Abstract
Xinguan No. 3 has been recommended for the treatment of coronavirus disease 2019 (COVID-19); however, its potential mechanisms are unclear. This study aims to explore the mechanisms of Xinguan No. 3 against COVID-19 through network pharmacology and molecular docking. We first searched the ingredients of Xinguan No. 3 in three databases (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, Traditional Chinese Medicines Integrated Database, and The Encyclopedia of Traditional Chinese Medicine). The active components and their potential targets were predicted through the SwissTargetPrediction website. The targets of COVID-19 can be found on the GeneCards website. Protein interaction analysis, screening of key targets, functional enrichment of key target genes, and signaling pathway analysis were performed through Search Tool for the Retrieval of Interacting Genes databases, Metascape databases, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. Finally, the affinity of the key active components with the core targets was verified by molecular docking. The results showed that five core targets had been screened, including MAPK1, NF-κB1, RELA, AKT1, and MAPK14. Gene ontology enrichment analysis revealed that the key targets were associated with inflammatory responses and responses to external stimuli. KEGG enrichment analysis indicated that the main pathways were influenza A, hepatitis B, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Therefore, Xinguan No. 3 might play a role in treating COVID-19 through anti-inflammatory, immune responses, and regulatory responses to external stimuli.
1 Introduction
Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by a new type of coronavirus, which was named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) [1]. The early symptoms of the disease include fever, cough, chest tightness, dyspnea, fatigue, abdominal distension and diarrhea, sore throat, and myalgia [2,3,4], which would gradually progress to severe respiratory diseases and affect other tissues and organs [5]. According to the latest data released by the World Health Organization on March 8, 2022, the confirmed cases in the world reached 445,096,612, and the confirmed deaths reached 5,998,301 [6]. Although people in some districts have already been vaccinated with vaccines against COVID-19 on a large scale, there is still a possibility for them to be infected with variants of SARS-CoV-2 [7]. In addition, some anti-COVID-19 drugs (e.g., paxlovid and molnupiravir) with good therapeutic effects seem perspective. However, they are too expensive [8]. So, it is of great significance to find safe, dependable, and inexpensive drugs to prevent COVID-19 as soon as possible.
Traditional Chinese Medicine (TCM) has played a vital role in preventing and treating COVID-19. Many TCM prescriptions have been officially recommended for the treatment of COVID-19 [9]. Studies have shown that TCM formulas could increase the cure rate and slow down the aggravation of the disease [10]. Xinguan No. 3, also known as Jing Fang Huo Pu Jie Du Mixture, is a TCM formula for treating COVID-19 in “Sichuan prevention control technology guide for novel coronavirus pneumonitis by TCM” [11]. It consists of 19 Chinese herbal medicines, including the aboveground part of Schizonepeta tenuifolia Briq., the root of Saposhnikovia divaricata (Turcz.) Schischk., the rootstock of Ligusticum chuanxiong Hort., the root of Angelica dahurica (Fisch. ex Hoffm.) Benth. et Hook. f. or A. dahurica (Fisch. ex Hoffm.) Benth. et Hook. f. var. formosana (Boiss.) Shan et Yuan, the aerial parts of Mentha haplocalyx Briq., the root of Platycodon grandiflorum (Jacq.) A. DC., the stem bark, root bark, or branch bark of Magnolia officinalis Rehd. et Wils. or M. officinalis Rehd. et Wils. var. biloba Rehd. et Wils., the tubers of Pinellia ternata (Thunb.) Breit., Medicinal Fermented Mass, the rootstock of Atractylodes macrocephala Koidz., the mature seed of Coix lacryma-jobi L. var. ma-yuen (Roman.) Stapf, the ripe seed of Dolichos lablab L., the ripe seed of Prunus armeniaca L. var. ansu Maxim., P. sibirica L., P. mandshurica (Maxim.) Koehne or P. armeniaca L., the ripe fruit of Crataegus pinnatifida Bge. var. major N. E. Br. or C. pinnatifida Bge., the sclerotia of Poria cocos (Schw.) Wolf, the aboveground part of Pogostemon cablin (Blanco) Benth., the leaves of Perilla frutescens (L.) Britt., the mature fruit of Amomum kravanh Pierre ex Gagnep. or A. compactum Soland ex Maton, and the rhizomes of Phragmites communis Trin (Table 1). The prescription of Xinguan No. 3 mainly comes from Jing Fang Bai Du Powder (JFBDP) and Huo Pu Xia Ling Decoction (HPXLD). JFBDP has a good curative effect on fever, headache, cough, and phlegm. It is often used to treat epidemic and infectious diseases, such as acute viral upper respiratory tract infections [12]. HPXLD has a therapeutic effect on diarrhea, fever, bloating, and headache. Xinguan No. 3 has the functions of relieving the exterior with the warm pungent and removing dampness with aromatic. For mild cases of COVID-19, the Xinguan No. 3 may effectively alleviate fever and cough symptoms.
