
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-01102
00030
10.1097/MD.0000000000035529
3
5900
Research Article
Systematic Review and Meta-Analysis
Network pharmacology-based study on the mechanism of action of Trollius chinensis capsule in the treatment of upper respiratory tract infection
https://orcid.org/0000-0002-8050-9872
Wu Jun MD 386212785@qq.com
a
Zhang Hai-Ping MD 592005427@qq.com
a
Gao Jing-Wen BD ilovegaojingwen@foxmail.com
a
Liu Zhi-Feng MD 240486941@qq.com
a
Jin Lei MD a*
a Department of Gastroenterology, Hubei No. 3 People’s Hospital of Jianghan University, Wuhan, China.
* Correspondence: Lei Jin, Department of Gastroenterology, Hubei No. 3 People’s Hospital of Jianghan University, Wuhan, 430000, China (e-mail: 15634619@qq.com).
06 9 2024
06 9 2024
103 36 e3552911 3 2023
14 9 2023
15 9 2023
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Background:

Upper respiratory tract infection (URTI), one of the most common respiratory diseases, has a high annual incidence. Trollius chinensis capsule has been used to treat URTI in China. However, the underlying-mechanisms remain unclear.

Methods:

Network pharmacology was used to explore the potential mechanism of action of Trollius chinensis capsule in URTI treatment. The active compounds in Trollius chinensis were obtained from the TCMSP, SymMap, and ETCM databases. The TCMSP, PubChem, and SwissTargetPrediction databases were used to predict potential targets of Trollius chinensis. URTI-associated targets were gathered from GeneCards and DisGeNET databases. The key targets and signaling pathways associated with URTI were selected by network topology, GO, and KEGG pathway enrichment analysis. Molecular docking was used to verify the binding activity between active compounds and key targets.

Results:

Quercetin, pectolinarigenin, beta-sitosterol, acacetin and cirsimaritin are major active compounds in Trollius chinensis capsule. Eighty one candidate therapeutic targets were confirmed to be involved in protection of Trollius chinensis capsule against URTI. Among them, 7 key targets (TP53, IL6, AKT1, CASP3, CXCL8, MMP9, and EGFR) were verified to have good binding affinities to the main active compounds. Furthermore, enrichment analyses suggested that inflammatory response, virus infection and oxidative stress related biological processes and pathways were possibly the potential mechanism.

Conclusion:

Overall, the present study clarified that quercetin, pectolinarigenin, beta-sitosterol, acacetin and cirsimaritin are proved to be the main effective compounds of Trollius chinensis capsule treating URTI, possibly by acting on the targets of IL6, AKT1, CASP3, CXCL8, MMP9 and EGFR to play anti-infectious, anti-viral, and anti-oxidative effects. This study provides a new understanding of the active compounds and mechanisms of Trollius chinensis capsule in URTI treatment from the perspective of network pharmacology.

molecular docking
network pharmacology
Trollius chinensis
upper respiratory tract infection
OPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Upper respiratory tract infection (URTI) accounts for the vast majority of respiratory tract infections in hospital outpatient clinics, including tonsillitis, nasopharyngitis (common cold), pharyngitis, and otitis media.[1] The etiology of URTI includes bacterial and viral infections. Most URTI cases are mild and self-limiting, but some can cause serious complications, especially in elderly patients and children, leading to heavy social and economic burdens on individuals and society. The current URTI management strategies are designed to alleviate the symptoms and prevent the spread of respiratory tract infections,[2] as the majority of URTI cases do not require antibiotic treatment.[3] Early intervention can alleviate the severity of symptoms, reduce virus transmission, and prevent progression to lower respiratory tract infection. Many medications can be used to relieve symptoms of URTI, such as Tylenol, Ganmaoling granule, and Trollius Chinensis Capsule. Cutting off the source of infection is the most effective way to reduce respiratory transmission, such as avoiding crowds, maintaining social distancing, keeping indoor air circulation, wearing masks, and reducing virus replication. This study suggests that Trollius chinensis capsule has antiviral and anti-inflammatory effects. Therefore, the Trollius chinensis capsule used in the Treatment of URTI is just in line with the management strategy.

Traditional Chinese medicine (TCM) has been used for many years to treat a variety of diseases, including URTI. Trollius chinensis is a perennial herb that is widely distributed in Mongolia and the northeastern regions of China. As a well-known Chinese folk herbal medicine, it has been used to treat respiratory infections owing to its actions of heat-clearing and detoxification.[4] There are several studies that have reported the use of Trollius chinensis to treat URTI.[5–7] However, the specific mechanism is unclear.

Network pharmacology is a tool used to study the active ingredients of TCM, which can systematically and comprehensively analyze the mechanism of action of drugs.[8] The network pharmacology-based approach is a promising research tool through which we can gain a more comprehensive and detailed understanding of the relationship between drugs and diseases.[9,10] So far, many studies have applied this method to explore the mechanisms of TCM against respiratory tract infections and have achieved satisfactory results.[11,12]

In this study, we applied network pharmacology and molecular docking to explore the mechanism of action of Trollius chinensis in URTI treatment. The flowchart of the study is shown in Figure 1.

Figure 1. The flowchart of this study.

2. Materials and methods

2.1. Construction of ingredient database and screening of active compounds

The main ingredient of Trollius chinensis capsule is Trollius chinensis. The active compounds were collected from Traditional Chinese Medicine Systems Pharmacology Database (TCMSP, https://old.tcmsp-e.com/tcmsp.php,Version 2.3),[13] SymMap (http://www.symmap.org/),[14] and ETCM (http://www.tcmip.cn/ETCM/index.php/Home/Index/).[15] The predicted targets of Trollius chinensis were collected from TCMSP, PubChem database (https://pubchem.ncbi.nlm.nih.gov/)[16] and SwissTargetPrediction (http://www.swisstargetprediction.ch/).[17] We screened out potential pharmacologically active compounds based on ADME-related properties (absorption, distribution, metabolism, and excretion) from the TCMSP, SymMap, and ETCM databases. The screening criteria were as follows: oral bioavailability ≥ 30% and drug-likeness ≥ 0.18. The predicted targets were verified using UniProtKB ID, and gene names and proteins were obtained from the UniProt database (https://www.uniprot.org/).[18]

2.2. Predicting URTI-associated targets

The URTI-associated targets were obtained from GeneCards (https://www.genecards.org/, Version 5.9)[19] and DisGeNET (https://www.disgenet.org/, DisGeNET 7.0).[20]

2.3. Protein-protein interaction (PPI) data

We used the Venny website (https://bioinfogp.cnb.csic.es/tools/venny/index.html, Version 2.1.0) to identify the common targets of the URTI-associated targets and predicted Trollius chinensis targets. The STRING database (https://cn.string-db.org/, Version 11.5)[21] was used to obtain the PPI data. The conditions were limited to “H. sapiens” and “multiple proteins.” In our study, the minimum required interaction score was set as the highest confidence level (0.900) to construct the PPI data.

