key: cord-0944107-u94wkoyz
authors: Zeng, Yijia; Lou, Guanhua; Ren, Yuanyuan; Li, Tingna; Zhang, Xiaorui; Wang, Jin; Huang, Qinwan
title: Network pharmacology-based analysis of zukamu granules for the treatment of COVID-19
date: 2021-01-02
journal: Eur J Integr Med
DOI: 10.1016/j.eujim.2020.101282
sha: 0c213b4cb1057d9bb24ade257495a77e48741ded
doc_id: 944107
cord_uid: u94wkoyz

INTRODUCTION: Zukamu granules may play a potential role in the fight against the Coronavirus, COVID-19. The purpose of this study is to explore the mechanisms of Zukamu granules using network pharmacology combined with molecular docking. METHODS: The Traditional Chinese Medicine systems pharmacology (TCMSP) database was used to filter the active compounds and the targets of each drug in the prescription. The Genecards and OMIM databases were used for identifying the targets related to COVID-19. The STRING database was used to analyze the intersection targets. Compound - target interaction and protein-protein interaction networks were constructed using Cytoscape to decipher the anti-COVID-19 mechanisms of action of the prescription. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway and Gene Ontology (GO) enrichment analysis was performed to investigate the molecular mechanisms of action. Finally, the interaction between the targets and the active compounds was verified by molecular docking technology. RESULTS: A total of 66 targets were identified. Further analysis identified 10 most important targets and 12 key compounds. Besides, 1340 biological process, 43 cell composition, and 87 molecular function items were obtained (P < 0.05). One hundred and thirty pathways were obtained (P < 0.05). The results of molecular docking showed that there was a stable binding between the active compounds and the targets. CONCLUSION: Analysis of the constructed pharmacological network results allowed for the prediction and interpretation of the multi-constituent, multi-targeted, and multi-pathway mechanisms of Zukamu granule as a potential source for supportive treatment of COVID-19.

In December 2019, a series of unexplained pneumonia cases occurred in Wuhan, China. On 12 January 2020, the World Health Organization (WHO) temporarily named this new virus as the 2019 novel coronavirus (2019-nCoV). On 11 February 

To screen the bioactive compounds with anti-COVID-19 activities, the TCMSP and text mining tools were used. The ADME parameter-based virtual screening of the compounds was utilized to further identify anti-COVID-19 compounds using an oral bioavailability (OB) threshold OB ≥ 30%, a drug-likeness (DL) threshold DL ≥ 0.18. After that, the common compounds and unique compounds were identified for the next analysis.

When OB ≥ 30% and DL ≥ 0.18 were selected as filter standards, the compounds which could not meet the filter standards but were proved to be the main effective compounds were retained. After eliminating the repeated compounds, 139 kinds of effective compounds were selected as candidate compounds (Table1). A, B, C, D, E, F, G, H, I, J, and K were the common compounds. The compound • target network of the 139 compounds and the corresponding targets was constructed with Cytoscape 3.7.2 (Fig. 2) . The analysis of the compound-target network showed 303 nodes (10 drug nodes, 11 common compound nodes, 128 endemic compound nodes, and 154 target nodes) and 1645 edges in total. All the regular hexagons in the network represented compounds, circles represented drugs, and diamonds represented targets. All the edges represented the interaction between drugs and compounds or compounds and targets. The compound -target network indicated that the same compound could interact with multiple targets, and each target was often associated with multiple compounds. 

The 154 drug targets were matched with the 1354 novel coronavirus pneumonia or pulmonary fibrosis-related targets to identify 66 intersection targets. The result was shown in Fig 3. Sixty-six kinds of intersection targets were imported into STRING with the gene type selected as Homo sapiens. Setting the medium confidence to 0.400 and hiding the disconnected nodes in the network, the protein-protein interaction information could be obtained. The information was visualized (Fig 4) . The network comprised 65 nodes and 691 edges. Further network topology analysis showed that the average node degree was 20.9, and the local clustering coefficient was 0.684, indicating the multi-targeted properties of the drug compounds studied. 

