Archives of oral biology · 2025
Exosome-related lactylation gene signature defines diagnostic biomarkers of periodontitis through integrative bulk and single-cell transcriptomics.
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Liang Xueyi, Fu Runxi, Chen Xiaochuan
Аннотация
BACKGROUND: Exosomes and lactylation modification have been increasingly recognized as key regulators of diseases, yet their integrative role in periodontitis remains unclear. No diagnostic model based on exosome-related lactylation genes (ERLGs) has been previously established for periodontitis. This study aimed to explore ERLGs as potential diagnostic biomarkers for periodontitis.
METHODS: Integration of bulk and single-cell RNA sequencing (scRNA-seq) datasets, machine learning modeling, and experimental validation were employed to identify ERLG signatures and dissect their cellular context in periodontitis.
RESULT: We identified 53 ERLGs that were significantly dysregulated in periodontitis and closely linked to metabolic reprogramming and immune regulation. ERLG-based clustering robustly distinguished periodontitis from healthy tissues and revealed distinct immune infiltration patterns. A machine learning-based diagnostic model using eight core ERLGs (AK3, CHST1, CHST2, MERTK, EGLN3, CXCR4, DSC2, KCNN4) achieved excellent predictive performance (AUC=0.938 in training cohort; validated in two independent cohorts). scRNA-seq uncovered heterogeneous lactylation modification patterns across major cell populations. Fibroblast subclustering revealed three disease-sensitive subsets (Fib_NTRK3, Fib_TNXB, Fib_MFAP5) that were reduced in periodontitis and exhibited reduced lactylation scores. Endothelial cell subclustering identified a disease-enriched Endo_TGM2 population characterized by high lactylation scores, activation of TGF-β, PI3K-AKT-mTOR, and TNFα/NF-κB signaling, and dynamic differentiation trajectories, and strong interactions with fibroblasts. Inflammatory stimulation enhanced exosomal lactylation and derived ERLG dysregulation in human gingival fibroblasts.
CONCLUSION: This study establishes the first ERLG-based diagnostic model for periodontitis, demonstrates its robust predictive capability and delineates cell type-specific lactylation remodeling, which providing mechanistic insights and potential signature for diagnosis.
METHODS: Integration of bulk and single-cell RNA sequencing (scRNA-seq) datasets, machine learning modeling, and experimental validation were employed to identify ERLG signatures and dissect their cellular context in periodontitis.
RESULT: We identified 53 ERLGs that were significantly dysregulated in periodontitis and closely linked to metabolic reprogramming and immune regulation. ERLG-based clustering robustly distinguished periodontitis from healthy tissues and revealed distinct immune infiltration patterns. A machine learning-based diagnostic model using eight core ERLGs (AK3, CHST1, CHST2, MERTK, EGLN3, CXCR4, DSC2, KCNN4) achieved excellent predictive performance (AUC=0.938 in training cohort; validated in two independent cohorts). scRNA-seq uncovered heterogeneous lactylation modification patterns across major cell populations. Fibroblast subclustering revealed three disease-sensitive subsets (Fib_NTRK3, Fib_TNXB, Fib_MFAP5) that were reduced in periodontitis and exhibited reduced lactylation scores. Endothelial cell subclustering identified a disease-enriched Endo_TGM2 population characterized by high lactylation scores, activation of TGF-β, PI3K-AKT-mTOR, and TNFα/NF-κB signaling, and dynamic differentiation trajectories, and strong interactions with fibroblasts. Inflammatory stimulation enhanced exosomal lactylation and derived ERLG dysregulation in human gingival fibroblasts.
CONCLUSION: This study establishes the first ERLG-based diagnostic model for periodontitis, demonstrates its robust predictive capability and delineates cell type-specific lactylation remodeling, which providing mechanistic insights and potential signature for diagnosis.