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Article

Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum

by
Jiaxue Wang
1,2,3,†,
Hui Tao
2,3,†,
Zhenlong Wang
2,3,
Wei An
2,3,
Ya Zhao
2,3,
Bing Han
2,3,
Jinquan Wang
2,3,
Xiuzhu Sun
1,* and
Xiumin Wang
2,3,*
1
College of Grassland Agriculture, Northwest A&F University, Xianyang 712100, China
2
Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2024, 14(21), 9687; https://doi.org/10.3390/app14219687
Submission received: 16 September 2024 / Revised: 16 October 2024 / Accepted: 21 October 2024 / Published: 23 October 2024
(This article belongs to the Section Agricultural Science and Technology)
Browse Figures
Versions Notes

Abstract

:
Cirsium japonicum, a traditional herb, exhibits significant anti-inflammatory activity. However, the main components and potential mechanisms of C. japonicum remain unclear. The aim of this study is to investigate the anti-inflammatory mechanism of Cirsium japonicum through network pharmacology and cellular experiments. The effective components of and potential targets for the anti-inflammatory activity of C. japonicum were identified using a traditional Chinese medicine systematic pharmacology database, the TCMSP analysis platform, and the GeneCards database. The drug–component–target–disease network diagram was constructed using Cytoscape 3.8.0 software, while the protein interaction network diagram was created using the STRING database and Cytoscape 3.8.0 software. Gene ontology (GO) enrichment and KEGG pathway enrichment analysis were carried out using the DAVID database. Molecular docking between key targets and active components was constructed with AutoDock 4.2.6 software to determine the best binding target. The results revealed that 14 active components of C. japonicum targeted 171 anti-inflammatory proteins. GO function enrichment analysis yielded 173 items, while KEGG pathway enrichment analysis identified 48 signaling pathways related to inflammation regulation. Molecular docking showed a strong affinity of sitosterol, stigmasterol, and other components with key targets such as peroxisome proliferator-activated receptor α recombinant protein (PPARA) and cyclooxygenase-2 (PTGS2). Vanillin, one active ingredient of C. japonicum, inhibited the release of lipopolysaccharide (LPS)-induced inflammatory factors in RAW264.7 cells. These findings suggest that C. japonicum may exert its anti-inflammatory effects by modulating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signal pathway (PI3K-Akt) and apoptin signal pathway, highlighting the multi-component, multi-target, and multi-channel molecular mechanism underlying its anti-inflammatory properties. Finally, the anti-inflammatory effect of vanillin, an effective component of C. japonicum, was verified by cell experiments. This study provides a new understanding of the pharmacological mechanisms of C. japonicum in the treatment of inflammatory conditions.

