Network of “drug-target-SARS-CoV-2 Related Genes” Through Integrated Analysis of Pharmacology and Geo Database

Network of “drug-target-SARS-CoV-2 Related Genes” Through Integrated Analysis of Pharmacology and Geo Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Network of “drug-target-SARS-CoV-2 Related Genes” Through Integrated Analysis of Pharmacology and Geo Database Jin ping Hou, Yong heng Wang, Yu meng Chen, Yi hao Chen, Xiao Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-117894/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Coronavirus Disease 2019 (COVID-19) respiratory disease rapidly caused a global pandemic and social and economic disruption. The combination of Traditional Chinese medicine (TCM) and Conventional Western medicine (CWM) is more effective for COVID-19 treatment. Moreover, TCM and CWM are important data source for developing new drug targets and promote strategies treat SARS-CoV-2 infections. However, many studies have analyzed the therapeutic mechanism of CWM or TCM alone for COVID-19, it is still unclear the interaction mechanism between TCM and CWM on COVID-19. Methods This paper integrates network pharmacology and GEO database to mine and identify COVID-19 molecular therapeutic targets, providing potential targets and new ideas for COVID-19 gene therapy and new drug development. It includes: 1) using TCMSP, TTD, PubChem and CTD databases to analyze drug interactions and associated phenotypes for SARS-CoV-2, to correlate drug and disease interaction mechanisms to screen key drug targets; 2) using GEO database to correlate differential genes and drug targets to screen potential antiviral gene therapy targets, to construct regulatory network and key points of SARS-CoV-2 therapeutic drugs; 3) using computer simulation of molecular docking to screen virus-related proteins for new drugs. Results Integrated analysis of network pharmacology discovered that baicalein, estrone and quercetin are the pivotal active ingredients in TCM and CWM. Combining drug target genes in pharmacology database and virus induced genes in GEO database, the result showed the core hub genes related to COVID-19: STAT1 , IL1B , IL6 , IL8 , PTGS2 and NFKBIA , and these genes were significantly downregulated in A549 and NHBE cells by SARS-CoV-2 infection. Moreover, chemical interaction and molecular docking analysis of hub genes showed that folic acid might as be potential therapeutic drug for COVID-19 treatment, and SARS-CoV-2 nucleocapsid phosphoprotein was a potential drug target. The network of “drug-target-SARS-CoV-2 related genes” provide noval potential compounds and targets for further studies of SARS-CoV-2. Conclusions Integrated analysis of network pharmacology and big data mining provided noval potential compounds and targets for further studies of SARS-CoV-2. Our research implied folic acid and SARS-CoV-2 N as therapeutic target in TCM and CWM. Our research also suggests that targeting SARS-CoV-2 N protein is likely to be a common mechanism of TCM and CWM. On the one hand, the identification of pivotal genes provides a target for COVID-19 molecular therapy, on the other hand, it provides ideas for the analysis of interaction mechanism between virus and host. Internal Medicine COVID-19 SARS-CoV-2 traditional Chinese medicine big data mining antivirus Figures Figure 1 Figure 1 Figure 2 Figure 2 Figure 3 Figure 3 Figure 4 Figure 4 Figure 5 Figure 5 Introduction The recently discovered beta-coronavirus severe acute respiratory syndrome (SARS-CoV-2), the causative agent of Coronavirus Disease 2019 (COVID-19) respiratory disease rapidly caused a global pandemic and social and economic disruption 1,2 . As of October 23th, 2020, a total of over 41 million cases of COVID-19 were reported and more than 1100 thousand lives were claimed globally ( https://covid19.who.int ). SARS-CoV-2 is sense single‐stranded genomic RNA virus, approximately 30 kb in length, encodes four structural proteins: the spike (S), membrane (M), envelope (E) and nucleocapsid (N) proteins 3 . S protein binds to angiotensin-converting enzyme (ACE2) receptor on the cytoplasmic membrane of type 2 lung cells and intestinal epithelium. After binding, S protein was cleaved by the host membrane serine protease TMPRSS2, which promoted the virus entry 4 .These structural proteins are components of the mature virus and play a crucial role in viral structure integrity, or as in the case of the spike protein, for viral entry into the host 5,6 . The SARS-CoV-2 genome contains 14 open reading frames (ORFs), preceded by transcriptional regulatory sequences (TRSs) 7 . The structural and accessory proteins are translated from a set of nested sub-genomic (g) RNAs. Underlining how SARS-CoV-2 hijacks the host during infection absolutely could promote the development, characterization, and deployment of an effective vaccine or antibody prophylaxis or treatment against SARS-CoV-2 and could prevent morbidity and mortality and curtail its epidemic spread 8 . Traditional Chinese medicine (TCM) formulations had been originally developed for earlier viral diseases and been used in COVID-19 treatment 9 . TCM was extensively wildly used to control the epidemic in China 10 . Shufeng Jiedu Capsule and Lianhua Qingwen capsule have good clinical effect on patients with COVID-19 11,12 . The further study of TCM may lead to the identification of novel antivirus compounds for the treatment of SARS-CoV-2 or other emerging fatal viral diseases 9 . The antiviral medicine mainly target on host protease or other cellular proteins, such as teicoplanin, arbidol, chloroquine and derivatives, and niclosamide; and target on viral proteins, such as niclosamide, lopinavir/ritonavir, remdesivir, favipiravir, and ribavirin 13 . Combination of TCM and conventional Western medicine (CWM) can effectively inhibit SARS-CoV-2 replication and prevent the production of proinflammatory cytokines induced by SARS-CoV-2 infection. Many studies have only analyzed the therapeutic mechanism of CWM or TCM alone for COVID-19, so this study integrated the mechanism of CWM and TCM together to explore their generality and characteristics. Based on the concept of "disease-target-compound-drug", network pharmacology systematically analyzes the mechanism of drug action and its interaction network from multiple levels and angles 14,15 . Network pharmacology combines the information of genome, proteome, drugs, diseases and other related databases with experimental verification data through the method of big data mining, so as to establish an inter related network of " disease-target-compound-drug " 16,17 . Therefore, this method can be used to systematically analyze the intervention and influence of TCM and CWM on COVID-19, reveal the mechanism of action of drugs in human body, correlate the pharmacophores and interactions of various drugs, and screen the core targets. In addition, the accumulation of big data resources is conducive to the screening of molecular markers for disease prevention 18 . Biomolecular targets are the basis of accurate diagnosis and personalized drug design, which can be used to capture specific disease signals in the early stage to formulate appropriate treatment plans 18 . Screening of molecular targets for COVID-19 therapy is of great significance for understanding the interaction mechanism between SARS-CoV-2 and host. Therefore, it is of great significance to integrate network pharmacology and GEO database to screen COVID-19 molecular therapeutic targets with clinical value. Identifying the regulatory effects of core hub genes based on big data mining can provide potential targets and new ideas for COVID-19 gene therapy and new drug development. Materials And Methods 2.1 Active compounds and drug target genes Chemical ingredients and chemical target genes were collected from the Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform (TCMSP) Database (http://tcmspw.com/tcmsp.php). In TCMSP database (http://tcmspw.com/tcmsp.php), the final candidate active ingredients were screened, according to the oral bioavailability (OB) ≥30%, drug-likeness (DL) ≥0.18, drug half-life (HL)≥10. The compound target genes were imported into UniProt (https://www.uniprot.org/) that defining the species as human, and obtained the gene names corresponding to the target genes. The COVID-19 treatment plan (issued in the sixth version) issued by the office of the National Health and Health Committee of China is selected as follows: Ephedra Herba (Mahuang), Cinnanmmi Cortex (Rougui), Alisma Orientale (Sam.) Juz. (Zexie), P olyporus Umbellatus(Pers)Fr. (Zhuqin), Atractyloes Macrocephala Koidz. (Baizhu), Arum Ternatum Thunb (Banxia), Asteris Radix Et Rhizoma (Zikou), Farfarae Flos (Donghua), Belamcandae Rhizome (Shegan), Asari Radix Et Rhizoma (Xixin), Rhizoma Dioscoreae (Shanyao), Aurantii Fructus Immaturus (Zhishi), Fortunes Bossfern Rhizome (Guanzong), Radix Cynanchi Paniculati (Xuchangqing), Eupatorium Fortunei Turcz (Peilan), Scutellariae Radix (Huangqin), Radix Bupleuri (Chaihu), Artemisia Annua L. (Qinghao), Isatidis Folium (Daqingye), Polygoni Cuspidati Rhizoma Et Radix (Huzhang), Verbenae Herb (Mabiancao), Phragmitis Rhizoma (Lugen), Citri Grandis Exocarpium (Huajuhong), Notopterygii Rhizoma Et Radix (Qianghuo), Arecae Semen (Binlang), Amygdalus Communis Vas (Xingren), Licorice (Gancao), Pogostemon Cablin (Blanco) Benth .(Huoxiang), Magnolia Officinalis Rehd Et Wils (Houpu), Atractylodes Lancea (Thunb.)Dc .(Cangshu), Amomum Tsao-Ko Crevostet (Caoguo), Radix Rhei Et Rhizome (Shengdahuang), Anemarrhenae Rhizoma (Zhimu), Radix Paeoniae Rubra (Chishao), Figwort Root (Xuansen), Forsythiae Fructus (Lianqiao), Cortex Moutan (Danpi), Coptidis Rhizoma (Huanglian), Lepidii Semen Descurainiae Semen (Tinglizi), Panax Ginseng C. A. Mey. (Rensen), Cornus Officinalis Sieb. Et Zucc. (Shanzhuyu), Arum Ternatum Thunb .(Fabanxia), Citrus Reticulata (Chenpi), Codonopsis Radix (Dangsen), Glehniae Radix (Beishasen), Schisandre Chinensis Fructus (Wuweizi), Mori Follum (Sangye), Prragmitis Rhizoma (Lugen), Radix Salviae (Dansen). 2.2 Differential expressed genes (DEGs) induced by SARS-CoV-2 infection NHEB and A549 cell lines The differential expression genes (DEGs) between EGFR-TKI sensitive and resistance were calculated from public dataset of Gene Expression Omnibus (GEO). GSE147507 dataset include SARS-CoV-2 infected NHEB and A549 cell lines, and the uninfected group. SARS-004 group indicated that SARS-CoV-2 infected NHBE cells at MOI 2, Cov-002 group indicated that SARS-CoV-2 infected A549 cells at MOI 0.2, and the control group were the corresponding uninfected cell lines. For assay the deregulation gene expression, “limma” package of R software was employed to test the DEGs. mRNAs with log2 fold change (|log2FC|, P < 0.01) considered to be differentially expressed mRNAs. 2.3 Identify core sub-network from compound-target network For identifying core targets from compounds-targets network, the overlap of DEGs and targets were extracted. There eight overlap targets were identified. We selected eight targets and their neighbors (only compounds) as core sub-network. 2.4 Construction of Gene Enrichment Analysis To better understand the processes associated with COVID-19 and Chinese medicine treatment, we performed GO terms and KEGG pathway enrichment analyses. The GO enrichment analysis provides three structured networks of defined terms to describe gene attributes. Enriched GO terms are classified according to biological process (BP), molecular function (MF), and cellular component (CC). The KEGG (http://www.genome.jp/kegg/) is a database for large-scale systematic analysis of molecular interaction networks of genes or proteins. The DAVID bioinformatics resources consist of an integrated biological knowledge base analytic tools, aimed at systematically extracting biological meaning from large gene or protein lists. We used the web-based search engine, DAVID, to determine over-represented GO terms and KEGG pathways with thresholds of an enrichment score > 2, count > 5, and P < 0.05 and analyzed COVID-19-related pathways and GO terms. Venn diagram and bubble graphs of were performed using the OmicShare tools, a free online analysis tool (http://www.omicshare.com/tools). Significant pathway terms of KEGG were mapped into a bubble graph. The big and higher bubbles represent those highly significantly enriched pathway terms. 2.5 Protein-Protein Interaction Network Analysis Protein-protein interaction (PPI) networks included information on the biological processes and molecular functions of cells. We used the online search tool for recurring instances of neighboring genes (STRING, Version 9.1) (http://www.string-db.org) to predict the interactions. The Cytoscape software 3.7.2 (http://cytoscape.org/) was used to visualize networks. To identify crucial relationships in the PPI network, potentially overlapping modules that were densely connected were subsequently identified using Molecular Complex Detection (MCODE) plugin in the Cytoscape program. The MCODE plugin was used to re‐analyze the clusters among the network according to the k‐core = 2. A value of P < 0.01 was considered the significant threshold. 2.6 Network Construction of KEGG Pathway To better analysis the holistic mechanism of Chinese herbs in endometriosis treatment, the subnetworks pathway was compiled by following the procedures: All targets of Chinese herbs in endometriosis treatment were submitted to an online tool KEGG Search Pathway (https://www.genome.jp/kegg/tool/map_pathway1.html). Based on the mechanism of endometriosis, multiple pathways were integrated and overlapped according to cross-talk targets in these maps. Based on the cross-talk of the pathways, we further constructed a targets-pathways network of Chinese medicine treatment. 