Pueraria Lobata Radix (RP) and Salviae Miltiorrhizae Radix (RS) in the treatment of type 2 diabetes mellitus (T2DM) based on network pharmacology and molecular docking.  To explore the potential molecular mechanism of  The chemical constituents and core targets of RP and RS were searched by Traditional Chinese Medicine System Pharmacology (TCMSP); target genes related to T2DM were obtained through GeneCards database, component target network diagram was constructed, intersection genes of active compounds and T2DM were synthesized, protein-protein interaction (PPI) relationship was obtained, and core targets were screened by using Cytoscape 3.7.2. Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed utilizing R studio 4.0.4 according to David database. Based on molecular docking, the screened active components of RP and RS were verified by molecular docking with the core target using Discovery Studio 2019.  There were totally 92 components and 29 corresponding targets in the component target network of RP and RS drug pair, of which 6 were the core targets of RP and RS in the treatment of T2DM. Molecular docking results showed that the active compounds of puerarin, formononetin, tanshinone iia, and luteolin had better binding activity with AKT1, VEGFA, NOS3, PPARG, MMP9, and VCAM1, respectively. Among them, puerarin showed significant effects in activating NOS3 pathway and luteolin exhibited significant effects in activating MMP9 pathway, respectively. The main biological processes mainly including xenobiotic stimulus, response to peptide, gland development, response to radiation, cellular response to chemical stress, response to oxygen levels, and the main signal pathways include response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, and response to oxygen levels.  Network pharmacology is an effective tool to explain the action mechanism of Traditional Chinese Medicine (TCM) from the overall perspective. RP and RS pair could alleviate T2DM via the molecular mechanism predicted by the network pharmacology, which provided new ideas and further research on the molecular mechanism of T2DM. In recent decades, the prevalence of T2DM has gradually increased worldwide. The prevalence of DM in the world has increased by 102.9% from 1990 to 2010 and is the country with the largest number of diabetic patients in the world [Diabetes mellitus (DM) is a group of metabolic diseases characterized by increased blood glucose due to genetic and environmental effects, resulting in defective insulin secretion and reduced sensitivity of target cells to insulin. Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, mainly associated with the accumulation of lipids in the insulin The important cause of T2DM is the disorder of lipid metabolism, therefore, it is especially important to control blood sugar and regulate blood lipid in the process of T2DM treatment in time . At prese, single-target development of drugs is difficult to treat T2DM and prevent the occurrence of complications such as glucose disorders and gastrointestinal tract, thus reducing the quality of life of patients .Traditional Chinese Medicine (TCM) treatment of T2DM is based on single-target superposition and, multicomponent and multitarget synergistic toxicity dispersion effect to achieve better efficacy and lower toxicity group, which is a complex theoretical system to achieve multidimensional regulation from a whole . DM beloPueraria Lobata Radix (RP) and Salviae Miltiorrhizae Radix (RS) are a common pair of medicine contained in the famous Chinese medicine book “Shi Jinmo on Medicine.” RP is the dried root of Pueraria lobata (Willd.) Ohwi, which has the functions of quenching thirst, raising yang, relieving diarrhea, relieving muscle and fever, and activating meridians [Salvia miltiorrhiza Bunge, which has the effects of activating blood circulation, cooling the blood and clearing the heart, and removing irritation and calming the mind [eridians . RS is athe mind . The comthe mind , 11. Modthe mind . The maithe mind .Pueraria Lobata Radix and Salviae Miltiorrhizae Radix (RP-RS) paired exhibits varies effects on T2DM or diabetes related diseases [Previous studies have been demonstrated that diseases , 14–17. http://temspw.com/temsp.php), according to the set the bioavailability (OB) > 20 and drug-like properties (DL) > 0.18, screening out eligible active ingredients and related targets.The chemical components of RP and RS were obtained with the help of the Traditional Chinese Medicine System Pharmacology Analysis Platform  database, with “Type 2 diabetes mellitus or T2DM” as the search term, the targets related to T2DM were obtained, and the correlation value (relevance score > 18) was used as the screening condition, and the screening results were used as candidate target genes of T2DM.Through the GeneCards (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to obtain the intersection genes of the active ingredient target and the T2DM target, and imported into STRING11.0 (http://www.string-db.org/) to obtain the protein interaction relationship. Using Cytoscape 3.7.2 draws the interaction network, analyzes the key targets by degree value, and constructs the interaction network of RP-RS in the treatment of T2DM.The effect of drugs on disease treatment is ultimately manifested in protein interactions. The Venn diagram , the targets of RP-RS for the treatment of T2DM were collected and imported into R studio 4.0.4 (The species was limited to “Human”). GO functional annotation and KEGG pathway enrichment analysis were performed on the intersection target genes of RP-RS and T2DM, respectively (P < 0.05). Potential targets were analyzed for biological process (BP), cellular component (CC), and molecular function (MF), and KEGG was used for pathway enrichment analysis of potential targets. To validate the anti-T2DM mechanism of RP-RS across the key targets and multiple pathways, the KEGG mapper functional analysis was used to mark the target genes on the pathway associated with T2DM.GO enrichment is a commonly used method for analysis of omic data, which is usually used to discover the enrichment degree of GO term in differential genes. Through GO enrichment analysis, the genes in the difference table can be classified according to their functions to achieve the purpose of annotation and classification of genes . KEGG ishttps://www.rcsb.org/) database, use PyMOL 2.5 to perform protein and ligand separation, dehydrogenation, water addition, and other operations on the original PDB protein molecule, and use AutoDock 1.5.7 software. The above compound and protein formats were converted to pdbqt format using Discovery Studio 2019 software for molecular docking and mapping of compounds and core targets.According to the analysis of network pharmacology results, molecular docking software was used to predict the key targets of RP-RS intervention on the main active components. The 3D structure of the compound was constructed using Chem 3D of ChemOffice software and saved in.mol2 format. Download the protein structure of the target from the PDB , MOL012297-puerarin , MOL07154-tanshinone iia , and MOL000392-formononetin , respectively .P < 0.05)  . BP main < 0.05) , mainly The key targets predicted by network pharmacology were AKT1 (1H10), VEGFA (1BJ1), NOS3 (1M9J), PPARG (2VV4), MMP9 (1GKC), and VCAM1 (1VSC), respectively. Molecular docking results of the top 4 main active ingredients and 6 core targets are shown in Current research on T2DM has focused on several pathological alterations such as obesity-related insulin resistance and defective insulin secretion as well as decreased B-cell mass through B-cell apoptosis, and has a close association with inflammatory alterations, immune gene defects, and impaired mitochondrial function , 21. In β-cell apoptosis [β-cells in diabetic mice, and increased serum insulin levels; and its protective effects on β-cells may be mediated by modulating the phosphatidylinositol 3-kinase/protein kinase B pathway, thereby exerting hypoglycemic effects and improving glucose tolerance [κB in human monocytes and the release of the proinflammatory factor TNF-α, with potential preventive and therapeutic activity in diabetes mellitus [In this study, a holistic analysis was performed using network pharmacology from multigene, multipassage, and multitarget, and 92 active ingredients of RP-RS for the treatment of T2DM were identified; the most important of which were puerarin, formononetin, tanshinone iia, and luteolin. The phytochemicals are estrogenically active polyphenolic nonsteroidal phytochemicals, which are commonly found in various plants. It has been shown that formononetin has therapeutic effects on diabetes, such as formononetin exhibited hypoglycemic effects in mice with tetraoxacillin-induced type 1 diabetes by inhibiting pancreatic poptosis . It was poptosis . Pueraripoptosis . It was olerance . In prevolerance . In addiolerance  and diabolerance . Previoumellitus . It was mellitus . Recent mellitus . Therefoα and FoxO1 to stimulate gluconeogenic gene expression [α protein in hepatocytes [α leads to inhibition of fatty acid oxidation in the liver, promoting PGC-1α activity in insulin-resistant hepatocytes may be able to eliminate lipid imbalance in T2DM patients [2+ in the early stages of diabetes, which leads to increased intracellular flow and activation of NOS, causing more endothelial cells to be produced. The end effector molecule of B-cell damage is NO, and NO can cause apoptosis by inducing endoplasmic reticulum stress-related apoptotic factors, indirectly damaging islet cells, and affecting insulin synthesis, and the activity of NOS will be reduced in diabetic patients in the later stages, while plasma NO levels will be reduced [α and IL-6 production, and the expression of these proinflammatory cytokines induces an increase in MMP9 expression in an autocrine and paracrine manner [Core targets including AKT1, VEGFA, NOS3, PPARG, MMP9, and VCAM1 have been identified as important by network pharmacological analysis. AKT1 is involved in several regulatory processes, including glucose metabolism, and AKT mediates insulin signaling and interacts with the transcription factors PGC-1pression . AKT is atocytes . This papatients . It was patients . It was patients . The NOSpatients . It has  reduced . PPARG,  reduced . Previou reduced . DM is n reduced . Hypergle manner . Studiese manner . VCAM1 ie manner . In addie manner .Through GO functional annotation analysis of the intersection target genes, it was found that RP-RS may regulate T2DM through various BP, cellular composition CC and MF. The GO enrichment analysis showed that RP-RS exhibits efficacy mainly via xenobiotic stimulus, response to peptide, response to oxygen levels, membrane raft, membrane microdomain, transcription requlator complex, DNA-binding transcription factor binding, RNA polymerase I-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding, etc. The KEGG pathway enrichment results revealed that a total of 136 signaling pathways were enriched, mainly related to response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, response to oxygen levels, and so on. The results of molecular docking validation showed that the key components screened by network pharmacology showed stable binding to the core targets NOS3 and MMP9, among which, the docking scores of puerarin and NOS3 were the highest, followed by those of luteolin and MMP9, and the stronger the interaction, the more stable the conformation of the compounds, indicating that puerarin showed good effects in activating NOS3 and luteolin exhibited good effects in activating MMP9 pathways, respectively.In conclusion, a total of 92 active ingredients of RP-RS were obtained. The active components of RP-RS mainly including puerarin, formononetin, tanshinone iia, and luteolin which are closely associated with T2DM. A total of 29 intersecting target genes was acquired between drugs (RP-RS) and diseases (T2DM). Among them, the core target genes in treatment of T2DM mainly including VEGFA, MMP9, AKT1, NOS3, VCAM1, and PPARG, respectively. In addition, puerarin showed significant anti-T2DM effects in activating NOS3 and luteolin exhibited significant anti-T2DM effects in activating MMP9 pathways, respectively. The biological function exhibits efficacy mainly via positive regulation of xenobiotic stimulus, response to peptide, response to oxygen levels, membrane raft, and membrane microdomain. The signaling pathways that exerting their therapeutic effect on T2DM mainly including response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, and response to oxygen levels. From the perspective of modern molecular biology, it was confirmed that RP-RS has good therapeutic effect on T2DM, the multitarget and multipath effects of TCM may rely on its therapeutic characteristics.