1. Introduction

The traditional herb Cirsium japonicum (Radix Cirsii Japonici) is widely distributed in most regions of China, as well as in North Korea and Japan, typically growing along roadsides, in grassy areas, and in fields [1]. C. japonicum has been widely used for the treatment of hemorrhagic fever, hepatitis, coagulation disorders, and urinary tract disorders [2,3,4,5,6]. This plant has been the subject of some scientific investigation, particularly regarding its phytochemistry and potential health benefits. Several studies have demonstrated that the aqueous extract of C. japonicum exhibits effective therapeutic potential against infectious jaundice and chronic hepatitis [7]. Additionally, it displays inhibitory activity against Mycobacterium tuberculosis, Neisseria meningitidis, Mycobacterium diphtheriae, Staphylococcus aureus, Mycobacterium typhi, Mycobacterium paratyphi, and Mycobacterium anthracis [8]. Furthermore, it has been found to ameliorate metabolic disorders associated with the steatohepatic conditions induced by high-fat diets [9]. Aqueous, alkaline, and acidic alcoholic extracts of C. japonicum, as well as aqueous extracts of its leaves, have demonstrated antihypertensive effects [10]. Clinical reports have also documented the use of root tablets for hypertension treatment [11]. Additionally, C. japonicum has been reported to enhance immunity, promote lipid metabolism, exhibit diuretic and hepatoprotective properties, enhance ethanol metabolizing enzyme activity, and reduce lipid peroxidation [12]. Furthermore, it also possesses anti-inflammatory, antioxidant, and antitumor activity [13,14,15]. Another study demonstrated that C. japonicum flower has potent anti-cancer efficacy by inactivating Akt1 in the phosphoinositide 3-kinase/Akt (PI3K/Akt) signaling pathway, which reduces the severity of inflammation in mice and inhibits the expression of apoptosis-related protein [16,17]. However, the main components of C. japonicum and the potential mechanisms of its anti-inflammatory activities remain unclear.
The rapid advancement of systems biology, bioinformatics, and pharmacology has led to network-based drug discovery becoming a promising approach for developing effective medications. In 2007, Hopkins et al. introduced the concept of “network pharmacology”, which utilizes systematic biology to analyze drug intervention and potential therapeutic targets for diseases [18,19,20,21,22,23]. Network pharmacology emphasizes a shift from the traditional “one target, one drug” strategy to a novel “network target, multi-component” strategy [24]. In the field of traditional Chinese medicine research, it is widely employed due to its holistic and systematic nature, which aligns with the principles of traditional Chinese medicine prescriptions [25,26,27]. Molecular docking is also extensively utilized in material basis research on traditional Chinese medicine as a computer-aided drug design method that relies on the interactions and affinities between targets and active compounds [28,29,30,31,32,33].
Network pharmacology employs network methods to analyze the intricate interplay among drugs, diseases, and targets, while also investigating the synergistic effects of multiple components on diseases. This approach closely aligns with the theoretical framework of holistic concepts and diagnosis and treatment in Chinese medicine, serving as a contemporary scientific methodology for elucidating the material basis of traditional Chinese medicines’ efficacy and mechanisms of action. It facilitates the visualization analysis of compound–target–signaling pathways through multidisciplinary approaches such as systems biology, bioinformatics, and multi-omics linkage. To some extent, this analysis reveals the therapeutic effectiveness of each active ingredient in natural plants [34,35,36,37]. In recent years, network pharmacology has been widely used to predict the mechanism of action between natural active compounds and diseases.
In the present study, network pharmacology and molecular docking methods were used to investigate the anti-inflammatory action mechanism of active compounds from C. japonicum elucidating their synergistic actions through multiple targets and pathways. In addition, the anti-inflammatory effect of vanillin, a potent component of C. japonicum, was also assessed in mouse macrophages.

2. Materials and Methods

2.1. Prediction and Identification of Active Compounds and Potential Targets of C. japonicum

The active ingredient screening of C. japonicum was performed using TCMSP [38] (https://tcmsp-e.com, accessed on 27 September 2023), a comprehensive pharmacology database and analytical platform for traditional Chinese medicine. The selection criteria included an oral bioavailability (OB) threshold of ≥20% and a drug-like property (DL) threshold of ≥0.1, based on the parameter information and standards provided by TCMSP. Subsequently, the target names were converted into corresponding Gene symbols using the UniProt database (https://www.uniprot.org, accessed on 27 September 2023). Cytoscape 3.8.0b (Cytoscape Consortium, San Diego, CA, USA) software was employed to construct a network diagram illustrating the interactions between active compounds and targets in C. japonicum.

2.2. Construction of Networks and Pathway Analysis

The GeneCards database (https://www.genecards.org, accessed on 5 August 2024) was used to perform a search for relevant antimicrobial targets, while the keyword “Anti-inflammation” was employed to explore disease genes of significance. The drug–component–target–disease network diagram was constructed by intersecting the retrieved anti-inflammatory gene target with the active ingredient gene target of C. japonicum.
By importing the target genes corresponding to the active compounds of C. japonicum and the target genes of inflammation into the Draw Venn Diagram website, a Venn diagram was generated to identify common targets for disease treatments.
The anti-inflammatory targets of C. japonicum were imported into the STRING database (https://string-db.org, accessed on 26 July 2023) to construct protein interaction networks. Subsequently, the obtained data were visualized and analyzed using Cytoscape 3.8.0 software.
The potential anti-inflammatory targets of C. japonicum were retrieved and inputted into the DAVID database (https://david.ncifcrf.gov, accessed on 12 January 2024) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis. The identifiers were selected as “OFFICIAL-GENE-SYMBOL” and the species was specified as “Homo sapiens”. The selection of “Homo sapiens” aimed to predict the functional distribution of the targets. GO analysis included biological processes (BPs), cellular components (CCs), and molecular functions (MFs), with the top ten items chosen for each category to facilitate visualization. Additionally, KEGG signaling pathway analysis was visualized using the online tool OmicShare (https://www.omicshare.com, accessed on 18 October 2023).

