The JingFangFuZiLiZhong formula (JFFZLZ) shows a good effect against UC in the clinic. Hence, we aim to investigate the mechanisms between JFFZLZ and UC We obtained active constituents and related targets from public databases. The overlapped genes between JFFZLZ and UC targets were further analysed by enrichment analysis. The active constituents and hub targets were used to construct molecule docking analysis. We finally screened out nine hub targets and their expressions were verified in the Gene Expression Omnibus database and UC rats’ colon tissues after JFFZLZ treatment.via regulating the hub targets.The results implied that JFFZLZ mainly regulated signal transduction, metabolites production, and inflammation pathways. The expression of STAT3, CXCL8, IL6, CXCL12, TNF, TP53, and PTPN11 were both upregulated in colon tissues of UC patients and UC rats. While RELA, EGFR, and TP53 were downregulated in UC patients, but upregulated in UC rats. Furthermore, JFFZLZ could repair UC rats’ colon mucosal damage and promote the healing of ulcers JFFZLZ could reduce the inflammatory infiltration and repair UC rats’ colon mucosal damage.Through the network pharmacology-based strategy and public database mining, we obtained the hub targets and key pathways between JFFZLF and UC.The mechanism of JFFZLZ against UC was inhibition of inflammatory response and oxidative stress by regulating the expression of the hub targets.These results elucidated that the anti-UC effect of JFFZLZ was closely related to the inhibition of inflammatory response, inhibition of oxidative stress, and repairing colon mucosal damage through different signal pathways. The findings could contribute to a better understanding of the regulation mechanisms in JFFZLZ against UC. Ulcerative colitis (UC) is a chronic inflammatory disease with recurrent attacks and unknown aetiology, which has caused a heavy health burden around the world . TypicalHerba Schizonepetae (JingJie), Radix Saposhnikoviae (FangFeng), Radix Aconiti Lateralis Preparata (FuZi), Radix Ginseng (RenShen), Rhizoma Zingiberis (GanJiang), Radix Glycyrrhiizae (GanCao), and Rhizoma Atractylodis Macrocephalae (BaiZhu). On the basis of our clinical experience and observation, the JFFZLZ could alleviate the clinical symptoms, suppress the inflammatory reaction, and regulate inflammatory factors of UC patients. From the perspective of TCM, the core theory of JFFZLZ is to regulate host immunity, reduce the inflammatory response, and improve clinical symptoms [There have been a considerable amount of TCM studies on treating UC, such as Rhubarb Paeony decoction , BaiTouWsymptoms .in vivo UC rat model with JFFZLZ treatment and detected the efficacy of JFFZLZ on UC rats by Haematoxylin–Eosin (HE) staining of colon tissues. We also analysed the hub targets expression in rats’ colon tissues by real-time quantitative polymerase chain reaction (RT-qPCR), ELISA, and Western blot (WB).Although the JFFZLZ has a good effect against UC, its potential pharmacological mechanisms are not fully elucidated. Meanwhile, the network pharmacology approach, also known as system pharmacology, has emerged as a powerful tool to explore the connection between drugs and diseases ,22. TherHerba Schizonepetae (JingJie), Radix Saposhnikoviae (FangFeng), Radix Aconiti Lateralis Preparata (FuZi), Radix Ginseng (RenShen), Rhizoma Zingiberis (GanJiang), Radix Glycyrrhiizae (GanCao), and Rhizoma Atractylodis Macrocephalae (BaiZhu). The active constituents of each herb in JFFZLZ were harvested by use of the online databases TCMSP (http://tcmspw.com/tcmsp.php) and TCMID (http://www.megabionet.org/tcmid/). Oral bioavailability (OB) and Drug-likeness (DL) are used to filter active compounds [The herbal formula of JingFangFuZiLiZhong formula (JFFZLZ) comprises seven traditional Chinese medicines, including ompounds . The crihttp://prediction.charite.de) databases. After that, all proteins were converted into genes with the help of the Uniprot database (https://www.uniprot.org/) and acquired gene ID. Furthermore, the JFFZLZ constituent − target network was visualised through Cytoscape .The putative JFFZLZ targets were retrieved in TCMSP, TCMID, and SuperPred (http://www.disgenet.org/), GeneCards (https://www.genecards.org/), MalaCards , and TTD (http://db.idrblab.net/ttd/) databases. The Keyword “ulcerative colitis (UC)” and “Homo sapiens” were used to search UC-related genes.Candidate UC targets were acquired from DisGeNET (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was constructed to visualise the overlapped genes (OGEs) between the JFFZLZ targets and UC targets.The Venny diagram .Gene Ontology (GO) function enrichment and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment are used to annotate functional characteristics and signal pathways involved in the targets . The enrhttps://string-db.org/cgi/input.pl). In this study, OGEs were inputted into the STRING database with confidence scores ≥ 0.7 as well as species limited to “Homo sapiens”. Then, PPI data were introduced into Cytoscape (version 3.7.2) to construct and visualise a PPI network, and the non-connection genes were removed. After obtaining the PPI network, the top 15 hub targets were acquired by the cytoHubba plugin of Cytoscape (version 3.7.2) according to the degree. Further, the top 15 genes were selected as hub targets based on the results of degree, and the PPI network in the top 15 genes was constructed by Cytoscape (version 3.7.2). Next, the enrichment analysis of KEGG in hub targets was performed by the ClueGO plugin using hub targets. And, the filtering terms of retrieval results in hub target KEGG were defined as the value of p ≤ 0.05, Cluster ≥ 3, and Kappa Score = 0.4. Bubble Diagrams were drawn by Sangerbox tools.The protein-protein interaction (PPI) network was derived based on the STRING database (https://www.rcsb.org) with species and resolutions limited to “Homo sapiens” and 2.0–3.0 °A. Moreover, the 2D structures of the common constituents were obtained from the TCMSP Database as ligands. Next, the molecular docking simulation was performed by AutoDockTools software (version 1.5.6), and the result was visualised in PyMOL software (version 2.2.0). The binding energy threshold condition ≤ −5.0  was considered as the most favourable docking pose.Then, the common constituents and hub targets were used to construct a molecular docking network. The 3D structures of the hub target protein receptors were retrieved from the RCSB PDB database (http://www.ncbi.nlm.gov/geo/), where the keywords were set as “ulcerative colitis (UC)”, “Normal”, and “homo sapiens”. The gene expression data for the analysis stem from the GDS3268, GDS2642, GDS3119, GDS4519, and GDS4365 datasets. For more, GDS3268 was composed of 129 patients with UC and 73 people with normal. GDS2642 was composed of 9 patients with UC and 4 people with normal. GDS3119 was composed of 21 patients with UC and 5 people with control. GDS4519 was composed of 10 pairs of twins patients with UC and 10 pairs of twins people with healthy. GDS4365 was composed of 30 patients with UC and 13 people with control. Furthermore, the mean value of different probes was taken as the final expression value of the gene, for multiple probes corresponding to the same chip.The expression of hub targets was verified in Gene Expression Omnibus (GEO) database 2016-0006). All animals were raised in the Barrier Environmental Animal Laboratory of Dongzhimen Hospital of Beijing University of Chinese Medicine (license: SYXK(Beijing)2015-0001) and maintained under the National Standards for Laboratory Animals of China (GB 14925-2010). Our study was carried out in compliance with the ARRIVE guidelines. This study was approved by the Ethics Committee of Laboratory Animals of Dongzhimen Hospital of Beijing University of Chinese Medicine (No.20-51). Animals were kept separately in an SPF laboratory, with the breeding environment: temperature 25 ± 1 °C, humidity 50 ± 10%, free to food and drinking water, 12-hour day and night alternation, as well as adaptable feeding for 5 days.Concentrate granules of Senna Folium were procured from Tcmages Pharmaceutical Co., Ltd . Hydrocortisone sodium succinate was purchased from Tianjin biochem Pharmaceutical Co., Ltd  and dextran sulphate sodium (DSS) was purchased from MP Biomedicals . The JFFZLZ granules were a compound preparation of Chinese herbal medicine, including Herba Schizonepetae , Radix Saposhnikoviae , Radix Aconiti Lateralis Preparata , Radix Ginseng , Rhizoma Zingiberis , Radix Glycyrrhiizae , and Rhizoma Atractylodis Macrocephalae , was also purchased from Tianjin biochem Pharmaceutical Co., Ltd .After five days of adaptive feeding, all the rats were fed with 2 ml/100g/d of 15% Sennae Folium decoction concentrate granule solution for 21 days; at the same time, they were given hydrocortisone succinate sodium with 0.15 ml/100g/d at a concentration of 5 mg/ml subcutaneously injection in the neck for 10 days. From the 22nd day, the rats were given 3% DSS solution and normal water alternately for a total of three cycles. In the first cycle, the DSS solution was administered for 5 days and then the normal drinking water for seven days; and the second cycle was the same as the first cycle of administration; while, in the third cycle, the rats were only given DSS for 8 days. If diarrhoea, bloody mucus, congestion, oedema, erosion, and ulcer appeared in the colonic mucosa, the model was established successfully. The whole establishment day of the chronic relapsing UC rat model was 53 days.n = 18) were divided randomly into three groups: blank group (BG), model group (MG), and JFFZLZ group (JFFZLZG), with 6 rats in each group. In the BG and MG, rats were administered 0.9% saline solution via oral gavage. The JFFZLZG rats were administrated by 14.8 g/kg/d  JFFZLZ granule via oral gavage. The JFFZLZ granule was dissolved in distilled water and given to rats. Rats were treated by gavage at a dose of 1 ml/100g/d, whose drug administration period was 28 days. After the intervention, the proximal colon tissues (0-4 cm above the anus) were fixed with 4% paraformaldehyde, then been paraffin-embedded, dewaxed, rehydrated, and stained with Haematoxylin–Eosin staining (HE). Meanwhile, the distal colon tissues (4–8 cm above the anus) were put into the cryopreservation tubes and stored frozen at −80 °C.Rats  according to the manufacturer’s instructions. Total RNA reverse transcription to cDNA was performed with the All-in-One First-Strand Synthesis Master Mix (with dsDNase) . RT-qPCR was performed using the Real-time PCR Detection System  with the Taq SYBR® Green qPCR Premix . The relative abundance of mRNA was using GAPDH as an internal control gene. The primers were provided in Rat colon tissues were lysed with potent RIPA buffer  adding a complete protease inhibitor cocktail . The tissue homogenates were centrifuged at 12,000 g at 4 °C for 10 min, and the supernatant containing proteins was collected. Then, protein concentration was determined with a BCA kit . The resulting supernatant was mixed with SDS-PAGE protein loading buffer (5X)  and the mixture was placed in a metal bath at 100 °C for 5 min to denature the protein. Next, an aliquot of 30 μg protein from each sample was separated using 10% SDS-PAGE  and transferred to PVDF membranes. The PVDF membranes were incubated with primary antibodies against STAT3  and NF-kBp65  at 4 °C overnight, and then incubated with secondary HRP-coupled antibodies  for 1 h at room temperature. The immunoreactivity was determined using enhanced chemiluminescence reagents  and analysed with Image J.g at 4 °C for 10 min, and the supernatant containing proteins was collected. Then, protein concentration was determined with a BCA kit . Then add 100 µL samples into the appropriate wells, and finally detected the OD value at 450 nm by the microplate reader. The