To explore the potential mechanisms of Yupingfeng Powder (YPFP) in the treatment of allergic diseases by using network pharmacology and molecular docking technology.  The active components and targets of YPFP were screened by the TCMSP database. The targets associated with atopic dermatitis, asthma, allergic rhinitis, and food allergy were obtained from GeneCards and OMIM databases, respectively. The intersection of the above disease-related targets was identified as allergy-related targets. Then, allergy-related targets and YPFP-related targets were crossed to obtain the potential targets of YPFP for allergy treatment. A protein-protein-interaction (PPI) network and a drug-target-disease topology network were constructed to screen hub targets and key ingredients. Next, GO and KEGG pathway enrichment analyses were performed separately on the potential targets and hub targets to identify the biological processes and signaling pathways involved. Finally, molecular docking was conducted to verify the binding affinity between key ingredients and hub targets.  In this study, 45 active ingredients were identified from YPFP, and 48 allergy-related targets were predicted by network pharmacology. IL6, TNF, IL1B, PTGS2, CXCL8, JUN, CCL2, IL10, IFNG, and IL4 were screened as hub targets by the PPI network. However, quercetin, kaempferol, wogonin, formononetin, and 7-O-methylisomucronulatol were identified as key ingredients by the drug-target-disease topological network. GO and KEGG pathway enrichment analysis indicated that the therapeutic effect of YPFP on allergy involved multiple biological processes and signaling pathways, including positive regulation of fever generation, positive regulation of neuroinflammatory response, vascular endothelial growth factor production, negative regulation of cytokine production involved in immune response, positive regulation of mononuclear cell migration, type 2 immune response, and negative regulation of lipid storage. Molecular docking verified that all the key ingredients had good binding affinity with hub targets.  This study revealed the key ingredients, hub targets, and potential mechanisms of YPFP antiallergy, and these data can provide some theoretical basis for subsequent allergy treatment and drug development. Allergies are a class of recurrent, chronic inflammatory diseases. Over the past few decades, the incidence of allergic diseases has continued to rise, especially in developing countries . Many crTraditional Chinese medicine (TCM) has a long history of treating allergic diseases. The “holistic concept” of TCM emphasizes the correlation between the various components of the human body, which coincides with allergy whole-course management concept of modern medicine. Due to the good clinical efficacy and few side effects, TCM treatment of allergy is widely used in China and has been gradually accepted worldwide as a major treatment for complementary and alternative medicine , 10.β1 [Yupingfeng Powder (YPFP) originated from the ancient Chinese medicine book “Danxi Xinfa,” which is composed of three Chinese herbs: Huangqi, Fangfeng, and Baizhu. In allergic diseases, YPFP shows good effects on both improving symptoms and controlling recurrence –13. It hβ1 . The polβ1 , 17. HowWith the rapid development of system biology and system pharmacology, network pharmacology has transformed the drug research models from “single disease-single target” to “multiple disease-multiple target”, which provides a new way for us to systematically study the pharmacodynamic mechanism of TCM compounds , 19. Thehttps://tcmspw.com/tcmsp.php), which is a unique TCM systematic pharmacology platform that integrates TCM active ingredients, potential targets, associated diseases, and pharmacokinetic data. Using “Huangqi,” “Fangfeng,” and “Baizhu” as keywords, oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 as screening conditions, a total of 45 active ingredients were obtained. Download the molecular structures of the screened active ingredients to prepare for subsequent molecular docking.The active ingredients of YPFP were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://www.uniprot.org). After removing duplicate genes, 221 YPFP-related target genes were obtained.Targets corresponding to the active ingredients of YPFP were predicted from TCMSP and translated into gene names using UniProtKB (https://www.genecards.org) and OMIM (https://www.omim.org). The data from the two databases were merged to remove the duplicates, and the related genes for each keyword were obtained. Cross the five keyword-related genes by “UpSet” and “VennDiagram” R package to obtain allergy-related target genes.Allergy-related target genes were searched for the keywords “atopic dermatitis,” “atopic eczema,” “asthma,” “allergic rhinitis,” and “food allergy” by GeneCards (https://bioinfogp.cnb.csic.es/tools/venny) was used to cluster YPFP-related target genes and allergy-related target genes, and the obtained overlaps were considered as potential target genes of YPFP therapy for allergy.Venny2.1 (https://www.string-db.org), with the species set as “Homo sapiens” and a confidence score ≥0.4. Each node in the network represents a protein, and the connection lines between nodes represent functional associations. The relevant results were exported in TSV format and extracted by Cytoscape v_3.9.1, and the top 10 hub genes were identified by the McCreight (MCC) method using the “Cytohubba” plug-in.The PPI network was plotted by entering the potential target genes into the STRING database  was used for the KEGG and GO analyses of the potential genes and hub genes, respectively. The enrichment degree was calculated according to the input number/background number, with P value ≤ 0.05 and an input number ≥2 as the inclusion criteria. Based on descending order of enrichment degree, the top 15 KEGG pathway and top 15 GO items of potential genes and hub genes were separately plotted as bubble diagrams by the “ggplot2” R package. Then Cytoscape v_3.9.1 was used to construct topological networks to identify the common biological functions and signaling pathways of hub genes and potential genes in the top 15 GO and KEGG results. In addition, the “ggalluvial” R package was used to draw the Sankey diagram of hub genes and enriched signal pathways. KEGG Mapper (https://www.genome.jp/kegg/mapper) is a collection of tools for KEGG mapping, which visualizes the relevant pathways of acquired genes through “Search Pathway” and “Search & Color Pathway” tools. We input potential genes into KEGG Mapper and set different colors , and then got the most relevant signaling pathway and displayed them.KOBAS 3.0 (https://clab.labshare.cn/cb-dock/php/). It predicts the binding region of a given protein, calculates the center and size using a curvation-based cavity detection method, connects with ligand binding sites queried by Autodock Vina, and then sorts binding patterns based on Vina scores and provides interactive 3D visualization of binding patterns. The 3D structures of the hub targets obtained by RSCB PDB and the 2D structures of the key ingredients obtained by TCMSP were uploaded to the CB-Dock website. After automatically identifying cavity size, calculating center and size, molecular docking, and configurational scoring were performed. Cavity size is the protein-ligand interaction interface predicted by CB-Dock based on the concave surface of protein [To verify the binding affinity of the hub targets of the PPI network to the key ingredients of the topological network, we performed molecular docking. CB-Dock is an online molecular docking website , the target genes corresponding to the 5 keywords were 1140, 1677, 1445, 1912, and 1241, respectively , allograft rejection, graft-versus-host disease, IL-17 signaling pathway, asthma, leishmaniasis, pertussis, chagas disease (American trypanosomiasis), and rheumatoid arthritis. Additionally, the Sankey diagram showed that most hub genes were enriched in the IL-17 pathway. The Sankey diagram and signal pathway diagram are shown in Figures Molecular docking was used to evaluate the binding affinity between the hub targets of the PPI network and the key ingredients of the YPFP-Target-Allergy topological network. The results showed that the minimum vina score of each docking module was less than −5.0 kcal/mol, indicating a good binding affinity of the hub targets to the key ingredients . To bettAtopic dermatitis , food allergies, asthma, and allergic rhinitis are the most common allergy-related diseases with similar epigenetic and physiopathological characteristics , 23. NumTCM has a unique theoretical system for the treatment of allergies. According to the theory of TCM, “qi deficiency” and “wind evil” are the two pathogenic bases of allergic diseases. Therefore, invigorating qi and dispelling wind are the main treatment principles. YPFP, a TCM compound with the above dual effects, is considered an effective and safe complementary and alternative therapy for allergy. To reveal the underlying mechanism of YPFP for allergy treatment, we described the relationships between active ingredients, targets, and signaling pathways in combination with network pharmacology and molecular docking.β were pro-inflammatory genes, while IL4, IL10, and IFNG were antiinflammatory genes. In the acute phase of inflammation, IL-6 is an important mediator of host immunity, but its sustained release can induce chronic inflammation or even a “cytokine storm” [β participate in the recruitment of neutrophils [γ, and IL-10 necessarily show corresponding changes [A PPI network was constructed with 48 genes co-expressed by allergy and YPFP. The 10 hub genes obtained by the PPI network all suggest the association of allergy and inflammatory response. IL6, TNF, CCL2, CXCL8, and IL1e storm” . Mast cee storm” . In locatrophils –29. Besitrophils . TH2 typtrophils . TH2 poltrophils , 32. As  changes , 32. PTG changes –35. Furt changes . Thus, aAccording to the screening conditions , a total of 45 active ingredients and 221 targets related to YPFP were identified, indicating that YPFP exerts its pharmacological effects in the treatment of allergies via multiple targets. The top five active ingredients obtained by the YPFP-Target-Allergy topological network were quercetin, kaempferol, wogonin, formononetin, and 7-O-methylisomucronulatol. As for quercetin, existing studies have indicated that quercetin relieves asthmatic tracheal spasm by inhibiting the expression of MUC5AC in airway epithelial cells and improves allergic inflammation by regulating TH1/TH2 balance and reducing antigen-specific IgE antibodies , 38. Kaeβ, TNF, IL-6, and PTGS2-induced prostaglandins also act on the thermoregulatory centers of allergic diseases [β, and leptin that drive hypoinflammatory responses and have been proven to contribute to the pathogenesis of allergy [GO analysis revealed that the main biological processes of YPFP  included neuroinflammation, fever regulation, vascular endothelial growth, lipid storage, and inflammatory regulation. The pathogenesis of neuroinflammation in allergic diseases has long been reported. Patients with chronic allergic inflammation have similar patterns of neuroinflammatory gene expression, which may