Evodia rutaecarpa is a tree native to Korea and China, and its fruit (hereafter referred to as Evodia) exhibits remarkable antitumour properties. However, little is known about its mechanism of action in NPC. In this study, we employed network pharmacology to identify targets of active Evodia compounds in nasopharyngeal carcinoma and generate an interaction network.  Nasopharyngeal carcinoma (NPC), a neoplasm of the head and neck, has high incidence and mortality rates in East and Southeast Asia.  The active ingredients of Evodia and targets in NPC were obtained from multiple databases, and an interaction network was constructed via the Cytoscape and STRING databases. The key biological processes and signalling pathways were predicted using Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses. Molecular docking technology was used to identify the affinity and activity of target genes, and The Cancer Genome Atlas and Human Protein Atlas databases were used to analyse differential expression. Cell Counting Kit-8 (CCK-8) and Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) dual-fluorescence staining were used for experimental verification.  Active Evodia compounds included quercetin, isorhamnetin, and evodiamine, and important NPC targets included MAPK14, AKT1, RELA, MAPK1, JUN, and p53, which were enriched in lipid and atherosclerosis signalling pathways. Additionally, we verified the high affinity and activity of the active compounds through molecular docking, and the target proteins were verified using immunohistochemistry and differential expression analyses. Furthermore, CCK-8 assays and Annexin V-FITC/PI dual-fluorescence staining showed that isorhamnetin inhibited the proliferation of NPC cells and induced apoptosis.  Our results identified the molecular mechanisms of Evodia and demonstrated its ability to alter the proliferation and apoptosis of NPC cells through multiple targets and pathways, thereby providing evidence for the clinical application of Evodia. Nasopharyngeal carcinoma (NPC) is an Epstein–Barr virus (EBV) associated epithelial head and neck neoplasm, with the highest incidence and mortality rates occurring in East and Southeast Asia and a 5-year survival rate of only approximately 50% for patients with advanced disease , 2. NPC Tetradium ruticarpum (A.Juss.) T.G.Hartley (Rutaceae)) is the dry and nearly mature fruit of rutaceous plants (Evodia rutaecarpa (Juss.) Benth. var. Officinalis (Dode) Huang or E. rutaecarpa (Juss.) Benth. var. bodinieri (Dode) Huang). Recent studies have shown that the main Evodia compounds are alkaloids, bitterin, volatile oil, and flavonoids, among which quercetin, evodiamine, and isorhamnetin are the main active compounds [The use of traditional Chinese medicine (TCM) to treat NPC is attaching increasing attention. TCM has fewer side effects and adverse reactions than Western chemotherapeutic drugs and presents a lower risk of patients developing drug resistance. Evodia  database and analysis platform to retrieve the chemical composition of Evodia . To obtaTo obtain NPC targets, we used “nasopharyngeal carcinoma” as a keyword for searches of the following five databases: GeneCard, OMIM, PharmGkb, the therapeutic targets database, and DrugBank. We merged the search results, deleted duplicate items, and obtained all NPC-related target genes.We matched the target-prediction results of the effective active Evodia compounds with the search results of NPC-related target genes and then used the Venn R package to compile a Venn diagram comprising targets of the effective active Evodia compounds and NPC target genes. We extracted cross-target Evodia genes and NPC targets as a common gene set to determine potential Evodia compounds for the treatment of NPC and then analysed the results.The screening results for the active Evodia compounds were imported into Cytoscape along with the NPC target-screening results  to constThe nodes in the network represent a protein target or active compound, and the edges represent the interaction between these biomolecules. A node's impact increases with the number of direct connections with other nodes. The centrality between nodes is a measurement of the shortest path between them, and the closeness centrality is defined as the reciprocal of the average value of the shortest distances between a node and all other nodes in the network. The larger the value, the greater the centrality of the node, which also relates to the speed at which a signal is transmitted from one node to another , 16. TheHomo sapiens species, a confidence level of 0.7, and hidden disconnected nodes in the network, we performed a PPI network analysis and downloaded the results in TSV format.A PPI is formed when two or more protein molecules form a complex through non-covalent bonds. PPIs and the resulting networks are crucial in most biological functions and processes . The STRThe file was then imported into Cytoscape to conduct topology analysis, and CytoNCA was employed to calculate the six parameters used to evaluate the topological properties of the nodes within the interaction network . These iGO and KEGG pathway enrichment analyses were conducted to analyse the target genes. An absolute value of the logarithmic fold change (>0.5) was the criterion for judging the significance of differentially expressed genes , 20. Thehttps://autodock.scripps.edu/) were used to optimize the structures of key compounds and targets, which were used to determine three-dimensional (3D) chemical structures and perform energy minimization and format conversion [Target protein structures were obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB), and the two-dimensional structures of the active Evodia compounds were obtained from the PubChem database . ChemBionversion . PyMol wBased on the high specificity of antibody binding to an antigen, immunohistochemistry can reveal the relative distribution and abundance of proteins. Immunohistochemical data were obtained from the Human Protein Atlas (HPA), and the expression of target genes in normal and cancer tissues was compared . Transcrμmol·L−1) of isorhamnetin and a positive control  were used to treat NPC CNE-2 cells. Cell Counting Kit-8 (CCK-8) assays were used to detect the proliferation of CNE-2 cells, and Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) dual-fluorescence staining was employed to detect the apoptosis rate [Different mass concentrations . The experimental verification results were processed using SPSS software , and data were visualized using GraphPad Prism software . The false discovery rate (FDR) method was used to adjust the P-value for multiple comparisons, and statistical significance was set to P < 0.05 (FDR <0.05).R software (v.3.6.1) was used to conduct statistical analyses. Unpaired In the TCMSP database, 30 active Evodia compounds were selected via screening, where OB greater than 30% and DL greater than 0.18 were the restrictive conditions . TogetheAdditionally, 495 Evodia targets were obtained from the TCMSP database. These genes were integrated through the UniProt database, and invalid and duplicate targets were deleted. In addition, 170 target genes associated with the effective active compounds were acquired .By integrating information from the GeneCard, OMIM, PharmGkb, TTD, and DrugBank databases, we obtained 8,953 gene targets for NPC . We mappβ-sitosterol, evodiamine, goshuyuamide, and rutaecarpine as the primary Evodia compounds associated with potential NPC targets.We used Cytoscape software to draw a diagram of the active compounds in the Evodia-NPC-target interaction network. The docking results for the 24 active Evodia compounds and 153 NPC targets  resultedWe then used STRING to process the NPC targets of Evodia compounds to construct a PPI network, resulting in a network of 127 nodes and 1,006 edges . ScoringP < 0.05 (Q < 0.05), resulting in the identification of 2,118, 56, and 181 GO targets related to biological processes, cellular components, and molecular functions, respectively. The biological processes included responses to drugs, generation of reactive oxygen species (ROS), cellular response to chemical stress, response to lipopolysaccharides, and response to molecules of a bacterial origin. The top 20 GO terms are displayed in Figures We used the Bioconductor R software package to conduct GO enrichment analysis of key targets. The screening was performed using a threshold of KEGG enrichment analysis of the 153 Evodia targets showed that they were enriched in 177 signalling pathways, including those related to hepatitis B, fluid shear stress and atherosclerosis, chemical carcinogenesis-receptor activation, and lipid and atherosclerosis, each of which play important roles in NPC development and progression. We displayed the top 20 KEGG enrichment results in Figures −1, indicating that they possessed good binding activity. The results identified hydrogen bonding and π-π-stacking interactions, which suggested potential favourable interactions between the compounds and NPC targets.Based on the KEGG enrichment analysis, the top six target proteins in the PPI network and their corresponding active compounds were selected for molecular docking. Data for the following proteins were downloaded from the PDB: MAPK14 (PDB ID: 2RG6), AKT1 (PDB ID: 7NH5), RELA (PDB ID: 5URN), JUN (PDB ID: 5T01), MAPK1 (PDB ID: 7NR9), and p53 (PDB ID: TBWN). Docking of the 3D structures with their respective compounds was visualized using PyMol, with lower binding energy designating a high-affinity interaction . The binTo verify the potential of the identified proteins as therapeutic targets, we analysed the differential expression of proteins in NPC tissues and cancer-adjacent tissues. Immunohistochemical results from the HPA database showed that JUN expression in cancer tissues was higher than that in normal nasopharyngeal tissues, whereas antibody staining levels of MAPK14, AKT1, and MAPK1 in cancer tissues were lower than those in adjacent tissues. However, we observed no significant differences in RELA and p53 expression between tumour and normal nasopharyngeal tissues .We used the TCGA database to expand the sample size for verification and obtain more accurate results. The box plot of the differential expression analysis  showed tμmol·L−1) for 24, 36, and 48 h significantly inhibited cell proliferation compared with cisplatin treatment /Akt/mTOR, Wnt/pathways . Additiopathways  and indupathways , 39. FurIsorhamnetin is a representative flavonoid compound that exhibits anti-tumour activity primarily by inhibiting tumour cell proliferation, inducing apoptosis, and inhibiting signal transduction. For example, Cai et al. reported that isorhamnetin inhibits the proliferation and metastasis of androgen-independent prostate cancer cells by targeting the endogenous apoptotic pathway and selectively inhibiting PI3K/AKT/mTOR signalling .Natural products are highly complex, and their effects on the treatment of diseases may result from the combined action of multiple compounds. Our results showed that quercetin, evodiamine, and isorhamnetin were associated with many NPC targets. GO enrichment analysis showed that NPC targets of Evodia compounds were involved in biological processes related to NPC development, including drug response, generation of ROS, and cellular response to chemical stress. Furthermore, Chaaben et al. reported that dysfunctional gene-driven redox stress pathways may increase the risk of disease in NPC-susceptible individuals .κB, and p53, which are important in cell proliferation, apoptosis, migration, and invasion. A previous study showed that LOX-1 alters ROS production, leading to lipid peroxidation and DNA damage, and promotes atherosclerosis and cancer progression, suggesting that LOX-1 inhibition may be a promising therapeutic strategy [Uncontrolled tumour cell proliferation is related to abnormal gene expression and signal transduction. The current findings suggested that Evodia may interfere with these pathological processes to inhibit NPC cell proliferation and promote their apoptosis. Our KEGG pathway analysis showed that Evodia compounds affect NPC via multiple signalling pathways that overlap with lipid- and atherosclerosis-related signalling pathways. Analysis of these signalling pathways revealed 37 common biomarkers, including lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1), AKT, nuclear factor (NF) strategy . TherefoRELA is an important target gene in NF-κB signalling; Lu and Yarbrough [RELA dephosphorylation inhibits tumour development [RELA methylation and subsequent activation of multiple downstream genes contribute to the regulation of breast cancer progression [The generated PPI network identified six targets for molecular docking studies, namely MAPK14, AKT1, RELA, JUN, MAPK1, and p53. AKT1 is a key regulator of PI3K/AKT signalling and participates in cell proliferation and apoptosis . MAPK14 arbrough  reportedelopment , and othgression , 51. JUNgression . TP53 isE. rutaecarpa has achieved remarkable results in clinical practice and is an important component in the combination therapy of NPC with other drugs. Reports on the mechanisms of action of natural plants in the treatment of NPC are scarce; therefore, this study offers new insights into the study of Evodia and its applications as adjuvant therapy. Elucidation of the mechanisms through which E. rutaecarpa exerts curative effects will be beneficial. In this study, we identified the effective active components of Evodia and target molecules in NPC. Our findings suggested that continued evaluation of evodiamine and other active components is warranted for their possible application in treating NPC and for supporting the scientific basis of using natural plants to treat NPC.As a TCM,


