Ma Sijia, He Hongbing, Ren Xiaobin
Department of Periodontology, Kunming Medical University School and Hospital of Stomatology, Kunming, 650106, People's Republic of China.
Yunnan Key Laboratory of Stomatology, Kunming, 650106, People's Republic of China.
J Inflamm Res. 2024 Dec 27;17:11659-11678. doi: 10.2147/JIR.S498739. eCollection 2024.
Periodontitis represents an inflammatory disease with multiple contributing factors, affecting both oral and systemic health. The mechanisms linking mitochondrial dysfunction to immune responses in periodontitis remain unclear, limiting the development of individualized diagnostic and therapeutic approaches.
This study aims to elucidate the roles of mitochondrial dysfunction and immune responses in the pathogenesis of periodontitis, identify distinct molecular subtypes, and discover robust diagnostic biomarkers to support precision medicine approaches.
Single-cell RNA sequencing and transcriptome data from periodontitis patients were analyzed to identify gene signatures linked to macrophages and mitochondria. Consensus clustering was applied to classify molecular subtypes. Potential biomarkers were identified using five machine learning algorithms and validated in clinical samples through qPCR and IHC.
Four molecular subtypes were identified: quiescent, macrophage-dominant, mitochondria-dominant, and mixed, each exhibiting unique gene expression patterns. From 13 potential biomarkers, eight were shortlisted using machine learning, and five (BNIP3, FAHD1, UNG, CBR3, and SLC25A43) were validated in clinical samples. Among them, BNIP3, FAHD1, and UNG were significantly downregulated (p < 0.05).
This study identifies novel molecular subtypes and biomarkers that elucidate the interplay between immune responses and mitochondrial dysfunction in periodontitis. These findings provide insights into the disease's heterogeneity and lay the foundation for developing non-invasive diagnostic tools and personalized therapeutic strategies.
牙周炎是一种由多种因素导致的炎症性疾病，会影响口腔和全身健康。线粒体功能障碍与牙周炎免疫反应之间的联系机制尚不清楚，这限制了个性化诊断和治疗方法的发展。
本研究旨在阐明线粒体功能障碍和免疫反应在牙周炎发病机制中的作用，识别不同的分子亚型，并发现可靠的诊断生物标志物以支持精准医学方法。
分析牙周炎患者的单细胞RNA测序和转录组数据，以识别与巨噬细胞和线粒体相关的基因特征。应用一致性聚类对分子亚型进行分类。使用五种机器学习算法识别潜在的生物标志物，并通过qPCR和免疫组化在临床样本中进行验证。
识别出四种分子亚型：静止型、巨噬细胞主导型、线粒体主导型和混合型，每种亚型都表现出独特的基因表达模式。从13种潜在生物标志物中，通过机器学习筛选出8种入围，其中5种（BNIP3、FAHD1、UNG、CBR3和SLC25A43）在临床样本中得到验证。其中，BNIP3、FAHD1和UNG显著下调（p < 0.05）。
本研究识别出了新的分子亚型和生物标志物，阐明了牙周炎中免疫反应与线粒体功能障碍之间的相互作用。这些发现为该疾病的异质性提供了见解，并为开发非侵入性诊断工具和个性化治疗策略奠定了基础。