concentration of target protein was calculated according to the standard curve and total protein concentration.The expression of IL-6 and TNF‐α in rat colon tissues was determined by using ELISA Kit  according to the manufacturer’s instructions. The rat colon tissues were lysed with 2X Lysis Buffer  adding a complete protease inhibitor cocktail . The tissue homogenates were centrifuged at 12,000 p-value <0.05 was considered to indicate a statistically significant difference. Significance levels are *p < .05, **p < .01, ***p < .001, and ns: not significant.Data for graphing was processed with GraphPad Prism 8.0 software. Statistical analyses were performed using the SPSS 25.0 statistical software package. One‐way analysis of variance (ANOVA) was used if the data as per normal distribution and homogeneity of variance, and the LSD test was used to assess the differences between the two groups. Otherwise, the Kruskal–Wallis H test was used. A Supplement Table 2). According to the potential active constituents of JFFZLZ, there were 1,946 targets acquired from the TCMSP, TCMID, and SuperPred databases after removing the redundancy (Supplement Table 3). Then we constructed the interaction network between active constituents and targets of JFFZLZ by Cytoscape. Among the shared herb molecules, the proportion of beta-sitosterol was the highest constituent (6/7), followed by daucosterol (3/7). While stigmasterol, folinic acid, quercetin, isoquercitrin, deltoin, heptadecane, tridecanoic acid, and kaempferol were the lowest molecules (2/7) (Supplement Table 3). Finally, we obtained 279 overlapped genes (OGEs) between the JFFZLZ putative targets and UC targets  . Next, a targets , the detSupplement Table 4.To gain insights into the main biological functions of JFFZLZ against UC, the GO and KEGG pathway enrichment analysis for the OGEs between the JFFZLZ putative targets and UC targets were performed. The enrichment analysis results revealed that the GO-Cellular Component (CC) of OGEs were predominantly involved in the vesicles, plasma membranes, cytoplasm, and extracellular matrix . The enrTo better understand the relationship between different targets, the 279 OGEs were used to construct the PPI network through the STRING database and Cytoscape after eliminating non-interaction proteins. The PPI network contained 224 nodes and 1303 edges , represeTo better understand the signal pathways and interactions between the constituents and target genes, we further constructed the molecular docking network between the active constituents and the hub targets. We first analysed the constituents in JFFZLZ according to the common degree and binding energy. As The GDS3268 dataset contained inflamed and un-inflamed colon epithelial biopsies of 129 UC patients from different anatomical locations of the gastrointestinal (GI) tract . The GDSTo further confirm the efficacy and mechanism of JFFZLZ against UC, we treated the UC rats with JFFZLZ and observed colon pathological changes by HE staining. The results showed significant colonic mucosal erosion and ulcers in the model group (MG) compared to the blank group (BG) , and significant inflammatory cell infiltration including neutrophils, eosinophils, plasma cells, and lymphocytes in the mucosal and submucosal layers . And, after JFFZLZ treatment, there were no apparent erosion and ulcers in the colonic mucosal, surface epitheliums and glands regenerated, and part of the glands appeared nodular. Meanwhile, the infiltration of inflammatory cells in the colon mucosal and submucosal layers significantly alleviated after JFFZLZ treatment compared to the MG rats  . Then, wFor the treatment of UC, glucocorticoids and mesalazine are the main options . Howevervia the interactions between these active constituents and their targets. Meanwhile, we also explored the targets of UC to understand its pathogenesis. We found that the inflammatory cytokines, signal transduction, autophagy, apoptosis, and cell metabolism accounted for the high proportion in UC. However, the detailed interaction information between key active constituents and targets needed further investigation.In this study, we first investigated and analysed the possible mechanisms of JFFZLZ against UC based on network pharmacology. We found that the key active constituents in JFFZLZ were stigmasterol, folinic acid, quercetin, isoquercitrin, beta-sitosterol, daucosterol, deltoin, heptadecane, tridecanoic acid, and kaempferol. These constituents were observed with inhibiting effects on colon inflammation. For instance, stigmasterol and beta-sitosterol had anti-inflammatory and antioxidant functions ; LikewisNext, we constructed the GO-BP, GO-CC, GO-MF, and KEGG enrichment analysis. The result revealed that the GO-CCs were particularly related to the vesicles, cytoplasm, and extracellular matrix, both relating to the exosomes. The previous study has shown that exosomes could mediate the functional transfer of genetic materials between immune cells like macrophages, Treg cells, and neutrophils, which affects immune response ,42. The via activation of RELA signalling, which promoted inflammatory response as well as induced carcinogenesis [Then, we identified the nine hub targets based on the PPI network and obtained the core KEGG signal pathway. In our study, the core targets were STAT3, CXCL8, IL6, CXCL12, TNF, RELA, TP53, and PTPN11. The core pathway related to UC was inflammatory bowel disease referring to IL6, TNF, RELA, and STAT3. Among the targets, CXCL8 and CXCL12 are chemokines, and STAT3, IL6, TNF, RELA belong to inflammation-related factors. As chemokines, CXCL8 and CXCL12 could recruit macrophages, T cells, B cells, dendritic cells, and other immune cells to induce inflammation ,51. Besiogenesis .Supplement Fig. 1). As the result of the GEO dataset analysis, STAT3, CXCL8, CXCL12, IL6, PTPN11, and TNF all upregulated in UC patients, while RELA, EGFR, and TP53 were downregulated, compared to control people. At the same time, compared with the blank group, the nine hub genes were all upregulated in colon tissues of UC rats. And after JFFZLZ treatment, these genes’ expression was downregulated to normal levels. On protein level, the key proteins like IL-6, TNF-α, NFKBp65, and STAT3 of the inflammatory bowel disease pathway had the same change trend. These findings indicated that JFFZLZ could reduce the UC disease by reducing the expression of inflammatory factors and inhibiting the inflammatory pathways. Moreover, JFFZLZ may delay the carcinogenesis of UC, which needed to be further investigated and validated in clinical research.We further constructed the molecular docking network between the active constituents and nine hub targets. The result showed that Stigmasterol, beta-sitosterol, quercetin, and kaempferol had enhanced binding energy with nine targets. Among the above docking network, the beta-sitosterol-TNF was the highest docking network. To further verify the differential expression of the above genes in UC and normal subjects, we detected these genes’ expression in the GEO datasets and UC rat model. The treatment of JFFZLZ against UC was effective, and drugs were safe and had no toxicity (in vivo experiments, we found that the underlying mechanisms of JFFZLZ treatment on UC were related to cell oxidation, inflammatory response, signal transductions, gene expression, and metabolism. Meanwhile, it’s pivotal for the regulation of JFFZLZ on the hub targets. In the clinic, the combination of these targets could be used to evaluate the therapeutic efficacy for JFFZLZ and may also be applicable in other drugs. These findings could make several contributions to the treatment of UC at present and assist in providing a potential therapeutic strategy for UC in the future.To sum up, through the network pharmacology, data mining, and Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Fritillaria thunbergii Miq.  is frequently used for its heat-clearing and phlegm-resolving properties in herbal compounds for the treatment of IPF. However, the specific mechanisms underlying the effects of ZBM against IPF have not yet been reported. In this study, we applied a systematic analysis strategy based on network pharmacology to explore the probable core targets and major pathways of ZBM against IPF. In addition, molecular docking simulation and quantitative real-time polymerase chain reaction (qRT-PCR) were performed to preliminarily investigate the possible mechanisms underlying the therapeutic effects of ZBM on IPF. We collected a total of 86 components of ZBM and used network pharmacology analysis to screen nine presumptive targets of ZBM against IPF. The molecular-docking results indicated that the components of ZBM exhibited good binding activity with presumptive targets. The qRT-PCR results also suggested that ZBM may partly alleviate IPF by regulating the expression of presumptive targets. This study laid the foundation for further clinical applications of ZBM and the development of IPF-related therapeutic products.Idiopathic pulmonary fibrosis (IPF) is a long-term, distressing, and age-related interstitial lung disease characterized by a complicated etiology and irreversible progression.  Idiopathic pulmonary fibrosis (IPF) is a long-term, distressing, and age-related interstitial lung disease characterized by the overactivation of lung fibroblasts and overexpression of extracellular matrix components, which are associated with the remodeling of the lung structure and an irreversible decline in lung function –3. In EuFritillaria thunbergii Miq. , which are mainly produced in Zhejiang, China, have shown wide clinical use in TCM [China, with thousands of years of history in the practice and development of traditional Chinese medicine (TCM), has established its own unique TCM-based treatment system. With the deepening of TCM research, this medical system has gained increasing recognition among clinicians and researchers due to its promising therapeutic effects and relatively mild side effects , 10. Thee in TCM , 12. In e in TCM , 14. Mode in TCM –17. In ae in TCM –20. Howee in TCM , 22.Unlike research on modern drugs, which are identified by targeting specific proteins, TCM research is based on systematic analysis strategies to elucidate the mechanisms of drugs, thereby accounting for the complex composition and extensive coverage of these drugs. With recent improvements in systems biology and biological network equilibrium theory, network pharmacology has gradually emerged as a new model of drug molecule design characterized by “network target and multicausal analysis” , 24. NetIn this study, the main compounds of ZBM were collected using the TCM systems pharmacology (TCMSP) database, TCM Database@Taiwan, and the relevant literature. Secondly, compounds with superior pharmacokinetic properties and druggable properties were screened by the SwissADME analysis tool, and network pharmacology analyses were conducted to predict crucial targets and possible mechanisms of ZBM against IPF. In addition, the affinity between components and targets was analyzed by molecular-docking simulations, which verified the reliability of the network pharmacology analysis. Finally, the partial mechanism of ZBM against IPF was verified by experiments. This study provides a new direction for research strategies to elucidate the mechanisms of TCM against IPF from a holistic perspective. The graphic abstract for the study is presented in Fritillariae Thunbrgii Bulbus” was used as a keyword to obtain chemical information from the TCMSP database, an open system pharmacology database for capturing the relationships among drugs, targets, and diseases [https://old.tcmsp-e.com/tcmsp.php; obtained on April 16, 2022). Similarly, “Fritillariae Thunbrgii Bulbus” was used as the search term to collect the relevant chemical composition information in the TCM Database@Taiwan, a noncommercial