2.4. GO (biological processes [BP], molecular function [MF], and cellular component [CC]) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis

The Hiplot website (https://hiplot.com.cn,Version 0.2.0) is a comprehensive and easy-to-use web service that boosts publication-ready biomedical data visualization, with versatile task plugins, such as basic statistics, regression, clustering, genomics, transcriptomics, time series, meta-analysis, and risk models. We chose the Hiplot website for the GO and KEGG pathway enrichment analysis.

2.5. Construction of protein interaction networks

The network visualization software Cytoscape[22] (3.9.1) was used to construct the following networks: compound-compound target network, the Trollius chinensis acting on the URTI target network is based on the targets common between the URTI-associated targets and the predicted Trollius chinensis targets, PPI network, and pathway-target network, which included the top 20 pathways and their corresponding targets in KEGG enrichment analysis.

2.6. Key targets molecular docking verification

Molecular docking combines compounds with protein receptors, and calculates the matching mode and affinity between them through computer simulation, which can predict ligand-target interactions at a molecular level, or describe structure-activity relationships. The 3D (three-dimensional) chemical structures of the active compounds as ligands in the SDF format were downloaded from the PubChem database. Open Babel[23] (2.4.1) was used to convert the SDF file into PDBQT format. The crystal structures of the target proteins as receptors were obtained from the RCSB PDB database (https://www.rcsb.org/).[24] The PyMOL and AutoDockTools[25] (1.5.6) were employed to pretreat the crystal structure of the target proteins, including removal of water molecules, extraction of ligands, determination of docking box size, removal of impurities, and add hydrogen atoms and oxygen atoms.[26] The AutoDock Vina[27] (1.1.2) was used to carry out the molecular docking. Protein-Ligand Interaction Profiler (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index)[28] was used to identify and label non-covalent bonds between the receptor and ligand.

This study did not involve animal experiments or human clinical trials, and all data were from known online databases, which did not involve ethical issues.

3. Results

3.1. Compound-compound target network

Seven active compounds were screened using the TCMSP and SymMap databases (Table 1). A total of 377 targets of the 7 active compounds were identified from the TCMSP, PubChem, and SwissTargetPrediction databases (see Table S1, Supplemental Digital Content, http://links.lww.com/MD/N507, which shows the predicted targets of Trollius Chinensis). A compound-compound target network was constructed to obtain more information about the 7 compounds and their corresponding targets at the overall level (Fig. 2). It consisted of 384 nodes and 719 edges. Network analysis (see Table S2, Supplemental Digital Content, http://links.lww.com/MD/N507, which shows the compound-target network) using Cytoscape showed that quercetin (MOL1, degree = 274), pectolinarigenin (MOL2, degree = 111), beta-sitosterol (MOL3, degree = 79), acacetin (MOL5, degree = 142), and skrofulein (MOL7, degree = 108) had more connections to targets.

Table 1 The 7 active compounds of Trollius Chinensis.

Figure 2. Compound-compound target network. It consists of 384 nodes, and 719 edges. The orange nodes represent the active compounds, the blue-green nodes represent the predicted targets of the active compounds. The edges represent the interaction between compounds and targets. The size of the node is directly proportional to the degree of interaction.

3.2. The Trollius chinensis acting on URTI target network

URTI-associated targets were obtained using GeneCards and DisGeNET. In total, 680 targets were screened (see Table S3, Supplemental Digital Content, http://links.lww.com/MD/N507, which lists the URTI associated targets). The Venny website (2.1.0) was used to obtain common targets between the URTI-related targets and the predicted Trollius chinensis targets. Finally, 81 targets were screened (Fig. 3; see Table S4, Supplemental Digital Content, http://links.lww.com/MD/N507, which lists the 81 common targets between the URTI related targets and the predicted Trollius Chinensis targets). We chose Cytoscape to generate the Trollius chinensis acting on the URTI target network (Fig. 4; see Table S5, Supplemental Digital Content, http://links.lww.com/MD/N507, which displays the Trollius Chinensis acting on URTI target network). The network contained 87 nodes and 143 edges. Notably, Sitosterol was not displayed in the network because it had nothing in common with the 81 targets. Moreover, the data showed that quercetin (MOL1) had the largest number of connections with the targets. Pectolinarigenin (MOL2) and acacetin (MOL5) were closely related to other targets, second only to quercetin.

Figure 3. The 81 common targets between the URTI-related targets and the predicted Trollius chinensis targets. URTI = upper respiratory tract infection.

Figure 4. The Trollius chinensis acting on URTI target network. It consists of 87 nodes, and 143 edges. The orange nodes represent the active compounds, the blue-green nodes represent the common targets between the URTI related targets and the predicted Trollius chinensis targets. The edges represent the interaction between compounds and targets. The size of the node is directly proportional to the degree of interaction. URTI = upper respiratory tract infection.

3.3. PPI network

The STRING database and Cytoscape were used to obtain a PPI network. There were 80 nodes and 2732 edges in the network (Fig. 5). The average degree centrality was 68.3; the average closeness centrality (CC) was 56.191; the average betweenness centrality was 47.150. Betweenness centrality, CC, and degree centrality of the following 23 targets were higher than the average values: interleukin 6 (IL6), TP53, RAC-alpha serine/threonine-protein kinase (AKT1), TNF, IL1B, VEGFA, JUN, CASP3, CCL2, CXCL8, MYC, MMP9, PPARG, epidermal growth factor receptor (EGFR), IL10, IFNG, PTGS2, HIF1A, EGF, CCND1, ERBB2, CAV1, and mitogen-activated protein kinase 1 (see Table S6, Supplemental Digital Content, http://links.lww.com/MD/N507, which illustrates the result of PPI network).

Figure 5. PPI network, which was completed in the Cytoscape and CytoHubba. It consists of 80 nodes, and 2732 edges. The nodes represent the common targets between the URTI-related targets and the predicted Trollius chinensis targets. The edges represent the interaction between the targets. Size of the node and shades of color are proportional to the degree of interaction. The darker the color, the larger the figure, the higher and more significant the score. PPI = protein-protein interaction, URTI = upper respiratory tract infection.

3.4. GO and KEGG enrichment analysis

The Hiplot website was used to conduct the GO and KEGG pathway enrichment analysis. We selected the top 20 GO and KEGG enrichment analysis for further investigation (Figs. 6 and 7; see Table S7, Supplemental Digital Content, http://links.lww.com/MD/N507, which shows the details of the top 20 of the enrichment results of target genes). GO enrichment analysis, including BP, cell composition (CC), and MF, was performed. The top 5 BP items were: response to lipopolysaccharides, response to molecules of bacterial origin, response to metal ions, cellular response to chemical stress, and regulation of cell-cell adhesion. The top 5 CC items were: membrane raft, membrane microdomain, vesicle lumen, external side of the plasma membrane, and cytoplasmic vesicle lumen. The top 5 MF items were: cytokine receptor binding, receptor ligand activity, signaling receptor activator activity, cytokine activity, and phosphatase binding.

Figure 6. Top 20 of GO enrichment analysis. (A) biological processes, (B) cellular components, (C) molecular function. GO = gene ontology.

Figure 7. Top 20 of KEGG enrichment analysis. KEGG = Kyoto Encyclopedia of Genes and Genomes.