The constitution and potential targets of Xinguan No. 3
| Herb | Chinses name | Abbreviation | Dosage (g) | No. of total compounds | No. of compounds passing ADME filtration | No. of protein targets |
|---|---|---|---|---|---|---|
| The aboveground part of Schizonepeta tenuifolia | Jing Jie | JJ | 15 | 218 | 103 | 559 |
| The root of Saposhnikovia divaricata | Fang Feng | FF | 15 | 226 | 134 | 780 |
| The rootstock of Ligusticum chuanxiong | Chuan Xiong | CX | 15 | 342 | 176 | 860 |
| The root of Angelica dahurica or A. dahurica var. formosana | Bai Zhi | BZhi | 15 | 331 | 160 | 814 |
| The aerial parts of Mentha haplocalyx | Bo He | BH | 15 | 228 | 113 | 508 |
| The root of Platycodon grandiflorum | Jie Geng | JG | 30 | 142 | 21 | 401 |
| The stem bark, root bark, or branch bark of Magnolia officinalis or M. officinalis var. biloba | Hou Pu | HP | 15 | 235 | 140 | 884 |
| The tubers of Pinellia ternata | Ban Xia | BX | 15 | 196 | 83 | 734 |
| The rootstock of Atractylodes macrocephala | Bai Zhu | BZhu | 30 | 112 | 79 | 540 |
| Medicinal Fermented Mass | Jian Qu | JQ | 15 | 5 | 1 | 26 |
| The mature seed of Coix lacryma-jobi var. ma-yuen | Yi Yi Ren | YYR | 30 | 65 | 22 | 258 |
| The ripe seed of Dolichos lablab | Bai Bian Dou | BBD | 30 | 27 | 8 | 54 |
| The ripe seed of Prunus armeniaca var. ansu or P. sibirica or P. mandshurica or P. armeniaca | Ku Xing Ren | KXR | 15 | 118 | 56 | 544 |
| The ripe fruit of Crataegus pinnatifida var. major or C. pinnatifida | Shan Zha | SZ | 30 | 159 | 42 | 487 |
| The sclerotia of Poria cocos | Fu Ling | FL | 30 | 83 | 24 | 418 |
| The aboveground part of Pogostemon cablin | Guang Huo Xiang | GHX | 15 | 152 | 66 | 441 |
| The leaves of Perilla frutescens | Zi Su | ZS | 15 | 368 | 188 | 713 |
| The mature fruit of Amomum kravanh or A. compactum | Dou Kou | DK | 15 | 93 | 57 | 614 |
| The rhizomes of Phragmites communis | Lu Gen | LG | 30 | 46 | 25 | 243 |
With the development of systems biology and bioinformatics, network pharmacology has become one of the effective methods for discovering new drugs and exploring the mechanism of drug action [13,14]. Molecular docking is a computer-aided drug design method for predicting drug-target interactions. Molecular docking supports the systematic exploration of the binding modes of drug molecules to receptors [15]. Exploring the mechanism of TCM via integrating network pharmacology and molecular docking technology has been a research hotspot nowadays. In this study, we combined molecular docking and network pharmacology to reveal the underlying mechanisms and active components of the anti-COVID-19 effects of Xinguan No. 3. These studies will be beneficial for the development and clinical application of Xinguan No. 3 in the treatment of COVID-19.
2 Materials and methods
2.1 Acquiring the intersecting gene base of Xinguan No. 3 and COVID-19
All ingredients of Xinguan No. 3 were retrieved from three databases: The Encyclopedia of Traditional Chinese Medicine [16] (ETCM, http://www.tcmip.cn/ETCM/index.php/Home/Index/), Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform [17] (TCMSP, http://www.tcmspw. com/tcmsp.php), and Traditional Chinese Medicines Integrated Database [18] (TCMID, http://www. megabionet.org/tcmid/). By the way, Jian Qu is supplemented from the current literature rather than the databases mentioned above. Then, we screened each retrieved ingredient based on gastrointestinal (GI) and Drug-likeness (DL) by the SwissADME database, with the screening criteria being based on “high” GI absorption, and DL was satisfied with at least two “yes” at the same time [19]. The potential active ingredients were obtained after screening. Potential target information related to these active ingredients was predicted from the SwissTargetPrediction [20] (http://www.swisstarget prediction.ch/). The targets were sent to the UniProt database (https://www. uniprot.org/) for normalization. The targets related to COVID-19 were retrieved from the Human Gene Database [21] (GeneCards, https://www.genecards.org/) with the keyword “COVID-19.” The targets were also sent to the UniProt Database for normalization. Venny (version 2.1.0) was used to obtain Xinguan No. 3 and COVID-19-related gene set (intersection target set).
2.2 Protein–protein interaction (PPI) analysis and key targets screening
The intersecting target set was analyzed by the Search Tool for the Retrieval of Interacting Genes [22] (STRING, https://string-db.org/) to build a PPI network. Targets with confidence scores more outstanding than 0.9 were selected for analysis of protein interactions. The results were exported in TSV format and imported into Cytoscape (version 3.8.2). The network analysis in Cytoscape (version 3.8.2) [23] is used to analyze the topology parameters of the PPI network. The larger the degree value, the more likely it will become the core target in Xinguan No. 3 to treat COVID-19. In this study, the top 20 targets with the highest degree were selected as the key targets.