database for providing downloads of TCM small molecules for virtual screening [https://tcm.cmu.edu.tw/; obtained on April 16, 2022). Additional chemical composition information was obtained from relevant review articles [“diseases  . Compounds that met all of the following screening conditions were analyzed in the next step: (1) high gastrointestinal (GI) absorption in the pharmacokinetics analysis; (2) 4 or 5 YES conditions in the drug-likeness analysis; and (3) no violations within the YES condition in the drug-likeness analysis. After screening, 17 compounds with superior properties were obtained, and a library of these compounds was established. Detailed information regarding these compounds is presented in the Supporting Information  or pharmacokinetic performance of small molecules on the basis of specific models  . Then, we pasted the SMILES information of the components into the Swiss Target Prediction platform  to obtain compound targets and retain targets with probability values >0. Compounds for which no targets were provided on the Swiss Target Prediction website were imported into the TCMSP for target collection. After removing duplicate values, the target cluster of compounds with superior properties was constructed. Detailed information is presented in the Supporting Information (Supplementary Information from the simplified molecular input line entry system (SMILES) for components with superior properties in the compound library was obtained from the PubChem website (gorithms  (https:/https://ctdbase.org/) is a publicly available database for studying chemical exposures and their biological effects [https://www.genecards.org) is an integrative database providing functional information on the relationship between human genes and disease [https://www.omim.org/) is an online database for describing the association between disease phenotypes and their causative genes [https://db.idrblab.net/ttd/) is a publicly accessible database for information on targets known to be of therapeutic value for diseases [https://go.drugbank.com/) is a database providing detailed drug data, drug target information, and computerized drug predictions [https://www.bioinformatics.com.cn; obtained on April 29, 2022) to represent potential targets of ZBM against IPF. Specific information regarding the targets is provided in the Supporting Information , GeneCards, Online Mendelian Inheritance in Man Database (OMIM), Therapeutic Target Database (TTD), and DrugBank Database were searched to obtain IPF-related targets. CTD  was applied to analyze the intersection target cluster. The data analyzed by the STRING database was exported in the TSV format and imported into Cytoscape software  for network analysis. The medians of the three parameters, “betweenness centrality,” “closeness centrality,” and “degree,” were calculated by the software. The crucial gene cluster was screened by retaining genes whose corresponding data values were greater than medians. Network analysis and visualization of the crucial gene clusters were performed in Cytoscape software. Detailed information regarding the crucial gene clusters is presented in the Supporting Information  to analyze the biological links among them. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to obtain information regarding the biological function and signaling pathways of the crucial gene clusters. Then, the microbiographic mapping platform  and Cytoscape software were employed to visualize the results by drawing a dot bubble chart, GO term chart, and the “compound-target-pathway” network. The correspondence information between active compounds, core targets, and pathways was input into Cytoscape, which performed network analysis among these elements and sorted them according to the “degree” value.The list of crucial genes obtained in https://www.rcsb.org/, obtained on May 10, 2022, PDB IDs are shown in https://www.pymol.org/), hydrogen bonds were added to protein molecules in MGLTools software . The proteins were then processed by calculating their Gasteiger charge values and assigning AD4 atom types and saved in the PDBQT format as receptor molecules. By searching the positions of the existing ligands in protein receptors, the docking boxes of the functional pockets of protein receptors were determined to prepare for molecular docking. Subsequently, compound molecules in the SDF 3D format downloaded from PubChem were converted to the PDB 3D format, and the 3D compound molecules were imported into MGLTools software for the detection of roots and selection of torsions. Then, the processed compound molecules were saved in the PDBQT format as ligand molecules and treated together with the processed receptor protein molecules to perform molecular docking simulation in AutoDock Vina software  (creening ) to obtaFritillaria thunbergii Miq. material was purchased from the Chinese herbal medicine market  and authenticated by Dr. Ding Qi . A total of 10.03 g of the TCM material was soaked for 2 h with ten times the amount of double distilled water and then refluxed for 1 h. After filtering the extracted liquid, the drug residues were extracted with eight times the amount of water under reflux for 1 h. The two filtrated liquids were combined, concentrated, and dried to finally obtain 1.68 g of ZBM extract powder (a yield of 16.75%). The extract powder was dissolved in dimethyl sulfoxide (DMSO) before the experiment.The TCM 2. When the cells were ready for plate laying, they were digested with 0.25% trypsin and inoculated at a density of 3 × 104 cells/well in 96-well plates or a density of 4 × 105 cells/well in 6-well plates. After starvation for at least 4 h, the cells in the administration groups were treated with transforming growth factor-β1 (TGF-β1) stimulators  for an additional 48 h in the absence or presence of ZBM extracts (0.01–10 μg/mL).Human embryonic lung fibroblast cell line MRC-5 (BNCC353614) was purchased from BeNa Culture Collection . A basal culture of MRC5 cells was established using Dulbecco's Modified Eagle Medium (DMEM) containing 12% fetal bovine serum, and the cells showed good proliferation at 37°C and 5% COβ1. Briefly, the day after cell inoculation into 96-well plates, cells were starved with serum-free medium for at least 4 h. The cells were then treated with ZBM  and TGF-β1 for an additional 48 h. After adding 10% CCK-8 reagent to the cell-containing wells, the plates were allowed to react for an additional 1-2 h at 37°C under light-proof conditions. Finally, the optical density at 450 nm was detected using a multifunctional microplate reader.The cell counting kit-8  was used to detect the proliferation of MRC-5 cells after treatment with ZBM extracts and TGF-−ΔΔct calculation method. Primers were purchased from Sangon Biotech ; detailed information regarding the primers is presented in Quantitative real-time polymerase chain reaction (qRT-PCR) is a useful technique for quantitative analysis of gene expression . RNA exthttps://www.graphpad.com/) was used for statistical analysis of the data. The paired sample T-test was applied for comparison between the two groups. A P value <0.05 indicated that the difference was statistically significant.All experimental data were obtained from at least three independently performed experiments. GraphPad Prism 8.0 , GeneCards (1000 targets), OMIM (339 targets), TTD (30 targets), and DrugBank (30 targets) databases. A total of 2081 IPF-related targets were obtained to construct the target cluster of disease after removing duplicates. The results were visualized with Venn diagrams .A total of 134 cross-targets were identified from the intersection of the target cluster of compounds with superior properties and the target cluster of disease . The STRThe list of 46 core targets was pasted into the DAVID database for KEGG and GO enrichment analysis to further investigate the role of core targets in the development of IPF. The top 10 items in the biological process (BP), cellular component (CC), and molecular function (MF) analysis results in the GO enrichment analysis were presented in the form of a merged bar graph in Based on the top 10 signaling pathways obtained above, the compound-target-pathway network, including 17 compounds with superior properties and 31 core targets, was constructed by Cytoscape software to analyze the relationships among them. As shown in We simulated the binding of the compounds and the targets through molecular-docking analyses to further analyze the connection between the targets and compounds. After treatment, five compounds were used as small molecule ligands and nine target proteins were used as docking receptors. Using AutoDock Vina software, semiflexible simulated docking was performed between them. The docking results were presented as affinity scores obtained with AutoDock Vina software . The larβ1 stimulators, ZBM at a concentration of 0.01–10 μg/mL showed no significant effect on the cell viability of MRC-5 cells, suggesting that ZBM is safe and reliable within a concentration range of 0.01–10 μg/mL. In comparison with the control group (no stimulation or drug), the cell viability in the model group (only TGF-β1 treatment) increased significantly after administration of the TGF-β1 stimulator (10 ng/mL). However, in the group that received simultaneous administration of TGF-β1 and drugs, ZBM at concentrations of 0.1–10 μg/mL significantly inhibited the cell proliferation effect induced by TGF-β1.We conducted in vitro experiments to further verify the correctness of the network pharmacology method and the results of molecular docking. First, we investigated the effect of the ZBM extract on the viability of MRC-5 cells. As shown in α-smooth muscle actin (α-SMA) is one of the characteristics of lung myofibroblasts in comparison with normal lung fibroblasts [α-SMA and collagen expression in MRC-5 cells to determine the effect of ZBM on the process of fibroblast transdifferentiation into myofibroblasts. As shown in β1-stimulated group, while administration of 0.1–10 μg/mL ZBM resulted in significant improvement, indicating that ZBM can inhibit the transformation of fibroblasts into myofibroblasts.Lung myofibroblasts are key effector cells of pulmonary fibrosis . Overexproblasts . Therefoβ1-stimulated group, while administration of 0.1–10 μg/mL ZBM resulted in various degrees of improvement in these expression levels, indicating that ZBM inhibited IPF partially by regulating the expression levels of these targets and that the systematic analysis strategy based on network pharmacology was reliable.Combined with the results of the above network pharmacology, we performed additional experimental analysis of the core targets to investigate the possible mechanisms underlying the therapeutic effects of ZBM on IPF. As shown in Although many risk factors for IPF, including smoking, drug exposure, pollutant exposure, viral infection, and genetic susceptibility, have been identified after decades of clinical research, therapeutic options based on the complex pathogenesis of this disease have still not been developed . Multiplβ1. We found that ZBM significantly reduced the mRNA expression levels of ACTA2, COL1A2, and COL3A1 in the TGF-β1-stimulated lung fibroblasts. This result suggested that ZBM had an antipulmonary fibrosis effect in vitro.The existing understanding of the pathogenesis of IPF has been enhanced by ongoing research. In general, under the stimulation of multiple risk factors, repeatedly injured alveolar epithelial cells undergo abnormal activation or apoptosis, resulting in the release of multiple cytokines to promote abnormal recruitment and activation of lung fibroblasts , 44, 45.For complex diseases, most drug studies focused on a single drug or a single target end in failure. Consequently, with the development of the field of network pharmacology, an increasing number of studies are