The top 15 KEGG pathways were as follows: lipid and atherosclerosis, fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, Kaposi sarcoma-associated herpesvirus infection, human cytomegalovirus infection, hepatitis C, hepatitis B, influenza A, Epstein-Barr virus infection, Chagas disease, TNF signaling pathway, proteoglycans in cancer, C-type lectin receptor signaling pathway, measles, and IL-17 signaling pathway. Most of these pathways are associated with viral infections, inflammatory responses, and oxidative stress. The data (Table 2) of the top 15 KEGG pathways and targets involved in each pathway were used to construct the pathway-target network (Fig. 8; see Table S8, Supplemental Digital Content, http://links.lww.com/MD/N507, which shows the Pathway-target network: the top 15 KEGG pathways and their corresponding targets) using Cytoscape-3.9.1. The network contained 68 nodes and 305 edges. It can be seen from the network that there were 12 targets involved in at least 10 pathways, which were as follows: NFKB1, mitogen-activated protein kinase 1, P IK3R1, PIK3CA, AKT1, inhibitor of nuclear factor kappa-B kinase subunit beta, TNF, IL6, NFKBIA, CASP8, JUN, and CASP3. It suggested that these targets may play a more important role in the treatment of URTI.

Table 2 The top 15 of KEGG pathways and targets involved in each pathway.

ID	Description	P value	Count	Targets	
hsa04933	AGE-RAGE signaling pathway in diabetic complications	9.04103E-25	22	VEGFA/TNF/THBD/STAT1/	
SELE/CCL2/MAPK1/PIK3R1	
/PIK3CA/SERPINE1/NFKB1	
/MMP2/JUN/CXCL8/IL6/	
IL1B/IL1A/ICAM1/CASP3/	
BCL2/CCND1/AKT1	
hsa05418	Fluid shear stress and atherosclerosis	2.51977E-24	24	NCF1/VEGFA/TP53/TNF/	
THBD/SELE/CCL2/PIK3R1	
/PIK3CA/NFKB1/NFE2L	
/MMP9/MMP2/JUN/IL1B/IL1A/	
IKBKB/IFNG/ICAM1/HMOX1/	
GSTM1/CAV1/BCL2/AKT1	
hsa05417	Lipid and atherosclerosis	3.08679E-22	26	NCF1/TP53/TNF/SELE/CCL2	
/MAPK1/PPARG/PIK3R1/PIK3CA	
/NFKBIA/NFKB1/NFE2L2/MMP9	
/MMP1/JUN/CXCL8/IL6/IL1B	
/IKBKB/ICAM1/CD40LG/CASP8	
/CASP3/BCL2/FASLG/AKT1	
hsa05142	Chagas disease	1.89083E-21	20	TNF/CCL2/MAPK1/PIK3R1	
/PIK3CA/SERPINE1/NOS2	
/NF KBIA/NFKB1/JUN/IL10/	
CXCL8/IL6/IL2/IL1B/IKBK/IFNG/CASP8/FASLG/	
AKT1	
hsa04668	TNF signaling pathway	1.38367E-20	20	TNF/SELE/CCL2/PTGS2/	
MAPK1/PIK3R1/PIK3CA/	
NFKBIA/NFKB1/MMP9/	
JUN/IRF1/CXCL10/IL6/	
IL1B/IKBKB/ICAM1/CASP8/	
CASP3/AKT1	
hsa04625	C-type lectin receptor signaling pathway	8.9858E-20	19	TNF/SYK/STAT1/PTGS2/	
MAPK1/PIK3R1/PIK3CA/	
NFKBIA/NFKB2/NFKB1/	
JUN/IRF1/IL10/IL6/IL2/IL1B	
/IKBKB/CASP8/AKT1	
hsa05219	Bladder cancer	5.61078E-19	14	VEGFA/TP53/RB1/MAPK1/MYC/MMP9/MMP2/MMP1	
/CXCL8/ERBB2/EGFR	
/EGF/BRAF/CCND1	
hsa05160	Hepatitis C	7.19465E-19	21	TP53/TNF/STAT1/RB1/MAPK1/PIK3R1/PIK3CA/	
NFKBIA/NFKB1/MYC/	
CXCL10/IKBKB/IFNG/EGFR	
/EGF/CASP8/CASP3/BRA/	
CCND1/FASLG/AKT1	
hsa05161	Hepatitis B	1.40441E-18	21	TP53/TNF/STAT1/RB1/MAPK1/PIK3R1/PIK3CA/	
NFKBIA/NFKB1/MYC/	
MMP9/JUN/CXCL8/IL6/	
IKBKB/CASP8/CASP3/	
BRAF/BCL2/FASLG/AKT1	
hsa05167	Kaposi sarcoma-associated herpesvirus infection	3.59605E-18	22	VEGFA/TP53/SYK/STAT1/	
RB1/PTGS2/MAPK1/PIK3R1/	
PIK3CA/NFKBIA/NFKB1/	
MYC/JUN/CXCL8/IL6/IKBKB/	
ICAM1/HIF1A/CASP8/	
CASP3/CCND1/AKT1	
hsa05164	Influenza A	4.42352E-18	21	TNF/STAT1/CCL2/MAPK1/PLG/PIK3R1/PIK3CA/NFKBIA	
/NFKB1/CXCL10/CXCL8/IL6/	
IL1B/IL1A/IKBKB/IFNG/ICAM1	
/CASP8/CASP3/FASLG/AKT1	
hsa04657	IL-17 signaling pathway	1.1461E-17	17	TNF/CCL2/PTGS2/MAPK1	
/NFKBIA/NFKB1/MMP9/MMP1	
/JUN/CXCL10/CXCL8/IL6/IL1B	
/I KBKB/IFNG/CASP8/CASP3	
hsa05163	Human cytomegalovirus infection	9.04179E-17	22	VEGFA/TP53/TNF/CCL2	
/RB1/PTGS2/MAPK1/PIK3R1	
/PIK3CA/NFKBIA/NFKB1/	
MYC/CXCL8/IL6/IL1B/IKBKB	
/EGFR/CASP8/CASP3/	
CCND1/FASLG/AKT1	
hsa05169	Epstein-Barr virus infection	1.44538E-16	21	TP53/TNF/SYK/STAT1/RB1	
/PIK3R1/PIK3CA/NFKBIA/NFKB2	
/NFKB1/MYC/JUN/CXCL10/IL6/I	
KBKB/ICAM1/CASP8/CASP3/BCL2/CCND1/AKT1	
hsa05212	Pancreatic cancer	2.81487E-16	15	VEGFA/TP53/STAT1/RB1/MAPK1/PIK3R1/PIK3CA/	
NFKB1/IKBKB/ERBB2/EGFR	
/EGF/BRAF/CCND1/AKT1	
KEGG = Kyoto Encyclopedia of Genes and Genomes.

Figure 8. The pathway–target network, which were the top 15 of KEGG pathways and targets involved in each pathway. The network has 68 nodes and 305 edges. The orange nodes represent the KEGG pathways, the blue-green nodes represent the targets involved in the top 15 of KEGG pathways. The edges represent the interaction between the pathways and targets. The size of the node is directly proportional to the degree of interaction. KEGG = Kyoto Encyclopedia of Genes and Genomes.