2.3 Functional enrichment analysis and core targets determination
To investigate the biological functions of the intersection target set, we performed gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Metascape database [24] (http://metascape.org/gp/). GO enrichment analysis included three categories, including biological processes (BP), molecular functions (MF), and cellular components (CC). The functional categories were found and sorted by the P-value. To find the potential core targets for the treatment of COVID-19 with Xinguan No. 3, we plotted the enrichment network map of key targets in the first 15 pathways. We analyzed the importance of key targets in the pathway by degree values to obtain the core targets.
2.4 Construction of network visualization
The Drug-Component-Target-Disease network was built through Cytoscape (version 3.8.2). Among them, the core components with degree value greater than twice that of active components were selected. The establishment of visual drug–core components–key targets–pathway network reflects the complex relationships among active compounds, their potential targets, and pathways.
2.5 Molecular docking
The core components of Xinguan No. 3 were docked with the core targets (RELA, NF-κB1, AKT1, MAPK1, and MAPK14). In addition, we also docked between ACE2, Mpro, and the core components to explore the effects of Xinguan No. 3 on SARS-CoV-2 [25,26,27,28]. The crystal structure of low-resolution protein receptors was downloaded from RCSB Protein Data Bank (PDB) (https://www. rcsb.org/). The “Protein Preparation Wizard” module of Maestro 11.9 was used to optimize the structure of proteins. After optimization, the binding site of the original ligand in its crystal structure is appointed as the docking site of each protein, and the binding site of protein without the original ligand is predicted. The receptor grid file is generated by the “receptor grid generation panel.” The core components were optimized by the “Ligprep” module. Finally, the “glide docking” module is used to dock and score the treated active components and proteins.
3 Results
3.1 Main chemical compounds and protein targets of Xinguan No. 3
It is generally believed that the therapeutic effect of Chinese herbal medicines is achieved through the interaction of multiple active components/targets. Therefore, we searched for the chemical compounds and protein targets of the 19 herbal medicines used to formulate Xinguan No. 3. We first collected the chemical components of each plant in three Chinese herbal medicine databases. We then screened them by ADME to determine the chemical components that can be absorbed orally because Xinguan No. 3 is administered orally. After identifying those components that can be absorbed orally, we predicted the corresponding targets for each chemical component. Each herbal ingredient, the total number of its compounds, the number of orally absorbable compounds, and the number of their protein targets are listed in Table 1. The GeneCards database was used to screen for targets related to COVID-19. A total of 682 COVID-19 targets were retrieved using a filter set to score greater than or equal to the median. These 682 targets were combined with targets of Xinguan No. 3 to obtain a total of 153 intersection targets.
3.2 Construction and analysis of the Herb–Compound–Target network
To visualize the network topology status of Xinguan No. 3 treatment of COVID-19, we built a Drug–Component–Target–Disease network that reflects the complex relationships between drugs, active components, targets, and disease through Cytoscape (version 3.8.2). There were 19 drug nodes, 1,049 component nodes, 113 gene nodes, and 7,680 kinds of edges, as shown in Figure 3. In the network, the degree value represents the number of threads connected to one node and other nodes in the network. For components, the larger the degree value, the more targets it corresponds to, and the more likely it is component that plays a significant role in Xinguan No. 3. Therefore, we screened 241 components as core components using the 2-fold degree value as the screening criterion.
3.3 Construction and analysis of the PPI network
PPIs are essential for most BPs [29]. In this study, we constructed a PPI network to analyze the interaction of the intersection targets. The confidence score of PPI networks can be seen as an indicator of protein sequence pairs [30]. The confidence score of protein sequence pairs is related to the probability of the protein participating in biological regulation. Therefore, we selected the core targets with a confidence score greater than 0.9, of which 112 targets were enriched. As shown in Figure 1, the PPI network finally holds 112 nodes and 419 edges. The shape and color of the nodes represent the magnitude of the degree value, indicating the importance of the targets. Therefore, we chose the top 20 targets as the key targets which have a higher degree value than other targets, for example, RELA, JUN, STAT3, MAPK14, TNF, NFKB1, PIK3R1, STAT1, MAPK3, MAPK1, IL6, EGFR, and NFKBIA. They may be the most important potential targets in Xinguan No. 3 treatment for COVID-19 (Figure 2).
The Herb–Compound–Target–Disease network. Yellow circles are drugs of Xinguan No. 3. Green circles are the active components. Light green circles are the duplicate components. Orange circles are intersection targets.
The PPI network of 113 targets. The size and brightness of the nodes indicate the importance of the targets.
3.4 GO enrichment and KEGG pathway analysis
To further understand the mechanism of Xinguan No. 3 in treating COVID-19, the intersection targets were imported into Metascape for analysis. With P < 0.05 as the screening condition, a total of 368 BPs, 106 MFs, and 73 CCs were obtained. The top 15 GO items of each category were selected to draw cell component enrichment map, MF enrichment map, and BP enrichment map (Figure 3). Among them, BPs are the most important. The enrichment results of BPs show that the intersection targets are connected to the regulation of cytokine production, positive regulation of cytokine production, MAPK cascade, response to bacterium, regulation of defense response, positive regulation of response to external stimulus, and so on. For MFs, these targets are associated with kinase activity, cytokine receptor binding, transcription factor binding, etc. For the analysis of CC, these targets are mainly on the side of the membrane, lytic vacuole, and the lysosome.
