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Background <p>Traditional Chinese medicine has long been valued for its diverse bioactive properties, with chrysin emerging as a potential agent for preventing H1N1 infection. However, its specific targets and underlying mechanisms of chrysin remain to be elucidated.</p> Methods <p>In this study, we employed predictive algorithms from the SEA, Swiss, and PharmMapper databases to identify targets of chrysin, while H1N1 infection-related genes were sourced from the OMIM and GeneCards databases. We then constructed a protein–protein interaction network using Cytoscape 3.9.1, and then performed Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Finally, we performed molecular docking with AutoDock Vina algorithm to predict interaction sites.</p> Results <p>We identified 30 target genes of chrysin, with VEGFA, SRC, PTGS2, EGFR, HIF1A, MMP9, APP, IL2, MMP2, and PLG ranking as the top candidates. Enrichment analysis revealed involvement of multiple signaling pathways (including ROS, relaxin, and EGFR pathways) associated with inflammation, infection, and cell apoptosis. Molecular docking studies further supported the strong affinity of chrysin for its target proteins.</p> Conclusions <p>We have identified diverse signalling pathways and targets by which chrysin prevents H1N1 infection. This work highlights several molecular targets for further investigation and supports the potential of chrysin as a basis for antiviral development.</p>

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Unveiling the targets and pathways of chrysin against H1N1 through network pharmacology

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Weili Lu,
  • Housheng Zheng,
  • Zhou Jian,
  • Liang Ye,
  • Lu Wang

摘要

Background

Traditional Chinese medicine has long been valued for its diverse bioactive properties, with chrysin emerging as a potential agent for preventing H1N1 infection. However, its specific targets and underlying mechanisms of chrysin remain to be elucidated.

Methods

In this study, we employed predictive algorithms from the SEA, Swiss, and PharmMapper databases to identify targets of chrysin, while H1N1 infection-related genes were sourced from the OMIM and GeneCards databases. We then constructed a protein–protein interaction network using Cytoscape 3.9.1, and then performed Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Finally, we performed molecular docking with AutoDock Vina algorithm to predict interaction sites.

Results

We identified 30 target genes of chrysin, with VEGFA, SRC, PTGS2, EGFR, HIF1A, MMP9, APP, IL2, MMP2, and PLG ranking as the top candidates. Enrichment analysis revealed involvement of multiple signaling pathways (including ROS, relaxin, and EGFR pathways) associated with inflammation, infection, and cell apoptosis. Molecular docking studies further supported the strong affinity of chrysin for its target proteins.

Conclusions

We have identified diverse signalling pathways and targets by which chrysin prevents H1N1 infection. This work highlights several molecular targets for further investigation and supports the potential of chrysin as a basis for antiviral development.