1. Introduction
Acute pneumonia (AP) is an acute infection of the lung parenchyma caused by bacteria, viruses, fungi, protozoa, and various physicochemical factors,[1] and it affects about 150 million people globally every year. AP often has a rapid onset and can cause severe lung damage. If left untreated, AP can progress to acute respiratory distress syndrome and can lead to death. AP causes at least 2 million deaths globally, and more than eight hundred thousand of these deaths occur globally, mainly in children under the age of 5.[2–4]
So far, pneumonia is often treated with multiple antibiotics or hormones. However, this treatment causes irreversible damage to patients’ physical functions, such as pulmonary fibrosis, and even leads to the development of antibiotic-resistant bacteria.[5] The outbreak of the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has increased the incidence of AP and resulted in incalculable economic and population losses worldwide. Therefore, it is essential to identify more effective and less toxic treatments for AP.
Herbal medicine is a traditional Chinese medicine (TCM) that has been used to treat various medical conditions for over 2000 years.[6] Various herbal formulas, such as Jinhua Qingan Granules, Lianhua Qinwen Capsules, Huashi Baidu San, and Xuebi Jin, were found to be effective in preventing and treating COVID-19.[7]
The Ruhao Dashi formula (RDF, Patent No. 202010234178.8) is an experimental herbal formula composed of 6 herbs, Herba Elsholtziae (HE), Sweet worm Wood (SW), Lonicera Japonica (LJ), Magnolia Officinalis (MO), Rhizoma Atractylodis (RA), and Amomum Tsao-ko (AT). This treatment was found to have good antiinflammatory and antibacterial effects clinically. However, the complex composition of herbs found in RDF and their possible synergistic effects make it difficult to explain its mode of action in treating AP. Novel bioinformatics techniques such as network pharmacology and molecular docking could be used to understand the molecular pathways involved behind the mode of action of RDF and hence facilitate its development into a pharmacological drug. Network pharmacology uses high throughput screening to evaluate the complex relationships between the drug-target-gene-disease pathways to make a preliminary prediction on the mode of action of a specific drug.[8] Molecular docking is used to design drugs based on the interaction between receptors and drug molecules. This method is often used to simulate the binding mode and affinity of molecular interactions.[9]
In recent years, network pharmacology and molecular docking techniques have been widely used to study the mechanism of action of herbal remedies from TCM. Akt is a serine/threonine protein kinase that includes Akt1, Akt2, and akt3. Recent studies have shown that Akt is activated in a dose-dependent manner during SARS-CoV-2 infection. This suggests that Akt may be a possible therapeutic target for novel coronavirus pneumonia.[10] This inference was demonstrated by Xia et al who used network pharmacology and molecular docking techniques to identify Akt1 as a potential target for the treatment and prevention of novel coronavirus pneumonia with Lianhua Qingwen Capsules, providing a direction for subsequent research on Lianhua Qingwen Capsules for the treatment of pneumonia induced by COVID-19.[11] Phosphatidylinositol-4,5-bisphosphate 3-kinase/protein kinase B (PI3K/Akt) signaling pathway is an intracellular pathway of great importance in the cell cycle process, which is associated with cellular quiescence, proliferation, cancer, and longevity.[12–14] Tao et al found the key pathway PI3K/Akt signal pathway of Huashi Baidu formula against COVID-19 with the help of network pharmacology and molecular docking technology, providing a research basis for the treatment of COVID-19 in the later stage.[15]
Therefore, this study aimed to use network pharmacology and molecular docking to evaluate the drug-target-gene-disease pathways of RDF involved in the treatment of AP and to explore the potential role of these targets in developing new pharmacological treatments.
2. Materials and methods
The research process was divided, as shown in Figure 1. First, a search for drug components and targets related to RDF and AP was performed using established databases. Subsequently, protein-protein interaction (PPI) network analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to identify the potential molecular mechanisms behind RDF and to construct a network of “target pathways.” Finally, molecular docking was performed to visualize the interaction pattern between the active molecules and the target proteins.
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Figure 1.: The workflow of the study on the potential mechanisms of RDF for the treatment of AP is summarized and described. The active ingredients and corresponding targets of RDF were obtained from the TCMSP database. AP-related targets were downloaded from 3 different databases and common targets were identified by intersecting the ingredient targets with AP-related targets. The common targets were entered into the STRING database to construct PPI networks, and the Cytoscape software and CytoNCA plug-in were used to screen the PPI networks for core targets among the common targets. GO and KEGG enrichment analyses were performed to construct a “target-pathway” network. Finally, the potential targets of RDF for AP treatment were validated by molecular docking. AP = acute pneumonia, GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, PPI = protein-protein interaction, RDF = Ruhao Dashi formula, TCMSP = Traditional Chinese Medicine Systems Pharmacology.
2.1. Identification of relevant drug components and targets
The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and analysis platform (http://tcmspw.com/tcmsp.php) is a unique network pharmacology platform for TCM.[16,17] The TCMSP database was searched to identify the acting proteins of 6 components of RDF (HE, SW, LJ, MO, RA, and AT).[18,19] The target proteins were converted to gene names using the UniProt database (https://www.uniprot.org/), and the organism species was restricted to “Homo sapiens” for normalization.[20] A “drug-component-common target” network diagram was constructed using Cytoscape 3.9.0 (reference) and distributed according to the degree of Centrality (DC) values.
