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鉴定与丙酸代谢相关的基因作为脓毒症发展的生物标志物和治疗靶点。

Identifying propionate metabolism-related genes as biomarkers of sepsis development and therapeutic targets.

作者信息

Yang Lechen, Shang Weifeng, Chen Dongjie, Qian Hang, Zhang Sheng, Pan Xiaojun, Huang Sisi, Liu Jiao, Chen Dechang

机构信息

Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.

Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.

出版信息

Sci Rep. 2025 Jul 8;15(1):24531. doi: 10.1038/s41598-025-06463-2.

Abstract

The treatment of sepsis is challenging due to unclear mechanisms. Propionate is increasingly seen as critical to sepsis pathophysiology by bridging gut microbiota and immunity, but the mechanisms remain unclear. Our study analysed differences in propionate metabolism in peripheral blood mononuclear cells from septic patients and healthy controls using single-cell RNA-seq (scRNA-seq) data. Differentially expressed genes (DEGs) analysis, pathway enrichment, transcription factor (TF) prediction, intercellular communication, and trajectory inference were used to explore the role of propionate metabolism in sepsis. We constructed a sepsis diagnostic model using LASSO and machine learning (XGBoost, CatBoost, NGBoost) with bulk RNA-seq data. scRNA-seq analysis revealed that propionate metabolism was highest in plasma cells (PCs), which can be classified into high and low metabolism groups, identifying 9,155 DEGs. High propionate metabolism was associated with metabolism such as short-chain fatty acids, while low metabolism was related to negative regulation of wound healing. The DoRothEA regulator algorithm showed TFs such as IRF4, ARID3A, FOXO4, and ATF2 were activated in high propionate metabolism subgroups, whereas NR5A1, BCL6, and CDX2 were activated in low subgroups. Cell-cell communication revealed that both groups interacted primarily with B cells and neutrophils, with the high propionate metabolism PCs showing more significant interactions. The receptor-ligand pairs primarily involved were VEGFA-FLT1 and VEGFB-FLT1, and the high propionate metabolism PCs and B cells might interact through BMP8B-BMPR2. Trajectory analysis indicated differentiation from B cells, first to low, then high propionate metabolism PCs. Finally, the LASSO algorithm identified 13 key genes, with the CatBoost model achieving perfect diagnostic performance (AUC = 1.000). These 13 key genes were validated through in vitro experiments. Collectively, these findings suggest that propionic acid metabolism may be a potential target for diagnosing and treating sepsis, offering new insights into its pathophysiology.

摘要

由于机制尚不清楚,脓毒症的治疗具有挑战性。丙酸通过连接肠道微生物群和免疫,越来越被视为脓毒症病理生理学的关键因素,但其机制仍不清楚。我们的研究使用单细胞RNA测序(scRNA-seq)数据分析了脓毒症患者和健康对照外周血单个核细胞中丙酸代谢的差异。通过差异表达基因(DEG)分析、通路富集、转录因子(TF)预测、细胞间通讯和轨迹推断来探索丙酸代谢在脓毒症中的作用。我们使用LASSO和机器学习(XGBoost、CatBoost、NGBoost)以及批量RNA-seq数据构建了脓毒症诊断模型。scRNA-seq分析显示,浆细胞(PC)中的丙酸代谢最高,可分为高代谢组和低代谢组,共鉴定出9155个DEG。高丙酸代谢与短链脂肪酸等代谢相关,而低代谢与伤口愈合的负调控有关。DoRothEA调节因子算法显示,IRF4、ARID3A、FOXO4和ATF2等TF在高丙酸代谢亚组中被激活,而NR5A1、BCL6和CDX2在低亚组中被激活。细胞间通讯显示,两组主要与B细胞和中性粒细胞相互作用,高丙酸代谢的PC显示出更显著的相互作用。主要涉及的受体-配体对是VEGFA-FLT1和VEGFB-FLT1,高丙酸代谢的PC和B细胞可能通过BMP8B-BMPR2相互作用。轨迹分析表明从B细胞分化而来,首先是低丙酸代谢的PC,然后是高丙酸代谢的PC。最后,LASSO算法确定了13个关键基因,CatBoost模型实现了完美的诊断性能(AUC = 1.000)。这13个关键基因通过体外实验得到验证。总的来说,这些发现表明丙酸代谢可能是诊断和治疗脓毒症的潜在靶点,为其病理生理学提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb79/12238408/c17de5fd13a5/41598_2025_6463_Fig1_HTML.jpg

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