2.7 Network Construction We established a network analysis of the relevant compound targets. The components of the target networks for the herbs were constructed using the Cytoscape software 3.7.2. The nodes in each network were evaluated based on three indices: degree, node betweenness, and node closeness. Degree indicated the number of edges between a single node and other nodes in a network. Node closeness represented the inverse of the sum of the distance from one node to other nodes. The importance of a node in a network was indicated by the values of these indices, with higher values indicating greater importance. 2.8 Component-molecular target docking From RSCB PDB database（https://www.rcsb.org/）Download the 3D structure pdb format files of SARS-CoV-2 related protein, angiotensin converting enzyme II ( ACE2 ) and angiotensin converting enzyme ( ACE ) remove ligands and non-protein molecules (such as water molecules) in target proteins by using discovery studio 2020 client software, and then save them as PDB suffix files. From PubChem database（https://pubchem.ncbi.nlm.nih.gov/）Download the SDF format file of compound 2D structure. Using pyrx software, we first upload the protein file after water removal and hydrogenation, convert it into pdbqt format file, then upload the compound file to minimize its energy, and convert it into pdbqt format file. Finally, Vina is used for docking. The binding energy is less than 0, which indicates that the ligand and the receptor can spontaneously bind. The active components with binding energy ≤-5.0kj/mol were selected as the screening basis of antiviral particles for COVID-19 target. Results 3.1 Identification of effective compounds and target genes of COVID-19 clinical TCM Effective compounds and target genes were identified through integrated bioinformatics analysis based on TCMSP, TTD, PubChem, CTD and GEO datasets (Fig.S1). According to the COVID-19 pneumonia treatment plan (issued in the sixth version) issued by the office of the national health and Health Committee of China, TCMSP database was used to search the active ingredients in 49 kinds of TCM in COVID-19 prescription, and the parameters OB > 30%, DL > 0.18, HL > 8 were used as the conditions to screen the active ingredients (gypsum, ginger, etc.) were the relevant results. A total of 374 active components and 8355 target genes were obtained by analyzing and summarizing the active components of drugs (Table S1). Amygdalus Communis Vas , Radix Bupleuri , Scutellariae Radix , Citrus Reticulata , Pogostemon Cablin (Blanco) Benth , Ephedra Herba , Notopterygii Rhizoma Et Radix , Atractylodes Lancea (Thunb.)Dc , Magnolia Officinalis Rehd Et Wils , Arecae Semen , Amomum tsaoko Crevost et Lemarie , Anemarrhenae Rhizoma , Forsythiae Fructus , Licorice and Arum Ternatum Thunb were repeated drugs in 10 different prescriptions. Furthermore, the correlation analysis of these effective compounds showed that there was a hub effective component MOL000422 (kaempferol, C 15 H 10 O 6 ) presented in the 10 prescriptions (Fig.1A). PubChem analysis showed the hub compound structure (Fig.1B and C). There are 77 kinds of effective components associated with prescriptions 1, 3, 4, 6, 7, 9 and 10 (Table S2). Each prescription has its own unique active ingredients for patients with different diseases. Prescription 1 has 23 unique active ingredients, prescription 2 has 3, prescription 3 has 3, prescription 4 has 3, prescription 6 has 2, prescription 7 has 2, prescription 8 has 8, prescription 9 has 3, prescription 10 has 43 (Fig.1A). In addition, TCM target genes were collected for GO and KEGG pathway analysis, and the result implied that target genes significantly involved positive regulation of transcription from RNA polymerase II promoter, nucleus and protein binding category (Fig.1D), and mainly involved in pathway in cancer, TNF signaling and Osteoclast differentiation (Fig.1E). 3.2 Screening of joint effective compounds and drug-target genes by analysis of combination of Chinese and Western Medicine We firstly constructed Venn graphs of TCM and CWM compounds and target genes based on TCMSP and TTD/PubChem database, and found 3 hub compounds and 64 target genes (Fig.2A and B). We further analyzed 3D structure of 3 hub compounds (Baicalein, Estrone and Quercetin) based on NCBI PubChem, and the result showed the three structures are similar, Baicalein and Quercetin have large conjugation systems, and Estrone only has conjugation of benzene rings (Fig.S2B). Hub target genes significantly involved in cytosol and protein binding category, and pathway in cancer and Hepatitis B, according to GO and KEGG pathway analysis (Fig. 2C and Fig.S2A). Moreover, Cytoscape was used to construct the regulatory network of “Traditional Chinese medicine-Hub compound-Target gens” (Fig.2D), which indicated that Quercetin had more complex network systems and Estrone had less target genes. MAPK14 , MAPK3 , MCL1 connected Quercetin and Baicalein, OPRM1 , ADRB2 , SCN5A , EGF and ACE connected Estrone and Quercetin. 3.3 Identification of differentially expressed genes (DEGs) induced by SARS-CoV-2 infection based on GEO database: GSE147507 This project is based on GEO database to download the original data of GSE147507. The data set explored the response of NHBE cell lines and A549 cell lines to SARS-CoV-2 infection at the transcriptional level, with a total of 110 samples. The raw data was analyzed by R language and the conditions P 1 defined as differential genes. The results showed that there were 1177 differentially expressed genes in A549 cells and 1407 genes in NHBE cells. The volcano plots of DEGs among Cov-set and SARS-set were shown in Fig.3A and B. Venn analysis showed that 8 up-regulated genes and 113 down regulated genes were the same in the two kinds of cells (Fig.3C and Table 1). NCBI database was used to analyze the tissue expression profile characteristics of hub differential genes (Fig.S3). The results showed that the up-regulated differentially expressed genes induced by SARS-CoV-2 had high tissue specificity. SYNE1 was highly expressed in ovary, CDKN2C in adipose tissue, gnpda1 in kidney, RPAGD in goiter, ANKH in pancreas and PRODH in small intestine. The down-regulated genes induced by virus were highly expressed in bone marrow, lymph node, spleen, lung, cecum, gallbladder and bladder. In addition, the expression levels of pivotal genes in pancreas and liver tissues were significantly lower than those in other tissues. Among all DEGs, only C3 , C1R , C1S , ASS1 , ERRF11 , KYNU , HSD11B1 , SERPINA3 , SAA1 and CFB were significantly expressed in liver tissues, while ERRF11 , SERPINA3 and BCL2A1 were significantly expressed in pancreas. The DEGs KEGG pathway enrichment showed DEGs significantly involved in pathway of Herpes simplex infection, influenza A, Measles and TNF signaling (Fig3D). GO analysis results indicated DEGs were enrichment in type I interferon signaling pathway, defense response to virus, cytoplasm, cytosol and protein binding (Fig.3E). The above results indicated that SARS-CoV-2 infection wildly down-regulated the immune response of host. 3.4 Identification of key hub DEGs by integrating internet-pharmacology and GEO database In order to further screen molecular targets for COVID-19 regulation, and provide targets for exploring the mechanism of interaction between virus and host and screening new drugs, we further conduct molecular docking between drug targeted genes (DTGs) and differential expressed genes (DEGs) induced by SARS-CoV-2 infection, and screen core pivotal genes. Venn analysis was carried out on the target genes of ten TCM prescriptions and the DEGs induced by virus infection. The results showed that the number of key genes in the target genes of SARS-004 group was more than that of Cov-002 group (Fig.4A). These joint genes were down regulated by viral infection in both Cov and SARS groups (Fig.4B and C). Furthermore, Venn analysis was carried out on the related genes of each prescription and GEO set, and found the common associated genes (Fig.S4A and B). ICAM1 , IL1A , PTGS2 , SLPI , STAT1 , TOP2A were the common key genes in CoV and SARS groups. Except TOP2A, they were all down regulated by virus infection (Fig. S4C). Besides, based on TTD (Therapeutic Target Database), GEO and TCMSP databases, the drug targets of Chinese and Western medicine and the differential expressed genes induced by virus were analyzed, and 14 core pivotal genes were obtained. IL6 , IL8 , IL1B , NFKBIA , PTGS2 , STAT1 and THBD were all present in both Cov and SARS groups. Except THBD , other genes were down regulated by viral infection in both groups (Fig.4D and E). Finally, the interaction compounds of core hub genes were analyzed by CTD database, and two common compounds, folic acid and ozone, were screened (Fig.S4E). Folic acid can down regulate the expression of core hub genes, and zone can up regulate the expression of core hub genes. 3.5 Identification of Component-molecular target docking The hub genes and SARS-CoV-2 related genes were respectively done component-molecular target docking with compounds. The lower the Binding energy is, the firmer the binding between compound and molecular target. This means the compound have greater ability to make a difference. The binding energy was lower than 5KJ/mol as the screening condition, and the docking results were listed (Table 2 and Table 3), indicating that these compounds had good binding ability to target proteins. Among the four compounds, the binding energy between folic acid and target protein were -7.2 / kcal.mol，-6.8/kcal.mol，-6.5/kcal.mol，-8.5/kcal.mol，-10.0/kcal.mol and -6.8/kcal.mol, generally lower than that of other compounds. NFKBIA and PTGS2 also have low binding energy with compounds (Table 2 and Fig. 5A). All the four compounds had lower binding energy with ACE2 , among which folic acid was the most significant. The binding energy between the compounds and ACE2 is slightly higher than that of the compounds with ACE . Among the eight proteins of SARS-COV-2, Nucleocapsid Phosphoprotein (N) had the lowest binding energy with compounds. The results with the minimum binding energy for each protein were selected for visualization ( Table 3 and Fig. 5B). Discussion SARA-Cov-2 infection molecular therapeutic network model is built based on intelligent computer and big data integration bioinformatics. The project mainly relies on database analysis to analyze the interactions and related phenotypes among SARA-Cov-2 drugs, and analyze the key drug targets by correlation analysis. Infected cells and normal cells were investigated by GEO database. This research mainly relies on the published data of the database to integrate bioinformatics analysis to obtain the key drug targets and potential targets of gene therapy, explore the mechanisms of effective compounds in the treatment of COVID-19. The category of "epidemic disease" in traditional Chinese medicine has accumulated rich experience in fighting epidemics for thousands of years. After analyzing the active compounds of ten TCM prescriptions, a common core compound, kaempferol, was found. It has been shown that kaempferol not only has some anti-inflammatory, anti-cough and expectorant effects, but also lowers blood glucose and prevents vascular complications of diabetes 19 . Coronavirus infection can induce COVID-19 patients with normal glucose tolerance to increase blood glucose, sustained hyperglycemia is not only conducive to virus replication in the body, but also reduce the body's ability to resist infection, hyperglycemia patients develop serious illness and face a higher risk of death 20,21 . This indicates that TCM can play a full therapeutic role in COVID-19. In addition, GO function was performed on the target sites of TCM, and the results showed that the target genes were enriched in the positive regulation of transcription from RNA polymerase II promoter, oxidation-reduction process, protein binding. Conducting KEGG pathway analysis revealed that target genes were mainly involved in pathway in cancer, TNF signaling pathway, and osteoclast differentiation. It was shown that these terms play a key role in the treatment of COVID-19 by TCM. In the association analysis of TCM and CWM, three central compounds were identified, namely, baicalein, estrone and quercetin. Baicalein is a flavonoid with antihyperglycemic activity 22 , which, like kaempferol, can be useful in patients with diabetes mellitus. Quercetin, like baicalein, is a flavonoid that has the beneficial effects on inflammation and immunity 23 . Study had shown that quercetin may decreases the frequency and duration of respiratory tract infections as an effective intervention; however, more research is needed 24 . Natural estrogens are mainly estradiol, estrone and estriol. Numerous studies had shown that estrogens are associated with the development of liver cancer and work by binding to estrogen receptors 25 . What's more, it had shown that estrogen treatment silences the inflammatory reactions and decreases virus titers leading to improved survival rate in animal experiments 26 . It is evident that TCM and CWM are effective in targeting patients with comorbid diabetes, liver damage, and kidney damage. GO analysis was performed on the overlapping fraction of targets obtained from target association analysis of TCM and CWM to obtain target enrichment in cytosol and protein binding category. KEGG analysis was performed to obtain target enrichment in the pathway in cancer and Hepatitis B. The commonality between TCM and CWM in the treatment of COVID-19 was shown. In the analysis of differential genes induced by viral infection, up-regulated genes such as gnpda1 were found to be highly expressed in the kidney, RPAGD in the thyroid, ANKH in the pancreas, PRODH in the small intestine, and down-regulated genes were highly expressed