2.2. Acquisition of AP-related genes
The keyword “acute pneumonia” was used to identify the targets related to AP from the GeneCards (http://www.genecards.org/), OMIM (http://www.omim.org/), and TTD (http://db.idrblab.net/ttd/) databases.[21–23] The Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/) was used to draw a Venn diagram to illustrate the targets of the active ingredients within the RDF, the AP-related genes, as well as the screened common targets and their corresponding active ingredients. Cytoscape 3.9.0 was used to construct the “drug-component-target” network according to the distribution of the DC value.
2.3. Construction of the PPI network
The common AP-related targets and the active ingredients of RDF were inserted into the STRING database (https://string-db.org/) to identify additional core targets.[24] The minimum interaction score was set to 0.4. The results of the PPI analysis were subsequently exported and visualized using the Cytoscape software version 3.9.0. The CytoNCA plug-in was used to calculate the betweenness centrality, closeness centrality (Cc), DC, and the eigenvector centrality (EC) mean scores of the PPI network. The nodes with all 4 parameters above the mean scores were retained to construct the core PPI network.
2.4. GO and KEGG enrichment analysis
The Metascape database (http://metascape.org/) brings together more than 40 independent knowledge bases commonly used to integrate functional enrichment analysis, interaction analysis, gene registration, and membership search.[25] This database was therefore used to perform the KEGG functional enrichment analysis. A P value below .05, a minimum count of 3, and an enrichment factor of 1.5 were used to perform the analysis. GO analysis was performed to evaluate the biological functions potentially targeted by RDF, including the biological process (BP), molecular function (MF), and cellular component (CC). The top 20 pathways revealed by KEGG analysis and the top 10 pathways identified with the GO analysis were imported into the microbiotope network analysis platform for mapping.
2.5. Construction of “targets-pathways” network
A “target-pathway” network was constructed for the 31 core targets obtained from the first 20 pathways identified by the KEGG enrichment analysis. The Cytoscape software version 3.9.0 was used to construct and analyze the network topology parameters.
2.6. Molecular docking
The strength of the binding activity of the RDF targets to the receptors was compared with that of aspirin, a common nonsteroidal antiinflammatory drug used to treat AP. For this analysis, protein targets with a degree value greater than or equal to 10 in the “target-pathway” network and active ingredients with a degree value greater than or equal to 15 in the “drug -component-common target” network were selected for docking. The 3D structures of target proteins were downloaded from the RCSB protein database in PDB format (https://www.rcsb.org/), and the 2D structures of the active ingredients were downloaded from the PubChem database in SDF format (https://pubchem.ncbi.nlm.nih.gov/). The ChemBio 3D software calculate and export the 3D structure by minimizing energy. The PyMOL software version 2.4.0 was used to dehydrate the receptor protein, while the Autodock Tools software was used to carry out the protein hydrogenation and charge calculation. The number of docking times was set to 50.
The stability and strength of the ligand and receptor binding for both RDF and aspirin were calculated based on the binding energy. And the binding activity was assessed by calculating the minimum binding efficiency. General binding energy <−4.25 kcal/mol indicates that the ligand and the receptor can bind in the natural state, and binding energy <−5.0 kcal/mol indicates that the ligand has good binding activity with the receptor. And binding energy of <−7.0 kcal/mol indicates a solid binding activity between the ligand and the receptor.
Finally, Autodock Tools software was used to dock the receptor protein with the small molecule ligands of the RDF active compounds, and the PyMoL software version 2.4.0 was used to visualize and process the docked molecules.
3. Results
3.1. Screening of potential targets for RDF
A total of 50 active ingredients and 236 drug-related targets were retrieved from the TCMSP and UniProt databases. The “drug-ingredient-target” network is shown in Figure 2. The GeneCards, OMIM, and TTD databases revealed 2155, 169, and 6 AP-related target genes, respectively. After removing the duplicate genes and combining the search results, 2283 AP-related genes remained. Figure 3A shows the evaluation of the intersection of drug-related target genes and disease-related genes revealing 134 common target genes corresponding to 46 active ingredients. The “drug-component-common target” interaction network consists of 186 nodes and 619 edges, with target genes distributed according to the DC values, as shown in Figure 3B.
Figure 2.: Drug-the active ingredients-targets network of RDF. The orange inverted triangle represents the 6 herbs in RDF, and the green graph represents the active ingredients. The purple squares represent target genes, which are distributed according to DC values, the larger the DC value, the closer to the center. DC = degree of centrality, RDF = Ruhao Dashi formula.
Figure 3.: (A) Venn diagram of common targets of active ingredients and AP-related targets. After preliminary screening, 236 active ingredient targets, 2283 disease-related targets, and 134 common targets were identified. (B) Drug-ingredient-common targets network. Including 134 common targets and 46 active ingredients. The orange inverted triangle represents the 6 herbs in the RDF, the purple square represents the active ingredients in the RDF, and the green diamond represents the common targets, which are distributed according to DC values, the closer to the center the greater the DC value. AP = acute pneumonia, DC = degree of centrality, RDF = Ruhao Dashi formula.
3.2. PPI network and core subnetwork analysis
As shown in Figure 4, a PPI network with 134 nodes and 2108 edges was initially constructed using the STRING database. Figure 5 shows that after removing the nodes with betweenness centrality below 117.4, Cc below 0.5, DC below 31.5, and EC below 0.07, 33 nodes and 457 edges remained. The remaining nodes and edges were used to construct the core PPI network, as shown in Figure 6.
Figure 4.: Protein-Protein interaction (PPI) network. (A) PPI network exported from STRING database. (B) Annotations for the nodes and edges in the PPI network.
