Yili Luo, Jianpeng Liu, Wangqiang Feng, Da Lin, Mengji Chen, Haihua Zheng
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Machine learning models, such as random forests and decision trees, were employed to screen hub genes and key transcription factors (TFs) associated with AMD.</p><p><strong>Results: </strong>Distinct cell clusters were identified in the present study, especially the T/NK cluster, with a notable increase in NK cell abundance observed in AMD. Cell-cell communication analyses revealed altered interactions, particularly in NK cells, indicating their potential role in AMD pathogenesis. HdWGCNA highlighted the turquoise module, enriched in inflammation-related pathways, as significantly associated with AMD in NK cells. The SCENIC analysis identified key TFs in NK cell regulatory networks. 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引用次数: 0
摘要
背景:年龄相关性黄斑变性(AMD)是导致老年人中心视力丧失的主要原因,已成为全球日益关注的健康问题:本研究的重点是揭示自然杀伤细胞(NK)在AMD中的复杂参与,阐明其免疫反应和细胞因子的调控作用:方法:利用单细胞RNA-seq分析基因表达总库(Gene Expression Omnibus)的转录组数据。应用高维加权基因共表达网络分析(hdWGCNA)和单细胞调控网络推断与聚类分析(SCENIC)揭示早期AMD患者NK细胞的调控机制。采用随机森林和决策树等机器学习模型筛选与AMD相关的枢纽基因和关键转录因子(TFs):结果:本研究发现了不同的细胞群,尤其是T/NK细胞群,观察到AMD患者的NK细胞数量明显增加。细胞-细胞通讯分析表明,细胞间的相互作用发生了改变,特别是在NK细胞中,这表明它们在AMD发病机制中的潜在作用。HdWGCNA突出显示了绿松石模块,该模块富含炎症相关通路,与NK细胞中的AMD显著相关。SCENIC 分析确定了 NK 细胞调控网络中的关键 TFs。通过机器学习,整合枢纽基因和TFs确定了CREM、FOXP1、IRF1、NFKB2和USF2是AMD的潜在预测因子:这一综合方法增强了我们对 NK 细胞动态、信号改变和 AMD 潜在预测模型的了解。鉴定出的TFs为分子干预提供了新途径,并凸显了NK细胞与AMD发病机制之间错综复杂的关系。总之,这项研究为促进我们对 AMD 的了解和管理提供了宝贵的见解。
Background: Age-related Macular Degeneration (AMD) poses a growing global health concern as the leading cause of central vision loss in elderly people.
Objection: This study focuses on unraveling the intricate involvement of Natural Killer (NK) cells in AMD, shedding light on their immune responses and cytokine regulatory roles.
Methods: Transcriptomic data from the Gene Expression Omnibus database were utilized, employing single-cell RNA-seq analysis. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and single-cell regulatory network inference and clustering (SCENIC) analysis were applied to reveal the regulatory mechanisms of NK cells in early-stage AMD patients. Machine learning models, such as random forests and decision trees, were employed to screen hub genes and key transcription factors (TFs) associated with AMD.
Results: Distinct cell clusters were identified in the present study, especially the T/NK cluster, with a notable increase in NK cell abundance observed in AMD. Cell-cell communication analyses revealed altered interactions, particularly in NK cells, indicating their potential role in AMD pathogenesis. HdWGCNA highlighted the turquoise module, enriched in inflammation-related pathways, as significantly associated with AMD in NK cells. The SCENIC analysis identified key TFs in NK cell regulatory networks. The integration of hub genes and TFs identified CREM, FOXP1, IRF1, NFKB2, and USF2 as potential predictors for AMD through machine learning.
Conclusion: This comprehensive approach enhances our understanding of NK cell dynamics, signaling alterations, and potential predictive models for AMD. The identified TFs provide new avenues for molecular interventions and highlight the intricate relationship between NK cells and AMD pathogenesis. Overall, this study contributes valuable insights for advancing our understanding and management of AMD.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.