2.3. Molecular Docking

The structural formulas of the active compounds of C. japonicum were downloaded from TCMSP. The top five active compounds with the highest degree values and the top five core targets were selected. Subsequently, their corresponding structures were acquired by querying the target names on the Uniprot website. Additionally, the PDB structural formulas of these targets were obtained from the RCSB PDB website. The active compounds underwent energy minimization using Chem3D 19.0 and were exported as ligand files in pdb format. In Discovery Studio 2019 (Accelrys, San Diego, CA, USA), receptor proteins underwent dehydrogenation and deligation processes before being hydrogenated again and exported as receptor files in MOL2 format.
The AutoDockTools-1.5.6 (The Scripps Research Institute, La Jolla, CA, USA) software was utilized to conduct molecular docking analysis between the active ingredient and the core target, aiming to determine the binding energy and validate the therapeutic potential of C. japonicum’s key active ingredient in inflammation treatment [39]. A lower binding energy indicates a more stable binding conformation and a higher probability of ligand–receptor interaction.

2.4. Verification of Anti-Inflammatory Activity of Active Compounds of C. japonicum in RAW264.7 Cells

The CCK-8 method was used to detect the cytotoxicity of vanillin (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China), one of the active compounds of C. japonicum. RAW264.7 cells (2.5 × 104 cells/mL, 100 μL) were added into 96-well plates and cultured at 37 °C in 5% CO2 for 24 h, and 100 μL vanillin (0.039, 0.078, 0.156, 0.313, 0.625, 1.25, 2.5, and 5.0 mg/mL) was added to each well. Wells containing only the culture medium served as the negative control. After incubation for 24 h, 10 μL CCK-8 was added into each well and incubated for another 2 h. The absorbance of each well was measured at 450 nm and the survival rate of cells was calculated as follows: Cell viability (%) = [(As − Ab)/(Ac − Ab)] × 100%, where As represents the experimental group, Ac represents the control group, and Ab represents the blank group.
To determine the cytotoxicity of vanillin, after overnight culture in a 6-well plate (1 × 105 cells/well, 2 mL medium/well), the cells were pre-treated with vanillin (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China), one of the active compounds of C. japonicum, for 3, 6, 9, and 12 h, respectively. This was followed by lipopolysaccharide (LPS) treatment for an additional 24 h. At the end of the scheduled experiments, the culture supernatant was collected from each well and used to determine the inflammatory cytokines, including tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), interleukin 1β (IL-1β), and interleukin 10 (IL-10), respectively. They were measured using commercial assay kits (Rpworld (Beijing) Co., Ltd., Beijing, China) according to the manufacturer’s instructions. Absorbance at a wavelength of 520 nm was then measured using a microplate reader.

2.5. Statistical Analysis

Data from cell experiments were analyzed using GraphPad Prism 9 (GraphPad Software, Inc., Solana Beach, CA, USA) statistical software, and the results are presented as means ± standard deviations. Statistical analysis was conducted using analysis of variance (ANOVA), and differences between groups were assessed using the least significant difference test. A p-value of <0.05 was considered statistically significant.

3. Results

3.1. Identification of Active Compounds in C. japonicum and Prediction of Their Molecular Targets

A total of 18 active compounds were obtained from the TCMSP database with an OB of ≥20% (Table 1). Among them, 14 active compounds were found to have corresponding targets, resulting in the identification of 171 targets. However, rhodopsin, β-amyl acetate, cyclopropane, and pectin did not show any corresponding targets. To convert these identified targets into gene names specific to Homo Sapiens, a search was conducted using “Homo Sapiens” as the keyword in the UniProt database. After removing duplicate genes from the results obtained, a total of 85 unique genes remained. The interaction between active compounds and their respective targets in C. japonicum were visualized using Cytoscape 3.8.0 software (Figure 1). To identify target genes associated with inflammation, searches were performed using “Inflammation” as a keyword in both the OMIM and GeneCards databases. This filtering process resulted in the identification of a total of 2924 disease-related targets.

