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

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 N...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 1962 - 11
Hlavní autoři:	Yadalam, Pradeep K., Natarajan, Prabhu Manickam, Ardila, Carlos M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 14.01.2025 Nature Publishing Group Nature Portfolio
Témata:	631/114 692/308 Anaerobic bacteria Apoptosis Autoencoder Bone loss Cell death Cell survival Deep learning Gene expression Gene Expression Profiling 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 - metabolism Periodontitis - microbiology Periodontitis - pathology Porphyromonas gingivalis Pyroptosis Pyroptosis - genetics Science Science (multidisciplinary) TLR4 protein Toll-like receptors Transcriptome Transcriptomics Variational autoencoders Variational autoencoders Inflammasome Periodontitis K means clustering
ISSN:	2045-2322, 2045-2322
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-86455-4