Investigating the effects of Laggera pterodonta on H3N2-Induced inflammatory and immune responses through network pharmacology, molecular docking, and experimental validation in a mice model
© 2024 The Authors..
For centuries, Laggera pterodonta (LP), a Chinese herbal medicine, has been widely employed for treating respiratory infectious diseases; however, the mechanism underlying LP's effectiveness against the influenza A/Aichi/2/1968 virus (H3N2) remains elusive. This study aims to shed light on the mechanism by which LP combats influenza in H3N2-infected mice. First, we conducted quasi-targeted metabolomics analysis using liquid chromatography-mass spectrometry to identify LP components. Subsequently, network pharmacology, molecular docking, and simulation were conducted to screen candidate targets associated with AKT and NF-κB. In addition, we conducted a series of experiments including qPCR, hematoxylin-eosin staining, flow cytometry, immunohistochemistry, and enzyme-linked immunosorbent assay to provide evidence that LP treatment in H3N2-infected mice can reduce pro-inflammatory cytokine levels (TNF-α, IL-6, IL-1β, and MCP-1) while increasing T cells (CD3+, CD4+, and CD8+) and syndecan-1 and secretory IgA expression. This, in turn, aids in the prevention of excessive inflammation and the fortification of immunity, both of which are compromised by H3N2. Finally, we utilized a Western blot assay to confirm that LP indeed inhibits the AKT/NF-κB signaling cascade. Thus, the efficacy of LP serves as a cornerstone in establishing a theoretical foundation for influenza treatment.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2024 |
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Erschienen: |
2024 |
Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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Enthalten in: |
Heliyon - 10(2024), 8 vom: 30. Apr., Seite e29487 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Chen, Yaorong [VerfasserIn] |
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Links: |
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Themen: |
Immunity |
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Anmerkungen: |
Date Revised 27.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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doi: |
10.1016/j.heliyon.2024.e29487 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM371544041 |
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520 | |a For centuries, Laggera pterodonta (LP), a Chinese herbal medicine, has been widely employed for treating respiratory infectious diseases; however, the mechanism underlying LP's effectiveness against the influenza A/Aichi/2/1968 virus (H3N2) remains elusive. This study aims to shed light on the mechanism by which LP combats influenza in H3N2-infected mice. First, we conducted quasi-targeted metabolomics analysis using liquid chromatography-mass spectrometry to identify LP components. Subsequently, network pharmacology, molecular docking, and simulation were conducted to screen candidate targets associated with AKT and NF-κB. In addition, we conducted a series of experiments including qPCR, hematoxylin-eosin staining, flow cytometry, immunohistochemistry, and enzyme-linked immunosorbent assay to provide evidence that LP treatment in H3N2-infected mice can reduce pro-inflammatory cytokine levels (TNF-α, IL-6, IL-1β, and MCP-1) while increasing T cells (CD3+, CD4+, and CD8+) and syndecan-1 and secretory IgA expression. This, in turn, aids in the prevention of excessive inflammation and the fortification of immunity, both of which are compromised by H3N2. Finally, we utilized a Western blot assay to confirm that LP indeed inhibits the AKT/NF-κB signaling cascade. Thus, the efficacy of LP serves as a cornerstone in establishing a theoretical foundation for influenza treatment | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Immunity | |
650 | 4 | |a Inflammation | |
650 | 4 | |a Influenza A/Aichi/2/1968 virus (H3N2) | |
650 | 4 | |a Laggera pterodonta | |
650 | 4 | |a Molecular docking | |
650 | 4 | |a Network pharmacology | |
700 | 1 | |a Chen, Zexing |e verfasserin |4 aut | |
700 | 1 | |a Wang, Wanqi |e verfasserin |4 aut | |
700 | 1 | |a Wang, Yutao |e verfasserin |4 aut | |
700 | 1 | |a Zhu, Jinyi |e verfasserin |4 aut | |
700 | 1 | |a Wang, Xinhua |e verfasserin |4 aut | |
700 | 1 | |a Huang, Wanyi |e verfasserin |4 aut | |
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