3.2. Screening of Potential Anti-Inflammatory Targets of C. japonicum

A search in the GeneCards database yielded 1809 identified inflammatory targets. Through an intersection analysis between the antibacterial and active ingredient targets of C. japonicum, visualized using a Venn diagram generated by the Draw Venn Diagram (ugent.be) online tool, we identified a total of 32 potential anti-inflammatory targets (Figure S1). Subsequently, they were imported into Cytoscape 3.8.0 software to draw the drug–ingredient–target–disease network diagram (Figure 2).

3.3. Construction of Protein–Protein Interaction (PPI) Network and Results of Core Gene Screening

The 32 anti-inflammatory genes identified were input into the STRING online database, resulting in the acquisition of a PPI network map consisting of 29 anti-inflammatory genes after excluding unrelated genes (Figure S2), while network analyzer function analysis was employed to identify potential key targets associated with C. japonicum’s anti-inflammatory properties, such as PPARG, PTGS2, BDNF, GCG, and PPARA. This identification was made by evaluating the Closeness, Betweenness, and Degree values.

3.4. GO and KEGG Enrichment Function Analysis

A total of 32 common targets of the active compounds of C. japonicum and inflammation were subjected to GO enrichment analysis using the David database. This analysis revealed 20 cellular components, including the extracellular region, extracellular space, and extracellular exosome. Furthermore, this analysis identified 23 molecular processes that were significantly enriched, primarily involving heme binding, peroxidase activity, protein binding, and prostaglandin binding. Exosome-related processes were also observed. Moreover, a total of 130 biological processes were discovered through this analysis, encompassing the regulation of blood pressure, inflammatory response, prostaglandin biosynthesis, nitric oxide synthesis, and nitric oxide-mediated signal transduction (Figure 3).
KEGG enrichment analysis further revealed the involvement of various pathways, including the relaxin signaling pathway, AGE-RAGE signaling pathway in diabetic complications, pathways associated with neurodegeneration—multiple diseases, HIF-1 signaling pathway, etc. (Figure 4).

3.5. Molecular Docking Validation

Four active compounds (oleic acid, methyl linoleate, leguminol, and sterol) were subjected to molecular docking with six key targets (PPARG, PTGS2, BDNF, GCG, PPARA, and EDN1), and the binding energies after docking are presented in Table S1. Lower AutoDock docking scores indicate a higher ability of the molecules to bind to the targets compared to the target itself. A lower AutoDock score signifies a stronger binding ability between the molecule and the target, as well as a reduced energy requirement for binding. It is evident that certain active components of C. japonicum (such as sterol and stigmasterol) exhibit a robust affinity towards several key targets, including PPARA, PPARG, PTGS2, and EDN1. Molecular docking results demonstrate that sitosterol binds to the active sites of PPARA and PTGS2 proteins while forming hydrogen bond interactions with ASN-299, THR-60 and LYS-546 (Figure 5a,b). Vanillin binds to the active sites of BDNF and PPARA proteins while forming hydrogen bond interactions with ARG-88, TY-52, ARG-87, ARG-409, and ASP-419 (Figure 5c,d). Stigmasterol binds specifically to the active site of PPARA protein while forming a hydrogen bond interaction with GLY-296 (Figure 5e).

3.6. Effect of Vanillin on Inflammatory Factors in RAW264.7 Cells Challenged with LPS

The cytotoxicity of vanillin was not observed at low concentrations (0.039–0.313 mg/mL) (Figure S3). However, at higher concentrations of 0.625, 1.25, and 2.5 mg/mL, the cell survival rates of RAW 264.7 treated with vanillin were 86%, 21.33%, and 5.8%, respectively. Therefore, concentrations of 0.1, 0.2, and 0.4 mg/mL of vanillin were selected for anti-inflammatory experiments.
The levels of inflammatory factors, such as TNF-α, IL-6, IL-1β, and IL-10, were assessed using competitive enzyme-linked immunosorbent assay (ELISA). In comparison to the blank control group, the levels of TNF-α, IL-6, and IL-1β in the model group were significantly increased (p < 0.005), while IL-10 showed a significant decrease (p < 0.01) (Figure 6). These findings indicate a substantial alteration in the levels of inflammatory cytokines within the cells due to LPS-induced inflammation. However, upon the addition of vanillin, there was a notable reduction in TNF-α, IL-1β, and IL-6 levels within the cells (p < 0.005) (Figure 6a–c). Notably, 0.2 mg/mL of vanillin demonstrated the most effective inhibition of IL-1β (p < 0.0001), while 0.05 mg/mL of vanillin displayed optimal inhibitory effects on both IL-6 and TNF-α; furthermore, 0.2 mg/mL of vanillin significantly increased the concentration of IL-10 (Figure 6d).