in the bone marrow, lymph nodes, spleen, lung, cecum, gallbladder, and bladder. In addition, key genes were expressed at significantly lower levels in pancreatic and liver tissues. It is hypothesized that these genes are key targets in the clinical symptoms of COVID-19 that cause fever, cough, chest tightness, and diarrhea and sore throat symptoms. After GO functional analysis of the differential genes, it was found that the differential genes were mostly enriched in the type I interferon signaling pathway, defense response to virus, cytoplasm, cytosol, and protein binding. KEGG analysis revealed that the differential genes were mostly enriched in herpes simplex infection, influenza A, and TNF signaling pathways. It is evident that SARS-CoV-2 infection affects the immune response of the host. It had shown that after infection with SARS-CoV-2, uncontrolled inflammatory mediators in the body, causing a cytokine storm, will lead to an excessive immune response, consistent with the results of differential gene enrichment analysis 27 . In addition, the results of GO classification showed that both TCM drug target genes and virus induced genes were significantly involved in protein binding of molecular functions. Whether TCM can inhibit protein synthesis more effectively, thus hindering the replication and proliferation of virus in the host? After analyzing the drug targets and differential genes separately, we obtained six core hub genes, namely IL6 , CXCL8 , IL1B , NFKBIA , PTGS2 , STAT1 , by correlating them to establish a " disease-target-compound-drug " regulatory network. SARS-CoV-2 causes activation of different immune cells that helps to secrete proinflammatory cytokine IL6 and other inflammatory cytokines then causes cytokine storm 28 . Both IL8 and IL1B encode proteins that are cytokines and, like IL6 , are more abundant in critically ill patients and statistically different (P<0.05) 27,29 . Analysis of the KEGG pathway for six core hub genes yielded four more important nodes: legionellosis, interleukin-6-mediated signaling pathway, IL-17 signaling pathway, and Leishmaniasis. Among them Legionellosis may involve pneumonia.IL-17 signaling pathway plays an important role in both acute and chronic inflammatory responses. This reveals the mechanism of herbal and Western medicine in the treatment of COVID-19. Identification of potential molecular targets is essential for drug repurposing 30-32 . Through the construction of molecular docking model between hub host gene and hub compound, it was found that folic acid (FA) and protein ILB, PTGS2, STAT1 were stable binding, and estrogen was stable binding to IL6, IL8. Estrone has good binding properties to proteins, and exogenous estrogen is recommended as a drug for the prevention and treatment of COVID-19 26 . Folic acid may be a potential drug target. FA had a relatively lower binding energy to the protein, suggesting that it works better as a drug to treat COVID-19. This suggests that folic acid is a potential drug to treat COVID-19. FA have preventive effects of on Zika virus-associated poor pregnancy outcomes in immunocompromised mice. Mice with FA treatment showed lower viral burden and better prognostic profiles in the placenta including reduced inflammatory response, and enhanced integrity of BPB 33 .It had indeed been shown to related to pregnant women with COVID-19 34 . However, the mechanism by which folic acid has apparently protected pregnant women during the COVID-19 pandemic has not been determined. For the precise treatment of COVID-19, our research performed molecular docking between drug compounds and viral proteins. The role of N in RNA recognition, replicating, transcribing the viral genome, and modulating the host immune response is indispensable 35 . The 3CL protease and the NSP13 helicase are crucial to viral replication 36,37 . The spike proteins help in fusing into the host and the NSP9 replicase plays a major role in viral replication 38 .The ORF3a protein can activate the NLRP3 inflammasome by promoting TRAF3‐dependent ubiquitination of ASC 39 . The ORF7A protein blocks cell cycle progression at G0/G1 phase via the cyclin D3/pRb pathway 40 .The ORF8 can mediate immune suppression and evasion activities potentially 41 . These proteins play an important role in the viral infection of the host, so they are selected for molecular docking with the effective compounds obtained from the analysis. 3CL Protease and ORF3a protein, ORF8 also have low binding energy with the compounds. The binding of Phosphoprotein N to folic acid was stable, indicating that it can be targeted for molecular therapy. Together, above results showed that the binding energy between FA and N is the lowest, so it was speculated that N might be the target of folic acid in the treatment of COVID-19. Conclusions In general, through the analysis of the effective compounds and drug targets of COVID-19 clinical TCM and CWM, we found the pivotal genes and effective compounds, and further established the "drug-target-virus infection" molecular network through the GEO database combined with the virus induced differential genes, and screened out the potential new drugs (FA) and new molecular targets (SARS-CoV-2 N protein and PTGS2) for COVID-19 treatment, which promote underlying the interaction mechanism between virus and host, and provide a new insight for the development of new drugs. Abbreviations COVID-19 : Coronavirus Disease 2019 TCM : Traditional Chinese medicine CWM : Conventional Western medicine SARS-CoV-2 : severe acute respiratory syndrome S : spike M : membrane E : envelope N : nucleocapsid ACE2 : angiotensin-converting enzyme ORFs : open reading frames TRSs : transcriptional regulatory sequences TCMSP : Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform DEGs : differential expression genes GEO : Gene Expression Omnibus BP : biological process MF : molecular function CC : cellular component PPI: Protein-protein interaction ACE: angiotensin converting enzyme DTGs : drug targeted genes N : SARS-COV-2, Nucleocapsid Phosphoprotein TTD: Therapeutic Target Database FA : folic acid Declarations Availability of data and materials All data and materials in the current study are included in this published article. Conflict of Interest Statement The authors declare no conflicts of interest. Ethics statements No animal studies are presented in this manuscript. No human studies are presented in this manuscript. No potentially identifiable human images or data is presented in this study. Consent to publish All authors agree to publish. Funding This work was supported by Science and Technology Planning Project of Yuzhong District of Chongqing City, 2020050, and the Chongqing Municipal Education Commission Foundation, KJQN202000403. ACKNOWLEDGMENTS We gratefully thank American Journal Experts for editing this manuscript. References 1 Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579 , 265-269, doi:10.1038/s41586-020-2008-3 (2020). 2 Wang, C., Horby, P. W., Hayden, F. G. & Gao, G. F. A novel coronavirus outbreak of global health concern. Lancet 395 , 470-473, doi:10.1016/S0140-6736(20)30185-9 (2020). 3 Kim, D. et al. The Architecture of SARS-CoV-2 Transcriptome. 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Tables Table 1 Total number of differentially expressed genes in GSE147507 dataset Category Numbers of DEGs Cov002_mock_vs_Co002_CoV2 SARS004_mock_vs_SARS004_CoV2 Venn Up-regulated 349 677 8 Down-regulated 828 730 105 Total number 1177 1407 113 Table 2 Binding energy (/kcal.mol) of effective compounds to hub genes Compounds Chemical formula IL1B IL6 IL8 NFKBIA PTGS2 STAT1 Baicalein C 15 H 10 O 5 -7.1 -6.9 -6.1 -7.9 -8.8 -6.2 Estrone C 18 H 22 O 2 -6.4 -7.4 -6.5 -8.1 -8.4 -6.5 Quercetin C 15 H 10 O 7 -7.1 -7.0 -6.1 -8.4 -9.1 -6.5 Folic acid C 19 H 19 N 7 O 6 -7.2 -6.8 -6.5 -8.5 -10.0 -6.8 Table 3 Binding energy (/kcal.mol) of effective compounds to SARS-Cov-2 related genes Compounds Baicalein Estrone Quercetin Folic acid Chemical formula C 15 H 10 O 5 C 18 H 22 O 2 C 15 H 10 O 7 C 19 H 19 N 7 O 6 ACE -7.9 -8.3 -8.0 -8.6 ACE2 -6.5 -7.0 -6.0 -7.5 SARS-Cov-2 3CL -7.8 -8.3 -8.0 -7.7 SARS-Cov-2 helicase-NSP13 -7.6 -7.4 -7.0 -7.9 SARS-Cov-2 Spike -5.8 -5.5 -5.6 -6.6 SARS-Cov-2 NSP9 -7.3 -7.4 -7.0 -7.4 SARS-Cov-2 Nucleocapsid Phosphoprotein -8.0 -7.8 -7.8 -8.3 SARS-Cov-2 ORF3a -7.7 -8.0 -7.5 -7.6 SARS-Cov-2 ORF7a -5.1 -5.8 -5.6 -6.5 SARS-Cov-2 ORF8 -7.6 -7.9 -7.6 -7.6 Supplementary Files Figure.S1.tif Fig.S1 Workflow of the present study. GEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; CTD: Comparative Toxicogenomics Database; TTD: Therapeutic Target Database; HPA: Human Protein Atlas. Figure.S1.tif Fig.S1 Workflow of the present study. GEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; CTD: Comparative Toxicogenomics Database; TTD: Therapeutic Target Database; HPA: Human Protein Atlas. Figure.S2.tif Fig.S2 GO and KEGG analysis of TCM and CWM target genes (A) GO analysis of target genes. (B) 3D structures of hub compounds. Figure.S2.tif Fig.S2 GO and KEGG analysis of TCM and CWM target genes (A) GO analysis of target genes. (B) 3D structures of hub compounds. Figure.S3.tif Fig.S3 Tissue expression profile of intersecting genes The NCBI database was used to analyze the tissue expression profile of intersecting genes in different human tissues, and MeV.5 was used for visualization. The red font is the up-regulated differential genes, the yellow box indicates that genes with low expression in the tissue, the blue box indicates the high expression of genes in kinds of tissues, the red box indicates that genes are highly expressed in liver tissues. Figure.S3.tif Fig.S3 Tissue expression profile of intersecting genes The NCBI database was used to analyze the tissue expression profile of intersecting genes in different human tissues, and MeV.5 was used for visualization. The red font is the up-regulated differential genes, the yellow box indicates that genes with low expression in the tissue, the blue box indicates the high expression of genes in kinds of tissues, the red box indicates that genes are highly expressed in liver tissues. Figure.S4.tif Fig.S4 Screening of key hub genes based on TTD, GEO and TCMSP datasets (A) Venn analysis of TCMSP and GEO datasets in Cov-group. (B) Venn analysis of TCMSP and GEO datasets in SARS-group. (C) The fold change of “TCMSP-GEO” joint genes after virus infection. (D) Venn analysis of genes in TTD, GEO and TCMSP datasets. (E) Venn analysis of chemical interaction. Figure.S4.tif Fig.S4 Screening of key hub genes based on TTD, GEO and TCMSP datasets (A) Venn analysis of TCMSP and GEO datasets in Cov-group. (B) Venn analysis of TCMSP and GEO datasets in SARS-group. (C) The fold change of “TCMSP-GEO” joint genes after virus infection. (D) Venn analysis of genes in TTD, GEO and TCMSP datasets. (E) Venn analysis of chemical interaction. Figure.S5.tif Fig.S5 Molecular docking of effective compounds to hub genes and SARS-CoV-2 related genes (A) The two-dimensional pattern that shows the hub genes of effective compounds on the associated proteins. (B) The two-dimensional pattern that shows the SARS-CoV-2 related genes of effective compounds on the associated proteins. Figure.S5.tif Fig.S5 Molecular docking of effective compounds to hub genes and SARS-CoV-2 related genes (A) The two-dimensional pattern that shows the hub genes of effective compounds on the associated proteins. (B) The two-dimensional pattern that shows the SARS-CoV-2 related genes of effective compounds on the associated proteins. SupplementeryTables.docx Table S1 Effective ingredient and target gene analysis of traditional Chinese medicine for COVID-19 Table S2 Statistical results of Venn analysis of effective compounds From user_list1 to user_list10 respectively indicated “Qingfei Detox Soup”, “Cold dampness lung syndrome”， “Damp heat syndrome”，“Damp-toxin depression lung syndrome”，“ Cold dampness syndrome”，“ Lung Closure Syndrome”，“ Trachea burnt certificate”，“ Internal and external withdrawal”，“ Pulmonary spleen Qi deficiency syndrome”，“ Qi-yin deficiency syndrome”. SupplementeryTables.docx Table S1 Effective ingredient and target gene analysis of traditional Chinese medicine for COVID-19 Table S2 Statistical results of Venn analysis of effective compounds From user_list1 to user_list10 respectively indicated “Qingfei Detox Soup”, “Cold dampness lung syndrome”， “Damp heat syndrome”，“Damp-toxin depression lung syndrome”，“ Cold dampness syndrome”，“ Lung Closure Syndrome”，“ Trachea burnt certificate”，“ Internal and external withdrawal”，“ Pulmonary spleen Qi deficiency syndrome”，“ Qi-yin deficiency syndrome”. GraphicalAbstract.tif Graphical Abstract The green line represents the results of pure network pharmacology analysis of traditional Chinese medicine, while the red line represents the hub compounds and their drug targets obtained from the integration of traditional Chinese medicine and Western medicine. The genes in the box are the pivotal genes between the differential genes induced by virus and the target genes of drug action. In addition, a new compound with potential effect on viral N protein was found by molecular docking. Network of “drug-target-SARS-CoV-2 related genes” was constructed through integrated analysis of pharmacology and GEO database, which provided a new molecular therapeutic target and drug screening direction for molecular of SARS-CoV-2. GraphicalAbstract.tif Graphical Abstract The green line represents the results of pure network pharmacology analysis of traditional Chinese medicine, while the red line represents the hub compounds and their drug targets obtained from the integration of traditional Chinese medicine and Western medicine. The genes in the box are the pivotal genes between the differential genes induced by virus and the target genes of drug action. In addition, a new compound with potential effect on viral N protein was found by molecular docking. Network of “drug-target-SARS-CoV-2 related genes” was constructed through integrated analysis of pharmacology and GEO database, which provided a new molecular therapeutic target and drug screening direction for molecular of SARS-CoV-2. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-117894","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":5490751,"identity":"8b74cfb4-b35b-43ae-8dad-9f88f169f3f5","order_by":0,"name":"Jin ping Hou","email":"","orcid":"","institution":"Chongqing Medical University Affiliated Children's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin","middleName":"ping","lastName":"Hou","suffix":""},{"id":5490752,"identity":"b288214d-0379-47b9-991e-43f2463fed71","order_by":1,"name":"Yong heng Wang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong","middleName":"heng","lastName":"Wang","suffix":""},{"id":5490753,"identity":"9cfef25d-e523-4a2d-a316-45189deadb6d","order_by":2,"name":"Yu meng Chen","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"meng","lastName":"Chen","suffix":""},{"id":5490754,"identity":"243be817-d437-4891-96c0-3ceccd7a45c9","order_by":3,"name":"Yi hao Chen","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"hao","lastName":"Chen","suffix":""},{"id":5490755,"identity":"c9672dd9-8547-4c8a-a1bc-2e3157a9609e","order_by":4,"name":"Xiao Zhu","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Zhu","suffix":""},{"id":5490756,"identity":"9138d61c-16eb-42ad-9987-4a4570f784e0","order_by":5,"name":"Rui si Qin","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"si","lastName":"Qin","suffix":""},{"id":5490757,"identity":"c7649b34-3782-42f3-b83c-e192db06d1d7","order_by":6,"name":"Tingting Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACCQiVwMDAfIABziZSCxtMKfFaeAyI08I/u/nY58o2uzx+6Z6PH97mHGbgZ88xYPi5A48ld44lzzzbllwsOefsZsm52w4zSPa8MWDsPYNbi4FEjjFjY9uBxA03crcx8wK1GNzIMWBmbMOnJf8zWMv+GznPwFrsCWvJYYbYIpHDBrFFgoAWiRtpxowN55ITZwAZQL+k80iceVZwsBePFv4ZyY8ZG8rsEvtnJD/88HabtRx/e/LGBz/xaAEDRjYogweMGBgOENAABH8QWkbBKBgFo2AUYAAASAhRVOgPz3QAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0089-9402","institution":"Chongqing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2020-11-28 18:25:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-117894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-117894/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4025073,"identity":"53113ded-0add-424e-9ff5-43cc2ecb4830","added_by":"auto","created_at":"2020-12-04 17:44:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1055093,"visible":true,"origin":"","legend":"Effective ingredient and target gene analysis of traditional Chinese medicine\n(A) Venn analysis of effective compound of TCM. (B) 2D structure of hub compound. (C) 3D structure of hub compound. (D) Gene_Ontology (GO) analysis of target genes. (E) KEGG pathway of target genes. \n","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/49064f3a9026cc374312e5c3.jpg"},{"id":4025061,"identity":"22ca8047-0090-4451-9374-07750280f20a","added_by":"auto","created_at":"2020-12-04 17:44:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1055093,"visible":true,"origin":"","legend":"Effective ingredient and target gene analysis of traditional Chinese medicine\n(A) Venn analysis of effective compound of TCM. (B) 2D structure of hub compound. (C) 3D structure of hub compound. (D) Gene_Ontology (GO) analysis of target genes. (E) KEGG pathway of target genes. \n","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/f1e9a08a5ba752383b63210f.jpg"},{"id":4025075,"identity":"ef9eed82-2a64-461a-9197-9a3d747085e8","added_by":"auto","created_at":"2020-12-04 17:44:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1524015,"visible":true,"origin":"","legend":"Integrated analysis of COVID-19 clinical TCM and CWM\n(A) Venn analysis of effective compounds based on TCMSP, TTD and PubChem database. (B) Venn analysis of drug target genes based on TCMSP, TTD and PubChem. (C) KEGG pathway of target genes. (D) Network of “TCM-compounds-target genes”.\n","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/e8b85fdaac209b8b8200657b.jpg"},{"id":4025063,"identity":"5856cf33-3f99-46cc-a2db-0d039e7d7ddf","added_by":"auto","created_at":"2020-12-04 17:44:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1524015,"visible":true,"origin":"","legend":"Integrated analysis of COVID-19 clinical TCM and CWM\n(A) Venn analysis of effective compounds based on TCMSP, TTD and PubChem database. (B) Venn analysis of drug target genes based on TCMSP, TTD and PubChem. (C) KEGG pathway of target genes. (D) Network of “TCM-compounds-target genes”.\n","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/a0155914b6897ff0c36afbc5.jpg"},{"id":4025077,"identity":"8f0c3d31-bf6b-4896-9472-1a29577dc56c","added_by":"auto","created_at":"2020-12-04 17:44:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1151966,"visible":true,"origin":"","legend":"Identification of differentially expressed genes (DEGs) in GSE147507 dataset\n(A) The volcano plots of the distribution of DEGs in Cov-group. (B) The volcano plots of the distribution of DEGs in SARS-group. (C) Venn analysis of DEGs in Cov- and SARS- groups. (D) KEGG pathway of intersection genes. (E) GO analysis of intersection genes. \n","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/78d0123a0d1b2be197c97955.jpg"},{"id":4025065,"identity":"99239a07-36f8-4e4c-8afb-a0e0027b912e","added_by":"auto","created_at":"2020-12-04 17:44:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1151966,"visible":true,"origin":"","legend":"Identification of differentially expressed genes (DEGs) in GSE147507 dataset\n(A) The volcano plots of the distribution of DEGs in Cov-group. (B) The volcano plots of the distribution of DEGs in SARS-group. (C) Venn analysis of DEGs in Cov- and SARS- groups. (D) KEGG pathway of intersection genes. (E) GO analysis of intersection genes. \n","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/b4ef95de2848e4fab9f1ec84.jpg"},{"id":4025079,"identity":"ec6f74a1-6228-487f-b4c4-2240e1b2598b","added_by":"auto","created_at":"2020-12-04 17:44:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1026484,"visible":true,"origin":"","legend":"Molecular docking of drug target genes (DTG) and virus induced differential expressed genes (VIDEGs)\n(A) Venn analysis of DTG and VIDEGs in different TCM prescriptions. (B) The fold change value of “TCM-DTG-VIDEGs” genes in SARS-group. The integrated genes were screened based on TCM target genes and viral induced genes in SARS-group, and the fold change of hub genes were analyzed in GSE147507. (C) The fold change value of “TCM-DTG-VIDEGs” genes in Cov-group. The integrated genes were screened based on TCM target genes and viral induced genes in Cov-group, and the fold change of hub genes were analyzed in GSE147507. (D) The fold change value of “TTD-TCMSP-GEO” genes in SARS-group. (E) The fold change value of “TTD-TCMSP-GEO” genes in Cov-group. * indicates that the gene is present in both Cov-group and SARS group. (F) The Chemical interaction of key hub genes. We screened chemical interaction of genes that present in venn analysis of “TTD-TCMSP-GEO” and both SARS-group and Cov-group.\n","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/ecf7a5286638b12185b5436e.jpg"},{"id":4025067,"identity":"c0ae1cb6-54c6-4d2d-83d6-e8c67fd3231c","added_by":"auto","created_at":"2020-12-04 17:44:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1026484,"visible":true,"origin":"","legend":"Molecular docking of drug target genes (DTG) and virus induced differential expressed genes (VIDEGs)\n(A) Venn analysis of DTG and VIDEGs in different TCM prescriptions. (B) The fold change value of “TCM-DTG-VIDEGs” genes in SARS-group. The integrated genes were screened based on TCM target genes and viral induced genes in SARS-group, and the fold change of hub genes were analyzed in GSE147507. (C) The fold change value of “TCM-DTG-VIDEGs” genes in Cov-group. The integrated genes were screened based on TCM target genes and viral induced genes in Cov-group, and the fold change of hub genes were analyzed in GSE147507. (D) The fold change value of “TTD-TCMSP-GEO” genes in SARS-group. (E) The fold change value of “TTD-TCMSP-GEO” genes in Cov-group. * indicates that the gene is present in both Cov-group and SARS group. (F) The Chemical interaction of key hub genes. We screened chemical interaction of genes that present in venn analysis of “TTD-TCMSP-GEO” and both SARS-group and Cov-group.\n","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/3b3787f68852c44476c8fead.jpg"},{"id":4025081,"identity":"0d0a66cf-d0b0-4825-b631-07a14943d0a4","added_by":"auto","created_at":"2020-12-04 17:44:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2021427,"visible":true,"origin":"","legend":"Molecular docking of effective compounds to hub genes and SARS-CoV-2 related genes\n(A) The three-dimensional pattern that shows the hub genes of effective compounds on the associated proteins. (B) The three-dimensional pattern that shows the molecular docking of the SARS-CoV-2 related genes and effective compounds.\n","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/c36436b6bdde5522a9e196ca.jpg"},{"id":4025069,"identity":"bf143806-1777-4735-a8bb-5fa9d2f49cc8","added_by":"auto","created_at":"2020-12-04 17:44:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2021427,"visible":true,"origin":"","legend":"Molecular docking of effective compounds to hub genes and SARS-CoV-2 related genes\n(A) The three-dimensional pattern that shows the hub genes of effective compounds on the associated proteins. (B) The three-dimensional pattern that shows the molecular docking of the SARS-CoV-2 related genes and effective compounds.\n","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/e9460b5900402b06c6e9ec03.jpg"},{"id":13625180,"identity":"5f0d306d-460b-478a-aa52-bf0c559e0ca3","added_by":"auto","created_at":"2021-09-17 07:25:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2029493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/ab52d8e8-5c50-4eda-9350-eeddf407f45c.pdf"},{"id":4025074,"identity":"0b31bec3-1537-4d3a-b886-188dc1fb35ca","added_by":"auto","created_at":"2020-12-04 17:44:53","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1091352,"visible":true,"origin":"","legend":"Fig.S1 Workflow of the present study.\nGEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; CTD: Comparative Toxicogenomics Database; TTD: Therapeutic Target Database; HPA: Human Protein Atlas.\n","description":"","filename":"Figure.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/60b71c253defe5483448b984.tif"},{"id":4025062,"identity":"ccd3e7d7-ea63-4a68-82fd-e90a8d7b0462","added_by":"auto","created_at":"2020-12-04 17:44:46","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1091352,"visible":true,"origin":"","legend":"Fig.S1 Workflow of the present study.\nGEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; CTD: Comparative Toxicogenomics Database; TTD: Therapeutic Target Database; HPA: Human Protein Atlas.\n","description":"","filename":"Figure.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/8cf95ba26bf554a8bb1042a1.tif"},{"id":4025076,"identity":"39f0a7a7-1804-4a1f-a811-9c0bfe8d57b6","added_by":"auto","created_at":"2020-12-04 17:44:53","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2617260,"visible":true,"origin":"","legend":"Fig.S2 GO and KEGG analysis of TCM and CWM target genes\n(A) GO analysis of target genes. (B) 3D structures of hub compounds.\n","description":"","filename":"Figure.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/734118267e7480cd616f7a91.tif"},{"id":4025064,"identity":"1ffcc3bd-8889-4b14-a568-46eab0368af6","added_by":"auto","created_at":"2020-12-04 17:44:47","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2617260,"visible":true,"origin":"","legend":"Fig.S2 GO and KEGG analysis of TCM and CWM target genes\n(A) GO analysis of target genes. (B) 3D structures of hub compounds.\n","description":"","filename":"Figure.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/4c893163072f102faa61901d.tif"},{"id":4025078,"identity":"da8a6539-615a-4714-8cfd-687ce18bd09b","added_by":"auto","created_at":"2020-12-04 17:44:53","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2494788,"visible":true,"origin":"","legend":"Fig.S3 Tissue expression profile of intersecting genes\nThe NCBI database was used to analyze the tissue expression profile of intersecting genes in different human tissues, and MeV.5 was used for visualization. The red font is the up-regulated differential genes, the yellow box indicates that genes with low expression in the tissue, the blue box indicates the high expression of genes in kinds of tissues, the red box indicates that genes are highly expressed in liver tissues.\n","description":"","filename":"Figure.S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/9c85ac586827044caf16d603.tif"},{"id":4025066,"identity":"9a186625-475e-408e-8e58-0630844c3685","added_by":"auto","created_at":"2020-12-04 17:44:47","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2494788,"visible":true,"origin":"","legend":"Fig.S3 Tissue expression profile of intersecting genes\nThe NCBI database was used to analyze the tissue expression profile of intersecting genes in different human tissues, and MeV.5 was used for visualization. The red font is the up-regulated differential genes, the yellow box indicates that genes with low expression in the tissue, the blue box indicates the high expression of genes in kinds of tissues, the red box indicates that genes are highly expressed in liver tissues.