employing synergistic, multicompound approaches for therapeutic drug development, which can favorably accelerate clinical translation . In thisα-SMA in pulmonary fibrosis and interactions with the TGF-β signaling pathway, promoting the development of pulmonary fibrosis [β1 stimulation and reduced after administration of ZBM, suggesting that ZBM has a regulatory effect on the PI3K/AKT pathway. Mouse double minute 2 (MDM2) mediates the ubiquitination of p53  signaling pathway is considered to be a major regulator of IPF, through its involvement in cell growth, differentiation, and metabolism . Activatfibrosis . Specififibrosis . In our s of IPF ) and is s of IPF . Furtherfibrosis –57. In oβ1 signaling pathway, which is critical for the fibrotic process [β-induced epithelial-mesenchymal transition (EMT) is dependent on the activation of the MAPK signaling cascade [β/Smad2 signaling pathway and the activation of the MAPK pathway, thereby inhibiting the process of EMT and improving pulmonary fibrosis [β1 and ZBM and found that ZBM could significantly reduce the TGF-β1-induced elevation of MAPK1 and MAPK3 expression in MRC-5 cells. Therefore, we speculate that ZBM may partially prevent the development of pulmonary fibrosis by inhibiting the MAPK signaling pathway.The mitogen-activated protein kinase (MAPK) signaling cascade, which is responsible for the transmission of extracellular signals to intracellular targets, is critical for cell growth, survival, and stress . The MAP process . TGF-β-i cascade . Mefunidfibrosis . In our β1 stimulation in MRC-5 cells, indicating that ZBM regulated the JAK signaling pathway. On the basis of these findings, we speculate that ZBM can also exert antipulmonary fibrosis effects through the JAK/STAT signaling axis. Interestingly, the epidermal growth factor receptor (EGFR), a member of the ErbB tyrosine kinase receptor family, was shown to participate in wound healing and tissue regeneration by activating downstream PI3K/AKT, MAPK/ERK, and STAT signaling pathways [Janus kinases (JAKs) are a group of intracellular tyrosine kinases that form the JAK/STAT signaling cascade with downstream signal transducers and activators of transcription (STAT), which play a regulatory role in the development of fibrosis . The JAKpathways . In our In summary, the use of ZBM in the management of IPF may be based on a multitarget and multipathway approach. In this study, we used a systematic analysis strategy based on network pharmacology to predict the potential mechanisms underlying the effects of ZBM against IPF. Our results suggested that ZBM improves IPF partly by influencing the trans-differentiation of fibroblasts to myofibroblasts through multiple targets such as EGRF, AKT1, MAPK1, SRC, PIK3CA, MAPK3, and JAK3 and multiple pathways such as the PI3K/AKT, MAPK, and JAK/STAT signaling pathways. This study provided new insights for elucidating the mechanism of TCM against IPF and will facilitate the further development of drugs related to ZBM.


R were used to identify overlapping differential expressed genes (oDEGs) and significant GO and KEGG enrichment terms. We constructed a PPI (protein–protein interaction) network through the STRING database and identified eight key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using six topological measures, betweenness, closeness, eccentricity, degree, MCC, and MNC, in the Analyze Network tool in Cytoscape. Three online databases GSCALite, Network Analyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks, and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm support vector machine (SVM). We suggested four TFs  and four miRNAs  as key transcriptional and posttranscriptional regulators of KGs. Finally, we proposed 16 candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat for BC through molecular docking analysis. Using BC cell lines, we validated that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed results might play an effective role in the treatment of BC patients.Accurate identification of molecular targets of disease plays an important role in diagnosis, prognosis, and therapies. Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study was to accurately identify a set of molecular targets and small molecular drugs that might be effective for BC diagnosis, prognosis, and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles  collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages, LIMMA and clusterProfiler, in  Breast cancer (BC) is the most common cancer diagnosis and the leading cause of cancer-related deaths in women worldwide. It was calculated that 2,261,419  new cases and 684,996  deaths occurred in BC in 2020 . In Chinde novo technique takes approximately 14 years and costs 800 million dollars, yet many pharmaceutical companies are working on it  and highlighted their role as molecular targets. We also proposed 16 KG-guided candidate drugs  for BC treatment and validated them https://www.ncbi.nlm.nih.gov/geo/)  database (ov/geo/) . The worov/geo/) , GSE2943ov/geo/) , GSE1246ov/geo/) , GSE4256ov/geo/) , GSE4582ov/geo/) , GSE1081ov/geo/) , GSE6521ov/geo/) , and GSEov/geo/)  were useov/geo/)  includinp value and adjusted p value  and logFC (fold change), accordingly. The cutoff criteria adj.P.Val < 0.01 and | logFC | > 1.5 were considered to select significant DEGs. Overlapping upregulated and downregulated DEGs were screened using the Venn diagram web tool (https://bioinformatics.psb.ugent.be/webtools/Venn/).We normalized all datasets by using log2-transformation, after which we used the normalized data for further analysis. Then, the Limma (version: 3.14) package  in RStudThe PPI network of oDEGs was constructed using the online database STRING  (v 11.5) and was visualized through the Cytoscape software . Then, wp-value cutoff = 0.05 and q-value cutoff = 0.05 were selected as the cutoff criteria (GO (Gene Ontology) function in three categories BPs , CC (Cellular Component), and MF (MF) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyzed through the R/Bioconductor package “clusterProfiler” (version: 3.14). criteria .The regulatory interaction network TFs (Transcription factors)-KGs-miRNAs (microRNAs) to detect transcriptional and posttranscriptional regulatory factors of KGs. The Network Analyst  web-baseR was used to generate the ROC curves  RNA-seq data (independent data) by using the GEPIA database . Then, wC curves .R was used to visualize the molecular docking results.To explore KG-guided candidate drugs for BC by molecular docking analysis, we considered eight KGs as drug target proteins (receptors). Subsequently, the 77 KG-associated meta-drug agents (ligands) were selected from the GSCALite database on the basis of a positive Spearman rank correlation with at least one KG . We downR software (Version 4.0.4). Moderated t-statistics were utilized to testH0  in both the case and control groups) versus H1  between the case and control groups). Statistically significant differences were considered when p < 0.05.All gene expression profiles were statistically analyzed by the The BC cell lines MCF-7 and MDA-MB-231 cells were cultured in Dulbecco’s modified Eagle’s medium  containing 10% fetal bovine serum  supplemented with penicillin (100  µ/ml) and streptomycin . Masitinib was purchased from MedChemExpress  and dissolved in dimethyl sulfoxide (DMSO). The cells were treated with different doses of masitinib for 24 or 48 h. After treatment, the cells were harvested with lysis buffer for immunoblot analyses or fixed with 4% paraformaldehyde for immunofluorescent staining.The cells were seeded in 96-well plates and cultured overnight. After treatment with masitinib at the indicated dose, a 10 µl solution of cell counting kit-8 (CCK-8) reagent was added to each well and incubated for 1 h in the dark. The absorbance was measured at 450 nm to calculate cell viability.The cells were treated with masitinib at different concentrations. After treatment, the cells were incubated with Hoechst 33342  and PI  for 10 min. The cells were then imaged with an inverted IX71 microscope system .Immunoblot analyses were performed as described previously . In brieWe used the LIMMA statistical approach to identify DEGs and considered the threshold adj.P.Val < 0.01 and |log2 fold change (FC) | >1.5 for selecting significantly upregulated and downregulated DEGs. The DEGs in each dataset are presented using volcano plots We visualized the PPI network of oDEGs in The GO functional and KEGG pathway enrichment analysis showed that 148 GO-BP terms, 27 GO-CC terms, 12 GO-MF terms, and 13 KEGG terms were enriched by oDEGs, and the top 10 terms of each category are represented in Regulatory interaction networks (TFs-KGs-miRNAs) are visualized in The expression patterns of KGs are displayed by box plots for independent data (TCGA RNA-seq data) in The ROC curves of the cancer prediction/classification model to test the prognostic power of our proposed KGs are presented in To explore the candidate drugs for BC, we considered eight KG-based proteins EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 as drug targets. We collected eight KG-associated drugs from the GSCALite database and considered them meta-drug agents. Then, we performed molecular docking analysis between our proposed receptors and the meta-drug agents. The binding affinity score matrix between the ordered receptors and the ordered drug agents is displayed in To further confirm the potential roles of the candidate drugs on BC, we selected one of the candidates masitinib to examine the effects on two BC cell lines. In both MCF-7  and MDA-BC is the cause of a high prevalence and leading mortality rate for women worldwide and is constantly increasing every year. The 5-year overall survival rate for BC patients is still very low, although there has been significant progress in systematic treatment over the past few decades. Therefore, research needs to be improved to discover potential molecular targets and effective candidate drugs that could play a significant role in improving survival rates and reducing mortality in BC patients.in silico analysis proposed that TOP2A is related to BC tumorigenesis and progression and has been highlighted as a pathogenetic process  and six upregulated KGs  through the PPI network analysis ; Table 3 process . BIRC5 w process .To investigate the pathogenetic processes of KGs, we selected four BP terms, six CC terms, three MF terms, and five KEGG terms based on directly associated with at least two KGs from the top ten terms of each category . SeveralFour TFs  and four miRNAs  were considered transcriptional and posttranscriptional and were connected to all KGs in the regulatory interaction network . Furtherin silico, in vivo, and in vitro analyses  were proposed based on BAS < −7 through molecular docking analysis ; Table 5analyses . Specifianalyses . The druanalyses . This dranalyses . Dasatinanalyses . Fedratianalyses . Dabrafeanalyses . Tramatianalyses . Belinosanalyses . HoweverMasitinib is an antitumor drug used primarily for mast cell tumors, but also used in solid tumors . It inhiIn this study, we identified eight stable BC-guided KGs EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 using well-established bioinformatics and network-based tools and highlighted their pathogenetic processes, regulatory factors, prognostic power, and drug molecules. Then, we proposed 16 potential candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat through molecular docking analysis. Furthermore, we verified the proposed results through literature review and different databases. Finally, we examined one of the candidates masitinib to identify the anti-breast cancer effects. We found that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed molecular biomarkers and repurposing candidate drugs may play an important role in the diagnosis and therapy of BC.


