Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis

• Published in: Scientific reports 2025-01, Vol.15 (1), p.1962-11, Article 1962
• Main Authors: Yadalam, Pradeep K., Natarajan, Prabhu Manickam, Ardila, Carlos M.
• Format: Article
• Language: English
• Subjects: 631/114; 692/308; Anaerobic bacteria; Apoptosis; Bone loss; Cell death; Cell survival; Cluster Analysis; Deep Learning; Gene expression; Gene Expression Profiling - methods; Gum disease; Humanities and Social Sciences; Humans; Hypoxia; Inflammasome; Inflammasomes; Inflammasomes - genetics; Inflammasomes - metabolism; K means clustering; multidisciplinary; NLR Family, Pyrin Domain-Containing 3 Protein - genetics; NLR Family, Pyrin Domain-Containing 3 Protein - metabolism; Periodontal diseases; Periodontitis; Periodontitis - genetics; Periodontitis - microbiology; Periodontitis - pathology; Porphyromonas gingivalis; Pyroptosis; Pyroptosis - genetics; Science; Science (multidisciplinary); TLR4 protein; Toll-like receptors; Transcriptome; Transcriptomics; Variational autoencoders
• ISSN: 2045-2322; 2045-2322
• 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.