\n","description":"","filename":"Figure.S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/62bf0e2462b4408ffba8aee0.tif"},{"id":4025080,"identity":"815ed4be-da53-4d51-bc62-688588a634cc","added_by":"auto","created_at":"2020-12-04 17:44:54","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2074284,"visible":true,"origin":"","legend":"Fig.S4 Screening of key hub genes based on TTD, GEO and TCMSP datasets\n(A) Venn analysis of TCMSP and GEO datasets in Cov-group. (B) Venn analysis of TCMSP and GEO datasets in SARS-group. (C) The fold change of “TCMSP-GEO” joint genes after virus infection. (D) Venn analysis of genes in TTD, GEO and TCMSP datasets. (E) Venn analysis of chemical interaction.\n","description":"","filename":"Figure.S4.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/9f7ab5b2f9e38a2979a7acc3.tif"},{"id":4025068,"identity":"a482a86a-d2d5-4bf7-adcc-93e04eb95ae8","added_by":"auto","created_at":"2020-12-04 17:44:48","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2074284,"visible":true,"origin":"","legend":"Fig.S4 Screening of key hub genes based on TTD, GEO and TCMSP datasets\n(A) Venn analysis of TCMSP and GEO datasets in Cov-group. (B) Venn analysis of TCMSP and GEO datasets in SARS-group. (C) The fold change of “TCMSP-GEO” joint genes after virus infection. (D) Venn analysis of genes in TTD, GEO and TCMSP datasets. (E) Venn analysis of chemical interaction.\n","description":"","filename":"Figure.S4.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/bf4cdca8722dd0628bd886ad.tif"},{"id":4025082,"identity":"a5c6b874-7c7d-403a-9e1d-8f3b1ac9cc23","added_by":"auto","created_at":"2020-12-04 17:44:54","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3293888,"visible":true,"origin":"","legend":"Fig.S5 Molecular docking of effective compounds to hub genes and SARS-CoV-2 related genes\n(A) The two-dimensional pattern that shows the hub genes of effective compounds on the associated proteins. (B) The two-dimensional pattern that shows the SARS-CoV-2 related genes of effective compounds on the associated proteins.\n","description":"","filename":"Figure.S5.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/c1fe36105d51295e4cb2b1dc.tif"},{"id":4025070,"identity":"0e956984-73b5-43dc-8889-dd02ebfe4a37","added_by":"auto","created_at":"2020-12-04 17:44:49","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3293888,"visible":true,"origin":"","legend":"Fig.S5 Molecular docking of effective compounds to hub genes and SARS-CoV-2 related genes\n(A) The two-dimensional pattern that shows the hub genes of effective compounds on the associated proteins. (B) The two-dimensional pattern that shows the SARS-CoV-2 related genes of effective compounds on the associated proteins.\n","description":"","filename":"Figure.S5.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/be1a47d905068c5c0d37bd5f.tif"},{"id":4025083,"identity":"dcf11fa0-bc13-4bbb-b71a-b51b05d2f159","added_by":"auto","created_at":"2020-12-04 17:44:54","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":64284,"visible":true,"origin":"","legend":"Table S1 Effective ingredient and target gene analysis of traditional Chinese medicine for COVID-19\nTable S2 Statistical results of Venn analysis of effective compounds\nFrom user_list1 to user_list10 respectively indicated “Qingfei Detox Soup”, “Cold dampness lung syndrome”， “Damp heat syndrome”，“Damp-toxin depression lung syndrome”，“ Cold dampness syndrome”，“ Lung Closure Syndrome”，“ Trachea burnt certificate”，“ Internal and external withdrawal”，“ Pulmonary spleen Qi deficiency syndrome”，“ Qi-yin deficiency syndrome”.\n","description":"","filename":"SupplementeryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/ff787d0501dac6bf1c7e0e04.docx"},{"id":4025071,"identity":"fd02c026-efd9-47a1-9d22-be293f6f06ff","added_by":"auto","created_at":"2020-12-04 17:44:49","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":64284,"visible":true,"origin":"","legend":"Table S1 Effective ingredient and target gene analysis of traditional Chinese medicine for COVID-19\nTable S2 Statistical results of Venn analysis of effective compounds\nFrom user_list1 to user_list10 respectively indicated “Qingfei Detox Soup”, “Cold dampness lung syndrome”， “Damp heat syndrome”，“Damp-toxin depression lung syndrome”，“ Cold dampness syndrome”，“ Lung Closure Syndrome”，“ Trachea burnt certificate”，“ Internal and external withdrawal”，“ Pulmonary spleen Qi deficiency syndrome”，“ Qi-yin deficiency syndrome”.\n","description":"","filename":"SupplementeryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/3d240abf2a7cae5eea99bf4d.docx"},{"id":4025084,"identity":"70bdb12d-cc05-40cb-9dee-0f1d009a7b48","added_by":"auto","created_at":"2020-12-04 17:44:55","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3359184,"visible":true,"origin":"","legend":"Graphical Abstract\nThe green line represents the results of pure network pharmacology analysis of traditional Chinese medicine, while the red line represents the hub compounds and their drug targets obtained from the integration of traditional Chinese medicine and Western medicine. The genes in the box are the pivotal genes between the differential genes induced by virus and the target genes of drug action. In addition, a new compound with potential effect on viral N protein was found by molecular docking. Network of “drug-target-SARS-CoV-2 related genes” was constructed through integrated analysis of pharmacology and GEO database, which provided a new molecular therapeutic target and drug screening direction for molecular of SARS-CoV-2. \n","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/041827a41338c33b032924b0.tif"},{"id":4025072,"identity":"419e4ab4-c09d-414a-8bc9-d435f6517ffb","added_by":"auto","created_at":"2020-12-04 17:44:49","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3359184,"visible":true,"origin":"","legend":"Graphical Abstract\nThe green line represents the results of pure network pharmacology analysis of traditional Chinese medicine, while the red line represents the hub compounds and their drug targets obtained from the integration of traditional Chinese medicine and Western medicine. The genes in the box are the pivotal genes between the differential genes induced by virus and the target genes of drug action. In addition, a new compound with potential effect on viral N protein was found by molecular docking. Network of “drug-target-SARS-CoV-2 related genes” was constructed through integrated analysis of pharmacology and GEO database, which provided a new molecular therapeutic target and drug screening direction for molecular of SARS-CoV-2. \n","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-117894/v1/13128dc0858e12b25415011f.tif"}],"financialInterests":"","formattedTitle":"\u003cp\u003eNetwork of “drug-target-SARS-CoV-2\u0026nbsp;Related Genes” Through Integrated Analysis of Pharmacology and Geo Database\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe recently discovered beta-coronavirus severe acute respiratory syndrome (SARS-CoV-2), the causative agent of Coronavirus Disease 2019 (COVID-19) respiratory disease rapidly caused a global pandemic and social and economic disruption\u003csup\u003e1,2\u003c/sup\u003e. As of October 23th, 2020, a total of over 41 million cases of COVID-19 were reported and more than 1100 thousand lives were claimed globally (\u003ca href=\"https://covid19.who.int\"\u003ehttps://covid19.who.int\u003c/a\u003e). SARS-CoV-2 is sense single‐stranded genomic RNA virus, approximately 30 kb in length, encodes four structural proteins: the spike (S), membrane (M), envelope (E) and nucleocapsid (N) proteins\u003csup\u003e3\u003c/sup\u003e. S protein binds to angiotensin-converting enzyme (ACE2) receptor on the cytoplasmic membrane of type 2 lung cells and intestinal epithelium. After binding, S protein was cleaved by the host membrane serine protease TMPRSS2, which promoted the virus entry \u003csup\u003e4\u003c/sup\u003e.These structural proteins are components of the mature virus and play a crucial role in viral structure integrity, or as in the case of the spike protein, for viral entry into the host\u003csup\u003e5,6\u003c/sup\u003e. The SARS-CoV-2 genome contains 14 open reading frames (ORFs), preceded by transcriptional regulatory sequences (TRSs) \u003csup\u003e7\u003c/sup\u003e. The structural and accessory proteins are translated from a set of nested sub-genomic (g) RNAs. Underlining how SARS-CoV-2 hijacks the host during infection absolutely could promote the development, characterization, and deployment of an effective vaccine or antibody prophylaxis or treatment against SARS-CoV-2 and could prevent morbidity and mortality and curtail its epidemic spread\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTraditional Chinese medicine (TCM) formulations had been originally developed for earlier viral diseases and been used in COVID-19 treatment\u003csup\u003e9\u003c/sup\u003e. TCM was extensively wildly used to control the epidemic in China \u003csup\u003e10\u003c/sup\u003e. Shufeng Jiedu Capsule and Lianhua Qingwen capsule have good clinical effect on patients with COVID-19\u003csup\u003e11,12\u003c/sup\u003e. The further study of TCM may lead to the identification of novel antivirus compounds for the treatment of SARS-CoV-2 or other emerging fatal viral diseases \u003csup\u003e9\u003c/sup\u003e. The antiviral medicine mainly target on host protease or other cellular proteins, such as teicoplanin, arbidol, chloroquine and derivatives, and niclosamide; and target on viral proteins, such as niclosamide, lopinavir/ritonavir, remdesivir, favipiravir, and ribavirin\u003csup\u003e13\u003c/sup\u003e. Combination of TCM and conventional Western medicine (CWM) can effectively inhibit SARS-CoV-2 replication and prevent the production of proinflammatory cytokines induced by SARS-CoV-2 infection. Many studies have only analyzed the therapeutic mechanism of CWM or TCM alone for COVID-19, so this study integrated the mechanism of CWM and TCM together to explore their generality and characteristics. Based on the concept of \"disease-target-compound-drug\", network pharmacology systematically analyzes the mechanism of drug action and its interaction network from multiple levels and angles \u003csup\u003e14,15\u003c/sup\u003e. Network pharmacology combines the information of genome, proteome, drugs, diseases and other related databases with experimental verification data through the method of big data mining, so as to establish an inter related network of \" disease-target-compound-drug \" \u003csup\u003e16,17\u003c/sup\u003e. Therefore, this method can be used to systematically analyze the intervention and influence of TCM and CWM on COVID-19, reveal the mechanism of action of drugs in human body, correlate the pharmacophores and interactions of various drugs, and screen the core targets. In addition, the accumulation of big data resources is conducive to the screening of molecular markers for disease prevention \u003csup\u003e18\u003c/sup\u003e. Biomolecular targets are the basis of accurate diagnosis and personalized drug design, which can be used to capture specific disease signals in the early stage to formulate appropriate treatment plans \u003csup\u003e18\u003c/sup\u003e. Screening of molecular targets for COVID-19 therapy is of great significance for understanding the interaction mechanism between SARS-CoV-2 and host. Therefore, it is of great significance to integrate network pharmacology and GEO database to screen COVID-19 molecular therapeutic targets with clinical value. Identifying the regulatory effects of core hub genes based on big data mining can provide potential targets and new ideas for COVID-19 gene therapy and new drug development.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Active compounds and drug target genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChemical ingredients and chemical target genes were collected from the Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform (TCMSP) Database (http://tcmspw.com/tcmsp.php). In TCMSP database (http://tcmspw.com/tcmsp.php), the final candidate active ingredients were screened, according to the oral bioavailability (OB) \u0026ge;30%, drug-likeness (DL) \u0026ge;0.18, drug half-life (HL)\u0026ge;10. The compound target genes were imported into UniProt (https://www.uniprot.org/) that defining the species as human, and obtained the gene names corresponding to the target genes.\u003c/p\u003e\n\u003cp\u003eThe COVID-19 treatment plan (issued in the sixth version) issued by the office of the National Health and Health Committee of China is selected as follows: \u003cem\u003eEphedra Herba\u003c/em\u003e (Mahuang), \u003cem\u003eCinnanmmi Cortex\u003c/em\u003e (Rougui), \u003cem\u003eAlisma Orientale\u003c/em\u003e\u003cem\u003e(Sam.) Juz.\u003c/em\u003e(Zexie), P\u003cem\u003eolyporus Umbellatus(Pers)Fr.\u003c/em\u003e(Zhuqin), \u003cem\u003eAtractyloes Macrocephala Koidz.\u003c/em\u003e(Baizhu), \u003cem\u003eArum Ternatum Thunb\u003c/em\u003e(Banxia), \u003cem\u003eAsteris Radix Et Rhizoma\u003c/em\u003e(Zikou), \u003cem\u003eFarfarae Flos\u003c/em\u003e(Donghua), \u003cem\u003eBelamcandae Rhizome\u003c/em\u003e(Shegan), \u003cem\u003eAsari Radix Et Rhizoma\u003c/em\u003e(Xixin), \u003cem\u003eRhizoma Dioscoreae\u003c/em\u003e(Shanyao), \u003cem\u003eAurantii Fructus Immaturus\u003c/em\u003e(Zhishi), \u003cem\u003eFortunes Bossfern Rhizome\u003c/em\u003e(Guanzong), \u003cem\u003eRadix Cynanchi Paniculati\u003c/em\u003e(Xuchangqing), \u003cem\u003eEupatorium Fortunei Turcz\u003c/em\u003e(Peilan), \u003cem\u003eScutellariae Radix\u003c/em\u003e(Huangqin), \u003cem\u003eRadix Bupleuri\u003c/em\u003e(Chaihu), \u003cem\u003eArtemisia Annua L.\u003c/em\u003e(Qinghao), \u003cem\u003eIsatidis Folium\u003c/em\u003e(Daqingye), \u003cem\u003ePolygoni Cuspidati Rhizoma Et Radix\u003c/em\u003e(Huzhang), \u003cem\u003eVerbenae Herb\u003c/em\u003e(Mabiancao), \u003cem\u003ePhragmitis Rhizoma\u003c/em\u003e(Lugen), \u003cem\u003eCitri Grandis Exocarpium\u003c/em\u003e(Huajuhong), \u003cem\u003eNotopterygii Rhizoma Et Radix\u003c/em\u003e(Qianghuo), \u003cem\u003eArecae Semen\u003c/em\u003e(Binlang), \u003cem\u003eAmygdalus Communis Vas\u003c/em\u003e(Xingren), \u003cem\u003eLicorice\u003c/em\u003e(Gancao), \u003cem\u003ePogostemon Cablin\u003c/em\u003e\u003cem\u003e(Blanco) Benth\u003c/em\u003e.