3.5. Molecular docking

Because the 5 active compounds (quercetin, pectolinarigenin, beta-sitosterol, acacetin, and cirsimaritin) interacted well with the 81 common targets, we chose the top 20 key targets from the PPI network for further verification by molecular docking. We selected results that met the following conditions: the binding energies of the same target, and all 5 active compounds were <−5 kcal/mol (Table 3). Ultimately, 7 key targets (TP53, IL6, AKT1, CASP3, CXCL8, MMP9, and EGFR) exhibited strong binding activities with 5 compounds, and they interacted with each other by forming hydrogen bonds, hydrophobic interactions, and π-stacking (Table 4). The binding patterns of some key targets with highly active compounds are shown in Figure 9.

Table 3 The results of molecular docking: the binding energies of all targets with 5 compounds are <−5 kcal/mol.

Key targets	PDB ID	Binding energy (kcal/mol)	
Acacetin	Betasitosterol	Cirsimaritin	Pectolinarigenin	Quercetin	
AKT1	6CCY	−7.8	−8	−7.5	−7.9	−8.4	
CASP3	6CL0	−6.9	−6.9	−6.7	−6.5	−7.6	
CXCL8	6WZM	−6.2	−6.5	−6	−6.1	−6.1	
EGFR	7OXB	−8.2	−8.7	−7.8	−7.3	−8.2	
IL6	4CNI	−6.9	−7.6	−6.7	−6.7	−7.1	
MMP9	6ESM	−10.4	−8.4	−9.6	−9.9	−10.4	
TP53	6SI3	−7.8	−5.3	−7.2	−7.4	−8.4	

Table 4 Molecular docking analysis of 5 compounds and 7 key targets.

Items	NonCovalent bond	
Hydrophobic interactions	Hydrogen bonds	π-Stacking	
AKT1	acacetin	LEU156, VAL164, ALA177, MET227, PHE438	ALA230	–	
beta-sitosterol	PHE161, VAL164, ALA177, LYS179, LEU181, VAL164	–	–	
cirsimaritin	PHE161, PHE161, LEU181, LEU181, ILE186	GLY159, PHE161, GLY162	–	
Pectolinarigenin	GLU234, PHE438	LYS179, ASP292	–	
quercetin	LEU156, ALA177, THR211, THR291, PHE438	LYS179, GLU198, GLU198, THR211, ALA230, ALA230, ASP292	–	
CASP3	acacetin	TRP206, TRP206, ASP253	SER205, SER205, ARG207, ARG207, ARG207	PHE256 , PHE256	
beta-sitosterol	TYR204, TYR204, TRP206, PHE250, PHE256, PHE256, PHE256	–	–	
cirsimaritin	PHE256	ARG207, ARG207, ARG207, SER251, ASP253	–	
pectolinarigenin	TRP206, ASP253	ARG207, ARG207	PHE256, PHE256	
quercetin	TYR204, ARG207, PHE256	ARG64, ARG64, SER120, HIS121, GLN161, ARG207, ARG207	–	
CXCL8	acacetin	PHE21, PHE21, LEU43, LEU43	TYR13, SER44, ASP45	PHE21	
beta-sitosterol	TYR13, PHE17, PHE17, PHE17, PHE17, PHE21, PHE21, PHE21, LEU43	–	–	
cirsimaritin	PHE17, PHE21, PHE21, LEU43, LEU43	LYS15, SER44, ASP45	PHE21	
pectolinarigenin	PHE17, PHE21, PHE21, LEU43, LEU43	LYS15, SER44, ASP45	PHE21	
quercetin	PHE21, PHE21	SER44, SER44, ASP45, ASP45, ARG47, ARG47	PHE21	
EGFR	acacetin	LEU718, ALA743, LEU792, LEU844, LEU844, THR854, ASP855	LYS745, MET493, ARG841, THR854	–	
beta-sitosterol	LEU718, PHE723, VAL726, VAL726, THR854	–	–	
cirsimaritin	PHE723, VAL726, LYS745, LEU844	LYS745, MET793, MET793, GLY796	–	
pectolinarigenin	PHE723, VAL726, LEU844	MET793	–	
quercetin	ALA743, MET793, LEU844, LEU844, THR854, ASP855	LYS745, MET793, ARG841	–	
IL6	acacetin	TYR32, ALA55	ASP56, ASP56, TYR103, SER108	PHE106	
beta-sitosterol	ASP31, LEU46, TYR49, TYR103, TYR103, TYR103, TYR104, TYR104, PHE106	–	–	
cirsimaritin	TYR32	ASP56, TYR103	PHE106	
pectolinarigenin	TYR32	ASP56, TYR103	PHE106	
quercetin	ALA55	ASP56, ASP56, ARG100, ARG100, GLU101, TYR103, SER108	PHE106	
MMP9	acacetin	LEU187, LEU188, LEU222, VAL223, HIS226, TYR248	ALA189, ALA189, GLN227	HIS226	
beta-sitosterol	TYR179, TYR179, LEU187, LEU187, HIS190, PHE192, PHE192	TYR248	–	
cirsimaritin	LEU222, VAL223, HIS226, TYR248	LEU188, ALA189, GLN227, ARG249	HIS226	
pectolinarigenin	LEU222, VAL223, HIS226, LEU243, TYR248	LEU188, ALA189, GLN227, HIS236	HIS226	
quercetin	LEU187, LEU188, VAL223, HIS226, TYR248, TYR248	ALA189, HIS226, GLN227, LEU243, MET247	–	
TP53	acacetin	THR150, PRO151, PRO222	LEU145, VAL147, THR230	–	
beta-sitosterol	TRP146, TRP146, VAL147, THR150, PRO222, PRO223, ASP228	–	–	
cirsimaritin	VAL147, THR150, PRO151, PRO222, PRO223	VAL147, SER220, THR230	–	
pectolinarigenin	THR150, RO151, PRO222, PRO223	VAL147	–	
quercetin	THR150, RO151, PRO222, PRO223	VAL147, PRO152, THR155, GLU221, ASP228, THR230	–	

Figure 9. The binding patterns of some key targets with highly active compounds.

4. Discussion

20% to 40% of hospital outpatients’ diseases and 12% to 35% of inpatients’ diseases are acute respiratory infections. URTI accounts for 87.5% of all respiratory infections, including tonsillitis, nasopharyngitis (common cold), pharyngitis and otitis media. Viral infections are the cause of most URTI, and in most cases, antibiotics are not required.[1] At this time, the focus of our medication was to relieve the patients’ symptoms. Many drugs such as Tylenol and Compound Paracetamol and Amantadine Hydrochloride Tablets are used to treat URTI. Their main components are acetaminophen, amantadine, ephedrine, dextromethorphan, chlorpheniramine and so on. These drugs are all combination formulations that can cause adverse reactions as well as treat the disease, such as drowsiness, rashes, digestive symptoms, and even liver and kidney function impairment. Chinese herbal medicines, such as Trollius chinensis, have been used for many years in China to prevent a variety of diseases in a multi-target and multi-component manner.[29] Trollius chinensis has anti-inflammatory[30] and antibacterial effects.[31] It is used to treat URTI in Chinese folk medicine. It is a single component, causes few adverse reactions, and has the effect of relieving the symptoms of URTI and antiviral. This is good news for people who cannot tolerate multiple adverse reactions, especially children and the elderly. However, its specific mechanism of action remains unclear. Therefore, we applied network pharmacology and molecular docking to explore the mechanism of action in the treatment of URTI.