Figure 5.: Cytoscape software used to identify key subnets. The larger the DC value, the larger the point, the darker the color, and the closer to the center. DC = degree of centrality.
Figure 6.: Core PPI network. CytoNCA plug-in in Cytoscape used to filter PPI networks, select targets with DC, EC, CC, and BC larger than their average values, and construct core PPI networks. (A) Core target PPI networks exported from STRING database. (B) Core target PPI networks processed using Cytoscape software. The larger the DC value, the larger the points, the darker the color. The larger the DC value, the larger the points, the darker the color, and the more centered the position. BC = betweenness centrality, CC = cellular component, DC = degree of centrality, EC = eigenvector centrality, PPI = protein-protein interaction.
3.3. GO and KEGG enrichment analysis
The GO analysis revealed 976 BP, 40 CC, and 55 MF, as shown in Figure 7A. After sorting according to the degree of significance, the BPs and MFs related to the action of RDF on AP were identified. These BPs were involved in the regulation of oxygen levels, hypoxia, and positive regulation of cell death. On the other hand, the MFs were mainly involved in the enrichment of growth factor receptor binding, cytokine receptor binding, and cell proliferation. Several factors include growth factor receptor binding, cytokine receptor binding, transcription coregulator binding, core promoter sequence-specific DNA binding, DNA-binding transcription binding, cytokine activity, protein domain specific binding, transcription factor binding, signaling receptor activator activity, and protease binding enriched the MFs. The CCs were mainly enriched in membrane raft, microdomain, caveola, plasma membrane raft, and platelet alpha granule.
Figure 7.: (A) Histogram showing the results of GO enrichment analysis, including the top 10 terms in BP, MF, and CC, respectively. (B) Bubble diagram showing the 20 results of KEGG enrichment analysis (count > 10). BP = biological process, CC = cellular component, GO = gene ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, MF = molecular function.
A total of 126 enriched pathways were obtained by KEGG analysis, and 20 pathways were screened for gene counts above 10. Out of these 20 pathways, 16 were related to disease pathways, and 4 were related to inflammatory pathways. The disease pathways were related to cancer, human cytomegalovirus infection, fluid shear stress and atherosclerosis, microRNAs in cancer, lipid, and atherosclerosis, human papillomavirus infection, proteoglycans in cancer, Chagas disease, hepatitis C, Kaposi sarcoma-associated herpesvirus infection and others. The inflammation-related signaling pathways were PI3K-Akt, mitogen-activated protein kinase (MAPK), and interleukin 17 (IL-17), which is shown in Figure 7B. RDF could be used to relieve AP through these pathways.
3.4. Target-pathway network
Figure 8 shows the “target-pathway” network for treating AP and the network nodes’ calculated degree values. The final network consisted of 51 nodes, 20 pathways, 31 targets, and 234 edges. A more significant degree value indicates a larger node with more related connections. As shown in Table 1, the degree values of the IKBKB, AKT1, TNF, CASP3, TP53, CASP8, and NFKBIA genes were higher in the “target-pathway” network. Therefore, based on these findings, we concluded that RDF acted on 31 targets through these signaling pathways. Several pathways were interlinked through common targets, suggesting a synergistic effect of the RDF components in treating AP.
Table 1 -
Top 20 AP-related pathway enriched by 33 core target genes.
| ID |
Description |
Gene ratio |
P value |
P adjust |
q value |
Count |
| hsa05200 |
Pathways in cancer |
23/33 |
2.12419E−33 |
2.12419E−33 |
7.3497E−31 |
23 |
| hsa05163 |
Human cytomegalovirus infection |
14/33 |
7.79952E−22 |
7.79952E−22 |
8.99545E−20 |
14 |
| hsa05418 |
Fluid shear stress and atherosclerosis |
13/33 |
1.24104E−22 |
1.24104E−22 |
2.147E−20 |
13 |
| hsa05206 |
MicroRNAs in cancer |
12/33 |
3.23935E−16 |
2.12419E−33 |
8.00583E−15 |
12 |
| hsa05417 |
Lipid and atherosclerosis |
12/33 |
3.87194E−18 |
7.79952E−22 |
1.91385E−16 |
12 |
| hsa05165 |
Human papillomavirus infection |
12/33 |
7.11471E−16 |
7.79952E−22 |
1.53856E−14 |
12 |
| hsa05205 |
Proteoglycans in cancer |
12/33 |
2.16654E−18 |
2.29741E−19 |
1.24937E−16 |
12 |
| hsa04151 |
PI3K-Akt signaling pathway |
12/33 |
1.59053E−15 |
6.31551E−18 |
3.05735E−14 |
12 |
| hsa05142 |
Chagas disease |
11/33 |
6.81241E−20 |
7.79952E−22 |
5.89273E−18 |
11 |
| hsa05160 |
Hepatitis C |
11/33 |
9.21693E−18 |
7.79952E−22 |
3.18906E−16 |
11 |
| hsa05167 |
Kaposi sarcoma-associated herpesvirus infection |
11/33 |
9.8848E−17 |
7.79952E−22 |
2.63088E−15 |
11 |
| hsa04010 |
MAPK signaling pathway |
11/33 |
9.88436E−15 |
6.31551E−18 |
1.79999E−13 |
11 |
| hsa04657 |
IL-17 signaling pathway |
10/33 |
4.54971E−18 |
7.79952E−22 |
1.96775E−16 |
10 |
| hsa04625 |
C-type lectin receptor signaling pathway |
10/33 |
1.30435E−17 |
7.79952E−22 |
4.10279E−16 |
10 |
| hsa04668 |
TNF signaling pathway |
10/33 |
2.81252E−17 |
7.79952E−22 |
8.10944E−16 |
10 |
| hsa05161 |
Hepatitis B |
10/33 |
1.24038E−15 |
7.79952E−22 |
2.52453E−14 |
10 |
| hsa05166 |
Human T-cell leukemia virus 1 infection |
10/33 |
3.00296E−14 |
7.79952E−22 |
4.58249E−13 |
10 |
| hsa05132 |
Salmonella infection |
10/33 |
9.49849E−14 |
7.79952E−22 |
1.31459E−12 |
10 |
| hsa05215 |
Prostate cancer |
10/33 |
6.31551E−18 |
6.31551E−18 |
2.42796E−16 |
10 |
| hsa05010 |
Alzheimer disease |
10/33 |
7.01827E−12 |
1.62041E−14 |
5.64726E−11 |
10 |
AP = acute pneumonia, IL-17 = interleukin 17, TNF = tumor necrosis factor.