4. Discussion

C. japonicum, a widely accessible and cost-effective Chinese herbal medicine, possesses various functions such as bacteriostasis, anti-inflammation, blood coagulation and hemostasis, blood pressure reduction, and anti-tumor effects [40]. One study has shown that the combined treatment of Aralia elata and C. japonicum has anti-inflammatory effects on RAW 264.7 cells and demonstrates protective effects against dextran sulfate sodium (DSS)-induced colitis in mice and acetic acid-mediated colitis in dogs [41]. However, the precise mechanism underlying its anti-inflammatory action remains unclear. This study employs network pharmacology and molecular docking to investigate the anti-inflammatory effects of C. japonicum, followed by in vitro experimental verification at the cellular level.
Flavonoids, widely distributed in Cirsium, are also the most abundant component and the main active ingredient of Cirsium. They possess various biological activities, such as anti-oxidation, anti-tumor effects, anti-inflammation, and liver, cardiovascular, and cerebrovascular protection [42,43,44]. In this study, 14 effective components of C. japonicum were obtained from the TCMSP website, primarily including methyl linoleate, oleic acid, stigmasterol, and sitosterol. Previous studies have demonstrated that stigmasterol exhibits potent anti-cancer properties, while also showing effects against osteoarthritis and inflammation. Moreover, it displays potent activity against parasites, fungi, and bacteria, while also exhibiting immunomodulatory and neuroprotective effects through its antioxidant properties [45,46,47,48,49,50]. One study conducted by Feng et al. [51] has revealed that stigmasterol significantly inhibits colon shortening and reduces colitis severity by suppressing pro-inflammatory IL-1β, IL-6, and cyclooxygenase-2 (COX-2) monocyte chemotactic protein release. Additionally, stigmasterol improves intestinal function and regulates fat metabolism to alleviate hepatic steatosis in rats by fortifying the intestinal barrier and enhancing bile acid metabolism [52]. β-sitosterol, a phytosterol with anti-inflammatory properties, exerts regulatory effects on blood glucose metabolism [53]. Xiao et al. [54] demonstrated that β-sitosterol reduces serum TNF-α levels in rats, thereby decelerating the progression of gastric mucosa damage through a decreased release and aggregation of inflammatory factors within the gastric mucosa. Moreover, the combination of β-sitosterol and aspirin can enhance the anti-inflammatory efficacy of aspirin [55].
Based on network pharmacology, we conducted a systemic analysis on the active components, targets, related pathways, and biological processes of C. japonicum. Through analyzing the relevant database of network pharmacology, a total of 14 active components and their corresponding 171 gene targets were identified for C. japonicum. A drug–component–target–disease network diagram was constructed to reveal that 14 effective active components in C. japonicum can synergistically act on 32 anti-inflammatory targets (Figure 2). The 14 anti-inflammatory components primarily consist of methyl linoleate, oleic acid, β-starch acetate, and sterols (such as stigmasterol sitosterol). Among them, oleic acid can inhibit the LPS-induced inflammatory reaction by down-regulating the expression of the nuclear factor kappa-B (NF-κB) signaling pathway [56]. Methyl linoleate effectively inhibits the expression of IL-1β in THP-1 cells.
In the PPI network visualization analysis, ADRB2, PTGS2, NOS3, BDNF, and PPARG have been identified as potential core targets for the anti-inflammatory effects of C. japonicum (Figure S1). Among them, ADRB2 is a crucial β2-adrenergic receptor involved in maintaining hepatocellular carcinoma cell proliferation and survival. Additionally, it has been found to attenuate osteoarthritis-like defects in temporomandibular joints when conditionally detected in mice [57,58]. PTGS2, also known as COX-2 enzyme, plays a significant role in the inflammatory response by catalyzing arachidonic acid conversion to prostate H2, which triggers the inflammatory cascade [59]. The expression of PTGS2 is regulated by various stress-related factors and serves as an important regulator; up-regulated PTGS2 significantly contributes to inflammation regulation through glucagon production [60]. Nitric oxide (NO) plays a crucial role in regulating various aspects of vascular function, including smooth muscle cell proliferation and migration, vascular tone, endothelial permeability, and endothelial–leukocyte interactions. It serves as a key anti-atherogenic factor in the