Seven active compounds were screened, including quercetin, pectolinarigenin, beta-sitosterol, acacetin, and cirsimaritin. Quercetin, a flavonoid, which was found in fruits and vegetables, has specific biological characteristics that may improve mental/physical performance and reduce the risk of infection.[32] These biological characteristics form the potentially beneficial basis for our immunity against disease, including anti-inflammatory, antiviral, antioxidant benefits, as well as capillary permeability.[33] Quercetin has been proved to have a strong anti-pathogenic effect on several pathogenic factors of URTI.[34,35] Quercetin has a long-lasting and strong anti-inflammatory effect,[36,37] its anti-inflammatory properties can be expressed on different types of cells in animal and human models.[38–46] Quercetin disrupts the association between tyrosine-phosphorylated phosphatidylinositol 3-kinase and myeloid differentiation factor-88, and inhibits mitogen-activated protein kinase/activator protein 1 and IKK/NF-κB-induced production of inflammatory mediators in RAW 264.7 cells.[45] Our study also confirmed that quercetin is the most important compound of Trollius chinensis in the treatment of URTI. Acacetin is a di-hydroxy and mono-methoxy flavone, which is present in various plants, including Damiana, black locust, and silver birch.[47] Several reports showed that acacetin has anti-inflammatory activity against a variety of inducers, including lipopolysaccharide and D-galactosamine. Acacetin reduces TNF- α and IL-1β levels in tissues and increases the activity of nuclear factor erythroid 2-related factor 2 (NRF-2) and heme oxygenase-1 to protect against lipopolysaccharides (LPS) induced injury.[48] Acacetin inhibits the 5-hydroxy-eicosa-tetra-enoic acid, leukotriene B4, TNF-α, NO and prostaglandin E production, and inhibits the cyclooxygenase-2 and 5-lipoxygenase activity in macrophages.[49–51] Acacetin suppresses IL-6, toll-like receptor 4, TNF-α, tumor necrosis factor-related apoptosis-inducing ligand, eotaxin-1, IL-5, and decreases the P38, extracellular signal-related kinase, phosphorylation of C-Jun N-terminal kinase, and expression of NF-κB.[52–56] This study shows that acacetin is one of the most important compounds in Trollius chinensis, suggesting that it plays an important role of Trollius chinensis in the treatment of URTI. Literature shows that pectolinarigenin strongly inhibited 5-lipoxygenase-catalyzed leukotriene production and cyclooxygenase-2-mediated PGE2 synthesis from LPS-treated RAW 264.7 cells, leading to reduced production of eicosanoid.[57] Cirsimaritin restrains influenza A virus replication by down-regulating the expression of NF-κB signaling pathway.[58] Beta-sitosterol can inhibit the expression of neutrophil chemokines GRO-α, GRO-β, and IL-8, and may interfere with the signal transduction mediated by protein kinase-C, thus playing an anti-inflammatory role.[59] Several studies had shown that beta-sitosterol can play an anti-inflammatory role in different tissues and organs.[60–63] Pharmacological mechanisms of salmoxanthin is not available in the literature, so the therapeutic effect was not discussed here. These compounds were all classified as flavonoids. The classification of the compounds and the analysis of the compound-target network and Trollius chinensis acting on the URTI target network indicated that flavonoids with good pharmacokinetic properties played a vital role in the treatment of URTI.

PPI network analysis showed that several targets, such as TP53, IL6, TNF, AKT1, MMP9, CASP3, and CXCL8, may play a key role in the biological process of T. chinensis URTI treatment. Current evidence strongly suggests that the expression of inflammatory genes is inversely associated with TP53 expression.[64,65] The reason for this is currently unclear. IL6 is a member of one of the 3 classic inflammatory signaling pathways (IL-6/JAK/STAT3 pathway), and is involved in a variety of inflammatory responses and inflammation-related diseases.[66] IL6 is immediately and temporarily expressed when the body is under stress, such as infection or tissue damage, to alert us to a defensive response. When this stress state is eliminated, the body stops producing IL6 in a series of negative regulation.[67] As an important inflammatory cytokine,[68] TNF can induce the production of cytokines such as IL-6 and participate in the process of oxidative stress and inflammation.[69] A previous study reported that AKT1 regulates the innate immunity of macrophages by mediating the production of mitochondrial hydrogen peroxide.[70] That the lack of AKT1 in macrophages promotes inflammation.[71] AKT1 level is crucial for the activation of inflammatory response. Zhang et al[72] found that MMP9 reduced the production of RANK, TNF-α, IL-1β, RANKL, TLR4, and TLR2 in LPS-stimulated MC3T3-E1 cells. MMP9 increased the expression of osteocalcin and osteoprotegerin in LPS-stimulated cells. It also plays a protective role against LPS-induced inflammation. Caspases are intracellular cystic cysteine proteases, that mediate cell death and inflammation, and play a unique role in apoptosis and inflammation. Casp3 is the main mediator of apoptosis and necrotic cell death. Pharmacological inhibitors of casp3 can protect against cell death after irradiation, indicating that the activation of casp3 is the key to apoptosis induced by genotoxic stress.[73] Inflammation is a self-defense mechanism, that can be caused by infection and tissue damage. The CXCL8-CXCR1/2 (receptors of CXCL8) axis gathers neutrophils at the infected site, induces neutrophil oxidative burst and particle release, eliminates inflammatory stimulation, and eliminates pathogenic bacteria.[74,75] Therefore, this axis can prevent further infection and tissue damage.[76] Its destruction will seriously affect the immune mechanism of the host against infection, and may even lead to death.

According to the GO enrichment analysis results, the top 5 BP, MF, and CC were mostly related to cell signal transduction, which was also in line with the biological processes at the cellular and molecular levels after suffering from URTI. KEGG enrichment analysis showed that the signaling pathways of Trollius chinensis in the treatment of URTI were mainly related to viral infection, inflammatory response, and oxidative stress. Several of the top 15 pathways in KEGG enrichment analysis were associated with different viral infections, indicating that Trollius chinensis may somehow inhibit multiple viruses. In particular, the influenza A pathway, has been identified to be associated with URTI.[77,78] Thus, we can speculate that Trollius chinensis is also likely to have an inhibitory effect on the virus associated with URTI.