By comparing the number of related targets of each target, a total of 30 core targets were identified from the protein-protein interaction network (Fig 5) , and the top ten targets were IL6, INS, EGFR, VEGFA, ALB, CASP3, MAPK8, CCND1, MYC, and FOS. The compounds related to these targets were acacetin, naringenin, aloe-emodin, luteolin, beta-sitosterol, beta-carotene, quercetin, kaempferol, licochalcone A, apigenin, lupeol, and hesperidin. 

Through the GO enrichment analysis, 1340 biological process (BP) items were obtained, and the top five were cellular response to chemical stress, response to steroid hormone, response to oxidative stress, response to nutrient levels, and cellular response to oxidative stress (Fig 6) . Forty-three cell composition (CC) items were obtained, and the top five were vesicle lumen, transcription regulator complex, membrane raft, membrane microdomain, and membrane region (Fig 7) . Eighty-seven molecular function (MF) items were obtained, and the top five were DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, DNA-binding transcription activator activity, RNA polymerase II-specific, DNA-binding transcription activator activity and ubiquitin protein ligase binding (Fig 8) . The research group visualized the first 20 results. The larger the bubble was, the more the enriched genes were. The smaller the P. adjust was, the redder the color of the bubble was. A total of 130 results were identified according to the KEGG pathway enrichment analysis, mainly involving PI3K-Akt signaling pathway, Kaposi sarcoma-associated herpesvirus infection, Epstein-Barr virus infection, Human cytomegalovirus infection, Fluid shear stress and atherosclerosis (Fig 9) . 

The value of binding energy is less than 0, indicating that the ligand can spontaneously bind to the receptor. As far as we know, the more stable the binding conformation is, the lower the binding energy is. In this study, the binding energy value ≤ -5.0 kcal/mol was selected as the filter standard. Luteolin and quercetin were selected as the representative compounds, and CASP3, EGFR, VEGFA, and IL6 were selected as the targets ( Table 2 ). The results showed that all the values were less than -5 kcal/mol, indicating that there was a stable binding between the compounds and the targets. The results were shown in Fig 10 and 

COVID-19 is a global pandemic. In severe cases, massive alveolar damage and progressive respiratory failure may lead to death, and the counts of lymphocyte, monocyte, leucocyte, infection-related biomarkers, inflammatory cytokines, and T cells are changed in severe patients [8] . One possible sequela of COVID-19 is pulmonary fibrosis, which leads to chronic breathing difficulties, long-term disability and affects patients' quality of life [9] [10] [11] . Zukamu granule is widely used in the treatment of cold, cough, fever without sweat, sore throat, and stuffy nose by Uygur people because of its functions of regulating abnormal temperament, clearing away heat, sweating, and dredging the orifices. Zukamu granule plays a significant role in the prevention and treatment of COVID-19, which improves the clinical cure rate [12] [13] [14] [15] . However, no study to date has examined the mechanisms of its action, and there is a lack of molecular-level research. Therefore, it is of great significance to study the mechanisms of action of Zukamu granule and explore potential targets for clinical use. With this aim in mind, in this research 139 active compounds in Zukamu granule were identified, including 11 common compounds. By analyzing the drug related targets and the COVID-19 related targets, sixty-six intersection targets were identified. A protein-protein interaction network was constructed with 65 intersecting targets after removing one free target, and 30 core targets were identified from the network. The most important ten core targets were IL6, INS, EGFR, VEGFA, ALB, CASP3, MAPK8, CCND1, MYC, and FOS. IL6 is the core target in PPI network, which indicates that it plays a key role in PPI network. When COVID-19 infects the upper and lower respiratory tract, it can cause a mild or highly acute respiratory syndrome with consequent release of pro-inflammatory cytokines, including interleukin (IL)-6. It is reported that IL-6 can act on fibroblasts, induce their activation and migration, and promote the occurrence of pulmonary fibrosis. Suppression of IL-6 has been shown to have a therapeutic effect in many inflammatory diseases [16] . Insulin (INS) is associated with the pathogenesis of diabetes, and its abnormality may lead to acute complications related to hyperglycemia, and patients with COVID-19 may be at risk of increased complications. Epidermal growth factor receptor (EGFR) is the prototypical member of a family of receptor tyrosine kinases known as the ErbB receptors. EGFR signaling regulates wound healing and repair in normal tissue, it has also been associated with fibrotic disease in various organs. Research shows that pulmonary fibrosis is caused by a hyperactive host response to lung injury mediated by EGFR signaling [17] . The combination of VEGF and VEGFR mediates angiogenesis, provides nutrients for the synthesis of extracellular matrix and collagen fibers, and aggravates pulmonary fibrosis [18] . Serum albumin is a multifunctional protein known to interact with a range of exogenous and endogenous compounds. The earlier studies indicated that the stressed and inflamed cells increase the uptake of albumin [19] [20] [21] [22] . Therefore, the severity of COVID-19 patients is closely related to the level of serum albumin. Caspase-3, onto which there is a convergence of the intrinsic and extrinsic apoptotic pathways, is the main executioner of apoptosis [23] . The high expression of Caspase-3 can increase the apoptosis of infected cells [24] . MAPK8 can be activated by various pro-inflammatory and stress stimuli, and plays a key role in the proliferation, differentiation and production of inflammatory cells [25] . Cyclin D1, a member of the cyclin protein family, has been identified as an indispensable factor for regulating the cell cycle. It can mediate osteoarthritis chondrocyte apoptosis through the WNT3/b-catenin signaling pathway [26] . Muscarinic acetylcholine receptor is closely related to airway diseases. Parasympathetic nerves release acetylcholine onto muscarinic receptors (M1-M5). Stimulation of M1 and M3 muscarinic receptors causes bronchoconstriction [27] . C-FOS is involved in the regulation of inflammation in asthma. Its expression level could be increased by the factors involved in the air-ways inflammation of asthma (histamine, eicosanoids, and cytokines) [28] . The increase of C-FOS expression in fibroblasts leads to fibrous dysplasia [29] . From the above analysis, Zukamu granule may play a role in the prevention or treatment of COVID-19 and pulmonary fibrosis by regulating the expression levels of these ten core targets.