GO and KEGG enrichment analysis of 113 core targets.
KEGG pathway enrichment analyses (2D) of the 113 intersecting targets showed that the main signaling pathways involved Influenza A, Hepatitis B, Hepatitis C, Epstein–Barr virus infection, Toll-like receptor signaling pathway, Chagas disease (American trypanosomiasis), NOD-like receptor signaling pathway, Measles, TNF signaling pathway, Th17 cell differentiation, and so on.
Then, we have constructed a clearer network diagram of the relationship between drugs, core active ingredients, key targets, and pathways to express the role of Xinguan No. 3 more concisely and intuitively in the fight against COVID-19, as shown in Figure 4. After that, we further constructed a network map of key targets and pathways, as shown in Figure 5. The results showed that the five genes (MAPK1, NFKB1, RELA, AKT1, and MAPK14) could be considered as core targets and may be potential therapeutic targets for the treatment of Xinguan No. 3, according to the degree value. These networks reveal that Xinguan 3 exerted its anti-coronavirus effect through multiple components, targets, and pathways.
Drug–Core active component–Key target–pathway network. The dark green squares are the medicinal herbs, the light green squares are the core active ingredients, the yellow octagons are the key targets, and the purple V shapes are the pathways.
Key target–pathway network. The yellow octagons are the key targets, and the red V shapes are the pathways.
3.5 Molecular docking simulation for core components
The genetic names of the docking targets were imported into the UniPort database to determine the protein identifier, and the structures of the proteins were downloaded in the PDB database, including MAPK1 (PDB ID: 4QP9), NFKB1 (PDB ID: 2O61), RELA (PDB ID: 6NV2), AKT1 (PDB ID: 4EKL), MAPK14 (PDB ID: 6SFI), ACE2 (PDB ID: 1R42), and Mpro (PDB ID: 6LU7). This study docked the core active ingredients with the target proteins. Figure 6 shows the binding pattern of the core active ingredients and the target proteins. The docking results show that magnolignan B and isolariciresino can be well bound to dock with seven target proteins, and the score of docking pairs was less than 5.5 kcal/mol, as shown in Table 2. When ligands bind to receptors, it is generally believed that the lower the binding energy, the higher the binding affinity of the ligand to the target protein.
Binding mode of molecular docking, taking magnolignan B as an example. (a) MAPK1, (b) NFKB1, (c) RELA, (d) AKT1, (e) MAPK14, (f) ACE2, and (g) Mpro.
Molecular docking results (docking score greater than 5.5 kcal/mol)
| No. | Compounds | Docking score | Source | ||||||
|---|---|---|---|---|---|---|---|---|---|
| RELA | NF-KB1 | AKT1 | MAKP1 | MAKP14 | Mpro | ACE2 | |||
| HP46 | Magnolignan B | –6.20 | –6.79 | –6.93 | –8.52 | –6.90 | –8.48 | –5.66 | HP |
| BX17 | Isolariciresino | –5.65 | –6.61 | –6.33 | –8.18 | –7.45 | –6.94 | –5.55 | BX |
| GHX8 | Apigenin-7-olate | –6.26 | –7.45 | –7.39 | –8.10 | –8.35 | –7.50 | — | GHX |
| CF49 | Apigenin | –6.28 | –7.44 | –7.34 | –7.94 | –8.34 | –7.51 | — | JJ, BH, ZS, and GHX |
| CF207 | Genkwanin | –5.83 | –7.79 | –6.53 | –8.35 | –8.47 | –6.79 | — | BH and GHX |
| CF70 | Quercetin | –5.82 | –7.26 | –7.03 | –7.89 | –8.09 | –6.15 | — | JJ, GHX, and ZZ |
| CF23 | Luteolin | –6.50 | –6.99 | –7.12 | –7.86 | –7.26 | –6.44 | — | JJ, BH, ZS, GK, and JG |
| JJ33 | 2-(3,4-Dihydroxyphenyl)-5-hydroxy-4-oxo-4H-chromen-7-olateluteolin-7-olate (1-) | –6.5 | –6.98 | –7.12 | –7.86 | –7.26 | –6.44 | — | JJ |
| DK19 | 1,7-Diphenyl-3,5-dihydroxy-1-heptene | –5.51 | –7.65 | –5.91 | –8.14 | –8.44 | –6.65 | — | DK |
| JJ37 | Schizonepetoside | –5.64 | –6.43 | –7.66 | –8.28 | –7.3 | –7.25 | — | JJ |
| CF185 | Rhamnocitrin | –5.62 | –7.87 | –5.72 | –7.69 | –8.34 | –6.68 | — | DK and GXH |
| HP73 | Reticulin | –6.08 | –7.42 | –6.45 | –7.65 | –7.63 | –6.84 | — | HP |
| FF61 | Primetin | –5.87 | –6.61 | –6.78 | –8.31 | –7.35 | –7.19 | FF | |
| CF208 | Acacetin | –5.63 | –6.36 | –6.15 | –7.14 | –8.71 | –6.59 | — | BH and JG |
| BH28 | 5-hydroxy-2-(4-methoxyphenyl)-4-oxo-4H-chromen-7-olate | –5.63 | –6.16 | –6.15 | –7.18 | –8.72 | –6.61 | — | BH |
| CX29 | Senkyunolide-L | –6.03 | –7.15 | –5.97 | –7.38 | –6.89 | –6.51 | — | CX |
| CF32 | Carvacrol | –6.02 | –6.20 | –6.96 | –7.73 | –6.4 | –5.84 | — | BZHI, BH, ZS, and DK |