However, the herbal preparation is chemically complex and the diversity of potential targets makes difficult to determine its mechanism of action. To gain insight into its mechanism of action, we analyzed “Salvianolic acid for injection” (SAFI), a traditional Chinese herbal medicine with anti-IS effects, using computational systems pharmacology. The potential targets of SAFI, obtained from literature mining and database searches, were compared with IS-associated genes, giving 38 common genes that were related with pathways involved in inflammatory response. This suggests that SAFI might function as an anti-inflammatory agent. Two genes associated with inflammation (PTGS1 and PTGS2), which were inhibited by SAFI, were preliminarily validated in vitro. The results showed that SAFI inhibited PTGS1 and PTGS2 activity in a dose-dependent manner and inhibited the production of prostaglandin E2 induced by lipopolysaccharide in RAW264.7 macrophages and BV-2 microglia. This approach reveals the possible pharmacological mechanism of SAFI acting on IS, and also provides a feasible way to elucidate the mechanism of traditional Chinese medicine (TCM).Ischemic stroke (IS) is an acute neurological injury that occurs when a vessel supplying blood to the brain is obstructed, which is a leading cause of death and disability. S Traditional Chinese medicine (TCM) has played a significant role in treatment of a large variety of diseases for thousands of years and is still widely used nowadays. TCM uses prescriptions which usually consist of different components, such as plants, animals or minerals, depending on the disease to treat. However, due to the presence of a large variety of chemical components in prescriptions, it is difficult to identify their active component, its mechanism of action and to establish clinical parameters in order to test their therapeutic efficacy. In addition, many prescriptions have more than one single active component, and these may act synergistically to produce therapeutic benefit. These impediments have long delayed the modernization of TCM .In the last years, the development of network pharmacology has made possible the exploration of mechanisms of action at the molecular level of many herbal preparations used in TCM. This has generated a new research paradigm for translating TCM from an experience-based medicine to an evidence-based medicine system . In addiIschemic stroke (IS) is an acute neurological injury caused by the narrowing or occlusion of the blood supply arteries of the brain, which is a major leading cause of death worldwide and the number one cause for acquired long-term disability, resulting in a global annual economic burden . ThromboSalvia miltiorrhiza and undergoes further separation and purification steps using standard chemical procedures. Its main water-soluble components include salvianolic acid B, rosmarinic acid, lithospermic acid, salvianolic acid D, and salvianolic acid Y  is a TCM preparation, approved by the Chinese Food and Drug Administration since 2011, which is extracted from c acid Y . SAFI hac acid Y .via comparison with disease genes of ischemic stroke. Differences and similarities between SAFI and the approved IS therapeutic drugs were also compared by signaling pathways enrichment analysis. In addition, the potential important targets of SAFI treatment for IS were validated by QSAR (quantitative structure-activity relationship) model. Finally, the key targets were validated experimentally using enzymatic and cell-based assays.In order to investigate the molecular mechanism of SAFI, we first collected information on its major components through literature mining, and compared their similarities with those of approved IS therapeutic drugs . Subsequently, SAFI targets, collected from extensive literature mining, and IS drugs targets were enriched for biological process analysis. Predicted SAFI inhibition and activation targets were extracted by signed random walk with restart (SRWR) method, and were enriched for biological process and signaling pathways. Potential important targets were found The whole workflow is illustrated in https://www.cnki.net/) and English-language literature through PubMed (https://pubmed.ncbi.nlm.nih.gov/). Then, the components contained in SAFI were collated manually and standardized through PubChem database (https://pubchem.ncbi.nlm.nih.gov/).In order to collect information on SAFI main components, “salvianolic acid for injection” was used as the key word to obtain Chinese-language literature through CNKI .IS-associated genes were collected from the DisGeNET database  by searcAnti-IS drugs collection were referred to “Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018” , and theCor) was then calculated, and Z-score was used to evaluate the significance of the correlation using the formulaE(Cor) and δ(Cor) are the mean and standard deviation of the Pearson correlation coefficients between the influence score vector of drug targets and those of 1000 groups of random contrast disease genes, each of which contained the same number of randomly selected proteins as the disease seed nodes.To evaluate the efficacy of a drug on disease, the correlation of the drug targets and the genes associated with the disease from the perspective of network propagation was used . BrieflyThe similarity of two drugs was evaluated based mainly on chemical structure, comprehensive targets or cellular function fingerprints. Drug clusters with similar features were obtained using hierarchical clustering.The chemical structure similarity was measured based on the Morgan fingerprint using RDKit in python. Molecular fingerprints encode molecular structure in a series of binary digits (bits) that represent the presence or absence of particular substructures in the molecule. Comparing fingerprints enable us to determine the similarity between two molecules. Once SMILES strings are converted to scalar fingerprints, the Tanimoto coefficient was used as the similarity score to measure the absolute similarity between two molecules.dAA⟩ and ⟨dBB⟩, to the mean shortest distance ⟨dAB⟩ between targets module A and B. The smaller the SAB, the closer the topological distance between the two drugs, that is, the more similar the functions of the two drugs. BNet was used as the background human PPI network.The target similarity between two drugs was measured based on comprehensive targets of compounds. Two drugs acting on same targets could be considered to have the same effect. For multi-target drugs, drugs whose targets are very close in the PPI network show similar effects . Here, tThe cellular function fingerprint similarity of two drugs was measured based on a “compound-target-cellular function” heterogeneous network using the PathSim method Yizhou . As descGiven a set of N compounds to be clustered, an N × N similarity matrix was generated. Finally, the similarity matrix was used to perform hierarchical cluster, which was executed by the R package hClust.The SRWR algorithm, which was proposed in the research of social networks for measuring trust and distrust in signed social networks , was useAs described in the literature , supposep-value have been previously described  was more enriched with the genes significantly inhibited by SAFI (identified by SRWR) than would be expected by chance. We used IS-associated disease genes collected from DisGeNET as distinct validation gene sets. The detailed methods to define the escribed .p-value, and a term with p-value <0.05 was considered being significant.For the purpose of exploring the biological function of genes correlated with IS, GO, Kyoto Encyclopedia of Gene and Genomes (KEGG) and tissue enrichment analysis were performed using DAVID. Significance of each term was assessed with a 50 values in Schrödinger’s Ligprep module were converted to predicted IC50 values (PIC50). The QSAR model based on traditional methods was generated for key common genes through Schrodinger’s AutoQSAR module.The dataset of key common genes in IS and SAFI was obtained from the ChEMBL database , and pro2 and Q2 respectively  models. The process of descriptor generation, feature selection, and creation of a large number of QSAR models has been automated into a single workflow in AutoQSAR . These mectively . AutoQSA50 values, Pharmaron (Beijing) was commissioned to perform in vitro PTGS1 and PTGS2 enzymatic assays. The detailed information about the assays, such as the reagents, instruments, assay procedure, data analysis, and calculation is presented in To evaluate the influence on PTGS1 and PTGS2 by SAFI and obtain ICIn order to validate the influence of SAFI on PTGS2 activity, RAW264.7 macrophages were plated in 96-well plate and pretreated with different concentrations of SAFI for 1 h. Then cells were stimulated by lipopolysaccharide  for 18 h to induce PTGS2 expression. Similarly, BV-2 microglia were treated by LPS(0.1 μg/ml) and different concentrations of SAFI for 24 h to induce PTGS2 expression. The prostaglandin E2 content in supernatants, generated by cyclooxygenase 2  in the conversion of arachidonic acid, was assayed with enzyme linked immunosorbent assay according to the protocol of manufacturer.p-value <0.05 was considered statistically significant.Values are represented as mean ± SD. Statistical significance was determined by One-way ANOVA in GraphPad Prism 8. In order to identify the important targets of SAFI in the treatment of IS, we first extracted its components through literature mining. It was found that salvianolic acid B, rosmarinic acid, lithospermic acid, salvianolic acid D, and salvianolic acid Y were major chemical components of SAFI, and four constituents present in the rat blood including salvianolic acid B, rosmarinic acid, lithospermic acid and salvianolic acid D were determined as effective Quality Markers of SAFI, identified by LC-MS/MS  Figure . SubsequPTGS2 gene was targeted by three main components of SAFI, salvianolic acid B, rosmarinic acid, and salvianolic acid D, suggesting an important role in the treatment of IS. Functional annotation of the 38 common genes was performed by enrichment analysis. As showed in negative regulation of apoptotic process and inflammatory response. Annotated pathways indicated that inflammatory associated pathways such as TNF signaling pathway, HIF signaling pathway, and NF-kappa B signaling pathway were significantly enriched in KEGG pathways  recommends 16 drugs to treat IS, representing five different mechanisms of action, including antiplatelet , anticoagulant , antilipemic , antihypertensive  and neoroprotectant . Next, the main compounds in SAFI were compared with the recommended anti-IS drugs based on similarity of chemical structure, functions and targets. The results showed that the main compounds of SAFI were not clustered with the five types of anti-IS drugs based on chemical structure , indicatZ-score was applied. Z-score, which provides a quantitative measure for the significance of the correlation between two genes, were set as greater than 3. Z-score indicated that the targets of both SAFI and its main components have a close link with IS-associated genes, especially salvianolic acid B (Z-score = 20.994) and rosmarinic acid (Z-score = 19.023), with a total of more than 200 IS-associated genes . These results suggest that SAFI could inhibit target tissue proteins to exert its anti-IS effects.To investigate whether the top inhibited genes by SAFI were expressed in IS target tissues, tissue enrichment analysis was conducted using DAVID. As shown in inflammatory response, platelet activation and cell adhesion, which are all anti-IS associated process. KEGG enrichment analysis indicated that the top inhibited genes are involved in PI3K-Akt, TNF, NF-kappa B, and HIF signaling pathways, among others, which are associated with inflammatory response  and Q2  represent the availability of the model (the better model the closer to 1). By default, the dataset was divided into a 75% training set  and a 25% test set . We used two models with Q2 > 0.5 in top10 to predict the activity of the compound on the target. The model score of PTGS1/2 with predictive ability is shown in PTGS1 and PTGS2 like two anti-IS drugs, aspirin and NS-398.Based on the intersection analysis and SRWR results, 50 of 0.04 μg/ml  were preliminarily validated in vitro. SAFI inhibited PTGS1 and PTGS2 activity in a dose-dependent manner and inhibited the production of prostaglandin E2 induced by LPS in RAW264.7 macrophages and BV-2 microglia. Cytokines, like tumor necrosis factor (TNF), modulate tissue injury