Figure 8.: Target-pathway network. The purple diamond represents the pathway and the blue square represents the target gene. the larger the DC value, the larger the shape and the darker the color. DC = degree of centrality.
3.5. Target-compounds docking analysis
Aspirin and 14 active ingredients with a degree-value greater than or equal to 15 in the “drug-component-common target” network, for a total of 15 ligand molecules. In addition, 11 targets with a degree of 10 or higher were selected as receptor molecules in the “target-pathway” network.
The molecular docking results in Table 2 show that the binding activity of the active ingredients of RDF to the receptor was significantly better than that of aspirin. The binding energy of beta-carotene to TNF, TP53, NFKBIA, and IL1B was the lowest among the 15 ligand molecules, indicating the most potent activity. The molecular docking patterns are illustrated in Figure 9.
Table 2 -
Docking results of core target proteins and core active components.
| Compound\Binding energy (kcal/mol) |
IKBKB (4KIK) |
AKT1 (1H10) |
TNF (1A8M) |
CASP3 (1CP3) |
TP53 (1AIE) |
CASP8 (3H11) |
NFKBIA (6Y1J) |
FOS (1A02) |
MYC (5I4Z) |
CTNNB1 (3TX7) |
1L1B (5R8E) |
| Quercetin |
−6.38 |
−5.86 |
−5.40 |
−6.17 |
−6.52 |
−7.50 |
−7.26 |
−7.66 |
−5.11 |
−4.83 |
−7.95 |
| Luteolin |
−6.60 |
−6.38 |
−5.42 |
−6.10 |
−6.63 |
−7.48 |
−7.99 |
−6.96 |
−5.47 |
−5.49 |
−8.25 |
| Kaempferol |
−6.70 |
−6.41 |
−5.66 |
−6.12 |
−6.25 |
−7.11 |
−7.47 |
−7.94 |
−5.17 |
−5.78 |
−7.79 |
| Wogonin |
−6.72 |
−5.95 |
−5.58 |
−5.76 |
−6.53 |
−7.01 |
−7.32 |
−6.86 |
−5.01 |
−5.40 |
−6.54 |
| Isorhamnetin |
−6.68 |
−6.08 |
−5.12 |
−6.24 |
−6.51 |
−5.98 |
−7.94 |
−8.07 |
−5.19 |
−5.81 |
−8.00 |
| Beta-sitosterol |
−6.75 |
−7.20 |
−5.66 |
−8.66 |
−8.20 |
−7.19 |
−8.66 |
−7.45 |
−6.95 |
−8.29 |
−9.23 |
| Beta-carotene |
−7.63 |
−7.34 |
−8.96 |
−8.62 |
−10.26 |
−8.63 |
−9.66 |
−8.47 |
−6.91 |
−7.91 |
−9.90 |
| Hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone |
−6.68 |
−5.81 |
−6.28 |
−6.15 |
−6.24 |
−5.65 |
−7.06 |
−8.11 |
−4.83 |
−5.79 |
−7.76 |
| Stigmasterol |
−8.01 |
−7.67 |
−8.04 |
−8.95 |
−8.45 |
−7.53 |
−8.87 |
−8.04 |
−7.56 |
−9.30 |
−9.53 |
| Acacetin |
−6.54 |
−6.12 |
−7.51 |
−6.20 |
−6.17 |
−7.80 |
−8.00 |
−7.88 |
−4.90 |
−6.56 |
−7.25 |
| Moslosooflavone |
−6.83 |
−5.90 |
−6.68 |
−5.90 |
−6.63 |
−7.34 |
−7.43 |
−7.58 |
−5.05 |
−5.94 |
−6.80 |
| 3β-acetoxyatractylone |
−6.18 |
−6.28 |
−7.06 |
−6.32 |
−5.98 |
−7.18 |
−6.38 |
−6.27 |
−5.67 |
−6.92 |
−7.24 |
| Artemetin |
−5.84 |
−5.49 |
−6.15 |
−5.53 |
−6.65 |
−6.45 |
−7.40 |
−5.55 |
−4.68 |
−5.69 |
−7.67 |
| Eucalyptol |
−6.94 |
−6.87 |
−8.69 |
−6.93 |
−6.69 |
−8.26 |
−7.22 |
−7.93 |
−6.03 |
−7.51 |
−7.46 |
| Aspirin |
−4.30 |
−7.19 |
−8.49 |
−5.73 |
−5.54 |
−6.79 |
−5.82 |
−4.63 |
−5.39 |
−5.45 |
−5.58 |
The lower the binding energy and the redder the color, the better the binding activity.
