRTN 和 IL2RG 可能是牙周炎的潜在生物标志物，通过调节脱氧肌苷参与 ABC 转运体途径，可能影响疾病的进展。