Picrorhizae Rhizoma (PR) extract based on network pharmacology and molecular docking. The potential chemicals of PR were screened through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and relevant literatures. Corresponding targets of active ingredients were found with the help of the UniProtKB database, and therapeutic targets for cancer action were screened with the help of the GeneCards database. We used Cytoscape software to construct the compound-target-pathway network of PR extract. We utilized the STRING database to obtain the protein-protein interaction (PPI) network. We used DAVID database combining Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, molecular docking was employed for initial efficacy checking. We have identified 16 potential active components of PR through screening, involving 112 disease action targets. Utilizing the GeneCards database, 112 intersecting targets between PR extract and cancer were found, which mainly exerts anticancer effects by regulating tumor necrosis factor (TNF), recombinant caspase 3 (CASP3), c-Jun NH2-terminal kinase (JNK)/JUN, epidermal growth factor receptor (EGFR), and estrogen receptor-1 (ESR1) with some other target genes and pathways associated with cancer. The major anticancer species are prostate cancer, colorectal cancer, small cell lung cancer, etc. In the molecular docking study, herbactin had a strong affinity for TNF. Based on network pharmacology and molecular docking studies, PR and their compounds have demonstrated potential anticancer activities against several key targets. Our preliminary findings provide a strong foundation for further experiments with PR constituents.This study sought to explore the anticancer mechanism of  Picrorhizae Rhizoma (PR) is a perennial herb in the Scrophulariaceae family with a similar name to Rhizoma Coptidis (RC), and both are products of cold clearing heat and dampness, improving the removal of gastrointestinal dampness and treating dampness and dampness, which are the same herbal medicines for dampness and laxity and dysentery, distributed in Sichuan, Yunnan, Tibet, and Himalayas, with main birth in India. PR is mainly effective in clearing heat, etc. Modern pharmacology has shown that PR has antidiabetic [diabetic , blood gdiabetic , hepatopdiabetic , 4, protdiabetic , protectdiabetic , 7, and Simultaneously, PR extract has been found to have potential against a wide range of tumors and cancers such as renal cancer , esophagIn this study, the methods of network pharmacology and molecular docking were applied to predict the multitarget multipathway synergy of PR extract against cancer. Technically, the synergistic effects with multicomponents may target multipathways and multitargets as most of traditional Chinese medicine has been reported previously. And that artificial intelligence-based platforms such as network pharmacology and molecular docking offer new perspectives for studying and applying TCM in mainstream medicine within a limited amount of time and resources. This study comprehensively and systematically reveals their mechanism of action, which is of great significance for the development and utilization of anticancer efficacy of PR, and provides reference for basic and clinical cancer research. The study step flow diagram is presented in As per the hypothesis and objective of the study, we have used several bioinformatics software, tools, and reference databases during analyses .The active components of PR were identified from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The screening conditions were OB  ≥ 30% and DL (similarity of patent medicine) ≥ 0.18. After screening combined with literature mining, the reported active ingredients were collected for supplementation.TCMSP input into PR was used to search and curate compounds action targets. The targets of action of the active ingredient were imported into the UniProtKB database into corresponding gene names, and the human gene (“Homo sapiens”) was screened. If no information can be found in UniProtKB, use PubChem database to check the simplified molecular-input line-entry system (SMILES) number to Swiss Target Prediction database to predict the possible target information. Further, targets with a likelihood greater than 0.1 were filtered and duplicates were removed.Find keywords “cancer,” “neoplasm,” and “tumor” from the GeneCards database. Delete duplicate genes. The active components and disease targets of drugs were matched. Further, Venny (version 2.1) software was used to draw a Venn diagram to determine the potential anticancer targets according to active ingredients of PR.The obtained common anticancer targets were imported into STRING database in the form of gene symbols, the interactions between proteins were analyzed, and the protein-protein interaction (PPI) network diagram of PR extract core targets for the treatment of cancer was obtained. The species was selected as “Homo sapiens,” and PPI with a minimum interaction value (“minimum required interaction score”) > 0.4 were selected. Hide free dots, download PPI graphics, and save as “tsv.” format.Then, the Cytoscape software (version 3.8.2) was used to construct a network map of drug and disease targets to show the relationship between PR extract and various cancer targets, to explore the mechanism of the anticancer effect of PR extract.P values. Screening GOTERM_CC_DIRECT, GOTERM_BP_DIRECT, and GOTERM_MF_DIRECT, the top 15 for each of direct are summarized into a new table. KEGG pathway selects the top 20 data, all ordered in the order sample group, gene ratio, Q value, count, and description, where description is equivalent to term and Q value is equivalent to FDR. And output the results as a bubble plot in the ImageGP online sketch drawing software.The GO functional enrichment analysis was performed to further analyze the roles of target proteins of TCM compounds in gene function with a rough understanding of their differential gene enrichment. Then, KEGG pathway enrichment analysis was performed to know the genes and their pathway, which help to understand the significantly changed metabolic pathways under the experimental conditions. Active ingredient corresponding targets and cancer-related common targets were entered into the DAVID database. GO enrichment analysis of target genes and KEGG pathway enrichment analysis were performed. Data were downloaded and ranked from small to large based on the corrected The 3D chemical structure of PR extract was downloaded from PubChem. The crystal structures of target proteins were obtained from the Protein Data Bank. The key active ingredients and core targets after screening were subjected to molecular docking validation. The protein receptor was optimized by PyMOL software to remove the attached ligands, heteroatoms, and water molecules before docking , 15. ThePicrorhizae Rhizoma” compounds were retrieved, and 55 effective compounds were identified. A total of 10 active ingredients were screened based on OB ≥ 30% and DL ≥ 0.18. Six reported active ingredients were collected in combination with literature mining [Through TCMSP, “e mining . A totalThrough TCMSP data platform, UniProtKB database, and PubChem database, a total of 203 targets were collected from the 16 active ingredients of PR. After screening of human genes (“Homo sapiens”), there were 183 targets. The repeated targets in each compound were removed, and the gene names of 112 related targets were obtained.By using the GeneCards database, 29162 cancer genes  S1 were P value is less than 1.0e − 16. Among them, nodes represent proteins, and each edge indicates a protein-protein interaction relationship. The greater the number of lines, the stronger the association  items, 69 cellular component (CC) items, and 101 molecular function (MF) items. The top 15 items of each component were imported into ImageGP for visualization  signaling pathways. As shown in The role of PR extract in treatment was further verified by molecular docking technology and further verified the results of network pharmacology. A total of 30 key targets were screened using PPI networking and analyzed by Cytoscape. The average shortest path, intermediate number, and degree of freedom in the network are calculated. TNF, CASP3, JUN, EGFR, ESR1, and HSP90AA1 which are the top six proteins with a large intermediate number, large degree value, and small average shortest paths were chosen for molecular docking with the screened sixteen active components. Recorded docking scores indicated that all candidates displayed docking scores between 0.11 and -6.51 kcal/mol. According to AutoDock software, 0.11 kcal/mol displayed candidates have the lowest activity, where -6.51 kcal/mol displayed candidates which have the highest affinity for the specific target enzyme. When the binding energy of ligand and receptor is less than 0, it indicates that it can bind spontaneously. Then, select the best compound from each target and visualize it, as shown in β-Sitosterol can form hydrogen bonds with the amino acids of EGFR (MET-795), and HSP90AA1 (SER-72) residues combine to form hydrogen bonds. These interactions allow proteins to form stable compounds with compounds. The results suggest that the active ingredient herbacetin can form hydrogen bonds with the amino acid residues of TNF . (5S)-5,9-Dihydroxy-4-(4-hydroxyphenyl)-5,6-dihydro-1-benzoxo can form hydrogen bonds with the amino acid residues of CASP3 . Hederagenin can form hydrogen bonds with the amino acid residues of JUN (GLU-293), and ESR1 (LYS-416) and can bind well with the corresponding target proteins. β-sitosterol, hederagenin, picroside IV, scrophuloside A_QT, herbacetin, and other 16 anticancer active ingredients were confirmed. Among them, herbacetin belongs to flavonoids, which are widely distributed and have a variety of biological activities. It can induce apoptosis of HepG2 cells and play an anticancer role. Hederagenin belongs to triterpenoids, which have a wide range of physiological activities. Pharmacological properties have been shown to be anti-inflammatory and hepatoprotective from antitumor aspects. Hederagenin can inhibit gastric, cervical, and colon cancer cells [β-Sitosterol belongs to tetracyclic triterpenes and is a natural small molecule with antitumor effects. Scrophuloside A is a phenolic glycoside, and phenolic glycosides can also prevent tumors. Picroside I, picroside IV, picroside III, picroside II, catalpol, 6-feruloylcatalpol, and aucubin all belong to iridoids, which are widely distributed in traditional Chinese medicine.In this study, the method of network pharmacology was used to explore the complex network of multicomponent, multitarget, and multichannel anticancer potency of PR extract. First, several