ArticleScientific reports2025
Identifying propionate metabolism-related genes as biomarkers of sepsis development and therapeutic targets.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of immune cell mitochondrial dysfunction characteristics and clinical predictive biomarkers in sepsis via multi-cohort machine learning and single-cell RNA sequencing.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- Short-Chain Fatty Acids in Sepsis: Mechanisms of Action and Therapeutic Advances.Biomedicines · 2026Review
- Network-based prioritization of sepsis-associated metabolites and in vivo validation of diosmetin in sepsis-associated acute kidney injury.Molecular biology reports · 2026Article
- Gut microbiota and sepsis: mechanisms, clinical correlations, and therapeutic prospects.Frontiers in medicine · 2026Article
- Gut barrier-microbiota crosstalk in sepsis: from pathogenesis to potential therapies.Frontiers in immunology · 2026Review
- Research advances on the role of programmed endothelial cell death in sepsis.Cell death discovery · 2025Review
- Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, and machine learning.Frontiers in immunology · 2025Article
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9 authors.
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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.
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