be associated with the outcome of neuroimmune interactions , 45. Fevdiseases , 47. Obediseases , 49. Adi allergy , 51. Ang allergy , 52. As κB, ERK, and P38 MAPK signaling pathways [α/β [KEGG pathway analysis revealed that YPFP treatment of allergy involves multiple signaling pathways. Examples include malaria, African trypanosomiasis, leishmaniasis, and Chagas disease (American trypanosomiasis), which are associated with parasitic infections. Numerous epidemiological and immunological studies have supported parasite expose-mediated host defense as a potential risk factor for allergy –55. Inflpathways . Even inpathways , 66, 67.pathways . Recent ays [α/β . In concα mediates the inflammatory response in the early stages of anaphylaxis. Studies have shown that quercetin inhibits the production of COX-2 and delays angiogenesis by inactivating P300 histone acetyltransferase (HAT) [κB signaling pathways to reduce inflammatory responses associated with PTGS2 expression [α, IL-1β, and IL-6 by inhibiting caspase-1 activity and regulating NF-κB activation and translocation [Molecular docking verified the good binding effect of key ingredients and hub targets. The binding scores of PTGS2 to quercetin, kaempferol, wogonin, and formononetin, IL-6 to kaempferol and formononetin, and TNF to kaempferol, wogonin, and formononetin were all less than −8.5 kcal/mol, indicating that the bound molecular structure was more stable. PTGS2 (COX-2) is an important mediator in promoting inflammation and tumor growth. Il-6 is involved in inducing the transition from acute to chronic inflammation. TNF-se (HAT) . Kaempfepression . Formonolocation . The aboHowever, there are still some limitations to this study. For example, the database information may be incomplete, and the interaction between active ingredients is not considered. Nonetheless, this study provides a new approach for further exploring the molecular mechanism of YPFP in allergies and a new idea for the study of targeted therapy mechanisms.β, PTGS2, CXCL8, JUN, CCL2, IL10, IFNG, and IL4 are the main therapeutic targets. This study indicates that YPFP has the characteristics of multicomponent, multitarget, and multipathway in allergy treatment, which provides more evidence for the further application of YPFP in allergy.In this study, we combined network pharmacology and molecular docking technology to study the molecular mechanism of YPFP in the treatment of allergy. According to the research, YPFP plays an antiallergic role mainly through inhibiting inflammatory infiltration, regulating neuroinflammation, regulating fever, inhibiting vascular endothelial growth, and improving lipid storage, involving malaria, African trypanosomiasis, inflammatory bowel disease (IBD), allograft rejection, graft-versus-host disease, IL-17 signaling pathway, asthma, leishmaniasis, pertussis, Chagas disease (American Trypanosomiasis) and Rheumatoid arthritis and other signaling pathways. Quercetin, Kaempferol, Wogonin, Formononetin, and 7-o-methylisomucronulatol are the key active ingredients of YPFP. IL6, TNF, IL1


Objective. This study is aimed at screening out effective active compounds of Qizhen capsule (QZC) and exploring the underlying mechanisms against gastric cancer (GACA) by combining both bioinformatic analysis and experimental approaches. Weighted gene coexpression network analysis (WGCNA), network pharmacology, molecular docking simulation, survival analysis, and data-based differential gene and protein expression analysis were employed to predict QZC's potential targets and explore the underlying mechanisms. Subsequently, multiple experiments, including cell viability, apoptosis, and protein expression analyses, were conducted to validate the bioinformatics-predicted therapeutic targets. The results indicated that luteolin, rutin, quercetin, and kaempferol were vital active compounds, and TP53, MAPK1, and AKT1 were key targets. Molecular docking simulation showed that the four abovementioned active compounds had high binding affinities to the three main targets. Enrichment analysis showed that vital active compounds exerted therapeutic effects on GACA through regulating the TP53 pathway, MAPK pathway, and PI3K/AKT pathway. Furthermore, data-based gene expression analysis revealed that TP53 and JUN genes were not only differentially expressed between normal and GACA tissues but also correlated with clinical stages. In parallel, in vitro experimental results suggested that QZC exerted therapeutic effects on GACA by decreasing IC50 values, downregulating AKT expression, upregulating TP53 and MAPK expression, and increasing apoptosis of SGC-7901 cells. This study highlights the potential candidate biomarkers, therapeutic targets, and basic mechanisms of QZC in treating GACA, providing a foundation for new drug development, target mining, and related animal studies in GACA. Gastric cancer (GACA), a type of severe fatal disease, continues to be the world's most common and deadly cancer, affecting the health of people, especially the elders. It is the fifth most common cancer and the third leading cause of cancer-related death worldwide, with a high mortality rate of up to 75% and an estimated 783,000 deaths in 2018 . HoweverTraditional Chinese medicine (TCM) is of great importance in fighting against cancer; its excellent curative effects have attracted increasing attentions worldwide . Qizhen Weighted gene coexpression network analysis (WGCNA) is a functional bioinformatic method used to identify gene modules for therapeutic targets based on gene coexpression analysis. By systematically analyzing the correlation between modules and clinical features, the modules that are closely associated with clinical characteristics can be proposed, and genes that may exhibit critical functions can be predicted . Networkin vitro experimental validations  to investigate the molecular mechanisms of QZC against GACA. The results of this study will deepen our understanding of the molecular mechanisms of QZC against GACA and provide useful information for the diagnosis, treatment, and prognosis of GACA. A flowchart of the technical strategy is shown in In this study, we employed both bioinformatic analyses  and n = 404) were downloaded from TCGA data portal  in January 2021. To get valuable information, the clinical metadata from 404 samples were screened. A total of 67 samples with incomplete data were excluded, and the remaining 337 samples were included for further analysis. Consequently, a total of 15,073 genes that were differently expressed between gastric cancer samples were subjected to WGCNA to identify key modules that were closely associated with GACA.GACA RNA sequencing data . The TOM similarity was used to derive two useful metrics of weights and distances. The dynamic tree-cut algorithm was employed to pinpoint the gene coexpression module with minModuleSize of 50 and mergeCutHeight of 0.01 [The WGCNA package was adopted to construct gene coexpression networks. Based on pairwise Pearson correlation coefficient matrices, a similarity matrix was constructed. Subsequently, the correlation coefficients were calculated by distance correlation. As distance correlation coefficients are always positive, they define an unsigned network in which positive and negative correlations are treated equally. With a power adjacency function, an adjacency matrix was obtained from the similarity matrix. The integration function (pickSoftThreshold) was utilized to select an appropriate soft threshold power  of 0.01 . Module  of 0.01 . In addihttps://tcmspw.com/), ETCM (https://www.tcmip.cn/), and TCMID (https://119.3.41.228:8000/tcmid/)) [https://pubchem.ncbi.nlm.nih.gov/) were uploaded to the TargetNet webserver (http://targetnet.scbdd.com) and Swiss Target Prediction database (http://www.swisstargetprediction.ch/). Protein targets with a prediction score > 0.9 were retained. Targets of compounds documented in TCMSP were also collected. Predicted protein target information including name and UniProt ID was obtained from the UniProt database (https://www.uniprot.org/). Only human targets were retained for the following analysis.By combining various databases as well as biomedical literatures, biomolecular networks can be constructed for a compound . The chetcmid/))  and litetcmid/)) –33. We utcmid/)) . In addiThe predicted target information of bioactive compounds and the related information were introduced into Cytoscape 3.8.0 to establish a compound-target network . Nodes rhttps://string-db.org/) is a robust algorithm that provides not only known and predicted protein interactions but also direct and indirect protein relationships [Homo sapiens,” and a confidence interaction score of 0.9 was chosen [Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 10.5 (ionships . Genes os chosen . The dathttps://david.ncifcrf.gov/) to elucidate the role of targets in gene function and signaling pathways. The species was set to “Homo sapiens.” P ≤ 0.05 were considered statistically significant. GO functional enrichment classified genes into three categories, including cellular component (CC), molecular function (MF), and biological process (BP). According to the results of KEGG analysis, Cytoscape 3.8.0 was used to set up a “target-pathway” network to show potential targets and pathways that were involved in QZC against GACA.GO functional enrichment and KEGG pathway analysis were performed using the David v6.7 database (https://www.kegg.jp/kegg/tool/map_pathway2.html) to screen out target genes that were involved in pathways related to cancer.Selected target genes were introduced into KEGG Mapper (https://www.rcsb.org/) [Molecular docking simulation can be utilized to assess the interaction between ligands and receptors based on geometric complementarity, energy complementarity, and chemical environmental complementarity . The 3D sb.org/) , processsb.org/) . Subsequsb.org/) . Eventuahttp://kmplot.com/analysis/) is an online tool that was used to perform survival analysis of the top eight PPI genes in GACA patients [P < 0.05 indicated the genes were associated with survival of GACA patients.Kaplan-Meier Plotter . After deleting samples with incomplete data, a total of 407 samples including 32 normal samples and 375 GACA samples were included. Subsequently, we analyzed the difference in expression levels of the top eight PPI genes between normal and GACA tissues, as well as in the four clinical stages. P < 0.05 were considered to be statistically significant [https://www.proteinatlas.org/).The mRNA sequencing data of 443 GACA patients were downloaded from TCGA database  for 2 h. The optical density (OD) at 450 nm was measured using a microplate reader . The 50% inhibitory concentration (IC50) values were calculated using GraphPad Prism 8.0 software.A CCK-8 kit  was adopted to assess the effects of luteolin, rutin, quercetin, or kaempferol on the proliferation of SGC-7901 cells . SGC-7901 cells were seeded into 96-well plates  at a density of 1 × 104 cells/mL, followed by incubation overnight at 37°C with 5% CO2. SGC-7901 cells were treated with RSM for 24 h and then collected by trypsinization. The treated cells were washed with cold PBS  and then centrifuged. Finally, we treated them with Annexin