endothelium [61]. Endothelial-type nitric oxide synthase 3 (NOS3), encoded by a gene located on chromosome 7q35-36, is responsible for maintaining vascular homeostasis and regulating endothelial function. NOS3 genetic polymorphisms have been demonstrated to exert an impact on NO levels and lipid profiles, and they are associated with hypertension [62], as well as with diabetic foot ulcers [63]. BDNF represents a crucial class of neurotrophic factors that play an essential role in regulating neuronal proliferation, differentiation, maturation, and pro-neuronal regeneration; it constitutes a fundamental factor in ongoing depression research [64]. BDNF and its receptor, tyrosine kinase receptor B (TrκB), have been implicated in the pathogenesis of various neurological disorders [65]. Furthermore, activation of the BDNF/TrκB signaling pathway has shown potential for ameliorating memory deficits in rats with Alzheimer’s disease [66]. PPARG belongs to the nuclear transcription factor superfamily as a subtype of peroxisome proliferator-activated receptor and has been demonstrated to mitigate inflammatory responses by inhibiting the NF-κB signaling pathway, making it a promising therapeutic molecular target for diverse malignant tumors [67].
In GO and KEGG pathway analyses of 32 targets related to anti-inflammatory effects of C. japonicum, it was revealed that the biological processes primarily involved in C. japonicum include response to LPS, negative regulation of cellular regulatory processes, response to hypoxia, negative regulation of macrophage-derived foam cell differentiation, and response to activity (Figure 3). The anti-inflammatory activity of C. japonicum mainly involves signaling pathway processes, such as the relaxin signaling pathway, arginine biosynthesis pathway, prostate cancer pathway, AGE-RAGE pathway in diabetic complications, apocynin signaling pathway, and PI3K-Akt signaling pathway (Figure 4). Among these pathways, the PI3K/Akt signaling pathway is a crucial intracellular mechanism that responds to extracellular signals and regulates various cellular and molecular functions, including metabolism, survival, growth, and angiogenesis. Its involvement in gastritis has also gained significant attention in recent years due to its role in cell growth, proliferation, apoptosis, as well as blood glucose regulation [68]. Several studies have demonstrated that cytokines such as TNF-α and IL-6 can attenuate inflammatory responses by modulating the PI3K-Akt signaling pathway [69]. Molecular docking results further confirmed the ability of active compounds from C. japonicum, including sitosterol, vanillin, stigmasterol, etc., to bind key targets (such as BNDF, PPARA, PPARA, etc.) and form hydrogen bonding interactions (Figure 5). Notably, vanillin exhibited a higher binding affinity than oleic acid with binding energies ranging from −5.54 to −3.35 kal (Table S1). Moreover, vanillin effectively suppressed the expression of pro-inflammatory factors (such as IL-6 and IL-1β) in mouse macrophages while promoting the expression of anti-inflammatory factor IL-10 (Figure 6). These findings suggest that C. japonicum possesses significant preventive and therapeutic potential against LPS-induced inflammation in mouse macrophages.
Certainly, the utilization of network pharmacology for analysis provides only theoretical insights. Due to the necessity of simplifying biological processes, it may not fully capture the intricate interactions within an organism. Consequently, this can lead to an inadequate comprehension of the mechanisms of action and to potential oversights of the dynamic changes and temporal factors in biological processes, resulting in misinterpretations regarding their effects [70]. Simultaneously, while molecular docking identifies known binding sites, practical applications may encounter variations or multiple binding sites for a target, which can affect the accuracy of docking results [71]. Notably, although there are limited reports on the presence of vanillin in C. japonicum, a few studies indicate that a significant amount of vanillic acid is found in this plant, necessitating the use of liquid chromatography–mass spectrometry (LC-MS) analysis. Previous research has demonstrated that this phenomenon may be attributed to the solvents employed during the extraction process. Some studies suggest that commonly used solvents such as acetone, methanol, and sulfuric acid can induce the oxidation of vanillin into vanillic acid, resulting in the detection of vanillic acid rather than vanillin as the final compound [72,73,74,75], which requires further experimental verification.