KEGG enrichment analysis indicated several inflammation-related pathways as follows: AGE-RAGE signaling pathway in diabetic complications, which is related to oxidative stress. In this pathway, the receptor RAGE activates NADPH oxidase 2 and causes excessive reactive oxygen species production.[79] The TNF signaling pathway, which plays an important role in the inflammatory response.[80] The key active compound quercetin achieve its anti-inflammatory effects by interfering with the TNF signaling pathway.[81] TNF, a key target in PPI network analysis, induces the production of cytokines such as IL-6 and subsequently participates in inflammatory responses and oxidative stress processes.[69] C-type lectin receptor signaling pathway. C-type lectins are a superfamily of proteins, that can recognize a wide range of ligands and regulate a variety of physiological functions. Its physiological functions involve homeostasis, cell death, cancer, antimicrobial immunity (including viruses, fungi, bacteria and parasites) and autoimmunity.[82] And it plays an important role in innate immune response, involved in pathogen recognition, phagocytosis, inflammatory signal transduction and antigen processing.[83] IL-17 signaling pathway. IL-17 mediated inflammation is essential for microbial clearance, while uncontrolled IL-17 signaling can cause immunopathological changes.[84,85] Meanwhile, most of the key targets obtained by PPI network analysis were involved in these inflammation-related pathways.

Five active compounds (quercetin, pectolinarigenin, beta-sitosterol, acacetin, and cirsimaritin) were confirmed to have good binding activities with 7 key targets (TP53, IL6, AKT1, CASP3, CXCL8, MMP9, and EGFR) by molecular docking. These results suggest that the 5 active compounds and 7 key targets may play an important role in the treatment of URTI. Therefore, further experimental verification is required.

However, this study had some limitations. First, as a disease with multiple causes, we only focused on the factors of viral infection, not bacteria and other factors of pathogenic infection. Second, owing to the limited conditions, our study was not verified experimentally. The mechanism of action of Trollius chinensis in the treatment of URTI requires comprehensive in vivo and in vitro studies. Third, although 5 active compounds and 7 key targets were confirmed to participate in the mechanism of action, the potential up-down relationship between them remains to be explored further.

5. Conclusion

This study preliminarily explored the mechanism of action of Trollius chinensis in the treatment of URTI. Through network pharmacology and molecular docking, it was verified that quercetin, pectolinarigenin, beta-sitosterol, acacetin and cirsimaritin were the key active compounds acting on the targets of IL6, AKT1, CASP3, CXCL8, MMP9, and EGFR, which may play a therapeutic role in anti-infection, anti-virus, and anti-oxidative stress in the treatment of URTI.

Author contributions

Conceptualization: Jun Wu.

Data curation: Jun Wu, Hai-Ping Zhang, Jing-Wen Gao, Zhi-Feng Liu.

Formal analysis: Jun Wu.

Investigation: Jun Wu.

Methodology: Jun Wu.

Project administration: Jun Wu.

Resources: Jun Wu.

Software: Jun Wu.

Supervision: Jun Wu.

Validation: Jun Wu, Hai-Ping Zhang, Jing-Wen Gao, Zhi-Feng Liu.

Visualization: Jun Wu, Hai-Ping Zhang, Jing-Wen Gao, Zhi-Feng Liu.

Writing – original draft: Jun Wu.

Writing – review & editing: Jun Wu, Lei Jin.

Supplementary Material

Abbreviations:

3D three-dimensional

ADME absorption, distribution, metabolism, and excretion

AGE-RAGE signaling pathway advanced glycationend product-receptor of advanced glycationend product signaling pathway

AKT1 RAC-alpha serine/threonine-protein kinase

BP biological processes

CASP3 Caspase-3

CASP8 caspase-8

CC cellular component

CC closeness centrality

CXCL8 interleukin-8

EGFR epidermal growth factor receptor

ETCM Encyclopaedia of Traditional Chinese medicine

GO gene ontology

GRO-α(β) growth regulated oncogene-α(β)

IKBKB inhibitor of nuclear factor kappa-B kinase subunit beta

IKK/NF-κB IκB kinase/nuclear factor-κB

IL6 interleukin 6

JUN transcription factor AP-1

KEGG Kyoto Encyclopedia of Genes and Genomes

LPS lipopolysaccharides

MC3T3-E1 cells mouse embryo osteoblast precursor cells

MF molecular function

MMP9 matrix metalloproteinase-9

MOL1 quercetin

MOL2 pectolinarigenin

MOL3 beta-sitosterol

MOL4 sitosterol

MOL5 acacetin

MOL6 salmoxanthin/ trollixanthin

MOL7 skrofulein/ cirsimaritin

NFKBIA nuclear factor-kappa-B inhibitor alpha

NRF-2 nuclear factor erythroid 2-related factor 2

PGE2 prostaglandin E2

PIK3CA phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform

PIK3R1 phosphatidylinositol 3-kinase regulatory subunit alpha

PPI protein-protein interaction

RANKL receptor activator of nuclear factor kappa-B ligand

RAW 264.7 cells mouse monocyte macrophage leukemia cells

RCSB PDB RCSB Protein Data Bank

TCM traditional Chinese medicine

TCMSP Traditional Chinese Medicine Systems Pharmacology Database

TLR toll-like receptors

TNF tumour necrosis factor

TP53 cellular tumor antigen p53

URTI upper respiratory tract infection

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Wu J, Zhang H-P, Gao J-W, Liu Z-F, Jin L. Network pharmacology-based study on the mechanism of action of Trollius chinensis capsule in the treatment of upper respiratory tract infection. Medicine 2024;103:36(e35529).
==== Refs
References

