Role of gut microbiota metabolites against vein graft restenosis: insights from network pharmacology, molecular docking and molecular dynamic simulation

Gut microbiota metabolites are increasingly recognized for their role in modulating chronic disease progression. However, their potential impact on vein graft restenosis (VGR) remains unexplored. This study aimed to elucidate the mechanisms by which gut microbiota and its metabolites attenuate VGR using an integrated approach of network pharmacology, molecular docking, and molecular dynamics (MD) simulations. Gut microbiota, metabolites, and human gut targets were obtained from the gutMGene database. Metabolite targets were predicted using SwissTargetPrediction and Similarity Ensemble Approach, while disease targets were collected from GeneCards, Online Mendelian Inheritance in Man (OMIM), and DrugBank. Overlapping targets were used to construct both a protein-protein interaction (PPI) network and a gut microbiota–metabolites–targets–VGR (GM-M-T-V) network to identify key microbiota, core metabolites, and hub targets. Enrichment analysis investigated associated biological processes, cellular components, molecular functions, and signaling pathways. Drug-likeness and toxicity were evaluated with SwissADME and ADMETlab 2.0. Molecular docking and MD simulations assessed the binding affinity and dynamic characteristics of target-metabolite complexes. Integrated data from relevant databases identified 260 gut microbiota, 251 metabolites, 404 metabolite targets, 238 human gut targets, and 741 VGR-related targets. Among these, 16 overlapping targets were identified for further analysis. Enrichment analysis highlighted significant involvement of the relaxin signaling pathway, while PPI topology analysis pinpointed AKT1, NFKB1, EGFR, PTGS2, and PPARG as hub targets. Quercetin was prioritized as the core metabolite based on its top network connectivity, favorable drug-likeness prediction, and manageable in silico-predicted hepatotoxicity/genotoxicity risks in light of its absent clinical toxicity. Molecular docking revealed that quercetin bound to four hub targets (AKT1, NFKB1, EGFR, PPARG) with affinities (ranging from−6.0 to−8.9 kcal/mol) comparable or superior to positive controls. MD simulations further suggested favorable structural stability and binding affinity of the EGFR–quercetin complex. This integrative study elucidates the role of gut microbiota metabolites against VGR, identifying the microbial metabolite quercetin as a promising multi-target therapeutic agent primarily via the relaxin signaling pathway, which provides a mechanistic groundwork for a novel potential treatment strategy.

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Journal
BMC Medical Genomics
Published
2025-12-08
DOI
https://doi.org/10.1186/s12920-025-02290-6
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
0.00
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article

Role of gut microbiota metabolites against vein graft restenosis: insights from network pharmacology, molecular docking and molecular dynamic simulation

Yunpeng Bai, Xiankun Liu, Yiming Bai, Ziang Li et al.
BMC Medical Genomics
Gut microbiota and health
article

Role of gut microbiota metabolites against vein graft restenosis: insights from network pharmacology, molecular docking and molecular dynamic simulation

Yunpeng Bai, Xiankun Liu, Yiming Bai, Ziang Li, Zhigang Guo
article en

Abstract

Gut microbiota metabolites are increasingly recognized for their role in modulating chronic disease progression. However, their potential impact on vein graft restenosis (VGR) remains unexplored. This study aimed to elucidate the mechanisms by which gut microbiota and its metabolites attenuate VGR using an integrated approach of network pharmacology, molecular docking, and molecular dynamics (MD) simulations. Gut microbiota, metabolites, and human gut targets were obtained from the gutMGene database. Metabolite targets were predicted using SwissTargetPrediction and Similarity Ensemble Approach, while disease targets were collected from GeneCards, Online Mendelian Inheritance in Man (OMIM), and DrugBank. Overlapping targets were used to construct both a protein-protein interaction (PPI) network and a gut microbiota–metabolites–targets–VGR (GM-M-T-V) network to identify key microbiota, core metabolites, and hub targets. Enrichment analysis investigated associated biological processes, cellular components, molecular functions, and signaling pathways. Drug-likeness and toxicity were evaluated with SwissADME and ADMETlab 2.0. Molecular docking and MD simulations assessed the binding affinity and dynamic characteristics of target-metabolite complexes. Integrated data from relevant databases identified 260 gut microbiota, 251 metabolites, 404 metabolite targets, 238 human gut targets, and 741 VGR-related targets. Among these, 16 overlapping targets were identified for further analysis. Enrichment analysis highlighted significant involvement of the relaxin signaling pathway, while PPI topology analysis pinpointed AKT1, NFKB1, EGFR, PTGS2, and PPARG as hub targets. Quercetin was prioritized as the core metabolite based on its top network connectivity, favorable drug-likeness prediction, and manageable in silico-predicted hepatotoxicity/genotoxicity risks in light of its absent clinical toxicity. Molecular docking revealed that quercetin bound to four hub targets (AKT1, NFKB1, EGFR, PPARG) with affinities (ranging from−6.0 to−8.9 kcal/mol) comparable or superior to positive controls. MD simulations further suggested favorable structural stability and binding affinity of the EGFR–quercetin complex. This integrative study elucidates the role of gut microbiota metabolites against VGR, identifying the microbial metabolite quercetin as a promising multi-target therapeutic agent primarily via the relaxin signaling pathway, which provides a mechanistic groundwork for a novel potential treatment strategy.

BMC Medical GenomicsVol. 19(1)
Tianjin Chest Hospital (CN), Shandong Provincial QianFoShan Hospital (CN), Shandong First Medical University (CN), Tianjin Medical University (CN)
Openalex Percentile: Top 65%
Gut microbiota and health
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