(Huoxiang), \u003cem\u003eMagnolia Officinalis Rehd Et Wils\u003c/em\u003e(Houpu), \u003cem\u003eAtractylodes Lancea (Thunb.)Dc\u003c/em\u003e.(Cangshu), \u003cem\u003eAmomum Tsao-Ko Crevostet\u003c/em\u003e(Caoguo), \u003cem\u003eRadix Rhei Et Rhizome\u003c/em\u003e(Shengdahuang), \u003cem\u003eAnemarrhenae Rhizoma\u003c/em\u003e(Zhimu), \u003cem\u003eRadix Paeoniae Rubra\u003c/em\u003e(Chishao), \u003cem\u003eFigwort Root\u003c/em\u003e(Xuansen), \u003cem\u003eForsythiae Fructus\u003c/em\u003e(Lianqiao), \u003cem\u003eCortex Moutan\u003c/em\u003e(Danpi), \u003cem\u003eCoptidis Rhizoma\u003c/em\u003e(Huanglian), \u003cem\u003eLepidii Semen Descurainiae Semen\u003c/em\u003e(Tinglizi), \u003cem\u003ePanax Ginseng C. A. Mey.\u003c/em\u003e(Rensen), \u003cem\u003eCornus Officinalis Sieb. Et Zucc.\u003c/em\u003e(Shanzhuyu), \u003cem\u003eArum Ternatum Thunb\u003c/em\u003e.(Fabanxia), \u003cem\u003eCitrus Reticulata\u003c/em\u003e(Chenpi), \u003cem\u003eCodonopsis Radix\u003c/em\u003e(Dangsen), \u003cem\u003eGlehniae Radix\u003c/em\u003e(Beishasen), \u003cem\u003eSchisandre Chinensis Fructus\u003c/em\u003e(Wuweizi), \u003cem\u003eMori Follum\u003c/em\u003e(Sangye), \u003cem\u003ePrragmitis Rhizoma\u003c/em\u003e(Lugen), \u003cem\u003eRadix Salviae\u003c/em\u003e(Dansen).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Differential expressed genes (DEGs) induced by SARS-CoV-2 infection\u003c/strong\u003e\u003cstrong\u003eNHEB and A549 cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differential expression genes (DEGs) between EGFR-TKI sensitive and resistance were calculated from public dataset of Gene Expression Omnibus (GEO). GSE147507 dataset include SARS-CoV-2 infected NHEB and A549 cell lines, and the uninfected group. SARS-004 group indicated that SARS-CoV-2 infected NHBE cells at MOI 2, Cov-002 group indicated that SARS-CoV-2 infected A549 cells at MOI 0.2, and the control group were the corresponding uninfected cell lines. For assay the deregulation gene expression, \u0026ldquo;limma\u0026rdquo; package of R software was employed to test the DEGs. mRNAs with log2 fold change (|log2FC|, P \u0026lt; 0.01) considered to be differentially expressed mRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Identify core sub-network from compound-target network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor identifying core targets from compounds-targets network, the overlap of DEGs and targets were extracted. There eight overlap targets were identified. We selected eight targets and their neighbors (only compounds) as core sub-network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Construction of Gene Enrichment Analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the processes associated with COVID-19 and Chinese medicine treatment, we performed GO terms and KEGG pathway enrichment analyses. The GO enrichment analysis provides three structured networks of defined terms to describe gene attributes. Enriched GO terms are classified according to biological process (BP), molecular function (MF), and cellular component (CC). The KEGG (http://www.genome.jp/kegg/) is a database for large-scale systematic analysis of molecular interaction networks of genes or proteins. The DAVID bioinformatics resources consist of an integrated biological knowledge base analytic tools, aimed at systematically extracting biological meaning from large gene or protein lists. We used the web-based search engine, DAVID, to determine over-represented GO terms and KEGG pathways with thresholds of an enrichment score \u0026gt; 2, count \u0026gt; 5, and P \u0026lt; 0.05 and analyzed COVID-19-related pathways and GO terms. Venn diagram and bubble graphs of were performed using the OmicShare tools, a free online analysis tool (http://www.omicshare.com/tools). Significant pathway terms of KEGG were mapped into a bubble graph. The big and higher bubbles represent those highly significantly enriched pathway terms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Protein-Protein Interaction Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction (PPI) networks included information on the biological processes and molecular functions of cells. We used the online search tool for recurring instances of neighboring genes (STRING, Version 9.1) (http://www.string-db.org) to predict the interactions. The Cytoscape software 3.7.2 (http://cytoscape.org/) was used to visualize networks. To identify crucial relationships in the PPI network, potentially overlapping modules that were densely connected were subsequently identified using Molecular Complex Detection (MCODE) plugin in the Cytoscape program. The MCODE plugin was used to re‐analyze the clusters among the network according to the k‐core = 2. A value of P \u0026lt; 0.01 was considered the significant threshold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Network Construction of KEGG Pathway\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better analysis the holistic mechanism of Chinese herbs in endometriosis treatment, the subnetworks pathway was compiled by following the procedures: All targets of Chinese herbs in endometriosis treatment were submitted to an online tool KEGG Search Pathway (https://www.genome.jp/kegg/tool/map_pathway1.html). Based on the mechanism of endometriosis, multiple pathways were integrated and overlapped according to cross-talk targets in these maps. Based on the cross-talk of the pathways, we further constructed a targets-pathways network of Chinese medicine treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Network Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established a network analysis of the relevant compound targets. The components of the target networks for the herbs were constructed using the Cytoscape software 3.7.2. The nodes in each network were evaluated based on three indices: degree, node betweenness, and node closeness. Degree indicated the number of edges between a single node and other nodes in a network. Node closeness represented the inverse of the sum of the distance from one node to other nodes. The importance of a node in a network was indicated by the values of these indices, with higher values indicating greater importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Component-molecular target docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom RSCB PDB database（https://www.rcsb.org/）Download the 3D structure pdb format files of SARS-CoV-2 related protein, angiotensin converting enzyme II (\u003cem\u003eACE2\u003c/em\u003e) and angiotensin converting enzyme (\u003cem\u003eACE\u003c/em\u003e) remove ligands and non-protein molecules (such as water molecules) in target proteins by using discovery studio 2020 client software, and then save them as PDB suffix files. From PubChem database（https://pubchem.ncbi.nlm.nih.gov/）Download the SDF format file of compound 2D structure. Using pyrx software, we first upload the protein file after water removal and hydrogenation, convert it into pdbqt format file, then upload the compound file to minimize its energy, and convert it into pdbqt format file. Finally, Vina is used for docking. The binding energy is less than 0, which indicates that the ligand and the receptor can spontaneously bind. The active components with binding energy \u0026le;-5.0kj/mol were selected as the screening basis of antiviral particles for COVID-19 target.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of effective compounds and target genes of COVID-19 clinical TCM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffective compounds and target genes were identified through integrated bioinformatics analysis based on TCMSP, TTD, PubChem, CTD and GEO datasets (Fig.S1). According to the COVID-19 pneumonia treatment plan (issued in the sixth version) issued by the office of the national health and Health Committee of China, TCMSP database was used to search the active ingredients in 49 kinds of TCM in COVID-19 prescription, and the parameters OB \u0026gt; 30%, DL \u0026gt; 0.18, HL \u0026gt; 8 were used as the conditions to screen the active ingredients (gypsum, ginger, etc.) were the relevant results. A total of 374 active components and 8355 target genes were obtained by analyzing and summarizing the active components of drugs (Table S1). \u003cem\u003eAmygdalus Communis Vas\u003c/em\u003e, \u003cem\u003eRadix Bupleuri\u003c/em\u003e, \u003cem\u003eScutellariae Radix\u003c/em\u003e, \u003cem\u003eCitrus Reticulata\u003c/em\u003e, \u003cem\u003ePogostemon Cablin (Blanco) Benth\u003c/em\u003e, \u003cem\u003eEphedra Herba\u003c/em\u003e, \u003cem\u003eNotopterygii Rhizoma Et Radix\u003c/em\u003e, \u003cem\u003eAtractylodes Lancea (Thunb.)Dc\u003c/em\u003e, \u003cem\u003eMagnolia Officinalis Rehd Et Wils\u003c/em\u003e, \u003cem\u003eArecae Semen\u003c/em\u003e, \u003cem\u003eAmomum tsaoko Crevost et Lemarie\u003c/em\u003e, \u003cem\u003eAnemarrhenae Rhizoma\u003c/em\u003e, \u003cem\u003eForsythiae Fructus\u003c/em\u003e, \u003cem\u003eLicorice \u003c/em\u003eand \u003cem\u003eArum Ternatum Thunb\u003c/em\u003e were repeated drugs in 10 different prescriptions. Furthermore, the correlation analysis of these effective compounds showed that there was a hub effective component MOL000422 (kaempferol, C\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e) presented in the 10 prescriptions (Fig.1A). PubChem analysis showed the hub compound structure (Fig.1B and C). There are 77 kinds of effective components associated with prescriptions 1, 3, 4, 6, 7, 9 and 10 (Table S2). Each prescription has its own unique active ingredients for patients with different diseases. Prescription 1 has 23 unique active ingredients, prescription 2 has 3, prescription 3 has 3, prescription 4 has 3, prescription 6 has 2, prescription 7 has 2, prescription 8 has 8, prescription 9 has 3, prescription 10 has 43 (Fig.1A). In addition, TCM target genes were collected for GO and KEGG pathway analysis, and the result implied that target genes significantly involved positive regulation of transcription from RNA polymerase II promoter, nucleus and protein binding category (Fig.1D), and mainly involved in pathway in cancer, TNF signaling and Osteoclast differentiation (Fig.1E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Screening of joint effective compounds and drug-target genes by analysis of combination of Chinese and Western Medicine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe firstly constructed Venn graphs of TCM and CWM compounds and target genes based on TCMSP and TTD/PubChem database, and found 3 hub compounds and 64 target genes (Fig.2A and B). We further analyzed 3D structure of 3 hub compounds (Baicalein, Estrone and Quercetin) based on NCBI PubChem, and the result showed the three structures are similar, Baicalein and Quercetin have large conjugation systems, and Estrone only has conjugation of benzene rings (Fig.S2B). Hub target genes significantly involved in cytosol and protein binding category, and pathway in cancer and Hepatitis B, according to GO and KEGG pathway analysis (Fig. 2C and Fig.S2A). Moreover, Cytoscape was used to construct the regulatory network of \u0026ldquo;Traditional Chinese medicine-Hub compound-Target gens\u0026rdquo; (Fig.2D), which indicated that Quercetin had more complex network systems and Estrone had less target genes. \u003cem\u003eMAPK14\u003c/em\u003e, \u003cem\u003eMAPK3\u003c/em\u003e,\u003cem\u003e MCL1\u003c/em\u003e connected Quercetin and Baicalein, \u003cem\u003eOPRM1\u003c/em\u003e, \u003cem\u003eADRB2\u003c/em\u003e, \u003cem\u003eSCN5A\u003c/em\u003e, \u003cem\u003eEGF\u003c/em\u003e and \u003cem\u003eACE\u003c/em\u003e connected Estrone and Quercetin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Identification of differentially expressed genes (DEGs) induced by SARS-CoV-2 infection based on GEO database: GSE147507\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is based on GEO database to download the original data of GSE147507. The data set explored the response of NHBE cell lines and A549 cell lines to SARS-CoV-2 infection at the transcriptional level, with a total of 110 samples. The raw data was analyzed by R language and the conditions P \u0026lt; 0.05 and |log2-FC|\u0026gt;1 defined as differential genes. The results showed that there were 1177 differentially expressed genes in A549 cells and 1407 genes in NHBE cells. The volcano plots of DEGs among Cov-set and SARS-set were shown in Fig.3A and B. Venn analysis showed that 8 up-regulated genes and 113 down regulated genes were the same in the two kinds of cells (Fig.3C and Table 1). NCBI database was used to analyze the tissue expression profile characteristics of hub differential genes (Fig.S3). The results showed that the up-regulated differentially expressed genes induced by SARS-CoV-2 had high tissue specificity. \u003cem\u003eSYNE1\u003c/em\u003e was highly expressed in ovary, \u003cem\u003eCDKN2C\u003c/em\u003e in adipose tissue, gnpda1 in kidney, \u003cem\u003eRPAGD\u003c/em\u003e in goiter, \u003cem\u003eANKH\u003c/em\u003e in pancreas and \u003cem\u003ePRODH\u003c/em\u003e in small intestine. The down-regulated genes induced by virus were highly expressed in bone marrow, lymph node, spleen, lung, cecum, gallbladder and bladder. In addition, the expression levels of pivotal genes in pancreas and liver tissues were significantly lower than those in other tissues. Among all DEGs, only \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eC1R\u003c/em\u003e, \u003cem\u003eC1S\u003c/em\u003e, \u003cem\u003eASS1\u003c/em\u003e, \u003cem\u003eERRF11\u003c/em\u003e, \u003cem\u003eKYNU\u003c/em\u003e, \u003cem\u003eHSD11B1\u003c/em\u003e, \u003cem\u003eSERPINA3\u003c/em\u003e, \u003cem\u003eSAA1\u003c/em\u003e and \u003cem\u003eCFB\u003c/em\u003e were significantly expressed in liver tissues, while \u003cem\u003eERRF11\u003c/em\u003e, \u003cem\u003eSERPINA3\u003c/em\u003e and \u003cem\u003eBCL2A1\u003c/em\u003e were significantly expressed in pancreas.