V-FITC  and propidium iodide (PI) at 37°C for 40 min in the dark. SGC-7901 cells were then placed in a water bath under light-proof conditions. The cell circle was analyzed with a flow cytometer .SGC-7901 cells in the logarithmic growth phase treated by luteolin, rutin, quercetin, and kaempferol were collected and seeded into 96-well plates  at a density of 5 × 10μL of preformulated cell lysis mix (cell lysis solution : protease inhibitor : phosphatase inhibitor = 98 : 1 : 1) for 15 min and then transferred to new 1.5 mL EP tubes. After centrifuging at 12,000 r/min for 15 min, the supernatant was collected. Protein concentration was determined using a BCA Protein Assay Kit . Protein separation was achieved by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and resolved proteins were transferred onto a PVDF membrane . After sealing with 5% skim milk  at room temperature for 2 h, the protein-containing PVDF membrane was washed with TBST solution. Primary antibodies against p-AKT , AKT , p-P53 , P53 , p-MAPK , MAPK , or GAPDH (endogenous reference) were added, respectively, and incubated overnight at 4°C. After washing the membrane, corresponding secondary antibodies were added and incubated for 1 h at room temperature, and an ECL kit was used for visualization. The gray values for each protein band were recorded using a fully automated chemiluminescence image analyzer. GAPDH was used as an internal reference to calculate the relative expression change of each protein. The protein's gray values were calculated by ImageJ and presented using a bar graph.SGC-7901 cells were lysed with 200 t-tests were performed to compare quantitative data between groups. P < 0.05 was defined as statistically significant.All data were shown as the mean ± SD. GraphPad Prism 8.0 software was used to analyze experimental results. Student's β = 2  was chosen as the soft-cutoff parameter , blue and turquoise modules were predicted to be key modules  and 2,328 lines . The fivHomo sapiens.” Among them, 24 nodes with BC values > the mean of 0.0173, CC values > the mean of 0.3677, and degree values > the double of 18.692 were defined as the major nodes. And these 24 nodes were predicted to be the potential targets for QZC treating GACA, which included TP53, EP300, AKT1, MAPK1, SRC, RELA, JUN, HSP90AA1, MAPK8, RHOA, MAPK14, ESR1, NR3C1, CDK1, EGFR, RB1, CCND1, RXRA, CXCL8, EDN1, PRKCA, VEGFA, FOS, and PRKACA.To optimize gene ranges that might be highly related to QZC's active compounds, we screened target genes overlapping blue module, turquoise module, and compound targets by Venn. A total of 314 overlapping target genes were considered potential targets for QZC against GACA . Next, tP < 0.01 after KEGG enrichment analysis. The findings suggested that QZC might exert its effects through the abovementioned pathways in treating GACA. The top 30 KEGG pathways and their correlations with targets are displayed in Figures Next, GO enrichment analyses, including biological process (BP), molecular function (MF), and cellular component (CC), were performed to explore the underlying mechanisms of QZC against GACA. In addition, KEGG functional enrichment analysis was also used to gain insights into the mechanisms of QZC in treating GACA. A total of 1,425 GO terms were obtained from GO enrichment analysis of 24 potential target genes, of which 1,230, 107, and 88 terms were from BP, MF, and CC, respectively. The top 10 highly enriched terms for BP, MF, and CC are shown in −1 was used as the key criterion [π-stacking (PHE520), π-cation interactions (ARG156), and salt bridges . In conjunction with literature analysis, the kinase structural domain of SRC is usually located in a docking groove directly below the active site cleft to bind substrates or inhibitory fragments, such as VAL364 and PHE520, which can be linked to kinases via hydrophobic interactions [π-cation interactions, and salt bridges [60c-src activities. S-fold-enhanced kinase activity has been measured using the pp60c-src truncation mutant, and translation was found to be terminated after Glu 517 [c-src in tumor-derived 4AT and 4BT cell lines [After network analysis, 8 PPI targets  were selected to analyze their molecular docking with 9 highly related QZC compounds  using AutoDock Vina. The docking scores of 8 PPI targets with 9 highly related QZC compounds were shown as a heat map . Bindingriterion . The binriterion . A totalractions , 52. Notractions . These s bridges . GLN362 ions ARG1, and sal Glu 517 . Other sll lines . In summll lines –61.To investigate the prognostic values of the top eight PPI genes, Kaplan-Meier plotter analysis was performed. Analysis results showed that they were not significantly associated with GACA patients' survival. We also found that abnormal expression of these eight PPI genes was not associated with unfavorable RFS or OS in GACA patients either, suggesting that the top eight PPI genes could not be used as biomarkers for GACA .TP53, AKT1, EP300, HSP90AA1, JUN, MAPK1, RELA, and SRC) in normal and tumor tissues. Results shown in TP53, EP300, HSP90AA1, JUN, RELA, and SRC (P < 0.05). Based on clinical characteristics, GACA patients were usually divided into four clinical stages (Stages I-IV). According to the data acquired from TCGA-STAD datasets, correlations between eight PPI gene levels and four clinical stages were analyzed . The expression levels of the top eight PPI proteins in normal and tumor tissues were also explored based on the HPA database. As shown in TCGA database was used to explore the expression patterns of eight PPI genes , suggesting that luteolin had the strongest inhibitory effect on SGC-7901 cell proliferation among the four, followed by quercetin (62.63), kaempferol (89.59), and rutin (414.4), respectively  could inhibit SGC-7901 cells' proliferation. According to the literature, the tested concentrations of four active compounds were set to 10, 20, 40, 80, and 100 ectively . CombiniFlow cytometric analysis was carried out to evaluate the apoptosis of SGC-7901 cells induced by luteolin, rutin, quercetin, and kaempferol. Results  demonstrThree major targets  predicted by both PPI analysis and molecular docking simulation were further investigated to verify their promotive effects on the apoptosis of SGC-7901 cells through Western blot. As shown in GACA is a common cancer with high mortality . Scientiβ-catenin, P53-independent pathway, PI3K/AKT, JAK/STAT, MAPK, P53, and NF-κB signaling pathways [A total of 94 active compounds in QZC and 471 targets were confirmed, which suggested that QZC might treat GACA through multiple targets. Based on the molecular docking simulation results, quercetin, luteolin, kaempferol, and rutin showed high molecular docking values with targets and were anticipated as key active compounds for the anti-GACA effect of QZC. The inhibitory effects of luteolin, rutin, quercetin, and kaempferol on SGC-7901 cell proliferation were verified by the CCK-8 assay, which echoed the molecular docking simulation results. As for quercetin, existing studies have indicated that quercetin can exert its antiproliferation effects by altering cell cycle progression, inhibiting cell proliferation, promoting apoptosis, inhibiting angiogenesis and metastasis progression, and affecting autophagy . In termpathways . TherefoTP53 and JUN genes were not only significantly differentially expressed between normal and GACA tissues but also correlated with clinical stages, suggesting that these two genes could serve as underlying biomarkers for GACA patients' prognosis. According to molecular docking simulation results, TP53, MAPK1, and AKT1 that showed a strong affinity with QZC active compounds were considered hub protein targets, and these three hub protein targets were highly likely to interact with QZC active compounds to inhibit GACA cell proliferation. TP53, one of the most frequently mutated genes in human cancer, is a multifunctional transcription factor that induces cell cycle arrest and apoptosis by regulating the expression of its target genes and noncoding genes [The PPI results demonstrated that TP53, AKT1, EP300, HSP90AA1, JUN, MAPK1, RELA, and SRC were the top eight proteins having more interactions with other protein targets. These eight corresponding genes were subjected to surviving analysis. Of them, ng genes . Therefong genes . TP53 hang genes . MAPK1, ng genes . Many reng genes . It is rng genes . AKT1 hang genes . AKT1 isng genes . Its funng genes . In adding genes . Therefong genes .Abnormal proliferation and insufficient apoptosis in tumor cells are believed to be the two main factors contributing to the onset and development of cancer. Consequently, finding a way to inhibit the proliferation and promote apoptosis of tumor cells would be a potential and effective strategy for cancer treatment. To date, plenty of studies have proved that TCM active compounds fight against cancers by both suppressing tumor cell proliferation and inducing tumor cell apoptosis through diverse signaling pathways . The floKEGG pathway analysis showed that genes identified as potential therapeutic targets were enriched in several cancer-related signaling pathways, such as PI3K/AKT signaling pathway, and MAPK signaling pathway. Among them, the PI3K/AKT pathway is often excessively activated in a variety of cancers , and sevThe central idea of TCM has a lot in common with network pharmacology, which is capable of systematically interpreting the treatment process of complicated diseases. In addition to network pharmacology, WGCNA, and data-based gene and protein expression analysis, this study also included molecular experiments to investigate the underlying mechanisms by which QZC exerts effects on GACA at both cellular and molecular levels. Although many advantages are apparent when using the combined approaches, the limitations cannot be neglected. First, a few pivotal targets and active compounds may be ignored if the information from databases was incomplete. Second, various signaling pathways through which QZC exerts its therapeutic effects on GACA were predicted by combined bioinformatic analyses, but experimental verification of the involvement of each pathway was not fully completed. Third, although our results provided important information about the mechanisms by which QZC exerts effects on GACA, further exploration is still needed owning to the complicated therapeutic mechanisms.In summary, luteolin, rutin, quercetin, and kaempferol were identified as the crucial active compounds in QZC. AKT1, MAPK1, and TP53 were predicted to be hub protein targets. Molecular docking simulation suggested that these four active compounds could bind to the three hub protein targets. QZC acts on GACA possibly via regulating multiple signaling pathways, including the MAPK signaling pathway, PI3K/AKT signaling pathway, and TP53 signaling pathway. According to the experimental validation, we believed that QZC exerted therapeutic effects on GACA by regulating the expression of hub target proteins, inhibiting tumor cell proliferation, and enhancing tumor cell apoptosis. Our study elucidated the potential pharmacological mechanisms of QZC in the treatment of GACA and provided new ideas for developing targeted auxiliary anticancer drugs.


Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787‐induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an  Angiogenesis is the physiological process of new blood vessel formation. Excessive or insufficient angiogenesis occurs in more than 70 diseases such as cancer, endometriosis, chronic wounds, and stroke . ExcessiThe discoveries of endogenous pro-angiogenic and anti-angiogenic molecules and elucidation of their respective signalling pathways have led to the development of clinically effective anti-angiogenesis drugs such as monoclonal antibodies , small mIn traditional Chinese medicine (TCM), many botanical drugs are used in the treatment of angiogenic diseases by modulating angiogenesis-related targets or pathways . HoweverLigand-based target prediction methods rely on the principle of chemical similarity, which assumes that compounds with similar chemical structure should exhibit similar biological effects . While tin silico is a well-established computational technique capable of inferring the MoA of putative compounds by utilizing known bioactivity information , Deoxycholic Acid  and Tetrahydropalmatine  were purchased from Sigma-Aldrich, Gillingham, Dorset, United Kingdom. Ferulic acid , Ursodeoxycholic Acid , Isoalantolactone  Cinobufotalin  and Ginsenosides Rg1 , Ginsenosides Rb3  and Ginsenosides Rc  were purchased from Cayman Chemical, Ann Arbor, Michigan, United States. 1 beta-hydroxyalantolactone  were purchased from Bioruler, Connecticut, United States.® X-100, rabbit anti-human von Willebrand factor antibody (F-3520), and mouse anti-rabbit IgG-alkaline phosphatase (A9919) were purchased from Sigma-Aldrich, Gillingham, Dorset, United Kingdom. Human recombinant VEGF and precast gels were purchased from Invitrogen Life Technologies, Paisley, United Kingdom. PTK787  was purchased from MedChem Express, Monmouth Junction, United States. Pronase E were purchased from Solarbio, Shanghai, China.Human umbilical vein endothelial cells (HUVECs) and human dermal fibroblasts (HDFs) were purchased from PromoCell Cells  and were subsequently cultured in PromoCell’s Endothelial Cell Growth Medium 2 (EGM-2) containing 2% foetal bovine serum (FBS), human epidermal growth factor (EGF), human basic fibroblast growth factor (bFGF), insulin-like growth factor (IGF), human vascular endothelial growth factor (VEGF), ascorbic acid and heparin. Endothelial Cell Growth Medium-2 as well as their respective bullet kits were purchased from PromoCell. Trypsin 0.005%/EDTA 0.01% solution, Dulbecco A Phosphate Buffered Saline (PBS), 1-StepTM NBT/BCIP (Pierce Protein Research) were purchased from Thermo Scientific, Loughborough, United Kingdom. Foetal bovine serum (FBS), dimethyl sulfoxide (DMSO), paraformaldehyde, Tritonin vitro endothelial tube formation assay  using the accession number GSE85871 , with the options set to “aromatize,” and “keep largest fragment.”The compound data set used for this work comprised 100 active TCM individual metabolites as published in previous work , which wt-Test is performed on the contingency table for the number of active and inactive target predictions in the promoter or inhibitor set of individual metabolites (a) and (b), respectively, compared to the number of active and inactive predictions in the background (putative inactive) TCM set, (c) and (d), respectively. The output of the enrichment calculation is the Odds Ratio (OR), defined as:Prediction IncluDinG INactivity (PIDGIN) (version 3) was used to conduct target prediction for the 100 individal metabolites mentioned above, by which the probability of the association between a compound and a drug target could be predicted. We used the following function options: bioactivity threshold is 10 μM. A background of 4,041 TCM molecules were selected from the TCM database at TCM@TAIWAN and SuperTCM . The indp-value is generated, where a value below 0.05 indicates when enrichment can be considered significant, and thus where we may reject the null hypothesis . Given the biases in chemical space the application of statistical tests needs to be interpreted with care; however, the above framework provides an empirical way to prioritize proteins more (and less) likely involved in a particular MoA, given a set of ligand-target interactions in two datasets. Target Prediction enrichment profile hierarchical clustering analysis for MoA hypothesis generation was then conducted using Seaborn Clustermap (version 0.11).An OR score over 1.0 represents enrichment for a target in the promoter or inhibitor set , whilst a score below 1.0 represents enrichment in the reference TCM set . A Fishers Exact Raw CEL data downloaded from GEO were first normalized by Robust Multiarray Average  using Afhttps://www.kegg.jp/kegg/) in the analysis and performed pathway enrichment using limma (version 3.38.3) (p < 0.05 (adjusted for multiple testing) as significantly enriched. We kept only pathways which have significant enrichment (adjusted p ≤ 0.05) in two or more TCM individual metabolites for visualization. Pathway enrichment profile hierarchical clustering analysis for MoA hypothesis generation was then conducted using Seaborn Clustermap (version 0.11).We used Over-Representation Analysis (ORA)  implemen 3.38.3) . As thisWe next trained a decision tree implemented in Scikit-learn (version 0.22)  to mergeA Random Forest classifier as implemented in Scikit-learn (version 0.22) was trained. Random Forest Classifier  functionin vitro tube formation assay .In the 12‐days co‐culture model, when human fibroblasts are co‐cultured with endothelial cells, the fibroblasts secrete the necessary matrix components that act as a scaffold for tube formation . In contin vivo . Briefly2, 0.16 mM MgSO4). Healthy, hatched zebrafish embryos were picked out and staged by time and morphological criteria . Randomization was used to assign embryos to different experimental groups and to the drug treatment.Transgenic zebrafish (Tg [vegfr2: GFP]) were maintained in 3 L polystyrene aquarium tanks with constant aeration and flow water systems at 28°C ± 1°C under a 14‐hr light/10‐hr dark photoperiod. Food with brine shrimp was fed twice per day. For breeding, adult zebrafish were placed in 1.5 L breeding tanks overnight and were separated by a transparent barrier that was removed on the following morning. Zebrafish embryos were raised in culture water  embryos were dechorionated with 1 mg/mL of Pronase E before treatment. Control embryos were treated with the equivalent amount of DMSO solution . Model embryos were treated with 0.2 μg/mL PTK787. Positive control embryos were treated with 0.2 μg/mL PTK787 and 40 μM ginsenoside Rg1. The zebrafish embryos of test groups were treated with 0.2 μg/mL PTK787 and test compound samples. After 24 h of incubation at 28.0°C ± 1°C with a 14‐hour light/10‐hour dark cycle, embryos were anaesthetized with 0.02% tricaine methane sulfonate and photographed under a fluorescence microscope . The length of intersegmental vessels (ISVs) between the trunk and tail of each embryo was measured with the Image Pro Plus 5.0 by a user blinded to the exposure groups.In pro-angiogenic experiments , the indThe experiment procedures were conducted according to the standard ethical guidelines that were approved by the Ethics Committee of the Biology Institute of Shangdong Academy of Science (SWS20210306).https://www.ibm.com/products/spss-statistics). p-Values below 0.05 were considered as statistically significant. All zebrafish data were processed by GraphPad Prism 6.0 software (https://www.graphpad.com/). After statistical analysis data were shown as mean ± SEM. The comparison between groups was performed by student’s test. p-values below 0.05 were considered as statistically significant.All in tube formation assay data were rendered as means ± SEM and the statistical results were analysed by a one-way ANOVA in SPASS 20.0  and four clusters of metabolites (C1-C4), shown in in vitro and in vivo, through the modulation of VEGF, platelet-derived growth factor (PDGF) and hypoxia-inducible factor-1 alpha (HIF-1α)  . However(HIF-1α) . More reOf the targets in MoA2 there is a pronounced pattern in compound cluster 2, where all the five constituent metabolites  have high predicted bioactivities for Galectin-3 and Galectin-9 both of which have been shown to be relevant to angiogenesis. Galectin-3 protects against ischemic stroke by promoting neuro-angiogenesis . While Gvia cAMP/protein kinase A (pkA)/Rac1-dependent signal pathway  observed high predicted bioactivities. The targets present in MoA3 have known association with angiogenesis. For example, G-protein coupled bile acid receptor 1 (GPBAR) can influence angiogenesis through suppressing the proinflammatory cytokine production and phagocytic function of macrophages, and by enhancing the barrier function  pathway . Next, t pathway . Finally pathway .via stimulation of the NF-κB (nuclear factor kappa of activated B cells) and p38 MAPK pathways . Studies have also demonstrated that bile acids can induce endothelial dysfunction by enhancing expression of intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion protein 1 (VCAM-1), and E-selectin pathways . Hydrophpathways .We next analysed the biological pathway(s) transcriptionally modulated after administration of TCM metabolite in MCF7 cells using Over-Representation Analysis (ORA). The heatmap shows 5 MoA clusters; Cluster 1 and 2 have very well-defined similarities in pathway activity whereas Clusters 3, 4, and 5 are smaller and less well-defined, although we were still able to find literature evidence for inhibiting or promoting angiogenesis.Cluster 1 metabolites have activity toward a range of angiogenesis pathways especially related to cancer and the immune system. Firstly, the B cell receptor signalling pathway which describes the role of B cells in immunity. The role of B cells in tumour initiation, progression, and angiogenesis is still debated. However, there is clinical evidence regarding their association with good prognosis of cancer patients and potential anti-tumour effect . SecondlThe pathways enriched in metabolites cluster 2 are related to the metabolism of various chemicals including glycerolipid, galactose, beta-alanine and alpha-linolenic acid. Angiogenesis has traditionally been viewed from the perspective of how endothelial cells (ECs) coordinate migration and proliferation in response to growth factor activation to form new vessel branches . Howevervia promotion of VEGF expression  had the highest importance score by Gini importance . This prFrom the decision tree using both TP and DEGs descriptors , it can in vitro endothelial tube formation assay (p < 0.05) and 1 µM (p < 0.05). And results in p < 0.01) and curculigoside promoted angiogenesis in ISVs at 160 µM (p < 0.01). However, despite the reported anti-angiogenic and anti-tumour activities of a deoxycholic acid derivative (p > 0.05).Having established enriched prediction protein targets  and geneon assay  and a zeon assay . The indrivative  and antirivative , Figure n = 49 out of 100) with which to subsequently