in experimental stroke and affect infarct evolution, making them good targets for potential future stroke therapy (via direct inhibition of PTGS1 and PTGS2. It should be noted that PTGS1 and PTGS2 are involved in inflammation not only via the arachidonic acid (AA) metabolic pathway, but also through the maintenance of cerebral blood flow, synaptic plasticity, and cerebrovascular regulation (In this study, we have systemically analyzed the mechanism of action of SAFI on IS using the network pharmacology combined computational prediction and experimental validation. We identified 38 genes shared by datasets of SAFI targets, SAFI main components targets and IS-associated genes. GO and KEGG enrichment analysis of this common shared gene set indicates their association with inflammation. Two genes associated with inflammation ( therapy . Nuclear therapy . HIF-1α  therapy . SAFI an therapy . Interes therapy . SAFI ha therapy . Therefogulation . Due to In this study, we have used network pharmacology combined with computational prediction to elucidate the molecular mechanisms of SAFI acting on IS, and demonstrated the feasibility of the approach in investigating its mode of action. A similar approach can be used for a variety of herbal medicine used in TCM for a variety of diseases, thus contributing to its modernization.The emergence of TCM databases such as HERB, TCMSP and others makes relatively easy to obtain the chemical components and corresponding targets of TCM, saving a lot of time and cost of experiments . HoweverPropagation-like algorithms, such as SRWR, have been used in the identification of proteins or genes influenced by drugs or diseases, and can simulate the spread of active compound-induced activation or inhibition to a group of targets on a particular signaling network. Using tQSAR has been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities in order to obtain a reliable statistical model for prediction of the activities of new chemical entities . Since tPTGS1 and PTGS2 were found in a dataset of genes shared between SAFI and its components’ targets and IS-associated genes, and their binding activities were further predicted by QSAR. The effect of SAFI on the enzymatic activities of PTGS1 and PTGS2 confirmed the data deduced from network pharmacology combined with computational prediction. Due to the limitations of network pharmacology and the complicated pathogenesis progress of IS, more extensive and in-depth research is still needed to corroborate these data to elucidate other action mechanisms of SAFI in the treatment of IS.In summary, in this study we deduce the mode of action of SAFI on IS from the levels of the components, targets, pathways, network, and activity prediction. SAFI putative targets are significantly enriched in several pathways associated with inflammation, which is critical in IS.


Although knowledge of biological pathways is essential for interpreting results from computational biology studies, the growing number of pathway databases complicates efforts to efficiently perform pathway analysis due to high redundancies among pathways from different databases, and inconsistencies in how pathways are created and named. We introduce the PAthway Communities (PAC) framework, which reconciles pathways from different databases and reduces pathway redundancy by revealing informative groups with distinct biological functions. Uniquely applying the Louvain community detection algorithm to a network of 4847 pathways from KEGG, REACTOME and Gene Ontology databases, we identify 35 distinct and automatically annotated communities of pathways and show that they are consistent with expert-curated pathway categories. Further, we demonstrate that our pathway community network can be queried with new gene sets to provide biological context in terms of related pathways and communities. Our approach, combined with an interpretable web tool we provide, will help computational biologists more efficiently contextualize and interpret their biological findings. Perfoal tasks ) using aal tasks ) to handal tasks  and REACal tasks ). These Since many pathway gene set databases offer unique advantages, it has become common practice to perform pathway enrichment analysis across multiple databases. Unfortunately, the difficulty of interpreting pathway-level results is increasing as the number of available pathways and databases grows. Figure et al.  framework. Our framework uniquely relies on the Louvain community detection algorithm to cluster pathways into communities based on their gene-level similarities Figures  and 2. FFor our analyses, we constructed a pathway graph comprised of pathways from KEGG , REACTOM10 from Fisher's exact test measuring the significance of gene overlap . This process generated a sparsely connected graph in which each node represents a pathway, and edges indicate similarity of pathways with respect to shared genes.Using the PAC method for identifying communities of related pathways involves two steps: 1) construction of the pathway network, and 2) detection of pathway communities Figure . For theSupplementary Methods andFor the second step, we identified communities of related pathways from this network using the Louvain community detection algorithm ,23 usinghttps://gitlab.cs.washington.edu/nbbwang/P-COM.Finally, after confirming the consistency of our learned communities with curated categories within each database Figure , we learTo automatically generate descriptions for our communities that were independent of expert-curated category labels, we developed a method based directly on the names of member pathways. For each community, we pre-processed member pathway names from MSigDB and identified commonly appearing terms. After pre-processing these names to remove database identifiers and common stop words , we counted instances of all 3-mers across community members and used the most commonly appearing 3-mer (with at least three appearances) as a label to describe our community. In the case of a tie, we selected the 3-mer whose source pathways had the highest average hubness within their community. When no 3-mers made at least three appearances, we repeated the process with 2-mers. p-values between the new gene set and each pathway already in the graph , we first validated it by again using the MSigDB Hallmark pathways, because some of their founders are pathways in our graph. Thus, we evaluated whether, when queried, these Hallmark gene lists tended to be assigned to communities containing many of their founder gene sets. Finally, as described below, we created an interactive web tool to help users query a list of genes and identify candidate communities most closely associated with it.https://pathwaycommunities.cs.washington.edu/, using the Plotly Dash framework. The tool lets users explore the community and pathway networks to gain a clearer understanding of how pathways relate to each other and the higher level processes learned by each community. It offers several views. First, a community-level view shows users the community graph . Together, these different pages for examining the learned communities of pathways, along with the ability to query a new gene set, may allow users to more effectively interpret their biological findings in the context of known pathways.Because the pathway graph contains thousands of pathways and learned communities contain up to 492 pathways, our integrative communities are not amenable to static visualizations. Therefore, we created an interactive web tool, available at To demonstrate an application of our method to new gene sets, we used an example of a differential gene expression analysis in breast cancer samples. Data was provided by the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. The database includes breast cancer sequencing data from 2,509 patients; gene expression measurements from biopsied breast cancer samples, including 24 368 measured genes; and phenotypic or treatment labels associated with each sample (e.g. estrogen receptor status and whether the patient was treated with chemotherapy).t-tests and identified 8,984 genes significantly differently expressed between groups  to compare the resulting communities with the curated categories (see Methods for details) from each database. Figure To learn a unified set of communities across all 4847 gene sets from the four different sources , we constructed a joint network and used the Louvain algorithm with a resolution of 0.4 . Furthermore, purple cells in Figure All Hallmark pathways have associated sets of Although the examples illustrated above and in Figure Finally, we also use Hallmark pathways to validate our method for querying new gene sets in the PAC framework. Because each Hallmark pathway has a set of associated founder gene sets (some of which are in our pathway network), we use our querying method to assign each hallmark pathway to a community in our network; we can then determine if there is agreement between the community assigned via our method and communities containing its founders. For 74% of Hallmark pathways, Figure To highlight high-level relationships among our pathway communities, we visualize our pathway community graph with their automatically learned labels Figure . First, Finally, to demonstrate that our approach produces informative pathway communities, we explored individual networks within communities. As an example, Figure https://pathwaycommunities.cs.washington.edu/.Breast cancers are commonly classified by their estrogen receptor (ER) status—i.e. whether estrogen receptors are expressed in cancer cells (ER+) or not (ER−). These cancer subtypes manifest in markedly different ways and require different treatment regimens because ER+ cancers rely heavily on estrogen to grow and reproduce, whereas ER- cancers do not ,26. ThusFigure P < 0.02 level in our gene set query. This helps the user visualize not only which pathways are enriched but also how they relate to each other .Next, we query the top genes against our communities . This approach considers not only the top enriched pathways, but the overall association of all pathways in each community with the query gene set. This analysis reveals that the top communities related to differentially expressed genes between ER+ and ER− broadly relate to growth factors and cell differentiation (community 20) and metabolic activity (community 1) Figure . InteresOur approach is not without limitations. In particular, because our graph is constructed using Fisher's exact test-based edges, our method for querying new gene sets also relies on the use of Fisher's exact test for pathway enrichment to be consistent with the rest of the graph's structure (since querying a new gene set involves simulating its addition as a node to the pathway network). Thus, our tool does not currently support the use of an alternative approach to compute pathway enrichment (e.g. GSEA); we may implement this functionality in the future. Additionally, although we empirically found that our automatic community annotation approach was consistent and interpretable, it relies primarily on the quality of pathway names. If the analysis were repeated with a new set of pathway databases that used uninformative pathway names, our approach for automatically labelling communities would not be applicable.et al. . Further, our approach may also complement existing methods of post-hoc disambiguation , as ourhttp://www.gsea-msigdb.org/gsea/downloads.jsp#msigdb). Curated categories for KEGG pathways are available at https://www.genome.jp/kegg/pathway.html. Curated categories for REACTOME pathways are available from their interactive web browser (https://reactome.org/PathwayBrowser/). GO relations are provided at http://geneontology.org/docs/download-ontology/ (we used the go-basic version).Molecular Signatures Database (MSigDB) is a collection of annotated gene sets, which include KEGG, REACTOME, GO, and Hallmark gene sets  cohort are available from the cBioPortal for cancer genomics. The specific dataset used in our study was downloaded here: https://networkx.org/), the Natural Language Tool Kit (https://www.nltk.org/) and Scikit-learn (https://scikit-learn.org/stable/).Python packages used in this study include the Community API (python-louvain.readthedocs.io/), NetworkX (https://gitlab.cs.washington.edu/nbbwang/P-COM. Our interactive webpage accompanying this paper is available at https://pathwaycommunities.cs.washington.edu/.Our code for reproducing our results is available at lqac044_Supplemental_FilesClick here for additional data file.