| CF144 | Diosmetin | –5.86 | –5.93 | –6.76 | –6.80 | –7.65 | –6.05 | — | JJ and BH |
| CX28 | Senkyunolide-F | –5.58 | –7.5 | –5.99 | –7.21 | –6.59 | –5.58 | — | CX |
| CX23 | Perlolyrine | –5.65 | –6.28 | –5.79 | –7.18 | –7.58 | –5.90 | — | CX |
| CX75 | Senkyunolide K | –6.07 | –6.75 | –6.64 | –6.23 | –6.68 | –6.23 | — | CX |
| CX84 | Butylidenephthalide | –5.75 | –6.3 | –7.04 | –6.44 | –6.75 | –5.76 | — | CX |
| CX85 | 3-Butyl-4,7-dihydroxy-3H-2-benzofuran-1-one | –6.03 | –6.78 | –6.54 | –5.89 | –6.84 | –5.94 | — | CX |
| CX46 | 4,7-Dihydroxy-3-butylphthalide | –6.03 | –6.75 | –6.56 | –5.89 | –6.84 | –6.01 | — | CX |
| BZHU19 | Ethyl 2-hydroxyquinoline-4-carboxylate | –6.21 | –5.54 | –5.94 | –7.52 | –6.63 | –6.27 | — | BZHU |
| CX114 | 3-Butylidene-4,5-dihydrophthalide | –5.63 | –6.28 | –6.73 | –6.55 | –6.62 | –5.57 | — | CX |
| CX92 | cnidium lactone | –5.84 | –6.29 | –6.41 | –6.20 | –6.55 | –5.60 | — | CX |
| CX83 | 3-Butyl-4,5,6,7-tetrahydro-3H-2-benzofuran-1-one | –5.64 | –6.20 | –5.63 | –6.06 | –6.09 | –5.53 | — | CX |
| CX43 | (3S)-3-Butyl-4,5,6,7-tetrahydro-3H-2-benzofuran-1-one | –5.64 | –6.20 | –5.63 | –5.98 | –6.09 | –5.53 | — | CX |
4 Discussion
Although SARS-CoV-2 has been effectively controlled in China, the number of confirmed cases of COVID-19 abroad is still increasing rapidly. In addition, the virus has produced a new variant called Omicron, with higher transmissibility, infectivity, and vaccine breakthrough properties [31]. Due to the lack of clinical studies of drugs against COVID-19 and the common presence of side effects, analyzing the exact components and ingredients of TCM is increasingly urgent [32]. There have been several TCM playing a significant role in fighting against epidemics since ancient times. Since the outbreak of COVID-19, some TCMs have been used in clinical practice, and their safety and efficacy have been demonstrated [33,34,35]. However, the active ingredients and molecular mechanisms of these drugs in the treatment of COVID-19 are still unclear.
In this study, 1,049 active components and 1,378 targets were found in total. Among them, 153 targets were related to the COVID-19, called intersection targets. And 112 highly expressed targets were obtained by analyzing the intersection targets of the PPI network, and the PPI network map was imported into Cytoscape (version 3.8.2), the higher the degree value, the stronger the Xinguan No. 3 effects on the core genes. The most important core targets were indicated, including RELA, IKBKG, JAK1, IL1B, IL2, AKT1, NFKBIA, IL6, MAPK1, MAPK3, STAT1, PIK3R1, NFKB1, TNF, MAPK14, STAT3, etc. It has been indicated that they played important roles in the PPI network. These genes of Xinguan No. 3, related to inflammatory responses [36,37], immuno-modulation [38], and cellular stress processes [39], may play a key role in treating COVID-19.
Then, KEGG enrichment analysis showed that the intersection targets of COVID-19 and Xinguan No. 3 were mainly concentrated in pathways with virus-induced diseases, such as Influenza A, Hepatitis B, Hepatitis C, Epstein–Barr virus infection, and Measles. In addition, Xinguan No. 3 participates in regulating immune and inflammatory pathways, such as Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Parasites, such as chagas disease, and toxoplasmosis, might also be involved in the disease. Five key targets, such as MAPK1, NFKB1, RELA, AKT1, and MAPK14 appeared more often in the top 15 pathways. Therefore, we assumed that these five genes can also be relative to the treatment of COVID-19 by Xinguan No. 3. For example, RELA and NF-KB1 are members of the NF-KB family, and the activation of NF-kB signals helps inhibit SARS-CoV-2 infection, delay induction of interferon signals, and ultimately inhibit viral replication [40]. MAPKs regulate a variety of cellular processes, including gene expression, immune responses, cell proliferation and apoptosis, and responses to oxidative stress [41]. AKT1 is one of the human AKT serine-threonine protein kinase families, and it has been documented that overexpressed AKT1 can enhance the transcription of viral genes and the synthesis of viral proteins [42]. The downregulation of the expression of the AKT1 gene promotes macrophage M1 polarization, leading to the occurrence of inflammation [43]. Overall, MAPK1, NFKB1, RELA, AKT1, and MAPK14 might be ideal targets for treating COVID-19.