validate through in-vitro and zebrafish assays. The model was trained using the 51 individual metabolites with literature evidence of angiogenic inhibition  and Prediction TP model (0.446 and 0.604) indicate that they are more likely to protome angiogenesis. However, they are both produced no effect on endothelial tube formation.Firstly, stachydrine hydrochloride and hyperoside were tested using the p < 0.05) at 10 μM and 1-beta-hydroxyalantolactone (p < 0.05) at 100 nM stimulated endothelial tube formation, while cinobufotalin (p < 0.05) at 100 nM and isoalantolactone (p < 0.05) at 1 μM inhibited endothelial tube formation. Therefore, further experiments were performed to determine the angiogenic effect of these four individual metabolites in vivo. The results in p < 0.01) at 40 μM and cinobufotalin (p < 0.01) at 20, 40, and 80 μM restored angiogenesis in ISVs against PTK787‐induced impairment. Tetrahydropalmatine worsened the effect of PTK787. Isoalantolactone caused lethality at 10 μM and higher concentrations Intriguingly, cinobufotalin showed contradictory angiogenic activities in vitro and in vivo , and these are worthy of further investigation in the future.Next results shown in  in vivo . It is pin vitro and in vivo models used in biological assessment.The relatively low probability scores of ginsenosides Rb3 and Rc from the Prediction Gene model (0.41 and 0.358) and Prediction TP model (0.242 and 0.210) indicate that they are more likely to inhibit angiogenesis than to stimulate it. Contrary to the model predictions, both individual metabolites stimulated angiogenesis in zebrafish model, overcoming the inhibitory effect of PTK787 . Hence, This study represents a novel composite approach to analyse the mode of action (MoA) of 51 pro- and antiangiogenic individual metabolites derived from TCM, based on the most important ten predicted protein targets, Differentially Expressed Genes (DEGs) and pathway enrichment signatures, culminating in the creation of an machine learning model to prospectively predict the angiogenic function of novel individual metabolites. Prior examples of the concept exist , for exaAnalysis of the PIDGIN predicted targets and enriched pathways by clustering , 3 demonin vivo and in vitro assessment of four selected individual metabolites derived from TCM known to have angiogenic modulating effects we were able to show that although these individual metabolites have been reported in the literature, they exhibit different activities due to the different pharmacological models used. For example, deoxycholic acid and ursodeoxycholic acid did not show angiogenic effects in ISVs or zebrafish in vivo despite being reported as angiogenic inhibitors in the literature. It is possible that the in vivo experimental results in this study using a simple zebrafish model of angiogenesis can differ from results observed using other animal models  reported in the literature. However, of the other two individual metabolites assessed we did report agreement between the prior literature and the in vivo and in vitro models. For example, ferulic acid and curculigoside stimulated endothelial tube formation and intersegmental vessels (ISVs) of zebrafish against PTK787-induced impairment.In addition to this, through in vitro and/or in vivo biological activities could detrimentally affect the predictive power of our first-generation machine learning model of angiogenesis modulation, leading to the prediction results not fully corroborating with the angiogenesis phenotype.Overall, the results found for deoxycholic acid and ursodeoxycholic acid signal caution is required when developing machine learning models based on literature evidence of inhibitory or pro-angiogenic individual metabolites. Uncertain designations resulting from contradictory n = 51) used in training the model, the accuracy and applicability of current machine learning protocol is expected to have certain applicability domain limitations. We then validated the prospective predictions of select individual metabolites using in vitro and in vivo assays. To our knowledge, this is the first time that ginsenosides Rb3 and Rc, cinobufotalin and 1-beta-hydroxyalantolacton which are present in TCM have been shown to promote angiogenesis in a zebrafish model. It is noteworthy that TCM in which the pro-angiogenic individual metabolites are present have historically been used to promote angiogenesis. For example, the Shexiang Baoxin pill which contains pro-angiogenic individual metabolites such as ginsenoside Rg1  and cinnamaldehyde , is widely used for the treatment of stable angina pectoris, chest pain or discomfort caused by coronary heart disease in China  widely used as a cardiotonic, diuretic, and hemostatic agent , which has been reported in the literature to have alleviated the progression of pulmonary fibrosis effect  remain to be confirmed by stringent biological verification in appropriate animal models of human disease and rigorous clinical trials.However, we did identify individual metabolites such as tetrahydropalmatine and isoalantolactone which were predicted to have angiogenic activity by the machine learning model but produced no inhibitory or pro-angiogenic effects Overall, the results indicate that, despite the limited data, the machine learning model in combination with gene expression analysis and predicted targets was able to predict the angiogenesis effects of individual metabolites. This is because it is often easier to distinguish no effect from either effect (so inactive vs. any modulator), than to classify functional effects . Moreover, the approach presented here allows for the preliminary elucidation of individual metabolite’s MoA based on target proteins, differentially expressed genes, and the final prospective prediction of the machine learning model. In contrast to conducting a phenotypic screen, for example, using ISV and zebrafish assays which were shown to disagree in cases in the present study, this approach therefore can help advance general understanding of the biological mechanisms  underlying compound angiogenic activity and inform future drug development of TCM metabolites. However, in future research, more in-depth studies need be carried out based on the prospective predictions of the machine learning model we developed with close attention to the choice of pharmacology models used to validate them.via angiogenesis. Future studies on the pharmacological mechanisms of modulation of angiogenesis by TCM should be investigated in depth when they are conducted. We also identified novel pro-angiogenic TCM metabolites for further research in the future. The machine learning approaches applied in this study could be easily expandable to elucidate molecular mechanisms of other TCM components and drugs.We performed a systematic analysis to explore the molecular angiogenic mechanisms of 51 TCM metabolites using bioinformatics approaches and assessed the possible angiogenesis-modulating activities of 49 TCM metabolites by a Machine Learning model and pharmacological models. Through this analysis, we identified that many of the TCM components possess diverse MoAs, and this may explain the applications of TCM in treating various symptoms and diseases


Osteoarthritis is a common degenerative disease with a high incidence, high disability rate, and poor prognosis. Clinical studies have shown that Bushen Huoxue formula can relieve joint swelling and pain and improve limb function and joint mobility, but there is a lack of high-quality scientific basis. Using network pharmacology and molecular docking technology to study the mechanism of Bushen Huoxue formula in the treatment of osteoarthritis.https://www.uniprot.org) database standardization of names. Then, the Venn diagram was created by taking the intersection of the active ingredient and the target of the disease, and the “active ingredient-target” network was constructed and analyzed using Cytoscape 3.7.2 software. At the same time, the intersecting targets were imported into the Search Tool for the Retrieval of Interaction Gene/Proteins database to build a protein-protein interaction network and to screen the core targets; the intersecting targets were visualized by using the Database for Annotation, Visualization and Integrated Discovery 6.8 database for gene ontology functional analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and construct the “active ingredient-target-pathway” network. Finally, the main active ingredients of the formula for tonifying the kidney and invigorating the blood were validated by molecular docking with the core targets.First, the active ingredients and corresponding target predictions of the formula were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the China National Knowledge Infrastructure. Meanwhile, the osteoarthritis disease targets were obtained through the genome annotation database platform (GeneCards) and the DrugBank database, and the target proteins obtained above were standardized using the Uniprot (A total of 194 active ingredients and 365 targets of the Bushen Huoxue formula were collected, 776 targets for osteoarthritis diseases and 96 targets for the intersection of active ingredients and diseases. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis yielded 104 relevant pathways, including tumor necrosis factor signaling pathways, cancer signaling pathways, nucleotide-binding oligomerization domain-like receptor signaling pathways, Toll-like receptors signaling pathways, and osteoclast differentiation, apoptosis, T-cell receptor signaling pathway, and other related pathways. The molecular docking results showed good binding of the main active ingredients to the core targets.This study shows that the treatment of osteoarthritis involves multicomponent, multitarget, and multipathway processes. The mechanism of anti-inflammatory, antioxidant, inhibition of cartilage matrix degradation, and reduction of subchondral bone destruction may be an important mechanism for the therapeutic effect. Epidemiological studies have found–4 that more than 50% of people over 65 years of age suffer from OA to varying degrees, with 8%–10% of these patients becoming disabled due to the disease, and the annual cost is increasing significantly, which places a heavy burden on families and society. Modern medical treatment of the disease mainly includes nonsteroidal anti-inflammatory drugs, glucocorticoids, analgesics, and joint injections, which only improve the symptoms but do not slow down the progression of the disease.,6 The disease belongs to the category of “paralysis” and “bone paralysis” in Chinese medicine. According to Chinese medical theory, the pathogenesis of the disease is based on the deficiency of the Bushen Huoxue formula in the ligaments.Osteoarthritis (OA) is a common degenerative bone disease characterized by synovial lesions, cartilage degeneration, subchondral bone collapse, periarticular osteophytes, muscle atrophy and weakness, and progressive joint pain, swelling, restricted movement, and even deformity.,10 Modern pharmacological studies have found that tonifying the kidney and invigorating the blood improve periarticular microcirculation, reduce the release of inflammatory factors,,13 scavenge oxygen-free radicals, inhibit the apoptosis of chondrocytes, and promote the repair of synovial membrane and chondrocytes for the treatment of OA.,17 Although clinical and basic studies have confirmed that tonifying the kidney and invigorating the blood can effectively improve the symptoms of OA, it is difficult to systematically analyze the relationship between components, targets, and pathways in purely experimental studies, whereas network pharmacology is based on bioinformatics and studies the relationship between “multicomponent-multitarget-multipathway,” which is in line with the complexity of the Chinese medicine compound.