compound target databases and disease target databases were searched; 16 main active components and 112 anticancer and antitumor targets of PR extract were identified. Based on the network pharmacology method, picroside I, er cells –19. β-SiThirty key targets such as TNF, EGFR, CASP3, and ESR1 were identified. The pathways in the cancer signal pathway are closely related to the anticancer effect of PR extract. The binding activity was simulated based on molecular docking, and the results showed that all of them had binding activity. Currently, molecular docking has been widely used by academicians, drug developers, and pharmaceutical companies to assess the potency of compounds against target enzymes associated with diseases or disorders with minimal resources and time , 21. Nevβ-sitosterol, hederagenin, scrophuloside A_QT, picroside IV, and herbacetin have the highest correlation with the target pathway. These ingredients can play multiple roles in the human body to achieve the effect of disease prevention and treatment. Through the visualization of PPI protein network analysis and Cytoscape software, we can see that the key targets of PR extract on cancer are TNF, CASP3, JUN, EGFR, ESR1, HSP90AA1, PPARG, PTGS2, MTOR, etc. TNF is a tumor necrosis factor, which is a cytokine that can directly kill tumor cells, but has no obvious toxic effect on normal cells. It is also one of the most potent bioactive factors to kill tumors. It is produced by activated macrophages, NK cells, and T lymphocytes and can inhibit osteoblasts and stimulate osteoclasts. It can be used as a cytokine for tumor biotherapy [It is interconnected with many disease targets in the anticancer and antitumor target network of active components of PR. It can be seen from this that picroside I, otherapy . TNF is otherapy , 23. CASotherapy . Similarotherapy . Loss ofotherapy –28. ESR1otherapy , 30. HSPotherapy –36. It notherapy . PPARG iotherapy –41. PTGSotherapy –45. MTORotherapy  is a tarThrough GO enrichment analysis, it is known that the anticancer effect of PR of extract is related to the association of cytoplasmic membrane and enzymes. The cytoplasmic membrane is an extremely thin layer of membrane that surrounds the cell surface, mainly composed of membrane lipids and membrane proteins. The basic role of the cytoplasmic membrane is to maintain the relative stability of the intracellular microenvironment. It also participates in the external environment for the exchange of materials, energy, and information and overall plays an active role in both the survival and differentiation of cells. Of the 112 targets that we screened, 68 all had effects on the cytoplasmic membrane. Of the top 30 targets in degree value, 20 all had an effect on the cytoplasmic membrane.Δ32 deletion and human cytomegalovirus infection with colorectal cancer in Tunisia [According to KEGG pathway analysis, 27 target genes are associated with cancer pathways contributing to the development and proliferation of metastatic cancer. Of these, 11 target genes are associated with colorectal cancer, 10 target genes with prostate cancer, and 10 target genes with small cell lung cancer. Through literature, it is known that estrogen plays a role in liver and breast cancer. Estrogen protects against liver cancer through genomic pathways, rapid transduction pathways, noncoding RNAs, tumor microenvironment, estrogen metabolites, and inhibition of hepatitis infection and replication . The exp Tunisia . Kaposi  Tunisia , relieve Tunisia , inhibit Tunisia , suppres Tunisia , promote Tunisia , and pro Tunisia . In addiβ-sitosterol has strong binding force with EGFR and HSP90AA1. Overall, flavonoid herbacetin exhibited the highest binding or docking score against TNF . Analyzing the interaction mode between proteins and ligands, it can be concluded that the extracts of PR can bind well to these selected targets and obtain lower binding energy mainly by forming multiple hydrogen bonds. In addition, molecular docking models provide evidence for how these compounds act on targets to inhibit cancer. It is preliminarily confirmed that many effective components of PR play a therapeutic role on cancer through key targets.Based on molecular docking scores, herbacetin, (5S)-5,9-dihydroxy-4-(4-hydroxyphenyl)-5,6-dihydro-1-benzoxocin-2-one, picroside I_qt, and hederagenin comparatively exhibited strong binding affinity against TNF, and In this study, the therapeutic effects of PR extract on cancer and tumor were preliminarily analyzed by means of network pharmacology and molecular docking. The potential anticancer and antitumor targets, related signal pathways, and biological processes of PR extract were predicted. It is revealed that the anticancer and antitumor effects of PR are the result of the joint action of multiple components, multiple targets, and multiple pathways. Network pharmacology of traditional Chinese medicine is the development and integration of ancient Chinese medicine and modern medicine in the interdisciplinary fields of network, pharmacology, biology, and computer. In network visualization, the main active components, anticancer and antitumor targets, and pathways of PR extract can be deduced from a large array of data integrations and calculations, which provides a theoretical basis for anticancer and antitumor research. This study provides a theoretical basis for the anticancer and antitumor mechanism of PR extract and its future experimental verification, in order to serve as a reference for later drug development and clinical application.


E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of  Automated design and build processes can rapidly accelerate work in synthetic biology and metabolic engineering. Here the authors present Galaxy-SynBioCAD, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology that they use to build and execute Galaxy scientific workflows from pathway design to strain engineering through the automated generation of scripts driving robotic workstations. Computation has become an essential tool in life science research. Synthetic biology, metabolic engineering and industrial biotechnology make no exception to that trend. As part of this endeavor, significant attention is being paid to the development of workflows adhering to design principles from engineering such as standardization and abstraction of modular parts, as well as the decoupling of design from fabrication.1. As an example, Cello2 applies the EDA approach to genetic circuits. Cello comprises several steps, which are connected and therefore need to use standardized input/output formats. Among those formats are Verilog to represent a logic function and Eugene3 to encode a set of parts and constraints between the parts. Although Cello achieved the compilation and standardization of several pieces of software for genetic design, in general, this is not true for most freely available synthetic biology and metabolic engineering design tools, where the fragmentation remains a significant barrier to adoption. Nonetheless, two main standards have emerged in the past two decades. The first, SBML4 is a biological modeling standard that has been developed by the systems biology community to encode strains and pathways. The second, SBOL5, is a data exchange standard specific to synthetic biology. SBOL has been developed to document genetic components  and their interactions for the purpose of biodesign engineering. SBOL can now encode complex genetic circuits, metabolic pathways, vectors, and plasmids. Complying with SBML and SBOL standards a suite of in silico genetic circuit design tools was recently proposed6.Following the electronic design automation (EDA) concept, there are many design automation tools for genetic circuits, these are extensively reviewed in Appleton et al.7. Briefly, from a given target compound and a given chassis strain, the first step consists of finding metabolic reactions that link the target compound to the native metabolites of the host strain. This step is carried out by retrosynthesis software tools13 and, should one wish to search for novel pathways or find pathways that produce unnatural target compounds, requires the use of reaction rules14. The result of retrosynthesis is a metabolic map and there is a need in a second step to enumerate the pathways linking the chassis metabolites to the target. There are many solutions for pathway enumeration and search15, which are sometimes integrated into the retrosynthesis software itself. The third step is to find the most promising enzyme sequences catalyzing the metabolic reactions. This can be achieved either through similarity search to enzyme annotated metabolic reactions18, or machine learning trained on metabolic databases20. Once the pathways have been annotated with enzyme sequences, they can be ranked in a fourth step. The ranking criteria are diverse, they can be among others based on thermodynamics21, predicted yield of the target22, target rate of production through flux balance analysis21, chassis cytotoxicity of the target and intermediates21, along with simpler criteria like pathway length.As for genetic circuits, there are plenty of software tools to assist the biosynthetic pathway design process23 to compute RBS sequences for different strengths, and design of experiments (DoE)25 to sample the space of possible constructs, which can be very large. The result of that step is a library of layouts representing either the same or different pathways. At this stage, one can either synthesize the whole layout DNA or, as it is most commonly done, synthesize individual DNA parts and use combinatorial DNA assembly methods to include variations of the control elements, such as promoters and RBS sequences. Several computational tools can be used to perform this sixth and last step of assembly design before constructing the pathways, these tools compute parts to be synthesized depending on the chosen assembly protocol. Computation tools to help the build tasks are sparser than for design. One can cite here Aquarium26, which provides instructions to a person or a robot to perform the assembly tasks along with Antha27, BioBlocks28, and DNA-BOT29. Engineered pathways are generally evaluated using HPLC or mass spectrometry analyses. Here too, computational tools can help in particular the workflows produced by OpenMS30 or Worlflow4Metabolomics31.In addition to the enzyme identities, there are multiple layout solutions and settings to engineer the top-ranked pathways. Indeed, the individual genes coding for the enzyme can be placed under different promoters, in a different order within an operon, with different RBS strength (if the chassis is a bacteria), and on different plasmids with different origins of replication if the engineering is performed on a plasmid. The fifth step deals with this issue by making use of tools such as the RBS calculatorConsidering the above, we are clearly at a stage where the pathway engineering process is not that far from being fully driven by computer software products. However, there are several hurdles that prevent this from happening even for tools covering pathway design only. First, the tools are not easily findable, they are stored in different places and the keywords to search online are not obvious. Secondly, some of the tools are difficult to access, some requiring registration, purchase, or access fees. Thirdly, almost none of the tools are interoperable and cannot be chained one after another to ensure that computational experiments are communicated well, and hence reproducible. Lastly, and perhaps most problematic for wider acceptance, the tools can be difficult to comprehend, requiring a level of expertize that limits their use by a large community.32. Indeed, without programming skills, scientists that need to use computational approaches are impeded by difficulties ranging from tool installation to determining which parameter values to use, to efficiently combining and interfacing multiple tools together in an analysis chain. Scientific workflows provide solutions where data is combined and processed into a configurable, structured set of steps. Existing systems often provide graphical user interfaces to combine different technologies along with efficient