Eventually, we screened the core active ingredients via molecular docking and obtained 29 active components which can have good binding energy with at least 6 target proteins. Among them, magnolignan B and isolariciresino had good binding energy with all target proteins, playing a therapeutic effect of Xinguan No. 3. Isolariciresino has only antioxidant activity [44], while the pharmacological effects of magnolignan B cannot be found in the literature so far. Therefore, the two components might possess the potential as antivirals for future research. Moreover, quercetin and luteolin in the 29 components obtained by docking have been confirmed to have therapeutic effects on COVID-19. Quercetin effectively reduces serum levels of ALP, q-CRP, and LDH, increases viral clearance, and helps improve early symptoms of SARS-CoV-2 infection [45,46]. The combination of supplementation with luteolin and palmitoylethanolamide can effectively improve the recovery of olfactory function in COVID-19, most notably in patients with long-term olfactory dysfunction [47].
5 Conclusion
In this study, we used network pharmacology and molecular docking technology to explore the potential mechanism of Chinese medicine Xinguan No. 3 in the treatment of COVID-19. The results have shown that Xinguan No. 3 might play a vital role in treating COVID-19 by inhibiting viral replication and improving inflammatory response. Two potential active components (magnolignan B and isolariciresino) have been found, providing a reference for the development of new drugs for the treatment of COVID-19. However, due to the limitations of network pharmacology and molecular docking, further experiments are needed to validate our results in the future.
Acknowledgments
The author is highly grateful to Yunsen Zhang from Chengdu University of Traditional Chinese Medicine for selflessly teaching us to use molecular docking software.
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Funding information: The studies were financed by the Sichuan Provincial Administration of Traditional Chinese Medicine (No. 2020yj019) and the Science and Technology Department of Sichuan Province (No. 2021YFS0410).
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Author contributions: Jiayan Peng and Kun Zhang conducted the experiments, performed data analysis, and wrote the article; Lijie Wang and Fang Peng collected the ingredients and revised the article; Chuantao Zhang, Kunlan Long, Jun Chen, and Xiujuan Zhou supported the study and provided the prescription information; Peiyang Gao and Gang Fan conceived and designed the study.
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Conflict of interest: The authors declare no conflict of interest.
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Ethical approval: The conducted research is not related to either human or animal use.
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Data availability statement: The data presented in this study are available on request from the corresponding author.
References
[1] Gao K, Zhu Y, Wang H, Gong X, Yue Z, Lv A, et al. Network pharmacology reveals the potential mechanism of Baiying Qinghou decoction in treating laryngeal squamous cell carcinoma. Aging (Albany NY). 2021;13(24):26003–21. 10.18632/aging.203786.Search in Google Scholar
[2] Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. COVID‐19 patients’ clinical characteristics, discharge rate, and fatality rate of meta‐analysis. J Med Virol. 2020;92(6):577–83.10.1002/jmv.25757Search in Google Scholar
[3] Ochani R, Asad A, Yasmin F, Shaikh S, Khalid H, Batra S, et al. COVID-19 pandemic: from origins to outcomes. A comprehensive review of viral pathogenesis, clinical manifestations, diagnostic evaluation, and management. Infez Med. 2021;29(1):20–36.Search in Google Scholar
[4] Xie J, Ding C, Li J, Wang Y, Guo H, Lu Z, et al. Characteristics of patients with coronavirus disease (COVID‐19) confirmed using an IgM‐IgG antibody test. J Med Virol. 2020;92(10):2004–10.10.1002/jmv.25930Search in Google Scholar
[5] Kevadiya BD, Machhi J, Herskovitz J, Oleynikov MD, Blomberg WR, Bajwa N, et al. Diagnostics for SARS-CoV-2 infections. Nat Mater. 2021;20(5):593–605. 10.1038/s41563-020-00906-z.Search in Google Scholar
[6] https://www.who.int/emergencies/diseases/novel-coronavirus-2019; Accessed March 8, 2022.Search in Google Scholar
[7] Hall VJ, Foulkes S, Saei A, Andrews N, Oguti B, Charlett A, et al. COVID-19 vaccine coverage in health-care workers in England and effectiveness of BNT162b2 mRNA vaccine against infection (SIREN): a prospective, multicentre, cohort study. Lancet. 2021;397(10286):1725–35. 10.1016/s0140-6736(21)00790-x.Search in Google Scholar