–21 Therefore, this article investigates the mechanism of action of the Bushen Huoxue formula in the treatment of OA by using network pharmacology and molecular docking techniques, and provides a scientific basis for revealing the effects of this Chinese medicine formula in the treatment of OA.Bushen Huoxue formula was first seen in Dacheng of traumatology. It is composed of 10 g of Radix rehmanniae, Radix Bupleurum, Semen Cuscutae, 3 g of Cortex Eucommiae, Fructus Lycii, Radix Angelicae sinensis, Cornu cervi pantotrichum, Herba Cistanches, Myrrh and Rhizoma dulcis, and 2 g of Safflower. The whole formula is effective in tonifying the kidneys and strengthening the bones, activating blood circulation, resolving blood stasis, and relieving swelling and pain. It has become a classic traditional Chinese medicine prescription for the treatment of OA. The formula is effective in relieving clinical symptoms such as swelling and pain in the joints, improving physical function and joint mobility, and improving the quality of life of patients in the middle and late stages.https://tcmspw.com/tcmsp.php) was used to search the active ingredients of the formula for tonifying the kidney and invigorating the blood: Radix Rehmanniae, Semen Cuscutae, Cortex Eucommiae, Fructus Lycii, Radix Angelicae Sinensis, Cornu Cervi Pantotrichum, Herba Cistanches, Myrrh, Radix Dulcis, and Rhizoma Hong Hua, and according to the absorption, distribution, metabolism, excretion module, the active ingredients with oral bioavailability oral bioavailability ≥ 30% and drug-like index drug-like ≥ 0.18 were screened. The molecule identifications were entered into the Related Targets to find the corresponding biological targets. Among the active ingredients of bone marrow, the literature was reviewed to screen for those associated with OA. The names of the active ingredient targets were corrected with standard names using the Uniprot database (https://www.uniprot.org). The targets corresponding to the active ingredients of each drug were deweighted and imported into the Cytoscape 3.7.2 software to construct a “drug-active-ingredient-target” network.The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) platform  and the DrugBank database (https://go.drugbank.com/), the results were combined and deduplicated using “Osteoarthritis” as the search. The results of the 2 databases were combined and deduplicated using the GeneCards database and the DrugBank database, using “Osteoarthritis” as the search term. The names of the disease targets were corrected to standard names using the Uniprot database.Using the GeneCards database (https://bioinfogp.cnb.csic.es). The intersecting targets were imported into Cytoscape 3.7.2 software, and the “active ingredient-target” network diagram was drawn.The targets corresponding to the active ingredients of the formula were intersected with the targets corresponding to OA and visualized by VENNY 2.1  network maps.http://david.ncifcrf.gov/), and gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out, and the results were presented as bar graphs and bubble plots, respectively. In addition, the results were imported into the Cytoscape 3.7.2 software to obtain the “active ingredient-key target-signaling pathway” map.The 96 intersecting targets between the Bushen Huoxue formula and OA were imported into the Database for Annotation, Visualization and Integrated Discovery database (http://www.rcsb.org/), imported from Schrödinger software. Ligprep was used for compound ionization, desalting, generation of tautomers, setting of stereoisomers, and other pretreatments. Then docking with the optimized protein receptor was performed using the glidedock module. The results are expressed in terms of the binding energy generated by docking the compound to the protein. In general, the lower the binding energy of the compound to the protein receptor, the stronger its affinity.The 8 active ingredients with the highest degree of “active ingredient-intersection target” were molecularly docked to the 4 core targets with the highest degree of PPI network. The compounds required for docking were downloaded and checked in PubChem compound identification record and processed through Chembio 3D for Minimize Energy. The required 4 core targets and corresponding protein data banks  were obtained from the Research Collaboratory for Structural Bioinformatics database  in the PPI network were docking with the eight active components  with the highest moderate value in the "active component intersection target" network. The molecular docking was performed with the 8 active ingredients  with the highest values in the “active ingredient-intersection target” network. The docking results are shown in Table ut Figs. .,23 after intraperitoneal injection of quercetin in osteoarthritic rats, it can activate the silent information regulator/AMP-activated protein kinase signaling pathway to inhibit endoplasmic reticulum stress-induced chondrocyte apoptosis. In addition, quercetin reduced serum IL-1β and TNF-α cytokine concentrations in OA mice, reducing the inflammatory response and maintaining the integrity of articular cartilage. β-sitosterol, dousterol, and coumarin are phytoestrogen-like substances that inhibit osteolysis of cartilage and subchondral bone by regulating nuclear factor kappa-B and osteoprotegerin/receptor activator of NF-κB/receptor activatorof NF-κB ligand signaling pathways, maintaining a dynamic balance between bone resorption and bone formation in the subchondral bone of the joint, thereby delaying the progression of arthritis.–26 Lignans and isochondroitin dihydroflavonoids are natural flavonoids with strong anti-inflammatory effects. It was found that lignans can effectively inhibit OA chondrocyte proliferation, downregulate the expression of c-Jun N-terminal kinase and p38 mitogen-activated protein kinase in chondrocytes, downregulate the expression of nitric oxide, TNF-α, and IL-6, reduce the inflammatory response, protect chondrocytes, and slow down cartilage degeneration. It also inhibited the expression of nitric oxide synthase and cyclo-oxygenase-2, as well as inhibiting mitogen-activated protein kinase-related extracellular signal-regulated kinase and p38 signaling pathways to reduce inflammation in osteoarthritic chondrocytes.,29 Baicalin can improve pain and expand a range of motion in OA model dogs, whereas targeted regulation of AKT1 reduces collagen degradation by decreasing the expression of proinflammatory factors IL-1β and TNF-α and decreasing the expression of cartilage matrix degradation-related enzymes matrix metalloproteinase-13 and a disintegrin and metalloproteinase with thrombospondin motif-5. The active ingredients of the Bushen Huoxue formula mainly include flavonoids and phytosterols, which delay chondrocyte apoptosis and reduce the destruction and resorption of articular cartilage and subchondral bone through anti-inflammatory and antioxidant pharmacological effects.In order to investigate the mechanism of action of this formula in the treatment of OA, this study used network pharmacology as the research method and molecular docking to validate the research results. A total of 194 active ingredients of the Bushen Huoxue formula were screened, and 96 key targets were obtained by intersecting the corresponding targets of active ingredients and OA targets. According to the “active ingredient-intersecting targets” network diagram, the highest active ingredients of Kidney and Blood Formula for OA treatment include quercetin, β-sitosterol, lignan, kaempferol, isoptin dihydroxyflavone, dousterol, coumarin, and baicalin. This reflects the multicomponent and multitarget characteristics of the Bushen Huoxue formula for OA treatment. Quercetin has anti-inflammatory and antioxidant effects. Animal studies have found that,TNF-α and IL-6 interact with each other and both promote the proliferation of synovial cells, which stimulates the production of matrix metalloproteinases and other cytotoxins and leads to bone erosion and cartilage destruction. Moulharat et al found that TNF-α increased the production of cyclooxygenase-2, IL-6, IL-8, monocyte chemoattractant protein-1, and regulated upon activation normal T cell expressed and secreted in chondrocytes, as well as prostaglandin E2 and nitric oxide levels, increased levels of matrix degrading proteases MMP-1, MMP-3, MMP-9, MMP-13, ADAMTS-4, and a disintegrin and metalloproteinase with thrombospondin motif-5, and inhibited synthesis of type II collagen and aggregates, leading to degradation of chondrocyte matrix components, collagen breakage, and joint destruction. In addition, both stimulate recruitment of articular leukocytes and receptor activator of NF-κB ligand differentiation, promote osteoblast maturation and differentiation, reduce chondrocyte proliferation, lead to disruption of bone homeostasis in articular cartilage and subchondral bone, and aggravate the progression of OA. The phosphoinositol-3 kinase-protein kinase B signaling pathway is one of the important pathways involved in apoptosis, and AKT1 is the most closely associated downstream factor with chondrocytes in this pathway. Current studies have found that AKT1 regulates neutrophils to induce their release of more inflammatory factors, and in addition, it inhibits pyrophosphate levels in chondrocytes to promote cartilage calcification.The targets screened for PPI intersection network core protein degree values greater than 50 were TNF, IL6, AKT1, and MAPK8. TNF and IL6 play a key role in the progression of inflammation. The current study found that TNF-α, IL-1, and other inflammatory factors, causing local inflammatory damage to the joint. In addition, TLR4 in the pathway binds to damage factors released from injured tissues, activating nuclear factor kappa-B and promoting the release of inflammatory factors, further aggravating the degree of joint damage. The subchondral bone of the joint disperses the mechanical load on the joint, while providing nutrients to the adjacent cartilage through the terminal vessels and secreting cytokines to maintain cartilage metabolism. It is found that the activation of osteoclast differentiation leads to the secretion of various proteases and cytokines, resulting in bone