TNF = tumor necrosis factor.
Figure 9.: Molecular docking patterns. The yellow lines represent the hydrogen bond interaction force, which is the main force promoting molecule binding with the active site.
4. Discussion
According to studies, the 6 herbs in the RDF have shown good pharmacological and therapeutic effects regarding antibacterial, antiinflammatory, and antiviral properties. The volatile oils, flavonoids, and terpenoids contained in HE have convincing antibacterial, antiviral, and antiinflammatory activities.[26,27] The apigenin and luteolin in HE inhibited Enterovirus 71 infection.[28] In addition apigenin-7-O-glucoside based on apigenin also showed inhibitory activity against the Hepatitis C virus.[29] Artemisinin and Artemisia amber from SW are used in research for the treatment of diseases such as malaria, rheumatoid arthritis, systemic lupus erythematosus, and allergic contact dermatitis. The World Health Organization recommends it as a first-line treatment for multi-drug-resistant malaria.[30,31] Chlorogenic and isochlorogenic acids in LJ inhibit microbial activity in vitro and in vivo.[32] Honokiol and Magnolol in MO have significant antiviral activity against murine norovirus and feline calicivirus.[33] The atractylone in RA has a good inhibitory effect on the influenza virus H1N1.[34,35] The active substance of AT is mainly the volatile oil it contains.[36] In recent years, a large number of studies have shown that AT has various pharmacological effects, such as sedation, antibacterial, and immunity enhancement.[37,38] In conclusion, all 6 herbs of RDF have significant pharmacological activities and have surprising effects in antiinflammatory, antibacterial, and antiviral aspects.
AP is a serious medical problem for which effective and sustainable therapeutic approaches are essential. Chinese herbal medicine is an effective means of treating the disease, but detailed mechanisms cannot be fully elucidated due to the complexity of herbal components and the synergistic effects among them. Network pharmacology and molecular docking techniques are often used to reveal the mechanisms of herbal components associated with diseases and to discover new active compounds from herbal formulations. Therefore, we performed network pharmacology and molecular docking analysis to explore the active compounds in RDF, the core target genes, and the potential mechanisms of RDF in the treatment of AP.
In this study, a total of 46 active ingredients with 134 common targets were obtained from multiple databases collected and screened. Today, there is widespread interest in natural compounds with high therapeutic value, low toxicity, and a wide range of pharmacological activities.[39–43] In this study, we found that the ingredient with the most relevant genes in the “drug-component-common target” network is quercetin. Quercetin is a major flavonol that is widely found in plants and has various biological functions such as antioxidant, antiinflammatory and antiviral.[44] It has been shown that quercetin may exert antiinflammatory and antioxidant effects by interfering with related signal transduction pathways (including AP-1 and NF-κB).[45] Moreover, Sul et al found that quercetin could prevent lipopolysaccharide (LPS)-induced oxidative stress and inflammation by modulating the NOX2/ROS/NF-kB signaling pathway.[46]
In addition to quercetin, other active ingredients from the RDF were found to have antiviral and antiinflammatory properties. Luteolin, which has a high degree value in the “drug-component-common target” network, is a common bioactive flavonoid polyphenolic compound. In vivo studies have shown that Luteolin significantly reduced the mRNA expression of LPS-induced inflammatory cytokines and chemokines in macrophages, and enhanced the antimicrobial capacity of lung epithelial cells and macrophages.[47] Furthermore, the protection of Luteolin against LPS-induced lung inflammation in mice with acute lung injury may be achieved through antioxidant effects and inhibit the ability of MEK/ERK and PI3K/Akt pathways in neutrophils, as well as MAPK and NF-κB pathways.[48,49] Similarly, kaempferol, which has a high degree value in the “drug-component-common target” network, is a flavonol and is one of the most active and important natural antiinflammatory compounds known.[50–52] Kaempferol can act as a modulator of pro-inflammatory enzyme activity to inhibit inflammation and control inflammation by inhibiting the expression of inflammatory genes. For example, kaempferol showed potent inhibition of the pro-inflammatory enzymes COX-1 and 2 in an in vitro cell-free assay system.[53] Kaempferol was shown to suppress the expression of LPS-induced MAPK pathway in human monocytic cell line THP- 1, which in turn reduced the inflammatory burden by inhibiting the production of monocyte-derived chemokine, interferon-gamma induced protein 10. Growth-related oncogene-alpha and IL-8.[54] In summary, these studies have demonstrated the importance of RDF in the fight against AP.
To explore the core targets of AP in RDF, we first constructed a PPI network indicating PPI, and then combined the results of KEGG analysis to construct a “target-pathway” network, revealing the target genes such as IKBKB, AKT1, TNF, CASP3, TP53, CASP8, NFKBIA, FOS, MYC, CTNNB1, IL1B, especially IKBKB, AKT1, and TNF, which have high degree value in the “target-pathway” network and maybe the core targets in the antiAP process of RDF. Among them, IKBKB is a protein-coding gene, and mutation will lead to immune deficiency in the human body.[55] Its encoding IKK1 and IKK2 can promote the phosphorylation of IκBα and NF-κB, leading to inflammation, and controlling the expression of IKBKB may be a method of inflammation treatment.[56] Akt1, also known as a protein kinase, is 1 of the 3 closely related serine/threonine protein kinases (Akt1, Akt2, and Akt3) and is a known oncogene. Akt activation is dependent on the PI3K pathway and is thought to be a key node in this pathway, and the PI3K/Akt signaling pathway is often associated with inflammation and cancer development.[57] In addition, TNF encodes a multifunctional pro-inflammatory cytokine of the tumor necrosis factor (TNF) superfamily, this cytokine is involved in a variety of BP, including cell proliferation, differentiation, apoptosis, lipid metabolism, and coagulation.[58,59] The cytokines of the TNF family induce rapid gene transcription for inflammation, cell survival, cell proliferation, and cell differentiation, mainly through activation of the NF-κB pathway.[60] Recently, NF-κB was found to regulate TNF expression after macrophage stimulation with LPS.[61] These data suggest that RDF antiAP may be regulated through multiple targets and confirm the great potential of RDF in the treatment of AP.