[8] Cully M. A tale of two antiviral targets – and the COVID-19 drugs that bind them. Nat Rev Drug Discov. 2022;21(1):3–5. 10.1038/d41573-021-00202-8.Search in Google Scholar
[9] Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7). Chin Med J (Engl). 2020;133(9):1087–95. 10.1097/cm9.0000000000000819.Search in Google Scholar
[10] Liu CX. Pay attention to situation of SARS-CoV-2 and TCM advantages in treatment of novel coronavirus infection. Chin Herb Med. 2020;12(2):97–103. 10.1016/j.chmed.2020.03.004.Search in Google Scholar
[11] http://wsjkw.sc.gov.cn/scwsjkw/zcwj11/2020/2/6/ac6fea21a3ad490aa0a73c9d70004ad6.shtml; Accessed November 28, 2021.Search in Google Scholar
[12] Zhao Y, Hu J, Zhang G, Guan Y, Cheng G, Qu H. Origin and application of Jingfang Baidu powder. Glob Traditional Chin Med. 2020;13(11):1996–2002.Search in Google Scholar
[13] Wang X, Wang ZY, Zheng JH, Li S. TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches. Chin J Nat Med. 2021;19(1):1–11. 10.1016/s1875-5364(21)60001-8.Search in Google Scholar
[14] Zhou Z, Chen B, Chen S, Lin M, Chen Y, Jin S, et al. Applications of network pharmacology in traditional Chinese medicine research. Evid Based Complement Altern Med. 2020;2020:1646905. 10.1155/2020/1646905.Search in Google Scholar PubMed PubMed Central
[15] Santos LHS, Ferreira RS, Caffarena ER. Integrating molecular docking and molecular dynamics simulations. Methods Mol Biol. 2019;2053:13–34. 10.1007/978-1-4939-9752-7_2.Search in Google Scholar
[16] Xu HY, Zhang YQ, Liu ZM, Chen T, Lv CY, Tang SH, et al. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019;47(D1):D976–D82. 10.1093/nar/gky987.Search in Google Scholar
[17] Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. 10.1186/1758-2946-6-13.Search in Google Scholar
[18] Huang L, Xie D, Yu Y, Liu H, Shi Y, Shi T, et al. TCMID 2.0: a comprehensive resource for TCM. Nucleic Acids Res. 2018;46(D1):D1117–d20. 10.1093/nar/gkx1028.Search in Google Scholar
[19] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. 10.1038/srep42717.Search in Google Scholar
[20] Gfeller D, Michielin O, Zoete V. Shaping the interaction landscape of bioactive molecules. Bioinformatics. 2013;29(23):3073–9. 10.1093/bioinformatics/btt540.Search in Google Scholar
[21] Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: integrating information about genes, proteins and diseases. Trends Genet. 1997;13(4):163. 10.1016/s0168-9525(97)01103-7.Search in Google Scholar
[22] Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–d12. 10.1093/nar/gkaa1074.Search in Google Scholar PubMed PubMed Central
[23] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. 10.1101/gr.1239303.Search in Google Scholar PubMed PubMed Central
[24] Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. 10.1038/s41467-019-09234-6.Search in Google Scholar PubMed PubMed Central
[25] Samuel RM, Majd H, Richter MN, Ghazizadeh Z, Zekavat SM, Navickas A, et al. Androgen signaling regulates SARS-CoV-2 receptor levels and is associated with severe COVID-19 symptoms in men. Cell Stem Cell. 2020;27(6):876–89. e12. 10.1016/j.stem.2020.11.009.Search in Google Scholar PubMed PubMed Central
[26] Busnadiego I, Fernbach S, Pohl MO, Karakus U, Huber M, Trkola A, et al. Antiviral Activity of Type I, II, and III Interferons Counterbalances ACE2 Inducibility and Restricts SARS-CoV-2. mBio. 2020;11(5):e01928-20. 10.1128/mBio.01928-20.Search in Google Scholar PubMed PubMed Central
[27] Wenzel J, Lampe J, Müller-Fielitz H, Schuster R, Zille M, Müller K, et al. The SARS-CoV-2 main protease M(pro) causes microvascular brain pathology by cleaving NEMO in brain endothelial cells. Nat Neurosci. 2021;24(11):1522–33. 10.1038/s41593-021-00926-1.Search in Google Scholar PubMed PubMed Central
[28] Sarkar A, Mandal K. Repurposing an Antiviral Drug against SARS-CoV-2 Main Protease. Angew Chem Int Ed Engl. 2021;60(44):23492–4. 10.1002/anie.202107481.Search in Google Scholar PubMed PubMed Central
[29] Athanasios A, Charalampos V, Vasileios T, Ashraf GM. Protein-Protein Interaction (PPI) Network: Recent advances in drug discovery. Curr Drug Metab. 2017;18(1):5–10. 10.2174/138920021801170119204832.Search in Google Scholar PubMed
[30] Xu W, Gao Y, Wang Y, Guan J. Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks. BMC Bioinforma. 2021;22(Suppl 6):485. 10.1186/s12859-021-04369-0.Search in Google Scholar PubMed PubMed Central