5. Conclusions

Network pharmacology analysis showed that 14 active compounds of C. japonicum targeted 171 anti-inflammatory proteins, including ADRB2, PTGS2, NOS3, BDNF, and PPARG. Moreover, it was found that C. japonicum has the potential to modulate the PI3K-Akt and apoptogen signaling pathways in inflammation regulation. Notably, one of the active compounds of C. japonicum exhibited remarkable anti-inflammatory activity in macrophages. The anti-inflammatory effects of this compound and its potential synergistic interactions with other compounds require further in vivo exploration or validation in the future. This study provides a theoretical and scientific basis for further understanding the anti-inflammatory mechanism of C. japonicum, as well as its potential development and application.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/app14219687/s1, Figure S1: Venn diagram of intersecting targets between anti-inflammatory genes and active compounds of C. japonicum; Table S1: Binding energies of key anti-inflammatory protein targets docked to active compounds of C. japonicum; Figure S2: Protein–protein interaction (PPI) network analysis; Figure S3: Cytotoxicity of vanillin against RAW 264.7 cells.

Author Contributions

Conceptualization, J.W. (Jiaxue Wang) and X.W.; Methodology, H.T., Z.W., Y.Z. and B.H.; Software, W.A.; Writing—original draft preparation, J.W. (Jiaxue Wang); Writing—review and editing, J.W. (Jiaxue Wang) and X.S.; Visualization, J.W. (Jiaxue Wang); Supervision, X.W. and J.W. (Jinquan Wang); Project administration, J.W. (Jiaxue Wang); Funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work has received funding support from the program of the Third Xinjiang Scientific Expedition Program (Grant No. 2022xjkk0404), supported by the Ministry of Science and Technology of the People’s Republic of China, 2022–2025.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are contained within the manuscript and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Active ingredients—target network diagram. Red represents: active ingredients; purple represents: target.
Figure 1. Active ingredients—target network diagram. Red represents: active ingredients; purple represents: target.
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Figure 2. Drug–ingredient–target–disease network diagram. Orange represents: C. japonicum; green represents: pathway; sky blue represents: ingredient; dark blue represents: target; purple represents: disease.
Figure 2. Drug–ingredient–target–disease network diagram. Orange represents: C. japonicum; green represents: pathway; sky blue represents: ingredient; dark blue represents: target; purple represents: disease.
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Figure 3. GO enrichment analysis.
Figure 3. GO enrichment analysis.
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Figure 4. KEGG enrichment analysis and key pathway network. Each data point represents a KEGG pathway, with the corresponding pathway names indicated on the left side of the axis. The x-axis displays the enrichment factor, which quantifies the ratio of differentially expressed proteins annotated to that specific pathway compared to all annotated proteins in the species. A higher enrichment factor signifies a more robust indication of significant enrichment for differential proteins within that particular pathway.
Figure 4. KEGG enrichment analysis and key pathway network. Each data point represents a KEGG pathway, with the corresponding pathway names indicated on the left side of the axis. The x-axis displays the enrichment factor, which quantifies the ratio of differentially expressed proteins annotated to that specific pathway compared to all annotated proteins in the species. A higher enrichment factor signifies a more robust indication of significant enrichment for differential proteins within that particular pathway.
