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

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Periodontitis*/genetics ; Periodontitis*/pathology ; Periodontitis*/microbiology ; Pyroptosis*/genetics ; NLR Family, Pyrin Domain-Containing 3 Protein*/genetics ; NLR Family, Pyrin Domain-Containing 3 Protein*/metabolism ; Transcriptome*; Humans ; Deep Learning ; Porphyromonas gingivalis ; Inflammasomes/metabolism ; Inflammasomes/genetics ; Cluster Analysis ; Gene Expression Profiling/methods
    • Abstract:
      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.
      Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
      (© 2025. The Author(s).)
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    • Contributed Indexing:
      Keywords: Inflammasome; K means clustering; Periodontitis; Variational autoencoders
    • Accession Number:
      0 (NLR Family, Pyrin Domain-Containing 3 Protein)
      0 (Inflammasomes)
      0 (NLRP3 protein, human)
    • Publication Date:
      Date Created: 20250114 Date Completed: 20250114 Latest Revision: 20250129
    • Publication Date:
      20250129
    • Accession Number:
      PMC11733260
    • Accession Number:
      10.1038/s41598-025-86455-4
    • Accession Number:
      39809940