methods for using them, and thus increase the efficiency of the scientists using them. In addition, workflow systems generally provide a platform for developers seeking a wider audience and broad integration of their tools, and can thus drive forward further developments in a specific field of research. Among existing workflow platforms, Galaxy is a system originally developed for genome analysis33 which now includes more than 8500 tools that can be found in the public ToolShed34.Scientific workflows help to address these issues by providing an open, web-based platform for performing findable and accessible data analyses linked to experimental protocols for all scientists irrespectively of their informatics expertize, along with interoperable and reproducible computations regardless of the particular platform that is being used35, a Galaxy set of tools for synthetic biology, metabolic engineering and industrial biotechnology. It allows one to easily create workflows from the incorporated toolset or use already developed shared workflows. The portal is a growing community effort where developers can add new tools and users can evaluate the tools performing design for their specific projects. The tools and workflows currently shared on the Galaxy-SynBioCAD portal cover an end-to-end metabolic pathway design and engineering process from the selection of strain and target to automated DNA parts assembly and strain transformation.Here, we introduce the Galaxy-SynBioCAD portalTo develop an integrated ecosystem, we selected software applications from among the computational tools mentioned above. Several criteria were used for this selection: the tools needed to (i) be relevant for pathway design and engineering, (ii) be published, (iii) be open-source , (iv) be well documented and deposited in GitHub, (v) make use of standard input/output, and (vi) exist as a standalone command-line tool. Within a workflow, each tool connected to one or more tools must share common file format for data exchange, i.e., each output file of a tool has to be compatible with the input file format of downstream tools in the workflow. The file format relies on the nature of the data  and the implementation choice made for each tool. Among the standard formats used, some are rather generic  while others are more specific to a scientific field .32, all selected tools are open-source with code available on GitHub and installable through the Conda package manager36 (cf. Tools design and integration process in the ‘Supplementary_Text’ file). Therefore, any user can install the tools needed on their own computer and run these as standalone programs or chain them together to process more complex calculations.The selected tools are further described in the ‘Supplementary_Text’ file . These tools can be divided in three categories: (i) those aimed at finding pathways to synthesize heterologous compounds in chassis organisms , (ii) those aimed at evaluating and ranking pathways  and (iii) those related to genetic design and engineering . Following FAIR principlesA Retrosynthesis workflow to enumerate the pathways enabling the synthesis of a given target chemical in a host chassis organism (cf. Retrosynthesis from target to chassis in Methods section and Retrosynthesis workflow in ‘Supplemetary_Text’ file).A Pathway analysis workflow to score and rank the pathways produced through Retrosynthesis step based on multiple criteria . Some criteria necessitated the development of specific methods for pathways thermodynamics (cf. Thermodynamics in Methods section) and theoretical product flux calculation . Pathway scoring is performed via Machine Learning using a training set of pathways extracted from literature and pathways validated by pathway engineering experts .37, Gibson38, and Ligation Chain Reaction (LCR)39 assembly methods. It also includes Design of Experiment (OptDoE tool) for combinatorial experimental design. The second workflow (BASIC assembly) generates plans for the Biopart Assembly Standard for Idempotent Cloning (BASIC) technique40. This workflow provides a direct link between machine-enabled design and automated assembly. It takes as input a pathway and generates a script to operate an Opentrons liquid handler robot performing assembly and chassis transformation.Two Genetic design and engineering workflows that produce assembly plans for plasmids encoding the pathways generated by the Retrosynthesis or Pathway analysis workflows (cf. Genetic design and engineering workflows in ‘Supplemetary_Text’ file). The first workflow generates plans for Golden GateTo go further in chaining tools, three types of Galaxy workflows are available on the Galaxy-SynBioCAD portal.In the reminder of the paper, a metabolic pathway is a succession of chemical reactions transforming reactants into products, while a construct is the assembly of genetic parts that encodes a metabolic pathway. Several constructs with different enzyme and regulatory sequences can encode for the same pathway. The Retrosynthesis and Pathways analysis workflows generate annotated SBML files describing pathways (cf. Pathway annotation in Methods section), which are taken as input to the Genetic design and engineering workflows to produce constructs, i.e., plasmids encoded in SBOL format along with assembly plans (in CSV files) and liquid handler instructions (python scripts directly executable by Opentrons).Retrosynthesis workflow was run at the Genoscope laboratory  for the production of lycopene in E. coli. We used iML151541 as a model for E. coli. The retrosynthetic map was composed of 12 unique compounds and 7 unique reactions, resulting in 3 “master” pathways (cf. Retrosynthesis from target to chassis in Methods section). Only the 10 best pathways were kept per master pathway, after reaction completion with cofactors and removal of duplicates only 9 pathways remained. Additional details are provided in the Supplementary file ‘Dataset The The Pathway Analysis workflow was run at the University Polytechnic of Valencia, Spain. The workflow took as inputs the list of nine pathways generated by the Retrosynthesis workflow. Results are shown in Fig. Pantoea ananas, enzymes CrtE (UniProt ID: P21684), CrtB (P21683), and CrtI (P21685) were predicted by the Selenzyme tool for the three-reaction pathway  and London . In both cases, as design input we used the top lycopene ranked pathway predicted by the Pathway analysis. Constraining the enzyme search within the organism crtE, crtB and crtI) were assembled in varying gene order in an operon, together with six different RBS-linkers  on a rectangular LB-agar plate (cf. Lycopene production in Methods section). Of these, only 30 (22 red + 8 white) and 33 (21 red + 12 white) constructs gave transformant colonies at Paris and London respectively  the scripts were used for the 88 constructs and to spot 10 µL of the transformed cells . In both the weak (J23116) and the high (J23105) promoter groups, low lycopene production was observed from constructs with more than one high-strength RBS , crtB-crtI-crtE , or crtI-crtE-crtB , constructs with more low-strength RBSes exhibited higher lycopene production. There was also an apparent preference for the crtI-crtE-crtB  among the highest producing constructs. Taken together, these data indicate that maximizing the expression of pathway genes can increase cellular burden, resulting in lower pathway productivity.An analysis of the number of successful transformants obtained in the two laboratories for the different combinations of promoter, RBS, and gene order indicates a preference for the weaker J23116 promoter Fig. . OverexpRBS Fig. . When coCriteria computed by the Pathway analysis workflow like target product flux, thermodynamic feasibility, pathway length, and enzyme availability score inform the user as to the best potential candidate pathway to produce a compound of interest. These criteria can be combined in a global score value. To that end, we developed a machine learning scoring tool  taking training data from literature and a validation trial conducted by metabolic engineering experts (cf. Acknowledgement section for the list of experts enrolled). The process is summarized in Fig. Literature data benchmarking. A list of 77 pathways corresponding to 60 expressed compounds in engineered organisms  was collected from the literature  pairs using the Retrosynthesis and Pathway analysis workflows. The set included the 5874 pathways generated for our literature benchmarking along with 2045 additional pathways corresponding to 20 additional  pairs taken from the LASER database44, which includes some pathways from B. subtilis. We next spliced the set in batches of 5 pathways synthesizing the same target in the same chassis. The predicted pathways best matching the literature pathways (when known) were included and the 4 remaining pathways were drawn randomly. We next recruited 40 experts in the metabolic engineering community (see Acknowledgement section) and asked them to select valid pathways in the list they received. To help the selection process, the experts received a clickable map of the 5 pathways , which returned a global score for each queried pathway. The global score distribution for the dataset of 7919 pathways is given in Fig. The machine learning global scoring process was used to rank the top 50 Galaxy-SynBioCAD pathways generated for 60 target molecules taken from our literature pathway training set. More precisely, a global score was calculated for all SynBioCAD generated pathways using machine learning. Concomitantly, literature or expert selected pathways were identified using the matching algorithm described in the ‘Supplementary_Text’ file. Results are shown in Fig. To assess the advantage of making use of a machine learning scoring schema, we computed a direct score based on (i) the inverse of the pathway length (favoring shorter pathway), (ii) the opposite normalized pathway free energy , (iii) the normalized averaged enzyme availability score, and (iv) the normalized FBA calculated product flux value. All these values were calculated using the Pathway analysis workflow  at different sites, and consequently the possibility of completing multi-partners design and engineering projects.We illustrated our workflows by designing and engineering a library of 88 pathway variants designed to produce lycopene in 45, written protocols without practical guidance can lead to problems, as protocols often contain ambiguities or rely on tacit knowledge. Here the possibility of running several times the same workflow that incorporates automated experiments provides a systematic way to quantify reproducibility  microbiome (380 publications), diseases like cancer (386 publications), and drug design and discovery (96 publications). At our request, a new Galaxy category named ‘Synthetic Biology’ was created, currently comprising 25 tools stored in the ToolShed.The Galaxy-SynBioCAD portal presented in this paper, proposes a set of synthetic biology and metabolic engineering computational tools in a Galaxy framework34, and these workflows could directly be incorporated into the portal to deal with data generated in the ‘Test’ step of the synthetic biology Design-Build-Test-Learn (DBTL) cycle. As mentioned in the introduction, several open-source software products deposited in GitHub47 could address the ‘Build’ step and eventually provide drivers to automated constructions using different robotic workstations beyond those provided by Opentrons. Regarding the ‘Learn’ step in DBTL, the OptDoE tool could easily be adapted to propose new designs as it was done in Carbonell et al.25. Other approaches to be considered are methods that make use of active machine learning as in Borkowski et al.48. Although all design examples provided in the current paper are for engineering pathways in host organisms, because of the recent development of models  for cell-free systems49, one can also consider adapting the portal for design and engineering in cell-free.The offering in Galaxy-SynBioCAD focuses on providing tools for pathway design and engineering. However, as Galaxy-SynBioCAD is a community effort, we anticipate our toolset will grow. Regarding pathway design tools, many of the software products