To annotate the functions of protein targets and related pathways, we further performed GO analysis and KEGG analysis. the results of GO analysis showed that target genes were mainly enriched in membrane raft, membrane microdomain, caveola, and plasma membrane raft loci, as well as biological functions such as cell death, cell proliferation, apoptosis, and hypoxia response. KEGG analysis showed that among the top 20 pathways, Pathways in cancer had the highest gene count, but inflammation-related pathways account for the largest proportion, including PI3K/Akt signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. Among these 4 inflammation-related pathways, the PI3K/Akt signaling pathway had the highest gene count and may be a key pathway for AP therapy. It is well known that the phosphatidylinositol-3-kinase, a conserved family of signal transduction enzymes, is implicated in the regulation of cell growth, cycle entry, migration, and survival.[62] Protein kinase B is activated via a PI3K pathway in which PI3K-mediated production of phosphatidylinositol-3,4,5-trisphosphate leads to the recruitment of Akt to the cell membrane and then initiates Akt phosphorylation by phosphoinositide-dependent kinase 1.[63] It was shown that the induction of Heme oxygenase-1 promoted by activation of PI3K/Akt pathway, can play an antiapoptotic and antiinflammatory role in the oxidative damage response to septic lung injury.[64–66] In addition, LPS-induced acute lung injury, including lung inflammation, oxidative stress, and apoptosis, could be prevented by inhibiting the PI3K/Akt/FoxO1 signaling pathway.[67] These findings are consistent with our view that the PI3K/Akt signaling pathway is more important in the involvement of RDF against AP.
Based on the network pharmacology we successfully screened 46 active ingredients and 31 targets, therefore, molecular docking further validated the predictions derived from network pharmacology. The docking results showed that almost all of the 14 active ingredients had strong binding activity to the 11 core targets, such as quercetin, beta-carotene, and Stigmasterol were significantly better than aspirin. The active ingredients of RDF regulate the MAPK signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, IL-17 signaling pathway, etc by acting on IKBKB, AKT1, TNF, TP53, NFKBIA, IL1B, and other targets. signaling pathway, etc to improve oxidative stress, regulate apoptosis, inhibit the inflammatory response, and treat acute lung injury caused by AP. The possibility of RDF for the treatment of AP was verified by molecular docking.
5. Conclusion
In this study, we used network pharmacology and molecular docking to screen for the active components of RDF and the molecular mechanisms involved in the treatment of AP. The functional enrichment analysis suggested that RDF could regulate the relevant targets through the MAPK, TNF, PI3K-Akt, and IL-17 signaling pathways. PPI molecular docking analysis showed that the 14 main active ingredients found in RDF had strong binding activities with the relevant AP targets. Most of these active components had a stronger binding activity when compared with aspirin.
Acknowledgments
We would like to thank TopEdit (www.topeditsci.com) for the English language revision.
Author contribution
Conceptualization: Xiuxiu Yi, Huifen Zhou.
Data curation: Xiuxiu Yi, Huifen Zhou, Yu He, Can Yang.
Formal analysis: Xiuxiu Yi.
Funding acquisition: Haitong Wan, Jing Chen
Methodology: Yu He, Li Yu.
Resources: Haitong Wan, Jing Chen.
Software: Li Yu.
Writing – original draft: Xiuxiu Yi, Huifen Zhou.
Writing – review & editing: Haitong Wan, Jing Chen.
References
[1]. Long ME, Mallampalli RK, Horowitz JC. Pathogenesis of pneumonia and acute lung injury. Clin Sci (Lond). 2022;136:747–69.
[2]. Centers for Disease Control and Prevention. Center for Disease Control and Prevention: pneumonia. Available at:
https://www.cdc.gov/pneumonia/causes.html [access date November 7, 2022].
[3]. World Health Organization. Pneumonia fact sheet. Available at:
https://www.who.int/news-room/fact-sheets/detail/pneumonia [access date November 7, 2022].
[4]. Sangeetha RK, Sagar P, Laxman G, et al. Emerging roles of inflammasomes in acute pneumonia. Am J Respir Crit Care Med. 2018;197:160–71.
[5]. Chalmers JD, Rother C, Salih W, et al. Healthcare-associated pneumonia does not accurately identify potentially resistant pathogens: a systematic review and meta-analysis. Clin Infect Dis. 2014;58:330–9.
[6]. Jiao X, Jin X, Ma Y, et al. A comprehensive application: molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine. Comput Biol Chem. 2021;90:107402.
[7]. Wei PF. Diagnosis and treatment protocol for novel coronavirus pneumonia (Trial Version 7). Chin Med J (Engl). 2020;133:1087–95.
[8]. Jin D, Zhang J, Zhang Y, et al. Network pharmacology-based and molecular docking prediction of the active ingredients and mechanism of ZaoRenDiHuang capsules for application in insomnia treatment. Comput Biol Med. 2021;135:104562.
