Evidence map›Paper›PMID 40628770›Full record

ArticleScientific reports2025

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

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

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Lechen Yang *Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.
Weifeng Shang *Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.
Dongjie Chen *Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Hang Qian *Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.
Sheng Zhang *Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.
Xiaojun PanDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.
Sisi HuangDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China.
Jiao LiuDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China. catherine015@163.com.
Dechang ChenDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, Shanghai, 200025, China. 18918520002@189.cn.

Funding

National Natural Science Foundation of China 82102244National Natural Science Foundation of China 82241044Shanghai "Rising Stars of Medical Talents" Youth Development Program SHWSRS(2024)_70
6 · The paper itself

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.

Indexed as

PropionatesSepsisBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansLeukocytes, MononuclearMaleSingle-Cell AnalysisTranscription FactorsBiomarkersPropionatesTranscription FactorsDiagnostic modelingMachine learningPlasma cellsPropionate metabolismSepsis

Identifiers

PMID40628770
PMCPMC12238408

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.