Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment
Chenchun Ding,1 Zhenzhen Guo,2 Quan Liao,1 Renjie Zuo,1 Junjie He,1 Ziwei Ye,2 Weibin Chen1 1Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of China; 2School of Pharmaceutical Sciences, Xiamen...
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Dove Medical Press
2024-11-01
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| Series: | Journal of Inflammation Research |
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| author | Ding C Guo Z Liao Q Zuo R He J Ye Z Chen W |
| author_facet | Ding C Guo Z Liao Q Zuo R He J Ye Z Chen W |
| author_sort | Ding C |
| collection | DOAJ |
| description | Chenchun Ding,1 Zhenzhen Guo,2 Quan Liao,1 Renjie Zuo,1 Junjie He,1 Ziwei Ye,2 Weibin Chen1 1Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of China; 2School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of ChinaCorrespondence: Weibin Chen, Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of China, Email cwbxmu@163.comPurpose: Idiopathic pulmonary fibrosis (IPF) is an irreversible respiratory disease. In this study, we evaluated the efficacy of salidroside (SAL), the main component of Rhodiola rosea, in treating IPF.Methods: The pharmacological effects of SAL against epithelial-mesenchymal transition (EMT) and IPF were assessed through in vivo and in vitro experiments. Targets for SAL in treating IPF were identified from various databases and a PPI network was constructed. Functional analyses of target genes were performed using GO, KEGG, DO, and GSEA. Core target genes were identified using LASSO logistic regression and support vector machine (SVM) analysis, followed by molecular docking simulations. Predicted targets and pathways were validated through Western blotting, qRT-PCR, and IHC.Results: Our results demonstrated that SAL ameliorated alveolar epithelial cells (AECs) EMT and mitigated bleomycin -induced pulmonary fibrosis. Through network pharmacology, we identified 74 targets for SAL in the treatment of IPF (PFDR< 0.05) and analyzed their biological functions. Based on these findings, we further applied machine learning techniques to narrow down 9 core targets (PFDR< 0.05). Integrating the results from molecular docking, KEGG, and GSEA analyses, we selected three key targets—IGF1, hypoxia-inducible factor 1-alpha (HIF-1α), and MAPK (PFDR< 0.05)—for further investigation. Our study revealed that SAL inhibits the IGF1 signaling pathway, thereby improving AECs senescence and cell cycle arrest. By inhibiting the HIF-1α pathway, SAL alleviates endoplasmic reticulum stress and reduces intracellular ROS accumulation. Moreover, SAL suppresses the activation of the MAPK signaling pathway, leading to a decrease in inflammation markers in AECs and lung tissue.Conclusion: Experimental results suggest that SAL effectively ameliorates BLM-induced EMT and IPF, likely through the inhibition of IGF1, HIF-1α, and MAPK signaling pathways. This study holds potential translational prospects and may provide new perspectives and insights for the use of traditional Chinese medicine in the treatment of IPF.Keywords: salidroside, idiopathic pulmonary fibrosis, network pharmacology, machine-learning, molecular docking |
| format | Article |
| id | doaj-art-3f77db6a85a04b06970d3f0c5e5f29a8 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-3f77db6a85a04b06970d3f0c5e5f29a82024-11-24T16:45:57ZengDove Medical PressJournal of Inflammation Research1178-70312024-11-01Volume 179453946797572Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis TreatmentDing CGuo ZLiao QZuo RHe JYe ZChen WChenchun Ding,1 Zhenzhen Guo,2 Quan Liao,1 Renjie Zuo,1 Junjie He,1 Ziwei Ye,2 Weibin Chen1 1Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of China; 2School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of ChinaCorrespondence: Weibin Chen, Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361102, People’s Republic of China, Email cwbxmu@163.comPurpose: Idiopathic pulmonary fibrosis (IPF) is an irreversible respiratory disease. In this study, we evaluated the efficacy of salidroside (SAL), the main component of Rhodiola rosea, in treating IPF.Methods: The pharmacological effects of SAL against epithelial-mesenchymal transition (EMT) and IPF were assessed through in vivo and in vitro experiments. Targets for SAL in treating IPF were identified from various databases and a PPI network was constructed. Functional analyses of target genes were performed using GO, KEGG, DO, and GSEA. Core target genes were identified using LASSO logistic regression and support vector machine (SVM) analysis, followed by molecular docking simulations. Predicted targets and pathways were validated through Western blotting, qRT-PCR, and IHC.Results: Our results demonstrated that SAL ameliorated alveolar epithelial cells (AECs) EMT and mitigated bleomycin -induced pulmonary fibrosis. Through network pharmacology, we identified 74 targets for SAL in the treatment of IPF (PFDR< 0.05) and analyzed their biological functions. Based on these findings, we further applied machine learning techniques to narrow down 9 core targets (PFDR< 0.05). Integrating the results from molecular docking, KEGG, and GSEA analyses, we selected three key targets—IGF1, hypoxia-inducible factor 1-alpha (HIF-1α), and MAPK (PFDR< 0.05)—for further investigation. Our study revealed that SAL inhibits the IGF1 signaling pathway, thereby improving AECs senescence and cell cycle arrest. By inhibiting the HIF-1α pathway, SAL alleviates endoplasmic reticulum stress and reduces intracellular ROS accumulation. Moreover, SAL suppresses the activation of the MAPK signaling pathway, leading to a decrease in inflammation markers in AECs and lung tissue.Conclusion: Experimental results suggest that SAL effectively ameliorates BLM-induced EMT and IPF, likely through the inhibition of IGF1, HIF-1α, and MAPK signaling pathways. This study holds potential translational prospects and may provide new perspectives and insights for the use of traditional Chinese medicine in the treatment of IPF.Keywords: salidroside, idiopathic pulmonary fibrosis, network pharmacology, machine-learning, molecular dockinghttps://www.dovepress.com/network-pharmacology-and-machine-learning-reveal-salidrosides-mechanis-peer-reviewed-fulltext-article-JIRsalidrosideidiopathic pulmonary fibrosisnetwork pharmacologymachine-learningmolecular docking. |
| spellingShingle | Ding C Guo Z Liao Q Zuo R He J Ye Z Chen W Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment Journal of Inflammation Research salidroside idiopathic pulmonary fibrosis network pharmacology machine-learning molecular docking. |
| title | Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment |
| title_full | Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment |
| title_fullStr | Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment |
| title_full_unstemmed | Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment |
| title_short | Network Pharmacology and Machine Learning Reveal Salidroside’s Mechanisms in Idiopathic Pulmonary Fibrosis Treatment |
| title_sort | network pharmacology and machine learning reveal salidroside rsquo s mechanisms in idiopathic pulmonary fibrosis treatment |
| topic | salidroside idiopathic pulmonary fibrosis network pharmacology machine-learning molecular docking. |
| url | https://www.dovepress.com/network-pharmacology-and-machine-learning-reveal-salidrosides-mechanis-peer-reviewed-fulltext-article-JIR |
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