listed in the introduction could be considered to be added to the portal. In particular, strain design including knockout genes to maximize targeted product fluxes could easily be implemented via the flux balance analysis tools. Additionally, there are already Galaxy workflows to take up and analyze metabolomics flow cytometry data in the ToolShed10 retrosynthesis software can easily be adapted to design biosensors. Such an adaptation has been proposed as a Galaxy-SynBioCAD workflow to predict and implement biosensors for the detection of various metabolites using the hydrogen peroxide compound as a metabolic hub, such that the metabolite signals are transduced to hydrogen peroxide before being sensed by the OxyR transcription factor50. Tools for genetic logic circuits engineering could also be considered. Also, as the cell resources consumption due to the level of expression of heterologous genes are not considered in the global score of a pathway, integrating in the overall design pipeline a tool enabling the prediction of the metabolic burden of genetic constructs based on enzyme expression levels would be of great interest and should be targeted for the future.All of the above-suggested additions could be implemented in our portal with relatively small efforts (cf. Tools design and integration process in ‘Supplementary_Text’ file). There are other applications that could be envisioned beyond pathway design and engineering. For instance, as shown in Delépine et al.51. The use of reaction rules allows estimating the outcomes of chemical transformations based on the generalization of reactions available in knowledge databases such as BRENDA52, MetaCyC52, Rhea53, or MetaNetX54. The degree of generalization is controlled by describing the surrounding environment of the reaction center up to a given diameter as described in Duigou et al.14. To ensure the accuracy of the predicted transformations that will outcome from the reaction rules, the RetroRules dataset provided by the Galaxy RetroRules tool has been validated by (i) checking that rules allow to reproduce the template reactions, and (ii) checking that results obtained by decreasing diameters are supersets of results obtained with higher diameters. Only the reaction rules that successfully passed the 2 checks are retained. The validation of this dataset has a success rate of 99.3%.Typically, the target compound, also named “source compound” is the compound of interest one wishes to produce, while the precursors are usually compounds that are natively present in a chassis strain. In the present implementation, the target can be any chemical that could be described by an InChI, and the chassis should be a metabolic model described in an SBML file. Starting from the source compound at the first iteration, reaction rules matching the chemical structure of the source are applied and newly predicted chemicals are generated. Reaction rules are generic descriptions of (bio)chemical reactions encoded into the community standard SMARTS10. Newly produced chemicals are scanned and kept for the next iteration if they are not within the set of available precursors. In that way, a new iteration is started using the previously collected chemicals as the new source set. The iterative process stops when either no new chemicals are discovered or the predefined number of steps is reached. RetroPath2.0 carries out this task.For each reaction rule, a score is calculated based on the ability to retrieve enzyme sequences catalyzing substrate to product transformations, the method is detailed in Delépine et al.The retrosynthesis tools RetroPath2.0 and RP2Paths output a set of pathways composed by chemical transformations based on reaction rules. To obtain reactions, we have to re-build them from template reactions which have been used to generate the rules. In addition, within a pathway one single chemical transformation can reference multiple rules. Such pathways will be called master pathways. For each master pathway, the algorithm takes each transformation and creates one fork per reaction rule referenced. Then for each reaction rule, again the algorithm creates a new fork per template reaction used to build the current rule. The enumeration of all forks create a set of slightly different pathways  for one master pathway. To perform the enumeration, datasets from RetroRules and MetaNetX are used. The reaction completion tool (rpCompletion) takes as input the CSV outputs of RP2paths and RetroPath2.0 and produces a collection of annotated SBML files. Those SBML files are “enriched” with additional information that are not stored as part of the normal SBML schema (cf. Pathway annotation section).Some results generated by the workflow produced in this study cannot be readily stored in the SBML files natively .Using the Minimal Information Required In the Annotation of Models (MIRIAM) conventions, one can store within the SBML file information that instructs the user on the provenance of the reactions and chemical species within the model by cross-references to a wide range of databases. However, this needs a third-party database lookup to match the database ID with its structural information. In this work, intermediate products are generated ad hoc references and may not necessarily have database entries whereas some other information cannot be contained under the MIRIAM annotations as it is: SMILES, InChI, InChIKey for chemical species, reaction rule ID (RetroRules ID), associated template reaction ID, and rule score based on the expected enzyme availability.55. As a result, this enriched file format (denoted rpSBML) is fully compliant with SBML version 3 specifications.As such, we elected to enrich the SBML format in such a way that our information can be stored directly within the SBML file without breaking any standard of the original file. Because SBML files are based on XML, new XML annotations are created outside the standard scope of an SBML file and thus are ignored by any standard SBML readers55. The SBML FBA package is used to define the FBA simulation conditions56. The tools also adhere to the MIRIAM annotation standard for the cross-references of chemical species to public databases57.Standard SBML extensions are also used in this project. The “groups” package is used to link the heterologous reactions and chemical species to identify them easily, as well as classifying the chemical species that are main actors in an heterologous pathway58) for the biomass reaction and record its flux. The upper and lower bounds of the biomass reaction are then set to the same amount, defined as a fraction of its previously recorded optimum. This ensures that any further FBA solution would have a fixed biomass production regardless of the conditions set for further analysis.We need metrics to rank heterologous pathways, this is why we developed an in-house Flux Balance Analysis (FBA) objective to simulate the flux of a target while considering the burden that the production of the target would cause on the cell. Under such simulation conditions, the analysis that returns a low flux may be caused by the starting native compound itself not having a high flux, or the cofactors required having a low flux, while the pathways with high flux would be caused by both the starting compound and the cofactors being in abundance. In either case, bottlenecks that limit the flux of the pathway may be identified and pathways that do not theoretically generate high yields can be filtered out. Furthermore, the production of heterologous molecules in an organism often causes a burden on the growth of the cell. To emulate such a condition, we use the method named ‘fraction of reaction’. We first perform FBA  are ignored. Such species are documented in the SBML file within the group named Thermodynamics is critical in synthetic pathway design by providing quantitative indicators to determine best metabolic pathways among a set of predicted ones. Thus, one can perform thermodynamic analysis to know whether a reaction direction of a pathway is feasible in physiological conditions.59 to compute the formation energy of chemical species and the Gibbs free energy for each chemical reaction and the heterologous pathway.In this work, we performed thermodynamic analysis for species, reactions and pathways. We use eQuilibratorrp_thermo_substituted_species. If a species has no known structure and is not substituted, then the reaction which involves this species will not have a thermodynamics value. Conversely thermodynamics can be computed by eQuilibrator for all reactions for which all species have been identified.For each species involved in a heterologous pathway, the first challenge is to find the corresponding compound in the eQuilibrator database. To find the right compound, we try to exactly match species ID, InChIKey, InChI or SMILES and stop with the first hit. Then, if no compound is found, in the last resort, the first part of species InChIKey is looked for within the eQuilibrator cache and when the result (a list) is not empty, the first compound is taken. If species have no known structure neither in eQuilibrator database nor in any public one, the user has the possibility to specify substitution for identifier, InChI and InChIKey for these species. This substitution is documented in the SBML file with the group named At the level of the pathway, we build a global pseudo-reaction linking chassis substrates to the target molecule and we compute thermodynamics with the eQuilibrator engine for the global pseudo-reaction.60 with a simplex algorithm.c is the objective function, A the stoichiometric matrix, and Building the global pseudo-reaction requires finding the appropriate stoichiometric coefficients such that the intermediate compounds of the pathway cancel out. A linear optimization program Eq.  can be sMNXM are species IDs from MetaNetX, CMPD are intermediate species within the heterologous pathway and not present in the chassis organism, and TARGET is the product of interest. We note that Rxn2 is the reaction to retain and CMPD3 and CMPD4 are species to remove as intermediate compounds. Thus, the parameters of linear solver are:As an example, let’s consider the following 3-reaction set:The solver outputs the following coefficients of reactions:The global pseudo-reaction for the reaction sets becomes:Pantoea ananas, i.e., 553 taxon ID. The combination of polycistronic constructs was built using 2 constitutive promoters (PJ23105 and PJ23116), 2 RBS linkers , 1 backbone (BASIC_SEVA_36_CmR-p15A.1) and enabling CDS permutation, resulting in a theoretical maximal number of constructs of 96 for 3 CDS. The labware IDs and parameters used with DNA-Bot parameters are listed in Tables 47 was developed to be fully compatible with the Opentrons APIv2 and enabling a command-line interface, whilst retaining the option of using an enhanced GUI for direct user control.From amongst the pathway predicted by the Pathway analysis workflow, the top-ranked one was selected with a score of 0.989. The search scope of the Selenzyme tool was restricted to the taxon ID of 40, and cloned into pTwist high copy vector  using Golden Gate. The resulting storage plasmids  were confirmed by sequencing.Lycopene genes were synthesized by Twist Bioscience, flanked by the LMP prefix and the LMS suffix sequenceswww.biolegio.com). For this work the standard BASIC linker set (Biolegio: BBP-18500) was used.Storage plasmids for lycopene genes and assembly vector BASIC_SEVA_36_CmRp15A.1 were prepared using Monarch® Plasmid Miniprep Kit  and E.Z.N.A.® Plasmid DNA Mini Kit . The samples were diluted to 200 ng/µl ready to use in the clip reactions. Plasmids coding for the lycopene pathway variants were constructed using Biopart Assembly Standard Idempotent Cloning (BASIC) method. Five-part BASIC reactions were performed, replacing the dropout mScarlet cassette in the assembly vector (BASIC_SEVA_36_CmRp15A.1) by a promoter and three genes with appropriate linkers. A collection of neutral and functional linkers (encoding RBS sequences) is available in a ready to use 96-well plate format  for 30 cycles , followed by a 5 min incubation at 60 °C at Micalis. Clip reactions were incubated in Opentrons Thermocycler Module for 20 cycles , followed by a 10 min incubation at 60 °C at Imperial. For clip reaction purification, 54 µL of Mag-Bind® TotalPure NGS magnetic beads  or AMPure XP ) were added; 150 µl 70% ethanol was used during wash steps; following re-suspension in H2O, 40 µL of the eluent was transferred to a fresh well. Constructs were assembled in volumes of 15 µL using 1.5 µL of each purified clip reaction in a solution of 1X assembly buffer . Assembly reactions were incubated at 50 °C for 45 min in a thermocycler  or Opentrons Thermocycler Module ). 