Yadalam Pradeep K, Natarajan Prabhu Manickam, Ardila Carlos M
Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technology Sciences, SIMATS, Saveetha University, Chennai, 600077, Tamil Nadu, India.
Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman University, Ajman, 346, United Arab Emirates.
Sci Rep. 2025 Jan 14;15(1):1962. doi: 10.1038/s41598-025-86455-4.
The NLRP3 inflammasome, regulated by TLR4, plays a pivotal role in periodontitis by mediating inflammatory cytokine release and bone loss induced by Porphyromonas gingivalis. Periodontal disease creates a hypoxic environment, favoring anaerobic bacteria survival and exacerbating inflammation. The NLRP3 inflammasome triggers pyroptosis, a programmed cell death that amplifies inflammation and tissue damage. This study evaluates the efficacy of Variational Graph Autoencoders (VGAEs) in reconstructing gene data related to NLRP3-mediated pyroptosis in periodontitis. The NCBI GEO dataset GSE262663, containing three samples with and without hypoxia exposure, was analyzed using unsupervised K-means clustering. This method identifies natural groupings within biological data without prior labels. VGAE, a deep learning model, captures complex graph relationships for tasks like link prediction and edge detection. The VGAE model demonstrated exceptional performance with an accuracy of 99.42% and perfect precision. While it identified 5,820 false negatives, indicating a conservative approach, it accurately predicted 4,080 out of 9,900 positive samples. The model's latent space distribution differed significantly from the original data, suggesting a tightly clustered representation of the gene expression patterns. K-means clustering and VGAE show promise in gene expression analysis and graph structure reconstruction for periodontitis research.
由TLR4调节的NLRP3炎性小体通过介导牙龈卟啉单胞菌诱导的炎性细胞因子释放和骨质流失在牙周炎中起关键作用。牙周疾病会营造低氧环境，有利于厌氧菌存活并加剧炎症。NLRP3炎性小体触发细胞焦亡，这是一种程序性细胞死亡，会放大炎症和组织损伤。本研究评估变分图自动编码器（VGAE）在重建与牙周炎中NLRP3介导的细胞焦亡相关的基因数据方面的功效。使用无监督K均值聚类分析了NCBI GEO数据集GSE262663，该数据集包含三个有或无低氧暴露的样本。此方法可在无先验标签的情况下识别生物数据中的自然分组。VGAE是一种深度学习模型，可捕捉复杂的图关系以执行链接预测和边缘检测等任务。VGAE模型表现出色，准确率达99.42%，且具有完美的精确率。虽然它识别出5820个假阴性，表明采用了保守方法，但它在9900个阳性样本中准确预测出4080个。该模型的潜在空间分布与原始数据有显著差异，表明基因表达模式呈现紧密聚类的表征。K均值聚类和VGAE在牙周炎研究的基因表达分析和图结构重建方面显示出前景。
