Shi Zhuang'e, Wang Fuping, Yang Lishun, Li Couwen, Gong Bing, Dai Ruanxian, Chen Guobing
Faculty of Life Science and Technology, Kunming University of Science and Technology, Department of Emergency Medicine, The Affiliated Hospital of Kunming University of Science and Technology, The First People's Hospital of Yunnan Province, Medical School, Kunming University of Science and Technology, Kunming, China.
Kunming Medical University, Department of Emergency Medicine, The First People's Hospital of Yunnan Province, Kunming, China.
Front Immunol. 2025 Aug 20;16:1640425. doi: 10.3389/fimmu.2025.1640425. eCollection 2025.
Sepsis is a global health challenge associated with high morbidity and mortality rates. Early diagnosis and treatment are challenging because of the limited understanding of its underlying mechanisms. This study aimed to identify biomarkers of sepsis through an integrated multi-method approach.
Mendelian randomization (MR) analysis was performed using data on 1400 plasma metabolites, 731 immune cell phenotypes, and sepsis genome-wide association studies. Single-cell RNA sequencing (scRNA-seq) data GSE167363 was used for cell annotation, differential expression analysis, Gene Set Enrichment Analysis (GSEA), transcription factor activity prediction, and cellular pseudotime analysis. The hub genes were identified via least absolute shrinkage and selection operator regression using GSE236713. The predictive models were constructed using the CatBoost, XGBoost, and NGBoost algorithms based on the data from GSE236713 and GSE28750. SHapley Additive ex Planations (SHAP) was used to filter the key molecules, and their expressions were confirmed via RT-qPCR of the peripheral blood mononuclear cells of the patients with sepsis and healthy controls.
Two-step MR revealed that glutamine degradant mediated the causal relationship between SSC-A on HLA-DR + NK and sepsis. ScRNA-seq analysis revealed distinct variations in the composition of immune cell phenotypes in the control and sepsis groups. NK cells were associated with glutamine metabolism. GSEA illustrated the top 10 pathways positively and negatively correlated in NK cells with high vs. low glutamine metabolism. Transcription factor prediction revealed opposing transcription factor profiles for these NK cells subsets. NK cell cellular pseudotime plot and immune cell infiltration analysis results were displayed. The predictive models achieved AUCs of 0.95 (CatBoost), 0.80 (XGBoost), and 0.62 (NGBoost). SHAP analysis identified SRSF7, E2F2, RAB13, and S100A8 as key molecular of the model. RT-qPCR revealed decreased SRSF7 expression and increased RAB13, E2F2, and S100A8 expression in sepsis.
SSC-A on HLA-DR + NK cells reduced the risk of sepsis by decreasing glutamine degradation. SRSF7, E2F2, RAB13, and S100A8 were identified as potential pathogenic biomarkers of sepsis.
脓毒症是一项全球性的健康挑战，其发病率和死亡率都很高。由于对其潜在机制的了解有限，早期诊断和治疗颇具挑战性。本研究旨在通过综合多种方法来识别脓毒症的生物标志物。
利用1400种血浆代谢物、731种免疫细胞表型的数据以及脓毒症全基因组关联研究进行孟德尔随机化（MR）分析。单细胞RNA测序（scRNA-seq）数据GSE167363用于细胞注释、差异表达分析、基因集富集分析（GSEA）、转录因子活性预测以及细胞伪时间分析。使用GSE236713通过最小绝对收缩和选择算子回归确定枢纽基因。基于GSE236713和GSE28750的数据，使用CatBoost、XGBoost和NGBoost算法构建预测模型。使用SHapley加法解释（SHAP）来筛选关键分子，并通过对脓毒症患者和健康对照的外周血单核细胞进行RT-qPCR来确认它们的表达。
两步MR显示谷氨酰胺降解产物介导了HLA-DR + NK上的SSC-A与脓毒症之间的因果关系。scRNA-seq分析揭示了对照组和脓毒症组中免疫细胞表型组成的明显差异。自然杀伤（NK）细胞与谷氨酰胺代谢有关。GSEA展示了在谷氨酰胺代谢高与低的NK细胞中正向和负向相关的前10条通路。转录因子预测揭示了这些NK细胞亚群相反的转录因子谱。展示了NK细胞的细胞伪时间图和免疫细胞浸润分析结果。预测模型的曲线下面积（AUC）分别为0.95（CatBoost）、0.80（XGBoost）和0.62（NGBoost）。SHAP分析确定SRSF7、E2F2、RAB13和S100A8为模型的关键分子。RT-qPCR显示脓毒症中SRSF7表达降低，而RAB13、E2F2和S100A8表达增加。
HLA-DR + NK细胞上的SSC-A通过减少谷氨酰胺降解降低了脓毒症风险。SRSF7、E2F2、RAB13和S100A8被确定为脓毒症的潜在致病生物标志物。