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Figure 5. Molecular docking patterns of key targets and specific active components of C. japonicum. (a) Sitosterol and PPARA (AutoDock Docking score: −8.67); (b) sitosterol and PTGS2 (score: −6.09); (c) vanillin and BDNF (score: −5.54); (d) vanillin and PPARA (score: −4.82); (e) stigmasterol and PPARA (score: −8.2).
Figure 5. Molecular docking patterns of key targets and specific active components of C. japonicum. (a) Sitosterol and PPARA (AutoDock Docking score: −8.67); (b) sitosterol and PTGS2 (score: −6.09); (c) vanillin and BDNF (score: −5.54); (d) vanillin and PPARA (score: −4.82); (e) stigmasterol and PPARA (score: −8.2).
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Figure 6. Effects of vanillin on inflammatory factors in RAW264.7 cells stimulated by LPS. Mouse macrophages were divided into five groups: CON group, LPS group, LPS + 0.4 mg/mL vanillin group, LPS + 0.2 mg/mL vanillin group, and LPS + 0.1 mg/mL vanillin group. After 24 h of DMEM culture, vanillin was added into cells stimulated by LPS (1 μg/mL) after 3 h, and the supernatants and proteins were taken after 24 h. (a–d) The expressions of cytokines. (a) IL-6; (b) IL-1β; (c) TNF-α; (d) IL-10. The levels of IL-6, IL-1β, TNF-α, and IL-10 were detected by ELISA kit. The data are expressed by the average standard deviation (n = 3). *: p < 0.05; **: p < 0.005; ***: p < 0.001; ****: p < 0.0001.
Figure 6. Effects of vanillin on inflammatory factors in RAW264.7 cells stimulated by LPS. Mouse macrophages were divided into five groups: CON group, LPS group, LPS + 0.4 mg/mL vanillin group, LPS + 0.2 mg/mL vanillin group, and LPS + 0.1 mg/mL vanillin group. After 24 h of DMEM culture, vanillin was added into cells stimulated by LPS (1 μg/mL) after 3 h, and the supernatants and proteins were taken after 24 h. (a–d) The expressions of cytokines. (a) IL-6; (b) IL-1β; (c) TNF-α; (d) IL-10. The levels of IL-6, IL-1β, TNF-α, and IL-10 were detected by ELISA kit. The data are expressed by the average standard deviation (n = 3). *: p < 0.05; **: p < 0.005; ***: p < 0.001; ****: p < 0.0001.
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Table 1. Screening of active compounds of C. japonicum.
Table 1. Screening of active compounds of C. japonicum.
Molecular IDMolecule NameOB (%)DL
MOL001641Methyl linoleate41.930.17
MOL001735Dinatin30.970.27
MOL001749ZINC0386043443.590.35
MOL002032DNOP40.590.4
MOL002879Diop43.590.39
MOL003180Widdrene53.810.12
MOL003344β-amyrin acetate42.060.74
MOL000359Sitosterol36.910.75
MOL000449Stigmasterol43.830.76
MOL004746(E,7S,11R)-3,7,11,15-tetramethylhexadec-2-en-1-ol49.630.13
MOL000057DIBP51.870.13
MOL005736Cyperene50.350.11
MOL005840PANA41.170.13
MOL005842Pectolinarigenin47.620.3
MOL005846Pectolinarin43.080.65
MOL000612(-)-Alpha-cedrene55.560.1
MOL000675Oleic acid33.130.14
MOL000635Vanillin51.99-
MOL000676DBP64.540.13
-: no data.
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MDPI and ACS Style

Wang, J.; Tao, H.; Wang, Z.; An, W.; Zhao, Y.; Han, B.; Wang, J.; Sun, X.; Wang, X. Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum. Appl. Sci. 2024, 14, 9687. https://doi.org/10.3390/app14219687

AMA Style

Wang J, Tao H, Wang Z, An W, Zhao Y, Han B, Wang J, Sun X, Wang X. Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum. Applied Sciences. 2024; 14(21):9687. https://doi.org/10.3390/app14219687

Chicago/Turabian Style

Wang, Jiaxue, Hui Tao, Zhenlong Wang, Wei An, Ya Zhao, Bing Han, Jinquan Wang, Xiuzhu Sun, and Xiumin Wang. 2024. "Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum" Applied Sciences 14, no. 21: 9687. https://doi.org/10.3390/app14219687

APA Style

Wang, J., Tao, H., Wang, Z., An, W., Zhao, Y., Han, B., Wang, J., Sun, X., & Wang, X. (2024). Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum. Applied Sciences, 14(21), 9687. https://doi.org/10.3390/app14219687

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