Myasthenia gravis (MG) is a rare and refractory autoimmune disease, and Qi Shen Di Huang (QSDH) drug formulary is an in-hospital herbal decoction with proven clinical efficacy in treating MG. Currently, most of the research on the QSDH drug formulary has concentrated on its clinical efficacy, and there is a lack of systematic study on the material basis. The active compounds and their mechanism of action have not been entirely determined. Therefore, this study sought to identify the active compounds in the QSDH drug formulary and analyze the key targets and potential mechanisms. We used ultra-performance liquid chromatography Q Exactive-mass spectrometry (UHPLC-QE-MS) and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database to identify and screen 85 active ingredients corresponding to 59 potential targets (17 herbs) associated with myasthenia gravis, and further identified AKT1 as the primary core target and the PI3K/AKT signaling pathway as the most substantial enriched pathway. Molecular docking and UPLC-MS analysis identified quercetin, luteolin, wogonin, kaempferol, laccasein, and epigallocatechin gallate are the core compounds of the QSDH drug formulary. In vivo rat studies showed that the QSDH drug formulary reduced Lennon's clinical score and decreased acetylcholine receptor antibody levels in peripheral blood rats with experimental autoimmune myasthenia gravis. In addition, the QSDH drug formulary downregulated P-PI3K/PI3K and P-Akt/Akt protein expression. Collectively, these findings describe the role and potential mechanism of the QSDH drug formulary in the treatment of MG, which exerts potential value by acting on AKT targets and regulating the PI3K/AKT signaling pathway and providing a theoretical reference for QSDH drug formulary application in the clinical treatment of MG. Myasthenia gravis (MG) is an autoimmune disease with lesions in the postsynaptic membrane of the neuromuscular junction (NMJ). Pathogenic antibodies include the common acetylcholine receptor antibody (AChR-ab) and the less common muscle-specific receptor tyrosine kinase antibody (Musk-ab), and the low-density lipoprotein receptor-related protein4 antibody (LRP4-ab) blocks the aggregation of acetylcholine receptors (AChR) and disrupts the function of AChR and the signaling between NMJ . FatigueThe QSDH drug formulary is based on the theory of “Fu Xie” and “Nao Sui” which proposes that the fundamental mechanism of this disease is “a deficiency of spleen and kidney and deficiency of brain marrow,” and the treatment should be essential in the basic principle of “JianPi YiQi BuSui” . The metThe current research on the QSDH drug formulary is mainly focused on clinical efficacy, lacking systematic material basis research, in-depth research on its overall chemical composition, the material basis of pharmacological efficacy, activity, and its active ingredients and specific mechanisms not fully understood. TCM research needs some new approaches and various network technologies have been evolving with the development of the information age. Cyberpharmacology has recently been an emerging discipline based on systems biology, multidirectional pharmacology, and proteomics. It combines pharmacology and information networks to construct molecular biological networks of diseases and drugs by analyzing the whole network to elucidate the pharmacological effects of drugs and pathogenesis of diseases through high-throughput histological data analysis and search of network databases . We can α subunit 97–116 peptide fragment . Complete Fuchsin adjuvant (CFA), incomplete Fuchsin adjuvant (IFA), . Dried powder of Mycobacterium tuberculosis H37RA . Rat antiacetylcholinesterase receptor antibody (AchR-Ab). Enzyme-linked immunosorbent assay (ELISA) kit . Phosphate buffer solution (PBS), . Phosphatidylinositol 3-kinase (PI3K) antibody, . Phosphatidylinositol 3-kinase (p-PI3K) antibody, . Phosphorylated protein kinase B (p-Akt) antibody, . Protein kinase B (Akt) antibody . Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) internal reference antibody . RIPA lysate (strong), protein prestain marker, BCA protein quantification kit . Ultrahigh-performance liquid chromatograph . High-resolution mass spectrometer (model Q Exactive focus). Infinite M200 pro multifunctional enzyme standard . Power Pac type electrophoresis instrument . Fluor chem E alpha chemiluminescence gel imaging system . Protein Simple.Methanol . Acetonitrile . L-2-chlorophenyl alanine . QSDH drug formulary . Prednisone acetate tablets . Rat-derived AchRμL of an extracted solution containing 1 μg/mL of internal standard and dissolved in 80% methanol. After the 30s vortex, the samples were homogenized at 45 Hz for 4 min and sonicated for 1 hour in an ice-water bath. After 1 hour in −40°C, the sample was centrifuged at 12000 rpm for 15 min at 4°C. Finally, the supernatant was obtained and put in a fresh 2 mL tube for LC-MS/MS analysis, taking 40 ul from each sample and pooling it as QSDH samples.The samples were crushed with a mixer mill for 30 s at 65 Hz. 100 mg of sample was added to 500 μm 2.1∗100 mm). The flow rate was set at 0.4 mL/min, and the sample injection volume was set at 5 μL. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% in acetonitrile (B). The multistep linear elution gradient program was as follows: 0–3.5 min, 95–85% A. 3.5-min, 85–70%A. 6–6.5, 70–70% A. 6.5–12 min, 70–30% A. 12–12.5 min, 30–30% A. 12.5–18 min, 30–0% A. 18–25 min, 0–0% A. 25–26 min, 0–95% A. 26–30 min, 95–95% A.LC-MS/MS analysis was performed on an Agilent ultra-high performance liquid chromatography 1290 UPLC system with a Waters UPLC BEH C18 column  or −3.6 kV (negative).https://tcmspw.com/tcmspsearch.php) database was used according to the conditions of oral bioavailability (OB) ≥ 30 and drug-like likeness (DL) ≥ 0.18. The active ingredients of QSDH drug formulary detected by UHPLC-QE-MS were screened. The targets corresponding to the chemical composition of the active ingredients were retrieved through the TCMSP database, and the target names were transformed to gene abbreviations through the UniProt (https://www.uniprot.org/) database [According to the principles of toxic pharmacokinetics (ADME), the TCMSP   [https://www.drugbank.ca/, updated on May 3, 2021) [https://www.disgenet.org/, updated on August 16, 2021) [http://db.idrblab.net/ttd/updated on June 1, 2021) [https://www.pharmgkb.org/, updated on October 4, 2021) [Using the keyword “myasthenia gravis,” enter the Genecards database  . DrugBan3, 2021) . DisGeNE6, 2021) . TTD dat1, 2021) . PharmGK4, 2021) . Take thIn order to determine the MG target of the QSDH drug formulary, the intersection target of the MG target and the corresponding target of the QSDH drug formulary active compound were obtained by Perl language analysis, and a Wayne diagram was drawn. The data of the intersection targets of the active compounds and their interactions were imported into Cytoscape3.8.2 software to construct a network of “TCM active components-intersectiontarget-disease,” and topological analysis was conducted to explore the pharmacological mechanism of the active components of QSDH prescription.https://www.string-db.org) [Homo sapiens, hide-free nodes, and retain default values for other parameters to obtain the intersection target interaction network. At the same time, the data were set with medium confidence >0.4 to ensure the reliability of the analysis.After importing the intersection targets obtained above into the STRING database (-db.org) , multiplhttps://hiplot.com.cn/, updated on May 4, 2021), and the species was Homo sapiens. The threshold P < 0.01 and the Gene Ontology, GO enrichment analysis, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis were set. R 4.0.3 software was used to draw bubble charts and bar charts from obtaining the biological process (BP), molecular function (MF), cellular components (CC), and main signal pathways involved in the treatment of MG by QSDH drug formulary.The intersection target was imported into the Hiplot database (https://pubchem.ncbi.nlm.nih.gov/) downloads the 3D structure of active ingredients in the PDB database (http://www1.rcsb.org/) to obtain the target protein 3D structure, AutoDockTools1.5.6, AutoDock Vina and PyMOL software were used for molecular structure processing and molecular docking.Based on the above research results, the network analyzer plug-in is used to analyze the network topology structure. Active components with the most significant degree value and the largest number of targets are selected as ligands, and core key genes are screened as receptors by the Cytohubba plug-in. The PubChem database  2016–0006. They were fed in the SPF barrier laboratory of the Animal Experiment Center of Changchun University of Chinese Medicine with free drinking water, indoor keep (21 ± 2)°C, relative humidity 50%–60%, and day and night alternating light and dark for 12 h. The experimental procedure was reviewed and approved by the Experimental Animal Ethics Committee of the Changchun University of Chinese Medicine (NO.2021228).