[1] Jain N Lodha R Kabra SK . Upper respiratory tract infections. Indian J Pediatr. 2001;68 :1135–8.11838568
[2] Wang Y Eccles R Bell J . Management of acute upper respiratory tract infection: the role of early intervention. Expert Rev Respir Med. 2021;15 :1517–23.34613861
[3] Grief SN . Upper respiratory infections. Prim Care. 2013;40 :757–70.23958368
[4] Yuan M Wang RF Wu XW An YN Yang XW . Investigation on Flos Trollii: constituents and bioactivities. Chin J Nat Med. 2013;11 :449–55.24359766
[5] Li H Zhang M Ma G . Radical scavenging activity of flavonoids from Trollius chinensis Bunge. Nutrition. 2011;27 :1061–5.21820869
[6] Wang R Wu X Liu L An Y . Activity directed investigation on anti-inflammatory fractions and compounds from flowers of Trollius chinensis. Pak J Pharm Sci. 2014;27 :285–8.24577916
[7] Qin Y Liang Y Ren D Qiu X Li X . Separation of phenolic acids and flavonoids from Trollius chinensis Bunge by high speed counter-current chromatography. J Chromatogr B Analyt Technol Biomed Life Sci. 2015;1001 :82–9.
[8] Hopkins AL . Network pharmacology. Nat Biotechnol. 2007;25 :1110–1.17921993
[9] Cheng F Desai RJ Handy DE . Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun. 2018;9 :2691.30002366
[10] Cheng F Kovacs IA Barabasi AL . Network-based prediction of drug combinations. Nat Commun. 2019;10 :1197.30867426
[11] Zhao J Tian S Lu D . Systems pharmacological study illustrates the immune regulation, anti-infection, anti-inflammation, and multi-organ protection mechanism of Qing-Fei-Pai-Du decoction in the treatment of COVID-19. Phytomedicine. 2021;85 :153315.32978039
[12] Tao Q Du J Li X . Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19. Drug Dev Ind Pharm. 2020;46 :1345–53.32643448
[13] Ru J Li P Wang J . TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6 :13.24735618
[14] Wu Y Zhang F Yang K . SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Res. 2019;47 :D1110–7.30380087
[15] Xu HY Zhang YQ Liu ZM . ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019;47 :D976–82.30365030
[16] Kim S Chen J Cheng T . PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49 :D1388–95.33151290
[17] Daina A Michielin O Zoete V . SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47 :W357–64.31106366
[18] UniProt C. UniProt:the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49 :D480–9.33237286
[19] Stelzer G Rosen N Plaschkes I . The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54 :1.30.1–1.30.33.
[20] Pinero J Ramirez-Anguita JM Sauch-Pitarch J . The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48 :D845–55.31680165
[21] Szklarczyk D Gable AL Nastou KC . The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49 :D605–12.33237311
[22] Shannon P Markiel A Ozier O . Cytoscape:a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13 :2498–504.14597658
[23] O’Boyle NM Banck M James CA Morley C Vandermeersch T Hutchison GR . Open babel: an open chemical toolbox. J Cheminform. 2011;3 :33.21982300
[24] Rose Y Duarte JM Lowe R . RCSB protein data bank: architectural advances towards integrated searching and efficient access to macromolecular structure data from the PDB Archive. J Mol Biol. 2021;433 :166704.33186584
[25] Seeliger D de Groot BL . Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des. 2010;24 :417–22.20401516
[26] Maier JK Labute P . Assessment of fully automated antibody homology modeling protocols in molecular operating environment. Proteins. 2014;82 :1599–610.24715627
[27] Trott O Olson AJ . AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31 :455–61.19499576
[28] Salentin S Schreiber S Haupt VJ Adasme MF Schroeder M . PLIP:fully automated protein-ligand interaction profiler. Nucleic Acids Res. 2015;43 :W443–7.25873628
[29] Chao J Dai Y Verpoorte R . Major achievements of evidence-based traditional Chinese medicine in treating major diseases. Biochem Pharmacol. 2017;139 :94–104.28636884
[30] Liu LJ Hu XH Guo LN Wang RF Zhao QT . Anti-inflammatory effect of the compounds from the flowers of Trollius chinensis. Pak J Pharm Sci. 2018;31 :1951–7.30150194
[31] Feng J Zhao D Xu Q . A new phenolic glycoside from Trollius chinensis Bunge with anti-inflammatory and antibacterial activities. Nat Prod Res. 2020;1 :8.
[32] Davis JM Murphy EA Carmichael MD . Effects of the dietary flavonoid quercetin upon performance and health. Curr Sports Med Rep. 2009;8 :206–13.19584608
[33] Chen S Jiang H Wu X Fang J . therapeutic effects of quercetin on inflammation, obesity, and type 2 Diabetes. Mediators Inflamm. 2016;2016 :9340637.28003714
[34] Chiang LC Chiang W Liu MC Lin CC . In vitro antiviral activities of Caesalpinia pulcherrima and its related flavonoids. J Antimicrob Chemother. 2003;52 :194–8.12837746
[35] Chen L Li J Luo C . Binding interaction of quercetin-3-beta-galactoside and its synthetic derivatives with SARS-CoV 3CL(pro): structure-activity relationship studies reveal salient pharmacophore features. Bioorg Med Chem. 2006;14 :8295–306.17046271
[36] Read MA . Flavonoids: naturally occurring anti-inflammatory agents. Am J Pathol. 1995;147 :235–7.7639322
[37] Orsolic N Knezevic AH Sver L Terzic S Basic I . Immunomodulatory and antimetastatic action of propolis and related polyphenolic compounds. J Ethnopharmacol. 2004;94 :307–15.15325736
[38] Chirumbolo S . The role of quercetin, flavonols and flavones in modulating inflammatory cell function. Inflamm Allergy Drug Targets. 2010;9 :263–85.20887269
[39] Yang D Liu X Liu M Chi H Liu J Han H . Protective effects of quercetin and taraxasterol against H2O2-induced human umbilical vein endothelial cell injury in vitro. Exp Ther Med. 2015;10 :1253–60.26622474
[40] Manjeet KR Ghosh B . Quercetin inhibits LPS-induced nitric oxide and tumor necrosis factor-alpha production in murine macrophages. Int J Immunopharmacol. 1999;21 :435–43.10454017
[41] Geraets L Moonen HJ Brauers K Wouters EF Bast A Hageman GJ . Dietary flavones and flavonoles are inhibitors of poly(ADP-ribose)polymerase-1 in pulmonary epithelial cells. J Nutr. 2007;137 :2190–5.17884996
[42] Kim HP Mani I Iversen L Ziboh VA . Effects of naturally-occurring flavonoids and biflavonoids on epidermal cyclooxygenase and lipoxygenase from guinea-pigs. Prostaglandins Leukot Essent Fatty Acids. 1998;58 :17–24.9482162
[43] Lee KM Hwang MK Lee DE Lee KW Lee HJ . Protective effect of quercetin against arsenite-induced COX-2 expression by targeting PI3K in rat liver epithelial cells. J Agric Food Chem. 2010;58 :5815–20.20377179
[44] Bureau G Longpre F Martinoli MG . Resveratrol and quercetin, two natural polyphenols, reduce apoptotic neuronal cell death induced by neuroinflammation. J Neurosci Res. 2008;86 :403–10.17929310
[45] Endale M Park SC Kim S . Quercetin disrupts tyrosine-phosphorylated phosphatidylinositol 3-kinase and myeloid differentiation factor-88 association, and inhibits MAPK/AP-1 and IKK/NF-kappaB-induced inflammatory mediators production in RAW 264.7 cells. Immunobiology. 2013;218 :1452–67.23735482
[46] Kempuraj D Madhappan B Christodoulou S . Flavonols inhibit proinflammatory mediator release, intracellular calcium ion levels and protein kinase C theta phosphorylation in human mast cells. Br J Pharmacol. 2005;145 :934–44.15912140