20 µL of DH5-alpha Competent E. coli  or home-made DH5-alpha competent E. coli  were distributed per well into 96-well plates; then, they were used for transformation reactions. In all, 5 µL of the assembly reactions were mixed with cells. Heat shock was conducted according to the manufacturer’s instructions. SOC media (125 µL) was transferred to each assembly and the reaction incubated at 37 °C for 1 h with lids off. Transformation reactions were spotted on plates (Thermo Scientific™ OmniTray™ Single well) each containing 40 mL LB-agar supplemented with 17.5 µg/mL chloramphenicol. The spotting protocol was run twice in order to spot 2 times 5 µL for each transformation reaction. The spotting step at Imperial was repeated using 40 µL of transformation reaction on a 12-well plate (Costar® 12-well 3737), each well containing 10 mL of LB_agar supplemented with 17.5 µg/mL chloramphenicol. 100 µL of each transformation reaction was plated manually on LB-agar plates containing 17.5 µg/mL chloramphenicol as well.DNA-BOT was executed as described in detail in the Genetic design and engineering workflow execution section. A clip reaction master mix was prepared by combining 3 µL of 10X NEB T4 DNA ligase buffer, 1 µL NEB BsaI-HF v2 (NEB #R3733), 1 µL T4 DNA Ligase , or Promega M1804 ) per 20 µL required for each reaction. The Opentrons OT-2 pipetted the 20 µL master mix for each reaction, plus 1 µL of Biolegio BASIC linkers and 1 µL of each DNA parts, together with sufficient Hg (10 min), washed by re-suspension in 1 mL water, re-pelleted at 5000×g (10 min), and the pellet re-suspended for extraction in 1 mL acetone. The cells in acetone were incubated at 55 °C for 20 min with continuous shaking , centrifuged at 19000 x g (10 min), and the supernatant transferred to a fresh tube. Lycopene absorbance of the supernatant was measured at 474 nm using a quartz cuvette (Hellma 104.002B-QS) in a spectrophotometer  or NanoDrop™ One UV-Vis ), and the pellet was dried at 50 °C for 48 h to determine the gDCW. Absorbance (OD474) was converted to molar concentration value by dividing by 150479, the molar extinction coefficient (ε) of lycopene61. The yield per gram dry cell weight (mg/gDCW) was calculated by dividing the absolute yield (mg) by the weight of the dried cell pellet.We selected 6 colonies at Micalis, and 8 colonies at Imperial, with different levels of red color  and sequenced them. This was followed by lycopene extraction from the 4 and 8 colonies, respectively, that sequenced correctly. To quantify lycopene production, 2 mL of overnight cultures grown in LB (Cm 17.5 μg/mL) were pelleted at 5000×62. The classifier was trained on 7919 Galaxy-SynBioCAD generated pathways (comprising 43392 reactions) used during the expert validation trial where 754 pathways were labeled positive. The training set can be found in the Supplementary file ‘Dataset The purpose of the machine learning model is to predict if a given predicted pathway is a valid one or not. To that end, we developed a classifier based on the XGBoost libraryFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesReporting SummaryPeer Review FileSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3


Folium Ginkgo (FG) against adriamycin-induced cardiotoxicity (AIC) through a network pharmacology approach.  To investigate the mechanism of  Active ingredients of FG were screened by TCMSP, and the targets of active ingredient were collected by Genclip3 and HERB databases. AIC-related target genes were predicted by Genecards, OMIM, and CTD databases. Protein-protein interaction (PPI) network was constructed by STRING platform and imported into Cytoscape software to construct the FG-active ingredients-targets-AIC network, and CytoNCA plug-in was used to analyze and identify the core target genes. The Metascape platform was used for transcription factor, GO and signaling pathway enrichment analysis.  27 active ingredients of FG and 1846 potential targets were obtained and 358 AIC target genes were retrieved. The intersection of FG and AIC targets resulted in 218 target genes involved in FG action. The top 5 active ingredients with most targets were quercetin, luteolin, kaempferol, isorhamnetin, and sesamin. After constructing the FG-active ingredients-targets-AIC network, CytoNCA analysis yielded 51 core targets, of which the top ranked target was STAT3. Ninety important transcription factors were enriched by transcription factor enrichment analysis, including RELA, TP53, NFKB1, SP1, JUN, STAT3, etc. The results of GO enrichment analysis showed that the effective active ingredient targets of FG were involved in apoptotic signaling, response to growth factor, cellular response to chemical stress, reactive oxygen species metabolic process, etc. The signaling pathway enrichment analysis showed that there were many signaling pathways involved in AIC, mainly including pathways in cancer, FOXO signaling pathway, AGE-RAGE signaling pathway in diabetic complications, signaling by interleukins, and PI3K-AKT signaling pathway,.  The study based on a network pharmacology approach demonstrates that the possible mechanisms of FG against AIC are the involvement of multicomponents, multitargets, and multipathways, and STAT3 may be a key target. Further experiments are needed to verify the results. Adriamycin is a broad-spectrum, highly effective anthracycline antitumor drug that can be used in the treatment of a variety of solid and hematologic malignancies, especially important in the treatment of breast cancer, sarcoma, lymphoma, leukemia, and other cancers. However, the time- and dose-dependent cardiotoxicity caused by adriamycin has severely limited its clinical application and therapeutic efficacy. Adriamycin-induced cardiotoxicity (AIC) is observed at a wide range of 3%-48% in adult and can manifest as both acute irreversible myocardial injury, left ventricular dysfunction, dilated cardiomyopathy, and heart failure one or more years after the end of treatment . AlthougFolium Ginkgo (FG) is the dried leaf of Ginkgo biloba, a plant of the Ginkgo family, which is naturally mild, with a slight air, slightly sweet, bitter, and astringent taste. FG has the effects of activating blood circulation, relieving pain, comforting the lungs and asthma, and reducing lipids, thus has been used in China as a traditional medicine for the treatment of asthma, bronchitis, and heart dysfunction for at least 5000 years [00 years , 5. Mode00 years . These a00 years , 8. The 00 years . Clinica00 years , 10. In 00 years . Studies00 years . Neverth00 years . Therefohttps://tcmspw.com/tcmsp.php) was searched for the chemical composition of FG using “Folium Ginkgo” as the keyword. The possible active ingredients in FG were screened with an oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, and the pharmacologic targets involved were identified. At the same time, using the screened active ingredients as keywords, the online tools Genclip3  and HERB (http://herb.ac.cn/) databases were used to further collect additional FG targets, and bioDBnet (https://biodbnet-abcc.ncifcrf.gov/db/db2db.php) was used to perform ID conversion of identified target gene. Finally, the results were summarized and organized to form the FG-targets database.The TCMSP database (https://www.genecards.org), the human Mendelian genetic database  and CTD (http://ctdbase.org/). After removing duplicate targets, gene names of targets were normalized and converted to gene ID by bioDBnet (https://biodbnet-abcc.ncifcrf.gov/db/db2db.php) to obtain the disease targets.The targets related to AIC were obtained by searching the Genecards database  to obtain the PPI network file. The species was selected as “Homo sapiens”, the interaction score parameter was set to 0.900, and the remaining were set as default. The “TSV” network file was obtained by removing the targets that did not interact with other proteins. The “TSV” file was imported into Cytoscape software, and the topological parameters of each target of the PPI network were analyzed by CytoNCA plug-in, and organized in an excel table, with the following parameters: betweenness, closeness, degree, local average connectivity (LAC), network centrality, and eigenvector. The median values of these six indicators were calculated separately, and the targets were identified as the core targets when all six indicators of the targets were greater than the median value. Subsequently, the core target network was constructed by Cytoscape software.The obtained common targets were imported into the STRING database (https://metascape.org/gp/index.html#/main/step1) to perform gene ontology (GO), transcription factor, and signaling pathway enrichment analysis, with the species setting as “H. sapiens” and the module setting as “Express Analysis”.The core target genes obtained above were imported into the online tool Metascape , of which anthracyclines are highly toxic. Therefore, patients receiving anthracycline chemotherapy often impair their vital energy with clinical symptoms including palpitations, chest tightness, shortness of breath, and weakness , 15. StuFG is a common Chinese medicine with the efficacy of activating blood circulation and removing blood stasis. A large number of basic and clinical studies have revealed a wide range of therapeutic effects of FG extract on cardiovascular and cerebrovascular diseases . FG has Unlike the traditional “single compound-disease” approach, network pharmacology is suitable for studying the multiple components and mechanisms of action of TCM from a holistic perspective . Therefoβ overexpression and promotes myocardial inflammation and apoptosis through activation of NF-κB signaling [κB signaling accelerated doxorubicin-induced apoptosis and fibrosis in cardiac cells [κB signaling [κB was mediated by interleukin signaling remained unclear. Foxo signaling is important for the regulation of cardiomyocyte development and survival. Doxorubicin has been shown to induce cardiomyocyte apoptosis and atrophy through CDK2-mediated activation of FOXO1 signaling [The results of GO enrichment analysis showed that the active ingredients of FG are involved in biological processes such as apoptosis, growth factor stimulation response, and cellular response to chemical stress and reactive oxygen metabolic processes, suggesting that the targets of FG are involved in apoptosis and oxidative stress regulation. Signaling pathway enrichment analysis showed that numerous signaling pathways, including cancer-related signaling pathway, Foxo signaling pathway, AGE- RAGE signaling pathway in diabetic complications, interleukin signaling pathway, and PI3K-AKT signaling pathway were involved in the mechanism of FG against AIC. Among them, as a classical pathways to maintain cell survival cycle, activation of PI3K-AKT signaling was demonstrated to effectively attenuate myocardial apoptosis and fibrosis induced by anthracycline –40. Consignaling , while aac cells . In suppignaling , howeverignaling , while oignaling , and actignaling , indicatignaling . AccordiIn summary, this study systematically investigated the active ingredients, targets, and signaling pathways of FG against AIC based on a network pharmacology approach. Quercetin, lignan, kaempferol, isorhamnetin, and sesquiterpene were identified as key components of FG in the treatment of AIC and STAT3 was identified as the key target. The synergistic effects of multiple ingredients, targets, and pathways may be implicated in FG action. Given the limitations of network pharmacology, further experimental validation of the effect of the key compounds of FG on important targets and signal such as STAT3 and FOXO signal AIC model is needed in the future, which could provide more support for our finding.