[31] Kannan S, Shaik Syed Ali P, Sheeza A. Omicron (B.1.1.529) – variant of concern – molecular profile and epidemiology: a mini review. Eur Rev Med Pharmacol Sci. 2021;25(24):8019–22. 10.26355/eurrev_202112_27653.Search in Google Scholar PubMed
[32] Pourkarim F, Pourtaghi-Anvarian S, Rezaee H. Molnupiravir: A new candidate for COVID-19 treatment. Pharmacol Res Perspect. 2022;10(1):e00909. 10.1002/prp2.909.Search in Google Scholar PubMed PubMed Central
[33] Zhao Z, Li Y, Zhou L, Zhou X, Xie B, Zhang W, et al. Prevention and treatment of COVID-19 using Traditional Chinese Medicine: A review. Phytomedicine. 2021;85:153308. 10.1016/j.phymed.2020.153308.Search in Google Scholar PubMed PubMed Central
[34] An X, Zhang Y, Duan L, Jin D, Zhao S, Zhou R, et al. The direct evidence and mechanism of traditional Chinese medicine treatment of COVID-19. Biomed Pharmacother. 2021;137:111267. 10.1016/j.biopha.2021.111267.Search in Google Scholar PubMed PubMed Central
[35] Du HZ, Hou XY, Miao YH, Huang BS, Liu DH. Traditional Chinese Medicine: an effective treatment for 2019 novel coronavirus pneumonia (NCP). Chin J Nat Med. 2020;18(3):206–10. 10.1016/s1875-5364(20)30022-4.Search in Google Scholar
[36] Hoyer KK, Dooms H, Barron L, Abbas AK. Interleukin-2 in the development and control of inflammatory disease. Immunol Rev. 2008;226:19–28. 10.1111/j.1600-065X.2008.00697.x.Search in Google Scholar PubMed
[37] Unver N, McAllister F. IL-6 family cytokines: Key inflammatory mediators as biomarkers and potential therapeutic targets. Cytokine Growth Factor Rev. 2018;41:10–7. 10.1016/j.cytogfr.2018.04.004.Search in Google Scholar PubMed PubMed Central
[38] Pemmaraju N, Chen NC, Verstovsek S. Immunotherapy and immunomodulation in myeloproliferative neoplasms. Hematol Oncol Clin North Am. 2021;35(2):409–29. 10.1016/j.hoc.2020.12.007.Search in Google Scholar PubMed
[39] Yin X, Riva L, Pu Y, Martin-Sancho L, Kanamune J, Yamamoto Y, et al. MDA5 governs the innate immune response to SARS-CoV-2 in lung epithelial cells. Cell Rep. 2021;34(2):108628. 10.1016/j.celrep.2020.108628.Search in Google Scholar PubMed PubMed Central
[40] Wang J, Ge W, Peng X, Yuan L, He S, Fu X. Investigating the active compounds and mechanism of HuaShi XuanFei formula for prevention and treatment of COVID-19 based on network pharmacology and molecular docking analysis. Mol Divers. 2021;26:1–16. 10.1007/s11030-021-10244-0.Search in Google Scholar PubMed PubMed Central
[41] Wang X, Zhang H, Abel AM, Young AJ, Xie L, Xie Z. Role of phosphatidylinositol 3-kinase (PI3K) and Akt1 kinase in porcine reproductive and respiratory syndrome virus (PRRSV) replication. Arch Virol. 2014;159(8):2091–6. 10.1007/s00705-014-2016-4.Search in Google Scholar PubMed
[42] Ahmad MK, Abdollah NA, Shafie NH, Yusof NM, Razak SRA. Dual-specificity phosphatase 6 (DUSP6): a review of its molecular characteristics and clinical relevance in cancer. Cancer Biol Med. 2018;15(1):14–28. 10.20892/j.issn.2095-3941.2017.0107.Search in Google Scholar PubMed PubMed Central
[43] Arranz A, Doxaki C, Vergadi E, Martinez de la Torre Y, Vaporidi K, Lagoudaki ED, et al. Akt1 and Akt2 protein kinases differentially contribute to macrophage polarization. Proc Natl Acad Sci U S A. 2012;109(24):9517–22. 10.1073/pnas.1119038109.Search in Google Scholar PubMed PubMed Central
[44] Sampei M, Arai MA, Ishibashi M. Total syntheses of schizandriside, saracoside and ( ±)-isolariciresinol with antioxidant activities. J Nat Med. 2018;72(3):651–4. 10.1007/s11418-018-1198-6.Search in Google Scholar PubMed
[45] Di Pierro F, Derosa G, Maffioli P, Bertuccioli A, Togni S, Riva A, et al. Possible therapeutic effects of adjuvant quercetin supplementation against early-stage COVID-19 infection: A prospective, randomized, controlled, and open-label study. Int J Gen Med. 2021;14:2359–66. 10.2147/ijgm.S318720.Search in Google Scholar
[46] Shohan M, Nashibi R, Mahmoudian-Sani MR, Abolnezhadian F, Ghafourian M, Alavi SM, et al. The therapeutic efficacy of quercetin in combination with antiviral drugs in hospitalized COVID-19 patients: A randomized controlled trial. Eur J Pharmacol. 2022;914:174615. 10.1016/j.ejphar.2021.174615.Search in Google Scholar PubMed PubMed Central
[47] D’Ascanio L, Vitelli F, Cingolani C, Maranzano M, Brenner MJ, Di Stadio A. Randomized clinical trial “olfactory dysfunction after COVID-19: olfactory rehabilitation therapy vs intervention treatment with Palmitoylethanolamide and Luteolin”: preliminary results. Eur Rev Med Pharmacol Sci. 2021;25(11):4156–62. 10.26355/eurrev_202106_26059.Search in Google Scholar PubMed
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