\u003c/p\u003e\n\u003cp\u003eThe DEGs KEGG pathway enrichment showed DEGs significantly involved in pathway of Herpes simplex infection, influenza A, Measles and TNF signaling (Fig3D). GO analysis results indicated DEGs were enrichment in type I interferon signaling pathway, defense response to virus, cytoplasm, cytosol and protein binding (Fig.3E). The above results indicated that SARS-CoV-2 infection wildly down-regulated the immune response of host.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Identification of key hub DEGs by integrating internet-pharmacology and GEO database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to further screen molecular targets for COVID-19 regulation, and provide targets for exploring the mechanism of interaction between virus and host and screening new drugs, we further conduct molecular docking between drug targeted genes (DTGs) and differential expressed genes (DEGs) induced by SARS-CoV-2 infection, and screen core pivotal genes. Venn analysis was carried out on the target genes of ten TCM prescriptions and the DEGs induced by virus infection. The results showed that the number of key genes in the target genes of SARS-004 group was more than that of Cov-002 group (Fig.4A). These joint genes were down regulated by viral infection in both Cov and SARS groups (Fig.4B and C). Furthermore, Venn analysis was carried out on the related genes of each prescription and GEO set, and found the common associated genes (Fig.S4A and B). \u003cem\u003eICAM1\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eSLPI\u003c/em\u003e, \u003cem\u003eSTAT1\u003c/em\u003e, \u003cem\u003eTOP2A\u003c/em\u003e were the common key genes in CoV and SARS groups. Except TOP2A, they were all down regulated by virus infection (Fig. S4C).\u003c/p\u003e\n\u003cp\u003eBesides, based on TTD (Therapeutic Target Database), GEO and TCMSP databases, the drug targets of Chinese and Western medicine and the differential expressed genes induced by virus were analyzed, and 14 core pivotal genes were obtained. \u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eIL8\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eSTAT1\u003c/em\u003e and \u003cem\u003eTHBD\u003c/em\u003e were all present in both Cov and SARS groups. Except \u003cem\u003eTHBD\u003c/em\u003e, other genes were down regulated by viral infection in both groups (Fig.4D and E). Finally, the interaction compounds of core hub genes were analyzed by CTD database, and two common compounds, folic acid and ozone, were screened (Fig.S4E). Folic acid can down regulate the expression of core hub genes, and zone can up regulate the expression of core hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Identification of \u003c/strong\u003e\u003cstrong\u003eComponent-molecular target docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hub genes and SARS-CoV-2 related genes were respectively done component-molecular target docking with compounds. The lower the Binding energy is, the firmer the binding between compound and molecular target. This means the compound have greater ability to make a difference. The binding energy was lower than 5KJ/mol as the screening condition, and the docking results were listed (Table 2 and Table 3), indicating that these compounds had good binding ability to target proteins. Among the four compounds, the binding energy between folic acid and target protein were -7.2\u003cstrong\u003e/\u003c/strong\u003ekcal.mol，-6.8/kcal.mol，-6.5/kcal.mol，-8.5/kcal.mol，-10.0/kcal.mol and -6.8/kcal.mol, generally lower than that of other compounds. \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003ePTGS2\u003c/em\u003e also have low binding energy with compounds (Table 2 and Fig. 5A). All the four compounds had lower binding energy with \u003cem\u003eACE2\u003c/em\u003e, among which folic acid was the most significant. The binding energy between the compounds and \u003cem\u003eACE2\u003c/em\u003e is slightly higher than that of the compounds with\u003cem\u003e ACE\u003c/em\u003e. Among the eight proteins of SARS-COV-2, Nucleocapsid Phosphoprotein (N) had the lowest binding energy with compounds. The results with the minimum binding energy for each protein were selected for visualization ( Table 3 and Fig. 5B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSARA-Cov-2 infection molecular therapeutic network model is built based on intelligent computer and big data integration bioinformatics. The project mainly relies on database analysis to analyze the interactions and related phenotypes among SARA-Cov-2 drugs, and analyze the key drug targets by correlation analysis. Infected cells and normal cells were investigated by GEO database. This research mainly relies on the published data of the database to integrate bioinformatics analysis to obtain the key drug targets and potential targets of gene therapy, explore the mechanisms of effective compounds in the treatment of COVID-19.\u003c/p\u003e\n\u003cp\u003eThe category of \"epidemic disease\" in traditional Chinese medicine has accumulated rich experience in fighting epidemics for thousands of years. After analyzing the active compounds of ten TCM prescriptions, a common core compound, kaempferol, was found. It has been shown that kaempferol not only has some anti-inflammatory, anti-cough and expectorant effects, but also lowers blood glucose and prevents vascular complications of diabetes\u003csup\u003e19\u003c/sup\u003e. Coronavirus infection can induce COVID-19 patients with normal glucose tolerance to increase blood glucose, sustained hyperglycemia is not only conducive to virus replication in the body, but also reduce the body's ability to resist infection, hyperglycemia patients develop serious illness and face a higher risk of death\u003csup\u003e20,21\u003c/sup\u003e. This indicates that TCM can play a full therapeutic role in COVID-19. In addition, GO function was performed on the target sites of TCM, and the results showed that the target genes were enriched in the positive regulation of transcription from RNA polymerase II promoter, oxidation-reduction process, protein binding. Conducting KEGG pathway analysis revealed that target genes were mainly involved in pathway in cancer, TNF signaling pathway, and osteoclast differentiation. It was shown that these terms play a key role in the treatment of COVID-19 by TCM.\u003c/p\u003e\n\u003cp\u003eIn the association analysis of TCM and CWM, three central compounds were identified, namely, baicalein, estrone and quercetin. Baicalein is a flavonoid with antihyperglycemic activity \u003csup\u003e22\u003c/sup\u003e, which, like kaempferol, can be useful in patients with diabetes mellitus. Quercetin, like baicalein, is a flavonoid that has the beneficial effects on inflammation and immunity\u003csup\u003e23\u003c/sup\u003e. Study had shown that quercetin may decreases the frequency and duration of respiratory tract infections as an effective intervention; however, more research is needed\u003csup\u003e24\u003c/sup\u003e. Natural estrogens are mainly estradiol, estrone and estriol. Numerous studies had shown that estrogens are associated with the development of liver cancer and work by binding to estrogen receptors\u003csup\u003e25\u003c/sup\u003e. What's more, it had shown that estrogen treatment silences the inflammatory reactions and decreases virus titers leading to improved survival rate in animal experiments \u003csup\u003e26\u003c/sup\u003e. It is evident that TCM and CWM are effective in targeting patients with comorbid diabetes, liver damage, and kidney damage. GO analysis was performed on the overlapping fraction of targets obtained from target association analysis of TCM and CWM to obtain target enrichment in cytosol and protein binding category. KEGG analysis was performed to obtain target enrichment in the pathway in cancer and Hepatitis B. The commonality between TCM and CWM in the treatment of COVID-19 was shown.\u003c/p\u003e\n\u003cp\u003eIn the analysis of differential genes induced by viral infection, up-regulated genes such as gnpda1 were found to be highly expressed in the kidney, \u003cem\u003eRPAGD\u003c/em\u003e in the thyroid, \u003cem\u003eANKH\u003c/em\u003e in the pancreas, \u003cem\u003ePRODH\u003c/em\u003e in the small intestine, and down-regulated genes were highly expressed in the bone marrow, lymph nodes, spleen, lung, cecum, gallbladder, and bladder. In addition, key genes were expressed at significantly lower levels in pancreatic and liver tissues. It is hypothesized that these genes are key targets in the clinical symptoms of COVID-19 that cause fever, cough, chest tightness, and diarrhea and sore throat symptoms. After GO functional analysis of the differential genes, it was found that the differential genes were mostly enriched in the type I interferon signaling pathway, defense response to virus, cytoplasm, cytosol, and protein binding. KEGG analysis revealed that the differential genes were mostly enriched in herpes simplex infection, influenza A, and TNF signaling pathways. It is evident that SARS-CoV-2 infection affects the immune response of the host. It had shown that after infection with SARS-CoV-2, uncontrolled inflammatory mediators in the body, causing a cytokine storm, will lead to an excessive immune response, consistent with the results of differential gene enrichment analysis\u003csup\u003e27\u003c/sup\u003e. In addition, the results of GO classification showed that both TCM drug target genes and virus induced genes were significantly involved in protein binding of molecular functions. Whether TCM can inhibit protein synthesis more effectively, thus hindering the replication and proliferation of virus in the host?\u003c/p\u003e\n\u003cp\u003eAfter analyzing the drug targets and differential genes separately, we obtained six core hub genes, namely \u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eCXCL8\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eSTAT1\u003c/em\u003e, by correlating them to establish a \" disease-target-compound-drug \" regulatory network. SARS-CoV-2 causes activation of different immune cells that helps to secrete proinflammatory cytokine \u003cem\u003eIL6\u003c/em\u003e and other inflammatory cytokines then causes cytokine storm\u003csup\u003e28\u003c/sup\u003e. Both \u003cem\u003eIL8\u003c/em\u003e and\u003cem\u003e IL1B\u003c/em\u003e encode proteins that are cytokines and, like \u003cem\u003eIL6\u003c/em\u003e, are more abundant in critically ill patients and statistically different (P\u0026lt;0.05)\u003csup\u003e27,29\u003c/sup\u003e. Analysis of the KEGG pathway for six core hub genes yielded four more important nodes: legionellosis, interleukin-6-mediated signaling pathway, IL-17 signaling pathway, and Leishmaniasis. Among them Legionellosis may involve pneumonia.IL-17 signaling pathway plays an important role in both acute and chronic inflammatory responses. This reveals the mechanism of herbal and Western medicine in the treatment of COVID-19.\u003c/p\u003e\n\u003cp\u003eIdentification of potential molecular targets is essential for drug repurposing\u003csup\u003e30-32\u003c/sup\u003e. Through the construction of molecular docking model between hub host gene and hub compound, it was found that folic acid (FA) and protein ILB, PTGS2, STAT1 were stable binding, and estrogen was stable binding to IL6, IL8. Estrone has good binding properties to proteins, and exogenous estrogen is recommended as a drug for the prevention and treatment of COVID-19\u003csup\u003e26\u003c/sup\u003e. Folic acid may be a potential drug target. FA had a relatively lower binding energy to the protein, suggesting that it works better as a drug to treat COVID-19. This suggests that folic acid is a potential drug to treat COVID-19. FA have preventive effects of on Zika virus-associated poor pregnancy outcomes in immunocompromised mice. Mice with FA treatment showed lower viral burden and better prognostic profiles in the placenta including reduced inflammatory response, and enhanced integrity of BPB\u003csup\u003e33\u003c/sup\u003e.It had indeed been shown to related to pregnant women with COVID-19 \u003csup\u003e34\u003c/sup\u003e. However, the mechanism by which folic acid has apparently protected pregnant women during the COVID-19 pandemic has not been determined.\u003c/p\u003e\n\u003cp\u003eFor the precise treatment of COVID-19, our research performed molecular docking between drug compounds and viral proteins. The role of N in RNA recognition, replicating, transcribing the viral genome, and modulating the host immune response is indispensable\u003csup\u003e35\u003c/sup\u003e. The \u003cem\u003e3CL\u003c/em\u003e protease and the \u003cem\u003eNSP13\u003c/em\u003e helicase are crucial to viral replication \u003csup\u003e36,37\u003c/sup\u003e. The spike proteins help in fusing into the host and the \u003cem\u003eNSP9\u003c/em\u003e replicase plays a major role in viral replication\u003csup\u003e38\u003c/sup\u003e.The \u003cem\u003eORF3a\u003c/em\u003e protein can activate the \u003cem\u003eNLRP3\u003c/em\u003e inflammasome by promoting TRAF3‐dependent ubiquitination of \u003cem\u003eASC\u003c/em\u003e\u003csup\u003e39\u003c/sup\u003e. The \u003cem\u003eORF7A\u003c/em\u003e protein blocks cell cycle progression at G0/G1 phase via the cyclin D3/pRb pathway\u003csup\u003e40\u003c/sup\u003e.The \u003cem\u003eORF8\u003c/em\u003e can mediate immune suppression and evasion activities potentially\u003csup\u003e41\u003c/sup\u003e. These proteins play an important role in the viral infection of the host, so they are selected for molecular docking with the effective compounds obtained from the analysis. 