On the basis of our analysis, acacetin, naringenin, aloe-emodin, luteolin, beta-sitosterol, beta-carotene, quercetin, kaempferol, licochalcone A, apigenin, lupeol, and hesperidin were found to be related to these 10 core targets. In an attempt to validate the obtained suggestions, references from the PubMed related to these 12 compounds were retrieved. As can be observed, several studies have established the link between those compounds and the different pathways in COVID-19 treatment. Acacetin, a natural flavonoid compound, has anti-oxidative and anti-inflammatory effects that can protect the sepsis-induced acute lung injury [30] . Naringenin is a flavonoid, which can significantly decrease the elevated pro-inflammatory cytokines like IL-1β, IL-6, TNF-α and NF-ҝβ levels [31, 32] . Aloe-emodin has anti-influenza, anti-bacterial and anti-inflammatory effects [33] [34] [35] . Luteolin, a natural flavonoid, has a significant anti-inflammatory effect, and its mechanism is related to the MAPK signaling pathway. Besides, luteolin has a role in reducing lung injury and myocardial fibrosis [36] [37] [38] . Beta-sitosterol has anti-inflammatory effects by inhibiting the occurrence of inflammatory reactions [39, 41] . Beta-carotene can mediate signal transduction and regulate gene expression [41] , and this may be related to its therapeutic effects. Quercetin is a natural bioflavonoid and has the activities of anti-inflammatory, anti-proliferative, anti-oxidant stress, and anti-angiogenic [42, 43] . Kaempferol, a flavonoid that exists in many plants and fruits, has the effects of anti-inflammatory and reducing pulmonary fibrosis [44, 45] . Lupeol, a diet triterpene, can inhibit the expression of EFGR and IL6 and has the modular effects on inflammation, oxidative stress, and angiogenesis. The mechanisms of action are related to the PI3K / Akt and p38 / ERK / MAPK pathways [46] [47] [48] . Licochalcone A, apigenin, and hesperidin can also inhibit inflammation and oxidative stress [49] [50] [51] [52] [53] [54] . We can conclude that these chemical constituents are the main active components in Zukamu granule. These compounds can act on the above ten core targets to regulate their expression levels, so as to play a pharmacodynamic role.