[9]. Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, et al. The light and dark sides of virtual screening: what is there to know? Int J Mol Sci. 2019;20:1375.
[10]. Appelberg S, Gupta S, Svensson AS, et al. Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells. Emerg Microbes Infect. 2020;9:1748–60.
[11]. Xia QD, Xun Y, Lu JL, et al. Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19. Cell Prolif. 2020;53:e12949.
[12]. King D, Yeomanson D, Bryant HE. PI3King the lock: targeting the PI3K/Akt/mTOR pathway as a novel therapeutic strategy in neuroblastoma. J Pediatr Hematol Oncol. 2015;37:245–51.
[13]. Peltier J, O’Neill A, Schaffer DV. PI3K/Akt and CREB regulate adult neural hippocampal progenitor proliferation and differentiation. Dev Neurobiol. 2007;67:1348–61.
[14]. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Prog Neurobiol. 2011;93:182–203.
[15]. Tao Q, Du J, Li X, et al. 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.
[16]. Dong Y, Zhao Q, Wang Y. Network pharmacology-based investigation of potential targets of astragalus membranaceous-angelica sinensis compound acting on diabetic nephropathy. Sci Rep. 2021;11:19496.
[17]. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.
[18]. Xu X, Zhang W, Huang C, et al. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci. 2012;13:6964–82.
[19]. Jia CY, Li JY, Hao GF, et al. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov Today. 2020;25:248–58.
[20]. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–12.
[21]. Stelzer G, Rosen N, Plaschkes I, et al. The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinform. 2016;54:1.30.1–1.30.33.
[22]. Amberger JS, Hamosh A. Searching Online Mendelian Inheritance in Man (OMIM): a knowledgebase of human genes and genetic phenotypes. Curr Protoc Bioinform. 2017;58:1.2.1–1.2.12.
[23]. Wang Y, Zhang S, Li F, et al. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res. 2020;48:D1031–41.
[24]. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.
[25]. Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.
[26]. Guo Z, Liu Z, Wang X, et al. Elsholtzia: phytochemistry and biological activities. Chem Cent J. 2012;6:147.
[27]. Liu AL, Wang HD, Lee SM, et al. Structure-activity relationship of flavonoids as influenza virus neuraminidase inhibitors and their in vitro anti-viral activities. Bioorg Med Chem. 2008;16:7141–7.
[28]. Qian S, Fan W, Qian P, et al. Apigenin restricts FMDV infection and inhibits viral IRES driven translational activity. Viruses. 2015;7:1613–26.
[29]. Ninfali P, Antonelli A, Magnani M, et al. Antiviral properties of flavonoids and delivery strategies. Nutrients. 2020;12:2534.
[30]. Cheong DHJ, Tan DWS, Wong FWS, et al. Anti-malarial drug, artemisinin and its derivatives for the treatment of respiratory diseases. Pharmacol Res. 2020;158:104901.
[31]. Uzun T, Toptas O. Artesunate: could be an alternative drug to chloroquine in COVID-19 treatment? Chin Med. 2020;15:54.
[32]. Shang X, Pan H, Li M, et al.
Lonicera japonica Thunb.: ethnopharmacology, phytochemistry and pharmacology of an important traditional Chinese medicine. J Ethnopharmacol. 2011;138:1–21.
[33]. Kim H, Lim CY, Chung MS. Magnolia officinalis and its honokiol and magnolol constituents inhibit human norovirus surrogates. Foodborne Pathog Dis. 2021;18:24–30.
[34]. Shi SJ, Qin Z, Kon SZ, et al. Screening of effective components of atractylodes macrocephala against influenza virus. Shizhen Tradit Chin Med Tradit Chin Med. 2012;23:565–6 [in Chinese, English abstract].
[35]. Li L, Kou S, Zhao J, et al. Bioinformatics analysis on the difference of immune regulation of herba agastache and atractylodes atractylodes on influenza a H1N1. J Tradit Chin Med. 2016;57:1011–4 [in Chinese, English abstract].
[36]. Qin HW, Yang TM, Yang SB, et al. Effects of different pre-drying and drying methods on volatile compounds in the pericarp and kernel of Amomum tsao-ko. Front Plant Sci. 2022;13:803776.
[37]. Sabulal N, Baby S. Chemistry of amomum essential oils. J Essent Oil Res. 2021;33:427–41.
[38]. Liu L, Zhao Y, Ming J, et al. Polyphenol extract and essential oil of Amomum tsao-ko equally alleviate hypercholesterolemia and modulate gut microbiota. Food Funct. 2021;12:12008–21.
[39]. Zhang HS, Zhang HY, Zai XM, et al. Study on the effect of three natural compounds of spartina alterniflora on reducing uric acid. Wild Plant Resour China. 2019;38:9–12 [in Chinese, English abstract].
[40]. Yang XN, Ji JL, Xu JX, et al. Natural herbs: a potential autophagy inducer in cancer therapy. Mod Oncol. 2019;27:879–85 [in Chinese, English abstract].
[41]. Cao Y, Hu YH, Zhang D, et al. Research progress in anti leukemia activity of natural compounds. J Southwest Med Univ. 2020;43:300–5 [in Chinese, English abstract].
[42]. An J, Chen B, Kang X, et al. Neuroprotective effects of natural compounds on LPS-induced inflammatory responses in microglia. Am J Transl Res. 2020;12:2353–78.