3CL Protease and ORF3a protein, ORF8 also have low binding energy with the compounds. The binding of Phosphoprotein N to folic acid was stable, indicating that it can be targeted for molecular therapy. Together, above results showed that the binding energy between FA and N is the lowest, so it was speculated that N might be the target of folic acid in the treatment of COVID-19.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn general, through the analysis of the effective compounds and drug targets of COVID-19 clinical TCM and CWM, we found the pivotal genes and effective compounds, and further established the \"drug-target-virus infection\" molecular network through the GEO database combined with the virus induced differential genes, and screened out the potential new drugs (FA) and new molecular targets (SARS-CoV-2 N protein and PTGS2) for COVID-19 treatment, which promote underlying the interaction mechanism between virus and host, and provide a new insight for the development of new drugs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e: Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTCM\u003c/strong\u003e: Traditional Chinese medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCWM\u003c/strong\u003e: Conventional Western medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-CoV-2\u003c/strong\u003e: severe acute respiratory syndrome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e: spike\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e: membrane\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e: envelope\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e: nucleocapsid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACE2\u003c/strong\u003e: angiotensin-converting enzyme\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORFs\u003c/strong\u003e: open reading frames\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTRSs\u003c/strong\u003e: transcriptional regulatory sequences\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTCMSP\u003c/strong\u003e: Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs\u003c/strong\u003e: differential expression genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e: Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e: biological process\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMF\u003c/strong\u003e: molecular function\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e: cellular component\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI:\u003c/strong\u003e Protein-protein interaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACE: \u003c/strong\u003eangiotensin converting enzyme\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDTGs\u003c/strong\u003e: drug targeted genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003cstrong\u003e: \u003c/strong\u003eSARS-COV-2, Nucleocapsid Phosphoprotein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTTD:\u003c/strong\u003e Therapeutic Target Database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFA\u003c/strong\u003e: folic acid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and materials in the current study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo animal studies are presented in this manuscript. No human studies are presented in this manuscript. No potentially identifiable human images or data is presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science and Technology Planning Project of Yuzhong District of Chongqing City, 2020050, and the Chongqing Municipal Education Commission Foundation, KJQN202000403.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully thank American Journal Experts for editing this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Wu, F.\u003cem\u003e et al.\u003c/em\u003e A new coronavirus associated with human respiratory disease in China. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e579\u003c/strong\u003e, 265-269, doi:10.1038/s41586-020-2008-3 (2020).\u003c/p\u003e\n\u003cp\u003e2\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Wang, C., Horby, P. 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R.\u003cem\u003e et al.\u003c/em\u003e An immunosuppressed Syrian golden hamster model for SARS-CoV infection. J Virology.\u0026nbsp; \u003cstrong\u003e380\u003c/strong\u003e (2008).\u003c/p\u003e\n\u003cp\u003e41\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Structure of SARS-CoV-2 ORF8, a rapidly evolving coronavirus protein implicated in immune evasion. J bioRxiv : the preprint server for biology.\u0026nbsp; (2020).\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Total number of differentially expressed genes in \u003c/strong\u003e\u003cstrong\u003eGSE147507 dataset\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"22%\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"62%\"\u003e\n\u003cp\u003eNumbers of DEGs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eCov002_mock_vs_Co002_CoV2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36%\"\u003e\n\u003cp\u003eSARS004_mock_vs_SARS004_CoV2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003eVenn\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eUp-regulated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36%\"\u003e\n\u003cp\u003e677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eDown-regulated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36%\"\u003e\n\u003cp\u003e730\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eTotal number\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36%\"\u003e\n\u003cp\u003e1407\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Binding energy (/kcal.mol) of effective compounds to hub genes \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemical formula\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e\u003cstrong\u003eIL1B\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e\u003cstrong\u003eIL6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e\u003cstrong\u003eIL8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e\u003cstrong\u003eNFKBIA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003ePTGS2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e\u003cstrong\u003eSTAT1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u003cstrong\u003eBaicalein\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e-7.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e-7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e-8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e-6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u003cstrong\u003eEstrone\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e-6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e-8.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e-8.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e-6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u003cstrong\u003eQuercetin\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e-7.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e-8.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e-9.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e-6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u003cstrong\u003eFolic acid\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eC\u003csub\u003e19\u003c/sub\u003eH\u003csub\u003e19\u003c/sub\u003eN\u003csub\u003e7\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e-7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"40\"\u003e\n\u003cp\u003e-6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e-10.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e-6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Binding energy (/kcal.mol) of effective compounds to SARS-Cov-2 related genes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eBaicalein\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eEstrone\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eQuercetin\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u003cstrong\u003eFolic acid\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemical formula\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eC\u003csub\u003e19\u003c/sub\u003eH\u003csub\u003e19\u003c/sub\u003eN\u003csub\u003e7\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eACE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-8.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-8.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-8.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eACE2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 3CL\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-8.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-8.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 helicase-NSP13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 Spike\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-5.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-5.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-6.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 NSP9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-7.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 Nucleocapsid Phosphoprotein\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-8.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-8.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 ORF3a\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-8.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003eSARS-Cov-2 ORF7a\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"88\"\u003e\n\u003cp\u003e-7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, SARS-CoV-2, traditional Chinese medicine, big data mining, antivirus","lastPublishedDoi":"10.21203/rs.3.rs-117894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-117894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eCoronavirus Disease 2019 (COVID-19) respiratory disease rapidly caused a global pandemic and social and economic disruption.\u003cstrong\u003e \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003ecombination of Traditional Chinese medicine (TCM) and Conventional Western medicine (CWM) is more effective for COVID-19 treatment. Moreover, TCM and CWM are important data source for developing new drug targets and promote strategies treat SARS-CoV-2 infections. However, many studies have analyzed the therapeutic mechanism of CWM or TCM alone for COVID-19, it is still unclear the interaction mechanism between TCM and CWM on COVID-19.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis paper integrates network pharmacology and GEO database to mine and identify COVID-19 molecular therapeutic targets, providing potential targets and new ideas for COVID-19 gene therapy and new drug development. It includes: 1) using TCMSP, TTD, PubChem and CTD databases to analyze drug interactions and associated phenotypes for SARS-CoV-2, to correlate drug and disease interaction mechanisms to screen key drug targets; 2) using GEO database to correlate differential genes and drug targets to screen potential antiviral gene therapy targets, to construct regulatory network and key points of SARS-CoV-2 therapeutic drugs; 3) using computer simulation of molecular docking to screen virus-related proteins for new drugs. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIntegrated analysis of network pharmacology discovered that baicalein, estrone and quercetin are the pivotal active ingredients in TCM and CWM. Combining drug target genes in pharmacology database and virus induced genes in GEO database, the result showed the core hub genes related to COVID-19:\u003cem\u003e STAT1\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e, \u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eIL8\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e, and these genes were significantly downregulated in A549 and NHBE cells by SARS-CoV-2 infection. Moreover, chemical interaction and molecular docking analysis of hub genes showed that folic acid might as be potential therapeutic drug for COVID-19 treatment, and SARS-CoV-2 nucleocapsid phosphoprotein was a potential drug target. The network of “drug-target-SARS-CoV-2 related genes” provide noval potential compounds and targets for further studies of SARS-CoV-2.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIntegrated analysis of network pharmacology and big data mining provided noval potential compounds and targets for further studies of SARS-CoV-2. Our research implied folic acid and SARS-CoV-2 N as therapeutic target in TCM and CWM. Our research also suggests that targeting SARS-CoV-2 N protein is likely to be a common mechanism of TCM and CWM. On the one hand, the identification of pivotal genes provides a target for COVID-19 molecular therapy, on the other hand, it provides ideas for the analysis of interaction mechanism between virus and host.\u003c/p\u003e","manuscriptTitle":"Network of “drug-target-SARS-CoV-2\u0026nbsp;Related Genes” Through Integrated Analysis of Pharmacology and Geo Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-04 17:44:44","doi":"10.21203/rs.3.rs-117894/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"957ce33e-ba4c-443a-bd73-0c4702508e66","owner":[],"postedDate":"December 4th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1337720,"name":"Internal Medicine"}],"tags":[],"updatedAt":"2020-12-10T19:34:28+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-04 17:44:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-117894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-117894","identity":"rs-117894","version":["v1"]},"buildId":"PuwtSGGF21Z2LVbsuOoB7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}