Suanzaoren-Wuweizi herb-pair (SWHP), composed of Zizyphi Spinosi Semen (Suanzaoren in Chinese) and Schisandrae Chinensis Fructus (Wuweizi in Chinese), is a traditional herbal formula that has been extensively used for the treatment of insomnia. The study aimed to explore the targets and signal pathways of Suanzaoren-Wuweizi (S-W) in the treatment of anxiety by network pharmacology, and to verify the pharmacodynamics and key targets of SWHP in mice.The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) as well as literature mining were used to obtain the main chemical ingredients of Suanzaoren and Wuweizi. The SwissTargetPrediction platform was used to predict drug-related targets. The GeneCards, TTD, DisGeNET and OMIM databases were used to obtain potential targets for the treatment of anxiety with the chemical components of S-W. Drug-disease intersection genes were selected, and a protein-protein interaction (PPI) network was constructed using STRING. The core targets of S-W in the treatment of anxiety were selected according to the topological parameters, and GO functional enrichment as well as KEGG pathways enrichment analyses were performed for potential targets. The relationship network of the “drug-active ingredient-disease-target-pathway” was constructed through Cytoscape 3.8.0. The pharmacodynamics of SWHP in the treatment of anxiety was evaluated by the elevated plus maze (EPM), the light/dark box test (LDB) and the open field test (OFT). The mechanisms were examined by measuring monoamine neurotransmitters in brain of mice.The results showed that there were 13 active ingredients for the treatment of anxiety in the network. This includes sanjoinenine, swertisin, daucosterol, schizandrer B, wuweizisu C and gomisin-A. Additionally, there were 148 targets, such as AKT1, TNF, SLC6A4, SLC6A3, EGFR, ESR1, HSP90AA1, CCND1, and DRD2, mainly involved in neuroactive ligand-receptor interactions, the Serotonergic synapse pathway and the cAMP signaling pathway. After 1 week of treatment, SWHP (2 and 3 g/kg) induced a significant increase on the percentage of entries into and time spent on the open arms of the EPM. In the LDB test, SWHP exerted anxiolytic-like effect at 2 g/kg. In the open-field test, SWHP (2 g/kg) increased the number of central entries and time spent in central areas. The levels of brain monoamines (5-HT and DA) and their metabolites  were decreased after SWHP treatment.The anti-anxiety effect of SWHP may be mediated by regulating 5-HT, DA and other signaling pathways. These findings demonstrated that SWHP produced an anxiolytic-like effect and the mechanism of action involves the serotonergic and dopaminergic systems, although underlying mechanism remains to be further elucidated. Anxiety, an aversive emotional state and commonly occurring mental disorder of the central nervous system, is contributing to an ever-increasing health burden worldwide. It’s comprised of a powerful emotional component associated with fearful thoughts and a physiological response. Kessler et al.  have repChinese therapeutic herbs are traditionally used in combination (combined according to their properties so as to extend their abilities) and always demonstrate better pharmacological effects combined than when used alone . Herb-paSWHP is a combination of Zizyphi Spinosae Semen  Hu ex H.F.Chou, officially recognized in the Chinese Pharmacopoeia) and Schisandrae Chinensis Fructus  Baill., officially recognized in the Chinese Pharmacopoeia). Ziziphi Spinosae Semen and Schisandrae Chinensis Fructus are documented in the Divine Husbandman’s Classic of the Materia Medica (the earliest dictionary of Chinese Materia Medica). The former is widely applied in China, Japan, Korea and other oriental countries to treat insomnia and anxiety symptoms –16, whilSWHP was originally recorded in Puji Fang  in the Ming Dynasty, a famous ancient medical manuscript, and was used to treat insomnia and to calm nerves. In TCM, SWHP is used to treat mental diseases in more than 262 prescriptions according to the Dictionary of Chinese Medicine Prescription , such as Anshen Pill, Yangxin Anshen Pill and Suanzaoren Powder. They are also applied in Zaoren Anshen Capsules and Jiannao Capsules, to treat the patients suffering from insomnia, forgetfulness, upset, dizziness and neurasthenia . MoreoveNetwork pharmacology is a new discipline which reveals the regulatory network of drugs in the body from the system level. The mechanism of drug action can be predicted by constructing the complex network relationship among “drugs, active components, targets and diseases”, particularly useful for the mechanism prediction of multi-target drugs developed from Traditional Chinese medicine –25.The monoaminergic system [serotonin (5-HT) and dopamine (DA)] in the brain has been postulated to play an important role in the pathophysiology of anxiety disorders –28. SeveThe aim of this study is to explore the targets and signal pathways of Suanzaoren-Wuweizi in the treatment of anxiety by utilizing network pharmacology. As well, to verify the anxiolytic-like effect of SWHP by using the elevated plus maze (EPM), the light/dark box test (LDB) and the open field test (OFT). In order to explore the potential mechanisms on monoaminergic system, also examined was the level of monoamines serotonin (5-HT), dopamine (DA) and their metabolites 5-HIAA and DOPAC in brain of mice. The study of these issues not only offers better guidance for the clinical application of SWHP, but also provides foundations for new drug discovery. Figure https://www.genecards.org/) . After one-week of treatment, SWHP (2 and 3 g/kg) led to a significant increase in the number of entries into the open arms from the vehicle. From 32.83 ± 1.93% to 42.42 ± 1.68% and 39.58 ± 1.45% respectively for 2 and 3 g/kg treatments  = 2.526, P < 0.05]. After one-week of treatment, SWHP at the dose of 2 g/kg significantly increased the number of transitions from the vehicle from 9.75 ± 0.72 to 13.33 ± 0.88  = 5.339, 𝑃 < 0.01], but not in the total distances  and their metabolites 5-HIAA and DOPAC . Compared with the vehicle group, SWHP (2 and 3 g/kg) and DZP significantly decreased the content of 5-HT from 123.33 ± 10.12 to 92.06 ± 9.58, 97.18 ± 4.61 and 92.58 ± 9.04  respectively. As well, decreased the content of 5-HIAA from 341.91 ± 15.54 to 275.51 ± 20.37, 282.94 ± 23.05 and 278.04 ± 22.95 , and decreased the content of DA from 520.91 ± 36.21 to 415.40 ± 33.04, 420.43 ± 18.25 and 409.67 ± 35.12 . SWHP (2 g/kg) and DZP also significantly decreased the content of DOPAC from 166.30 ± 14.46 to 128.08 ± 8.91 and 124.86 ± 8.56  when compared with the vehicle group .Table n = 11–12). *P < 0.05 and **P < 0.01 as compared with the vehicle group. One-way ANOVA with Student-Newman-Keuls post hoc test.Mice were administered vehicle, SWHP  or DZP for 7 days. Values were presented as mean ± S.E.M. , flavonoids  in Ziziphi spinosae Semen, and lignans  in Schisandrae chinensis Fructus, which is consistent with the types of chemical components predicted by network pharmacology of SWHP. This is also consistent with the literature, which report the components in Ziziphi spinosae Semen –59 and SThe target after the compatibility of S-W mainly focuses on AKT1, TNF, SLC6A4, SLC6A3, and DRD2. AKT/protein kinase B signaling pathway regulates cell growth and proliferation, and participates in cell processes, including apoptosis and glucose metabolism. Activation of AKT requires the activation of PI3K and PI3K-AKT pathway, which play an important role in improving anxiety and depression . RelatedThrough the construction of network pathways, GO enrichment analysis, and KEGG pathway enrichment analysis, we found that the active components of S-W may play a role in the treatment of anxiety by participating in neuroactive ligand-receptor interaction, serotonin synapse and the cAMP signaling pathway. It is known from literature that the receptor biogenic imines contained in the neuroactive ligand-receptor interaction signal pathway is an essential stimulating nerve tissue molecule, which controls and regulates many important biological functions after binding to the corresponding receptors. For example, emotion, memory and the endocrine system. The disorder of this pathway or the down-regulation of the receptor will subsequently cause anxiety , 69. TheIn the present study we provided convincing evidence that SWHP extract administered by oral route produces a specific anxiolytic-like effect in EPM, LDB and OFT after one-week treatment in mice and the effect is involved with the monoaminergic system.In the present study we provide convincing evidence that SWHP extract administered by oral route produces a specific anxiolytic-like effect in the EPM, LDB and OFT after one-week of treatment in mice, and the effect is involved with the monoaminergic system. The EPM is considered to be an etiologically valid animal model of anxiety because it uses natural stimuli  that can induce anxiety in humans –76. The The light/dark test is based on the innate aversion rodents have to brightly illuminated areas and on the spontaneous exploratory behavior of rodents in response to mild stressors, that is, novel environment and light , 79. PreThe OFT is used in studies of the neurobiological basis of anxiety and screening for anxiolytic compounds . The timAs a brain mechanism mediating effects of environmental factors, we focused on serotonin (5-HT) and DA systems in the present study. The current research on the etiology and pathogenesis of anxiety disorders has demonstrated that 5-HT is one of the key neurotransmitters modulating anxiety . 5-HT anNotably, the neurotransmitter system predicted in network pharmacology not only included the 5-HT system and DA system, but also included the GABA system and cAMP signal pathway as predicted key pathways. Therefore, it is necessary to also determine GABA, which was reported to be one of the mechanisms of DZP, and the key proteins in the cAMP signal pathway, such as PKA, ERK, and RAP1a in the later stage. This is required in order to more comprehensively explain the anti-anxiety mechanism of SWHP.In summary, the mechanism of SWHP for the treatment of anxiety was analyzed, and the reliability of the network pharmacology prediction was verified by animal experiments. Our findings open new perspectives for understanding SWHP for anxiety, and indicates that SWHP exerts an anxiolytic-like effect in the EPM, LDB and OFT, without affecting locomotor activity, in mice. In addition, this work provides evidence that the anxiolytic effect of SWHP appears to be mediated through the monoamine neurotransmitter levels. The findings of this study provide reference and a scientific basis for further study into the anti-anxiety effect of SWHP, as it provides an alternative safe and effective treatment with less side effects.