[43]. Azab A, Nassar A, Azab AN. Anti-inflammatory activity of natural products. Molecules. 2016;21:1321.
[44]. Liu SW, Liu JY. Advances in pharmacological effects of quercetin. Chin J Lung Dis (electron ed). 2020;13:104–6 [in Chinese, English abstract].
[45]. Chen T, Zhang X, Zhu G, et al. Quercetin inhibits TNF-α induced HUVECs apoptosis and inflammation via downregulating NF-kB and AP-1 signaling pathway in vitro. Medicine (Baltim). 2020;99:e22241.
[46]. Sul OJ, Ra SW. Quercetin prevents LPS-induced oxidative stress and inflammation by modulating NOX2/ROS/NF-kB in lung epithelial cells. Molecules. 2021;26:6949.
[47]. Miao J, Lin F, Huang N, et al. Improving anti-inflammatory effect of luteolin with nano-micelles in the bacteria-induced lung infection. J Biomed Nanotechnol. 2021;17:1229–41.
[48]. Kuo MY, Liao MF, Chen FL, et al. Luteolin attenuates the pulmonary inflammatory response involves abilities of antioxidation and inhibition of MAPK and NFκB pathways in mice with endotoxin-induced acute lung injury. Food Chem Toxicol. 2011;49:2660–6.
[49]. Lee JP, Li YC, Chen HY, et al. Protective effects of luteolin against lipopolysaccharide-induced acute lung injury involves inhibition of MEK/ERK and PI3K/Akt pathways in neutrophils. Acta Pharmacol Sin. 2010;31:831–8.
[50]. Crespo I, García-Mediavilla MV, Gutiérrez B, et al. A comparison of the effects of kaempferol and quercetin on cytokine-induced pro-inflammatory status of cultured human endothelial cells. Br J Nutr. 2008;100:968–76.
[51]. De MG, Malvar DC, Vanderlinde FA, et al. Antinociceptive and anti-inflammatory kaempferol glycosides from Sedum dendroideum. J Ethnopharmacol. 2009;124:228–32.
[52]. Hämäläinen M, Nieminen R, Vuorela P, et al. Anti-inflammatory effects of flavonoids: genistein, kaempferol, quercetin, and daidzein inhibit STAT-1 and NF-kappaB activations, whereas flavone, isorhamnetin, naringenin, and pelargonidin inhibit only NF-kappaB activation along with their inhibitory effect on iNOS expression and NO production in activated macrophages. Mediators Inflamm. 2007;2007:45673.
[53]. Lee JH, Kim GH. Evaluation of antioxidant and inhibitory activities for different subclasses flavonoids on enzymes for rheumatoid arthritis. J Food Sci. 2010;75:H212–7.
[54]. Huang CH, Jan RL, Kuo CH, et al. Natural flavone kaempferol suppresses chemokines expression in human monocyte THP-1 cells through MAPK pathways. J Food Sci. 2010;75:H254–9.
[55]. Scheidereit C. IkappaB kinase complexes: gateways to NF-kappaB activation and transcription. Oncogene. 2006;25:6685–705.
[56]. Yingkun N, Zhenyu W, Jing L, et al. Stevioside protects LPS-induced acute lung injury in mice. Inflammation. 2013;36:242–50.
[57]. Chen H, Zhou L, Wu X, et al. The PI3K/AKT pathway in the pathogenesis of prostate cancer. Front Biosci (Landmark Ed). 2016;21:1084–91.
[58]. Brenner D, Blaser H, Mak TW. Regulation of tumor necrosis factor signaling: live or let die. Nat Rev Immunol. 2015;15:362–74.
[59]. Chen G, Goeddel DV. TNF-R1 signaling: a beautiful pathway. Science. 2002;296:1634–5.
[60]. Hayden MS, Ghosh S. Regulation of NF-κB by TNF family cytokines. Semin Immunol. 2014;26:253–66.
[61]. Shakhov AN, Collart MA, Vassalli P, et al. Kappa B-type enhancers are involved in lipopolysaccharide-mediated transcriptional activation of the tumor necrosis factor alpha gene in primary macrophages. J Exp Med. 1990;171:35–47.
[62]. Morovicz AP, Mazloumi GF, Jacobsen RG, et al. Phosphoinositide 3-kinase signaling in the nucleolus. Adv Biol Regul. 2022;83:100843.
[63]. Hu H, Juvekar A, Lyssiotis CA, et al. Phosphoinositide 3-kinase regulates glycolysis through mobilization of aldolase from the actin cytoskeleton. Cell. 2016;164:433–46.
[64]. Kim H, Kim SR, Je J, et al. The proximal tubular α7 nicotinic acetylcholine receptor attenuates ischemic acute kidney injury through Akt/PKC signaling-mediated HO-1 induction. Exp Mol Med. 2018;50:1–17.
[65]. Pei L, Kong Y, Shao C, et al. Heme oxygenase-1 induction mediates chemoresistance of breast cancer cells to pharmorubicin by promoting autophagy via PI3K/Akt pathway. J Cell Mol Med. 2018;22:5311–21.
[66]. Xiao Q, Piao R, Wang H, et al. Orientin-mediated Nrf2/HO-1 signal alleviates H2O2-induced oxidative damage via induction of JNK and PI3K/AKT activation. Int J Biol Macromol. 2018;118(Pt A):747–55.
[67]. Cui H, Zhang Q. Dexmedetomidine ameliorates lipopolysaccharide-induced acute lung injury by inhibiting the PI3K/Akt/FoxO1 signaling pathway. J Anesth. 2021;35:394–404.
