基于孟德尔随机化、单细胞RNA测序和多机器学习方法探索系统性红斑狼疮谷胱甘肽代谢关键基因

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
Kejiang Wang, Xiaoqiong Li, Ying Tang, Lizhou Zhao
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引用次数: 0

摘要

系统性红斑狼疮(SLE)是一种复杂的自身免疫性疾病,其特征是免疫失调导致炎症和器官损伤。尽管SLE的全球发病率不断上升,但其病因尚不清楚。我们应用孟德尔随机化(MR)、多组学整合、机器学习(ML)和SHAP来鉴定与SLE相关的关键代谢物和基因,揭示谷胱甘肽途径的关键作用。对1400种血清代谢物进行MR分析,发现谷胱甘肽代谢途径显著富集。单细胞RNA测序(scRNA-seq)数据根据谷胱甘肽代谢评分将单核细胞分为Metabolism_high组和Metabolism_low组。使用GSEA、代谢途径活性评估、转录因子预测、细胞通讯分析和伪时间分析分析差异表达基因。建立了LASSO回归识别中心基因和机器学习模型(CatBoost、XGBoost、NGBoost)。使用SHAP方法解释这些模型。通过多个数据集验证了关键基因的表达。MR分析证实代谢产物在谷胱甘肽途径中富集,确定了9个枢纽基因。机器学习模型在验证集中的auc分别为0.85、0.80和0.83。SHAP分析显示LAP3是所有模型中贡献最大的基因。scRNA-seq数据显示,LAP3在SLE免疫微环境中发挥重要作用。跨多个数据集(training、Validation和GSE112087)的验证显示,SLE患者PBMCs中LAP3表达升高,auc分别为0.935、0.795和0.817,提示有很强的诊断潜力。谷胱甘肽代谢与SLE的发展密切相关,LAP3可能在其进展中起关键作用。谷胱甘肽代谢和LAP3均可作为SLE诊断和治疗的潜在靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Key Genes of Glutathione Metabolism in Systemic Lupus Erythematosus Based on Mendelian Randomisation, Single-Cell RNA Sequencing and Multiple Machine Learning Approaches

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterised by immune dysregulation leading to inflammation and organ damage. Despite the rising global incidence of SLE, its aetiology remains unclear. We applied Mendelian randomisation (MR), multi-omics integration, machine learning (ML), and SHAP to identify key metabolites and genes associated with SLE, revealing the crucial role of the glutathione pathway. MR analysis was performed on 1400 serum metabolites, revealing significant enrichment in the glutathione metabolic pathway. Single-cell RNA sequencing (scRNA-seq) data classified monocytes into Metabolism_high and Metabolism_low groups based on glutathione metabolism scores. Differentially expressed genes were analysed using GSEA, metabolic pathway activity assessment, transcription factor prediction, cellular communication analysis, and Pseudotime analysis. LASSO regression identified hub genes and machine learning models (CatBoost, XGBoost, NGBoost) were developed. The SHAP method was used to interpret these models. Expression of key genes was validated across multiple datasets. MR analysis confirmed that metabolites were enriched in the glutathione pathway, identifying nine hub genes. Machine learning models achieved AUCs of 0.85, 0.80, and 0.83 in the validation set. SHAP analysis highlighted LAP3 as the top contributing gene across all models. scRNA-seq data showed that LAP3 plays a significant role in the immune microenvironment of SLE. Validation across multiple datasets (training, validation, and GSE112087) revealed elevated LAP3 expression in PBMCs of SLE patients, with AUCs of 0.935, 0.795, and 0.817, respectively, suggesting strong diagnostic potential. Glutathione metabolism is closely associated with SLE development and LAP3 may play a key role in its progression. Both glutathione metabolism and LAP3 could serve as potential targets for SLE diagnosis and treatment.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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