resorption, bone destruction, loss of trabeculae, and fracture of the subchondral bone, disrupting the biomechanical balance of the subchondral bone, and promoting the release of inflammatory factors. The pathological changes in the synovial, cartilage, and subchondral bone are not described in this article.GO analysis results show that the key target gene-enrichment biological processes in the treatment of OA with the Bushen Huoxue formula are mainly related to inflammation, positive regulation of nitric oxide biosynthesis process, degradation of extracellular matrix, collagen catabolic process, etc., which are all related to inflammation, oxidative stress, destruction of articular cartilage, and subchondral bone. TLRs are a general term for a group of pattern recognition receptors, and it has been found that activation of these pathways can produce 1, except for IL-6 and β-sitosterol, which means that the active ingredients of the Bushen Huoxue formula can bind to the core target protein stably, indicating that the Bushen Huoxue formula can exert its therapeutic effect on OA by inhibiting the release of inflammatory mediators, regulating chondrocyte apoptosis, and modulating immune activity.The molecular binding energy between ligand and receptor was found to be less than −7 KJ mol−Although our study explored the mechanism of Bushen Huoxue formula in the treatment of OA, there are still some limitations. First of all, the research data are obtained from the literature, so we can not determine the authenticity and integrity of the results. Secondly, the quality of literature is not high enough, and there are few risk assessment projects that can be clearly put forward. In addition, further experiments are needed to confirm the prediction results.In this article, the mechanism of the formula for treating OA by tonifying the kidney and invigorating the blood was demonstrated through a network pharmacology approach and validated by molecular docking technology. It was demonstrated that the formula exerts its therapeutic effects on OA through multiple components, multiple targets and multiple pathways. The active ingredients in the formula, including quercetin, β-sitosterol, lignan, kaempferol, isopsoralen dihydroxyflavone, dousterol, coumarin, and baicalin, act on key target proteins such as TNF, IL6, AKT1, and MAPK8 to regulate the TNF signaling pathway, TLR signaling pathway, osteoclast differentiation, and other signaling pathways, thus acting as anti-inflammatory, antioxidant, inhibiting cartilage matrix degradation, and reducing subchondral bone destruction. It is a synergistic treatment for OA by inhibiting the degradation of cartilage matrix and reducing the destruction of subchondral bone.We thank Prof. Yufeng Wang for his critical guidance and problem solving for this thesis and all the people in this study.Main analysis and drafting of the article: Tingting Pang, Yufeng Wang, and Li Dong.Research design: Chang Liu and Huijuan Lou.Introduction and discussion sections: Jiahui Li, Siyuan Lei, and Jiangchun Zhang.Article preparation: Junjie Yao and Zhongxu Li.All authors wrote, read, and approved the article. Conceptualization: Yufeng Wang and Li Dong. Data curation: Tingting Pang, Chang Liu, and Huijuan Lou.Formal analysis: Tingting Pang, Junjie Yao, Jiahui Li, and Siyuan Lei.Funding acquisition: Tingting Pang and Chang Liu.Investigation: Tingting Pang, Junjie Yao, Yufeng Wang, and Li Dong. Methodology: Tingting Pang, Yufeng Wang, and Chang Liu.Project administration: Yufeng Wang and Li Dong.Resources: Tingting Pang, Chang Liu, Junjie Yao, and Zhongxu Li. Software: Tingting Pang, Jiahui Li, and Jiangchun Zhang.Supervision: Yufeng Wang and Li Dong.Validation: Yufeng Wang, Li Dong, Junjie Yao, and Jiahui Li.Visualization: Chang Liu.Writing-original draft: Tingting Pang, Yufeng Wang, and Jiahui Li.Writing-review and editing: Tingting Pang, Yufeng Wang, and Li Dong.


Safety has always been an important issue affecting the development of traditional Chinese medicine industry, especially for toxic medicinal materials, the establishment of risk prevention and control measures for toxic herbs is of great significance to improving the use of traditional Chinese medicine in clinical. Fuzi is a kind of traditional Chinese medicine and its toxicity has become the most important obstacle of limit in clinical using. In this paper, network pharmacology and molecular docking technology were used to analyze the main toxic components of Fuzi, the key targets and the mechanism of neurotoxicity. We carried out CCK-8 and WB assays, and detected LDH release and SDH activity. It was verified that aconitine caused neurotoxicity through a variety of pathways, including MAPK signaling pathway, pathways related to Akt protein, destruction of cell membrane integrity, damage of mitochondrial function affecting energy metabolism and apoptosis. What’s more, this study confirmed that aconitine could produce neurotoxicity by promoting apoptosis of hippocampus neuron and decreasing its quantity through Nissl Staining and TUNEL assay. This paper found and confirmed multiple targets and various pathways causing neurotoxicity of Fuzi, in order to provide reference for clinical application and related research. Aconitum Carmichaelii Debeaux , known as Chinese aconite, Chinese wolfsbane and monkshood, is the lateral root of  Debeaux . Fuzi isonchitis . Althougonchitis . The curIn 2007, the concept of “network pharmacology” was first proposed , in the In this study, on the basis of related literature reports, we aim to research the mechanism of neurotoxicity of Fuzi, using network pharmacology and molecular docking method, in order to provide reference for clinical application and related research. The workflow of this study is shown in http://herb.ac.cn/) and SymMap database (http://www.symmap.org/) were used to get all active compounds of Fuzi. Then, we retrieved the targets associated with active compounds from CTD database (http://ctdbase.org/), and our filter was set to “Interaction Count >1”. We collected all the targets, deleted duplicates and got candidate toxic compounds of Fuzi and potential target genes.HERB database (https://www.genecards.org/) and set “Relevance score ≥10” as a filter. Then the results of the two were combined and duplicated targets were removed in order to get potential targets of neurotoxicity.We searched potential targets of neurotoxicity by using “toxic encephalopathy” and “nerve toxicity” as the key words in GeneCards database (https://bioinfogp.cnb.csic.es/tools/venny/) to match the candidate targets of toxic compounds and the potential targets of neurotoxicity, and intersection target genes were the potential targets in the toxic effect of Fuzi on nerve.We used Venny 2.1.0 database (https://cn.string-db.org/) to build the PPI network interaction. Cytoscape 3.9.1 was used to construct and visualize the PPI network. CytoHubba, a network topology analysis plug-in in Cytoscape, was used for analyzing topology parameters of each target. The value of “Degree” was used as a reference for the importance of the core targets.All the targets were uploaded to String database  for performing GO biological process and KEGG pathway enrichment analysis (p < 0.05). GO enrichment analysis includes Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) analysis. KEGG is a bioinformatics resource for mining significantly altered metabolic pathways enriched in the gene list. GO and KEGG pathway analysis were visualized using the R programming language.Intersected target genes were uploaded to DAVID database (https://pubchem.ncbi.nlm.nih.gov/) was used to download the SDF format of key toxic compounds, and OpenBabel software was used to transfer into MOL2 format. The 3D structure of the protein was downloaded in PDB database (http://www.rcsb.org/).PubChem database , the chemical structure is shown in In Situ Cell Death Detection Kit was purchased from Roche . Alcohol and xylene were obtained from SINOPHARM . And paraformaldehyde was purchased from Sangon Biotech .Dulbecco’s Modified Eagle’s Medium (DMEM) was purchased from Hylone . 10% Fetal Bovine Serum (FBS) was obtained from GIBCO . The Cell Counting Kit-8 (CCK-8) was bought from Sigma-Aldrich . Penicillin-Streptomycin Solution was obtained from Allcare Biomedical Development . The primary antibodies against p-MAPK, MAPK1, p53, p-Akt, Akt, Caspase-3 and GAPDH were purchased from Cell Signaling Tech. . Trypsin, Tris-base, glutathione (GSH), acrylamide, SDS, AP, THMED and other reagents were obtained from Amresco (United States). Nissl Staining Solution and LDH Assay Kit were bought from Beyotime . SDH Assay Kit was obtained from Nanjing Jiancheng Bioengineering Institute . The 2.SH-SY5Y cells were obtained from cell bank in Shanghai Institutes for Biological Sciences . They were cultured in DMEM medium supplemented with 10% (v/v) FBS and 1% (v/v) penicillin-streptomycin solution, and maintained at 37°C in a humidified chamber with 5% CO3 cells per well. After incubating in a cell incubator overnight, SH-SY5Y cells were exposed to aconitine in concentrations of 0, 25, 50, 100, 200, 300, 400 μM for 24 and 48 h. The wells added amount of medium is the blank group. Then, 10 μL of CCK-8 solution was added to each well of the plate. After 1 h of incubating, we measured the optical density (OD) at 450 nm wavelength with a microplate reader. Each experiment was repeated three times. (Cell viability = [(ODExperiment group - ODBlank)/(ODControl group - ODBlank)]×100%)Cell viability was measured using CCK-8 assay. SH-SY5Y cells were seeded into 96-well plates in the concentration of 5×105 cells/well) were seeded into 6-well plates and were exposed to aconitine in concentrations of 0, 100, 200, 400 μM for 24 h. Cell proteins were extracted after treatment with RIPA lysis buffer. The BCA method was used to calculate the content of protein. According to the relative molecular mass of target protein, we prepared 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). An equal amount of the protein (20 μg per sample) was loaded onto SDS-PAGE for separation and transferred onto polyvinylidene difluoride (PVDF) membranes. Then we used 5% BSA for 1 h to block the membranes and used the primary antibodies including anti-p-MAPK, anti-MAPK1, anti-p53, anti-p-Akt, anti-Akt, anti-Caspase-3 and anti-GAPDH overnight at 4°C. After washed with TBST three times and further incubated with HRP-conjugated secondary antibodies for 1 h at room temperature, the protein bands were detected by the CheniDoc MP Imaging System.SH-SY5Y cells  of aconitine (1 mg/kg), while each rat in group II, which served as control, received an equal volume of normal saline.Twenty rats of SPF grade were used in this study. All rats were about six weeks old and divided into two equal groups for different experimental analysis. Each rat in group I  was given intraperitoneal injection  and xylene for 5 min (twice), and then fixed using neutral gum. The number of hippocampus neuron was visualized with an optical microscope at 40 × and 100 ×  magnification. Then