[47] Singh S Gupta P Meena A Luqman S . Acacetin, a flavone with diverse therapeutic potential in cancer, inflammation, infections and other metabolic disorders. Food Chem Toxicol. 2020;145 :111708.32866514
[48] Wu D Wang Y Zhang H Du M Li T . Acacetin attenuates mice endotoxin-induced acute lung injury via augmentation of heme oxygenase-1 activity. Inflammopharmacology. 2018;26 :635–43.28988328
[49] Fan SY Zeng HW Pei YH . The anti-inflammatory activities of an extract and compounds isolated from Platycladus orientalis (Linnaeus) Franco in vitro and ex vivo. J Ethnopharmacol. 2012;141 :647–52.21619922
[50] Kim HG Ju MS Ha SK . Acacetin protects dopaminergic cells against 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced neuroinflammation in vitro and in vivo. Biol Pharm Bull. 2012;35 :1287–94.22863927
[51] Srisook K Srisook E Nachaiyo W . Bioassay-guided isolation and mechanistic action of anti-inflammatory agents from Clerodendrum inerme leaves. J Ethnopharmacol. 2015;165 :94–102.25725433
[52] Warat M Szliszka E Korzonek-Szlacheta I Krol W Czuba ZP . Chrysin, apigenin and acacetin inhibit tumor necrosis factor-related apoptosis-inducing ligand receptor-1 (TRAIL-R1) on activated RAW264.7 macrophages. Int J Mol Sci . 2014;15 :11510–22.24979133
[53] Cho HI Park JH Choi HS . Protective mechanisms of acacetin against D-galactosamine and lipopolysaccharide-induced fulminant hepatic failure in mice. J Nat Prod. 2014;77 :2497–503.25382719
[54] Liu X Tian F Tian Y . Isolation and Identification of Potential Allelochemicals from Aerial Parts of Avena fatua L. and Their Allelopathic Effect on Wheat. J Agric Food Chem. 2016;64 :3492–500.27079356
[55] Huang WC Liou CJ . Dietary acacetin reduces airway hyperresponsiveness and eosinophil infiltration by modulating eotaxin-1 and th2 cytokines in a mouse model of asthma. Evid Based Complement Alternat Med. 2012;2012 :910520.23049614
[56] Liu H Wang YJ Yang L . Synthesis of a highly water-soluble acacetin prodrug for treating experimental atrial fibrillation in beagle dogs. Sci Rep. 2016;6 :25743.27160397
[57] Lim H Son KH Chang HW Bae K Kang SS Kim HP . Anti-inflammatory activity of pectolinarigenin and pectolinarin isolated from Cirsium chanroenicum. Biol Pharm Bull. 2008;31 :2063–7.18981574
[58] Yan H Wang H Ma L . Cirsimaritin inhibits influenza A virus replication by downregulating the NF-kappaB signal transduction pathway. Virol J. 2018;15 :88.29783993
[59] Lampronti I Dechecchi MC Rimessi A . Beta-Sitosterol reduces the expression of chemotactic cytokine genes in cystic fibrosis bronchial epithelial cells. Front Pharmacol. 2017;8 :236.28553226
[60] Jayaraman S Devarajan N Rajagopal P . beta-Sitosterol circumvents obesity induced inflammation and insulin resistance by down-regulating IKKbeta/NF-kappaB and JNK Signaling Pathway in Adipocytes of Type 2 Diabetic Rats. Molecules. 2021;26 :2101.33917607
[61] Kwon Y . Use of saw palmetto (Serenoa repens) extract for benign prostatic hyperplasia. Food Sci Biotechnol. 2019;28 :1599–606.31807332
[62] Yin Y Liu X Liu J . Beta-sitosterol and its derivatives repress lipopolysaccharide/d-galactosamine-induced acute hepatic injury by inhibiting the oxidation and inflammation in mice. Bioorg Med Chem Lett. 2018;28 :1525–33.29622518
[63] Yuk JE Woo JS Yun CY . Effects of lactose-beta-sitosterol and beta-sitosterol on ovalbumin-induced lung inflammation in actively sensitized mice. Int Immunopharmacol. 2007;7 :1517–27.17920528
[64] Brighenti E Calabrese C Liguori G . Interleukin 6 downregulates p53 expression and activity by stimulating ribosome biogenesis: a new pathway connecting inflammation to cancer. Oncogene. 2014;33 :4396–406.24531714
[65] Gudkov AV Gurova KV Komarova EA . Inflammation and p53: a tale of two stresses. Genes Cancer. 2011;2 :503–16.21779518
[66] Yeung YT Aziz F Guerrero-Castilla A Arguelles S . Signaling pathways in inflammation and anti-inflammatory therapies. Curr Pharm Des. 2018;24 :1449–84.29589535
[67] Tanaka T Narazaki M Kishimoto T . IL-6 in inflammation, immunity, and disease. Cold Spring Harb Perspect Biol. 2014;6 :a016295.25190079
[68] Shivappa N Hebert JR Rosato V . Inflammatory potential of diet and risk of oral and pharyngeal cancer in a large case-control study from Italy. Int J Cancer. 2017;141 :471–9.28340515
[69] Ge Q Chen L Tang M . Analysis of mulberry leaf components in the treatment of diabetes using network pharmacology. Eur J Pharmacol. 2018;833 :50–62.29782863
[70] West AP Brodsky IE Rahner C . TLR signalling augments macrophage bactericidal activity through mitochondrial ROS. Nature. 2011;472 :476–80.21525932
[71] Arranz A Doxaki C Vergadi E . Akt1 and Akt2 protein kinases differentially contribute to macrophage polarization. Proc Natl Acad Sci U S A. 2012;109 :9517–22.22647600
[72] Zhang H Liu L Jiang C Pan K Deng J Wan C . MMP9 protects against LPS-induced inflammation in osteoblasts. Innate Immun. 2020;26 :259–69.31726909
[73] Choudhary GS Al-Harbi S Almasan A . Caspase-3 activation is a critical determinant of genotoxic stress-induced apoptosis. Methods Mol Biol. 2015;1219 :1–9.25308257
[74] Chuntharapai A Kim KJ . Regulation of the expression of IL-8 receptor A/B by IL-8: possible functions of each receptor. J Immunol. 1995;155 :2587–94.7650389
[75] Hartl D Latzin P Hordijk P . Cleavage of CXCR1 on neutrophils disables bacterial killing in cystic fibrosis lung disease. Nat Med. 2007;13 :1423–30.18059279
[76] Samie A Dzhivhuho GA Nangammbi TC . Distribution of CXCR2 + 1208 T/C gene polymorphisms in relation to opportunistic infections among HIV-infected patients in Limpopo Province, South Africa. Genet Mol Res. 2014;13 :7470–9.25222246
[77] Kuiken T Riteau B Fouchier RA Rimmelzwaan GF . Pathogenesis of influenza virus infections: the good, the bad and the ugly. Curr Opin Virol. 2012;2 :276–86.22709515
[78] Pormohammad A Ghorbani S Khatami A . Comparison of influenza type A and B with COVID-19: a global systematic review and meta-analysis on clinical, laboratory and radiographic findings. Rev Med Virol. 2021;31 :e2179.33035373
[79] Wei M Li H Li Q . Based on network pharmacology to explore the molecular targets and mechanisms of Gegen Qinlian Decoction for the treatment of ulcerative colitis. Biomed Res Int. 2020;2020 :5217405.33299870
[80] Noack M Miossec P . Selected cytokine pathways in rheumatoid arthritis. Semin Immunopathol. 2017;39 :365–83.28213794
[81] Kashyap D Mittal S Sak K Singhal P Tuli HS . Molecular mechanisms of action of quercetin in cancer: recent advances. Tumour Biol. 2016;37 :12927–39.27448306
[82] Brown GD Willment JA Whitehead L . C-type lectins in immunity and homeostasis. Nat Rev Immunol. 2018;18 :374–89.29581532
[83] Li K Underhill DM . C-Type lectin receptors in phagocytosis. Curr Top Microbiol Immunol. 2020;429 :1–18.32060644
[84] Amatya N Garg AV Gaffen SL . IL-17 Signaling: the Yin and the Yang. Trends Immunol. 2017;38 :310–22.28254169
[85] McGeachy MJ Cua DJ Gaffen SL . The IL-17 family of cytokines in health and disease. Immunity. 2019;50 :892–906.30995505