μL of mixed emulsion, and Rα97-116 peptide dry powder and Mycobacterium tuberculosis H37RA powder were added to PBS. Each 200μL of PBS contained 50 μg of Rα97-116 dry powder and 1 mg of H37RA dry powder, and the peptide and dry powder were dissolved by ultrasonication on an ice box to make the peptide and dry powder fully fused in PBS until the solution was cloudy. The dissolved liquid is extracted into a 1 mL syringe. Another syringe extracts 200 μL of CFA, and a medical hose connects the two syringes to mix the PBS and CFA of the meaning peptide segment and bacteriophage powder to prevent protein denaturation, the whole process is carried out on the ice, repeatedly pumping two syringes back and forth until milky white liquid appears and the emulsion does not dissolve and does not spread, keeping the spherical shape floating on the water surface for a long time, then the mold-making drug is drug formularyted successfully. The equipped mold-making antigen emulsion was injected subcutaneously at five sites: the tail's base, both hind limbs' foot pads, and both sides of the back. The adjuvant control group was injected with a mixture of CFA and PBS in equal amounts, and the day of the first immunization was recorded as day 0. One booster immunization was given on day 30 and day 45, with the same preparation, injection volume, injection sites, and injection method as the first immunization, except that CFA was replaced with IFA. The adjuvant control group was injected with a mixture of IFA and PBS, with the same injection volume, injection sites, and injection method as the first immunization. The dose, injection site, and injection method were the same as the first immunization. The model was evaluated on day 60, and the change in body mass assessed the success, Lennon's clinical symptom grading method was used [Eight of 60 Lewis female rats were chosen randomly as the adjuvant control group, and the remaining 52 were referred to Baggi et al . The EAMwas used , and theThe removed rat spleen tissue was cut and put into RIPA lysis solution, lysed on ice for 30 min at 12000 r/min, centrifuged at 4°C for 10 min, the supernatant was taken, the protein concentration was measured by BCA method, the sample protein concentration was adjusted to the same, the protein samples were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to polyvinylidene fluoride (PVDF) membranes. After the end of the transfer, PI3K, p-PI3K, Akt, p-AKT primary antibodies (1 : 500), and secondary antibodies were incubated with 5% skimmed milk for 1 hour at room temperature. The transferred protein strips were developed by exposure using a chemiluminescence imaging system, and the images were acquired by quantitative optical density analysis.s) using one-way ANOVA. P < 0.05 indicates that the difference is statistically significant.GraphPad Prism 5.0 software was used for statistical analysis, and measures were expressed as mean plus or minus standard deviation  were searched and downloaded from the TCMSP website. According to OB ≥ 30% and DL ≥ 0.18 as screening conditions, 85 active compounds were obtained, as shown in A total of 822 targets related to myasthenia gravis were obtained from the GeneCards, Drugbank, DisGeNET, TTD, and PharmGKB databases using the keyword “myasthenia gravis,” as shown in Ased on Perl language analysis of 85 targets of active components of QSDH prescription and corresponding targets of MG disease, 59 intersection target proteins were obtained, considered as targets of QSDH drug formulary for MG treatment, as shown in To better explain the mechanisms by which the QSDH drug formulary acts concerning MG, we further evaluated its 59 intersecting targets to analyze their relationship. The 59 intersecting targets were imported into the STRING database to obtain protein interaction information, which was visualized using Cytoscape 3.8.2 software . There ap-value<0.01 and q-value<0.05, The top 10 entries obtained from GO enrichment analysis were performed, and bubble plotted using R4.0.3 software. A total of 1959 items were significantly enriched (P < 0.05), of which 1834 were enriched in biological process (BP), 35 in cellular component (CC), and 90 in molecular function (MF), The 59 intersecting targets were entered into the Hiplot online database, set P < 0.05), and the top 30 pathways were bar charted by R 4.0.3 software .As depicted in P < 0.01).On day 60 after the first immunization, the serum AChR-Ab levels of rats were analyzed by ELISA. As shown in P < 0.001), as shown in P < 0.001).The EAMG model was successfully established and then randomized to group administration of the intervention, with Lennon clinical score tests performed every seven days during treatment. During the administration treatment, the clinical symptoms of rats in the EAMG model group did not improve as the experiment progressed. Even individual rats showed signs of paralysis, convulsions, loose skin, jaws clinging to the bottom of the cage, and inability to eat, and the Lennon clinical score was significantly different from that of the adjuvant control rats (P < 0.01). After the intervention of each dosing group, the serum AChR-Ab levels of rats in the QSDH drug formulary low, medium, and high-dose groups and the prednisone acetate tablet group were significantly reduced compared with those in the EAMG model group  than in the adjuvant control group. The pathway-related proteins p-PI3K and p-AKT decreased to different degrees after each dose group of QSDH drug formulary low, medium, and high and positive drug treatment .Based on the core targets screened by network pharmacology and the results of the KEGG enrichment analysis, we further verified whether the QSDH drug formulary for MG treatment interferes with the PI3K/AKT signaling pathway. Western blot was used to detect the expression of the PI3K/AKT pathway-related proteins in the spleen tissues of each group of rats. Detection of protein expression of PI3K, phosphorylated PI3K (p-PI3K), AKT, and phosphorylated AKT (p-AKT). As Figures Network pharmacology is based on systems biology theory, integrates pharmacology, high-throughput sequencing, genomics, and other technologies, emphasizing the multichannel regulation of signaling pathways . Our stuγ in Th1 and downregulate IL-4 expression in Th2, thereby regulating immune function [The active ingredient-intersection target network showed that quercetin, epigallocatechin-3-gallate, luteolin, wogonin, kaempferol, and fisetin matched more targets, which may be the core compounds and essential material basis for the QSDH drug formulary to exert its therapeutic effect on MG. Studies have shown that epigallocatechin-3-gallate, quercetin, luteolin, wogonin, kaempferol, and fisetin have anti-inflammatory, antioxidant, immunomodulatory, and neuroprotective effects –26. Epigfunction . Luteolifunction . An expefunction . Fisetinfunction . The aboγ, and TNF-α in the peripheral serum of MG patients [The core targets obtained from screening intersecting targets in the PPI network by Cytohubba and CytoNCA plug-ins, of which the top three occupied by AKT1, STAT3, and TP53, were predicted to be the possible main targets of action of the QSDH drug formulary for the treatment of MG. Studies have reported that AKT1 enhances the differentiation of CD8+T cells, improves the proliferation of CD8+T cells, and inhibits the over-activation of DCs, thus regulating the immune response . The stupatients . The exppatients . STAT3, patients . TP53 ispatients .α activates the expression of glycolytic genes to provide energy for rapid activation of immune cells, suggesting that the PI3K/AKT signaling pathway regulates immune function in MG [Based on the KEGG pathway enrichment analysis results, we can see that the QSDH drug formulary exerts its effect on the treatment of MG mainly through signaling pathways such as the IL-17 signaling pathway, the HIF-1 signaling pathway, the PI3K-AKT signaling pathway, and the mTOR signaling pathway. The PI3K-AKT signaling pathway regulates cell proliferation, differentiation, apoptosis, and glucose transport and can regulate T cells' development, stability, and function . Studieson in MG . Our in We selected the core active ingredients quercetin, epigallocatechin-3-gallate, luteolin, wogonin, kaempferol, and fisetin as ligands and the first core target AKT1 as the receptor for molecular docking. Furthermore, we verified the pharmacodynamic substance basis of the QSDH drug formulary for MG treatment. The results showed that the main active compounds could stably bind to the core target AKT1 through hydrogen bonding. Among them, epigallocatechin-3-gallate formed the strongest hydrogen bond to AKT1 with asp-46, Glu-40, LYS-39, AlA-50, and GLN-47 amino acids near the active site. It further indicates that the QSDH drug formulary can treat myasthenia gravis by acting on the AKT1 target protein.In conclusion, 85 active pharmaceutical ingredients of the QSDH drug formulary were identified by UHPLC-QE-MS combined with the network pharmacology method. Preliminary prediction QSDH drug formulary can regulate the PI3K/AKT signaling pathway through quercetin, epigallocatechin-3-gallate, luteolin, wogonin, kaempferol, fisetin, and other active ingredients, acting on AKT1 target protein to inhibit inflammation, immune function, and cell metabolism, to achieve the purpose of treating MG. Provides new insights into the QSDH drug formulary's mechanism to alleviate MG disease's progression.