we used ImageJ software to count normal nerve cells.In Situ Cell Death Detection Kit. After being dewaxed, rehydrated and washed with distilled water three times, the sections were stained according to the manufacturer’s instructions and apoptotic cells were stained brown due to the binding of dUTP enzyme to their fragmented DNA. We used an optical microscope at 40× and 100 × magnification to observe apoptosis of hippocampus neuron. Then, we used ImageJ software to calculate the number of apoptotic cells.Apoptosis cells were measured by using p < 0.05 was considered statistically significant.All statistical analyses were preformed using GraphPad prism 8.0. Differences in multiple groups were analyzed by ANOVA and We used HERB and SymMap database to predict the toxic compounds of Fuzi, and a total of 177 active compounds were retrieved. All the active compounds were input into CTD database for compound retrieval, and 22 candidate toxic compounds of Fuzi were obtained. After we combined the targets of all toxic compounds and deleted the duplicates, a total of 1810 corresponding targets were obtained. Then toxic compounds with corresponding targets greater than 10 were selected as candidate core toxic compounds .In the GeneCards database, 252 potential targets were searched by using “toxic encephalopathy” as the key word, and 290 potential targets were searched by using “nerve toxicity” as the key word. All the collected target genes were merged and duplicated. Then we obtained 474 potential targets of neurotoxicity.Matching the candidate targets of toxic compounds and the potential targets of neurotoxicity, 133 genes were selected as potential targets in the toxic effect of Fuzi on nerve. Then, String database was used to identify the PPI network of the 133 intersection target genes, as shown in We uploaded the PPI network in the figure above to Cytoscape 3.9.1 and used the Network Analyzer function in the software to analyze the topology parameters of each target .The top 10 are selected as the core targets according to the ranking of Degree value, followed by ALB, AKT1, TP53, IL6, TNF, INS, CASP3, IL1B, EGFR and BDNF. The visualization results are shown in Subsequently, we used Cytoscape3.9.1 to construct the relationship network between candidate toxic compounds of Fuzi and the corresponding 10 core targets in p-value, as shown in To explore the toxic mechanisms, we imported 133 intersection targets into DAVID database for GO and KEGG pathway enrichment analyses. A total of 184 GO functional items were obtained, including 124 biological processes (BP), which mainly involved response to nutrient levels, gliogenesis, neuron death and aging, etc. There were 18 cellular components (CC), mainly including membrane raft, membrane microdomain, organelle outer membrane and vesicle lumen, etc. It also included 42 molecular function (MF), focusing on signaling receptor activator activity and receptor ligand activity. The first 10 items of enrichment results were visualized according to We screened 170 major signaling pathways in KEGG pathway enrichment results. The high ranking enriched pathways involved pathways of neurodegenerative multiple disease, MAPK signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, and various infection and tumor-specific pathways. Other pathways included lipid and atherosclerosis, Hepatitis B and Hepatitis C, endocrine resistance, apoptosis and so on. The most significant enriched 20 pathways in KEGG analysis were shown in Among them, MAPK1, TP53, TNF, EGFR, INS, CASP3, IL1B and BDNF in the core targets were all related to MAPK signaling pathway, which is an important mechanism of Fuzi leading to neurotoxicity.In order to further verify the molecular mechanism of neurotoxicity of Fuzi, we chose uracil, palmitic acid and aconitine, the key toxic substances of Fuzi, to preliminarily simulate their binding to the core targets .−1, this compound has a good binding activity with the core target protein, while when the binding energy is less than −7.0 kJ mol−1, this compound has a strong binding activity with the core target protein .Network pharmacology analysis showed that aconitine was the main toxic substance causing neurotoxicity of Fuzi, so we further investigated the toxic effect of aconitine on SH-SY5Y cells. The results of CCK-8 assay showed that aconitine at 300 and 400 μM concentrations could decrease SH-SY5Y cell viability following 24 h treatment and aconitine at all the different concentrations  significantly decreased SH-SY5Y cell viability after 48 h treatment, and the cell viability rate gradually decreased with the increase of concentration, indicating that aconitine had significant toxic effect on SH-SY5Y cells . MoreoveAccording to the results of the KEGG enrichment analysis, MAPK signaling pathway was an important mechanism of neurotoxicity caused by Fuzi. Therefore, we further evaluated the expression levels of p53, p-MAPK and MAPK1, which are the core target proteins in MAPK signaling pathway. As shown in Molecular docking results showed that Akt1 and Caspase-3 proteins were the core proteins of neurotoxicity caused by aconitine. Therefore, we assessed the expression level of p-Akt, Akt and Caspase-3 by western blot. As shown in LDH is a stable cytoplasmic enzyme and the release of LDH is a key feature of the plasma membrane damage. Compared with the control group, aconitine at different concentrations  significantly increased the release of LDH in SDH, belonging to cytochrome oxidase, is the only multi-subunit enzyme integrated on the membrane in the TCA cycle. It can provide electrons for the respiratory chain of mitochondrial oxygen demand and productivity, and is a marker enzyme of mitochondrial. As shown in The Nissl Staining result is shown in Compared with the control group, we can see a large number of TUNEL positive cells (brown) in the experimental group obviously. Its expression area is wider and its dyeing is heavier . The resBased on network pharmacology, molecular docking and experimental verification, this study systematically analyzed the potential mechanism of neurotoxicity caused by Fuzi, constructed the relationship network between candidate toxic compounds and neurotoxicity targets, and predicted the main toxic substances, potential targets and signaling pathways.Network pharmacology analysis showed that uracil, palmitic acid and aconitine might play a crucial role in the neurotoxicity of Fuzi. Uracil is an important component in RNA, and fluorouracil, an antitumor drug with similar structure, has long been reported to have obvious neurotoxicity . PalmitiThe result of PPI protein interaction network analysis showed that the potential target genes for neurotoxicity of Aconite were ALB, AKT1, TP53, IL6, TNF, INS, CASP3, IL1B, EGFR and BDNF. AKT1 is the core target of PI3K/AKT signaling pathway. MAPK1, TP53, TNF, EGFR, INS, CASP3, IL1B and BDNF are related to MAPK signaling pathway. GO function and KEGG pathway enrichment analysis also confirmed that MAPK signaling pathway played an important role in the neurotoxicity induced by Fuzi.Molecular docking results displayed that aconitine, the main toxic substance of Fuzi, exhibited good affinity with key targets ALB, AKT1 and CASP3, and the conformation of the binding site was stable. Based on the above results, the main toxic substance aconitine was selected for subsequent experimental verification, and its potential mechanism was discussed in depth.https://www.genome.jp/kegg/pathway.html), we found that MAPK and p53 proteins are mainly involved in the classical MAPK pathway, JNK and p38 MAPK pathwayand p53 signaling pathway, and we suspected they are the main signaling pathways of neurotoxicity caused by Fuzi.The results of WB assay confirmed that aconitine affects the transmission of MAPK signaling pathway by inhibiting the MAPK protein phosphorylation and promoting the expression of p53. In KEGG PATHWAY database (At the same time, the WB results showed that aconitine can inhibit the phosphorylation of the key protein Akt and promote the expression of Cleaved-caspase-3. Akt is the core protein of PI3K-Akt signaling pathway, and can control a variety of downstream signaling pathways, therefore, by adjusting the phosphorylation of Akt, Fuzi affects PI3K-Akt signaling pathway and controls various signaling pathways to cause neurotoxicity. And Cleaved-caspase-3, a kind of cysteine protease, is one of the key enzymes in the cell apoptosis pathways. Fuzi can increase its expression, and then promote the apoptosis of nerve cells in the resulting in neurotoxicity.Aconitine can also increase LDH release and reduce SDH activity. The increase in LDH release suggests that aconitine can destroy the integrity of neuronal cell membrane, and the decrease in SDH activity suggests that aconitine leads to mitochondrial damage, while the damage of cell membrane and mitochondrial dysfunction are the characteristics of irreversible cell damage . AconitiMitochondrial dysfunction can lead to energy deficiency, which is an important mechanism for neurotoxicity . Early sThe hippocampus is the most vulnerable areas of the brain and plays an important role in learning function impairment, memory loss and cognitive dysfunction . ApoptosIn fact, the mechanism of these pathways is not a single work, but the interaction influence causes neurotoxicity of Fuzi. However, these signaling pathways also play an important role in treatment in addition to inducing toxicity . We are Earlier studies have demonstrated that the toxicity of Fuzi mainly derives from diester diterpenne alkaloids including aconitine , which hin vivo (In our study, some drawbacks should be noted. Our research only contains the toxic components that have been confirmed at present and whether there are other toxic compounds in Fuzi needs to be further studied. In addition, most of the Chinese herbal medicine plays a role in treatment by the form of oral, so its efficacy and toxicity are closely related to metabolites. Unfortunately, our research didn’t predict all the bioactive metabolites of Fuzi in vivo .In recent years, gut microbiota has emerged as a new frontier to understand the development and progress of diseases, especially it can influence the development and diseases of the central nervous system along “microbiota-brain-gut axis” . ArachicThis research analyzed the potential mechanism of neurotoxicity caused by Fuzi through building the relationship network of “compounds - targets - neurotoxicity” using network pharmacology and molecular docking. The results showed that aconitine, the core toxic compound of Fuzi, caused neurotoxicity through multiple targets and multiple ways, including MAPK signaling pathway, pathways related to Akt protein, destroying cell membrane integrity, damaging mitochondrial function and affecting energy metabolism and cell apoptosis. At the same time, aconitine can promote apoptosis of hippocampus neuron and decrease its quantity, thus producing neurotoxicity. Our research provided reference for clinical application and related research.