Wu Huijian, Huang Linqing, Cai Shuting, Xiong Xiaoming, He Yan
School of Advanced Manufacturing, Guangdong University of Technology, Jieyang 522000, China.
School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China.
Genes (Basel). 2025 Aug 26;16(9):1005. doi: 10.3390/genes16091005.
: Periodontitis, a chronic inflammatory disease driven by host immune dysregulation, leads to progressive destruction of periodontal tissues. This study employed an integrative approach combining single-cell transcriptomics, hierarchical weighted gene co-expression network analysis (hdWGCNA), and deep learning algorithms to identify key biomarkers associated with neutrophil degranulation in periodontitis, aiming to establish diagnostic models for early detection and precision interventions. : We integrated single-cell RNA sequencing (scRNA-seq) data from human gingival tissues with bulk transcriptomic datasets. Pathogenic neutrophil subsets were characterized via pseudotime trajectory and cell-cell communication analyses. Hierarchical weighted gene co-expression network analysis (hdWGCNA) identified functional modules linked to degranulation. Machine learning and a convolutional neural network (CNN) model combining gene expression and immune cell profiles were developed for diagnosis. : scRNA-seq revealed a neutrophil subpopulation significantly increased infiltration in periodontitis, with cell-cell communication and pseudotime trajectory analyses demonstrating amplified inflammatory crosstalk. hdWGCNA identified the turquoise module enriched in PD-KEY-Neutrophils, containing hub genes linked to neutrophil degranulation and complement activation. Immune infiltration and non-negative matrix factorization linked high-degranulation neutrophil signatures to the periodontal immunity microenvironment. Machine learning demonstrated that the neutrophil degranulation-associated genes effectively distinguish diseased gingival tissue, suggesting their potential to predict periodontitis. Finally, integrating transcriptomic and immunological data, we developed a gene-immune CNN deep learning model accurately diagnosed periodontitis in diverse cohorts (AUC = 0.922). : Our study identified a pathogenic neutrophil subpopulation driving periodontitis through degranulation and inflammation. The neutrophil degranulation genes serve as critical biomarkers, offering new insights for clinical diagnosis and treatment of periodontitis.
牙周炎是一种由宿主免疫失调驱动的慢性炎症性疾病，会导致牙周组织的渐进性破坏。本研究采用了一种综合方法，将单细胞转录组学、分层加权基因共表达网络分析（hdWGCNA）和深度学习算法相结合，以识别与牙周炎中中性粒细胞脱颗粒相关的关键生物标志物，旨在建立早期检测和精准干预的诊断模型。：我们将来自人类牙龈组织的单细胞RNA测序（scRNA-seq）数据与批量转录组数据集进行整合。通过伪时间轨迹和细胞间通讯分析对致病性中性粒细胞亚群进行表征。分层加权基因共表达网络分析（hdWGCNA）确定了与脱颗粒相关的功能模块。开发了结合基因表达和免疫细胞谱的机器学习和卷积神经网络（CNN）模型用于诊断。：scRNA-seq显示牙周炎中一个中性粒细胞亚群的浸润显著增加，细胞间通讯和伪时间轨迹分析表明炎症串扰增强。hdWGCNA确定了富含PD-KEY-中性粒细胞的绿松石模块，其中包含与中性粒细胞脱颗粒和补体激活相关的枢纽基因。免疫浸润和非负矩阵分解将高脱颗粒中性粒细胞特征与牙周免疫微环境联系起来。机器学习表明，与中性粒细胞脱颗粒相关的基因能够有效区分患病的牙龈组织，表明它们具有预测牙周炎的潜力。最后，整合转录组学和免疫学数据，我们开发了一种基因-免疫CNN深度学习模型，能够在不同队列中准确诊断牙周炎（AUC = 0.922）。：我们的研究确定了一个通过脱颗粒和炎症驱动牙周炎的致病性中性粒细胞亚群。中性粒细胞脱颗粒基因作为关键生物标志物，为牙周炎的临床诊断和治疗提供了新的见解。