To further clarify the mechanisms of action, we carried out enrichment analysis of GO and KEGG. GO enrichment analysis showed that the effective compounds of Zukamu granule were mainly involved in the regulation of chemical stress, transcriptional regulation, inflammatory response, apoptosis, oxidative stress, and nutritional level. KEGG pathway enrichment analysis showed that the effective compounds were mainly involved in the inflammatory response, viral infection, cancer, apoptosis, and tissue repair related signaling pathways. Previous studies have shown that the development of COVID-19 and its sequelae (pulmonary fibrosis) is closely related to inflammation, apoptosis and angiogenesis [3, 55, 56] , and this is consistent with the result of our research. The results of molecular docking showed that the binding energy values between effective compounds and targets were less than -5.0 kcal/mol, indicating that there shows an affinity for the compounds and receptors. Based on all the above evidence, we can see that the core effective compounds of Zukamu granule may have the intervention effects on the COVID-19 through anti-inflammatory, anti-oxidant stress, regulation of apoptosis, and inhibition of pulmonary fibrosis.

In this study, we identified the active compounds and targets of Zukamu granule for the treatment of COVID -19, but further experimental or clinical verification of the findings of the present study is still needed.

The overall goal of this study is to explore the mechanisms of action of Zukamu granule for the treatment of COVID-19. We examine some previous work and propose that network pharmacology combined with molecular docking is a feasible method. After systematic analysis, we can know that Zukamu granule may have the intervention effects on the COVID-19 through anti-inflammatory, anti-oxidant stress, regulation of apoptosis, and inhibition of pulmonary fibrosis. This research provides a certain basis for clinical medication.

Yijia Zeng, Guanhua Lou, Jin Wang, and Qinwan Huang were guarantor of integrity of entire study and contributed to the study concepts and design. Yijia Zeng, Yuanyuan Ren, and Tingna Li contributed to the literature search and data collection. Yijia Zeng, Guanhua Lou, and Xiaorui Zhang contributed to the data acquisition and analysis. Yijia Zeng and Qinwan Huang contributed to the manuscript preparation and revision. All the authors discussed, edited and approved the final version.

This work was financially supported by the Xinglin Scholars Talent Promotion Plan of Chengdu University of Traditional Chinese Medicine (Grant number: QNXZ2018023; Grant number: XSGG2019008).

The authors declare that they have no conflicts of interest. of the node was, the larger the node size was, and the brighter the node color was. The larger the combined score was, the larger the edge size was, and the darker the color was. 

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Mechanisms of pulmonary fibrosis

We thank our alma mater Chengdu University of Traditional Chinese Medicine for the experimental platform provided for this study. Thank you all for your support and help.

The data used to support the findings of this study is available from the corresponding author upon request.

On behalf of me and my co-authors, I make the following statement: Yijia Zeng, Guanhua Lou, Jin Wang, and Qinwan Huang were guarantor of integrity of entire study and contributed to the study concepts and design. Yijia Zeng, Yuanyuan Ren, and Tingna Li contributed to the literature search and data collection. Yijia Zeng, Guanhua Lou, and Xiaorui Zhang contributed to the data acquisition and analysis. Yijia Zeng and Qinwan Huang contributed to the manuscript preparation and revision. All the authors discussed, edited and approved the final version.

The authors declare that they have no conflicts of interest. 

All sources of funding should also be acknowledged and you should declare any involvement of study sponsors in the study design; collection, analysis and interpretation of data; the writing of the manuscript; the decision to submit the manuscript for publication. If the study sponsors had no such involvement, this should be stated.

This work was financially supported by the Xinglin Scholars Talent Promotion Plan of Chengdu University of Traditional Chinese Medicine (Grant number: QNXZ2018023; Grant number: XSGG2019008). The study sponsors contributed to the study concepts.Signature (a scanned signature is acceptable, Print name but each author must sign) Yijia Zeng, Guanhua Lou, Yuanyuan Ren, Tingna Li, Xiaorui Zhang, Jin Wang, Qinwan Huang Graphical Abstract